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Genomics tool for monitoring engineered stormwater treatment wetlands LeNoble, Jesisca 2017

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 GENOMICS TOOL FOR MONITORING ENGINEERED STORMWATER TREATMENT WETLANDS  by  Jessica LeNoble  B.Eng., Dalhousie University, 2014 B.A., Dalhousie University, 2014   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF APPLIED SCIENCE  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Civil Engineering)  THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)    June 2017  © Jessica LeNoble, 2017 ii  Abstract In the context of this research, stormwater consists of precipitation that falls onto impervious surfaces and fails to infiltrate into the ground. Traditional stormwater management involves diverting stormwater into storm sewers followed by discharge to a watercourse. However, in Vancouver and elsewhere, there is a push from governments for a more integrated approach which makes use of low impact design (LIDs) features. For this reason, engineered wetlands, which are designed to optimize natural processes for water diversion and treatment, are becoming a more common and desirable treatment option for stormwater. However, there are barriers for the implementation of engineered wetlands and other LIDs because traditional water quality monitoring often does not provide a reliable enough validation that the wetlands are meeting water treatment objectives, thus leading to a lack of accountability for designers and operators. In this research, a genomics-based approach was applied at an operating stormwater treatment wetland (the Lost Lagoon wetland located in Stanley Park, Vancouver British Columbia, Canada), with the goal to provide proof of concept data to inform the development of a genomics-based tool for stormwater treatment wetlands and other LIDs. In addition, a laboratory based stormwater dosing study was performed to allow for cross comparison of results. Microbial communities and functional genes with known adaptations for the contaminants found in stormwater were correlated with contaminant levels to increase the reliability and certainty of findings. Results from DNA sequencing were compared using samples extracted from the Lost Lagoon wetland and several outcomes suggested that bacteria may correlate with the performance of treatment wetlands. This was generally supported further using results from samples extracted during the stormwater dosing study. Cost estimates performed for various treatment wetland monitoring scenarios suggested that in the future, a genomics-based monitoring approach may supply more accurate treatment performance data at a lower overall cost and effort level than traditional stormwater treatment monitoring. Proof of concept, for the application of genomics-based monitoring of stormwater treatment wetlands, was provided. It was demonstrated that genomics could supply benefits for future monitoring endeavours and that additional investigation into this field may be worthwhile.    iii  Lay Summary In this research, a novel treatment monitoring approach was applied at an operating stormwater treatment wetland (the Lost Lagoon wetland located in Stanley Park, Vancouver British Columbia, Canada). In addition, a laboratory based stormwater dosing study was performed to allow for comparison of results. Bacterial communities and genes with known adaptations for the contaminants found in stormwater were linked with contaminant levels to increase the reliability and certainty of treatment findings. Results were compared using samples collected from the Lost Lagoon wetland and several outcomes suggested that bacteria may correlate with the performance of treatment wetlands. This was generally supported further using results from samples collected during the stormwater dosing study. Cost estimates, performed for various treatment wetland monitoring scenarios, suggested that in the future this novel monitoring approach may supply more accurate treatment performance data at a lower overall cost and effort level than traditional stormwater treatment monitoring.   iv  Preface The research described in this document contains two parts of equal significance. First, a field study was conducted at the Lost Lagoon wetland in Stanley Park in Vancouver, British Columbia between June 2014 and December 2014. Second, a laboratory study was conducted using facilities at the University of British Columbia Vancouver Campus between November 2015 and April 2016. Both of these studies were student-led by the author of this document. In addition, the author was primarily responsible for the identification and design of the research program, the applications to funding agencies, the execution of both the field and laboratory studies, and the analyses of data. That being said, many individuals, including the author’s primary supervisor and collaborating supervisors, contributed advice, expertise, and constructive criticism throughout the design of the research program and the analyses that were conducted by the author. Specifically, Prof. James Atwater, Dr. Susan Baldwin, Dr. Dirk Van Zyl, Dr. Bill Mohn and Chris Johnston provided direction for the two components of this research. Additional contributions include:  Staff from the Stanley Park Ecology Centre assisted with sample collection during the field study;  Timothy Ma from the UBC Department of Civil Engineering conducted analyses for metals;  Staff from Microbiome Insights performed Illumina MiSeq sequencing;  Anastacia Kuzmin from the UBC Department of Zoology performed Illumina HiSeq sequencing; and  Dr. Ido Hatam contributed codes and support for the analyses of Illumina MiSeq data. Publications, Presentations and Data Deposition: A version of the field study results in Chapter 1 and Chapter 2 has been prepared for submission. Jessica LeNoble, James Atwater, Susan Baldwin, Chris Johnston, Ido Hatam. The application of genomics as a monitoring tool for the efficacy of engineered stormwater treatment wetlands: a case study using results from an operating stormwater treatment wetland in Stanley Park, Vancouver, British Columbia. A version of the laboratory study in Chapter 2 has been prepared for submission: Jessica LeNoble, James Atwater, Susan Baldwin, Chris Johnston, Ido Hatam. The application of genomics as a monitoring tool for the efficacy of engineered stormwater treatment wetlands: a proof of concept study using the results of a stormwater dosing experiment.   v  The outcome of this work has been presented in conferences as follows: Jessica LeNoble, James Atwater, Chris Johnston, Maria Egerton, Susan Baldwin ad Dirk Van Zyl. Genomics Tool for Monitoring Stormwater Treatment Wetlands. Poster session at the 14th Annual Genomics Forum: Global Impact of Genomics. Genome BC. Vancouver, Canada. May 13, 2016. Jessica LeNoble, James Atwater, Chris Johnston, Maria Egerton, Susan Baldwin ad Dirk Van Zyl. Genomics Tool for Monitoring Stormwater Treatment Wetlands. Poster session at the 3rd Annual Water and Environment Student Talks Conference: Where is Water Taking Us? University of British Columbia. Vancouver, Canada. June 7, 2016. Upon completion of this thesis and subsequent publications, raw microbial data will be deposited in the following repositories under project name, “Genomics Tool for Engineered Stormwater Treatment Wetlands.”  NCBI  MGRAST     vi  Table of Contents Abstract ......................................................................................................................................................... ii Lay Summary ................................................................................................................................................ iii Preface ......................................................................................................................................................... iv Table of Contents ......................................................................................................................................... vi List of Tables ................................................................................................................................................ xiii List of Figures ............................................................................................................................................ xviii List of Abbreviations and Symbols .............................................................................................................. xxv Acknowledgements .................................................................................................................................. xxvii Dedication ............................................................................................................................................... xxviii 1. Introduction ........................................................................................................................................... 1 1.1 Background ................................................................................................................................... 1 1.2 Motivation ..................................................................................................................................... 2 1.3 Objective and Study Goals ............................................................................................................. 3 1.4 Scope and General Research Activities.......................................................................................... 4 2. Chapter 1: Application of Traditional Water and Sediment Quality Monitoring Techniques for Validation of the Lost Lagoon Stormwater Treatment Wetland .................................................................................... 6 2.1 Introduction and Chapter Goal ...................................................................................................... 6 2.2 Chapter Objectives ........................................................................................................................ 6 2.3 Hypotheses .................................................................................................................................... 7 2.4 Literature Review .......................................................................................................................... 7  Regulatory Framework .............................................................................................................. 7  Urban Stormwater and Accepted Treatment Efficacy of Engineered Wetlands ..................... 10  Description and Sources of Common Pollutants in Urban Stormwater ........................... 10  Reasons for Toxicity for Common Pollutants in Urban Stormwater ................................ 12  Wetlands as an Urban Stormwater Control Measure ..................................................... 14  Barriers for Implementing Stormwater Treatment Wetlands ......................................... 16  Traditional Water and Sediment Quality Monitoring for Validating the Efficacy of Stormwater Treatment Wetlands ............................................................................................................................ 16  Visual Inspection.............................................................................................................. 16  Testing ............................................................................................................................. 17  Capacity Testing ........................................................................................................ 17  Synthetic Runoff Testing ............................................................................................ 17 vii   Monitoring ....................................................................................................................... 18  Study Site:  Lost Lagoon Stormwater Treatment Wetland ...................................................... 18  Precedent for Installation ................................................................................................ 18  Design, Installation, Maintenance and Monitoring Regime ............................................ 19  Design ........................................................................................................................ 19  Installation ................................................................................................................. 22  Maintenance and Monitoring Regime ....................................................................... 24  Best Management Practices Employed in the Lost Lagoon Wetland Design ................... 26  Previous Stormwater Quantity and Quality Data ............................................................ 27  Year 2000: Drainage Area and Calculation of Design Flow ........................................ 27  Year 2007: UBC Undergraduate Thesis ...................................................................... 27  Year 2013: Vancouver Board of Parks and Recreation Sediment Dredging Report ... 30 2.5 Methodology ............................................................................................................................... 33  Site Visits and Sampling Regime .............................................................................................. 33  Field Site Survey and Conditions at the Time of the Field Study Site Visit ....................... 33  Sampling Locations and Dates ......................................................................................... 37  Water Sampling Equipment............................................................................................. 39  Sediment Sampling Equipment ....................................................................................... 40  Sample Collection, Preservation, Storage and Transport ................................................ 41  Water Samples .......................................................................................................... 41  Sediment Samples ..................................................................................................... 42  Laboratory Analysis of Water Quality Parameters ................................................................... 42  Sample Handling and Preservation .................................................................................. 42  Analytical Methods .......................................................................................................... 42  Statistical Analyses .................................................................................................................. 43 2.6 Results and Interpretation ........................................................................................................... 43  Turbidity, Total Suspended Solids, Chemical Oxygen Demand and Total Organic Carbon ...... 44  Interpretation .................................................................................................................. 44  Turbidity Figures .............................................................................................................. 45  Totals Suspended Solids Figures ...................................................................................... 46  Chemical Oxygen Demand Figures .................................................................................. 47  Total Organic Carbon Figures .......................................................................................... 48  Statistical Scores for Site Comparison ............................................................................. 49 viii   Metals ...................................................................................................................................... 49  Interpretations ................................................................................................................ 49  Water Samples ................................................................................................................ 52  Surface Sediment Samples .............................................................................................. 55  10-cm Depth Sediment Samples ..................................................................................... 58  Statistical Scores for Site Comparison ............................................................................. 60  Mineral Oil and Grease ............................................................................................................ 61  Interpretation .................................................................................................................. 61  Mineral Oil and Grease Figures ....................................................................................... 62 2.7 Discussion and Conclusion .......................................................................................................... 63  Chapter Hypotheses ................................................................................................................ 63  Chapter Objectives .................................................................................................................. 64  Final Remarks .......................................................................................................................... 66 2.8 Limitations ................................................................................................................................... 67 3. Chapter 2: Application of Genomics-Based Monitoring Techniques for Complimentary Validation of the Lost Lagoon Stormwater Treatment Wetland ............................................................................................. 68 3.1 Introduction and Chapter Goal .................................................................................................... 68 3.2 Chapter Objectives ...................................................................................................................... 68 3.3 Hypotheses .................................................................................................................................. 68 3.4 Literature Review ........................................................................................................................ 69  Toxicity of Urban Stormwater ................................................................................................. 69  Influence of Urban Stormwater Contaminants on Microbial Communities ............................ 70  Known Microbial Adaptations to Urban Stormwater Contaminants ....................................... 72  DNA Sequencing and Data Analysis Methods .......................................................................... 75  DNA Sequencing Overview .............................................................................................. 75  Sequence Data Analysis Overview ................................................................................... 81 3.5 Methodology ............................................................................................................................... 83  Field Study Site Visits and Sampling Regime ............................................................................ 83  Column Study Preparation and Execution ............................................................................... 83  Sourcing and Confirmation of Uncontaminated Soil ....................................................... 83  Collection of Uncontaminated Soil .................................................................................. 83  Column Study Environment ............................................................................................. 84  Pre-Study Experiment ...................................................................................................... 84 ix   Column Configuration and Set-Up ................................................................................... 84  Column Water Dosing Regime and Environmental Controls ........................................... 85  Stormwater Dose Quality .......................................................................................... 85  Stormwater Dose Volume and Frequency ................................................................. 88  Column Study Sampling Regime .............................................................................................. 89  Sample Preservation, Transport, Pre-Processing, Storage and Quality Control ...................... 90  Laboratory Analysis of Water and Sediment Quality Parameters............................................ 91  Laboratory Preparation of Bacterial DNA ................................................................................ 91  Sample Handling and Preservation .................................................................................. 91  Extraction of DNA and Quality Control ............................................................................ 91  Quantification of DNA ..................................................................................................... 91  Sequencing for Comparison of Microbial Community Compositions ...................................... 91  Library Preparation and Quality Control .......................................................................... 92  Sequencing of the 16s rRNA Gene ................................................................................... 93  Sequencing for Comparison of Microbial Functional Gene Compositions .............................. 94  Sample Selection and Quality Control ............................................................................. 94  Library Preparation and Quality Control .......................................................................... 95  Cluster Generation .......................................................................................................... 95  Sequencing of Whole Bacterial Genomes ....................................................................... 95  Analysis of Bacterial Taxa Using the 16s rRNA Gene ............................................................... 96  Quality Filtering and Determination of Unique Sequences and Abundances .................. 96  Preparation of OTU Tables .............................................................................................. 97  Taxonomic Assignments .................................................................................................. 97  Bioinformatics ................................................................................................................. 97  Statistical Analyses on Data ............................................................................................. 98  Data Screening .......................................................................................................... 98  Alpha Diversity ........................................................................................................... 98  Community Composition ........................................................................................... 99  Indicator Species ..................................................................................................... 100  Analysis of Bacterial Functions Using Metagenomics ........................................................ 100  File Conversion and Sequence De-Multiplexing ............................................................ 100  Read Merging, Quality Filtering, and Contig Assembly .................................................. 100  Preparation of Function Lists ......................................................................................... 100 x   Analyses on Data ........................................................................................................... 101  Review of Results........................................................................................................... 101 3.6 Results and Interpretation ......................................................................................................... 101  Environmental Analysis ......................................................................................................... 101  Confirmation of Beaver Lake Bog Soil Quality ............................................................... 101  Preliminary Study .......................................................................................................... 102  Turbidity, TSS, COD and TOC ......................................................................................... 103  Interpretation .......................................................................................................... 103  Figures ..................................................................................................................... 103  Metals ............................................................................................................................ 106  Interpretation .......................................................................................................... 106  Figures ..................................................................................................................... 108  Water Samples ................................................................................................ 108  Surface Sediment Samples .............................................................................. 112  10-cm Depth Sediment Samples ..................................................................... 116  Microbial Community Analysis .............................................................................................. 120  Data Quality and Screening ........................................................................................... 120  Interpretation .......................................................................................................... 120  Field Study ............................................................................................................... 121  Sequence Depth Cutoff ................................................................................... 121  Comparison of Pseudo-Replicates and Outlier Screening ............................... 122  Laboratory Study ..................................................................................................... 124  Sequence Depth Cutoff ................................................................................... 124  Comparison of Pseudo-Replicates and Outlier Screening ............................... 126  Alpha Diversity ............................................................................................................... 128  Community Composition ............................................................................................... 130  Interpretation .......................................................................................................... 130  Field Study ............................................................................................................... 132  Water Samples ................................................................................................ 132  Surface Sediment Samples .............................................................................. 133  10-cm Depth Sediment Samples ..................................................................... 134  Laboratory Study ..................................................................................................... 135  Water Samples ................................................................................................ 135 xi   Surface Sediment Samples .............................................................................. 136  10-cm Depth Sediment Samples ..................................................................... 137  Indicator Species ........................................................................................................... 137  Interpretation .......................................................................................................... 137  Field Study ............................................................................................................... 138  Water Samples ................................................................................................ 138  Surface Sediment Samples .............................................................................. 139  10-cm Depth Sediment Samples ..................................................................... 139  Laboratory Study ..................................................................................................... 140  Water Samples ................................................................................................ 140  Surface Sediment Samples .............................................................................. 140  10-cm Depth Sediment Samples ..................................................................... 141  Microbial Functional Gene Analysis ....................................................................................... 141  Data Quality and Screening ........................................................................................... 141  Functional Gene Composition ....................................................................................... 142  Interpretation .......................................................................................................... 142  Figures ..................................................................................................................... 144  Metal Adaptation Genes ............................................................................................... 145  Interpretation .......................................................................................................... 145  Figures ..................................................................................................................... 146 3.7 Discussion and Conclusion ........................................................................................................ 149  Chapter Hypotheses .............................................................................................................. 149  Chapter Objectives ................................................................................................................ 151  Final Remarks ........................................................................................................................ 152 3.8 Limitations ................................................................................................................................. 152 4. Discussion .......................................................................................................................................... 153 4.1 Cost Comparison of Wetland Validation Techniques ................................................................ 153  Sample Collection .................................................................................................................. 153  Laboratory Analyses .............................................................................................................. 154  Total Cost of Data Acquisition ............................................................................................... 155 4.2 Cost Comparison of a Single Wetland Monitoring Event........................................................... 156 5. Conclusion .......................................................................................................................................... 157 6. Recommendations ............................................................................................................................. 158 xii  6.1 Follow-On Research................................................................................................................... 158 6.2 Application and Improvements of Study Methodology ............................................................. 159 Bibliography .............................................................................................................................................. 160 Appendix A: Acid Digestion Procedure for Water and Sediment Samples ................................................ 172 Appendix B: Historic Water and Sediment Quality Data for the Lost Lagoon Wetland ............................. 173 Appendix C: Rainfall Records During Lost Lagoon Wetland Site Visits ....................................................... 194 Appendix D: Temperature Records During Lost Lagoon Wetland Site Visits ............................................. 198 Appendix E: Delineation of the Lost Lagoon Wetland Watershed ............................................................. 201 Appendix F: Raw Measurements for the Lost Lagoon Wetland Field Study .............................................. 202 Appendix G: Raw Measurements for the Laboratory Column Test ........................................................... 211 Appendix H: Alpha Diversity ...................................................................................................................... 217 Field Study ............................................................................................................................................. 217 Water Samples ................................................................................................................................... 217 Surface Sediment Samples ................................................................................................................. 223 10-cm Depth Sediment Samples ........................................................................................................ 228 Laboratory Study ................................................................................................................................... 233 Water Samples ................................................................................................................................... 233 Surface Sediment Samples ................................................................................................................. 237 10-cm Depth Samples ........................................................................................................................ 241 Appendix I: Letters of Permission and Support for the Research Project .................................................. 246 Appendix J: Project Management – Timeline and Budget ......................................................................... 252 Appendix K: Independent Statistical Review by UBC Applied Statistics and Data Science Group ............. 256 Appendix L: Reflections on the Work ........................................................................................................ 261    xiii  List of Tables Table 1. Description of Common Pollutants in Urban Stormwater ............................................................. 10 Table 2. Sources of Common Pollutants in Urban Stormwater ................................................................... 11 Table 3. Reasons for Toxicity of Common Pollutants in Urban Stormwater ................................................ 13 Table 4. Concentration of Common Pollutants in Urban Stormwater, Treatment Guidelines and Removal Efficiency Using Engineered Wetlands ........................................................................................................ 15 Table 5. Elements of the Lost Lagoon Wetland Maintenance and Monitoring Regime  (Kerr Wood Leidal, 2002) ........................................................................................................................................................... 25 Table 6. 2007 Results for Plant Specimens in Lost Lagoon Wetland (Thoren et al., 2007) .......................... 28 Table 7. 2007 Results for Sediment Samples in Lost Lagoon Wetland (Thoren et al., 2007) ....................... 28 Table 8. 2007 Regression Analysis Results for Sediment Samples in Lost Lagoon Wetland (Thoren et al., 2007) ........................................................................................................................................................... 29 Table 9. Comparison of 2007 Wetland Results with Sediment Data for Washington State (Thoren et al., 2007) ........................................................................................................................................................... 29 Table 10. BC Residential Soil Standards and Metal Concentrations Measured in the Sediment of the Lost Lagoon Wetland Forebay ............................................................................................................................ 32 Table 11. Field Study Samples Taken ........................................................................................................... 38 Table 12. Confidence Levels for Wilcoxon Rank Test Between Entry and Exit for Environmental Parameters .................................................................................................................................................................... 49 Table 13. Confidence Levels for Wilcoxon Rank Test Between Exit and Lagoon for Environmental Parameters .................................................................................................................................................................... 49 Table 14. Confidence Levels for Wilcoxon Rank Test Between Entry and Lagoon for Environmental Parameters .................................................................................................................................................. 49 Table 15. Confidence Levels for Wilcoxon Paired Rank Test Between Entry and Exit for Metals ................ 60 Table 16. Confidence Levels for Wilcoxon Paired Rank Test Between Exit and Lagoon for Metals ............. 61 Table 17. Confidence Levels for Wilcoxon Paired Rank Test Between Entry and Lagoon for Metals .......... 61 Table 18. Comparison of Maximum Pollutant Concentrations Measured in Water Samples at the Lost Lagoon to British Columbia Treatment Guidelines ...................................................................................... 65 Table 19. Bacterial adaptations to metals in stormwater (adapted from Nies 1999; Das et al. 2016) ........ 73 Table 20. Common Conventional and Biochemical Techniques for Analyzing Microbial Diversity and Abundance (adapted from Fakruddin & Mannan, 2013) ............................................................................ 77 Table 21. Common Partial Community Analysis Molecular Techniques for Analyzing Microbial Diversity and Abundance (adapted from Fakruddin & Mannan, 2013; Rastogi & Sani, 2011) .......................................... 78 Table 22. Common Techniques for Analyzing Microbial Diversity and Abundance using Whole Community Analysis (adapted from Fakruddin & Mannan, 2013; Rastogi & Sani, 2011) ............................................... 80 Table 23. Common Software Applications for Sequence Data Analysis ...................................................... 82 xiv  Table 24. Urban Highway Stormwater Quality from Literature and Sediment Quality Data from the Lost Lagoon Wetland in 2013 ............................................................................................................................. 86 Table 25. Target Element Concentrations for Semi-Synthetic Stormwater Recipe ..................................... 87 Table 26. Chemical Additives Used for Semi-Synthetic Stormwater Supplementation ............................... 87 Table 27. Environment Canada Average Precipitation and Temperature Data for Vancouver Harbor (Environment Canada, 2016) ....................................................................................................................... 88 Table 28. Calculated Water Addition Volumes and Frequencies for Column Study .................................... 89 Table 29. Recipe for PCR Used During Library Preparation Prior to 16s rRNA Gene Sequencing ................ 92 Table 30. Conditions for PCR Used During Library Preparation Prior to 16s rRNA Gene Sequencing ......... 93 Table 31. Confirmation of Beaver Lake Bog Soil Quality ............................................................................ 102 Table 32. Measurements Recorded During Preliminary Column Test ....................................................... 103 Table 33. Adonis Whole Dataset Comparison of Field Study Water Samples by Location with Strata Adjustment for Date .................................................................................................................................. 132 Table 34. Adonis Pairwise Comparison of Field Study Water Samples by Location ................................... 132 Table 35. Adonis Whole Dataset Comparison of Field Study Surface Sediment Samples by Location with Strata Adjustment for Date ....................................................................................................................... 133 Table 36. Adonis Pairwise Comparison of Field Study Surface Sediment Samples by Location................. 133 Table 37. Adonis Whole Dataset Comparison of Field Study Depth Sediment Samples by Location with Strata Adjustment for Date .................................................................................................................................. 134 Table 38. Adonis Pairwise Comparison of Field Study Depth Sediment Samples by Location ................... 134 Table 39. Adonis Whole Dataset Comparison of Laboratory Study Water Samples by Exposure and Date .................................................................................................................................................................. 135 Table 40. Adonis Whole Dataset Comparison of Laboratory Study Surface Sediment Samples by Exposure and Date .................................................................................................................................................... 136 Table 41. Adonis Whole Dataset Comparison of Laboratory Study Depth Sediment Samples by Exposure and Date ........................................................................................................................................................... 137 Table 42. Summary Statistics for Sequence Data Prior to Quality Control and Screening ......................... 142 Table 43. Summary Statistics for Sequence Data After Quality Control and Screening ............................. 142 Table 44. Approximate Cost Per Day for Sample Collection ...................................................................... 153 Table 45. Approximate Cost Per Sample for Traditional Stormwater Quality Analysis .............................. 154 Table 46. Approximate Cost Per Sample for Genomics Stormwater Quality Analysis Using Length Comparison of Bacterial Communities ...................................................................................................... 154 Table 47. Approximate Cost Per Sample for Genomics Stormwater Quality Analysis Using Entry and Exit Comparison of Bacterial Functional Genes ............................................................................................... 155 Table 48. Environment Canada Temperature Records for July 1, 2015 through August 31, 2015 ............ 198 xv  Table 49. Environment Canada Temperature Records for September 1, 2015 through October 31, 2015 .................................................................................................................................................................. 199 Table 50. Environment Canada Temperature Records for November 1, 2015 through December 31, 2015 .................................................................................................................................................................. 200 Table 51. In Situ Recordings of Dissolved Oxygen, pH and Temperature in the Lost Lagoon Wetland Forebay at the Water Surface and Water Floor ...................................................................................................... 203 Table 52. Temperature, DO, pH, Conductivity, ORP - Raw Data - Field Samples ....................................... 204 Table 53. Turbidity - Raw Data - Field Samples.......................................................................................... 205 Table 54. Chemical Oxygen Demand – Raw Data – Field Samples ............................................................ 206 Table 55. Total Suspended Solids - Raw Data - Field Samples ................................................................... 207 Table 56. Total Organic Carbon - Raw Data - Field .................................................................................... 208 Table 57. Metals - Raw Data - Field Samples - Water ................................................................................ 209 Table 58. Metals - Raw Data - Field Samples - Sediment ........................................................................... 210 Table 59. Determination of the Water Content in Beaver Lake Bog Soil ................................................... 211 Table 60. Raw Data Recorded for the 2-Week Preliminary Column Study ................................................ 211 Table 61. Mass of Soil Added to Each Column for the Column Study ....................................................... 211 Table 62. Temperature, DO, pH, Conductivity, ORP - Raw Data – Column Log ......................................... 212 Table 63. Temperature, DO, pH, Conductivity, ORP - Raw Data – Column Log ......................................... 213 Table 64. Turbidity, TSS, COD, TOC - Raw Data - Column Study ................................................................ 214 Table 65. Metals - Raw Data - Column Study - Water ............................................................................... 215 Table 66. Metals – Raw Data - Column Study - Sediment ......................................................................... 216 Table 67. One Way ANOVA Test Result for Richness Comparison of Water Samples by Field Site Based on Chao1 Estimator ........................................................................................................................................ 218 Table 68. Tukey HSD Test Result for Diversity Comparison of Water Samples by Field Site Based on Chao1 Estimator ................................................................................................................................................... 218 Table 69. One Way ANOVA Test Result for Coverage Comparison of Water Samples by Field Site Based on Good’s Coverage ....................................................................................................................................... 219 Table 70. Tukey HSD Test Result for Coverage Comparison of Water Samples by Field Site Based on Good’s Coverage ................................................................................................................................................... 220 Table 71. One Way ANOVA Test Result for Diversity Comparison of Water Samples by Field Site Based on the Inverse Simpson Estimator .................................................................................................................. 221 Table 72. One Way ANOVA Test Result for Observed OTUs Comparison of Water Samples by Field Site Based on SOBS Calculation .................................................................................................................................. 222 Table 73. Tukey HSD Test Result for Observed OTUs Comparison of Water Samples by Field Site Based on SOBS Calculation ....................................................................................................................................... 222 xvi  Table 74. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on Chao1 Estimator ........................................................................................................................ 224 Table 75. Tukey HSD Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on Chao1 Estimator ................................................................................................................................... 224 Table 76. One Way ANOVA Test Result for Coverage Comparison of Surface Sediment Samples by Field Site Based on Good’s Coverage ........................................................................................................................ 225 Table 77. Tukey HSD Test Result for Coverage Comparison of Surface Sediment Samples by Field Site Based on Good’s Coverage .................................................................................................................................. 225 Table 78. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on the Inverse Simpson Estimator .................................................................................................. 226 Table 79. Tukey HSD Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on the Inverse Simpson Estimator ............................................................................................................. 227 Table 80. One Way ANOVA Test Result for Observed OTUs Comparison of Surface Sediment Samples by Field Site Based on SOBS Calculation ........................................................................................................ 228 Table 81. One Way ANOVA Test Result for Richness Comparison of 10-cm Depth Sediment Samples by Field Site Based on Chao1 Estimator ................................................................................................................. 229 Table 82. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Field Site Based on Good’s Coverage ................................................................................................................. 230 Table 83. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Field Site Based on the Inverse Simpson Estimator ........................................................................................... 232 Table 84. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Field Site Based on SOBS Calculation ................................................................................................................. 233 Table 85. One Way ANOVA Test Result for Richness Comparison of Water Samples by Column Based on the Chao1 Estimator ........................................................................................................................................ 234 Table 86. One Way ANOVA Test Result for Coverage Comparison of Water Samples by Column Based on the Good’s Coverage ................................................................................................................................. 235 Table 87. Tukey HSD Test Result for Coverage Comparison of Water Samples by Column Based on the Good’s Coverage ....................................................................................................................................... 235 Table 88. One Way ANOVA Test Result for Diversity Comparison of Water Samples by Column Based on the Inverse Simpson Estimator ........................................................................................................................ 236 Table 89. One Way ANOVA Test Result for Diversity Comparison of Water Samples by Column Based on SOBS Calculation ....................................................................................................................................... 237 Table 90. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on the Chao1 Estimator .................................................................................................................. 238 Table 91. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on the Good’s Coverage ................................................................................................................. 239 Table 92. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on the Inverse Simpson Estimator .................................................................................................. 240 xvii  Table 93. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on SOBS Calculation ........................................................................................................................ 241 Table 94. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on Chao1 Estimator ........................................................................................................... 242 Table 95. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on Good’s Coverage .......................................................................................................... 243 Table 96. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on the Inverse Simpson Estimator ..................................................................................... 244 Table 97. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on SOBS Calculation ........................................................................................................... 245 Table 98. Project Budget and Finances ..................................................................................................... 255    xviii  List of Figures Figure 1. Map of Stanley Park (City of Vancouver, 2016b) Highlighting the Lost Lagoon Wetland ............... 2 Figure 2. Map of Stanley Park (City of Vancouver, 2016b) Highlighting the Beaver Lake Bog ....................... 5 Figure 3. Visual Breakdown of the Regulatory Framework for Stormwater Management in Vancouver ...... 9 Figure 4. Illustration of the Lost Lagoon Wetland (Kerr Wood Leidal Associates Ltd., 1999) ...................... 21 Figure 5. Laying of Silt Curtain ..................................................................................................................... 23 Figure 6. Construction of the Berm ............................................................................................................. 23 Figure 7. Excavation of the Pools and Marshes ........................................................................................... 23 Figure 8. Vegetation Planted ....................................................................................................................... 23 Figure 9. Arial Shot Facing Northwest ......................................................................................................... 23 Figure 10. Arial Shot Facing Southeast ........................................................................................................ 23 Figure 11. Design Hydrograph for Lost Lagoon Wetland (adapted from Kerr Wood Leidal, 1999) ............. 27 Figure 12. 2007 Sample Sites in Lost Lagoon for Plants and Sediment (adapted from Thoren et al., 2007)28 Figure 13. Locations Sampled by Hemmera During the 2013 Sediment Investigation (Hemmera, 2013)... 31 Figure 14. Survey Map of Field Site ............................................................................................................. 34 Figure 15. Lost Lagoon ................................................................................................................................ 35 Figure 16. On Site Graphic of Treatment Process ........................................................................................ 35 Figure 17. Storm Sewer on the Stanley Park Causeway .............................................................................. 35 Figure 18. Access Point for the Lower Stormceptor .................................................................................... 35 Figure 19. Wetland Bypass to Lost Lagoon .................................................................................................. 35 Figure 20. Setting Forebay ........................................................................................................................... 35 Figure 21. High Marsh ................................................................................................................................. 36 Figure 22. Low Marsh .................................................................................................................................. 36 Figure 23. Sections of Low Marsh Showing Plant Damage and Beaver Activity .......................................... 36 Figure 24. Signs of Beaver Activity at Lost Lagoon ...................................................................................... 36 Figure 25. Access Point for the Wetland Outlet Control Valve System ....................................................... 36 Figure 26. Outlet Point to Lost Lagoon ........................................................................................................ 36 Figure 27. Field Study Sampling Locations at the Lost Lagoon Wetland ..................................................... 37 Figure 28. Overview of Field Sampling Process ........................................................................................... 39 Figure 29. Image of the Water Sampling Equipment .................................................................................. 39 Figure 30. Photograph of the Sediment Sampler ........................................................................................ 40 Figure 31. Barplot Comparison by Plot of Turbidity in Water Samples Collected During the Field Study ... 45 xix  Figure 32. Boxplot Comparison of Turbidity in Water Samples Collected During the Field Study ............... 45 Figure 33. Barplot Comparison by Plot of TSS in Water Samples Collected During the Field Study ............ 46 Figure 34 Boxplot Comparison of TSS in Water Samples Collected During the Field Study ........................ 46 Figure 35. Barplot Comparison by Plot of COD in Water Samples Collected During the Field Study .......... 47 Figure 36. Boxplot Comparison of COD in Water Samples Collected During the Field Study ...................... 47 Figure 37. Barplot Comparison by Plot of TOC in Water Samples Collected During the Field Study ........... 48 Figure 38. Boxplot Comparison of TOC in Water Samples Collected During the Field Study ...................... 48 Figure 39. Barplot Comparison by Plot of Metals Associated with Stormwater in Water Samples Collected During the Field Study ................................................................................................................................. 52 Figure 40. Barplot Comparison by Plot of Metals Associated with Stormwater in Water Samples Collected During the Field Study ................................................................................................................................. 53 Figure 41. Boxplot Comparison of Metals Associated with Stormwater for Water Samples Collected During the Field Study ............................................................................................................................................. 54 Figure 42. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study ................................................................................................................. 55 Figure 43. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study ................................................................................................................. 56 Figure 44. Boxplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study ................................................................................................................. 57 Figure 45. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study ................................................................................................................. 58 Figure 46. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study ................................................................................................................. 59 Figure 47. Boxplot Comparison of Metals Associated with Stormwater between Forebay and Exit for Samples taken at a Depth of 10 cm ............................................................................................................. 60 Figure 48. Comparison by Site of Total Mineral Oil and Grease in Water Samples Collected During the Field Study ........................................................................................................................................................... 62 Figure 49. Boxplot Comparison of Total Mineral Oil and Grease for Water Sampled Collected During the Field Study ................................................................................................................................................... 62 Figure 50. Barplot Comparison by Column of Turbidity in Water Samples ............................................... 104 Figure 51. Barplot Comparison by Column of Total Suspended Solids in Water Samples ......................... 104 Figure 52. Comparison by Column of Chemical Oxygen Demand in Water Samples ................................ 105 Figure 53. Comparison by Column of Total Organic Carbon in Water Samples ........................................ 105 Figure 54. Comparison of Turbidity, TSS, TOC, and COD in Field and Lab Studies ..................................... 106 Figure 55. Time Comparison of Metals Associated with Stormwater in Column Water Samples ............. 108 Figure 56. Time Comparison of Metals Associated with Stormwater in Column Water Samples ............. 109 xx  Figure 57. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Water Samples .................................................................................................................................................................. 110 Figure 58. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Water Samples .................................................................................................................................................................. 111 Figure 59. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment ....... 112 Figure 60. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment ....... 113 Figure 61. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Surface Sediment .................................................................................................................................................................. 114 Figure 62. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Surface Sediment .................................................................................................................................................................. 115 Figure 63. Barplot Time Comparison of Metals Associated with Stormwater in 10-cm Depth Sediment . 116 Figure 64. Barplot Time Comparison of Metals Associated with Stormwater in 10-cm Depth Sediment . 117 Figure 65. Comparison of Metals Associated with Stormwater in Field Study and Lab Study 10-cm Depth Sediment ................................................................................................................................................... 118 Figure 66. Comparison of Metals Associated with Stormwater in Field Study and Lab Study 10-cm Depth Sediment ................................................................................................................................................... 119 Figure 67. Rarefaction Curve Illustrating Minimum Depth Cut-off for Field Samples ............................... 121 Figure 68. Anosim Boxplot Between Pseudo-Replicate Samples Prior to Outlier Screening in the Field Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples) ....................................... 122 Figure 69. NMDS Plot Illustrating Suspected Outliers Among Field Samples ............................................ 123 Figure 70. Anosim Boxplot Between Pseudo-Replicate Samples After Outlier Screening in the Field Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples) ....................................... 124 Figure 71. Rarefaction Curve Illustrating Minimum Depth Cutoff for Column Samples ............................ 125 Figure 72. Anosim Boxplot Between Pseudo-Replicate Samples Prior to Outlier Screening in the Column Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples) ............................. 126 Figure 73. NMDS Plot Illustrating Suspected Outliers Among Column Samples ........................................ 127 Figure 74. Anosim Boxplot Between Pseudo-Replicate Samples After Outlier Screening in the Column Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples) ....................................... 128 Figure 75. NMDS Plot Comparing Field Study Water Samples .................................................................. 132 Figure 76. NMDS Plot Comparing Field Study Surface Sediment Samples ................................................ 133 Figure 77. NMDS Plot Comparing Field Study 10-cm Depth Sediment Samples ....................................... 134 Figure 78. NMDS Plot Comparing Laboratory Study Water Sediment Samples ........................................ 135 Figure 79. NMDS Plot Comparing Column Study Surface Sediment Samples............................................ 136 Figure 80. NMDS Plot Comparing Column Study 10-cm Depth Sediment Samples ................................... 137 Figure 81. Indicator Species Barplot for Field Study Water Samples ......................................................... 138 Figure 82. Indicator Species Barplot for Field Study Surface Sediment Samples ....................................... 139 xxi  Figure 83. Indicator Species Barplot for Field Study 10-cm Depth Sediment Samples .............................. 139 Figure 84. Indicator Species Barplot for Laboratory Study Water Sediment Samples ............................... 140 Figure 85. Indicator Species Barplot for Laboratory Study Surfaced Sediment Samples ........................... 140 Figure 86. Indicator Species Barplot for Laboratory Study 10-cm Depth Samples .................................... 141 Figure 87. NMDS Plot of KEGG Annotated Genes for Field Samples ......................................................... 144 Figure 88. NMDS Plot of KEGG Annotated Genes for Column Samples .................................................... 144 Figure 89. Relative Abundance of Genes Associated with Zinc Measured in Field Samples ...................... 146 Figure 90. Relative Abundance of Genes Associated with Zinc Measured in Column Samples ................. 146 Figure 91. Relative Abundance of Functional Associated with Manganese, Zinc and Iron Measured in Field Samples ..................................................................................................................................................... 147 Figure 92. Relative Abundance of Genes Associated with Manganese, Zinc and Iron Measured in Column Samples ..................................................................................................................................................... 147 Figure 93. Relative Abundance of CzcA Tolerance Gene Measured in Field Samples ............................... 148 Figure 94. Relative Abundance of CzcA Tolerance Gene Measured in Column Samples .......................... 148 Figure 94. Rainfall Recorded for Downtown Vancouver Between July 1 and July 15, 2015 ...................... 194 Figure 95. Rainfall Recorded for Downtown Vancouver Between July 15 and July 30, 2015 .................... 194 Figure 96. Rainfall Recorded for Downtown Vancouver between August 1 and August 15 ...................... 194 Figure 97. Rainfall Recorded for Downtown Vancouver Between August 17 and August 31, 2015 .......... 195 Figure 98. Rainfall Recorded for Downtown Vancouver Between September 1 and September 15, 2015 .................................................................................................................................................................. 195 Figure 99. Rainfall Recorded for Downtown Vancouver Between September 16 and September 30, 2015 .................................................................................................................................................................. 195 Figure 100. Rainfall Recorded for Downtown Vancouver Between October 1 and October 15, 2015 ...... 196 Figure 101. Rainfall Recorded for Downtown Vancouver Between October 16 and October 30, 2015 .... 196 Figure 102. Rainfall Recorded for Downtown Vancouver Between October 31 and November 14, 2015 196 Figure 103. Rainfall Recorded for Downtown Vancouver Between November 15 and November 29, 2015 .................................................................................................................................................................. 197 Figure 104. Rainfall Recorded for Downtown Vancouver Between November 30 and December 14, 2015 .................................................................................................................................................................. 197 Figure 105. Rainfall Recorded for Downtown Vancouver Between December 15 and December 29, 2015 .................................................................................................................................................................. 197 Figure 106. Delineation of Lost Lagoon Wetland Watershed Using ArcGIS Online Tool (2016) ................ 201 Figure 107. Depth Profile Measurements Taken in the Lost Lagoon Wetland Forebay ............................ 202 Figure 108. Barplot Between Field Site Water Samples for Richness Based on the Chao1 Estimator ....... 217 Figure 109. Boxplot Between Field Site Water Samples for Richness Based on the Chao1 Estimator ...... 217 xxii  Figure 110. ANOVA Residuals Between Field Site Water Samples for Richness Based on the Chao1 Estimator .................................................................................................................................................................. 217 Figure 111. Barplot Between Field Site Water Samples for Coverage Based on Good’s Coverage ........... 218 Figure 112. Boxplot Between Field Site Water Samples for Coverage Based on Good’s Coverage ........... 219 Figure 113. ANOVA Residuals Between Field Site Water Samples for Coverage Based on Good’s Coverage .................................................................................................................................................................. 219 Figure 114. Barplot Between Field Site Water Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................................................. 220 Figure 115. Boxplot Between Field Site Water Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................................................. 220 Figure 116. ANOVA Residuals Between Field Site Water Samples for Diversity Based on the Inverse Simpson Estimator ................................................................................................................................................... 221 Figure 117. Barplot Between Field Site Water Samples for Observed OTUs Based on the SOBS Calculation .................................................................................................................................................................. 221 Figure 118. Boxplot Between Field Site Water Samples for Observed OTUs Based on the SOBS Calculation .................................................................................................................................................................. 221 Figure 119. ANOVA Residuals Between Field Site Water Samples for Observed OTUs Based on the SOBS Calculation ................................................................................................................................................. 222 Figure 120. Barplot Between Field Site Surface Sediment Samples for Richness Based on the Chao1 Estimator ................................................................................................................................................... 223 Figure 121. Boxplot Between Field Site Surface Sediment Samples for Richness Based on the Chao1 Estimator ................................................................................................................................................... 223 Figure 122. ANOVA Residuals Between Field Site Surface Sediment Samples for Richness Based on the Chao1 Estimator ........................................................................................................................................ 223 Figure 123. Barplot Between Field Site Surface Sediment Samples for Coverage Based on Good’s Coverage .................................................................................................................................................................. 224 Figure 124. Boxplot Between Field Site Surface Sediment Samples for Coverage Based on Good’s Coverage .................................................................................................................................................................. 224 Figure 125. ANOVA Residuals Between Field Site Surface Sediment Samples for Coverage Based on Good’s Coverage ................................................................................................................................................... 225 Figure 126. Barplot Between Field Site Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................... 225 Figure 127. Boxplot Between Field Site Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................... 226 Figure 128. ANOVA Residuals Between Field Site Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator ........................................................................................................................ 226 Figure 129. Barplot Between Field Site Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation ................................................................................................................................................. 227 xxiii  Figure 130. Boxplot Between Field Site Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation ................................................................................................................................................. 227 Figure 131. ANOVA Residuals Between Field Site Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation ................................................................................................................................. 228 Figure 132. Barplot Between Field Site 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator ................................................................................................................................................... 228 Figure 133. Boxplot Between Field Site 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator ................................................................................................................................................... 229 Figure 134. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator ........................................................................................................................................ 229 Figure 135. Barplot Between Field Site 10-cm Depth Sediment Samples for Coverage Based on Good’s Coverage ................................................................................................................................................... 230 Figure 136. Boxplot Between Field Site 10-cm Depth Sediment Samples for Coverage Based on Good’s Coverage ................................................................................................................................................... 230 Figure 137. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Coverage Based on Good’s Coverage ....................................................................................................................................... 230 Figure 138. Barplot Between Field Site 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................... 231 Figure 139. Boxplot Between Field Site 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................... 231 Figure 140. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator ........................................................................................................................ 231 Figure 141. Barplot Between Field Site 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation ....................................................................................................................................... 232 Figure 142. Boxplot Between Field Site 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation ....................................................................................................................................... 232 Figure 143. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation ............................................................................................................................ 233 Figure 144. Barplot Between Column Samples for Richness Based on the Chao1 Estimator .................... 233 Figure 145. ANOVA Residuals Between Column Water Samples for Richness Based on the Chao1 Estimator .................................................................................................................................................................. 234 Figure 146. Barplot Between Column Water Samples for Coverage Based on the Good’s Coverage ....... 234 Figure 147. ANOVA Residuals Between Column Water Samples for Observed OTUs Based on the Good’s Coverage ................................................................................................................................................... 235 Figure 148. Barplot Between Column Water Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................................................. 235 Figure 149. ANOVA Residuals Between Column Water Samples for Observed OTUs Based on the Inverse Simpson Estimator .................................................................................................................................... 236 xxiv  Figure 150. Barplot Between Column Water Samples for Observed OTUs Based on the SOBS Calculation .................................................................................................................................................................. 236 Figure 151. ANOVA Residuals Between Column Water Samples for Observed OTUs Based on the SOBS Calculation ................................................................................................................................................. 237 Figure 152. Barplot Between Column Surface Sediment Samples for Richness Based on the Chao1 Estimator .................................................................................................................................................................. 237 Figure 153. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the Chao1 Estimator ........................................................................................................................................ 238 Figure 154. Barplot Between Column Surface Sediment Samples for Coverage Based on the Good’s Coverage ................................................................................................................................................... 238 Figure 155. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the Good’s Coverage ....................................................................................................................................... 239 Figure 156. Barplot Between Column Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator ................................................................................................................................................... 239 Figure 157. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the Inverse Simpson Estimator ........................................................................................................................ 240 Figure 158. Barplot Between Column Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation ................................................................................................................................................. 240 Figure 159. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation ....................................................................................................................................... 241 Figure 160. Barplot Between Column 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator ................................................................................................................................................... 241 Figure 161. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the Chao1 Estimator ............................................................................................................................. 242 Figure 162. Barplot Between Column 10-cm Depth Sediment Samples for Coverage Based on the Good’s Coverage ................................................................................................................................................... 242 Figure 163. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the Good’s Coverage ............................................................................................................................ 243 Figure 164. Barplot Between Column 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator .................................................................................................................................... 243 Figure 165. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the Inverse Simpson Estimator ............................................................................................................. 244 Figure 166. Barplot Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation ....................................................................................................................................... 244 Figure 167. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation ............................................................................................................................ 245    xxv  List of Abbreviations and Symbols Symbol Property (A)RISA (Automated) Ribosomal intergenic spacer analysis AA Atomic absorption ANOSIM Analysis of similarity ANOVA Analysis of variance BCTFA British Columbia Transportation Financing Authority bp Base-pair CAMERA Community cyberinfrastructure for advanced microbial ecology research CEME Civil engineering and mechanical engineering CLPP Community level physiological profiling COD Chemical oxygen demand Cond Conductivity Df Degrees of freedom DGGE Denaturing gradient gel electrophoresis DNA Deoxyribonucleic acid DO Dissolved oxygen FAME Fatty acid methyl ester analysis FISH Florescence in situ hybridization GAAS Genome relative abundance and average size GPS Global positioning system GUSTA ME Guide to statistical analysis in microbial ecology ICP-OES Inductively coupled plasma optical emission spectrometry IMG/M Integrated microbial genomes with microbiome samples KEGG Kyoto encyclopedia of genes and genomes KWL Kerr Wood Leidal Consulting Engineers Limited MAFFT Multiple alignment program for amino acid or nucleotide sequences MeanSqs Mean squares MG-RAST Metagenome rapid annotation subsystem technology MO&G Mineral oil and grease NMDS Non-metric multidimensional scaling ORP Oxidation-reduction potential OTU Operational taxonomic unit PCB Polychlorinated biphenyl PCR Polymerase chain reaction PC-SWMM Personal computer stormwater management model PLFA Phospholipid fatty acid PVC Polyvinyl chloride QIIME Quantitative insights into microbial ecology Q-PCR Quantitative polymerase chain reaction RAxML Randomized accelerated maximum likelihood RDP Ribosomal database project RFLP Restriction fragment length polymorphism rpoB Polymerase beta sub-unit rRNA Ribosomal ribonucleic acid SCSU Sole-carbon source utilization SPES Stanley Park Ecology Society SSCP Single strand confirmation polymorphism STAMP Strategies and techniques for analyzing microbial populations SumsOfSqs Sum of squares TACOA Taxonomic classification of environmental genomic fragments approach TGGE Temperature gradient gel electrophoresis TOC Total organic carbon xxvi  T-RFLP Terminal restriction fragment length polymorphism TSS Total suspended solids Turb Turbidity UBC University of British Columbia VOC Volatile organic compound   xxvii  Acknowledgements This project was generously financed through a partnership between the University of British Columbia, the Natural Sciences and Engineering Council of Canada through the Industrial Postgraduate Scholarship (IPS) program, Genome British Columbia, through the User Partnership Program (UPP), Kerr Wood Leidal Consulting Engineers Ltd. and the Stanley Park Ecology Society. In addition, the scope of this project required many partners and collaborators to whom I am most grateful: UBC Professors: 1. Prof. James Atwater, project supervisor and endless source of knowledge and support 2. Dr. Susan Baldwin, project support for the microbiology portion of the research 3. Dr. Dirk Van Zyl, project proposal assistance and support for the laboratory study 4. Dr. Bill Mohn, input for the microbial sampling plan, bioinformatics and analyses 5. Dr. Karen Bartlett, providing laboratory space for sample processing UBC Staff:  Paula Parkinson, laboratory training and support in the CEME Environmental Lab  Timothy Ma, laboratory support in the CEME Environmental Lab  Jonathan Taylor, laboratory training and support in the CHBE microbiology lab  Anastacia Kuzmin, whole genome sequencing  Dr. Ido Hatam, software and bioinformatics training and review UBC Students:  Cristina Kei Oliveira, laboratory and fieldwork assistance  Marie De Zetter, laboratory and fieldwork assistance  Michael Harvard, laboratory and fieldwork assistance  Shona Robinson, fieldwork assistance  Jeff MacSween, fieldwork assistance  Gal Av-Gay and Julian Ho, statistical consulting Kerr Wood Leidal Consulting Engineers Ltd. Staff:  Chris Johnston, financial support, project direction, and consulting  Patrick Lilley, biology assistance  Ryan Taylor, GIS assistance Stanley Park Ecology Society Staff:  Patricia Thomson, in-kind financial support for fieldwork  June Pretzer and Maria Egerton, assistance with fieldwork management  Paul Higginson, fieldwork assistance and local site resource Genome BC:  Aniko Takacs-Cox and Chen Wan, proposal development, sector and financial management Other:  Daniel Smith, laboratory and fieldwork assistance  Jamen Kaye, laboratory assistance  Nicholas Williams, fieldwork assistance  xxviii  Dedication This thesis is dedicated to my grade nine science teacher, Mr. Tobias Blaskovits. Without knowing it at the time, Mr. Blaskovits helped my awkward thirteen-year-old self find her niche in high school but more importantly, he was the first person to inspire my passion for environmental conservation, which has ultimately led to my pursuit of this research. Beyond this, Mr. Blaskovits connected me with the group pictured below, which includes some of my most treasured lifelong friends. Mr. Blaskovits continues to use hand-on approaches to help young students find their passion for science, engineering, and discovery. It is the teachers like Mr. Blaskovits that shape our future communities; they are deserving of our utmost appreciation and thanks.   Winning the Mind Grind in grade 9, 2007 In the photo: Mr. Tobias Blaskovitz, Edward Truong, Jessica LeNoble and Cody O’Neil photographed with CBC News Cast, Sandy Dawson and Mike Roberts Our mind grind team in grade 12, 2010 In the photo: Edward Truong, Peter Davidson, Cody O’Neil, Connor Vandenberg, Alexa Geddes, Leanna Gruendel, and Jessica LeNoble 1  1. Introduction 1.1 Background In the context of this research, stormwater consists of precipitation that falls onto impervious surfaces and fails to infiltrate into the ground. Traditional stormwater management involves diverting stormwater into storm sewers followed by discharge to a watercourse, which may or may not include prior treatment at a wastewater treatment facility. However, in Vancouver and elsewhere, there is a push by provincial and municipal governments to integrate stormwater treatment practices through the design and installation of low impact design features (British Columbia Ministry of Community, Sport, and Cultural Development, n.d.), which make use of natural processes to enhance the quality of discharged water, reduce the quantity of runoff, and recharge groundwater aquifers. For this reason, engineered wetlands, which are designed to optimize natural processes for water diversion and treatment, are becoming a more common and desirable treatment option for stormwater. However, there are still barriers to the implementation of these wetlands as low impact design techniques for stormwater. Traditional water quality monitoring often does not provide a reliable enough validation that the wetlands are meeting water treatment objectives. Adequate pollutant removal efficiency monitoring requires continuous inflow and outflow measurements over a two-year study period (Erickson, Weiss, & Gulliver, 2013); thus, this regime is highly intensive for both resources and labour. In addition, the potential for erroneous and uncollected data is accelerated by unpredictable weather and the potential for equipment wear due to urban vandalism and routine use over an extensive study period. With diverse priorities and competition for limited resources, municipalities are unlikely to fund adequate monitoring regimes for engineered wetlands and will either choose to avoid their installation or base decision making on inadequate analyses. As low impact design features become a greater priority, emerging analyses methods for monitoring pollutant removal efficiencies are of interest for application in the stormwater treatment sector. One such emerging analysis method for monitoring treatment effectiveness is the application of genomics, “the branch of molecular biology that is concerned with the structure, function, evolution, and mapping of genomes, or the complete set of DNA within a single cell of an organism.” (Oxford Univerisity Press, 2016) Because the toxicity of stormwater influences microbial life (Karlsson, Viklander, Scholes, & Revitt, 2010), analysis of the microbiology within engineered wetlands may compliment traditional water quality monitoring and improve the effectiveness of treatment wetlands in the future. The content in this thesis 2  provides data to support this claim. 1.2 Motivation In 1999, Kerr Wood Leidal Consulting Engineers Ltd. (KWL) was commissioned by the City of Vancouver for the design and commissioning of an engineered wetland, from here forward referred to as the Lost Lagoon wetland, which would treat stormwater exiting the newly expanded Stanley Park Causeway displayed by the map in Figure 1.  Figure 1. Map of Stanley Park (City of Vancouver, 2016b) Highlighting the Lost Lagoon Wetland At the time it was commissioned, the Lost Lagoon wetland employed many of the best engineering management practices available and, in doing so, the design received an award of excellence from The Consulting Engineers of British Columbia. However, since the wetland was installed, only limited assessment of its treatment effectiveness has been performed. Though treatment monitoring is desirable and necessary, because of reasons described in the previous section, adequate water treatment monitoring Lost Lagoon wetland 3  has not been performed. That being said, the Lost Lagoon wetland is a highly desirable site for the application of an emerging monitoring method because it was designed as an ideal treatment system with its only source of influent being stormwater diverted from the Stanley Park Causeway. There is a wealth of knowledge indicating that the toxic components of stormwater have an influence on bacteria at both the species and functional gene levels (Nies, 1999). This wealth of knowledge along with the desire to increase the use of low impact design features for stormwater treatment led to the motivation behind this research. 1.3 Objective and Study Goals Overall, the goal of this study was to provide proof of concept data that supports or rejects developing a genomics monitoring tool for low impact design features that treat stormwater, including engineered wetlands. This goal was achieved by splitting the study’s components into two chapters, with each chapter encompassing three objectives. Chapter 1: Apply traditional water and sediment quality monitoring techniques for validation of the Lost Lagoon wetland Using limited water and sediment sampled from the Lost Lagoon wetland: 1. Demonstrate that the Lost Lagoon wetland is meeting water quality treatment guidelines; 2. Demonstrate that the engineering best management practices employed in the design of the Lost Lagoon wetland have had some meaningful impact on the stormwater treatment efficiency; and 3. Identify knowledge gaps and opportunities for complimentary data analyses though the application of genomics. Chapter 2: Apply genomics monitoring techniques for complimentary validation of the Lost Lagoon wetland Using the same samples that were analysed in Chapter 1: 1. Apply genomics-based analysis methods to determine if there are shifts in the microbial communities and functional genes along the length of the Lost Lagoon wetland; 2. Determine if there is a correlation between the water and sediment quality, present over the study period, and the microbial communities and functional genes observed; and 3. Determine, through laboratory experimentation, if there are opportunities to expand and pursue genomics analyses at other stormwater treatment low impact design features. 4  1.4 Scope and General Research Activities The scope of this research can be differentiated into two parts described here. In the first part of the thesis, a field study was executed at the Lost Lagoon wetland in Stanley Park, British Columbia. The field study covered a six-month period between July, 2015 and December, 2015. Data obtained from the field study was analyzed in order to inform the conclusions of Chapter 1, where limited traditional water and sediment quality analyses were employed in an attempt to validate the Lost Lagoon wetland and to identify knowledge gaps and opportunities for complimentary analyses though the application of genomics. In the second part of this thesis, DNA was first extracted from the field samples taken at the Lost Lagoon wetland and next sequenced, analyzed, and compared at both the bacterial species level and the functional gene level. In addition to these analyses, a laboratory study was carried out using columns of uncontaminated natural sediment sourced from a bog near Beaver Lake as highlighted in Figure 2.   5   Figure 2. Map of Stanley Park (City of Vancouver, 2016b) Highlighting the Beaver Lake Bog  The laboratory study ran for a four-month period between December 2015 and March 2016; however, laboratory conditions were controlled and designed to mimic the weather observed at the Lost Lagoon wetland over the period between September 2015 and December 2015. During the laboratory study period, seventeen sediment columns were repeatedly dosed with either semi-synthetic stormwater or distilled water. At one month intervals, sediment columns were sacrificed and analyzed for both the traditional water and sediment quality parameters as well as DNA. The results obtained from the field and laboratory studies were subsequently used to inform the conclusions of Chapter 2, where genomics monitoring techniques were employed in an attempt to provide complimentary validation of the Lost Lagoon wetland and to determine if there may be future opportunities to expand and pursue genomics analyses at other low impact design stormwater treatment features.   Beaver Lake bog 6  2. Chapter 1: Application of Traditional Water and Sediment Quality Monitoring Techniques for Validation of the Lost Lagoon Stormwater Treatment Wetland 2.1 Introduction and Chapter Goal The contents of this chapter detail the background and environmental results of a field study that was undertaken at the Lost Lagoon wetland. Water and soil samples were collected from the wetland and environmental conditions were measured and analyzed. In addition, DNA was extracted and archived for future analyses in Chapter 2. Because the sampling regime was designed to optimize the collection of bacterial DNA, there were some limitations for the environmental analyses, which are further discussed later in this chapter. Most importantly, sampling of the wetland was performed over a six-month period, which is shorter than the required timespan needed to fully validate a stormwater treatment wetland. The goal of this chapter was to demonstrate that the wetland is an ideal field site to be used for the ‘proof of concept’ design of a genomics-based monitoring tool for stormwater treatment wetlands. This chapter identifies common challenges that result from traditional wetland testing and also provides a lead in for opportunities to apply genomics as a method to reduce said challenges. To illustrate the need for stormwater management, background details on stormwater toxicity and treatment requirements are first provided. Next, engineered wetlands and associated best management practices are described. The Lost Lagoon wetland is then given some background and the design features are described in order to provide context for the field sampling and analysis plan. Finally, the study methodology, results, discussion and conclusions are provided. 2.2 Chapter Objectives Based on the overall goal of this chapter, this chapter has three specific objectives. Using water and sediment sampled from the Lost Lagoon wetland: 1. Demonstrate that the Lost Lagoon wetland is meeting or exceeding water quality treatment guidelines; 2. Demonstrate that the engineering best management practices employed in the design of the Lost Lagoon wetland have had some meaningful impact on the stormwater treatment efficiency; and 3. Identify knowledge gaps and opportunities for complimentary data analyses though the application of genomics. 7  2.3 Hypotheses The Lost Lagoon wetland was designed to improve stormwater runoff quality through a variety of treatment mechanisms including filtration, sedimentation, adsorption, and biological uptake. Therefore, in order to prove that the wetland is meeting treatment guidelines and in order to begin to validate the treatment mechanisms within the wetland, two hypotheses must be true. 1. The concentrations of metals associated with stormwater decrease along the length of the wetland; and 2. The concentration of oil and grease decreases along the length of the wetland. 2.4 Literature Review In order to provide background and context for the objectives and hypotheses stated in this chapter, a review of relevant literature was performed. First, a description of the regulatory framework for stormwater treatment in Vancouver is supplied. Next, common pollutants in stormwater are given some context, including the pollutants’ origins, reasons for toxicity, expected concentration ranges, guidelines for treatment, and the expected treatment that is achievable using engineered wetlands. Barriers for implementing wetlands for stormwater treatment are described as well as a description of traditional monitoring techniques. Finally, the precedent, design components and best management practices, and past analyses of the Lost Lagoon wetland are described.  Regulatory Framework In Canada, a multi-jurisdictional approach provides the authority to discharge liquid waste and different regulations and guidelines come into force depending on the source and content of the liquid waste which is to be discharged. At the national level, there are federal regulations under Section 35(1) of the Fisheries Act (Government of Canada, 1985), which stipulate conditions for discharges to fish bearing receiving bodies. In addition, the federal Environmental Protection Act (Government of Canada, 1999) makes pollution prevention the cornerstone of national efforts to reduce toxic substances in the environment. However, these Acts do not explicitly regulate discharges of waste where the only source is stormwater. This is mainly due to the fact that management of the natural environment is largely a provincial jurisdiction in Canada and, thus, federal regulations on environmental matters are limited. Concerning stormwater, beyond the Fisheries Act and Environmental Protection Act, several federal guidelines and best management practices exist that collectively serve to provide a Canada-wide strategy for stormwater management and planning. These 8  guidelines are largely the result of a consensus among provincial governments reached through meetings of the Canadian Council of Ministers of the Environment. At the provincial level, British Columbia has adopted this federal strategy through application of its guideline for managing stormwater titled, Stormwater Planning: A guidebook for BC (Stephens, Graham, & Reid, 2002) and through enforcement of the Municipal Wastewater Regulation (Government of British Columbia, 2016). Within its suite of provincial regulations, British Columbia grants the authority to permit stormwater treatment and conveyance systems to municipalities. However, because municipalities do not hold an explicit right to jurisdictional power in Canada, the province of British Columbia still directly controls liquid waste discharges by requiring all municipalities to submit and adhere to an Integrated Liquid Waste and Resource Management Plan. Said plan must first be approved by the BC Ministry of the Environment before municipalities are granted implicit rights to regulate and permit the management of liquid waste, including stormwater. At the municipal level, by developing an Integrated Liquid Waste and Resource Management Plan, Metro Vancouver provides resources, and guidelines concerning stormwater that its fourteen member municipalities must adhere to prior to being granted local authority over stormwater management. Through applying Metro Vancouver’s liquid waste management plan, the City of Vancouver developed its target specific plan, namely the Citywide Integrated Stormwater Management Plan (City of Vancouver, 2016a). Within this plan, locally relevant best management practices are supplied in an easy to apply context for developers, planners and engineers. In addition, priorities for low impact design features are placed at a high significance concerning Vancouver’s sustainability goals. Because the guidelines and regulations for treating and conveying stormwater are managed within several documents and pieces of legislation, it is easy to become lost when attempting to discern what information is most applicable. For this reason, Figure 3 has been supplied as a summary of the regulatory framework concerning stormwater management in British Columbia.  9  FederalProvincialMunicipalFisheries and Oceans Canada Environment CanadaCanadian Council of Ministers of the EnvironmentMinistry of Community and Rural DevelopmentMinistry of EnvironmentCity of VancouverMetro Vancouver Stormwater Interagency Liaison GroupFisheries Act→ Regulates the disruption of fish habitats→ “No person shall carry on any work… that results in serious harm to fish that are part of a commercial, recreational or Aboriginal fishery.”Urban Stormwater Guidelines and Best Management Practices for Protection of Fish and Fish Habitat→ Provides target best management practices to reduce and mitigate the total runoff volume caused by increased urban development, mitigate water quality impacts to fish habitat, and  restrict the post-development peak runoff flow rate to that of the predevelopment peak runoff flow rate→ Enables infrastructure financing and provides co-funding to local governments for civic projectsMunicipal Wastewater Regulation→ Includes long term planning to phase out combined sewers and to increase infiltration of groundwater→ Includes site specific regulations for allowable effluent volumes and discharge quality→ Does not directly cover discharges from separate stormwater discharge facilitiesStormwater Planning: A guidebook for BC→ Includes a component requiring all municipalities to develop liquid waste management plans→ Provides guidelines for local governments to develop Integrated Stormwater Management Plans→ Provides five guiding principles for integrated stormwater management including: (1) agreeing that stormwater is a resource, (2) designing for the complete spectrum of rainfall events, (3) acting on a priority basis in at-risk drainage catchments, (4) planning at four scales – regional, watershed, neighbourhood, and site and (5) testing solutions and reducing costs by adaptive managementEnvironmental Protection Act→ Makes pollution prevention the cornerstone of national efforts to reduce toxic substances in the environment→ Provides tools to manage toxic substances, pollution, and wastesCanada Wide Strategy for the Management of Municipal Wastewater→ Requires that all facilities achieve minimum national performance standards and develop and manage site-specific effluent discharge objectives→ Does not directly cover discharges from separate stormwater discharge facilities Integrated Liquid Waste and Resource Management Plan→ Defines the roles of member municipalities and other jurisdictions in managing liquid waste→ Stipulates that all member municipalities are required to have an Integrated Stormwater Management Plan in Place→ Gives authority to member municipalities to own and maintain stormwater systems and to set local land use plans and community development standardsStormwater Management Guide→ Provides a guideline for member municipalities to meet the Integrated Liquid Waste and Resource Management PlanCitywide Integrated Stormwater Management Plan→ Aligns stormwater management with a broad set of citywide sustainability goals→ Sets infiltration, quality and conveyance targets for the full rainfall spectrum→ Includes specific targets for use of low impact designs for increased water infiltration and treatment→ Includes goals for enhancement of biodiversity focused or demonstration projects→ Includes plans to reduce and eliminate combined sewage systems→ Provides a best management design practices toolkit for developers, which includes components for engineered wetlands                    Figure 3. Visual Breakdown of the Regulatory Framework for Stormwater Management in Vancouver 10   Urban Stormwater and Accepted Treatment Efficacy of Engineered Wetlands  Description and Sources of Common Pollutants in Urban Stormwater As urbanization increases, construction and development lead to an increase in the total impervious surface area within watersheds. Because impervious surfaces limit the ability of water to infiltrate into the ground, unmitigated urbanization can lead to an increase in runoff volumes and peak flow rates. These larger faster runoffs yield more kinetic energy, which increases the opportunity for erosion and the movement of solid particles. In addition, roadways and vehicle traffic are sources of pollutants due to combustion of fossil fuels and mechanical wear. Thus, the quality of stormwater is degraded as a number of pollutants increase in concentration. Table 1 outlines the common pollutants of concern found in stormwater and Table 2 outlines the sources of said pollutants. Table 1. Description of Common Pollutants in Urban Stormwater Pollutant Description Alkalinity Water's capacity to neutralize acid measured as concentration of CaCO3 Chloride Concentration of dissolved Cl- Hardness Dissolved calcium and magnesium, measured as CaCO3 Nitrogen Nutrient existing as particulate, dissolved, nitrate, nitrite, and ammonium Phosphorus Nutrient existing in numerous particulate and dissolved forms Mineral Oil and Grease Total concentration of hydrocarbons Organic Carbon Degradable organic material in total or dissolved form pH Function of the number of hydrogen ions in a solution Solids Total concentration of suspended or dissolved particulates Temperature Thermal property Turbidity Cloudiness of water, an indirect measure of particulates Metals Concentration of As, Ag, Al, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, Pb, Sb, V, and/or Zn in total or dissolved form    11  Table 2. Sources of Common Pollutants in Urban Stormwater Pollutant Sources in Stormwater 1,2 Alkalinity Rainwater, rocks, soil and debris Chloride Road de-icing rock salts,  Hardness Rainwater, rocks, soil and debris Nitrogen Atmosphere, animal waste, vegetative matter and fertilizers Phosphorus Atmosphere, animal waste, vegetative matter and fertilizers Mineral Oil and Grease Atmosphere, vehicle coolants, gasoline, oils, lubricants, coal-tar based asphalt sealants Organic Carbon Animal waste, vegetation, oils, greases, grass clippings pH Rainwater, reduced buffering due to impervious surfaces Solids Atmosphere, pavement wear, vehicles, and road maintenance Temperature Changes in land use, surface cover and shading Turbidity Atmosphere, pavement wear, vehicles, road maintenance, vegetation Arsenic Atmosphere, fertilizers, animal waste, solid wastes Silver Diesel fuels, improper disposal of industrial wastes Aluminum Atmosphere, rocks, soil, and debris, vehicle exhaust, asphalt  Barium Vehicle wear Beryllium Vehicle wear Calcium Road de-icing rock salts, grease, atmosphere, rocks, soil and debris Cadmium Vehicle wear, tire fillers and insecticides Cobalt Atmosphere, vehicle wear Chromium Atmosphere, vehicle wear, moving engine parts and brake linings Copper Soil, bearing wear, engine parts, brake linings and radiator repair Iron Atmosphere, soil, vehicle wear, engine parts, and road structures Potassium Atmosphere and fertilizers Magnesium Road de-icing rock salts, soil, rocks and debris, rainwater Manganese Atmosphere, engine parts and gasoline additives Molybdenum Atmosphere, vehicle wear, brake linings Sodium Atmosphere, road de-icing rock salts, soil, rocks and debris Nickel Diesel fuel, lubricating oil, bushing wear, brake linings and asphalt Lead Tire fillers, lubricating oil/grease, vehicle wear and radiators Antimony Rubber tires, enamel paints and lacquers Vanadium Atmosphere Zinc Atmosphere, tire wear, vehicle wear, soil, rocks and debris 1 (Erickson, Weiss, & Gulliver, 2013) 2 (British Coloumbia Ministry of the Environment, 1992)   12   Reasons for Toxicity for Common Pollutants in Urban Stormwater Urban stormwater can have hydrological, chemical, biological or physical impacts on the environment; however, the greatest concern is usually biological integrity and habitat alteration (Erickson, Weiss, & Gulliver, 2013). As the concentration of certain pollutants increases in stormwater, a variety of toxic effects may become evident in the ecosystems of receiving water bodies. For this reason, untreated, unmitigated urban stormwater runoff is detrimental over time. Table 3 outlines the specific reasons for the toxicity of the common pollutants found in urban stormwater. 13  Table 3. Reasons for Toxicity of Common Pollutants in Urban Stormwater Pollutant Reasons for Toxicity 1 Alkalinity  Low alkalinity limits the buffering capacity of receiving water to moderate changes in pH Chloride  High chloride concentrations indirectly affect soil properties such as swelling, porosity, water retention, and saturated hydraulic conductivity  High chloride concentrations contribute to high salinity which can be lethal for freshwater species Hardness  Low hardness indirectly increases toxicity as cadmium, copper, nickel and lead toxicities increase as hardness decreases Nitrogen  High nitrogen concentrations increase plant growth in a process called eutrophication  Eutrophication leads to reduced water clarity and the presence of blue-green algae which decomposes, reducing the oxygen content of the receiving water body Phosphorus  High phosphorus concentrations increase plant growth in a process called eutrophication  Eutrophication leads to reduced water clarity and the presence of blue-green algae which decomposes, reducing the oxygen content of the receiving water body Mineral Oil and Grease  Reduce the ability of some organisms to reproduce, negatively impact the ability of some plant species to grow, and can be lethal in high concentrations  Can accumulate in the sediment of aquatic environments, reducing oxygen content as it slowly decomposes Organic Carbon  Degradation consumes oxygen and impairs aquatic life pH  Changes in pH can be lethal for aquatic organisms  pH can indirectly influence the toxicity of other toxic compounds, including heavy metals  Solids  High solids loadings contribute to oxygen consumption and eutrophication  High solids loadings are associated with higher concentrations of particle-bound pollutants, including heavy metals Temperature  Surges of elevated temperatures reduce dissolved oxygen content  Temperature can indirectly influence the toxicity of other compounds, such as ammonia Turbidity  High turbidity is associated with high particulate loadings and is associated with higher concentrations of particle-bound pollutants, including heavy metals Metals  Reduce the ability of some organisms to reproduce, negatively impact the ability of some plant species to grow, and can be lethal in high concentrations  Can bioaccumulate in the sediment of aquatic environments 1 (Erickson, Weiss, & Gulliver, 2013)    14   Wetlands as an Urban Stormwater Control Measure While the technology has improved in the last twenty years, wetlands have long been known to improve water quality (Kerr Wood Leidal Associates Ltd., 1999). Removal efficiencies for toxins associated with sediments can be as high as 90%, with average total removal efficiencies in the range of 60%-80% (Hawkins et al., 1997). The expected removal efficiencies for wetlands are comparable with other treatment options but wetlands provide the added benefit of enhanced habitats for wildlife and plants. Table 4 outlines the expected concentration of pollutants in stormwater, the guidelines for treatment in Canada and the removal efficiency expected from engineered wetlands.   15  Table 4. Concentration of Common Pollutants in Urban Stormwater, Treatment Guidelines and Removal Efficiency Using Engineered Wetlands Pollutant Concentration Removal Efficiency 4, % Stormwater 1,2 Guideline3  Alkalinity (mg/L) 8-1531 20 - Chloride (mg/L) 0.5-75.31 0.640 - Hardness (mg/L) 8.2-80.31 20 - Nitrogen (mg/L) 0.34-202 - -19α Phosphorus (µg/L) 64-44101 - 7 Mineral Oil and Grease (mg/L) 5.0-63.41 15 74 Organic Carbon (mg/L) 7.3-17.62 - 31 pH 6.2-8.72 6.5-9 - Solids (mg/L) 44-8091 20% above BL* -5 α Temperature - - - Turbidity - - - Arsenic (µg/L) 0-585 5 41 Silver (µg/L) 3.05 0.25 - Aluminum (µg/L) 26-71001 100 85 Barium (µg/L) 2-7921 - 34 Beryllium (µg/L) - - - Calcium (µg/L) 42-5061 - 67 Cadmium (µg/L) 0-405 1 - Cobalt (µg/L) - - - Chromium (µg/L) 0-405 2 61 Copper (µg/L) 22-70335 2 33 Iron (µg/L) 32-1250001 350 84 Potassium (mg/L) 5-1141 - -8 α Magnesium (mg/L) 113-7411 - 29 Manganese (µg/L) 112-69101 80 91 Molybdenum (µg/L) - 70 - Sodium (mg/L) 6.7-5481 - -19 α Nickel (µg/L) 0-1265 25 - Lead (µg/L) 73-17805 3 79 Antimony (µg/L)  6 - Vanadium (µg/L)  - - Zinc (µg/L) 5-23861 7.5 71 1 (Stime, 2014) 2 (British Columbia Research Corporation, 1992) 3 (CCME Guidelines for Protection of Aquatic Life, Freshwater, 2016) 4 (Hawkins et al., 1997) 5 (Geosyntec Consultants & Wright Water Engineers Inc., 2011) *BL = Baseline concentration αNegative values indicate that wetlands are a source of material   16   Barriers for Implementing Stormwater Treatment Wetlands While the popularity of low impact design features is increasing, these systems still make up a minority of all stormwater treatment systems in British Columbia. Even with the increase of literature, which indicates the importance of low impact design features for long term urban sustainability, the cost and uncertainty behind these types of systems still remain the primary reasons that the implementation of low impact designs is challenging. Specifically, for the case of engineered wetlands, as a stormwater control measure, construction costs and long term maintenance and monitoring costs are of primary concern for land developers. Because stormwater quality is variable in nature, treatment efficacy through natural processes is challenging to monitor and validate. Proper validation of these systems often requires a two-year sampling regime, which is unlikely to be prioritized by most municipalities.  Traditional Water and Sediment Quality Monitoring for Validating the Efficacy of Stormwater Treatment Wetlands  Visual Inspection Visual inspection is the first and least complex option for inspecting an engineered wetland. Visual inspection is performed by running through a pre-prepared checklist in order to see if the different components of the wetland qualitatively appear to be functioning as they were designed. The downside of visual inspection is that if there are no outward signs of malfunction, there is no guarantee that the field inspector will notice that the wetland is operating improperly. A typical visual inspection should involve review of the following wetland properties:  History of previous visual inspections and assessments;  Condition and extent of access to the wetland, including upstream and downstream areas;  Condition of the inlet and outlet structures;  Condition of each component of the wetland (i.e. forebay, low marsh, high marsh etc.);  Condition of water –moving or stagnant as designed;  Potential that an illicit discharge occurred;  Signs of erosion and deposition;  Health and condition of soil and vegetation;  Quantity of litter and debris; and  Stability of banks and sides of practices. (Erickson, Weiss, & Gulliver, 2013)   17  Taken together, assessment of these properties should indicate to a field inspector whether the wetland is being maintained properly by the owner and whether the wetland is likely functioning within its design constraints. Visual inspection gives no quantitative indication of water treatment efficacy.  Testing Testing involves preparing a series of measurements which are taken under synthetically controlled conditions. Testing is considerably more involved than visual inspection but requires fewer resources than monitoring, which requires taking measurements during natural runoff events. Two types of testing are common when assessing stormwater treatment practices, namely capacity testing and synthetic runoff testing. Capacity testing requires taking point measurements to determine surface infiltration/filtration capacity or the remaining sediment storage available in a specific space. Synthetic runoff testing measures the overall performance of a wetland, rather than only a series of point measurements  Capacity Testing Capacity testing using sediment retention tests can be of great value for assessing the sedimentation and thus solids removal performance of wetlands. Sediment retention tests require measurement of surface elevations using a level rod and a boat or using electronic sonar depth measurement equipment. Taken together with GPS or total station longitude and latitudes and design drawings, these measurements can provide an estimate of the retained sediment within a forebay or settling pond (Erickson, Weiss, & Gulliver, 2013). The rate and efficiency of sediment accumulation can then be estimated using predictions or measurements of the inlet water quality and the timespan that the wetland has been in operation. Infiltration/filtration testing estimates the saturated hydraulic conductivity at specific locations within stormwater treatment systems. In the case of engineered wetlands, these measurements are less valuable because the wetlands are generally designed to inhibit infiltration and to instead convey water to a receiving water body. Infiltration/filtration capacity testing would be valuable if there is suspicion that the wetland is not functioning as designed.  Synthetic Runoff Testing Synthetic runoff testing requires that a prescribed quantity and quality of synthetic stormwater is applied to a stormwater treatment practice during controlled conditions. In the case of engineered wetlands, theoretically, the wetland could be dosed with synthetic stormwater and the quality of water at the outlet could be measured over time. Conservative tracers such as chloride or rhodamine can be added to the synthetic stormwater in order to determine if there are dead zones or short circuiting in the wetland. The 18  accuracy of synthetic stormwater testing may be low because it is challenging to maintain representative and consistent suspended solids in synthetic stormwater (Erickson, Weiss, & Gulliver, 2013). This process is also limited by the amount of synthetic stormwater that can be prepared, either using a fire hydrant, water truck or other source. Synthetic stormwater testing is more practical for small stormwater systems like grit chambers and stormceptors.  Monitoring Monitoring is the most accurate option for validating stormwater treatment systems but it is also the most time-consuming, resource intensive, and costly. Typically, monitoring is only performed when visual inspection and testing do not meet site validation goals or when stakeholders wish to use the treatment site as a demonstration of effective best management practices. Quantitatively monitoring the treatment effectiveness of engineered wetlands is achieved by collecting influent and effluent samples along each stage of the treatment system and determining the samples’ pollutant concentrations through laboratory analyses. When developing a monitoring plan, it is necessary to follow standardized guidance procedures, which are described elsewhere (Erickson, Weiss, & Gulliver, 2013). Due to the nature of weather, influent water quality and quantity is highly variable and, in order to have statistically significant analyses, repeat monitoring is generally required for all storms over a study period of fourteen to twenty-four months. Monitoring of engineered wetlands has a high potential for errors or losses in data because weather is unpredictable and the likelihood of equipment malfunctions over a long field study period increases with time.  Study Site:  Lost Lagoon Stormwater Treatment Wetland  Precedent for Installation In June of 1999, the Vancouver Board of Parks and Recreation commissioned KWL to prepare a stormwater management plan, which would coincide with upgrades to the Stanley Park Causeway. These upgrades were part of a larger Stanley Park Causeway rehabilitation project, which was funded under the umbrella of the British Columbia Transportation Financing Authority (BCTFA) Lions Gate Bridge project. On June 30th 1999, staff from the Park’s Board and KWL held a workshop to develop recommendations for stormwater management along the causeway. The final recommendations included:  Discharging all pavement surface runoff to Lost Lagoon;  Treating the runoff through installation of an engineered wetland located in the northeast corner of Lost Lagoon; and 19   Adding spill interceptors in two locations.  Design, Installation, Maintenance and Monitoring Regime  Design Lost Lagoon was originally a saltwater passage between Vancouver and Stanley Park.  In 1916, the eastern end of Lost Lagoon was cut off from Coal Harbour (Clifford, 1932). While there is a carp population that was seeded in the lagoon, it is recognized that, due to its artificial design, Lost Lagoon is primarily an aesthetic feature in the park and not a sensitive aquatic habitat (Kerr Wood Leidal Associates Ltd., 1999). Compared to other habitats in Stanley Park, the aquatic life in Lost Lagoon is generally tolerant to changes in salinity and water quality conditions but it was recognized during the design of the stormwater management plan in 1999 that the input of additional stormwater to Lost Lagoon should not reduce the quality of Lost Lagoon. In order to maintain the water elevation in Lost Lagoon, it is augmented by the city drinking water supply though use of a fountain. Originally, it was thought that stormwater from the causeway could supplement the inflow from the fountain but calculations proved that the stormwater inflow from the causeway would be negligible. Before installation of the engineered wetland, the Stanley Park causeway was drained by catch basins which discharged stormwater into ditches on both sides of the road. This allowed the pollutants from the roadway to extend directly from the ditches into forested sections of Stanley Park. The new and revised drainage plan included a number of features to prevent contamination from the roadway from reaching forested areas. The causeway drainage plan had a number of provisions including:  Two oil/water stormceptors – Stormceptor Model #3000 online with the storm sewer and located on the upper end of the causeway near the pedestrian overpass and Stormceptor Model #4000 located near the Lost Lagoon wetland system;  A single discharge point for stormwater runoff located at the northeast corner of Lost Lagoon;  Ditch subdrains for redirection of clean shallow groundwater directly to existing creek systems;  A flow diversion structure at Lost Lagoon; and  An engineered wetland including a settling forebay and flow augmentation structure. The city drainage plan was said to ‘end’ at the discharge point of the stormceptor but the installation of engineered wetlands or ‘marshes’ was said to be required before discharging to Lost Lagoon.  The required size of the engineered wetland to be installed near Lost Lagoon was based on a design storm of 46 mm of rain in 24-hours as this was calculated to be ‘on-average’ the largest storm that would occur 20  within a six-month return period. The peak flow and total volume for the design storm were calculated to be 21 L/s and 1022 m3, respectively. Comparatively, the causeway storm sewer system was designed for a 100-year return period storm. Thus, flows exiting the causeway during infrequently occurring large storms were designed to be diverted around the treatment wetland. The final design of the Lost Lagoon wetland required construction of a berm to physically cut the wetland out of space along the side slope of Lost Lagoon. When the wetland was designed, sediments and low levels of oils, greases, nutrients, and organic matter were the primary contaminants of concern. De-icing salts were not considered to be of concern as the causeway very rarely requires de-icing. Thus, the wetland was designed to optimize removal of particulate matter through settling and removal of dissolved contaminants through adsorption on soil and bacterial processes associated with plant uptake. The engineered wetland was designed to include several separate components for removal of various types of pollutants. Figure 4 illustrates these components. The major components include:  A flow diversion structure, allowing flows greater than 25 L/s to bypass the wetland in order to prevent scouring and flooding;  A sedimentation forebay, promoting settling of particles, including grit and particle-bound contaminants;  Marsh terraces, allowing sustained contact between stormwater and soil and plant matter through extended settling, adsorption, and biological removal;  Deep pools, contributing to biological diversity, increasing biological removal;  Plants (e.g. Carex and Scirpex) specifically sourced to improve contaminant de-mobilization;  An outlet structure, promoting a long residence time (2 weeks), eliminating short-circuiting and dead zones;  Base flow inlets, helping sustain plant life during dry seasons by diverting surface watercourses if needed; and  An augmentation structure, allowing movement of lagoon water into the forebay in the event that supplemental water is required in a drought year.  21   Figure 4. Illustration of the Lost Lagoon Wetland (Kerr Wood Leidal Associates Ltd., 1999)22   Installation Following its design, the Lost Lagoon wetland was constructed during the summer of 2000 and was fully commissioned for stormwater treatment in the spring of 2001. During construction, the water level in Lost Lagoon was lowered to the lowest feasible level based on environmental and aesthetic considerations. A silt curtain was set down and construction of the berm commenced first. Construction of the wetland’s pools and marshes followed with subsequent construction of the access point and staging. Time was provided for expected settling and then final landscaping and planting was performed.  This coincided with a monitoring and inspection plan for sediment and design quality. Figure 5 though Figure 10 are pictures, courtesy of KWL, that illustrate the installation and final wetland as commissioned in year 2001. 23   Figure 5. Laying of Silt Curtain  Figure 6. Construction of the Berm  Figure 7. Excavation of the Pools and Marshes  Figure 8. Vegetation Planted  Figure 9. Arial Shot Facing Northwest  Figure 10. Arial Shot Facing Southeast24   Maintenance and Monitoring Regime The BC Ministry of Transportation is responsible for the drainage sewer system, including both stormceptors and the Vancouver Board of Parks and Recreation is responsible for operating and maintaining the wetland and surrounding features. The maintenance and monitoring regime for the wetland, as recommended by consultants at KWL includes several elements that occur during different seasons of the year and periodically. These elements are summarized in Table 5. Interestingly, there is no requirement for water or sediment quality testing, or testing of the treatment efficacy. Monitoring is performed only by visual inspection. KWL can be contacted directly for the manual on maintenance and monitoring of the Lost Lagoon wetland.   25  Table 5. Elements of the Lost Lagoon Wetland Maintenance and Monitoring Regime  (Kerr Wood Leidal, 2002) Period Activity Monthly  Visually inspect the inlet pool, wetland marsh, inlet and outlet chambers, and Stormceptor  Record the Lost Lagoon water level at the Lagoon outlet  Check the wetland water level and record the level at the outlet flow control chamber  Remove trash  Check that people are not entering or damaging the riparian areas Spring Maintenance (April)  Inspect and repair observation platforms and interpretive signs  Clean out the Stormceptors  Flush the inlet flow control chamber  Flush the outlet flow control chamber  Adjust the water level in Lost Lagoon to between 0.8 m and 0.9 m  Adjust the wetland outlet weir to an elevation 1.20 m  Remove weeds and undesirable plants by hand Summer Maintenance (July-August)  Inspect plants for water stress  Augment inflow or irrigate if required Fall Maintenance (October)  Clean out the Stormceptor  Flush the inlet flow control chamber  Flush the outlet flow control chamber  Adjust the water level in Lost Lagoon to 0.6 m  Adjust the wetland outlet weir to elevation 1.15 m Winter Maintenance (December)  Flush the inlet flow control chamber Annual Tasks  Inspect wetland plants for presence, abundance and condition  Inspect bottom contours and water depths relative to plans  Inspect sediment and outlet conditions  If plant harvesting for nutrient control is desired, perform in the late summer Periodic Tasks  2002, inspect plants twice per month during the summer  2011, sediment removal Every 5 Years  Settlement survey  Infill/replant wetland plants   26   Best Management Practices Employed in the Lost Lagoon Wetland Design When it was designed in 1999, the Lost Lagoon wetland employed many of the best management practices available to engineers. This was due to a strong desire by the City of Vancouver and the design consultants, KWL, to produce an effective and lasting treatment site in this high profile, public location. The best management practices incorporated into the wetland design for stormwater treatment included:  Installation of two stormceptors for overflow protection; o The first stormceptor reduces the degree of emulsification of spilled materials with stormwater by reducing the distance that contaminants travel before capture, thus increasing capture efficiency.  Installation of a flow diversion structure, preventing scouring, flooding and washout of the wetland;  Inclusion of a valved outlet from the forebay to the Lost Lagoon, allowing the marsh to be bypassed during maintenance;  Sizing the settling forebay to treat a 6-month return period design storm, allowing adequate treatment of most rainfall events that occur on the causeway;  Sizing the wetland as a whole to have a long enough hydraulic retention time (2 weeks) to allow for adequate contaminant removal;  Variation of the depths of terraces in the marsh system, allowing a diversity of plant and biotic life to take part in pollutant removal processes;  Inclusion of deep pools and low-flow channels, facilitating pollutant removal mechanisms;  Inclusion of an outlet setting pool, increasing stormwater residence time and reducing short-circuiting and under-treatment;  Incorporation of an adjustable weir at the outlet, ensuring the water level in the marsh remains constant;  Diversion of surface flow from nearby creeks, preventing flushing of the wetland during winter storms; and  Inclusion of an irrigation connection along the length of the wetland berm for the case of an extreme drought, eliminating the need to add water from Lost Lagoon to the wetland as this could introduce algae and excessive sediment loads to the wetland. The design consultants have a high interest in knowing whether these best management practices have contributed to the treatment efficacy of the wetland and this interest has been incorporated into the objectives of this study. 27   Previous Stormwater Quantity and Quality Data  Year 2000: Drainage Area and Calculation of Design Flow In 1999, staff at KWL calculated the drainage area feeding into the Lost Lagoon wetland to be 2.7 hectares. This drainage area along with precipitation data from a nearby weather station in North Vancouver was used to model the 6-month design storm flow, using the PC-SWMM model  (James, 2010). The calculations determined that a wetland design based on a maximum flow rate of 21 L/s would be able to treat over 92% of flow exiting the causeway on a yearly basis. Figure 11 illustrates the design hydrograph as retrieved from Kerr Wood Leidal Consulting Engineers Ltd. (1999)   Figure 11. Design Hydrograph for Lost Lagoon Wetland (adapted from Kerr Wood Leidal, 1999)  Year 2007: UBC Undergraduate Thesis In 2007, a group of undergraduate students, in the UBC Earth and Oceans Sciences Honors Environmental Science Program, performed an analysis on the Lost Lagoon wetland to evaluate its effectiveness so that the City of Vancouver could plan future maintenance. Carex obnupta and Scirpus acutus plant samples and sediment grab samples were collected at several locations between the wetland’s inlet and outlet. The group’s findings indicated that plants in the wetland had accumulated several metals associated with stormwater and that the water flowrate through the wetland contributed to higher metal uptake for plants. In addition, significant reductions in metal concentrations in the sediment were found for all metals except for arsenic. The group found that metal concentrations in the sediment were highest along the edges of the wetland, indicating that the water residence time led to an increase in the deposition of metals. Figure 12 illustrates the locations that the student group sampled for plants and sediment. Table 7 lists the mean metal concentrations for the plant and sediment samples, respectively. 0123450.05.010.015.020.025.030.018:00 0:00 6:00 12:00 18:00 0:00 6:00 12:0010-min Rain Depth (mm)Flow (l/s)TimeCombined Stormwater System Flow Rain28   Figure 12. 2007 Sample Sites in Lost Lagoon for Plants and Sediment (adapted from Thoren et al., 2007) Table 6. 2007 Results for Plant Specimens in Lost Lagoon Wetland (Thoren et al., 2007) Metal Concentration in Carex (ppm) Concentration in Scirpus (ppm) Transect 1 Transect 2 Transect 3 Outside Transect 1 Transect 2 Transect 3 Cd 1.1 2.8 0.37 0.5 0.66 0.53 0.33 Cu 75.2 119.7 30.3 24.9 68.0 38.7 30.2 Mn 383.3 730.7 664.3 453.0 311.0 580.7 463.7 Pb 26.4 34.3 8.95 6.21 30.1 9.69 7.76 Zn 146.0 236.0 77.7 0.44 109.1 184.0 97.6 Table 7. 2007 Results for Sediment Samples in Lost Lagoon Wetland (Thoren et al., 2007) Metal Mean Inlet Concentration (mg/kg dry weight) Mean Outlet Concentration (mg/kg dry weight) Percentage Decrease (%) As 3.9 2.5 36.4 Cd 0.5 0.1 73.1 Cr 28.7 20.6 28.3 Cu 66.9 23.9 64.3 Pb 27.4 5.3 80.6 Ni 26.7 21.7 18.7 Zn 132.0 57.8 56.2    Plant samples Sediment samples Transect 1 Transect 2 N Transect 3 29  Thoren et al (2007) employed a simple regression model to relate metal concentrations to the distance from the outlet. 𝑦 = 𝑎𝑒ି௞௫ Where,  a represents the y-intercept of the graph;  k represents the slope or removal efficiency; and  x represents the distance along the wetland. Regression analysis was accompanied by R2 and p-values, which represent the accuracy and suitability of the exponential model and the significance of the decrease, respectively. These results are summarized inTable 8. Table 8. 2007 Regression Analysis Results for Sediment Samples in Lost Lagoon Wetland (Thoren et al., 2007) Metal R2 k-value t-value P>|t| (p-value) As 0.348 -0.00693 -2.63 0.0207 Cd 0.614 -0.02083 -4.55 0.0005 Cr 0.494 -0.00618 -3.56 0.0035 Cu 0.549 -0.01681 -3.97 0.0016 Pb 0.549 -0.02414 -5.18 0.0002 Zn 0.6352 -0.01314 -4.76 0.0004 Thoren et al also compared the mean, maximum, and minimum metal concentrations in the sediment to average metal concentration in the soil of Washington State. These results are summarized in Table 9. Table 9. Comparison of 2007 Wetland Results with Sediment Data for Washington State (Thoren et al., 2007) Metal Washington State (g/kg dry weight)1  Lost Lagoon Wetland Mean (mg/kg dry weight)2 Lost Lagoon Wetland Max (mg/kg dry weight)2 Lost Lagoon Wetland Min (mg/kg dry weight)2 As 4.5 3.0 5.3 1.1 Cd 0.8 0.4 1.7 0.1 Cr 49.9 25.5 47.3 17.5 Cu 31 221 53.1 18.4 Pb 14 19.5 82.7 4.3 Zn 78 103.4 288.0 54.7 1 (Washington State Department of Transportation, 2007) 2 (Thoren et al., 2007) 30  Overall, the results from the 2007 assessment provide a promising reason to use the Lost Lagoon wetland as a research site for development of a genomics tool. Both plant and sediment samples indicate metals are retained within the wetland and stormwater treatment is occurring successfully. However, further evidence of these conclusions is still needed, using more recent samples and a greater depth of sampling.  Year 2013: Vancouver Board of Parks and Recreation Sediment Dredging Report In 2013, the City of Vancouver contracted Hemmera environmental consultants to perform an in-situ investigation of the sediment quality in the Lost Lagoon wetland. This project was executed in order to confirm that the sediment would not be classified as a hazardous waste prior to dredging and disposing of the sediment in a landfill. Grab samples were taken from eight locations in the wetland forebay and the samples from five of the eight locations underwent laboratory analysis. These locations are illustrated in Figure 13. Hemmera also unsuccessfully attempted to extract core sediment samples from the wetland but further results of this attempt were not recorded in their report.31   Figure 13. Locations Sampled by Hemmera During the 2013 Sediment Investigation (Hemmera, 2013)32  Field observations were recorded at the time of sampling and included the following:  “The sediment substrate at the sampling locations generally consisted of dark grey to black sand with trace silt, gravel, organics, and pine needles;  No marine fauna was observed by Hemmera;  A hydrogen sulfide (H2S) odor was noted in the majority of the sediment samples collected;  No petroleum hydrocarbon sheens were observed in the collected samples. However, a petroleum hydrocarbon-like odor was observed in two samples; and  The moisture content measured in the sample ranged from 68.8%-72.8%.” (Hemmera, 2013) The results provided by the laboratory analysis offer a number of important observations. Each sample submitted had concentrations of one or more metal constituents above soil guidelines and these constituents primarily included antimony, chromium, copper, molybdenum, lead and zinc. In addition, all samples had concentrations of HEPH above standards. Sodium and chloride ions as well as VOCs, PCBs, chlorinated hydrocarbons, and chlorinated/non-chlorinated phenols were measured to be below the allowable levels. Table 10 summarizes the regulatory levels and concentrations measured in the sediment of the Lost Lagoon wetland for the constituents of primary interest. These measurements indicate that a high contaminant loading was deposited and retained in the wetland forebay in the ten years prior to when the forebay was dredged. Table 10. BC Residential Soil Standards and Metal Concentrations Measured in the Sediment of the Lost Lagoon Wetland Forebay Metal Regulatory Standard (mg/kg dry weight)1 Measured Range (mg/kg dry weight)2 % In Excess Antimony 20 25-65 20-225 Chromium 100 100-140 0-40 Copper 90-150 350-650 153-620 Lead 150 160-240 7-60 Molybdenum 10 11-30 10-200 Zinc 450 600-1200 33-160 1 (British Columbia Ministry of Water, Land and Air Protection, 2011) 2 (Hemmera, 2013)   33  2.5 Methodology The primary focus of this chapter was to demonstrate that, overall, the Lost Lagoon wetland is meeting treatment guidelines and to lay the groundwork for the microbial analyses in Chapter 2. With this goal in mind, a strategic methodology was developed for the Lost Lagoon wetland field study. Specifically, sediment quality and long term treatment trends in the wetland were of greatest concern for the environmental sampling. A detailed description of the methodology employed to answer the objectives and hypotheses listed at the beginning of this chapter is supplied here.  Site Visits and Sampling Regime  Field Site Survey and Conditions at the Time of the Field Study Site Visit  On April 23, 2015 at 8:00 AM, an initial field site survey was conducted to assess the conditions of the wetland. GPS coordinates and digital photographs were taken at all points of interest and locations that indicated damage to the wetland features. Figure 14 illustrates a map of the field site and GPS locations of the photos.  Figure 15 through Figure 26 illustrate some of the relevant photos from the site visit. The initial site survey indicated that all of the major elements of the wetland were intact and are being maintained. However, there were signs of beaver activity, which required further investigation with park staff members.34   Figure 14. Survey Map of Field SiteFigure 24 Figure 17 Figure 16 Figure 15Figure Figure 18 Figure 20 Figure 21 Figure 23 Figure 22 Figure 19 Figure 26 Figure 25 35   Figure 15. Lost Lagoon  Figure 16. On Site Graphic of Treatment Process  Figure 17. Storm Sewer on the Stanley Park Causeway  Figure 18. Access Point for the Lower Stormceptor  Figure 19. Wetland Bypass to Lost Lagoon  Figure 20. Setting Forebay 36   Figure 21. High Marsh  Figure 22. Low Marsh   Figure 23. Sections of Low Marsh Showing Plant Damage and Beaver Activity  Figure 24. Signs of Beaver Activity at Lost Lagoon  Figure 25. Access Point for the Wetland Outlet Control Valve System  Figure 26. Outlet Point to Lost Lagoon37   Sampling Locations and Dates As sediment quality and long term treatment trends in the wetland were of greatest concern for the environmental sampling, the sampling locations and dates was optimized to obtain results that could both verify the treatment performance of the wetland and add to the microbial analyses in Chapter 2.  The hypotheses in this study require that there are differences in the sediment quality at the front and back end of the wetland. Therefore, initially a ‘search sampling’ methodology (Gilbert, 1987) was applied in order to divide the wetland into 6 areas for comparison as illustrated in Figure 27. These areas included: 1. The lower stormceptor; 2. The East side of the forebay, closest to the inlet pipe; 3. The centre of the forebay; 4. The West side of the forebay, furthest from the inlet pipe; 5. The settling pool closest to the outlet pipe; and 6. The exit pipe from the wetland, at the shore of Lost Lagoon.                  Figure 27. Field Study Sampling Locations at the Lost Lagoon Wetland In order to reduce the size of the comparison areas but retain statistical significance, the comparison areas were further divided into 1-m2 plots and a ‘systematic sampling’ methodology (Gilbert, 1987) was applied to select study plots at equal intervals using an aligned grid. Sampling of the study plots was also performed using systematic sampling, where samples were taken from the four corners and the center of each plot. 1 5.2 3.2 4.1 3.1 2.3 2.2 2.1 4.2 5.1 4.3 3.3 6.1 6.2 5.3 6.3 38  Seven samplings of the wetland occurred between July and December of 2015. As the sample area in the wetland was relatively large, compared to the resources available to the research team, not all study plots could be sampled on a given study day. The implications of this are further discussed in the Limitations and Recommendations sections of this thesis. Since sampling was to occur on public land in a treatment space that provides habitat for local birds and inner-city animals, great care was taken during the sampling process to reduce damage to the site. In addition, sampling plans were approved by staff at both the Vancouver Board of Parks and Recreation and at the Stanley Park Ecology Society. Documentation of approval and support for this study can be found in Appendix H. During the sampling events, three mediums were sampled – surface sediment at the wetland floor, sediment at a depth of 10 cm below the wetland floor, and water at the soil-water interface. In some cases, inaccessibility or inoperable equipment limited the number of samples that could be taken. This is further discussed in the study’s Limitations section. Table 11 summarizes the samples, which were taken from the field study site and Figure 28 provides an overview of the field sampling process. Table 11. Field Study Samples Taken Site Number Description # Days Sampled Dates Sample Medias 1 Stormceptor 1 Dec 16 Water 2.1 NW Corner Forebay 3 Sept 9, Oct 21, Dec 16 Water, depth and surface sediment 2.2 N Centre Forebay 2 Nov 11, Dec 16 Water, surface sediment 2.3 NE Corner Forebay 3 Oct 6, Nov 11, Dec 16 Water, depth and surface sediment 3.1 W Centre Forebay 2 July 21, Sept 9, Oct 21 Water, depth and surface sediment 3.2 Centre Forebay 2 Oct 21, Nov 11 Water, depth and surface sediment 3.3 E Centre Forebay 2 Oct 6, Nov 11 Water, depth and surface sediment 4.1 SW Corner Forebay 3 July 21, Sept 9, Oct 21 Water, depth and surface sediment 4.2 S Centre Forebay 2 Oct 21, Nov 11 Water, depth and surface sediment 4.3 SE Corner Forebay 2 Oct 6, Nov 11 Water, depth and surface sediment 5.1 NW Settling Pond 2 Sept 22, Nov 11 Water, depth and surface sediment 5.2 Centre Settling Pond 2 Sept 22, Nov 11 Water, depth and surface sediment 5.3 SE Settling Pond 2 Sept 22, Nov 11 Water, depth and surface sediment 6.1 W Exit 3 July 21, Sept 9, Oct 21 Water, depth and surface sediment 6.2 Centre Exit 3 July 21, Sept 9, Oct 21 Water, depth and surface sediment 6.3 E Exit 3 July 21, Sept 9, Oct 21 Water, depth and surface sediment   39   Figure 28. Overview of Field Sampling Process  Water Sampling Equipment As illustrated in Figure 29,  a syphon (Col-Parmar WZ-70607-00) and plastic tubing were used to extract two 1-L samples from each sampling location. All samples were taken while the researcher sat in a small dinghy. Figure 29. Image of the Water Sampling Equipment   Two 1 L water samples and 1 60 mL sediment core taken at each corner and the centre of a 1 m2 plot Allocated volumes from one plot are homogenized to form a composite sample representing the plot Allocated volumes from all three plots are homogenized to form a composite sample for measurement of mineral oil and grease 40   Sediment Sampling Equipment Sampling of sediment in the wetland represented a major challenge. Particularly in the wetland forebay, because the water depth exceeded 2 m in some locations, great care and accuracy was required to obtain a core of sediment. There was variability in the quality and consistency of the sediment with some areas being sandy soils and other areas being primarily clayey soils. In addition, sampling for microbiology and an interest in differences in the sediment at the surface of the wetland floor and below the surface of the wetland floor created additional challenges to ensure that mixing of the sediment layers did not occur during sampling. During method development, different apparatuses were tested for their ability to extract and hold a sediment core. After several trials, a successful custom sediment core sampling was built. To build the apparatus, first a 60-mL syringe with a diameter of ¾ inches was fit at the nose end to a ball valve. The ball valve was connected to a PVC pipe to be used as a sampling rod. Next, two circular stainless steel fittings were clamped over the handle of the syringe. Two screws were driven through the metals fittings and copper rods were connected to the screws. A second 60 mL syringe was clamped to the tail end of the first syringe using stainless steel fittings and a plastic O-ring. Two stainless steel fittings were clamped over each end of the handle of the second syringe and removable screws were driven through the stainless steel clamps and screwed into the copper rods. The nose of the second syringe was sanded off so that this end of the sampler could be driven into the wetland soil using a rubber mallet. After taking a sample, the second syringe was unscrewed and unhooked from the rest of the apparatus and a new syringe was put in place. This process was repeated for each sample. Figure 30 illustrates the sediment core sampling apparatus.  Figure 30. Photograph of the Sediment Sampler   ¾ inch ball valve Fittings First syringe O-rings Second syringe Rods Connection for PVC pipe 41   Sample Collection, Preservation, Storage and Transport  Water Samples Two 1 L samples were taken above the wetland floor using a syphon and plastic tubing. After a sample was taken, it was immediately poured into a clean 1 L plastic bottle and labelled. The syphon and tubing were then rinsed with distilled water and 90% ethanol solution. The plastics bottles were brought back to shore, where a small field lab processing site was set up. 500 mL of each sample was poured into a clean wide mouthed plastic bottle and immediately tested for environmental parameters using a YSI probe. On site measurements were recorded for DO, pH, temperature, conductivity, and redox potential. Composite water samples were prepared to represent each plot and preserved on site using the following protocols.  Total Organic Carbon (TOC) o Add 25 mL from each of 5 1-L bottles o Add 1 drop H3PO4 o Place in cooler on ice  Chemical Oxygen Demand (COD) o Add 20 mL from each of 5 1-L bottles o Add 1 drop H2SO4 o Place in cooler on ice  Metals o Add 10 mL from each of 5 1-L bottles o Add 1 drop HNO3 o Place in cooler on ice  Turbidity/Total Suspended Solids (TSS) o Add 100 mL from each of 5 1-L bottles o Place in cooler on ice  Mineral Oil and Grease (MO&G) o Add 50 mL from each of 15 1-L bottles (3 sets of 5) o Add 2 drops H2SO4 o Place in cooler on ice Samples were also homogenized for microbial analysis. This method is described in Chapter 2. 42  Water samples for environmental parameters were stored on ice and transported by truck to the CEME Environmental Laboratory at UBC. The samples were stored at <4 Celsius until further processing and analysis.  Sediment Samples During sampling, the researcher used a hard rubber mallet to drive the sampling apparatus into the sediment at the location of interest. The researcher then carefully pulled up the sampler, removed the syringe from the sampler apparatus, and wrapped both ends of the syringe in laboratory grade aluminum foil that was previously disinfected with ethanol. The syringe was immediately placed in a cooler on dry ice. For the next sampling event, the sampler was cleaned with ethanol and a new clean syringe was attached using an Allen key. Sediment samples for were stored on dry ice and transported by truck to the CEME Environmental Laboratory at UBC. The samples were stored at <-20 Celsius until analyzed.  Laboratory Analysis of Water Quality Parameters  Sample Handling and Preservation All equipment that was to come into contact with sediment was soaked in 10% bleach solution for a minimum of 24 hours prior to sample handling. All equipment was rinsed with nitric acid and then cleaned with 90% disinfectant grade ethanol between sampling. Sediment samples remained in the plastic syringes and were frozen at <-20 Celsius until further processing. Frozen syringes were then removed from the freezer. The first 1-cm of sediment content in the five syringes which corresponded to one sample plot were cut from each sediment sample and placed in a mortar. To keep the samples frozen during processing, the mortar was placed in a stainless steel bowl that was filled with crushed dry ice. The sediment was ground and homogenized to a fine consistency using a pestle and any large rocks and sticks were pulled out prior to placing the ground sample into a disinfected plastic Ziploc bag. Samples were placed back in the freezer at <-20˚C until further processing. The same process was followed for the last 1-cm of each sediment core. By this means, both the surface sediment and sediment at a depth of 10-cm could be analyzed.  Analytical Methods The researcher applied standard environmental laboratory tests based on equipment available in the CEME Environmental laboratory. The laboratory tests employed for each environmental parameter were: 43   Environmental parameters – YSI handheld multi-parameter instrument;  MO&G – USA EPA Method 1664 (United States Environmental Protection Agency, 1999);  TOC – USA EPA Method 415.3  (Potter & Wimsatt, 2005);  COD –  Hach Method 8000 (Hach, 2008);  Turbidity – USA EPA Method 180.1 (O’Del, 1993); and  TSS – Hach Gravimetric Method 8158 (Hach, 2007) Due to high organic content in the samples, water samples were digested for metal analysis using a custom protocol based on EPA method 3050-B  (United States Environmental Protection Agency, 1996) described in Appendix A. Metals were analyzed using ICP-OES on a Varian Liberty 100/200 apparatus. Samples were analyzed in triplicate and measurements included analysis of procedural and field blanks.  Statistical Analyses Analyses of the main parameters of interest, metals and mineral oil and grease, were first performed through visual assessment of the data. To compare the metal concentrations at each plot, bar graphs were prepared to illustrate the average concentrations of each metal that is associated with stormwater. Boxplots of the concentrations for each metal were used to provide a visual assessment of the symmetry of the distribution and the variability in the concentrations between the wetland entry (Site 2, 3, and 4) and the wetland exit (Site 5). Each media (water, surface sediment, and 10-cm depth sediment) was visualized individually because it is expected that that these medias will behave differently.  In order to compare the measured environmental pollutant and metal concentrations, Wilcoxon paired rank tests were performed between the results measured at the wetland entry, exit and the Lost Lagoon. The Wilcoxon rank test is the equivalent to the common paired student t-test for comparison of two means. However, the Wilcoxon rank test does not assume that the measurements are normally distributed and for this reason, the Wilcoxon rank test carries somewhat less weight. However, environmental samples tend not be normally distributed due to outliers at high concentration levels; thus, in this case, the Wilcoxon rank test is a better fit for the data.  2.6 Results and Interpretation In this section results and interpretation are supplied for the laboratory tests. Each environmental parameter is illustrated as a bar graph by plot and then by a boxplot between the wetland entry (Sites 2, 3, and 4), wetland exit (Site 5) and Lost Lagoon (Site 6). This method of visualization allows for comparison first along the width and length of the wetland and then between the major locations at the field site. After 44  the visual illustration, statistical comparisons are calculated. An interpretation of the data is supplied prior to the figures and statistical calculations.  Turbidity, Total Suspended Solids, Chemical Oxygen Demand and Total Organic Carbon  Interpretation Turbidity, TSS, COD, and TOC cannot be directly attributed to the stormwater entering the wetland from the roadway because sampling was performed during the autumn season and leaf matter from overhanging trees deposited directly into the wetland and contributed to the high solids content during the study period. That being said, a similar relationship for turbidity, TSS, COD, and TOC was observed between the various sites where water was sampled in the Lost Lagoon wetland. Generally, these parameters were measured to have higher averages in the wetland inlet than in the wetland outlet and also higher averages in the Lost Lagoon than in the wetland outlet. Figure 31 through Figure 38 graphically illustrate the relationship that was observed. In Table 12 through Table 14, a significant statistical difference is interpreted when p<0.05, or in other words, when there is at least 95% confidence that interpreting two medians as being different occurs when the two medians are truly different. No significant differences in the medians of turbidity, TSS, TOC, and/or COD were calculated between the wetland entry, wetland exit, and the Lost Lagoon. This is likely due to the high range of measurements over the sampling period caused by the contribution of organic matter over the autumn sampling season.   45   Turbidity Figures  Figure 31. Barplot Comparison by Plot of Turbidity in Water Samples Collected During the Field Study  Figure 32. Boxplot Comparison of Turbidity in Water Samples Collected During the Field Study   0501001502002503003504001 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Stormceptor Entry Exit LagoonNTU46   Totals Suspended Solids Figures  Figure 33. Barplot Comparison by Plot of TSS in Water Samples Collected During the Field Study   Figure 34 Boxplot Comparison of TSS in Water Samples Collected During the Field Study 02004006008001000120014001 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Stormceptor Entry Exit Lagoonmg/L47   Chemical Oxygen Demand Figures  Figure 35. Barplot Comparison by Plot of COD in Water Samples Collected During the Field Study   Figure 36. Boxplot Comparison of COD in Water Samples Collected During the Field Study 0200400600800100012001 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Stormceptor Entry Exit Lagoonmg/L48   Total Organic Carbon Figures  Figure 37. Barplot Comparison by Plot of TOC in Water Samples Collected During the Field Study   Figure 38. Boxplot Comparison of TOC in Water Samples Collected During the Field Study 0501001502002503003504004505001 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Stormceptor Entry Exit Lagoonmg/L49   Statistical Scores for Site Comparison Table 12 through Table 14 list the confidence levels (z-scores) computed using the Wilcoxon Paired Rank Test in the R standard package version 3.1.1, (R Core Team, 2016). Table 12. Confidence Levels for Wilcoxon Rank Test Between Entry and Exit for Environmental Parameters Turbidity TSS TOC COD 0.170 0.076 0.193 0.386  Table 13. Confidence Levels for Wilcoxon Rank Test Between Exit and Lagoon for Environmental Parameters Turbidity TSS TOC COD 0.067 0.097 0.115 0.425  Table 14. Confidence Levels for Wilcoxon Rank Test Between Entry and Lagoon for Environmental Parameters Turbidity TSS TOC COD 0.373 0.811 0.735 0.425  Metals  Interpretations Together, Figure 39 through Figure 41 illustrate the distribution of metals within the water samples obtained from the Lost Lagoon wetland. From Figure 39 and Figure 40, visually, there is a trend of decreasing metal concentrations along the length of wetland. However, Site 6.1, 6.2, and 6.3, where samples were taken from the shore of Lost Lagoon, show higher concentrations of several metals that are associated with stormwater. This could be due to additional drainage into the Lost Lagoon from neighbouring roadways including Lost Lagoon Drive and Chilco Street because it does not appear to be explained by the contribution of stormwater from the treatment wetland. Additional information is needed to explain this trend. Generally, Figure 41 also illustrates that the high concentrations of metals in some water samples at the wetland entry were no longer measured at the wetland exit. Figure 42 and Figure 43 illustrate the distribution of metals in the surface sediment at each plot along the study site. From these graphs, there is visual evidence that the concentration of metals is lower at the back end of the wetland compared to the front end of the wetland. There is also evidence that particle setting and adsorption are contributing to the decreasing concentration of metals in the stormwater because the plot with the highest metal concentrations, Site 3.2, is in the centre of the forebay, rather than at the beginning or end of the forebay. Figure 44 illustrates the variation of metals measured in surface sediment 50  between the entry and exit of the Lost Lagoon wetland and Lost Lagoon. At this resolution, there is also evidence that over the entire sampling regime, there are decreasing concentrations for some of the metals commonly associated with stormwater in the surface sediment. Figure 45 and Figure 46 illustrate the distribution of metals averaged by plot in the sediment at a depth of 10 cm below the floor of the Lost Lagoon wetland. From these graphs, there is some visual evidence that the concentration of metals is lower at the back end of the wetland compared to the front end; however, the results are less clear than with the surface sediment samples. In addition, Figure 47 illustrates the variation of metals measured in the sediment sampled at a depth of 10 cm below the wetland floor, between the entry and exit of the Lost Lagoon wetland and Lost Lagoon. The results in Figure 47 generally appear to be consistent with the results in the boxplot for surface sediment metal concentrations. Due to equipment malfunction, depth sediment samples were not obtained at Site 2.2, Site 3.2 and Site 4.2. Due to the variability in the measurements of metal concentrations, the statistical scores in Table 15 through Table 17 are complex and challenging to interpret. Overall, the observations include: Between the entry and exit of the Lost Lagoon wetland;  Insignificant differences were calculated between the water samples measuring cobalt and copper while significant differences were calculated between the water samples for barium, manganese, nickel, and zinc. Statistical conclusions could not be calculated for cadmium, chromium, molybdenum, lead, and antimony because measurements were too close to the detection limits of the analytical method.  Insignificant differences were calculated between the surface sediment samples for nickel and zinc while significant differences were calculated between the surface sediment samples for barium, chromium, copper manganese, and lead.  Statistical conclusions could not be calculated for cadmium, cobalt, molybdenum, and antimony because measurements were too close to the detection limits of the analytical method.  Finally, insignificant differences between depth sediment samples were calculated for barium, copper, manganese, nickel, and zinc while a significant difference was calculated for chromium. Statistical conclusions could not be calculated for cadmium, cobalt, molybdenum, lead, and antimony because measurements were too close to the detection limits of the analytical method. Between the entry to the wetland and Lost Lagoon; 51   No statistical differences were calculated. Between the exit of the wetland and Lost Lagoon;  Statistical differences were calculated between surface sediment samples were calculated for chromium, copper, and lead.  No other statistical differences were calculated.   52   Water Samples   Figure 39. Barplot Comparison by Plot of Metals Associated with Stormwater in Water Samples Collected During the Field Study 0501001502002501 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Stormceptor Entry Exit Lagoonµg/LCdCoMoNiSb53   Figure 40. Barplot Comparison by Plot of Metals Associated with Stormwater in Water Samples Collected During the Field Study 050010001500200025001 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Stormceptor Entry Exit Lagoonµg/LBaCrCuMnPbZn54   Figure 41. Boxplot Comparison of Metals Associated with Stormwater for Water Samples Collected During the Field Study   55   Surface Sediment Samples  Figure 42. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study 5.00010.00015.00020.00025.00030.00035.00040.00045.00050.0002.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Entry Exit LagoonCd (mg/kg)Co (mg/kg)Mo (mg/kg)Ni (mg/kg)Sb (mg/kg)56   Figure 43. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study 5.000205.000405.000605.000805.0001005.0001205.0001405.0002.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Entry Exit LagoonBa (mg/kg)Cr (mg/kg)Cu (mg/kg)Mn (mg/kg)Pb (mg/kg)Zn (mg/kg)57   Figure 44. Boxplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study   58   10-cm Depth Sediment Samples  Figure 45. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study 5.00015.00025.00035.00045.00055.00065.00075.0002.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Entry Exit LagoonCd (mg/kg)Co (mg/kg)Mo (mg/kg)Ni (mg/kg)Sb (mg/kg)59   Figure 46. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment Samples Collected During the Field Study 5.000105.000205.000305.000405.000505.000605.000705.000805.0002.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 5.1 5.2 5.3 6.1 6.2 6.3Entry Exit LagoonBa (mg/kg)Cr (mg/kg)Cu (mg/kg)Mn (mg/kg)Pb (mg/kg)Zn (mg/kg)60   Figure 47. Boxplot Comparison of Metals Associated with Stormwater between Forebay and Exit for Samples taken at a Depth of 10 cm  Statistical Scores for Site Comparison Table 15. Confidence Levels for Wilcoxon Paired Rank Test Between Entry and Exit for Metals Metal Water Surface  Depth Ba 0.016 0.047  0.661 Cd - -  - Co - -  - Cr - 0.000  0.000 Cu 0.723 0.000  0.077 Mn 0.003 0.031  0.776 Mo - -  - Ni 0.032 0.577  0.732 Pb - 0.004  - Sb - -  - Zn 0.002 0.123  0.281     61  Table 16. Confidence Levels for Wilcoxon Paired Rank Test Between Exit and Lagoon for Metals Metal Water Surface Depth Ba 0.174 0.236 0.525 Cd - - - Co - 0.152 1 Cr - 0.126 0.294 Cu 1 0.943 0.828 Mn 0.822 0.163 0.735 Mo - - - Ni - 1 1 Pb - 1 - Sb - - - Zn 1 0.455 0.282  Table 17. Confidence Levels for Wilcoxon Paired Rank Test Between Entry and Lagoon for Metals Metal Water Surface Depth Ba 0.372 0.075 0.525 Cd - - - Co - 0.046 - Cr - 0.010 0.371 Cu 1 0.009 0.269 Mn 0.546 0.691 0.635 Mo - - - Ni - 1 1 Pb - 0.010 - Sb - - - Zn 1 0.089 0.733  Mineral Oil and Grease  Interpretation Figure 48 and Figure 49 illustrate the change in mineral oil and grease along the length of the wetland. Based on the date sampled, mineral oil and grease had the most variable concentration at Site 2. This was expected because the level of mineral oil and grease measured in water samples is dependent on the influent quality, which could be highly variable depending on vehicle traffic and potential vehicle leakage onto the causeway. Mineral oil and grease was measured to be below guideline levels (30-mg/L) (Canadian Council of Ministers of the Environment, 2015) at Site 4, Site 5, or Site 6. Unfortunately, only one mineral oil and grease sample was taken at Site 1 at the exit of the stormceptor, therefore the variability of mineral oil and grease entering the wetland is unknown. Mineral oil and grease samples were only compared graphically because the sample size was too small to interpret statistical calculations.  62   Mineral Oil and Grease Figures  Figure 48. Comparison by Site of Total Mineral Oil and Grease in Water Samples Collected During the Field Study  Figure 49. Boxplot Comparison of Total Mineral Oil and Grease for Water Sampled Collected During the Field Study 0.0020.0040.0060.0080.00100.00120.00140.00Site 1 Site 2 Site 3 Site 4 Site 5 Site 6mg/L63  2.7 Discussion and Conclusion As described previously, the goal of this study is to provide proof of concept data that supports or rejects developing a genomics-based monitoring tool for low impact design features that treat stormwater, including engineered wetlands. In this chapter, data was gathered and analyses were conducted in order to provide background information for the treatment efficacy of a functioning stormwater treatment wetland, namely the Lost Lagoon wetland in Stanley Park, Vancouver. For this, an attempt was made to answer two hypotheses and to support three objectives.  Chapter Hypotheses To prove that the wetland is effectively treating stormwater and to begin to validate the treatment mechanisms within the wetland, it was previously stated that two hypotheses must be true. 1. The concentrations of metals associated with stormwater decrease along the length of the wetland; and 2. The concentration of oil and grease decreases along the length of the wetland. Regarding metal concentrations, for the three sample types, most metal concentrations visibly decreased between samples taken near the Lost Lagoon wetland entry and exit and this was confirmed by calculating and comparing the Wilcoxon rank test parameter between population medians. The same trend was not found when comparing the wetland entry and exit to the environment in Lost Lagoon. For this chapter’s purposes, this result effectively proves that the first hypothesis is true. Mineral oil and grease more clearly decreased between the wetland entry and exit. Variable and high mineral oil and grease concentrations were measured throughout the wetland forebay while low or undetectable levels of mineral oil and grease were measured at the wetland exit and in Lost Lagoon. For the purposes of this chapter, the measured results prove that mineral oil and grease decreases along the length of the Lost Lagoon wetland. The persistence of outliers throughout the dataset may have contributed to some of the statistical uncertainty in the results. In addition, the natural background levels of certain metals may outweigh the calculation of a difference between the wetland entry and exit, especially for metals that exist at only slightly elevated levels in stormwater, such as cobalt and antimony.   64   Chapter Objectives In order to support the goal of this study, to provide proof of concept data that supports or rejects developing a genomics monitoring tool for low impact design features that treat stormwater, including engineered wetlands, three objectives were previously stated for this chapter: 1. Demonstrate that the Lost Lagoon wetland is meeting water quality treatment guidelines; 2. Demonstrate that the engineering best management practices employed in the design of the Lost Lagoon wetland have had some meaningful impact on the stormwater treatment efficiency; and 3. Identify knowledge gaps and opportunities for complimentary data analyses though the application of genomics. For the first objective, comparison of the maximum pollutant concentrations (Table 18)  in the water samples collected at the entry and exit of Lost Lagoon wetland demonstrates that the wetland is generally meeting water quality treatment guidelines. The only exception for this, is the maximum point measurement for cadmium. The maximum cadmium concentration measured at the outlet was 4 µg/L and the guideline for effluent water is 1 µg/L (Canadian Council of Ministers of the Environment, 2015). The effluent guideline is the same as the method detection limit for the ICP instrument so, using the methodology employed here, it cannot be said with confidence whether this guideline is regularly exceeded.   65  Table 18. Comparison of Maximum Pollutant Concentrations Measured in Water Samples at the Lost Lagoon to British Columbia Treatment Guidelines  Pollutant Concentration Stormwater 1 Inlet Maximum Outlet Maximum Guideline3 Mineral Oil and Grease (mg/L) 5.0-63.41 108 5.4 30 Organic Carbon (mg/L) 7.3-17.6 327 148 - Solids (mg/L) 44-8091 1359 106 5 above BL Turbidity, NTU - 359 155 - Barium (µg/L) 0.2-0.7921 492 10 - Cadmium (ug/L) 0.035-2.31 18 4 1 Cobalt (µg/L)  34 1 - Chromium (µg/L) 0.01-0.132 238 1 - Copper (µg/L) 4.0-6.592 1552 123 2.0 Manganese (µg/L) 0.112-6.91    Molybdenum (µg/L)  25 20 73 Nickel (µg/L) .002-22.62 73 1 25 Lead (µg/L) 0.2-2.781 376 1 3 Antimony (µg/L)  185 1 - Zinc (µg/L) 6.5-27.5 2009 18 75 1 (Stime, 2014) 2 (British Columbia Research Corporation, 1992) 3 (Canadian Council of Ministers of the Environment, 2015) For the second objective, demonstrating that the best management practices employed in the design of the wetland have had some meaningful impact on stormwater treatment, one must review the wetland design and treatment capacity as a whole. Several different mechanisms, including sedimentation, adsorption, and plant uptake, are responsible for removing pollutants and the wetland was designed to optimize all of these mechanisms for long term stormwater treatment goals. The stormceptors incorporated as a pre-treatment step prior to inflow to the wetland were not studied at depth during this study. However, high levels of mineral oil and grease were measured in the forebay of the wetland, indicating that use of the stormceptors as the only treatment method would not meet effluent discharge guidelines. During the study period, there was no evidence of scouring or overflow from the wetland. In addition, sediment samples generally indicated that metal contaminant levels were higher on the front end of the wetland compared to the back end. This supports the notion that the overflow and diversion structures are beneficial to the overall treatment efficacy of the wetland. During the initial site visit at the Lost Lagoon wetland, photographs and documentation of the state of plant species was documented. There was evidence that the plant species had adapted well to the climate within 66  the wetland but that additional maintenance is required to remove some invasive species, including blackberry plants. Thoren et al (2007) demonstrated that two plant species selected for the wetland design, Carex obnupta and Scirpus acutus, were effective in taking up certain metal pollutants. Therefore, continued monitoring and maintenance of the planned plant species should continue. There was also evidence of animal activity where beavers had removed trees along the berm between the wetland and Lost Lagoon. The beaver activity requires close monitoring so that the wetland outlet does not become blocked, causing backflow and damage.  Finally, the sizing of the settling forebay for a 6-month design storm was of interest during this study. In the results, there is evidence that the highest metal loading is received at the centre of the forebay. Measurements for metals taken at Location 2 (the wetland entry) and Location 3 (the centre of the forebay) were consistently higher than measurements taken at Location 4 (in the forebay, furthest from the wetland entry). However, additional analysis of flow rates and settling within the forebay would be required to properly validate this treatment stage. For the third and final objective of this chapter, to identify knowledge gaps and opportunities for complimentary analysis through application of genomics, two statements can be made. First, while there is some evidence that the wetland is removing contaminants and meeting treatment objectives, there is still a lot of uncertainty in the results. Specifically, the results indicate that overall there is a significant decrease in contaminants along the length of the wetland but for some contaminants including cadmium, cobalt, lead, and zinc, more depth of analyses would be beneficial. Second, genomics provides an opportunity for complimentary analyses because microbial communities adapt and change due to the toxicity of pollutants. Specific species that thrive in contaminated environments will overtake other species, which due not have the same abilities. Over time, microbial communities also adapt and develop genetic tolerance mechanisms when exposed to pollutants. Analyzing species and gene differences between the microbial communities at the front and back end of the wetland would, thus, provide an additional resource to compliment uncertain pollutant treatment data.  Final Remarks The work described in this chapter effectively answered both study hypotheses and provided data in support of the three objectives described here. In doing so, this chapter has laid the foundation for Chapter 2, where microbial analyses were conducted to provide proof of concept data in support of developing a genomics tool for monitoring stormwater treatment wetlands. Overall, there is evidence of effective 67  stormwater treatment at the chosen field study site, the Lost Lagoon wetland in Stanley Park, Vancouver, but further analyses are required to properly validate said evidence. 2.8 Limitations Even though a wide range of techniques and several collection dates were incorporated into the environmental sampling design, single point in time measurements do not provide adequate proof that the wetland is meeting design targets. This is because the stormwater runoff entering the wetland is highly variable and the time it takes for stormwater to pass through the wetland is also variable. Therefore, one cannot directly compare water measurements taken at the front and back end of the wetland on a single date. Sediment sampling provides a clearer picture of long term treatment trends but there are still limitations because of the challenges with digesting organic rich samples prior to analysis using ICP-MS or other techniques. In contrast, there are many effective techniques to extract DNA from sediment and water samples and these are widely available from laboratory suppliers. In the future, analyzing the microbial response to contaminants may present itself as a valuable tool to validate environmental data. In addition to the variability within the wetland, there were several limitations during this section of the research study, which contributed to uncertainty in the results. These include:  Challenges accessing the wetland and stormceptor;  Equipment malfunctions with the core sediment sampler;  Budget limitations for the number of samples which could be processed;  Limits to the number of samples which could be obtained and processed in a single day; and  Challenges with the digestion of sediment samples.   68  3. Chapter 2: Application of Genomics-Based Monitoring Techniques for Complimentary Validation of the Lost Lagoon Stormwater Treatment Wetland 3.1 Introduction and Chapter Goal The contents of this chapter expand on the results of Chapter 1 by applying genomics-based approaches to support the conclusion that the Lost Lagoon wetland is effectively treating stormwater. In addition, this chapter provides data to support the application of genomics for validation of other low impact design sites that treat stormwater. This chapter first describes the toxicity of stormwater in relation to bacteria. Next, bacterial adaptions to stormwater exposure are described with the goal of identifying potential markers for effective stormwater treatment. In support of the study methodology, potential genomics approaches are compared for application in stormwater treatment monitoring. The study methodology is described, which includes the incorporation of a laboratory based study, with the goal to illustrate the adaptability of this study’s methodology for other low impact design sites. Finally, results, discussion, and conclusions are provided. 3.2 Chapter Objectives Based on the overall goals of this research, this chapter has three specific objectives. Using the same samples that were analysed in Chapter 1: 1. Apply genomics-based analysis methods to determine if there are shifts in the microbial communities and functional genes along the length of the Lost Lagoon wetland; 2. Determine if there is a correlation between the water and sediment quality, present over the study period, and the microbial communities and functional genes observed; and 3. Determine, through laboratory experimentation, if there are opportunities to expand and pursue genomics-based analyses at other stormwater treatment low impact design features. 3.3 Hypotheses In order to use microbial comparisons as a monitoring parameter for stormwater treatment, one would need to observe differences in the microbial communities that exist in the presence of stormwater compared to the microbial communities that do not exist in the presence of stormwater. One would then need to meaningfully capitalize on these differences by correlating adaptation to contamination. 69  In order to achieve said observations and correlations, this chapter attempts to answer three hypotheses: 1. There is a shift in the composition and function of the microbial communities that exist between the entry and exit of the Lost Lagoon wetland; 2. The shift in the composition and function of the microbial communities between the entry and exit of the Lost Lagoon wetland is influenced by the decreasing concentration of contaminants along the length of the wetland; 3. There are similarities across unconnected sites in the adaptations that take place within microbial communities due to exposure to stormwater. 3.4 Literature Review Like in Chapter 1, in order to provide background and context for the objectives and hypotheses stated in this chapter, a review of relevant literature was performed. First, a description of the toxicity of urban stormwater is provided. Next, the influence of urban stormwater contaminants on microbial communities is reviewed. After this, a summary from the literature of known microbial adaptations to stormwater is given. Finally, current methods for DNA sequencing and data analysis are discussed and compared for their advantages and disadvantages. This information advises the decisions that were made for the methodology presented in Chapter 2.  Toxicity of Urban Stormwater Numerous past and current studies examine the toxicity of highway stormwater from both an environmental and human health perspective and these studies generally conclude that stormwater has some toxic elements (Gjessing et al., 1984, Mulliss, Revitt, & Shutes, 1996, Marsalek et al., 1999, Karlsson et al., 2010).  Dutka et al. (1994) recommend assessment of toxicity through chronic effects testing for stormwater because, while the immediate effects due to exposure may not be severe, the prolonged effects of stormwater exposure are impactful. There are a variety of means to test toxicity including tests for cytotoxicity (cellular damage) and genotoxicity (genetic damage), which both tend to focus on toxic effects for bacteria. Because toxicity of stormwater is influenced by the quality of said stormwater, where temporal variability and uncertainty has already been discussed, many studies tend to focus their research efforts on the toxicity of sediments in locations that have been impacted by stormwater. However, sediment sampling introduces additional uncertainties because of chemical partitioning, bioavailability, and the small sample size (Marsalek et al., 1999).  Pitt, et al. (1995) identified gravity settling as the most important means of 70  reducing stormwater toxicity, where settling was shown experimentally to reduce stormwater toxicity by approximately 50%. However, in a review of four common toxicity testing methods for sediment and water samples, all samples were shown to include inherent uncertainties of between 10% and 50%, which limit the ability of toxicity testing to elucidate toxicity measurements (Marsalek et al., 1999). Beyond water quality, stormwater also produces environmentally toxic effects to receiving environments due to sediment loadings and alterations to stream morphology.  However, discussion of toxicity in this form is outside of the scope of this project. While toxicity of stormwater as whole is less studied, the toxicity of specific elements within stormwater are well known. For example, chromium causes oxidative damage and inhibits sulfate membrane transport in bacteria and nickel can be highly toxic as it inhibits cell multiplication (Das, Dash, & Chakraborty, 2016). However, the toxicity of stormwater is not equal to the sum of its parts due to the interaction of pollutants including metals, natural organic matter, and hydrocarbons. Likewise, bacteria have developed complex resistance pathways, which are often correlated. The influence of stormwater on bacteria and the complexity of stormwater toxicity is further discussed in the sections that follow.  Influence of Urban Stormwater Contaminants on Microbial Communities While there is a large body of literature that suggests that stormwater has toxic elements, the influence of stormwater on microbial communities, specifically bacteria, is lesser known. After an extensive review of literature, only a handful of published studies attempted to determine the influence of stormwater on the bacteria that reside within engineered wetlands or other low impact treatment systems (Nogaro et al., 2007; Hartman et al., 2008; Faulwetter et al., 2009; Karlsson et al., 2010; Truu, Juhanson, & Truu, 2009; Sun et al., 2013) Within the literature that was accessed, no study provided a dataset where bacteria were compared along the length of a stormwater treatment wetland. Nogoro et al. (2007) examined the influence of stormwater quality on microbial characteristics. Their results showed that biogeochemical processes, including aerobic respiration, denitrification, and fermentation as well as microbial metabolism and enzymatic activities were stimulated by the presence of stormwater and the natural organic matter. Nogoro et al. (2007) also concluded that hydrocarbons and heavy metals did not have significant effect on microbial processes. However, Nogoro et al. only examined total bacteria counts, a crude index of bacteria diversity (optical density) and hydrolytic and dehydrogenase activities. The authors did not examine the bacterial community at a species or gene level, likely because the sequencing technologies were not available at the time of their study. 71  Hartman et al. (2008) suggested that “soil bacteria regulate wetland biogeochemical processes, yet little is known about controls over their distribution and abundance.” While Hartman et al. (2008) did not specifically analyze stormwater treatment wetlands, they did perform a broad analysis of fifteen natural and restored wetlands. The analysis suggested that soil pH, land use, and restoration status greatly influenced bacterial composition and diversity but wetland type, soil carbon and nutrient concentrations had less of an impact. Land use was found to have the most significant impact on bacterial communities across all wetland sites even after accounting for wetland type and soil chemistry using pure-partial Mantel’s tests. Interestingly, Hartman et al. (2008) noted that the responses of bacterial communities were dominated by a few taxa (Acidobacteria and Proteobacteria) and the authors suggested that this yields a promising result for the application of bacteria as an indicator of wetland health.  Faulwetter et al. (2009) noted that the recent application of newer molecular and genetic analysis methods has begun a “new era of treatment wetland research.” In their literature review, Faulwetter et al. (2009) found that results up to 2009 confirmed the existence of microbial functional groups such as nitrifiers, denitrifiers and sulphate reducers that are responsible for pollutant removal but Faulwetter et al. also suggested that the future of this science would shift to the identification and linkage of the functional groups to the environmental factors of greatest influence. In 2009, Faulwetter et al. recognized the upcoming importance and value of microbial analysis in water treatment: “When we understand what controllable factors turn critical functional groups on and off we will be able to fully optimize performance for removal of a specific pollutant, or perhaps still be able to achieve the “perfect” treatment system that can satisfactorily remove virtually all pollutants from domestic wastewater, and/or other sources.” Sun et al. (2013) used 454 pyrosequencing of the 16S rRNA gene in order to investigate how estuaries responded to contaminants. While this study did not specifically address stormwater treatment wetlands, Sun et al. (2013) conclude that an abundant and pervasive core set of bacteria were largely responsible for mediating the response of the microbial community to contamination. Like Hartman et al. (2008), Sun et al. (2013) also found that the microbial community core was dominated by proteobacteria and acidobacteria. The authors observed that silt and metals together explained approximately 20% of the variation in the bacterial community and that salinity and temperature predicted approximately 11% of the microbial community. The research supported the notion that there is some functional redundancy within the bacteria of contaminated sediments but that our understanding of bacteria communities’ responses and resilience to contamination is still developing.  72   Known Microbial Adaptations to Urban Stormwater Contaminants While few studies on bacteria specifically focus on the changes of communities due to exposure to stormwater contaminants, there is a wider body of knowledge that focuses on the response of bacterial communities to metal exposure (Das, Dash, & Chakraborty, 2016). For example, one study, which compared two metal contaminated sites with an order of magnitude difference in contamination, suggested that adaptations of bacterial communities to metal exposure are subtle but significant and that the bacterial communities in freshwater sediments adapt to metal exposure without widespread changes to the bacterial population (Gillan et al. 2015). Adaptations of microbial communities may occur at either the genus/species level (e.g. Pseudomonas fluorescens, Alcaligenes faecalis, Ochrobactrum tritici, etc.) or at the gene level (e.g. CadB, ChrA, CopAB, etc.). Certain species of bacteria may be able to adapt to environments with elevated metal levels, which would otherwise be toxic for other bacteria, through application of elements within their genetic systems and/or through mechanisms for maintaining their internal ecosystem (Ryan et al. 2009). In their review of bacterial adaptations, Das et al. (2016) point out that bacteria are uniquely able to adapt to all types of extreme environments due to several features including their:  Small size;  High surface area to volume ratio; and  Ability to efficiently transfer genetic traits. In addition to these features, bacteria have developed three primary methods for metal resistance including: 1. Efflux of irritant metals outside the cell by transporters; 2. Transformation of metals into less toxic forms; and 3. Bioadsorption. Efflux requires that bacteria consume energy (ATP) to pump metal cations outside of the cell (Nies 2003). Transformation to a less toxic state requires that bacteria reduce metals to a dissimilar oxidation state. Bio-adsorption typically requires that bacteria bind metals onto their cellular surface – this typically involves formation of a biofilm, which can be highly complex and versatile (Harrison et al. 2006). Beginning in the 1970’s, numerous bacteria have been identified for their metal resistant traits. In 1999 Nies reviewed known metal resistance mechanisms to that date. In 2016, Das et al. updated the works of Nies with the goal of identifying opportunities for bioremediation. In their words, Das et al. (2016) state that “the ability of bacteria to resist toxic metals comes from a highly modified genetic system, by means 73  of which bacteria synthesize proteins enabling them to thrive in the presence of such elements. Bacteria survive by expressing several metal-resistant genes toward toxic metals.”  The relevant details from both summaries with respect to stormwater pollutants are summarized in Table 19. Table 19. Bacterial adaptations to metals in stormwater (adapted from Nies 1999; Das et al. 2016) Metal Adaptation Sources Antimony  Leishmania cells are able to gain resistance to arsenic and antimony by efflux. Rosenstein et al. 1992; Sanders et al. 1997 Arsenic  Aerobic bacteria, like Alcaligenes faecalis, are able to oxidize arsenic.  Leishmania cells are able to gain resistance to arsenic and antimony by efflux. Laverman et al. 1995 Dey et al. 1994 Cadmium  Resistance to cadmium in bacteria is based on cadmium efflux.  In Cyanobacteria, amplification of the smt metallothionein locus increases cadmium resistance and deletion of it decreases resistance.   In gram-negative bacteria, cadmium is detoxified by RND-driven systems like Czc, which is mainly a zinc exporter and Ncc, which is mainly a nickel exporter.  In gram-positive bacteria, the first example of a cadmium-exporting P-type ATPase was the Cad-A pump from S. aureus. Olafson et al. 1979 Gupta et al. 1992; Gupta et al. 1993; Turner et al. 1993 Thelwell et al. 1998; Nies 1995; Nies & Silver 1989b; Schmidt & Schlegel 1994 Nucifora et al. 1989; Silver et al. 1989 Chromium  To fight chromium toxicity, microbes have developed two mechanisms of chromium resistance. The first is a method of chromate efflux from the cells, and the second method involves enzymatic reduction of toxic Cr6+ to less toxic Cr3+.  The operon for chromium efflux if encoded in four genes, chr-BACF  Chr-R was identified as a chromate reductase gene. The general chromate transport reactions involve a family of chromate ion transporters.   Three other genes, chr-JKL, were later identified and proven to be involved in the chromium reduction process.  Chromium can also be reduced through bacterial excretion of enzymes but this process is lesser known.  Pseudomonas fluorescens strain LB300, was shown to reduce chromate and a broad variety of bacteria that are able to reduce chromate have since been found. Das et al. 2016   Branco et al. 2008 Gonzalez et al. 2005  Henne et al. 2009 Batool et al. 2012; Mishra et al. 2012 Bopp & Ehrlich 1988; Cervantes & Silver 1992 Cobalt  Resistance to cobalt in gram-negative bacteria is based on a trans-envelope efflux driven by a resistance, nodulation, cell division (RND) transporter.  Cobalt resistance seems always to be the by-product of resistance to another heavy metal, either nickel or zinc. Liesegang et al. 1993; Schmidt & Schlegel 1994  Nies et al. 1987 Copper  A major copper resistance mechanism in bacteria is encoded within four genes, cop-YABZ. Bacteria with these genes will show early copper retention followed by a metal efflux process.  Other bacteria, including E. Coli have been shown to a have a double regulatory mechanism for copper resistance, which is encoded in a sensing system controlled by the two genes, cus-RS, and this sensing Odermatt et al. 1992, 1993; Wunderli-Ye & Solioz 1999; Albarracin et al. 2008  Djoko et al. 2010 74  mechanism regulates metal efflux, which is controlled by four proteins cus-CFBA.  Some bacteria also have a copper efflux system where the regulatory gene, cue-R, regulates two genes, cop-A and Cue-O, which cause copper efflux.  Cso-R is another regulatory gene in bacteria, which in the presence of Cu+ de-represses copper resistance genes.  A Streptococcus strain was seen to have a copper transport operon named cop-YAZ in which cop-Y and cop-Z were established as heavy metal-binding proteins.  Pseudomonas fluorescens has been reported to possess a cop-RSCD operon for copper efflux.  Helicobacter pylori contains two separate operons for copper export and import, hpcop-AP.  Bacillus subtilis has another copper regulatory system, mediated and regulated by Ycn-Jk and Cso-R. Together, these genes maintain a state of copper homoeostasis.  An ATPase-driven copper efflux system is the main mechanism responsible for cytoplasmic copper removal: the multicopper oxidase Cue-O in E.coli and Enetrobactin oxidizes Cu (I) to Cu (II).  Yersiniabactin sequesters Cu (II) outside the bacterial cell protecting the bacteria from intracellular killing.    Djoko et al. 2010  Chang et al. 2014  Vats & Lee 2001  Hu et al. 2009 Ge & Taylor 1996  Chillappagari et al. 2009  Grass et al. 2004  Chaturvedi et al. 2012 Lead  Lead-tolerant bacteria have been isolated, and precipitation of lead phosphate within the cells of these bacteria has been reported.  Several bacteria, such as Arthrobacter spp., Bacillus megaterium, Pseudomonas marginalis, Citrobacter freundii, Staphylococcus aureus, and E. coli have been found to be resistant to lead.  The most studied lead efflux operon, named the pbr operon, was found to contain many structural genes, (pbr-TABCD) and one regulatory gene (pbr-R)  Metal immobilization by the process of extracellular sequestration is also important for regulating metal toxicity:  Lead binding by the negatively charged components of EPS has been demonstrated in P. aeruginosa strain CH07.  Pseudomonas marginalis is able to resist lead through sequestration of lead in an exopolymer.  Similarly, the EPS of Paenibacillus jamilae bioadsorbs lead  There are many enzymatic activities in the bacterial EPS which assist in toxic metal transformation by chemical reaction, precipitation, or entrapment.  Bioprecipitation of toxic metals to insoluble complex formation is another strategy which reduces metal bioavailability and toxicity:  Bacillus iodinium strain GP13 and Bacillus pumilus strain S3 were reported to precipitate lead as lead sulfide. Trajanovska et al. 1997; Levinson & Mahler 1998; Das et al. 2016   Borremans et al. 2001; Jarosławiecka & Piotrowska-Seget 2014  Das et al. 2016 De et al. 2007  Roane 1999 Morillo et al. 2008  Paul 2008 Das et al. 2016 De et al. 2008  75   A phosphate-solubilizing bacterium, E. cloacae, was found to resist lead by immobilizing lead as a insoluble lead phosphate mineral, pyromorphite Park et al. 2011 Nickel  Nickel is detoxified by sequestration and/or transport. It is bound to polyphosphate in S. aureus.  The best-known nickel resistance in bacteria, in Ralstonia sp. strain CH34 and related bacteria, is based on a nickel efflux pump driven by an RND transporter.  Nickel resistance in bacteria is generally mediated by efflux pumps. One such resistance mechanism has been studied in Cupriavidus metallidurans strain CH34 where it was reported that the presence of the efflux pump was encoded by the cnr-YHXCBAT gene system.  In Achromobacter xylosoxidans strain 31A, only one gene, nreB, was responsible for conferring the entire nickel resistance efflux system.  The ncc operon provides combined nickel, cobalt, and cadmium resistance.  Seven open reading frames (ORFs) were studied and designated ncc-YXHCBAN. The nucleotide sequence revealed significant similarity to the cnr and czc operons of Alcaligenes eutrophus strain CH34.  In E. coli, the rcn-A gene encodes a membrane-bound polypeptide which had the ability to confer resistance to nickel and cobalt.  Another efflux pump was identified in Helicobacter pylori and named czn-ABC, for cadmium, zinc, and nickel.  In another study, the nickel/cobalt transferase gene, NiCo-T, from Staphylococcus aureus was amplified and established as having high resistance Gonzalez & Jensen 1998 Nies 1999  Grass et al. 2000  Grass et al. 2005 Schmidt & Schlegel 1994  Tibazarwa et al. 2000   Rodrigue et al. 2005  Stahler et al. 2006  Zhang et al. 2007 Zinc  Two systems are used for zinc detoxification in bacteria, P-type efflux ATPases and RND-driven transporters.  In E. coli and Synechocysti, Znt-A and Zia-A are responsible for zinc efflux. Efflux pumps for cadmium resistance often also cause zinc efflux. Beard et al. 1997; Rensing et al. 1997b Thelwell et al. 1998  DNA Sequencing and Data Analysis Methods  DNA Sequencing Overview Early methods for DNA sequencing began in 1970 and were unautomated, extremely costly, and took years to complete; these methods are generally no longer in use and are described elsewhere (Chen, 1994). However, since 1995 when the first bacterial genome was sequenced (Fleischmann et al., 1995), scientific capabilities with DNA sequencing and genome-based analytics have rapidly increased. Loman et al. (2012) discuss how extremely rapid growth in this field has led to “an embarrassment of choice” between instruments and platforms and also that “vigorous competition between manufacturers has resulted in sustained technical improvements on almost all platforms.” There are numerous sequencing technologies available to researchers, each offering its own set of advantages and disadvantages. There are also 76  numerous precursors or alternate methods for analyzing bacterial diversity and function, some of which are still commonly used and others of which are being phased out due to out-competition from emerging/modern technologies. Selecting the right analysis method for a study depends on a number of factors including:  The goal of the study;  The sample media and the expected DNA quantity and quality obtainable during extraction;  The depth and quality of data required to achieve the study goal;  The availability of analysis technologies and institutional expertise for guidance; and  The study timeline and budget. Bacteria are highly concentrated in the natural environment; one gram of soil or sediment typically contains 1010 bacteria while one millilitre of seawater typically contains 106 bacteria (Torsvik et al., 1990). Because of the massive population, comparing bacterial diversity quickly becomes extremely complex. Bacterial diversity exists at three levels: within species (genetic), between species (species) and community (ecological) diversity (Harpole, 2010). Species diversity can be further broken down into two components – species richness and species distribution. Species richness refers to the total number of different species in the population while species distribution refers to the evenness of the different species in the population. Diversity studies can relay useful information about the stresses on an ecosystem; generally, a bacterial community that is diverse is more stable when responding to environmental stresses as it contains the genetic code for adaptability to change (Yannarell & Triplett, 2005). Diversity will change in response to stress and this can be monitored as a cause and effect relationship. Methods for analyzing microbial diversity and abundance can be categorized into three groups: conventional (culture-based), biochemical and molecular. Table 20 summarizes some of the most common conventional and biochemical analysis techniques.   77  Table 20. Common Conventional and Biochemical Techniques for Analyzing Microbial Diversity and Abundance (adapted from Fakruddin & Mannan, 2013) Method Description Advantages Disadvantages Plate counts  Culture bacteria on growth media followed by viable counts  Fast   Inexpensive   Un-culturable bacteria not detected   Bias towards fast growing bacteria Community level physiological profiling (CLPP)/ Sole-Carbon Source Utilization (SCSU) Pattern  Identify pure cultures of bacteria to the species level using their metabolic properties  Examine the functional capabilities of the microbial population  Compare metabolic capabilities of communities.  Fast   Highly reproducible   Relatively inexpensive   Able to differentiate microbial communities   Generates large amount of data   Option of using bacterial, fungal plates or site specific carbon sources  Only represents culturable fraction of community   Favours fast growing bacteria   Only represents those organisms capable of utilizing available carbon sources   Potential metabolic diversity, not in situ diversity   Sensitive to inoculum density  Phospholipid fatty acid (PLFA) analysis/Fatty acid methyl ester analysis (FAME)   Use the fatty acid composition of microorganisms to aid microbial characterization  Analyze the PLFA composition of the organisms since different subsets of a community have different PLFA patterns.  Culturing not required   Direct extraction from soil   Follow specific organisms or communities  Can be influenced by external factors   Results can be confounded by other microorganisms   Molecular techniques, can be further divided into partial community analysis techniques and whole community analysis techniques. These techniques can also be classified as first generation, next generation, or third generation methods based on the throughput, quality and depth of information obtained. Partial community analysis generally involves first generation PCR-based analysis techniques where DNA or RNA extracted from an environmental sample is used as a template to characterize microorganisms (Rastogi & Sani, 2011). Essentially, in partial community analysis, researchers determine the genetic signature in a sample by selecting and analyzing a specific gene that is conserved among all species such as the 16S rRNA gene or the RNA polymerase beta sub-unit (rpoB).  The disadvantage of partial community analysis is that researchers must compare to a database of known information in order to parcel out results from their samples; however, the growing databases of known species data have made these methods highly desirable in recent years. Technological advances in the throughput and depth of information that can be obtained in partial community analysis has led to the development of next generation methods including the Illumina MiSeq platform, which is a type of clone library analysis. Table 21 summarizes common partial community analysis techniques and their advantages and disadvantages. 78  Table 21. Common Partial Community Analysis Molecular Techniques for Analyzing Microbial Diversity and Abundance (adapted from Fakruddin & Mannan, 2013; Rastogi & Sani, 2011) Method Description Advantages Disadvantages Nucleic acid re-association and hybridization  Estimate diversity by measuring the genetic complexity of the microbial community (re-association)  Use specific probes  (e.g. FISH) on extracted DNA or RNA, or in situ to examine and quantify known sequences (hybridization)   Total DNA extracted   Not influenced by PCR biases   Can study DNA or RNA   Can be studied in situ   Lack of sensitivity   Sequences need to be in high copy number for detection   Dependent on lysing and extraction efficiency  DNA microarrays and DNA hybridization    Develop a microarray to elucidate function diversity of a community by identify specific target genes coding for enzymes such as nitrogenase, nitrate reductase, naphthalene dioxygenase etc.  Same as nucleic acid   hybridization   Thousands of genes can be analyzed   Increased specificity   Only detect the most abundant species   Need to culture organisms   Only accurate in low diversity systems  Denaturing (DGGE) and Temperature (TGGE) Gradient Gel  Electrophoresis    Use a linear gradient of DNA denaturants (DGGE) or temperature (TGGE) to separate DNA fragments (16S or 18S rRNA) of the same length but with different base-pair sequences and differentiate the fragments based on their mobility (Mühling et al., 2008)  Large number of samples can be analyzed simultaneously   Reliable, reproducible and rapid   PCR biases   Dependent on lysing and extraction efficiency   Sample handling can influence community  One band can represent more than one species   Detects dominant species  Single Strand Conformation Polymorphism (SSCP)  Analyze differences in the mobility of single stranded DNA on polyacrylamide gel, resulting from the folded secondary structure of DNA, which is dependent on DNA sequences  Same as DGGE/TGGE   No GC clamp   No gradient   PCR biases   Some ssDNA can form more than one stable conformation  Restriction  Fragment Length  Polymorphism (RFLP)   Blot electrophoresed digests from agarose gels onto membranes and hybridize with a probe prepared from cloned DNA segments of related organisms  Detect structural changes in microbial community   PCR biases   Banding patterns often too complex  Terminal Restriction Fragment Length Polymorphism  (T-RFLP)   Follow the same principle as RFLP except label one PCR primer with a fluorescent dye, perform PCR on the sample DNA using universal 16S rDNA primers and separate fragments by gel electrophoresis, where each unique fragment length can be counted as an OTU and the frequency of OTUs can be calculated (Liu et al., 1997)    Simpler banding patterns than RFLP   Can be automated   Large number of samples   Highly reproducible   Ability to compare differences between microbial communities   Dependent on extraction and lysing efficiency   PCR biases   Type of Taq can increase variability   Choice of restriction enzymes will influence community fingerprint  79  Ribosomal Intergenic Spacer Analysis (RISA)/Automated Ribosomal Intergenic Spacer Analysis (ARISA)  Detect sequence polymorphisms using silver staining in RISA or a fluorescently labeled forward primer in  ARISA  Use PCR to amplify the intergenic spacer (IGS) region between the 16S and 23S ribosomal subunits , denature and separate units on a polyacrlyamide gel and differentiate between bacterial strains and species based on heterogeneity (Fisher & Triplett, 1999).   Highly reproducible community profiles    Requires large quantities of DNA (for RISA)   PCR biases  Quantitative polymerase chain reaction (Q-PCR)  Use dyes or probes to measure the accumulation of amplicons in real time during each cycle of the PCR and quantify based on the exponential increase in amplicon concentration  Rapid  Successfully used for quantification of important physiological groups  Highly sensitive to starting template concentration  Requires microbe concentrations to be above detection limits Clone library method (e.g. MiSeq)  Clone and then sequence the individual gene fragments in an environmental sample (e.g. 16S rRNA genes) and compare to a known database such as GreenGenes or Silva  Most widely used method to analyze PCR products  The ‘gold standard’ for preliminary microbial diversity surveys  Large availability of data for comparison  16S rRNA gene is highly stable and conserved  Labor intensive  Time consuming  Expensive  May not decipher the entire microbial community composition  In contrast with partial community analysis techniques, whole community analysis techniques attempt to analyze all of the genetic information extracted from a sample. The first common modern method of whole community analysis to be developed was automated Sanger sequencing (Slatko et al., 2011); however, this was a first generation technique that was costly and highly time-intensive and while it did elucidate much insight into the link between microbial function and taxonomic identity, a large body of information was still poorly understood. Next generation sequencing methods emerged in 2005 and their advent has revolutionized the scientific understanding of microbial communities and relationships (Lagares et al., 2012). Table 22 summarizes the most common techniques for whole community analysis.   80  Table 22. Common Techniques for Analyzing Microbial Diversity and Abundance using Whole Community Analysis (adapted from Fakruddin & Mannan, 2013; Rastogi & Sani, 2011) Method Description Advantages Disadvantages Automated chain terminator (Sanger) sequencing  Sequence whole microbial genomes using a shotgun cloning method that involves (1) extraction of DNA from pure cultures, (2) random fragmentation of obtained genomic DNA into small fragments, (3) ligation and cloning of DNA fragments into plasmid vectors, and (4) bidirectional sequencing of DNA fragments  Small machines are available for low-throughput laboratories  Useful for some specific applications (e.g. finishing genomes)  Costly  Time-intensive  Sequencing low number of clones captures only dominant components of the microbial communities Metagenomics  Investigation of the collective microbial genomes retrieved directly from environmental samples without relying on cultivation or prior knowledge of the microbial communities (Riesenfeld et al. 2004)  Cost-effective  Higher throughput  Simpler library preparation  No cloning step  Steadily improving read lengths  Minimal hands on time  Long run time  Short read lengths  Some methods yield high error rates or biases  Expensive reagents Metatranscrip-tomics  Allows monitoring of microbial gene expression profiles in natural environments by studying global transcription of genes by random sequencing of mRNA transcripts pooled from microbial communities at a particular time and place  Suitable for measuring changes in gene expression and their regulation with changing environmental conditions  Prokaryotic microbial mRNA transcripts are not polyA tailed, so obtaining complementary DNA is not easy. Proteogenomics  Deals with the large-scale study of proteins expressed by environmental microbial communities at a given point in time  Rapid and sensitive  Protein biomarkers are more reliable and provide a clearer picture of metabolic functions than functional genes or even the corresponding mRNA transcripts of microbial communities  New emerging technology  Concerning whole community analysis, metagenomics is the focus of this study as the goal is to develop a genomics tool for stormwater treatment applications. Therefore, those interested in further detail of other whole community analysis methods should consult elsewhere (Rastogi & Sani, 2011). Various companies supply technologies for metagenome sequencing including the Roche 454 platform (Life Sciences), the HiSeq (Illumina), and the Ion Torrent Personal Genome Machine (Thermo Fisher) (Bragg & Tyson, 2014). Each of these technologies offers advantages and disadvantages with greater advantages established with 81  each upcoming model (ibid). Currently, the most popular platform is the Illumina HiSeq system. Bragg and Tyson (2014) describe the Illumina sequencing protocol: “The Illumina sequencing protocol begins by ligating template DNA to an adaptor sequence and thence onto a glass flow cell. The template DNA is subjected to bridge amplification, whereby each template is increased to roughly 1,000 copies. By using an isothermal polymerase and 3′ inactivated fluorescent nucleotides, Illumina is able to incorporate a solitary base each cycle. Each base addition is followed by an imaging step, which reads the fluorescent label.” There are numerous models of the HiSeq platform including the HiSeq, HiSeq 2000, HiSeq 2500, and HiSeq 3000/4000. With each model upgrade, the sequencing power and efficiency increases; however, the general analysis principles remain the same.  Sequence Data Analysis Overview Due to the complexity of data obtained, analysis of next generation sequenced data requires several steps. These are summarized as follows (Lagares et al., 2012): 1. Data filtering: Identifying and removing noisy reads based on quality scores; 2. Data trimming: Removing regions with a high likelihood of error; 3. Noise removal: Iteratively pre-clustering and deleting both chimeras and PCR artifacts; 4. Data clustering: Defining OTUs by linking sequences with a threshold for percent similarity (e.g. 97%); 5. Taxonomic assignment: Comparing with a reference alignment of known taxonomic assignments (for MiSeq) or classification based on sequence homology and composition (HiSeq); 6. Assembly of metagenomes: Finding overlaps between reads and building consensus sequences, so-called contigs, based on multiple alignments; 7. Gene annotation: Identifying metagenomic sequences using gene prediction tools; 8. Metabolic reconstruction: Using gene predictions to understand the metabolic potential of a microbial community; and 9. Comparative metagenomics: Searching for statistically significant differences between metagenomes using either taxonomic classifications or gene/metabolic annotations. There are various software tools available to researchers for these purposes. Table 23 summarizes some of the more common software applications and provides links to each tool’s website for more information.  82  Table 23. Common Software Applications for Sequence Data Analysis Application Name Webpage General sequence processing Mothur http://www.mothur.org/ QIIME http://www.qiime.org/ De-noising AmpliconNoise http://code.google.com/p/ampliconnoise/ DeNoiser http://www.qiime.org/ Clustering UCLUST http://www.drive5.com/usearch/  Mothur http://www.mothur.org/  DNACLUST http://sourceforge.net/projects/dnaclust/  CD-hit http://www.bioinformatics.org/cd-hit/ Alignment Mothur http://www.mothur.org/ MAFFT http://mafft.cbrc.jp/alignment/software/ PyNAST http://pynast.sourceforge.net/ RDP http://pyro.cme.msu.edu/ SILVA http://www.arb-silva.de/ Phylo-genetics RAxML http://sco.h-its.org/exelixis/software.html FastTree http://www.microbesonline.org/fasttree/ Community analysis QIIME http://www.qiime.org/ Mothur http://www.mothur.org/ MG-RAST http://metagenomics.anl.gov/ R (VEGAN, GGPlot) http://www.r-project.org/ Assembly Newbler http://454.com/ Celera Assember http://sourceforge.net/apps/mediawiki/wgs-assembler/index.php?title=Main_Page CLC Assembly cell www.clcbio.com Meta-IDBA http://i.cs.hku.hk/~alse/hkubrg/projects/metaidba/ Genovo http://cs.stanford.edu/group/genovo/ MetaORFA n.a. MetaVelvet http://metavelvet.dna.bio.keio.ac.jp/ Bambus 2 http://www.cbcb.umd.edu/software/bambus/ Short read gene prediction Orphelia http://orphelia.gobics.de/ Metagenemark http://exon.gatech.edu/metagenome/Prediction/ FragGeneScan http://omics.informatics.indiana.edu/FragGeneScan/ MetaGeneAnnotator http://metagene.cb.k.u-tokyo.ac.jp/ Metagenomics tools MEGAN http://ab.inf.uni-tuebingen.de/software/megan/welcome.html SOrt-ITEMS http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS/ WebCARMA/CARMA 3 http://www.cebitec.uni-bielefeld.de/brf/carma/carma.html Treephyler http://gobics.de/fabian/treephyler PhyloPhytiaS http://binning.bioinf.mpi-inf.mpg.de TACOA http://www.cebitec.uni-bielefeld.de/brf/tacoa/tacoa.html Phymm/PhymmBL http://www.cbcb.umd.edu/software/phymm/ Naïve Bayes classifier http://ratite.cs.dal.ca/rita/submission    MG-RAST http://metagenomics.anl.gov/ CAMERA http://camera.calit2.net/ IMG/M http://img.jgi.doe.gov GAAS http://sourceforge.net/projects/gaas/ SmashCommunity http://www.bork.embl.de/software/smash Meta-rep http://www.jcvi.org/metarep Xipe http://edwards.sdsu.edu/cgi-bin/xipe.cgi STAMP http://kiwi.cs.dal.ca/Software/STAMP  83  3.5 Methodology The primary focus of this chapter was to provide proof of concept microbial results which either support or reject further research towards a genomics-based monitoring tool for stormwater treatment wetlands and other low impact stormwater treatment sites. With this goal in mind, a two-part strategic methodology was developed. First, a field study was performed at the Lost Lagoon wetland with the collection of water and sediment samples described in Chapter 1. In support of this chapter’s first two objectives and hypotheses, DNA was extracted, sequenced, analyzed and compared with environmental data. Second, a laboratory study was designed and performed, in support of this chapter’s third objective and hypothesis. Like in the field study, water and sediment samples were collected over the experimental period and DNA was extracted, sequenced, analyzed and compared with environmental data. A detailed description of the methodology employed to answer the objectives and hypotheses listed at the beginning of this chapter is supplied here.  Field Study Site Visits and Sampling Regime Please consult section 2.5 for details of the field study site visits and sampling regime.  Column Study Preparation and Execution A four month long, laboratory study was carried out using columns of uncontaminated natural soil sourced from a bog near Beaver Lake as highlighted in Figure 2 in the introduction to this thesis. Columns were fed either semi-synthetic stormwater or distilled water and the contaminant levels and microbial responses were measured over time.  Sourcing and Confirmation of Uncontaminated Soil To confirm the soil quality prior to collection of uncontaminated park soil, a location that was believed to be free of stormwater contamination was sited near the Beaver Lake bog. On October 27, 2015, six samples were collected across the bog site and each sample site was marked with flag tape. The samples were packed into plastic freezer bags and were brought back to the laboratory and analyzed for metal content.  Collection of Uncontaminated Soil After soil quality was confirmed, a soil collection day was planned for November 11, 2015. Eight large coolers were disinfected. Coolers were scrubbed with laboratory dish detergent, soaked overnight with 5% bleach solution, allowed to dry, rinsed with 1% nitric acid solution, and sprayed and wiped with 95% 84  ethanol. Shovels were also cleaned with dish detergent, rinsed with bleach, and sprayed with ethanol prior to soil collection. On November 11, 2015, bog soil was collected from Stanley Park. A team of four workers shoveled soil into clean five gallon buckets and transferred the soil to the disinfected coolers. A total of eight coolers of soil were collected and transferred by truck to the UBC civil engineering department refrigerators, where they were stored at <4˚C until further processing.  Column Study Environment In order to run the study over four months, a clean temperature controlled room was prepared. The room was emptied of shelving and the walls, ceiling, refrigeration system, and floor were scrubbed with laboratory grade dish soap and tap water. Next the surfaces were sprayed with hospital grade germicide and allowed to stand for fifteen minutes. Following germicide, the surfaces were wiped clean with paper towel and then sprayed with 10% beach solution and allowed to stand for twenty-four hours. Finally, the surfaces were given a final cleaning with 95% disinfection grade ethanol.  Pre-Study Experiment Before the full laboratory experiment began, the column configuration was run and studied for one week on one column and the ORP, conductivity, pH, DO, and temperature were monitored and confirmed to be in the range of values measured in the Lost Lagoon wetland forebay.  Column Configuration and Set-Up Seventeen sediment columns were analyzed over a four-month period. The sediment columns were constructed from five gallon opaque PVC buckets. Before the study began, the buckets were scrubbed with laboratory grade dish detergent and soaked in a 10% bleach-water solution for twenty-four hours. To prevent preferential flow along the smooth inside of the buckets, the inner lining of each bucket was then roughed with coarse sand paper. The buckets were again washed with laboratory grade dish detergent and soaked in bleach solution for twenty-four hours. Buckets were rinsed twice with distilled water, rinsed once with 1% nitric acid, rinsed once with distilled water, and wiped clean with 95% disinfection grade ethanol. The same cleaning process was used for the column lids. On November 19, 2015, soil was packed into the laboratory columns. 2.5 L from each cooler was placed into a clean bucket and homogenized using a hand mixer. Large debris including sticks, rocks, leaves, roots etcetera, that had a length greater than 0.5 cm was removed. No garbage or fecal matter was observed in the soil. One litre of soil was then packed into each column using a clean rubber mallet. This process was 85  repeated eight times so that the height of soil packed into each column reach 15-cm. Columns were zeroed and weighed and the mass of soil added to each column was recorded. The columns were allowed to sit covered in the clean controlled room at <4˚C until further processing. On November 25, 2015, 8-L of distilled water was added to six columns, which would serve as study controls. 5-L of distilled water was added to each of eleven columns, which would serve as the object of the study, hereafter referred to as exposed columns. A 0.5 cm hole was drilled into the lid of each column and the holes were sealed with bungs. The lids were placed on top of each column while they reached temperature equilibrium with the control room. The temperature in the control room was then increased by 2˚C on the morning of each subsequent day until the temperature of the room reached 18˚C.  Column Water Dosing Regime and Environmental Controls  Stormwater Dose Quality Based on several resources, (Bratieres et al., 2008; Blecken et al., 2009; Lewis & Sjostrom, 2010; Zhang et al., 2015), a recipe was developed for semi-synthetic stormwater to be used as simulated urban runoff in the column study. A combination of real sediment and chemical additives was mixed with dechlorinated distilled water in order to achieve target TSS concentrations and to maintain consistent inflow, while also mimicking ‘natural’ conditions. On November 27, 2015, fine sediment, which was collected from Site 2.1 in the Lost Lagoon wetland forebay, was autoclaved, centrifuged and decanted to remove water, and baked at 105˚C for 48 hours. This sediment was frozen at <-4˚C prior to use in the stormwater recipe. Average values from literature as well as the predicted quality of sediment from Site 2.1 in the Lost Lagoon Wetland were used to set target stormwater quality and to prepare a stormwater ‘recipe’. Table 24 lists stormwater qualities found in literature and the 2013 Hemmera analyses for quality of sediment in the Lost Lagoon Wetland.    86  Table 24. Urban Highway Stormwater Quality from Literature and Sediment Quality Data from the Lost Lagoon Wetland in 2013 Element Washington1 British Columbia2 Blecken et al (2009) Dredged Sediment3 µg /L µg /L µg/L mg/kg Antimony 8.7 - - 64 Arsenic 2.6 10-130 - 7 Barium 84 - - 205 Cadmium 2.8 - 6.7 3.5 Chromium 18 10-110 - 135 Cobalt 4.4 0.7-30 - 11 Copper 72 13-288 95 650 Lead 61 10-3775 181.5 250 Molybdenum 9.5 - - 28 Nickel 12.9 2-126 - 46 Zinc 394 40-25500 587.3 1150 Phosphorus 500 - - - Nitrogen 2800 - - - TSS* 400-1200  155 - *mg/L 1Washinton State (EPA), 2007 2British Columbia Waste Management Group, 1992 3Hemmera, 2013  Based on data from the state of Washington, USA, it was assumed that average highway stormwater would have a TSS concentration of approximately 800 mg/L. In order to maintain more consistent metal levels, the stormwater recipe for the laboratory column study was prepared in a semi-synthetic fashion. The stormwater was prepared using a target TSS of 400 mg/L and the remaining metal concentrations were ‘topped up’ using chemical additives. Table 25 lists the target metal concentrations for the stormwater and the top up required using chemical additives.   87  Table 25. Target Element Concentrations for Semi-Synthetic Stormwater Recipe Element 800 mg/L TSS 400 mg/L TSS Target Chemical Top Up Required µg/L µg/L µg/L µg/L Antimony 51.2 25.6 65 39 Arsenic 5.6 2.8 100 97 Barium 164 82 205 123 Cadmium 2.8 1.4 5 4 Chromium 108 54 100 46 Cobalt 8.8 4.4 15 11 Copper 520 260 650 390 Lead 200 100 550 450 Molybdenum 22.4 11.2 30 19 Nickel 36.8 18.4 100 82 Zinc 920 460 1100 640 Phosphorus 0 0 500 500 Nitrogen - - 2800 2800  Chemical additives were selected based on previous work performed by Blecken et al., (2009) and based on common availability of these additives in the laboratory.  Table 26 lists the chemical additives that were used for the stormwater recipe. Concentrated volumes of the chemical additives were prepared in separate 1-L bottles for each element and the volumes were stored in a refrigerator at <4˚C for use over the duration of the study. Table 26. Chemical Additives Used for Semi-Synthetic Stormwater Supplementation Element Chemical Additive Molecular Mass % Element g/mol g/g Antimony K₂Sb₂(C₄H₂O₆)₂ 613.83 40% Arsenic As2O3 197.84 76% Barium BaCl2 208.23 66% Cadmium Cd(NO3)2•4H20 368.45 31% Chromium [Cr(H2O)6](NO3)3•3H2O 535.07 10% Cobalt Co(NO3)2•6H20 291.03 20% Copper CuSO4•5H20 267.70 24% Lead Pb(NO3)2 331.21 63% Molybdenum (NH4)6Mo7O24•4H20 1,235.86 8% Nickel NiCl2·6H2O 237.69 25% Zinc ZnCl2 136.30 48% Phosphorus KH2PO4 136.09 23% Nitrogen NH4NO3 80.04 35%  88  On November 30, 2015, semi-synthetic stormwater was prepared to match the target concentrations listed in Table 25. Stormwater was mixed in disinfected 5 gallon buckets and stored in sterile glass 2-L amber bottles at <4˚C until application. Batches of semi-synthetic stormwater were prepared at two week intervals.  Stormwater Dose Volume and Frequency To determine the stormwater dosing regime, the ratio of the top surface area sediment column to the Lost Lagoon wetland catchment area was calculated. The watershed catchment area was calculated in Appendix E: 𝐿𝑜𝑠𝑡 𝐿𝑎𝑔𝑜𝑜𝑛 𝑊𝑎𝑡𝑒𝑟𝑠ℎ𝑒𝑑 𝐶𝑎𝑡𝑐ℎ𝑚𝑒𝑛𝑡 𝐴𝑟𝑒𝑎 = 32143 𝑚ଶ The column top surface area was calculated using the measured diameter: 𝐶𝑜𝑙𝑢𝑚𝑛 𝑇𝑜𝑝 𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝐴𝑟𝑒𝑎 = 𝜋𝑟ଶ = 𝜋(0.14ଶ) = 0.061544 𝑚ଶ The ratio of column top surface area and catchment area was calculated using: 𝑅𝑎𝑡𝑖𝑜 =0.061544 𝑚ଶ32134 𝑚ଶ= 1.91×10ି଺𝑚ଶ𝑚ଶ Next, average weather data from Environment Canada weather station 1108446, Vancouver Harbour CS, which is located 1.82 km from Stanley Park was used to determine monthly average temperature and precipitation values for the field study site over the period between August and November. Table 27. Environment Canada Average Precipitation and Temperature Data for Vancouver Harbor (Environment Canada, 2016) Month Day Temperature Night Temperature Rain  Average Monthly Rain Average Rain ˚C ˚C Days/month mm mm August 23 14 10 39.5 4.0 September 20 11 11 48.2 4.4 October 14 7 20 126.8 6.3 November 10 3 23 183.4 8.0  Using the data from the Environment Canada Station and assuming that the entire catchment area drains to the Lost Lagoon wetland, the average drainage volume per storm was calculated as: 𝐷𝑟𝑎𝑖𝑛𝑎𝑔𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚ଷ) =𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑅𝑎𝑖𝑛 (𝑚𝑚)×𝐶𝑎𝑡𝑐ℎ𝑚𝑒𝑛𝑡 𝐴𝑟𝑒𝑎(𝑚ଶ)1000 𝑚𝑚𝑚 The ratio of the catchment area to the column area was used to scale the water volume to be added to each laboratory column during each ‘precipitation’ event: 89  𝐶𝑜𝑙𝑢𝑚𝑛 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿) = 𝐷𝑟𝑎𝑖𝑛𝑎𝑔𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 (𝑚ଷ) ×𝑅𝑎𝑡𝑖𝑜 ( 𝑚ଶ𝑚ଶ )×1000𝐿𝑚ଷ ×1000𝑚𝐿𝐿 The frequency of precipitation events, or ‘additions per week’, was calculated using the average number of rain days and the days in each month: 𝐴𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑠 𝑝𝑒𝑟 𝑤𝑒𝑒𝑘 (𝑛 𝑑𝑎𝑦𝑠) = 𝑅𝑎𝑖𝑛 ൬𝑑𝑎𝑦𝑠𝑚𝑜𝑛𝑡ℎ൰ ×7 𝑑𝑎𝑦𝑠𝑤𝑒𝑒𝑘30 𝑑𝑎𝑦𝑠𝑚𝑜𝑛𝑡ℎ Table 28. Calculated Water Addition Volumes and Frequencies for Column Study Month Drainage Volume Column Volume Additions per Week m3 mL n days August 127.0 243 2 September 140.8 270 3 October 203.8 390 5 November 256.3 491 5  3 L of stormwater were initially added to each exposed column. After the initial loading of stormwater, the lids were sealed to each column and the columns were made watertight using Parafilm. Column watering was achieved using a sterilized 500 mL glass flask and a peristaltic pump. A piece of sterile tubing was connected to each side of the pump. One piece of tubing was inserted into the 500 mL glass flask and one side of the tubing was inserted through the small hole in the lid of the sample column. The glass flask was filled with the appropriate quantity of stormwater, or distilled water for the controls, and the water was fed into the column using the pump. Prior to adding volumes of water to each column, an equal volume of water in the column was removed using the pump. To avoid contamination, plastic tubing was traded out between every column watering episode. All mobile equipment was soaked overnight in a 10% bleach solution prior to use. All stationary equipment was wiped clean with 95% disinfectant grade ethanol after every watering episode.  Column Study Sampling Regime As previously stated, the column study began with 17 sediment columns, six of which were fed with distilled water and eleven of which were fed with semi-synthetic stormwater. The nature of sample collection required that columns be sacrificed. On day zero, two columns that were fed with stormwater and one column that was fed with distilled water were sacrificed and analyzed. Sampling of sediment columns then occurred at four week intervals following the same procedure. Thus, ten stormwater columns and five distilled water columns were sacrificed and analyzed for the study. The remaining two columns were used 90  to collect daily measurements for dissolved oxygen, conductivity, pH, temperature, and redox potential using the handheld device that was described in Chapter 1. Sample collection for the column study was generally performed in the same manner as was performed in the Lost Lagoon wetland. 1 L plastic bottles were used to collect water samples near the soil-water interface. After collection of water, a peristatic pump was used to lower the water level in the column under investigation. For soil sampling, 5 disinfected 60 mL syringes, with sanded off ends, were carefully pressed into the soil layer at the centre and at four evenly spaced points around the column’s perimeter. The syringes were carefully pulled from the column, capped on both ends with aluminum foil and frozen at <-20˚C until further processing.  Sample Preservation, Transport, Pre-Processing, Storage and Quality Control Water samples collected at the Lost Lagoon wetland were preserved for DNA on site using the following procedure:  To a 100 mL glass jar, add 20 mL of sample from each of 5 1-L plastic bottles;  Cap the glass jar and place in a plastic zip-lock bag; and  Place the bag in a cooler on dry ice. Water samples collected during the laboratory study were combined and preserved by the same methods that were applied in the field. This includes taking samples for environmental parameters and contaminants. Both field and laboratory study sediment samples were prepared and preserved for laboratory testing and DNA extraction following the procedures that were applied during the field study at the Lost Lagoon Wetland. These procedures are described in Section 2.5.1.5. Field samples collected at the Lost Lagoon wetland were transported back to UBC using the methods described in Section 2.5.1.5. Laboratory study samples did not require additional transport. For both the field and laboratory studies, water samples that were frozen on dry ice during transport were thawed at 4˚C. 30 mL of sample water was filtered through a sterile filter paper with pore size of 0.45 µm.  Prior to filtering the water sample, the filtering apparatus was soaked in a 10% bleach solution overnight. The filtering apparatus was cleaned with 95% disinfectant grade ethanol between samples. Filter papers were rolled in on themselves, placed in individual sterile petri dishes, wrapped in aluminium foil and frozen at <-20˚C until DNA extraction. 91  Field and laboratory study sediment samples were pre-processed and stored according to the same procedure that is described in Chapter 1.  Laboratory Analysis of Water and Sediment Quality Parameters Laboratory analysis for water and sediment quality parameters for the column samples followed the same procedures as were applied during the field study at the Lost Lagoon wetland. These procedures are described in Chapter 1.  Laboratory Preparation of Bacterial DNA  Sample Handling and Preservation Prior to extracting DNA, water and soil samples from the field and lab studies were placed in sterile plastic bags, labelled and frozen at <-20˚C.  Extraction of DNA and Quality Control All equipment used during the DNA extraction process was soaked in bleach overnight and disinfected with 95% ethanol solution prior to and during use. The extraction of DNA was performed using Mobio PowerSoil® DNA Isolation Kits, catelog number 12888-100 (Qiagen, 2016) according to the manufacturer’s instructions. Soil samples were extracted without modification to the procedure. Because the contents of the water samples were filtered onto sterile 0.45µm filters, prior to extraction, the filters were cut into 2mm by 2mm squares and the squares were inserted into the bead tubes using sterile forceps. The manufacturer’s DNA extraction protocol was then followed without modification. After extraction, DNA aliquots were frozen at <-20˚C until further processing.  Quantification of DNA DNA samples were thawed and quantified for DNA concentration using fluorimetric analysis on the Qubit® 3.0 Fluorimeter (Thermo Scientific, catalogue #Q33216)  Sequencing for Comparison of Microbial Community Compositions Comparison of microbial community composition was achieved through sequencing and analysis of the 16s rRNA gene. Sequencing of the 16s rRNA gene was outsourced to Microbiome Insights, a Vancouver-based service company that has delivered microbial analyses to hundreds of both academic and industrial researchers. Prior to delivering DNA samples to Microbiome Insights, the researchers discussed with 92  Microbiome Insights staff to prepare a sequencing protocol that included the appropriate standards for quality control. After a satisfactory plan was established, samples were transported on dry ice to the Microbiome Insights facility, which is located approximately 300 m from the UBC chemical engineering building. Samples were frozen at <-80˚C until further processing. An electronic sample list with DNA concentrations and appropriate meta-data was also provided to Microbiome Insights. Samples were delivered in two batches. The first batch included the DNA extracts from all 185 field study samples. The second batch included DNA extracts from all 112 column study samples.  Library Preparation and Quality Control In preparation for sequencing of the 16s rRNA gene, the following procedures were performed. 10 µM index primer aliquots were arrayed into 96-well plates as recommended by Kozich et al., (2013) as follows:  A701 – A712 with A501 – A508   A701 – A712 with B501 – B508   B701 – B712 with B501 – B508   B701 – B712 with A501 – A508  Template DNA was aliquoted into a 96-well format with blank wells included for negative control. PCR reactions were performed using ThermoFisher Phusion Hot Start II DNA Polymerase (2 U/ μL). Each sample constituted a single PCR reaction. The PCR recipe and cycling conditions are indicated in Table 29  and Table 30. Table 29. Recipe for PCR Used During Library Preparation Prior to 16s rRNA Gene Sequencing  Volume  PCR Mix µL/reaction 100 5x Buffer 10 1000 MgCl 1 100 Forward Primer 1  Reverse Primer 1  dNTP 1 100 dH2O 33.5 3350 taq 0.5 50    template 2  total 50 5000   93    Table 30. Conditions for PCR Used During Library Preparation Prior to 16s rRNA Gene Sequencing  Temperature ˚C Time  98 2:00 30 cycles 98 0:20 55 0:15 72 0:30 72 10:00  4 hold   In order to validate PCR success, eleven random samples and the negative control were analyzed and validated using gel electrophoresis. PCR products were then cleaned using Agencourt Ampure XP beads with a 0.8:1 bead to sample ratio. Following cleaning, PCR products were eluted to a final volume of 20 μL.  10 μL of the clean PCR product were used for normalization using the Invitrogen SequalPrep kit, and the remaining 10 μL were stored for backup. The amplicon library was normalized as recommended by Invitrogen (1-2 ng/ μL), and 5 μL of each normalized sample was pooled into a single library per plate (ie. 4 pooled plates in a 384-sample sequencing run). Library pools were further concentrated using the DNA Clean & Concentrator kit, following the manufacturer’s instructions (Zymo Research). A dilution series was performed for each of the four pooled libraries for subsequent quality control steps.  Each pool was analyzed on the Agilent Bioanalyzer using the High Sensitivity DS DNA assay in order to determine the approximate library fragment size, and to verify library integrity.  Library pools containing unintended amplicons were purified using the Qiagen QIAquick Gel Extraction kit, following the manufacturer’s instructions (Qiagen). Pooled library concentrations were then determined using the KAPA Library Quantification Kit for Illumina and following the manufacturer’s instructions (Kapa Biosystems). Library pools were diluted to 4 nM and denatured into single strands using fresh 0.2 N NaOH as recommended by Illumina. The final library loading concentration was 8 pM, with an additional PhiX spike-in of 20%.   Sequencing of the 16s rRNA Gene The amplicon library was sequenced on the Illumina MiSeq using the MiSeq 500 Cycle V2 Reagent Kit (250 x 2).   94   Sequencing for Comparison of Microbial Functional Gene Compositions Sequencing for comparison of microbial functional genes was achieved through metagenome sequencing. Metagenome sequencing was outsourced to the UBC Beatty NextGen Sequencing Centre. The steps towards sequencing of metagenomes are described here.  Sample Selection and Quality Control Because meaningful results are dependent on sequence depth and quality, these parameters were of primary importance for the analysis of metagenomes. However, this also had to be balanced with the desire to maximize cost effectiveness. It was determined that a balance of sequence depth/quality and cost effectiveness could be reached when sequencing metagenomes, if six samples were sequenced per lane, using the Illumina HiSeq 2000. To maximize the diversity of samples and to ensure redundancy was achieved, DNA extracts were pooled to form each sample. Only DNA extracts that had a concentration between 5 ng/µL and 20 ng/µL were considered for possible pooling.  Prior to pooling samples, to ensure the DNA was not degraded, DNA extracts were visualized using gel electrophoresis. 12.5 µL of each of four DNA extracts were pooled to form a sample with a volume of 50 µL. Pooled samples were treated for RNA – 1 µL of RNASE A (Purelink-Introgen) was added to each pooled sample and the pooled samples were inclubated at 37 ˚C for twenty-five minutes. To re-purfiy samples, 2 µL of 5 M NaCL was added to each 50 µL pooled sample. Samples were inverted three to five times to mix. 90 µL of cold ethanol (100%) was added to each sample and the samples were inverted three to five times to mix. Samples were centrifuged at 10,000xg for 5 minutes. The liquid was decanted and the precipitate was allowed to air dry at room temperature. The DNA was then re-suspended in 45 µL of sterile Tris, containing no EDTA (Solution 6 from the Mobio Powersoil Reagent Kit). The DNA concentration in each pooled sample was quantified a second time using the Qubit Fluorimeter 2.0. Samples were selected based on the objectives of both field and lab studies and for the case of the field study, based on the known quality of environmental data. A breakdown of the pooled samples is as follows:  Lane 1 1. Four sediment samples (2 depth, 2 surface) pooled, site 2.1 September 9, 2015 2. Four sediment samples (2 depth, 2 surface) pooled, site 5.2, September 22, 2015 3. Four sediment samples (2 depth, 2 surface) pooled, site 2.1, October 20 2015 95  4. Four sediment samples (2 depth, 2 surface) pooled, site 3.1, October 20 ,2015 5. Four sediment samples (2 depth, 2 surface) pooled, site 4.1, October 20, 2015 6. Four sediment samples (2 depth, 2 surface) pooled, site 6.2, October 20, 2015  Lane 2 1. Four sediment samples (2 depth, 2 surface) pooled, Column 1, December 4, 2015 2. Four sediment samples (2 depth, 2 surface) pooled, Column 7, December 4, 2015 3. Four sediment samples (2 depth, 2 surface) pooled, Column 8, December 4, 2015 4. Four sediment samples (2 depth, 2 surface) pooled, Column 5, March 29, 2016 5. Four sediment samples (2 depth, 2 surface) pooled, Column 15, March 29, 2016 6. Four sediment samples(2 depth, 2 surface)  pooled, Column 16, March 29, 2016 Pooled samples were stored at <-20˚C until they were delivered to the Beatty NextGen Sequencing Centre. Samples were placed on dry ice during transportation to the Beatty NextGen Sequencing Centre, which is located approximately 400 m from the UBC chemical engineering laboratory, where the samples were originally stored. Upon delivery, samples were stored at <-20˚C until further processing.  Library Preparation and Quality Control Library preparation was performed following a standard Illumina protocol for the HiSeq 2000 analyzer. The TruSeq Nano DNA LT Library Prep kit was used following manufacturer’s instructions with settings for the Covaris M220 sonicator and 550bp insert size. Libraries were then validated using a Qubit Fluorimeter 2.0. Libraries were then sealed and stored at -20 ˚C for less than seven days. Libraries and the PhIX control were denatured and diluted according to manufacturer’s instructions. The prepared libraries and PhIX control were then combined at a ratio of 99:1.  Cluster Generation Cluster generation was performed using the cBot 2 system (SY-312-2001) following manufacturer’s instructions for preparation of reagents and consumables and for quality control.  Sequencing of Whole Bacterial Genomes Sequencing reagents were prepared following manufacturer’s instructions and the following chemistry settings:  SBS: HiSeq SBS Kit v4;  Index: HiSeq v4 Index; and 96   PE turnaround: HiSeq PE Cluster Kit v4. When programming the sequencing run, the SBS reagent kit was set to 250 cycles on the reagent screen. The sequencing flow cell was loaded following the manufacturer’s instructions for 100 base pair, paired-end sequencing and the sequencing run was executed.  Analysis of Bacterial Taxa Using the 16s rRNA Gene Analysis of bacterial taxa using the 16s rRNA gene was performed through combination of three common microbial software programs, namely USearch, Mothur, and the R package, Vegan. Initial quality filtering, bioinformatics treatment and preparation of OTU tables was performed in USearch.  Taxonomic assignments and calculation of alpha diversity and community composition parameters were performed in Mothur. Statistical analyses were performed in R. Further details and justification of input parameters are described below.  Quality Filtering and Determination of Unique Sequences and Abundances Fastq file names were returned in the formatted output from MiSeq (i.e. s1_R1_001 etc). All Fastq files were transformed to fasta files. Using USearch, sequences were truncated to 200 bp and shorter sequences were dropped so that only high quality sequences remained. An example of the code is as follows:  USearch -fastq_filter s1_R1_001.fastq -sample s1 -relabel @ -fastq_trunclen 200 -fastaout reads1.fa Next, all files were concatenated to a single file to be used to determine the abundance of each operational taxonomic unit (OTU):  copy/b read*.fa reads.fa Following this, all sequences were transformed and truncated again and base quality was accounted for by setting the fastq_maxee parameter to 1.0:  USearch -fastq_filter s1_R1_001.fastq -sample s1 -relabel @ -fastq_trunclen 250 -fastaout filtered1.fa -fastq_maxee 1.0 Again, all files were merged to a single file to be used for OTU calling:  copy/b filt*.fa filtered.fa A file was prepared with only unique sequences: 97   USearch -derep_fulllength filtered.fa -relabel Uniq -sizeout -fastaout uniques.fa Unique sequences were then sorted by abundance:  USearch -sortbysize uniques.fa -fastaout suniques.fa -minsize 1  Preparation of OTU Tables Unique sequences were pre-clustered with a threshold of 98% similarity:  USearch -cluster_smallmem suniques.fa -id 0.98 -maxdiffs 4 -centroids preclustered.fa The unique pre-clustered sequences were then sorted by size:  USearch -sortbysize preclustered.fa -fastaout preclustered.fa -minsize 1 OTUs were clustered with “-minsize 2” in order to remove singletons:  USearch -cluster_otus preclustered.fa -minsize 2 -otus otus_preuchime.fa -relabel Otu0 Chimera removal was performed using the rdp_gold.fa database:  USearch -uchime_ref otus_preuchime.fa -db rdp_gold.fa -strand plus -nonchimeras otus.fa An OTU table was prepared for the samples at 97% similarity with exported formats for both Mothur and Qiime applications:  USearch -USearch_global reads.fa -db otus.fa -strand plus -id 0.97 -otutabout willotutab1.txt -biomout willotutab.json -mothur_shared_out wsh1.shared  Taxonomic Assignments In Mothur, OTUs were classified and taxonomy was assigned using the Silva reference database for bacteria:  Mothur -classify.seqs (fasta=otus.fa, template=silva.bacteria.fasta, taxonomy=silva.bacteria.silva.tax)  Bioinformatics Alpha diversity and community composition parameters and indicator species analyses were calculated using Mothur. Sequences were subsampled to 9000 sequences for the field study and 5000 sequences for the lab study. Samples that had fewer than these numbers of sequences were dropped from the dataset. The sample code is as follows: 98   Mothur -count.groups(shared=current)  Mothur -summary.single(calc=coverage-sobs-chao-invsimpson, subsample=9000)  Mothur -rarefaction.single(shared=current, calc=sobs, freq=100)  Mothur -indicator(shared=current, design= current, processors=4)  Statistical Analyses on Data  Data Screening Before analyses, a number of data screening techniques were applied, using GUide to STatistical Analysis in Microbial Ecology (GUSTA ME) (Buttigieg & Ramette, 2014). These include:  Avoiding data dredging; Data dredging can occur when subsets of data are used to confirm hypotheses or when hypotheses are generated after the data is observed. Data dredging was avoided by not discarding data when it did not fit the hypotheses and by testing the hypotheses on more than one dataset.  Ensuring awareness and consideration of pseudoreplication in the study; Pseudoreplication occurs when dependent data is assumed to be independent. For example, if three measurements of the same sample are taken, then this data is dependent. In this study, pseudo-replicates were averaged prior to hypotheses testing and prior to visualization of the data.  Checking and correcting for missing values; Due to the nature of analysis using both ICP for metals and DNA sequencing for bacteria, some missing values occurred in the dataset. Samples with missing values were removed from the dataset prior to hypotheses testing. This generally was performed using is.na() parameter in R.   Screening for outliers; Microbial outliers were screened from the dataset using the Analysis of Similarity (ANOSIM) test in R. Outlier samples were removed from the dataset prior to analyses. This is further discussed in the results section.  Alpha Diversity In order to compare alpha diversity among the samples, four indicators were calculated using the Mothur summary.single command. These include:  Richness, or the number of different species present in a sample, based on the Chao1 estimator; 99   Coverage, or the percent of the total species present in a sample, based on the Good’s coverage calculation;  Diversity, (richness and evenness, or the relative abundances of species) based on the inverse Simpson estimator; and  Observed OTUs based on the SOBS calculation. To compare alpha diversity, samples were split into separate datasets for the three materials (water, surface sediment, and 10-cm depth sediment) and split into separate datasets for the field and laboratory studies. Calculations were performed based on the different sites within the wetland and based on the columns analyzed in the laboratory study. In order to illustrate the variation among the data, barplots and boxplots were prepared for the various indices. To identify if there were significant differences among the data, one-way ANOVA tests were calculated using the standard R package (R Core Team, 2016) and interpreted using a confidence of 95%. Confirmation of both positive and negative statistical results was performed using the Tukey HSD test in R.  Community Composition To compare community composition, samples were split into separate datasets for the three materials (water, surface sediment, and 10-cm depth sediment) and split into separate datasets for the field and laboratory studies. Statistical calculations were performed based on the different locations within the wetland (stormceptor, entry, exit and Lost Lagoon) for the field study and based on the dosing of columns and the date of sample extraction for the laboratory study. Plots and statistical calculations were completed in R using the R standard package and using the vegan package. OTU tables for the various datasets were imported from Mothur into RStudio. OTU data were log transformed and dissimilarity matrices were calculated using the vegdist function in vegan with standard inputs. Two-dimensional NMDS were prepared using the metaNMDS function in vegan while setting the dissimilarity index to Bray Curtis and the maximum number of tries equal to 100. Stressplots were prepared and NMDS were only accepted if the R2 in the stressplot was greater than 0.90 and the stress calculation was less than 0.20. Comparison between field study sites and laboratory study dosing was performed using the Adonis function in vegan with 999 permutations, the Bray Curtis dissimilarity index and the Bonferroni p-value adjustment. Fitting of environmental data was performed using the envfit statistic in vegan and 999 permutations. Statistical calculations for hypotheses tests were considered significant if the p-value was less than 0.05.   100   Indicator Species The same approach for splitting the dataset was applied for the indicator species comparisons as was applied for the alpha diversity comparisons and community composition comparisons. Indicator species were calculated using the indicator() function in Mothur. Indicator species were considered statistically significant if the R statistics was greater than 80 and the p-value was less than 0.05.  Analysis of Bacterial Functions Using Metagenomics Analysis of functional genes was performed using standard computational techniques. Initially, file conversion and de-multiplexing was performed using Illumina CASAVA software. Merging, assembly and quality filtering was performed using MetaVelvet. Bioinformatics treatments and preparation of functional lists were performed using MetaPathways. Additional analyses were performed using RStudio and Microsoft Excel.  File Conversion and Sequence De-Multiplexing All sequences passing the HiSeq Q30 filter were converted by the Beatty Biodiversity centre from bcl to FastQ format with barcodes extracted using standard input to the Illumina supported software, CASAVA 1.8.2.  Read Merging, Quality Filtering, and Contig Assembly Read merging, quality filtering and assembly of reads into contigs was performed using MetaVelvet (Namiki et al., 2011). The Kmer length was set to 31 and the minimum contig length was set to 100 bp.  Preparation of Function Lists The MetaPathways v2.5.3 pipeline was used to perform quality control, protein prediction, clustering and similarity based annotation on sequence datasets using several bioinformatics tools as described by the authors (Konwar et al., 2014). The MetaPathways pipeline features: 1. “Open reading frame (ORF) prediction using Prodigal with BLAST or LAST annotation against the MetaCyc, RefSeq, KEGG, and COG protein databases; 2. Taxonomic analysis using MEGAN, ML-TreeMap, 16S SSU and 23S LSU rRNA homology using the Silva and GreenGenes databases; and 3. Systematic creation of Environmental Pathway/Genome Databases (ePGDBs) mapping functional information onto the MetaCyc database of metabolic Pathways.” (Konwar et al., 2014). 101  Minimum sequence length was set to 70 bp and minimum ORF length was set to 20 bp. All other quality control indices were left as standard parameter inputs.  Analyses on Data Analysis were performed using the Vegan package in R and using basic graphing options in Microsoft Excel. Statistical analyses were not performed on this dataset because of the small sample size and because, at the time of publication, this is an area for future work.  Review of Results Upon completion of this project, statistical results were independently reviewed by a consultant at the UBC Applied Statistics and Data Group (ASDA). As a reference, results of this review are included in Appendix K. Some minor modifications to the description of the methodology were made to clarify outcomes of this review; however, the majority of recommendations were outside of the scope of this study and left for future follow on research. 3.6 Results and Interpretation  Environmental Analysis  Confirmation of Beaver Lake Bog Soil Quality In Table 31, a list is provided of the averages and standard deviations for the concentrations of all metals that were measured in order to confirm the quality of the soil at the Beaver Lake Bog. This was an essential first step because this soil was to be collected and packed into the sediment columns for the future study. The metals associated with stormwater runoff were of greatest interest. Barium, cadmium, cobalt, copper, manganese, molybdenum nickel, lead, and zinc were below detection limits or near/below the concentrations of metals measured at the exit to the Lost Lagoon wetland. The only stormwater metal of concern that measured above the levels in wetland exit was antimony. The reasons for this are unclear because other metals did not observe the same trend. The observation could be due to naturally occurring higher antimony levels in the bog soil or possibly some interference on the analytical instrument, where the antimony levels are quite close to the detection limit of 10 mg/kg dry weight.  102  Table 31. Confirmation of Beaver Lake Bog Soil Quality   As Ag Al B Ba Be Cd Co Cr Cu Fe K Li  mg/kg dry weight  Average Bog 1 Surface   1051.5  23.2    20.5 31.2 1388.6 39.5 27.0 Bog 2 Surface   1821.8 49.5     43.3 35.2 3345.9 48.2 25.6 Bog 3 Surface   706.5 38.0 23.2    4.5 24.1 1028.4 52.3 25.1 Bog 4 Surface   921.0 44.7 28.5    46.7 20.5 1740.0 30.5 25.0 Bog 5 Surface   1284.3  57.0    15.4 32.4 2166.8 40.0 25.1 Bog 6 Surface   1360.3 44.3     69.5 17.7 2511.2 40.2 25.0 Site 5 Surface   9342.3 29.3 14.5    16.1 61.3 3144.4 101.5 36.3  Standard Deviation Bog 1 Surface   173.3  4.5    14.3 0.6 57.6 2.5 2.4 Bog 2 Surface   217.6      19.1 3.3 585.0 5.6 0.4 Bog 3 Surface   17.3 10.1 0.8     1.5 117.3 4.5 0.1 Bog 4 Surface   159.6 2.0 6.2     3.1 66.8 5.2 0.1 Bog 5 Surface   113.5  3.5    12.8 0.6 201.4 0.0 0.2 Bog 6 Surface   249.8 4.6      0.9 522.6 4.2 0.2 Site 5 Surface   3961.9 7.5 13.4    8.8 19.4 3338.8 71.2 4.7                                 Mg Mn Mo Na Ni Pb Sb Se Si Sr Ti V Zn  mg/kg dry weight  Average Bog 1 Surface 726.9 24.4  626.5 5.8  12.9 81.4 1497.5  31.0  127.2 Bog 2 Surface 780.2   665.8 6.6  13.3 62.0 1379.7  67.3  122.6 Bog 3 Surface 302.1 20.6  528.3 4.1  14.2 78.4 1446.2  27.8  88.8 Bog 4 Surface 495.6 79.6  426.3 6.0  13.0 64.9 1601.5  33.4  94.9 Bog 5 Surface 827.8   761.5 4.3  15.3 46.5 1673.3  51.4  160.3 Bog 6 Surface 648.9 90.9  518.0 5.7  12.7 67.9 887.2  68.8  98.9 Site 5 Surface 858.5 103.3 7.0 1560.2 19.3 7.1 10.0  5286.4  184.6  253.6  Standard Deviation Bog 1 Surface 199.3 9.9  222.0 2.7  0.5 22.6 743.9    10.7 Bog 2 Surface 23.4   45.0 1.3  1.4 11.6 881.5  1.7  34.3 Bog 3 Surface 14.6 4.3  70.0 2.2  2.0 20.5 612.9  1.8  16.1 Bog 4 Surface 100.9   26.0 2.8  1.5 22.4 403.0    16.1 Bog 5 Surface 19.7   149.9 0.3   12.5 800.1     Bog 6 Surface 53.1 6.1  12.7   1.3 19.4 667.3  17.6  39.4 Site 5 Surface 1162.7 53.0  904.9 5.3 3.0   2980.2  19.2  127.2  Preliminary Study Prior to beginning the laboratory column study, a preliminary test was performed over one week. The results are recorded in Table 32. Measurements were collected and recorded for DO, pH, temperature, conductivity, and ORP using a YSI probe, as described previously. The measurements were taken at the surface of the column and at the soil-water interface (below a water depth of 30-cm). Measurements were compared to see if the conditions in the column would equilibrate to similar conditions as measured at the soil water interface in the Lost Lagoon wetland forebay. DO and pH generally equilibrated to the same range as measured in the forebay. Temperature measurements were not relevant because the preliminary test was operated at room temperature (approximately 22 ˚C) and the temperature in the forebay decreased over the autumn season. Conductivity measurements in the column were lower than the average measurement in the forebay; however, the column measurements were trending towards the forebay measurements. ORP measurements in the column also trended towards the levels measured in the forebay. Overall, the results of the preliminary study were considered adequate enough to continue moving forward with the column study. 103  Table 32. Measurements Recorded During Preliminary Column Test Time DO, mg/L pH Temperature, ˚C Conductivity, µS/cm ORP, mV Water Surface Zero 8.37 6.5 16.67 53.75 322.7 36 hrs 4.69 5.71 19.21 54.97 390.2 96 hrs 5.45 5.31 20.31 57.06 378.9 192 hrs 5.35 5.48 20.45 49.95 372.6 Soil-Water Interface Zero 7.64 7.14 16.75 64.12 309.1 36 hrs 5.19 6.15 19.2 58.13 384.7 96 hrs 5.8 5.49 20.14 66.24 373.5 192 hrs 3.99 5.44 20.36 94.33 376.6 Wetland Forebay Soil-Water Interface Average 3.53 5.15 11.65 153.73 285.16 St.Dev. 1.99 1.33 3.09 55.45 126.00   Turbidity, TSS, COD and TOC  Interpretation Turbidity, TSS, COD, and TOC were measured in the water samples of each lab study column at the time the columns were sacrificed. In Figure 50 through Figure 53, there was no observable trend in the measurements for any of these parameters with the exception that Column 1, which was sacrificed and analyzed on the first day of the column study, measured higher levels for all parameters. These higher measurements may have occurred because some organic material from the soil layer was stirred into the water layer during the initial addition of 30-L of water to the column. Unfortunately, the dataset was too small to make statistical comparisons between the columns. In Figure 54, turbidity, TSS, TOC, and COD are visually compared between the first and last week of column samples and between the Lost Lagoon wetland entry and exit. With the exception of Column 1 having higher measurements, the field and lab study measurements for these parameters are generally within the same range. These parameters may have an influence on the microbial populations present in the water samples. Thus, in order to use the laboratory results to verify the field results, it is essential that the same range of measurements exists between the two studies.   Figures 104   Figure 50. Barplot Comparison by Column of Turbidity in Water Samples  Figure 51. Barplot Comparison by Column of Total Suspended Solids in Water Samples  - 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00Week 1BlankColumn 1Week 4BlankColumn 2Week 8BlankColumn 3Week 12BlankColumn 4Week 16BlankColumn 5Week 1ExposedColumn 7Week 1ExposedColumn 8Week 4ExposedColumn 9Week 4ExposedColumn 10Week 8ExposedColumn 11Week 8ExposedColumn 12Week 12ExposedColumn 13Week 12ExposedColumn 14Week 16ExposedColumn 15Week 16ExposedColumn 16 - 100.00 200.00 300.00 400.00 500.00 600.00 700.00Week 1BlankColumn 1Week 4BlankColumn 2Week 8BlankColumn 3Week 12BlankColumn 4Week 16BlankColumn 5Week 1ExposedColumn 7Week 1ExposedColumn 8Week 4ExposedColumn 9Week 4ExposedColumn 10Week 8ExposedColumn 11Week 8ExposedColumn 12Week 12ExposedColumn 13Week 12ExposedColumn 14Week 16ExposedColumn 15Week 16ExposedColumn 16Dosed with distilled water                                   Dosed with semi-synthetic stormwater Dosed with distilled water                                   Dosed with semi-synthetic stormwater 105   Figure 52. Comparison by Column of Chemical Oxygen Demand in Water Samples  Figure 53. Comparison by Column of Total Organic Carbon in Water Samples   - 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00Week 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16 - 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00Week 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dosed with distilled water                                   Dosed with semi-synthetic stormwater Dosed with distilled water                                   Dosed with semi-synthetic stormwater 106   Figure 54. Comparison of Turbidity, TSS, TOC, and COD in Field and Lab Studies  Metals  Interpretation Together, Figure 55 and Figure 56 illustrate the distribution of metals within the water samples obtained from the columns. From Figure 55 and Figure 56, visually, there is a trend over time of slightly decreasing metal concentrations in the columns that were fed distilled water and of increasing metals concentrations in the columns that were fed stormwater. This trend is most clear for molybdenum, nickel, barium, copper, manganese, and zinc. Cadmium, cobalt, and antimony measurements were near detection limits and this may account for less clarity in the results. The slight decrease in metal concentrations in the columns that were fed distilled water may be due to some partial flushing of the soil as water in the columns was exchanged with distilled water on a regularly occurring basis, following the rain patterns in Vancouver. In Figure 57 and Figure 58, the metal concentrations, measured during week one and week sixteen of the laboratory study and at the entry and the exit of the Lost Lagoon wetland, are compared. Unfortunately, statistical tests could not be performed between the two studies because the dataset for the column study was too small in comparison to the field study. However, the metal concentrations in the water samples collected from the stormwater columns at week sixteen generally did reach the concentrations measured at the entry of the Lost Lagoon wetland and of the stormwater that was fed into them. Due to the stormwater recipe that was prepared, some metal concentrations differed, including that molybdenum and nickel concentrations were higher in the laboratory stormwater columns than in the wetland forebay 0.00100.00200.00300.00400.00500.00600.00700.00Entry Exit Column01 Column05 Column07 Column08 Column15 Column16Field Distilled Water StormwaterTurbidity, NTUTSS, mg/LTOC, mg/LCOD, mg/L107  and barium and that antimony concentrations were lower in the laboratory stormwater columns than in the wetland forebay. In Figure 59 through Figure 62, generally the same trends were observed for the surface sediment samples as were observed for the water samples. Molybdenum and nickel concentrations were also higher in the laboratory stormwater column surface sediment samples than in the wetland forebay and barium and antimony concentrations were lower in the laboratory stormwater column surface sediment samples than in the wetland forebay. Figure 63 through Figure 66 illustrate that the same trend over time was observed for the 10-cm depth samples as was observed for the water and surface sediment samples. However, metal concentrations in the depth samples taken in the column study were higher than were observed in the field study. This was generally true for all metals present in the semi-synthetic stormwater.  This may have an influence on the microbial communities present in these samples. That being said, overall, the column study achieved its goal regarding the metal concentrations, which was to mimic the concentrations in the Lost Lagoon wetland, in order to provide a dataset for microbial comparisons later on.   108   Figures  Water Samples  Figure 55. Time Comparison of Metals Associated with Stormwater in Column Water Samples 0100200300400500600700800Water Water Water Water Water Water Water Water Water Water Water Water Water Water WaterWeek 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dec 4 2015 Jan 4 2015 Feb 4 2016 Mar 4 2016 April 3 2016 Dec 4 2015 Dec 4 2015 Jan 4 2015 Jan 4 2015 Feb 4 2016 Feb 4 2016 Mar 4 2016 Mar 4 2016 April 3 2016 April 3 2016µg/LCdCoMoNiSbDosed with distilled water                                   Dosed with semi-synthetic stormwater 109   Figure 56. Time Comparison of Metals Associated with Stormwater in Column Water Samples 0.00100.00200.00300.00400.00500.00600.00700.00800.00900.00Water Water Water Water Water Water Water Water Water Water Water Water Water Water WaterWeek 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dec 4 2015 Jan 4 2015 Feb 4 2016 Mar 4 2016 April 3 2016 Dec 4 2015 Dec 4 2015 Jan 4 2015 Jan 4 2015 Feb 4 2016 Feb 4 2016 Mar 4 2016 Mar 4 2016 April 3 2016 April 3 2016µg/LBaCrCuMnPbZnDosed with distilled water                                   Dosed with semi-synthetic stormwater 110   Figure 57. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Water Samples   0.0050.00100.00150.00200.00250.00300.00350.00400.00450.00500.00µg/LBaCdCoCrSb111   Figure 58. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Water Samples 0.00500.001000.001500.002000.002500.00µg/LCuMnMoNiPbZn112   Surface Sediment Samples  Figure 59. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment 0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00100.00Week 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dec 4 2015 Jan 4 2016 Feb 4 2016 Mar 4 2016 April 3 2016 Dec 4 2015 Dec 4 2015 Jan 4 2016 Jan 4 2016 Feb 4 2016 Feb 4 2016 Mar 4 2016 Mar 4 2016 April 3 2016 April 3 2016mg/kg dry weightCdCoMoNiSbDosed with distilled water                                   Dosed with semi-synthetic stormwater 113    Figure 60. Barplot Comparison by Plot of Metals Associated with Stormwater in Surface Sediment 0.00100.00200.00300.00400.00500.00600.00Week 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dec 4 2015 Jan 4 2016 Feb 4 2016 Mar 4 2016 April 3 2016 Dec 4 2015 Dec 4 2015 Jan 4 2016 Jan 4 2016 Feb 4 2016 Feb 4 2016 Mar 4 2016 Mar 4 2016 April 3 2016 April 3 2016mg/kg dry weightBaCrCuMnNiPbZnDosed with distilled water                                   Dosed with semi-synthetic stormwater 114   Figure 61. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Surface Sediment 020406080100120140Column01 Column07 Column08 Exit Column05 Column15 Column16 Entrymg/kg dry weightCdCoCrMoSb115   Figure 62. Comparison of Metals Associated with Stormwater in Field Study and Lab Study Surface Sediment 0100200300400500600700800Column01 Column07 Column08 Exit Column05 Column15 Column16 Entrymg/kg dry weight BaCuMnNiPbZn116   10-cm Depth Sediment Samples  Figure 63. Barplot Time Comparison of Metals Associated with Stormwater in 10-cm Depth Sediment 0.0020.0040.0060.0080.00100.00120.00Week 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dec 4 2015 Jan 4 2016 Feb 4 2016 Mar 4 2016 April 3 2016 Dec 4 2015 Dec 4 2015 Jan 4 2016 Jan 4 2016 Feb 4 2016 Feb 4 2016 Mar 4 2016 Mar 4 2016 April 3 2016 April 3 2016mg/kg dry weightCdCoMoNiSbDosed with distilled water                                   Dosed with semi-synthetic stormwater 117    Figure 64. Barplot Time Comparison of Metals Associated with Stormwater in 10-cm Depth Sediment 0.00100.00200.00300.00400.00500.00600.00Week 1 Week 4 Week 8 Week 12 Week 16 Week 1 Week 1 Week 4 Week 4 Week 8 Week 8 Week 12 Week 12 Week 16 Week 16Blank Blank Blank Blank Blank Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed Exposed ExposedColumn 1 Column 2 Column 3 Column 4 Column 5 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16Dec 4 2015 Jan 4 2016 Feb 4 2016 Mar 4 2016 April 3 2016 Dec 4 2015 Dec 4 2015 Jan 4 2016 Jan 4 2016 Feb 4 2016 Feb 4 2016 Mar 4 2016 Mar 4 2016 April 3 2016 April 3 2016mg/kg dry weightBaCrCuMnPbZnDosed with distilled water                                   Dosed with semi-synthetic stormwater 118   Figure 65. Comparison of Metals Associated with Stormwater in Field Study and Lab Study 10-cm Depth Sediment 020406080100120Column01 Column07 Column08 Exit Column05 Column15 Column16 Entrymg/kg dry weightCdCoMoNiSb119   Figure 66. Comparison of Metals Associated with Stormwater in Field Study and Lab Study 10-cm Depth Sediment 0100200300400500600Column01 Column07 Column08 Exit Column05 Column15 Column16 Entrymg/kg dry weightBaCrCuMnPbZn120   Microbial Community Analysis  Data Quality and Screening  Interpretation Using the count.seq command in Mothur (Schloss et al., 2009), the average sequence count for the field samples (excluding blanks) was initially calculated to be 21,888. To ensure only high quality outputs were analyzed, the minimum cutoff was set to 9000 clones and by this means two DNA extracts were eliminated from the dataset. After setting the cutoff, the new average clone count for the field samples was calculated to be 22,084 clones. Because there was a range of counts obtained using the MiSeq platform, diversity analyses were performed by randomly subsampling 9000 clones from each sample present in the dataset. Figure 67 is a rarefaction curve for the field sample sequences, which was calculated using the rarefaction.single command and SOBS parameter in Mothur. This graph illustrates the sequence depth and cutoff for the field samples. From the figure, it is clear that there were more OTUs identified in the sediment samples (marked with black and brown lines) than in the water samples (marked with blue lines). This is expected because there is generally a higher level of microbial diversity in soil samples than in water samples when sampling in the natural environment, as was performed in this field study. Diversity analyses were performed by randomly subsampling 5000 clones from each sample present in the dataset. Using the ANOSIM function in R  (R Core Team, 2016), an assessment of outliers among pseudo-replicate field samples was performed. The boxplot output is illustrated in Figure 68. Three pseudo-replicate field samples were identified as likely to be including outliers and all three pseudo-replicates corresponded to water samples. Figure 69 is an NMDS plot, which illustrates the pseudo-replicates that contain outliers. Three outliers were identified and removed from the field study dataset and the ANOSIM calculation was performed a second time, as illustrated in Figure 70. The removal of outliers increased the R fit statistic for the water sample dataset from 0.912 to 0.974. The same screening procedures were performed on the dataset for the sequences obtained from samples taken during the laboratory column study (Figure 71). Sequence diversity was lower in this dataset, as was expected due to the controlled conditions in the laboratory. Before screening, the average number of clones (excluding blanks) was calculated to be 7138 clones. The minimum and maximum cutoffs were set to 5000 clones and 9000 clones, respectively. A maximum cutoff was set because three DNA extracts produced an unreasonably high level of clones (more than 500% above the average). This may be the result of a laboratory handling error because the samples were consecutively located on the sequencing plate for the MiSeq platform. After setting the cutoffs, the new average number of clones was calculated to be 6353. 121  Using the ANOSIM function (Figure 72), four outliers were identified among pseudo-replicates in the laboratory column study dataset. These outliers are illustrated using the NMDS plot displayed in Figure 73. After removal of outliers, the R fit statistic increased from 0.985 to 0.986 for the water samples dataset, from 0.906 to 0.908 for the surface sediment sample dataset, and from 0.904 to 0.951 for the depth sediment sample dataset. The recalculated ANOSIM output is illustrated in Figure 74.  Field Study  Sequence Depth Cutoff  Figure 67. Rarefaction Curve Illustrating Minimum Depth Cut-off for Field Samples   122   Comparison of Pseudo-Replicates and Outlier Screening  Figure 68. Anosim Boxplot Between Pseudo-Replicate Samples Prior to Outlier Screening in the Field Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples)  123   Figure 69. NMDS Plot Illustrating Suspected Outliers Among Field Samples  124   Figure 70. Anosim Boxplot Between Pseudo-Replicate Samples After Outlier Screening in the Field Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples)  Laboratory Study  Sequence Depth Cutoff 125   Figure 71. Rarefaction Curve Illustrating Minimum Depth Cutoff for Column Samples   126   Comparison of Pseudo-Replicates and Outlier Screening  Figure 72. Anosim Boxplot Between Pseudo-Replicate Samples Prior to Outlier Screening in the Column Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples)  127   Figure 73. NMDS Plot Illustrating Suspected Outliers Among Column Samples 128   Figure 74. Anosim Boxplot Between Pseudo-Replicate Samples After Outlier Screening in the Column Study (Left to right: Water, Surface Sediment, and 10-cm Depth Sediment Samples)  Alpha Diversity Please refer to Appendix H for the figures and tables that are referenced in this section. In  Table 67, one-way ANOVA comparison of the richness parameter using Chao1 indicated that there was a significant difference between the richness levels in the water samples, based on location (p-value = 129  0.008). Figure 109 through Figure 111 illustrate that lower richness was observed at Site 1 (stormceptor) and Site 6 (Lost Lagoon). The highest richness was observed at Site 5 (wetland exit). Alternately, in Table 69, comparison of coverage indicates a significant difference among locations (p-value = 0.004) with Site 1 and Site 6 having greater coverage than Site 2 through Site 5, as illustrated in Figure 112 through Figure 114. In Table 71, no significant difference (p-value = 0.237) was calculated for diversity among the water samples based on the Inverse Simpson index. Likewise, in Table 72, no significant difference (p-value = 0.130) was calculated for observed OTUs among the water samples based on SOBS. Figure 115 through Figure 120 graphically illustrate the calculations for diversity and observed OTUs. In Table 74, one-way ANOVA comparison of the richness parameter using Chao1 indicated that there was a significant difference between the richness levels in the surface sediment samples, based on location (p-value = 0.011). Figure 121 through Figure 123 illustrate that lower richness was observed at Site 6 (Lost Lagoon). Alternately, in Table 76, comparison of coverage indicates a significant difference among locations (p-value = 0.010) with Site 6 having greater coverage than Site 2 through Site 5, as illustrated in Figure 124 through Figure 126. In Table 78, a significant difference (p-value = 0.003) was calculated for diversity among the surface sediment samples based on the Inverse Simpson index. However, in Table 80, no significant difference (p-value = 0.095) was calculated for observed OTUs among the surface sediment samples based on SOBS. Figure 127 through Figure 129 graphically illustrate the calculations for diversity and observed OTUs. In Table 81, one-way ANOVA comparison of the richness parameter using Chao1 indicated that there was no significant difference between the richness levels in the 10-cm depth sediment samples, based on location (p-value = 0.727). Figure 130 through Figure 132 illustrate the similarity among sampling sites. Likewise, in Table 82, comparison of coverage indicated no significant difference among locations (p-value = 0.720), as illustrated in Figure 133 through Figure 135. In Table 83, no significant difference (p-value = 0.130) was calculated for diversity among the 10-cm depth sediment samples based on the Inverse Simpson index. In Table 84, no significant difference (p-value = 0.815) was calculated for observed OTUs among the 10-cm depth sediment samples based on SOBS. Figure 136 through Figure 141 graphically illustrate the calculations for diversity and observed OTUs. In Table 86, using the ANOVA test, a significant difference for diversity (p-value =0.0486) was calculated between water samples taken for the column study; however, this was not confirmed with the Tukey HSD test (p-value =0.0580).  No other significant differences were calculated for any of the indices. The comparisons are illustrated in Table 85 through Table 97 and in Figure 145 through Figure 168.  130   Community Composition  Interpretation Analysis of the bacterial communities in the field samples generally indicated significant differences based on the location where the samples were collected. This significance also held true after adjusting for the date when the samples were collected. Among the water samples taken during the field study, the hypothesis that no difference existed between field sites failed (p-value = 0.002). Further pairwise testing indicated that the Lost Lagoon had the most significantly different bacterial community from the wetland entry or forebay (p-value = 0.006) and a significantly different bacterial community from the wetland exit or settling pond (p-value = 0.03). Of importance, a significant difference was not calculated between the wetland entry and wetland exit (p-value = 0.492). In addition, no significant differences were determined between the stormceptor and any of the other sites for the bacterial communities identified in the water samples; however, this is likely due to a small sample size for the stormceptor. Figure 75 is an NMDS plot which illustrates the distance of dissimilarities among the field water samples. From this plot, there is a clear difference between the Lost Lagoon and the wetland; however, differences between the wetland entry and exit are more difficult to visually discern. Statistical outputs for the field water samples are summarized in Table 33 and Table 34. In addition, using the envfit statistic in the vegan package in R, only nickel had a significant correlation (p-value <0.05) with the bacterial communities in the field water samples. However, nickel correlated with the Lost Lagoon, which had higher concentrations of nickel than the wetland. The lack of correlations among metal concentration and the field water samples suggest that water sampling alone would not be an adequate technique for validating a stormwater treatment wetland, such as the Lost Lagoon wetland. Of greater significance among the bacterial community comparisons are the results between the surface sediment samples. In Figure 76, there are clear visual differences between the Lost Lagoon and the wetland forebay and wetland settling pond. These differences are supported by the statistical calculations summarized in Table 35 and Table 36 where all p-values are less than 0.05. In addition, using the envfit parameter, copper and chromium positively correlated with the wetland forebay samples and nickel negatively correlated with the wetland settling pond samples. The 10-cm depth sediment field samples yielded the same overall results as the surface sediment field samples. Barium concentrations also positively correlated with the wetland forebay. These results are illustrated in Figure 77 and summarized in Table 37 and Table 38. 131  The bacterial communities, identified among the samples extracted from the laboratory column samples, yielded interesting results. These results begin to show some potential causation between the application or dosing of stormwater and the response of bacteria to said stormwater. The NMDS plot in Figure 78 illustrates the response of bacteria in the water samples extracted over the duration of the column study. From the figure, generally, there is a departure between columns that were dosed with stormwater and between columns that were dosed with distilled water. Exposure to stormwater yielded a difference from distilled water (p-value = 0.035) even when accounting for the fact that the date that the samples were taken along the study period also yielded a significant impact (p-value = 0.003). Results positively correlated with four metals – manganese, nickel, chromium and copper. The statistical computations for the column water samples can be found in Table 39. The NMDS plot in Figure 79 illustrates a similar response of bacteria in the surface sediment samples extracted over the duration of the column study as was observed with the water samples. There is significant departure between columns that were dosed with stormwater and columns that were dosed with distilled water (p-value = 0.05). Results positively correlated with copper. This held true when considering the date in which samples were extracted and computations are summarized in Table 40. The NMDS plot in Figure 80 illustrates a different result for the 10-cm depth sediment samples extracted over the duration of column study. While there is some departure between the columns that were dosed with stormwater and the columns that were dosed with distilled water, there is no clear pattern with time for the progression of the bacterial communities in the columns that were dosed with distilled water. The summary of statistics for the 10-cm depth sediment samples can be found in Table 41.   132    Field Study  Water Samples  Figure 75. NMDS Plot Comparing Field Study Water Samples Table 33. Adonis Whole Dataset Comparison of Field Study Water Samples by Location with Strata Adjustment for Date  Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Length 3 2.201482 0.733827 5.13504 0.401122 0.002 Residuals 23 3.286835 0.142906 NA 0.598878 NA Total 26 5.488317 NA NA 1 NA Table 34. Adonis Pairwise Comparison of Field Study Water Samples by Location  Pairs F.Model R2 p.value p.adjusted 1 Lagoon vs Forebay 18.39609 0.505441 0.001 0.006 2 Lagoon vs Settling-Pond 13.37634 0.548743 0.005 0.03 3 Lagoon vs Stormceptor 1.588809 0.209362 0.266 1 4 Forebay vs Settling-Pond 2.45167 0.126039 0.082 0.492 5 Forebay vs Stormceptor 2.783385 0.188278 0.073 0.438 6 Settling-Pond vs Stormceptor 2.92018 0.368701 0.138 0.828    Lagoon Forebay Settling Pond Stormceptor 133   Surface Sediment Samples  Figure 76. NMDS Plot Comparing Field Study Surface Sediment Samples Table 35. Adonis Whole Dataset Comparison of Field Study Surface Sediment Samples by Location with Strata Adjustment for Date  Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Length 2 1.215679 0.60784 6.157626 0.339121 0.002 Residuals 24 2.36912 0.098713 NA 0.660879 NA Total 26 3.584799 NA NA 1 NA Table 36. Adonis Pairwise Comparison of Field Study Surface Sediment Samples by Location  Pairs F.Model R2 p.value p.adjusted 1 Lagoon vs Forebay 20.51019042 0.519111404 0.001 0.003 2 Lagoon vs Settling-Pond 22.04533539 0.710101376 0.005 0.015 3 Forebay vs Settling-Pond 6.611271881 0.248438779 0.001 0.003    Forebay Settling Pond Lagoon 134   10-cm Depth Sediment Samples  Figure 77. NMDS Plot Comparing Field Study 10-cm Depth Sediment Samples Table 37. Adonis Whole Dataset Comparison of Field Study Depth Sediment Samples by Location with Strata Adjustment for Date  Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Length 2 1.097689211 0.548844606 4.629741405 0.415979243 0.02 Residuals 13 1.541118444 0.118547573 NA 0.584020757 NA Total 15 2.638807656 NA NA 1 NA Table 38. Adonis Pairwise Comparison of Field Study Depth Sediment Samples by Location  Pairs F.Model R2 p.value p.adjusted 1 Lagoon vs Forebay 6.498867792 0.371387902 0.001 0.003 2 Lagoon vs Settling-Pond 14.66670227 0.70967792 0.016 0.048 3 Forebay vs Settling-Pond 5.200414149 0.366215668 0.004 0.012    Settling Pond Forebay Lagoon 135   Laboratory Study  Water Samples   Figure 78. NMDS Plot Comparing Laboratory Study Water Sediment Samples Table 39. Adonis Whole Dataset Comparison of Laboratory Study Water Samples by Exposure and Date  Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Exposure 1 0.167920169 0.167920169 2.007923907 0.121214106 0.035 Date 4 0.631997288 0.157999322 1.889294283 0.456210748 0.003 Residuals 7 0.58540126 0.083628751 NA 0.422575147 NA Total 12 1.385318718 NA NA 1 NA    136   Surface Sediment Samples  Figure 79. NMDS Plot Comparing Column Study Surface Sediment Samples Table 40. Adonis Whole Dataset Comparison of Laboratory Study Surface Sediment Samples by Exposure and Date  Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Exposure 1 0.067210634 0.067210634 1.777639956 0.085997169 0.05 Date 4 0.37405388 0.09351347 2.473318129 0.478608408 0.001 Residuals 9 0.340280217 0.037808913 NA 0.435394423 NA Total 14 0.781544732 NA NA 1 NA    137   10-cm Depth Sediment Samples  Figure 80. NMDS Plot Comparing Column Study 10-cm Depth Sediment Samples Table 41. Adonis Whole Dataset Comparison of Laboratory Study Depth Sediment Samples by Exposure and Date  Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Exposure 1 0.051649735 0.051649735 1.168781611 0.061211356 0.224 Date 4 0.394423785 0.098605946 2.231353513 0.467441217 0.001 Residuals 9 0.39771982 0.044191091 NA 0.471347427 NA Total 14 0.843793339 NA NA 1 NA  Indicator Species  Interpretation Indicator species were determined using Mothur for the three different sample types extracted during both the field study and the laboratory column study. In Figure 81 through Figure 83, the top indicator species are illustrated by relative abundance for the field sites. In Figure 84 through Figure 86, the top indicator species are illustrated by relative abundance for the laboratory columns that were sampled during week 16 of the column study. While significant indicator species were determined, there are no discernable patterns at the phylum level among the identified indicator species. One discernable difference was that, generally, there were a greater number of significant indicator species identified at the sites and in the columns that were not dosed with stormwater. This might suggest that some species of bacteria are 138  influenced by stormwater, either negatively (extinction) or positively (adaptation). Further research and repeat testing would be required in order to deduce statistics for this hypothesis, however.  Field Study  Water Samples  Figure 81. Indicator Species Barplot for Field Study Water Samples   139   Surface Sediment Samples  Figure 82. Indicator Species Barplot for Field Study Surface Sediment Samples  10-cm Depth Sediment Samples  Figure 83. Indicator Species Barplot for Field Study 10-cm Depth Sediment Samples   140   Laboratory Study  Water Samples  Figure 84. Indicator Species Barplot for Laboratory Study Water Sediment Samples  Surface Sediment Samples  Figure 85. Indicator Species Barplot for Laboratory Study Surfaced Sediment Samples  141   10-cm Depth Sediment Samples  Figure 86. Indicator Species Barplot for Laboratory Study 10-cm Depth Samples  Microbial Functional Gene Analysis  Data Quality and Screening Quality control removed an average of %1.80 percent of sequences and 1.79% of translated ORFs. Table 42 and Table 43 provide summary statistics for the quality control of sequence data.   142   Table 42. Summary Statistics for Sequence Data Prior to Quality Control and Screening  Sequences (#) Minimum Length Average Length Maximum Length Total Base Pairs Translated ORFs (amino) (#) Minimum Length Average Length Maximum Length Total Base Pairs October Site 2 1112254 61 109 2172 122159553 1032935 18 34 523 36034670 October Site 3 874221 61 107 1606 93989112 824868 18 34 460 28107007 October Site 4 862982 61 106 11286 91791432 801450 18 33 917 27091806 October Site 5 3313614 61 103 13962 344045197 3128608 15 32 1434 102697812 October Site 6 1367387 61 104 5548 142795471 1057221 15 33 805 35373718 September Site 2 795230 61 110 6160 881879676 739436 18 35 598 26103686 Column 1 886693 61 110 2208 98099327 832115 16 35 528 29327503 Column 7 1010237 61 111 1843 99794400 949845 17 35 511 33618691 Column 8 792921 61 108 1467 86045364 731896 18 34 377 25359107 Column 5 733631 61 145 27254 107065610 704410 18 45 1114 31937114 Column 15 1068593 61 165 16241 177132959 1026190 17 50 2197 51854855 Column 16 967645 61 147 12007 142540522 927311 18 45 1237 42313345 Table 43. Summary Statistics for Sequence Data After Quality Control and Screening  Sequences (#) Minimum Length Average Length Maximum Length Total Base Pairs Translated ORFs (amino) (#) Minimum Length Average Length Maximum Length Total Base Pairs October Site 2 1093496 70 110 2172 120966323 1032935 20 34 523 35458633 October Site 3 868657 70 107 1606 93627335 808769 20 34 460 27628016 October Site 4 849512 70 107 11286 90924595 784604 20 33 917 26593745 October Site 5 3309767 70 103 13962 343209220 3071139 20 32 1434 100954728 October Site 6 1284648 70 107 5548 137559094 1029315 20 33 805 34575323 September Site 2 778822 70 111 6160 87149331 724319 20 35 598 25658071 Column 1 874291 70 111 2208 97300822 813273 20 35 528 28758224 Column 7 997944 70 111 1843 111616320 929628 20 35 511 33007909 Column 8 777275 70 109 1467 85048533 715807 20 34 377 24879426 Column 5 723742 70 146 27254 106382831 693944 20 45 1114 31617064 Column 15 1045794 70 167 16241 175054974 1011977 20 50 2197 51425082 Column 16 953961 70 148 12007 141415083 914016 20 45 1237 41908807   Functional Gene Composition  Interpretation To visualize the composition of annotated genes using the KEGG database, NMDS plots were prepared in the same fashion as was performed for visualization of bacterial communities except this time using the relative abundance of annotated genes for the samples instead of the abundance of OTUs. Figure 87 is an NMDS plot for the metagenomes sequenced from a subset of sediment samples collected during the field study at the Lost Lagoon wetland. From Figure 87, based on the relative distance between samples, there is some indication that samples taken at the same location but a month apart have more similar metagenomes than samples that are taken on the same date but in different locations. There is also some evidence that Site 2, Site 3, and Site 4 (wetland forebay) have greater similarity to each other than they do to Site 5 (wetland settling pond) or Site 6 (Lost Lagoon). The dataset provides some interesting proof of concept results, though there are too few data points to form definitive conclusions. Likewise, Figure 88 is an NMDS plot for the metagenomes sequenced from sediment samples collected at the beginning and at the end of the column study. From Figure 88,  there is some evidence that after sixteen 143  weeks, the soil columns that were dosed with stormwater formed a separate cluster from the soil column that was dosed with distilled water. As with the field study, the dataset provides some interesting proof of concept for the utility of metagenomics in monitoring stormwater treatment but more data would be needed to strengthen preliminary findings   144   Figures  Figure 87. NMDS Plot of KEGG Annotated Genes for Field Samples   Figure 88. NMDS Plot of KEGG Annotated Genes for Column Samples   Stormwater, Week 16 Distilled water, Week 16 Distilled water, Week 1 Stormwater, Week 1 Stormwater, Week 1 Settling Pond Forebay Forebay Forebay Forebay Lost Lagoon 145   Metal Adaptation Genes  Interpretation Genes, which are known to be partially responsible for metal adaptation, (Table 19) were highlighted from the dataset and plotted based on their relative abundance. It was the desire of the author to compare mechanisms for tolerance of copper, lead and zinc in this section as these three metals showed clear trends of decreasing concentrations along the length of the Lost Lagoon wetland. However, few markers for copper and no markers for lead adaptations were present in the KEGG database from 2014. The one marker that was present in the dataset for copper (CusR) did not form an identifiable trend among samples taken during the field study or among samples taken during the column study. For these reasons, comparisons were instead performed using markers for zinc, manganese/zinc/iron, and cobalt/nickel tolerances. Figure 89 is a plot which illustrates the relative abundance of six genes that are relevant for zinc transport or resistance. The comparison is complicated because the genes do not all present the same trend. For example, the znuB gene has a downward trend between the wetland exit and entry; however, the zraP gene has an upward trend between the wetland exit and entry. The relative abundances of znuB is greater than the relative abundance of the other genes in this subset and this suggests that this gene may be more dominant in zinc transport than some of the others, though this is not conclusive. Of interest, in Figure 90, the znuB gene also has a greater relative abundance in the soil columns that were dosed with stormwater than in the soil columns that were dosed with distilled water. For this initial investigation, this suggests that znuB may be an important factor in zinc tolerance and that this could be an item for further investigation. A similar result, as was just described, is illustrated in Figure 91 for four genes associated with manganese/zinc/iron transport. In this figure, the two genes with the highest relative abundances (troB and sitB) have higher relative abundances in the wetland entry than in the wetland exit. However unlike in the previous example for zinc, the results for the column study, illustrated in Figure 92, do not present the same result. This adds to the complexity of the observations and illustrates that there are many influential factors at play. Finally, in Figure 93 and Figure 94, the relative abundances of the czcA sequence, which codes for cobalt and nickel resistance proteins (Gillan et al 2015), are compared for the field and column studies, respectively. CzcA expressed both a clear decrease in relative abundance along the length of the wetland and clear increase in relative abundance in soil columns that were dosed with stormwater compared to soil columns that were dosed with distilled water. Like znuB, CzcA also represents an item that may be useful for further investigation. 146   Figures  Figure 89. Relative Abundance of Genes Associated with Zinc Measured in Field Samples   Figure 90. Relative Abundance of Genes Associated with Zinc Measured in Column Samples 0.000%0.005%0.010%0.015%0.020%0.025%0.030%0.035%0.040%SeptSite2 OctSite2 OctSite3 OctSite4 OctSite5 OctSite6Relative AbundanceznuB; zinc transport system permease proteintroA, mntA, znuA; manganese/zinc/iron transport system substrate-binding proteinSLC30A1, ZNT1; solute carrier family 30 (zinc transporter), member 1zraP; zinc resistance-associated proteintroC, mntC, znuB; manganese/zinc/iron transport system permease proteinSLC39A4, ZIP4; solute carrier family 39 (zinc transporter), member 4GLI; zinc finger protein GLI0501001502002503003504000.000%0.002%0.004%0.006%0.008%0.010%Column1 Column7 Column8 Column5 Column15 Column16Concentration, mg/kg dry weightRealtive AbundanceznuB; zinc transport system permease proteintroD, mntD, znuB; manganese/zinc/iron transport system permease proteintroA, mntA, znuA; manganese/zinc/iron transport system substrate-binding proteinSLC30A1, ZNT1; solute carrier family 30 (zinc transporter), member 1zraP; zinc resistance-associated proteintroC, mntC, znuB; manganese/zinc/iron transport system permease proteinSLC39A4, ZIP4; solute carrier family 39 (zinc transporter), member 4GLI; zinc finger protein GLIZnEntry, [Zn] = 365 mg/kg Exit, [Zn] = 157 mg/kg Lagoon Wetland Entry                                             Wetland Exit         0 days                                                              120 days 147   Figure 91. Relative Abundance of Functional Associated with Manganese, Zinc and Iron Measured in Field Samples   Figure 92. Relative Abundance of Genes Associated with Manganese, Zinc and Iron Measured in Column Samples 0.0000%0.0005%0.0010%0.0015%0.0020%0.0025%0.0030%0.0035%0.0040%SeptSite2 OctSite2 OctSite3 OctSite4 OctSite5 OctSite6Realtive AbundancetroB, mntB, znuC; manganese/zinc/iron transport system ATP- binding proteinsitB; manganese/iron transport system ATP-binding proteintroD, mntD, znuB; manganese/zinc/iron transport system permease proteintroC, mntC, znuB; manganese/zinc/iron transport system permease protein05010015020025030035040000.0000050.000010.0000150.000020.0000250.000030.0000350.00004Column1 Column7 Column8 Column5 Column15 Column16Concentration, mg/kg dry weightRelative AbundancetroB, mntB, znuC; manganese/zinc/iron transport system ATP- binding proteinsitB; manganese/iron transport system ATP-binding proteintroD, mntD, znuB; manganese/zinc/iron transport system permease proteintroA, mntA, znuA; manganese/zinc/iron transport system substrate-binding proteinZnMnEntry, [Mn] = 349 mg/kg Exit, [Mn] = 161 mg/kg Lagoon Wetland Entry                                             Wetland Exit         0 days                                                              120 days 148    Figure 93. Relative Abundance of CzcA Tolerance Gene Measured in Field Samples   Figure 94. Relative Abundance of CzcA Tolerance Gene Measured in Column Samples 0.000%0.002%0.004%0.006%0.008%0.010%0.012%SeptSite2 OctSite2 OctSite3 OctSite4 OctSite5 OctSite6Relative AbundancenrsA, czcA; cation efflux systemprotein involved in nickel and cobalttolerance010203040506070809000.000020.000040.000060.000080.00010.00012Column1 Column7 Column8 Column5 Column15 Column16Concentration, mg/kg dry weightRelative AbundancenrsA, czcA; cation efflux system protein involved in nickel and cobalt toleranceCoNiEntry, [Ni] = 20 mg/kg, [Co] = 15 mg/kg Exit,  [Ni] = 5 mg/kg,  [Co] = 12 mg/kg Lagoon Wetland Entry                                             Wetland Exit         0 days                                                              120 days 149  3.7 Discussion and Conclusion While the goal of this study was to provide proof of concept data that supports or rejects developing a genomics monitoring tool for low impact design features that treat stormwater, including engineered wetlands, the goal of Chapter 2 was to expand on the results of Chapter 1 by applying genomics-based approaches to support the conclusion that the Lost Lagoon wetland is effectively treating stormwater. In addition, this chapter attempted to provide data to support the application of genomics for validation of other low impact design sites that treat stormwater. In this chapter experimentation was conducted and data was gathered and analyzed to provide proof that microbial analyses can support environmental analyses for the validation of a stormwater treatment wetland and possibly other similar systems. For this, an attempt was made to answer three hypotheses and to support three objectives.  Chapter Hypotheses To provide proof of concept results for the application of genomics-based analyses as a wetland validation technique, it was previously stated that three hypotheses must be true. 1. There is a shift in the composition and function of the microbial communities that exist between the entry and exit of the Lost Lagoon wetland; 2. The shift in the composition and function of the microbial communities between the entry and exit of the Lost Lagoon wetland is influenced by the decreasing concentration of contaminants along the length of the wetland; and 3. There are similarities across unconnected sites in the adaptations that take place within microbial communities due to exposure to stormwater. Regarding the first hypothesis, demonstrating a shift in the composition and function of microbial communities along the Lost Lagoon wetland, some important conclusions can be drawn. There was a significant difference in microbial community composition calculated between the wetland entry and exit for the surface sediment samples and for the 10-cm depth sediment samples but no significant difference was calculated for community composition between the water samples taken at the wetland entry and exit. Comparison of community diversity between the wetland entry and exit did not yield significant differences. However, a greater number of indicator species were identified at the wetland exit than at the wetland entry, suggesting that future analyses at a greater depth could focus on this element of the current study. For the proof of concept stage of analysis, the overall community composition comparisons suggest that long term trends are of greater importance for wetland validation and that further research could focus on sediment testing only. 150  Relating to functional genes for the first hypothesis, data was only obtained for a small subset of sediment samples collected during the field study and thus, final conclusions could not be drawn at the time of writing. Initial results suggest that there was some clustering of metagenomes based on the location where samples were collected in the wetland and that the date in which samples were collected was less important than the location in which samples were collected. These observations fit positively with the hypothesis that there is a shift in functional genes between the wetland entry and exit; however further investigation is required for validation of this hypothesis. Regarding the second hypothesis, correlating contaminants with microbial communities and functions, additional conclusions can be drawn. Some significant correlations were determined between the wetland entry and exit. However, challenges remain where metal concentrations are only slightly higher than detection limit concentrations using ICP-OES analysis. Copper, nickel, and chromium displayed the strongest correlations with the microbial communities (p-values < 0.05) between the wetland entry and wetland exit. Likewise, some functional gene sequences that are known to code for metal tolerances had higher relative abundances in samples that were measured to have higher metal concentrations. For example, this was evident for both the znuB gene, which codes for zinc resistance and the czcA gene, which codes for nickel/cobalt resistance. That being stated, there were great complexities among the functional genes data and it is important to evaluate the dataset as a greater whole before conclusions can be drawn. Finally, regarding the third hypothesis, determining if exposure to stormwater will shift the microbial communities at an unconnected site, unique and interesting conclusions can be drawn.   For the community bacteria compositions, no significant changes in microbial diversity were determined. However, for the water samples and surface sediment samples, there was a clear departure between the microbial communities in the sediment columns that were dosed with stormwater and the microbial communities that were dosed with distilled water. This trend was not evident in the 10-cm depth sediment samples, however. As with the wetland field study, there were also a greater number of indicator species identified in sediment columns that were dosed with distilled water over sediment columns that were dosed with stormwater. Interestingly, some similarities were present between the field and column study for functional genes. The znuB gene and the czcA gene both had higher relative abundances after sixteen weeks in the columns that were dosed with stormwater versus the column that was dosed with distilled water. However, the same result between the laboratory and field studies was not evident for genes associated with zinc/manganese/iron, thus observations are not conclusive and further exploration of the data and 151  experimentation is required. Metal resistance is regulated by a wide host of cellular functions and because the data output is so large, challenges arise in identifying the factors that have the greatest influence.   Chapter Objectives To support the goal of this study, to provide proof of concept data that supports or rejects developing a genomics-based monitoring tool for low impact design features that treat stormwater, including engineered wetlands, three objectives were previously stated for this chapter: 1. Apply genomics-based analysis methods to determine if there are shifts in the microbial communities and functional genes along the length of the Lost Lagoon wetland; 2. Determine if there is a correlation between the water and sediment quality, present over the study period, and the microbial communities and functional genes observed; and 3. Determine, through laboratory experimentation, if there are opportunities to expand and pursue genomics analyses at other low impact design features for stormwater treatment. Comparing the results of the hypotheses tests in this chapter serves to support the first objective, (i.e. determining if microbial shifts exist along the length of the Lost Lagoon wetland). While diversity and indicator species did not prove to be significant measures for comparison, microbial community composition presented clear shifts between the wetland entry and wetland exit, as was confirmed using common statistical techniques in microbiology, including the Adonis test in the R vegan package. Comparison of the metagenomes presented similar results and helped to confirm that there is a change in the microbial community between the entry and exit of the wetland.  For the second objective, determining if correlations exist between sample quality and bacteria, some important conclusions were drawn; however, this objective could, perhaps, be taken further with future research. Using the envfit statistic in the R vegan package, some significant correlations were calculated between metal concentrations and microbial communities but the noise present in the data presents challenges for validating conclusions. Similar results were observed for functional genes responsible for metal tolerances. Sequences that were present in greater relative abundances, such as znuB and czcA were illustrated to demonstrate some correlation with metal concentrations; however, this result was complicated by a wide array of additional sequences that may play a part in metal tolerance but are not present in a high enough quantity to be measurable or comparable. For the third and final objective in this chapter, performing laboratory experiments to determine if there may be opportunities to perform genomics analyses at other sites, the research presented here suggests that stormwater influences bacteria and that this may be exploited for treatment monitoring purposes. 152  Specifically, in the column study, the departure over time, of the microbial communities in the sediment columns that were dosed with stormwater from the sediment columns that were dosed with stormwater, suggests that there is causation between stormwater contamination and the composition of microbial communities. In addition, some similar results between the field and column studies, for dominant metal resistance genes, present promise for future research.  Final Remarks The work described in this chapter effectively provided data to inform the three hypotheses that were laid out in this chapter and this also supported the three objectives described here. In doing so, this chapter has provided some interesting proof of concept for the application of genomics analyses for stormwater treatment monitoring purposes, particularly for engineered wetlands. As time continues, the expansion of datasets for bacterial species and gene annotation will improve the quality of future data comparisons, only adding to the interest in this field of research. This will be particularly valuable for metals that are toxic in low concentrations and for metals that do not have strong documentation for bacterial tolerances. 3.8 Limitations Limitations for the environmental sampling in the Lost Lagoon wetland were described in Chapter 1 and similar limitations were present throughout the methodology described in Chapter 2. The limitations experienced here were mainly due to budget constraints that limited the number of unique samples that could be sequenced and analyzed. This was true for both the community analyses and functional gene analyses. Conclusions presented in this chapter are only true for the study presented here; they are not universal for wetland treatment systems. Further research at other stormwater treatment wetlands is required to validate the application of genomics as a viable treatment monitoring method. In addition, the suggestion of causation between stormwater contamination and microbial communities demonstrated in the laboratory column study requires repetition of the column study in its entirety before conclusions can be drawn. For the proof of concept, that genomics can be used to support monitoring and validating stormwater treatment wetlands, this chapter has laid a strong foundation for future research in support of designing a more concrete monitoring tool.   153  4. Discussion In Chapter 1, the results of traditional analyses techniques for monitoring and validating the efficacy of an operating stormwater treatment wetland, namely the Lost Lagoon wetland, were described. These analyses were performed to support the notion that genomics-based analyses can be applied as a tool to enhance and/or improve traditional monitoring techniques. Conclusions in Chapter 1 suggested that the uncertainty inherent to traditional monitoring for stormwater treatment may be an area where additional analyses may provide support. In Chapter 2, the results of genomics-based analyses at the Lost Lagoon wetland and a laboratory study for stormwater dosing were described. Conclusions in Chapter 2 suggest that including genomics-based analyses in stormwater treatment monitoring may provide greater certainty of treatment efficacy using a lower number of samples for analyses. Here, a cost comparison of the traditional stormwater treatment wetland validation method described by Erickson, Weiss and Gulliver (2013) is compared to the method described in Chapter 1 and Chapter 2. Theoretical costs are calculated based on a both a full validation, as well as a single compliance monitoring event, using the Lost Lagoon wetland that was analyzed in this study as an example. 4.1 Cost Comparison of Wetland Validation Techniques   Sample Collection For traditional stormwater treatment validation, Erickson, Weiss and Gulliver (2013) recommend the deployment of stormwater automatic samplers at the inlet and outlet pipes of the treatment wetland. Automatic samplers would be triggered during each storm event and a field technician would be required to visit the site to collect samples each time the automatic samplers were triggered. Using the Lost Lagoon wetland as an example, there are 166 storm events in Vancouver each year (Environment Canada, 2016) and thus sampling would need to occur at this frequency. For genomics-based treatment validation, the sampling methods described in this study would be applied. Two field technicians would be required to extract samples from the wetland at two week intervals over the rain season, which is approximately 8 months in Vancouver, thus 16 sampling events would be required. Table 44 lists the predicted labor cost for each sampling event based on a rate of $30 per hour per technician. Table 44. Approximate Cost Per Day for Sample Collection Sample Collection Labor Cost/Day Traditional $   90.00 Genomics $ 480.00  154   The cost of labor for sample collection using traditional methods is calculated to be:  166 𝑠𝑡𝑜𝑟𝑚𝑠 ×2 𝑦𝑒𝑎𝑟𝑠 ×$90𝑠𝑡𝑜𝑟𝑚= $29,880 Likewise, the cost of labor for sample collection using genomics-based methods is calculated to be: 16 𝑑𝑎𝑦𝑠 ×$480𝑑𝑎𝑦= $15,360  Laboratory Analyses Laboratory analyses of environmental samples presents a major cost for validating stormwater treatment wetlands. Table 45 lists the approximate cost per sample for a traditional wetland validation. Table 46 lists the approximate cost per sample for bacterial community analysis and corresponding analysis of trace metals in soil. Table 47 lists the approximate cost per sample for functional genes analyses with corresponding analysis of trace metals in water samples. Table 45. Approximate Cost Per Sample for Traditional Stormwater Quality Analysis Traditional Analyses Element Cost1 pH $   10.00 ORP $   10.00 Conductivity $   10.00 Turbidity $   10.00 TSS $   20.00 COD $   30.00 TOC $   30.00 Oil and Grease $   40.00 Trace Metals Water $ 125.00 Total $ 285.00 1(Canadian Association for Laboratory Accreditation, 2016) Table 46. Approximate Cost Per Sample for Genomics-Based Stormwater Quality Analysis Using Length Comparison of Bacterial Communities Genomics Analyses Element Cost Trace Metals Soil $ 150.001 MiSeq $   75.002 Total $ 225.00 1(Canadian Association for Laboratory Accreditation, 2016) 2(Microbiome Insights, 2016)  155  Table 47. Approximate Cost Per Sample for Genomics Stormwater Quality Analysis Using Entry and Exit Comparison of Bacterial Functional Genes Genomics Analyses Element Cost Trace Metals Water $ 125.001 HiSeq $ 850.002 Total $ 975.00 1(Canadian Association for Laboratory Accreditation, 2016) 2(University of British Columbia Beatty Biodiversity Sequencing Centre, 2016) Using the values listed in Table 44 through Table 47 the costs for traditional and genomics laboratory analyses are as follows. Traditional: 166 𝑠𝑡𝑜𝑟𝑚𝑠 ×3 (𝑡𝑟𝑖𝑝𝑙𝑖𝑐𝑎𝑡𝑒𝑠)×2 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠 ×2 𝑦𝑒𝑎𝑟𝑠 ×$285𝑠𝑎𝑚𝑝𝑙𝑒= $567,720 Genomics: 16 𝑑𝑎𝑦𝑠 ×3 (𝑡𝑟𝑖𝑝𝑙𝑖𝑐𝑎𝑡𝑒𝑠)×18 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠 ×$225𝑠𝑎𝑚𝑝𝑙𝑒= $194,400 16 𝑑𝑎𝑦𝑠 ×3 (𝑡𝑟𝑖𝑝𝑙𝑖𝑐𝑎𝑡𝑒𝑠)×2 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠  ×$975𝑠𝑎𝑚𝑝𝑙𝑒= $93,600 𝑇𝑜𝑡𝑎𝑙 = $194,400 + $93,600 = $288,000   Total Cost of Data Acquisition By summing the cost of sample collection and laboratory analyses, an estimate for the cost of data acquisition for both a traditional and a genomics-based wetland validation is calculated. Traditional: 𝑇𝑜𝑡𝑎𝑙 = $567,720 + $29,880 = $597,600 Genomics: 𝑇𝑜𝑡𝑎𝑙 = $288,000 + $15,360 = $303,360 Only the cost for sample collection and laboratory analyses were included in the cost estimate because these two factors were deemed to be the items of greatest significance. The cost of sampling equipment would be relatively small compared to the cost of laboratory analyses, for example. This cost estimate suggests that the genomics-based method described in this study may represent a lower cost option for data acquisition for validating stormwater treatment wetlands than traditional techniques. The cost and time for data analysis and reporting is also a major item but is not included here. 156  4.2 Cost Comparison of a Single Wetland Monitoring Event The results expressed in the previous section represent total cost figures for a full wetland validation study but it is expected that this level of effort would not be expended by a municipality that is operating a stormwater treatment wetland. From an engineering perspective, monitoring tends to only be performed on one date annually or even less frequently (Chris Johnston, personal communication). This is because regulatory agencies typically do not require performance monitoring for stormwater treatment systems for road runoff, even though contaminant concentrations may be greater than effluent guidelines. Single point in time monitoring events often provide inconclusive results, which can be a barrier for the installation of engineered wetlands. This was described further in Chapter 1. Strengthening the quality of single event monitoring data may be beneficial in the face of increasing regulatory requirements and a will among municipalities to implement low impact design features, such as engineered stormwater treatment wetlands. Thus, using the Lost Lagoon wetland as an example, if a municipality only monitored the performance of the wetland on one day, the monitoring regime would be quite limited. Based on past single point in time monitoring including that of Hemmera (2013) and Thoren et al (2007), the traditional single monitoring event would theoretically include sampling approximately nine soil and water samples from the entry and exit of the wetland followed by metal analyses performed in duplicate. This would represent a cost of: $275𝑠𝑎𝑚𝑝𝑙𝑒×9 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠 ×2 𝑆𝑖𝑡𝑒𝑠 ×2 (𝑑𝑢𝑝𝑙𝑖𝑐𝑎𝑡𝑒)[𝑎𝑛𝑎𝑙𝑦𝑠𝑒𝑠] + $480 [𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛]  = $9,900 If that same single monitoring event also included genomics-based analyses with nine locations selected for 16s bacterial community analysis and three locations selected for metagenome analysis, the cost increase would be: ($75𝑠𝑎𝑚𝑝𝑙𝑒×9 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠 ×2 𝑆𝑖𝑡𝑒𝑠 ×2 (𝑑𝑢𝑝𝑙𝑖𝑐𝑎𝑡𝑒) + $850×3 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑠 ×2 𝑆𝑖𝑡𝑒𝑠) [𝑎𝑛𝑎𝑙𝑦𝑠𝑒𝑠]+ $480 [𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛]  = $7,800 While this represents a greater cost to acquire data, the added benefit for confidence in results may be worth the expense. In addition, as methods and scientific understanding of genetic data processing increases in the future, it may one day be possible to drop the sample size or to exclude some of the metal analyses entirely. Thus, for future monitoring efforts, there is a significant financial opportunity for genomics-based methods to outcompete traditional methods for stormwater treatment monitoring, particularly for low impact design systems where microbiota influence treatment performance. 157  5. Conclusion The goal of this study was to provide proof of concept data to inform the development of a genomics-based tool for monitoring stormwater treatment wetlands. In the introduction, the motivation for improving wetland monitoring techniques was described and the Lost Lagoon wetland was illustrated as an ideal location to perform a case study. In Chapter 1, background details on stormwater contaminants, stormwater treatment wetlands and the Lost Lagoon wetland were outlined. Results of traditional monitoring were prepared and conclusions illustrated that there are shortcomings present with the status quo for traditional wetland monitoring. In Chapter 2, genomics-based methods were introduced as an additional technique for monitoring stormwater treatment wetlands. Results from DNA sequencing were compared using water and sediment samples extracted from the Lost Lagoon wetland and several outcomes suggested that bacteria may correlate with the performance of treatment wetlands. This was generally supported further using results from samples extracted during a stormwater dosing study using columns of soil sourced from the Stanley Park bog in Vancouver, British Columbia. The discussion immediately before this section provided a brief cost comparison of traditional validation and monitoring and genomics-based validation and monitoring. This cost comparison highlighted that an full wetland validation study may be less expensive using genomics and that a single point in time wetland monitoring event may be more expensive using genomics, though the improvement on data and confidence in results could be worth the cost increase. In addition to providing proof of concept data and cost analyses, this study also included method development which should serve to refine future genomics-based studies for stormwater treatment wetlands and other low impact design features. Specifically, it was found that sediment sampling provided the greatest promise when attempting to discern long-term stormwater treatment trends in both the field wetland study and in the laboratory stormwater dosing study. Within the limits of graduate studies research, this study achieved its goal. Proof of concept, for the application of genomics-based monitoring of stormwater treatment wetlands, was provided. It was demonstrated that genomics will supply benefits for future stormwater treatment monitoring endeavours and that additional investigation into this field is worthwhile.   158  6. Recommendations 6.1 Follow-On Research This study provided useful proof of concept results and preliminary conclusions. However, there are several facets which were not in the scope of this research project and could be continued with further. These include:  Measuring the quality of stormwater that enters and exits the Lost Lagoon wetland, through installation of an automatic sampler;  Performing a validation on the sizing of the Lost Lagoon wetland forebay based on flow velocities;  Performing more in depth analyses of other pollutants, including petrochemicals, exiting the Stanley Park Causeway and the analyzing the impact of these other pollutants on bacteria;  Statistically correlating indicator species with metal concentrations;  Statistically correlating known metal resistance genes with metal concentrations;  Sequencing a larger number of metagenomes to increase the confidence in this study’s findings;  Repeating the metagenome analyses using an updated and more widely accepted annotation tool, such as the MG-RAST server;  Performing some additional statistical analyses, as outlined by the independent review of this project included in Appendix K;  Repeating the field study at additional stormwater treatment wetlands of similar and different configurations and comparing the findings with the results illustrated here;  Repeating the column study using the same controls and comparing the findings with the results illustrated here;  Repeating the column study using modified controls and comparing the findings with the results illustrated here; and  Modifying the approach applied here for application at other low impact design sites such as retention ponds, absorbent landscapes, and swales, among others. Based on these facets that were outside the scope of this research project, there are several follow-on recommendations. First, at the time of publication, the twelve metagenomes, which were analyzed in this thesis, had been submitted to the MG-RAST server for gene annotation. Analysis of these results will be used to inform the articles (listed in the Preface to this thesis), which are currently in preparation and will be submitted for publication. After this analysis is confirmed and finalized, the main follow-on recommendation resulting from the present study is to repeat the sampling and analysis methodology at 159  additional stormwater treatment wetlands. Ideally, a follow-on study would perform the methodology recommended here at two or more additional sites – one with similar structure to that of the Lost Lagoon wetland and one with an alternate structure. Following this, results could be compared between wetlands and more significant conclusions could be drawn as to the validity in applying genomics as a monitoring tool for engineered wetlands. 6.2 Application and Improvements of Study Methodology The research presented here provided a broad analysis of data using several sample mediums. Based on outcomes described in Chapter 1 and Chapter 2, for follow-on phase applications of this research, some optimum choices for the sampling and analyses methodology include:  During the rainy season, collect field samples at either two or four week intervals for at least four months but preferably eight months if time and budgets permit;  Collect and analyze samples at the inlet and outlet of each wetland instead of along the entire length of each wetland;  Follow the environmental sampling protocols described in Chapter 1 for both sediment and water samples but analyze all samples for environmental parameters in triplicate instead of in duplicate;  Perform DNA analyses on surface sediment samples only;  To reduce the overall number of samples in the que for DNA sequencing, homogenize surface sediment samples across the entire wetland entry and entire wetland exit for each date sampled instead of on a 1 m plot basis for each date sampled; and  Follow the sequencing and bioinformatics methodologies described in Chapter 2 for both 16s and metagenome analyses but also consider modifying these techniques as new improvements become available. Taken together, these improvements should allow for a more streamlined comparison of the treatment efficacy within each wetland and between different wetlands for future applications of the tool described within this document.      160  Bibliography Albarracin VH, Avila AL, Amoroso MJ, Abate CM (2008) Copper removal ability by Streptomyces strains with dissimilar growth pat- terns and endowed with cupric reductase activity. FEMS Microbiology Letters, 288:141–148 Batool R, Yrjala K, Hasnain S. (2012). Hexavalent chromium reduction by bacteria from tannery effluent. Journal of Microbial Biotechnology 22:547–554 Beard SJ, Hashim R, Membrillo-Hernandez J, Hughes MN, Poole RK (1997) Zinc(II) tolerance in Escherichia coli K-12: evidence that the zntA gene (o732) encodes a cation transport ATPase. Molecular Microbiology, 25: 883-891 Blecken, G.-T., Zinger, Y., Deletić, A., Fletcher, T. D., & Viklander, M. (2009). Influence of intermittent wetting and drying conditions on heavy metal removal by stormwater bio-filters. Water Research, 43(18), 4590–4598. Bopp LH, Ehrlich HL (1988) Chromate resistance and reduction in Pseudomonas fluorescens strain LB300. Archives of Microbiology, 150: 426-431 Borremans B, Hobman JL, Provoost A, Brown NL, van Der Lelie D (2001) Cloning and functional analysis of the pbr lead resistance determinant of Ralstonia metallidurans CH34. Journal of Bacteriology, 183: 5651–5658 Branco R., Chung, A.P., Johnston T., Gurel, V., Morais P., Zhitkovich A. (2008) The chromate-inducible chrBACF operon from the transposable element TnOtChr confers resistance to chromium(VI) and superoxide. Journal of Bacteriology, 190(21):6996–7003 Bratieres, K., Fletcher, T., Deletic, A., & Zinger, Y. (2008a). Nutrient and sediment removal by stormwater biofilters: A large-scale design optimization study. Water Research, 42, 3930-3940. Bratieres, K., Fletcher, T.D., Alcazar, L., Le Coustumer, S.M., McCarthy, D.T. (2008b). Removal of nutrients, heavy metals and pathogens by stormwater biofilters. In: 11th International Conference on Urban Drainage. Edinburgh, Scotland, UK, 2008. British Columbia Ministry of Community, Sport, and Cultural Development. (n.d.). Environmental Infrastructure. Retrieved May 17, 2016, from LGD: Stormwater: http://www.cscd.gov.bc.ca/lgd/environment/stormwater.htm 161  British Columbia Ministry of the Environment. (1992). Urban Runoff Quality Guidelines for the Province of British Columbia. Vancouver, BC: Municipal Waste Branch: Environmental Protection Division. British Columbia Ministry of Water, Land and Air Protection. (2011). Environmental Management Act, 2004. Contaminated Sites Regulation. British Columbia, Canada. Buttigieg PL, Ramette A (2014) A Guide to Statistical Analysis in Microbial Ecology: a community-focused, living review of multivariate data analyses. FEMS Microbial Ecology, 90: 543–550. Government of Canada. Fisheries Act (1985). Retrieved from http://laws-lois.justice.gc.ca/PDF/F-14.pdf Canadian Association for Laboratory Accreditation (2016). Environmental Laboratory Price Guide. Retrieved from: http://www.enr.gov.nt.ca/programs/taiga-environmental-laboratory/price-guide. Canadian Council of Ministers of the Environment. (2015). Water Quality Guidelines for the Protection of Aquatic Life. Cervantes C, Silver S (1992) Plasmid chromate resistance and chromium reduction. Plasmid 27: 65-71 Chang FM, Coyne HJ, Cubillas C, Vinuesa P, Fang X, Ma Z, Ma D, Helmann JD, García-de los Santos A, Wang YX, Dann CE3rd, Giedroc DP (2014) Cu(I)-mediated allosteric switching in a copper-sensing operon repressor (CsoR). Journal of Biological Chemistry, 289(27): 19204–19217 Chaturvedi KS, Hung CS, Crowley JR, Stapleton AE, Henderson JP (2012) The siderophore yersiniabactin binds copper to protect pathogens during infection. Natural Chemical Biology 8:731–736 Chen, E.Y. Chapter One - The Efficiency of Automated DNA Sequencing, In Automated DNA Sequencing and Analysis, edited by MD. Adams, C. Fields and JC Venter, Academic Press, San Diego, 1994, Pages 3-10 Chillappagari S, Miethke M, Trip H, Kuipers OP, Marahiel MA (2009) Copper acquisition is mediated by YcnJ and regulated by YcnK and CsoR in Bacillus subtilis. Journal of Bacteriology, 191:2362–2370 City of Vancouver. (2016a). Integrated Rainwater Management Plan - Best Management Practice Toolkit, II, 48. City of Vancouver. (2016b). Trail Map of Stanley Park. Retrieved from http://vancouver.ca/files/cov/Stanley-Park-trails-map.pdf Clifford, G. (1932). A Biological Survey of Lost Lagoon. University of British Columbia. 162  Cole Parmer. (2016). Hand Syphon. Retrieved from: http://www.coleparmer.co.uk/Product/Hand_operated_water_and_chemical_siphon_drum_pump_32_strokes_gallon/WZ-70607-00 Das, S., Dash, H. R., Chakraborty, J. (2016). Genetic basis and importance of metal resistant genes in bacteria for bioremediation of contaminated environments with toxic metal pollutants. Applied Microbiology and Biotechnology, 100(7), 2967–2984. De J, Ramaiah N, Bhosle NB, Garg A, Vardanyan L, Nagle VL, Fukami K (2007) Potential of mercury resistant marine bacteria for detoxification of chemicals of environmental concern. Microbes of the Environment, 22:336–345 De J, Ramaiah N, Vardanyan L (2008) Detoxification of toxic heavy metals by marine bacteria highly resistant to mercury. Marine Biotechnology, 10:471–477 Dey S, Papadopoulou B, Haimeur A, Roy G, Grondin K, Dou D, Rosen BP, Ouellette M (1994) High level arsenite resistance in Leishmania tarentolae is mediated by an active extrusion system. Molecular Biochemistry, 67: 49-57 Djoko KY, Chong LX, Wedd AG, Xiao Z (2010) Reaction mechanisms of the multi-copper oxidase CueO from Escherichia coli support its functional role as a cuprous oxidase. Journal of the American Chemistry Society, 132:2005– 2015 Dutka, B., Marsalek, J., Jurkovic, A., Kwan, K., & Mcinnis, R. (1994). Eco-toxicological study of stormwater ponds under winter conditions. Zeitschrift Für Angewandte Zoologie, 80(1), 25–42. Edgar, R.C. (2013) UPARSE: Highly accurate OTU sequences from microbial amplicon reads, Nature Methods [Pubmed:23955772, dx.doi.org/10.1038/nmeth.2604]. Edgar, R.C. (2016), SINTAX, a simple non-Bayesian taxonomy classifier for 16S and ITS sequences, http://dx.doi.org/10.1101/074161. Edgar, R.C. (2016), UCHIME2: Improved chimera detection for amplicon sequences, http://dx.doi.org/10.1101/074252. Edgar, R.C. and Flyvbjerg, H (2015) Error filtering, pair assembly and error correction for next-generation sequencing reads, doi: 10.1093/bioinformatics/btv401. Edgar, RC (unpublished) http://drive5.com/usearch 163  Edgar,RC (2010) Search and clustering orders of magnitude faster than BLAST, Bioinformatics 26(19), 2460-2461. doi: 10.1093/bioinformatics/btq461 Edgar,RC, Haas,BJ, Clemente,JC, Quince,C, Knight,R (2011) UCHIME improves sensitivity and speed of chimera detection, Bioinformaticsdoi: 10.1093/bioinformatics/btr381 [Pubmed 21700674]. Environment Canada. (2016, December 1). Station Results - Historical Data - Vancouver Harbour CS. Vancouver, British Columbia, Canada. Erickson, A. J., Weiss, P. T., & Gulliver, J. S. (2013). Optimizing Stormwater Treatment Practices: A Handbook of Assessment and Maintenance. New York, United States: Springer Science and Business. ESRI. (2016, October). ARCGIS Map Maker and Analysis Tool. Retrieved from https://www.arcgis.com/home/webmap/viewer.html Fakruddin, M., & Mannan, K. S. Bin. (2013). Methods for Analyzing Diversity of Microbial Communities in Natural Environments. Ceylon Journal of Science (Biological Sciences), 42(1), 19–33.  Faulwetter, J. L., Gagnon, V., Sundberg, C., Chazarenc, F., Burr, M. D., Brisson, J., … Stein, O. R. (2009). Microbial processes influencing performance of treatment wetlands: A review. Ecological Engineering, 35(6), 987–1004. Fleischmann, R. D. et al. Whole-genome random sequencing and assembly of Haemophilus influenza Rd. Science 269, 496-512 (1995). Ge Z, Taylor DE (1996) Helicobacter pylori genes hpcopA and hpcopP constitute a cop operon involved in copper export. FEMS Microbiology Letters, 145:181–188 Geosyntec Consultants, & Wright Water Engineers Inc. (2011). International Stormwater Best Management Practices (BMP) Database Pollutant Category Summary Statistical Summary: Metals. International Stormwater BMP Database. Retrieved from www.bmpdatabase.org Gilbert, R. O. (1987). Statistical Methods for Environmental Pollution Monitoring. New York: John Wiley and Sons, Inc. Gillan, D. C., Roosa, S., Kunath, B., Billon, G., & Wattiez, R. (2015). The long-term adaptation of bacterial communities in metal-contaminated sediments: A meta-proteo-genomic study. Environmental Microbiology, 17(6), 1991–2005. 164  Gjessing, E., Lygren, E., Andersen, S., Berglind, L., Carlberg, G., Efraimen, H., … Martinsen, K. (1984). Acute toxicity and chemical characteristics of moderately polluted runoff from highways. The Science of the Total Environment, 33, 225–232. Gonzalez CF, Ackerley DF, Lynch SV, Matin A (2005). ChrR, a soluble Quinone reductase of Pseudomonas putida that defends against H2O2. Journal of Biological Chemistry 280:22590–22595 Gonzalez H, Jensen TE (1998) Nickel sequestering by polyphosphate bodies in Staphylococcus aureus. Microbiosphere, 93: 179-185 Government of British Columbia. (2016). Municipal Wastewater Regulation. B.C. Reg 87/2012. Government of Canada. (1999). Environmental Protection Act. Grass G, Fricke B, Nies, DH. (2005) Control of expression of a periplasmic nickel efflux pump by periplasmic nickel concentrations. Biometals. 18:437–448 Grass G, Grobe C, Nies DH (2000) Regulation of the cnr cobalt and nickel resistance determinant from Ralstonia sp. strain CH34. Journal of Bacteriology, 182:1390–1398 Grass G, Thakali K, Klebba PE, Thieme D, Muller A, Wildner GF, Rensing C (2004) Linkage between catecholate siderophores and the multi-copper oxidase CueO in Escherichia coli. Journal of Bacteriology, 186:5826–5833 Gupta A, Matsui K, Lo JF, Silver S (1999) Molecular basis for resistance to silver in Salmonella. Nat Med (in press) Gupta SD, Wu HC, Rick PD (1997) A Salmonella typhimurium genetic locus which confers copper tolerance in copper sensitive mutants of Escherichia coli. Journal of Bacteriology 179: 4977-4984 Hach. (2007). Hach Method 8158/8164. Solids, Non-filterable Suspended Solids; Total and Volatile: Adapted from USAEPA Standard Gravimetric Method in Standard Methods for Examination of Water and Wastewater Section 2450. Hach Company. Retrieved from http://www.hach.com/asset-get.download-en.jsa?code=56432. Hach. (2008). Portable Turbidimeter Model 2100P: Instrument and Procedure Manual. Hach Company. Harpole, W. (2010). Neutral theory of species diversity. Nature Education Knowledge. 1: 31. 165  Hartman, W. H., Richardson, C. J., Vilgalys, R., & Bruland, G. L. (2008). Environmental and anthropogenic controls over bacterial communities in wetland soils. Proceedings of the National Academy of Sciences of the United States of America, 105(46), 17842–17847. Hawkins, W. B., Rodgers, J. H., Gillespie, W. B., Dunn, A. W., Dorn, P. B., & Cano, M. L. (1997). Design and Construction of Wetlands for Aqueous Transfers and Transformations of Selected Metals. Ecotoxicology and Environmental Safety, 36, 238–248. Hemmera. (2013). In-Situ Sediment Investigation at Lost Lagoon Stormwater Treatment Wetland. Vancouver, BC. Henne KL, Nakatsu CH, Thompson DK, Konopka AE. (2009). High-level chromate resistance in Arthrobacteria sp. strain FB24 requires previously uncharacterized accessory genes. BMC Microbiology 9:199 Hu YH, Wang HL, Zhang M, Sun L (2009) Molecular analysis of the copper-responsive CopRSCD of a pathogenic Pseudomonas fluorescens strain. Journal of Microbiology, 47:277–286 James, W. (2010). User's Guide to SWMM. USA: CHI Management. Jarosławiecka A, Piotrowska-Seget Z (2014) Lead resistance in microorganisms. Microbiology 160:12–25 Karlsson, K., Viklander, M., Scholes, L., & Revitt, M. (2010). Heavy metal concentrations and toxicity in water and sediment from stormwater ponds and sedimentation tanks. Journal of Hazardous Materials, 178(1–3), 612–618. Kerr Wood Leidal Associates Ltd. (1999). Stanley Park Stormwater Management Plan. Prepared in Burnaby for the City of Vancouver Board of Parks and Recreation. Kerr Wood Leidal. (2002). Lost Lagoon Stormwater Treatment Wetland: Operation and Maintenance Manual. Vancouver, BC: Kerr Wood Leidal Associates Ltd. Konwar, K. M., Hanson, N. W., Pagé, A. P., & Hallam, S. J. (2013). MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinformatics, 14(1), 202. Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., & Schloss, P. D. (2013). Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Applied and environmental microbiology, 79(17), 5112-5120. 166  Laverman AM, Blum JS, Schaefer JK, Phillips EJP, Lovley DR, Oremland RS (1995) Growth of strain SES-3 with arsenate and other diverse electron acceptors. Applied Environmental Microbiology, 61:3556-3561 Levinson HS, Mahler I (1998) Phosphatase activity and lead resistance in Citrobacter freundii and Staphylococcus aureus. FEMS Microbiology Letters 161: 135-138 Lewis, J., & Sjöstrom, J. (2010). Optimizing the experimental design of soil columns in saturated and unsaturated transport experiments. Journal of Contaminant Hydrology, 115(1–4), 1–13. Liesegang H, Lemke K, Siddiqui RA, Schlegel H-G (1993) Characterization of the inducible nickel and cobalt resistance determinant cnr from pMOL28 of Alcaligenes eutrophus CH34. Journal of Bacteriology, 175: 767-778 Loman, N. J., Constantinidou, C., Chan, J. Z. M., Halachev, M., Sergeant, M., Penn, C. W., Pallen, M. J. (2012). High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity. Nature Reviews Microbiology, 10(9), 599–606. Marsalek, J., Rochfort, Q., Mayer, T., Servos, M., Dutka, B., & Brownlee, B. (1999). Toxicity testing for controlling urban wet-weather pollution: advantages and limitations. Urban Water, 1(1), 91–103. Meyer, F., Paarmann, D., D’Souza, M. D., Olson, R., Glass, E. M., Kubal, M., … Edwards, R. A. (2008). The metagenomics RAST server - A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics, 9(386). Retrieved from http://www.biomedcentral.com/1471-2105/9/386.%0A1.5 Microbiome Insights. (May 2016). Personal communication. Mishra R, Sinha V, Kannan A, Upreti RK. (2012). Reduction of chromium-VI by chromium resistant Lactobacilli: a prospective bacterium for bioremediation. Toxicology International, 19:25–30 Morillo JA, Garcia-Ribera R, Quesada T, Aguilera M, Ramos- Cormenzana A, Monteoliva-Sanchez M (2008) Biosorption of heavy metals by the exopolysaccharide produced by Paenibacillus jamilae. World Journal of Microbial Biotechnology, 24:2699–2704 Mulliss, R. M., Revitt, D. M., & Shutes, R. B. E. (1996). A statistical approach for the assessment of the toxic influences on Gammarus pulex (Amphipoda) and Asellus aquaticus (Isopoda) exposed to urban aquatic discharges. Water Research, 30(5), 1237–1243. 167  Namiki, T., Hachiya, T., Tanaka, H., Sakakibara, Y., 2011. MetaVelvet: An extension of Velvet assembler to de novo metagenome assembly from short sequence reads. ACM Conference on Bioinformatics, Computational Biology and Biomedicine. Nies D, Mergeay M, Friedrich B, Schlegel HG (1987) Cloning of plasmid genes encoding resistance to cadmium, zinc, and cobalt in Alcaligenes eutrophus CH34. Journal of Bacteriology, 169: 4865-4868 Nies DH (1995) The cobalt, zinc, and cadmium efflux system CzcABC from Alcaligenes eutrophus functions as a cation-proton-antiporter in Escherichia coli. Journal of Bacteriology 177: 2707-2712 Nies DH, Silver S (1989a) Metal ion uptake by a plasmid-free metal-sensitive Alcaligenes eutrophus strain. Journal of Bacteriology 171:4073-4075 Nies, D. H. (1999). Microbial heavy-metal resistance. Applied Microbiology and Biotechnology, 51(6), 730–750. Nies, D. H. (2003). Efflux-mediated heavy metal resistance in prokaryotes. FEMS Microbiology Rev, 27, 313–339. Nogaro, G., Mermillod-Blondin, F., Montuelle, B., Boisson, J. C., Bedell, J. P., Ohannessian, A., Gibert, J. (2007). Influence of a stormwater sediment deposit on microbial and biogeochemical processes in infiltration porous media. Science of the Total Environment, 377(2–3), 334–348. Nucifora G, Chu L, Misra TK, Silver S (1989) Cadmium resistance from Staphylococcus aureus plasmid pI258 cadA gene results from a cadmium-efflux ATPase. Proceedings of the National Academy of Sciences, USA, 86:3544-3548 O’Del, J. W. (1993). Determination of Turbidity by Nephelometry. Standard Methods for the Examination of Water and Wastewater, (August), 1–10. Odermatt A, Krapf R, Solioz M (1994) Induction of the putative copper ATPases, CopA and CopB, of Enterococcus hirae by Ag+ and Cu2+, and Ag+ extrusion by CopB. Biochemical Biophysics Res. Community, 202: 44-48 Olafson RW, Abel K, Sim RS (1979) Prokaryotic metallothionein: preliminary characterization of a blue-green algae heavy-metal binding protein. Biochemistry Biophysics Res. Community 89: 36-43 Oxford University Press. (2016). Oxford Dictionaries. Retrieved May 17, 2016, from Definition of Genomics in English: http://www.oxforddictionaries.com/us/definition/american_english/genomics 168  Park JH, Bolan N, Meghraj M, Naidu N (2011) Concomitant rock phosphate dissolution and lead immobilization by phosphate solubilising bacteria (Enterobacter sp.). Journal of Environmental Management, 92:1115–1120 Parks DH, Tyson GW, Hugenholtz P, Beiko RG. (2014). STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics, 30, 3123-3124. Paul D, Pandey G, Pandey J, Jain RK (2005) Accessing microbial diversity for bioremediation and environmental restoration. Trends in Biotechnology, 23:135–142 Paulsen, I. T., & Holmes, A. J. (2014). Environmental Microbiology: Methods and Protocols. (J. Walker, Ed.) (2nd ed.). USA: Humana Press. Pitt, R., Field, R., Lalor, M., & Brown, M. (1995). Urban stormwater toxic pollutants: assessment, sources, and treatability. Water Environment Research, 67(3), 260–275. Potter, B., & Wimsatt, J. (2005). Method 415.3 - Measurement of Total Organic Carbon, Dissolved Organic Carbon and Specific UV Absorbance at 254 NM in Source Water and Drinking Water. Washington, DC: U.S. Environmental Protection Agency. R Core Team. (2016). R: A language and environment for statistical computing. R. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ Rastogi G, Sani RS (2011) Molecular techniques to assess microbial community structure, function and dynamics in the environment. In Microbes and microbial technology: agricultural and environmental applications. Ahmad I, Ahmad F, Pichtel J, eds, pp 29–57. Springer, New York. Rensing C, Mitra B, Rosen BP (1997b) The zntA gene of Escherichia coli encodes a Zn(II)-translocating P-type ATPase. Proceedings of the National Academy of Science, USA 24: 14 326-14 331 Roane TM (1999) Lead resistance in two bacterial isolates from heavy metal-contaminated soils. Microbial Ecology. 37:218–224 Rodrigue A, Effantin G, Mandrand-Berthelot MA (2005) Identification of rcnA (yohM), a nickel and cobalt resistance gene in Escherichia coli. Journal of Bacteriology, 187:2912–2916 Rosenstein, R., Peschel, A., Wieland, B., & Götz, F. (1992). Expression and regulation of the antimonite, arsenite, and arsenate resistance operon of Staphylococcus Xylosus plasmid pSX267. Journal of Bacteriology, 174(11), 3676–3683. 169  Sanders, O, Rensing C, Kuroda M, Miltra B, Rosen, B. (1997). Antimonite is accumulated by the glycerol facilitator GlpF in Escherichia Coli.  Journal of Bacteriology. 179: 3365-3367. Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–41. https://doi.org/10.1128/AEM.01541-09 Schmidt T, Schlegel HG (1994) Combined nickel-cobalt-cadmium resistance encoded by the ncc locus of Alcaligenes xylosoxidans 31A. Journal of Bacteriology, 176: 7045-7054 Seitzinger, S., Harrison, J., Böhkle, J., Bouwman, A., Lowrance, R., Peterson, B., & Ven Drecht, T. (2006). Denitrification across waterscapes and landscapes: A synthesis. Applied Ecology, 16, 2064–2090. Silver S, Misra TK, Laddaga RA (1989) DNA sequence analysis of bacterial toxic heavy metal resistances. Biological Trace Element Resistance, 21:145-163 Stahler FN, Odenbreit S, Haas R, Wilrich J, Van Vliet AH, Kusters JG, Kist M, Bereswill S (2006) The novel Helicobacter pylori CznABC metal efflux pump is required for cadmium, zinc, and nickel resistance, modulation, and gastric colonization. Infection Immunology, 74: 3845–3852 Stephens, K. A., Graham, P., & Reid, D. (2002). Stormwater Planning: A Guidebook for British Columbia. Victoria: British Columbia Ministry of Water, Land, and Air Protection. Stime, S. E. (2014). Source Controls for Runoff Treatment: Hydrologic Response and Water Quality Attenuation. University of British Columbia. Sun, M. Y., Dafforn, K. A., Johnston, E. L., & Brown, M. V. (2013). Core sediment bacteria drive community response to anthropogenic contamination over multiple environmental gradients. Environmental Microbiology, 15(9), 2517–2531. Thelwell C, Robinson NJ, Turner, JA, Cavet JS (1998) An SmtB-like repressor from Synechocystis PCC 6803 regulates a zinc exporter. Proceedings of the National Academy of Sciences, USA 95: 10 728-10 733 Thoren, R., Shiu, R., Shum, J., Jensen, M., & Wells, S. (2007). The Effectiveness of Heavy Metal and Hydrocarbon Removal by the Stanley Park Stormwater Treatment Wetland at Lost Lagoon. University of British Columbia. 170  Tibazarwa C, Wuertz S, Mergeay M, Wyns L, van Der Lelie D (2000) Regulation of the cnr cobalt and nickel resistance determinant of Ralstonia eutropha (Alcaligenes eutrophus) CH34. Journal of Bacteriology, 182(5):1399–1409 Torsvik, V., Goksoyr, J. and Daae, F.L. (1990). High diversity in DNA of soil bacteria. Applied and Environmental Microbiology 56: 782–787. Trajanovska S, Britz ML, Bhave M (1997) Detection of heavy metal ion resistance genes in gram-positive and gram-negative bacteria isolated from a lead-contaminated site. Biodegradation 8: 113-124 Truu, M., Juhanson, J., & Truu, J. (2009). Microbial biomass, activity and community composition in constructed wetlands. Science of the Total Environment, 407(13), 3958–3971. Turner JS, Morby AP, Whitton BA, Gupta A, Robinson NJ (1993). Construction of Zn2+/Cd2+ hypersensitive cyanobacterial mutants lacking a functional metallothionein locus. Journal Biological Chemistry, 268: 4494-4498 United States Environmental Protection Agency. (1996). Method 3050B: Acid Digestion of Sediments, Sludges, and Soils, Revision 2. United States Environmental Protection Agency. (1999). Method 1664, Revision A: N-Hexane Extractable Material (HEM; Oil and Grease) and Silica Gel Treated N-Hexane Extractable Material (SGT-HEM; Non-polar Material) by Extraction and Gravimetry. Washington, D.C.: Office of Water. University of British Columbia Beatty Biodiversity Sequencing Centre. (2016). Illumina HiSeq Sequencing. Retrieved from: https://sites.google.com/site/biodiversitynextgensequencing/gallery. Vats N, Lee SF (2001) Characterization of a copper-transport operon, copYAZ, from Streptococcus mutans. Microbiology. 147:653–662 Washington State Department of Transportation. (2007). Untreated Highway Runoff in Western Washington. Seattle Washington. Wunderli-Ye H, Solioz M (1999) Copper homeostasis in Enterococcus hirae. In: Copper Transport and Its Disorders. Springer USA. pp. 255–264 Yannarell, A.C. and Triplett, E.W. (2005). Geographic and environmental sources of variation in lake bacterial community composition. Applied and Environmental Microbiology 71: 227–239. 171  Zhang YM, Yin H, Ye JS, Peng H, Zhang N, Qin HM, Yang F, He BY (2007) Cloning and expression of the nickel/cobalt transferase gene in E. coli BL21 and bioaccumulation of nickel ion by genetically engineered strain. Huan Jing Ke Xue 28(4):918–923 Zhang, K., Deletic, A., Page, D., & McCarthy, D. T. (2015a). Surrogates for herbicide removal in stormwater biofilters. Water Research, 81, 64-71. Zhang, K., Randelovic, A., Aguiar, L. M., Page, D., McCarthy, D. T., & Deletic, A. (2015b). Methodologies for Pre-Validation of Bio-filters and Wetlands for Stormwater Treatment. PLoS ONE, 10(5), 1–21.    172  Appendix A: Acid Digestion Procedure for Water and Sediment Samples Acid digestion of sludge and manure for metals on ICP This method is based on United States Environmental Protection Agency method 3050B. The method is not a total digestion technique. It is a very strong acid digestion that will dissolve almost all elements that could become environmentally available. Elements bound in silicate structures are not normally dissolved by this procedure. This procedure uses very strong acid and peroxide. These chemicals are highly corrosive and can cause severe burns. Wear a splash shield, lab coat and rubber apron and gloves when handling them. Equipment:  BD-46 block digester – set at 140˚C, which will give a tube temperature of 95˚C  Digestion tubes  Cold fingers  Reagents:  Concentrated nitric acid  Concentrated hydrochloric acid  30% hydrogen peroxide  Aqua regia – 1:3 volume ratio of hydrochloric acid and nitric acid  Procedure: 1. Put 5 mL of sample into a digestion tube 1 2. Add 5 mL of aqua regia or use a 1:1 volume ratio of nitric acid and hydrochloric acid 2 3. Add 1 mL of 30% hydrogen peroxide 3 4. Place a cold finger on the top of the tube 5. Heat at 95˚C for two hours in the block digester 6. Cool and make the volume up to 50 mL with deionized water 7. Filter through a hardened fast filter such as Whatman 54 or equivalent 8. Transfer to the appropriate autosampler test tubes 9. Run on the AA or the ICP  Digest a blank along with the samples. Standards should be made up in a matrix to match the samples (10% aqua regia or 1:1 volume ratio of hydrochloric acid and nitric acid). If performing trace metal analysis, use trace level concentrated acids. If a brown gas appears (NO2) during the digestion, then the digestion is not complete. Add more nitric acid in 1 mL increments to each tube until it disappears.] 1. If using this procedure for soils, weigh out 0.10 g of dry sample 2. A 1:1 volume mixture of nitric acid and hydrochloric acid is easier to work with 3. Do not add 30% hydrogen peroxide if there is little organics in the sample    173  Appendix B: Historic Water and Sediment Quality Data for the Lost Lagoon Wetland   174     175     176     177     178     179     180     181     182     183     184     185     186     187     188     189     190     191     192     193   194  Appendix C: Rainfall Records During Lost Lagoon Wetland Site Visits Blue stars indicate the dates when sampling was performed at the Lost Lagoon wetland. Data was retrieved from the Vancouver Harbour Weather Station (Environment Canada, 2016)  Figure 95. Rainfall Recorded for Downtown Vancouver Between July 1 and July 15, 2015  Figure 96. Rainfall Recorded for Downtown Vancouver Between July 15 and July 30, 2015  Figure 97. Rainfall Recorded for Downtown Vancouver between August 1 and August 15 195    Figure 98. Rainfall Recorded for Downtown Vancouver Between August 17 and August 31, 2015  Figure 99. Rainfall Recorded for Downtown Vancouver Between September 1 and September 15, 2015  Figure 100. Rainfall Recorded for Downtown Vancouver Between September 16 and September 30, 2015   196    Figure 101. Rainfall Recorded for Downtown Vancouver Between October 1 and October 15, 2015  Figure 102. Rainfall Recorded for Downtown Vancouver Between October 16 and October 30, 2015  Figure 103. Rainfall Recorded for Downtown Vancouver Between October 31 and November 14, 2015  197   Figure 104. Rainfall Recorded for Downtown Vancouver Between November 15 and November 29, 2015  Figure 105. Rainfall Recorded for Downtown Vancouver Between November 30 and December 14, 2015  Figure 106. Rainfall Recorded for Downtown Vancouver Between December 15 and December 29, 2015   198  Appendix D: Temperature Records During Lost Lagoon Wetland Site Visits Bold fonts indicate the dates when sampling was performed at the Lost Lagoon wetland. Data was retrieved from the Vancouver Harbour Weather Station (Environment Canada, 2016) Table 48. Environment Canada Temperature Records for July 1, 2015 through August 31, 2015 Date Maximum Mean Minimum  Date Maximum Mean Minimum Jul 1 2015 23.9 °C 20.6 °C 17.2 °C  Aug 1 2015 24.3 °C 20.6 °C 16.8 °C Jul 2 2015 24.6 °C 21.2 °C 17.8 °C  Aug 2 2015 23.9 °C 20.7 °C 17.5 °C Jul 3 2015 24.8 °C 20.7 °C 16.5 °C  Aug 3 2015 23.2 °C 18.8 °C 14.4 °C Jul 4 2015 24.6 °C 20.7 °C 16.7 °C  Aug 4 2015 22.5 °C 19.2 °C 15.9 °C Jul 5 2015 25.6 °C 20.9 °C 16.2 °C  Aug 5 2015 19.3 °C 16.9 °C 14.4 °C Jul 6 2015 27.1 °C 21.8 °C 16.5 °C  Aug 6 2015 22.4 °C 18.4 °C 14.4 °C Jul 7 2015 22.7 °C 18.6 °C 14.4 °C  Aug 7 2015 23.2 °C 18.4 °C 13.6 °C Jul 8 2015 23.8 °C 19.5 °C 15.2 °C  Aug 8 2015 21.8 °C 18.7 °C 15.6 °C Jul 9 2015 26.0 °C 20.4 °C 14.7 °C  Aug 9 2015 23.4 °C 19.6 °C 15.8 °C Jul 10 2015 22.9 °C 19.5 °C 16.0 °C  Aug 10 2015 22.6 °C 18.5 °C 14.4 °C Jul 11 2015 18.7 °C 17.3 °C 15.9 °C  Aug 11 2015 24.1 °C 19.4 °C 14.6 °C Jul 12 2015 24.2 °C 20.1 °C 16.0 °C  Aug 12 2015 26.8 °C 22.0 °C 17.1 °C Jul 13 2015 21.8 °C 19.3 °C 16.7 °C  Aug 13 2015 24.3 °C 20.1 °C 15.8 °C Jul 14 2015 22.3 °C 18.1 °C 13.8 °C  Aug 14 2015 18.8 °C 16.9 °C 15.0 °C Jul 15 2015 23.1 °C 18.5 °C 13.8 °C  Aug 15 2015 20.6 °C 17.6 °C 14.5 °C Jul 16 2015 21.9 °C 18.9 °C 15.9 °C  Aug 16 2015 22.2 °C 18.4 °C 14.5 °C Jul 17 2015 22.9 °C 18.2 °C 13.4 °C  Aug 17 2015 21.8 °C 17.0 °C 12.2 °C Jul 18 2015 26.4 °C 21.5 °C 16.5 °C  Aug 18 2015 22.8 °C 18.4 °C 14.0 °C Jul 19 2015 27.3 °C 22.6 °C 17.8 °C  Aug 19 2015 24.6 °C 20.6 °C 16.5 °C Jul 20 2015 24.3 °C 20.4 °C 16.5 °C  Aug 20 2015 22.4 °C 18.9 °C 15.3 °C Jul 21 2015 22.7 °C 19.5 °C 16.2 °C  Aug 21 2015 21.0 °C 17.7 °C 14.3 °C Jul 22 2015 22.5 °C 18.2 °C 13.8 °C  Aug 22 2015 24.0 °C 17.3 °C 10.6 °C Jul 23 2015 23.4 °C 17.7 °C 12.0 °C  Aug 23 2015 22.7 °C 17.2 °C 11.7 °C Jul 24 2015 18.1 °C 16.2 °C 14.2 °C  Aug 24 2015 22.8 °C 17.9 °C 12.9 °C Jul 25 2015 21.8 °C 17.9 °C 14.0 °C  Aug 25 2015 20.2 °C 16.0 °C 11.7 °C Jul 26 2015 18.3 °C 16.1 °C 13.9 °C  Aug 26 2015 21.8 °C 16.8 °C 11.7 °C Jul 27 2015 21.4 °C 17.3 °C 13.1 °C  Aug 27 2015 25.4 °C 20.1 °C 14.7 °C Jul 28 2015 22.4 °C 18.1 °C 13.7 °C  Aug 28 2015 21.5 °C 18.3 °C 15.0 °C Jul 29 2015 24.2 °C 19.5 °C 14.7 °C  Aug 29 2015 21.4 °C 18.3 °C 15.1 °C Jul 30 2015 26.4 °C 20.2 °C 14.0 °C  Aug 30 2015 20.2 °C 16.9 °C 13.6 °C      Aug 31 2015 16.5 °C 15.4 °C 14.2 °C       199    Table 49. Environment Canada Temperature Records for September 1, 2015 through October 31, 2015 Date Maximum Mean Minimum   Date Maximum Mean Minimum Sep 1 2015 16.2 °C 15.0 °C 13.7 °C  Oct 1 2015 15.0 °C 11.5 °C 8.0 °C Sep 2 2015 17.0 °C 13.4 °C 9.8 °C  Oct 2 2015 14.6 °C 11.9 °C 9.1 °C Sep 3 2015 16.4 °C 13.3 °C 10.2 °C  Oct 3 2015 15.9 °C 11.4 °C 6.8 °C Sep 4 2015 16.9 °C 12.3 °C 7.6 °C  Oct 4 2015 16.9 °C 11.5 °C 6.1 °C Sep 5 2015 18.8 °C 13.6 °C 8.4 °C  Oct 5 2015 16.4 °C 11.4 °C 6.3 °C Sep 6 2015 16.7 °C 14.3 °C 11.9 °C  Oct 6 2015 17.7 °C 12.8 °C 7.8 °C Sep 7 2015 20.3 °C 15.9 °C 11.5 °C  Oct 7 2015 15.4 °C 14.3 °C 13.1 °C Sep 8 2015 19.8 °C 16.8 °C 13.8 °C  Oct 8 2015 17.2 °C 15.2 °C 13.1 °C Sep 9 2015 22.7 °C 18.5 °C 14.3 °C  Oct 9 2015 18.8 °C 15.3 °C 11.8 °C Sep 10 2015 20.2 °C 16.2 °C 12.1 °C  Oct 10 2015 17.8 °C 15.8 °C 13.8 °C Sep 11 2015 20.1 °C 16.8 °C 13.5 °C  Oct 11 2015 15.6 °C 12.1 °C 8.6 °C Sep 12 2015 20.6 °C 17.7 °C 14.8 °C  Oct 12 2015 13.2 °C 12.2 °C 11.1 °C Sep 13 2015 17.6 °C 15.7 °C 13.8 °C  Oct 13 2015 14.5 °C 11.4 °C 8.2 °C Sep 14 2015 15.7 °C 12.1 °C 8.5 °C  Oct 14 2015 13.1 °C 9.7 °C 6.2 °C Sep 15 2015 17.1 °C 13.7 °C 10.3 °C  Oct 15 2015 14.6 °C 10.3 °C 6.0 °C Sep 16 2015 19.2 °C 14.6 °C 9.9 °C  Oct 16 2015 15.4 °C 10.1 °C 4.7 °C Sep 17 2015 18.1 °C 15.3 °C 12.4 °C  Oct 17 2015 16.5 °C 13.4 °C 10.2 °C Sep 18 2015 16.8 °C 14.6 °C 12.4 °C  Oct 18 2015 14.3 °C 13.4 °C 12.4 °C Sep 19 2015 17.1 °C 15.5 °C 13.9 °C  Oct 19 2015 14.5 °C 12.6 °C 10.6 °C Sep 20 2015 20.6 °C 16.7 °C 12.8 °C  Oct 20 2015 14.4 °C 11.6 °C 8.7 °C Sep 21 2015 15.9 °C 12.2 °C 8.5 °C  Oct 21 2015 13.8 °C 10.0 °C 6.2 °C Sep 22 2015 16.8 °C 12.0 °C 7.2 °C  Oct 22 2015 13.0 °C 10.0 °C 6.9 °C Sep 23 2015 18.2 °C 12.1 °C 6.0 °C  Oct 23 2015 12.5 °C 8.9 °C 5.2 °C Sep 24 2015 17.5 °C 14.2 °C 10.8 °C  Oct 24 2015 12.2 °C 8.2 °C 4.1 °C Sep 25 2015 15.6 °C 12.4 °C 9.2 °C  Oct 25 2015 13.5 °C 11.0 °C 8.4 °C Sep 26 2015 15.5 °C 11.4 °C 7.2 °C  Oct 26 2015 14.5 °C 11.8 °C 9.0 °C Sep 27 2015 14.9 °C 10.5 °C 6.1 °C  Oct 27 2015 14.0 °C 9.8 °C 5.5 °C Sep 28 2015 15.7 °C 10.8 °C 5.9 °C  Oct 28 2015 12.0 °C 10.6 °C 9.2 °C Sep 29 2015 16.6 °C 11.8 °C 7.0 °C  Oct 29 2015 15.4 °C 12.7 °C 10.0 °C Sep 30 2015 17.1 °C 12.1 °C 7.1 °C  Oct 30 2015 13.6 °C 11.9 °C 10.2 °C           Oct 31 2015 15.8 °C 12.5 °C 9.2 °C     200  Table 50. Environment Canada Temperature Records for November 1, 2015 through December 31, 2015 Date Maximum Mean Minimum   Date Maximum Mean Minimum Nov 1 2015 11.6 °C 9.4 °C 7.1 °C  Dec 1 2015 10.8 °C 8.0 °C 5.2 °C Nov 2 2015 10.8 °C 7.7 °C 4.6 °C  Dec 2 2015 11.0 °C 8.9 °C 6.8 °C Nov 3 2015 10.1 °C 6.2 °C 2.3 °C  Dec 3 2015 13.9 °C 10.5 °C 7.1 °C Nov 4 2015 8.7 °C 5.5 °C 2.2 °C  Dec 4 2015 10.4 °C 7.4 °C 4.4 °C Nov 5 2015 10.9 °C 7.8 °C 4.6 °C  Dec 5 2015 9.4 °C 8.2 °C 6.9 °C Nov 6 2015 10.6 °C 7.9 °C 5.2 °C  Dec 6 2015 10.7 °C 9.1 °C 7.4 °C Nov 7 2015 11.1 °C 9.4 °C 7.7 °C  Dec 7 2015 10.3 °C 9.4 °C 8.5 °C Nov 8 2015 10.3 °C 7.9 °C 5.4 °C  Dec 8 2015 13.8 °C 11.0 °C 8.1 °C Nov 9 2015 9.6 °C 6.1 °C 2.5 °C  Dec 9 2015 10.6 °C 8.6 °C 6.5 °C Nov 10 2015 8.9 °C 5.0 °C 1.0 °C  Dec 10 2015 10.8 °C 8.3 °C 5.8 °C Nov 11 2015 10.2 °C 7.1 °C 4.0 °C  Dec 11 2015 11.3 °C 7.0 °C 2.6 °C Nov 12 2015 9.2 °C 6.2 °C 3.2 °C  Dec 12 2015 8.7 °C 5.1 °C 1.5 °C Nov 13 2015 12.2 °C 10.1 °C 8.0 °C  Dec 13 2015 8.6 °C 7.3 °C 5.9 °C Nov 14 2015 8.9 °C 7.7 °C 6.4 °C  Dec 14 2015 6.7 °C 4.3 °C 1.8 °C Nov 15 2015 8.1 °C 7.0 °C 5.8 °C  Dec 15 2015 5.3 °C 3.6 °C 1.8 °C Nov 16 2015 6.9 °C 3.3 °C -0.3 °C  Dec 16 2015 5.3 °C 2.7 °C 0.1 °C Nov 17 2015 13.1 °C 9.3 °C 5.5 °C  Dec 17 2015 3.4 °C 2.1 °C 0.8 °C Nov 18 2015 7.5 °C 5.5 °C 3.4 °C  Dec 18 2015 8.5 °C 5.7 °C 2.9 °C Nov 19 2015 6.7 °C 2.7 °C -1.4 °C  Dec 19 2015 7.3 °C 5.4 °C 3.5 °C Nov 20 2015 6.1 °C 1.5 °C -3.1 °C  Dec 20 2015 7.6 °C 5.0 °C 2.3 °C Nov 21 2015 6.2 °C 1.4 °C -3.4 °C  Dec 21 2015 5.9 °C 4.0 °C 2.0 °C Nov 22 2015 6.9 °C 2.7 °C -1.6 °C  Dec 22 2015 4.9 °C 2.4 °C -0.1 °C Nov 23 2015 5.5 °C 4.3 °C 3.0 °C  Dec 23 2015 6.1 °C 3.6 °C 1.1 °C Nov 24 2015 8.6 °C 3.2 °C -2.2 °C  Dec 24 2015 5.0 °C 3.6 °C 2.1 °C Nov 25 2015 6.3 °C 1.2 °C -4.0 °C  Dec 25 2015 5.1 °C 2.2 °C -0.7 °C Nov 26 2015 6.4 °C 1.4 °C -3.7 °C  Dec 26 2015 3.8 °C 1.9 °C -0.1 °C Nov 27 2015 6.3 °C 1.2 °C -4.0 °C  Dec 27 2015 5.4 °C 3.3 °C 1.2 °C Nov 28 2015 5.2 °C 0.3 °C -4.7 °C  Dec 28 2015 4.6 °C 2.1 °C -0.4 °C Nov 29 2015 4.3 °C -0.1 °C -4.5 °C  Dec 29 2015 5.6 °C 1.9 °C -1.9 °C Nov 30 2015 7.5 °C 1.1 °C -5.3 °C  Dec 30 2015 2.6 °C -1.0 °C -4.5 °C           Dec 31 2015 2.7 °C -1.4 °C -5.4 °C      201  Appendix E: Delineation of the Lost Lagoon Wetland Watershed The ArcGIS online watershed area calculation tool was used for the calculation of the Lost Lagoon wetland watershed. Below is an illustration of the output.  Figure 107. Delineation of Lost Lagoon Wetland Watershed Using ArcGIS Online Tool (2016)    Area = 32,143 m2 202  Appendix F: Raw Measurements for the Lost Lagoon Wetland Field Study   Figure 108. Depth Profile Measurements Taken in the Lost Lagoon Wetland Forebay  1.12 m 1.45 m 1.40 m 0.79 m 1.08 m 1.28 m 1.50 m 1.52 m 1.42 m 1.50 m 1.14 m 0.60 m 1.14 m 1.52 m 1.55 m 1.39 m 1.49 m 1.49 m 1.52 m 1.49 m 1.50 m 1.42 m 0.60 m 0.78 m 0.32 m 1.04 m 0.77 m 1.39 m 0.75 m 1.38 m 0.75 m 203   Table 51. In Situ Recordings of Dissolved Oxygen, pH and Temperature in the Lost Lagoon Wetland Forebay at the Water Surface and Water Floor Oct 16 2015 Depth Site DO pH Temp   mg/L  Celsius Surface 1.2 0.94 5.2-5.3 12.5-12.7 Floor 1.2 0.85 Surface 1.1 1.04 Floor 1.1 0.98 Surface 1.3 0.77 Floor 1.3 0.19 Surface 2.1 1 Floor 2.1 0.85 Surface 2.2 0.8 Floor 2.2 0.67 Surface 2.3 0.43 Floor 2.3 0.34    204  Table 52. Temperature, DO, pH, Conductivity, ORP - Raw Data - Field Samples Date Site Temp DO pH Cond ORP 21-Jul 6.1 18.66 2.37 6.09 51.23529 -6.6 21-Jul 6.1 18.97 6.25 6.28 50.47059 23.6 21-Jul 6.1 20.17 9.02 6.38 271.4706 8.7 21-Jul 6.2 19.37 4.93 7.09  94 21-Jul 6.2 19.75 4.93 6.57  59.5 21-Jul 6.2 18.82 5.97 6.2 73.41176 10.9 21-Jul 6.3 20.36 8.81 6.42 52.76471 33 21-Jul 6.3 20.17 8.63 6.42 86.41176 11.1 21-Jul 6.3 20.53 9.92 6.49 61.17647 24.1 08-Sep 2.1 15.4  8.35 58.11765 219.5 08-Sep 2.1 15.32  7.89 57.35294 208.8 08-Sep 2.1 15.52  7.65 58.88235 202.4 08-Sep 4.1 15.44  7.17 58.11765 252.9 08-Sep 4.1 15.6  7.17 58.88235 206.5 08-Sep 4.1 15.61  7.13 58.88235 162.5 08-Sep 6.2 15.07  7.01 55.82353 214.9 08-Sep 6.2 16.08  7.07 35.17647 196.1 08-Sep 6.2 16.18  7.14 91.76471 186.6 22-Sep 5.1 15.3 4.5 6.66 58.11765 39 22-Sep 5.1 15 2.5 6.27 58.88235 20 22-Sep 5.1 15.4 1.5 6 61.17647 45 22-Sep 5.2 18.5 3.5 6.5 58.88235  22-Sep 5.2 17 3.5 6.68 58.88235  22-Sep 5.2 16.6 1 6.57 35.17647  22-Sep 5.3 16.95 2.4 6.5 60.41176 21 22-Sep 5.3 17 1.6 6.72 63.47059 32 22-Sep 5.3 16.5 1.2 6.64 52 38 06-Oct 2.3 12.71 0.13 5.81 247.2 447 06-Oct 2.3 12.61 0.15 5.47 171.7 421.6 06-Oct 2.3 12.62 0.16 5.41 164 404.1 06-Oct 3.3 14.78 4.94 5.66 201.5 557.6 06-Oct 3.3 14.97 2.34 5.6 190.1 500.7 06-Oct 3.3 14.96 1.75 5.55 178.3 481.8 06-Oct 4.3 15.62 1.52 5.29 216.9 622.4 06-Oct 4.3 15.63 0.27 4.95 217.8 576.1 06-Oct 4.3 15.55 1.14 5.63 324.8 613.6 20-Oct 2.1 13.29 1.88 5.55 160 335 20-Oct 2.1 13.23 2.8 5.52 98 218 20-Oct 2.1 13.35 1.3 5.4 170 171 20-Oct 3.1 13.68 1.7 5.35 180 320 20-Oct 3.1 13.78 1.7 5.4 179 314 20-Oct 3.1 13.8 2 5.4 179 313 20-Oct 4.1 15.55 1.6 5.3 190 313 20-Oct 4.1 15.92 2  188 335 20-Oct 4.1 15.94 2.1 5.4 189 321 20-Oct 6.1 18.46 5.4 5.4 24 228 20-Oct 6.1 17.57 5.4 5.4 15 219 20-Oct 6.1 17.86 5.8 6.6 23 232 20-Oct 6.2 19.57 6.6 5.7 24.8 198.6 20-Oct 6.2 18.37 6.7 5.65 19.6 136 20-Oct 6.2 18.41 5.9 5.9 16.98 146 20-Oct 6.3 17.84 7.02 5.92 21.53 199.5 20-Oct 6.3 18.3 7.81 6.03 29.45 215.3 20-Oct 6.3 18.14 7.79 6.17 20.05 230.5 11-Nov 2.2 9.71 5.59 4.23 111.2 202.1 11-Nov 2.3 9.75 5.5 4.4 112.2 182.5 11-Nov 3.1 9.62 5.7 4.03 109.9 148.3 11-Nov 3.2 9.72 5.61 3.15 134.5 195.5 11-Nov 3.3 9.73 5.44 2.8 155.2 177.5 11-Nov 4.2 9.76 5.61 3.65 119.5 216.2 11-Nov 5.1 8.65 5.25 2.78 132.5 171 11-Nov 5.2 8.48 5.32 6.79 133.7 114.3 11-Nov 5.3 8.37 5.31 2.75 144.5 121.8 16-Dec 1.1 8.47 10 5.15 375.5 90 16-Dec 2.1 7.81 1.84 5.87 235.7 297 16-Dec 2.2 7.3 4.53 5.69 169.7 340 16-Dec 2.3 7.66 3.35 6 200.3 249.5    205  Table 53. Turbidity - Raw Data - Field Samples Date Site Turbidity, NTU Date Site Turbidity, NTU 21-Jul 6.1 50.0 06-Oct Lab Blank 0.40 21-Jul 6.1 48.5 06-Oct Lab Blank 0.29 21-Jul 6.1 50.4 06-Oct Lab Blank 0.38 21-Jul 6.2 94.0 20-Oct 6.1 22 21-Jul 6.2 100.0 20-Oct 6.1 22.15 21-Jul 6.2 93.0 20-Oct 6.1 19.7 21-Jul 6.3 54.7 20-Oct 6.2 58.1 21-Jul 6.3 53.6 20-Oct 6.2 57.3 21-Jul 6.3 57.2 20-Oct 6.2 41.65 21-Jul Field Blank 0.4 20-Oct 6.3 11.75 21-Jul Field Blank 0.3 20-Oct 6.3 9.44 21-Jul Field Blank 0.4 20-Oct 6.3 10.945 08-Sep 2.1 143 20-Oct Lab Blank 0.645 08-Sep 2.1 161 20-Oct Lab Blank 0.47 08-Sep 2.1 147 11-Nov 2.2 15.2 08-Sep 4.1 91.7 11-Nov 2.2 14.8 08-Sep 4.1 104 11-Nov 2.2 14.7 08-Sep 4.1 89.9 11-Nov 2.3 15.3 08-Sep 6.2 144 11-Nov 2.3 13.7 08-Sep 6.2 155 11-Nov 2.3 13.2 08-Sep 6.2 155 11-Nov 3.1 13 08-Sep Field Blank 0.30 11-Nov 3.1 17.3 08-Sep Lab Blank 0.20 11-Nov Lab blank 0.12 08-Sep Trip Blank 0.12 11-Nov Lab blank 0.18 06-Oct 2.3 32.9 16-Dec 1 1.9 06-Oct 2.3 28 16-Dec 1 2.6 06-Oct 2.3 42 16-Dec 1 2.8 06-Oct 3.3 20.1 16-Dec 2.1 11.3 06-Oct 3.3 22.4 16-Dec 2.1 11.1 06-Oct 3.3 25.6 16-Dec 2.1 11.4 06-Oct 4.3 17.8 16-Dec 2.2 16.6 06-Oct 4.3 20.5 16-Dec 2.2 16.6 06-Oct 4.3 17.9 16-Dec 2.2 19.5 06-Oct Field Blank 0.16 16-Dec 2.3 12 06-Oct Field Blank 0.19 16-Dec 2.3 12.2 06-Oct Field Blank 0.13 16-Dec 2.3 11.7    206  Table 54. Chemical Oxygen Demand – Raw Data – Field Samples Date Site COD, mg/L Date Site COD, mg/L 21-Jul Field Blank 10 06-Oct 3.3 42 21-Jul Field Blank - 06-Oct 3.3 59 21-Jul Field Blank - 06-Oct 3.3 51 21-Jul Lab Blank 0 06-Oct 4.3 36 21-Jul Trip Blank 6 06-Oct 4.3 79 21-Jul 6.2 232 06-Oct 4.3 28 21-Jul 6.2 172 20-Oct 2.1 1160 21-Jul 6.2 144 20-Oct 2.1 1256 21-Jul 6.1 116 20-Oct 2.1 1176 21-Jul 6.1 144 20-Oct 3.1 167 21-Jul 6.1 - 20-Oct 3.1 189.5 21-Jul 6.3 115 20-Oct 3.1 212 21-Jul 6.3 129 20-Oct 4.1 131.5 21-Jul 6.3 - 20-Oct 4.1 107 08-Sep Field Blank 12 20-Oct 4.1 108 08-Sep Field Blank 18 20-Oct 6.1 21 08-Sep Field Blank 8 20-Oct 6.1 34 08-Sep Lab Blank 0 20-Oct 6.1 46 08-Sep Lab Blank - 20-Oct 6.2 102 08-Sep Lab Blank 4 20-Oct 6.2 72 08-Sep Trip Blank 10 20-Oct 6.2 55 08-Sep Trip Blank 12 20-Oct 6.3 32 08-Sep Trip Blank 4 20-Oct 6.3 24 08-Sep 2.1 382 20-Oct 6.3 35 08-Sep 2.1 324 20-Oct Lab Blank - 08-Sep 2.1 300 20-Oct Lab Blank - 08-Sep 4.1 459 11-Nov 3.1 29 08-Sep 4.1 433 11-Nov 3.1 26 08-Sep 4.1 435 11-Nov 3.1 - 08-Sep 6.2 277 11-Nov 4.2 18 08-Sep 6.2 344 11-Nov 4.2 36 08-Sep 6.2 351 11-Nov 4.2 - 06-Oct Field Blank 30 11-Nov 3.2 39 06-Oct Field Blank 43 11-Nov 3.2 46 06-Oct Field Blank 10 11-Nov 3.2 - 06-Oct Lab Blank 0 11-Nov 2.2 74 06-Oct Lab Blank - 11-Nov 2.2 95 06-Oct Lab Blank 4 11-Nov 2.2 - 06-Oct Trip Blank - 11-Nov 3.3 1 06-Oct Trip Blank - 11-Nov 3.3 1 06-Oct Trip Blank - 11-Nov 3.3 - 06-Oct 2.3 28 11-Nov 2.3 37 06-Oct 2.3 81 11-Nov 2.3 96 06-Oct 2.3 20 11-Nov 2.3 - 11-Nov 5.1 55 11-Nov Lab blank - 11-Nov 5.1 41 16-Dec 1 8 11-Nov 5.1 - 16-Dec 1 19 11-Nov 5.2 157 16-Dec 1 13 11-Nov 5.2 124 16-Dec 2.1 28 11-Nov 5.2 - 16-Dec 2.1 24 11-Nov 5.3 67 16-Dec 2.1 26 11-Nov 5.3 69 16-Dec 2.2 30 11-Nov 5.3 - 16-Dec 2.2 22 11-Nov Lab blank -7 16-Dec 2.2 39 11-Nov Lab blank - 16-Dec 2.3 936    16-Dec 2.3 925     207  Table 55. Total Suspended Solids - Raw Data - Field Samples Date Site TSS, mg/L Date Site TSS, mg/L 21-Jul Field Blank 0.0 06-Oct 3.3 41 21-Jul Field Blank - 06-Oct 3.3 33 21-Jul Field Blank - 06-Oct 3.3 52 21-Jul lab blank 0.0 06-Oct 4.3 41 21-Jul trip blank 0.0 06-Oct 4.3 33 21-Jul 6.2 66.0 06-Oct 4.3 45 21-Jul 6.2 67.0 20-Oct 2.1 1306 21-Jul 6.2  20-Oct 2.1 1359 21-Jul 6.1 10.0 20-Oct 2.1 - 21-Jul 6.1 - 20-Oct 3.1 327 21-Jul 6.1 31.0 20-Oct 3.1 366 21-Jul 6.3 37.0 20-Oct 3.1 - 21-Jul 6.3 40.0 20-Oct 4.1 157 21-Jul 6.3 42.0 20-Oct 4.1 111 08-Sep Field Blank - 20-Oct 4.1 - 08-Sep Field Blank - 20-Oct 6.1 37 08-Sep Field Blank 5 20-Oct 6.1 38 08-Sep Lab Blank - 20-Oct 6.1 - 08-Sep Lab Blank 1 20-Oct 6.2 97 08-Sep Lab Blank - 20-Oct 6.2 106 08-Sep Trip Blank 0 20-Oct 6.2 - 08-Sep Trip Blank 4 20-Oct 6.3 28 08-Sep Trip Blank 5 20-Oct 6.3 32 08-Sep 2.1 179 20-Oct 6.3 - 08-Sep 2.1 174 20-Oct Lab Blank 7 08-Sep 2.1 170 20-Oct Lab Blank 5 08-Sep 4.1 59 11-Nov 3.1 33 08-Sep 4.1 70 11-Nov 3.1 23 08-Sep 4.1 70 11-Nov 3.1 - 08-Sep 6.2 103 11-Nov 4.2 14 08-Sep 6.2 88 11-Nov 4.2 44 08-Sep 6.2 100 11-Nov 4.2 - 06-Oct Field Blank  11-Nov 3.2 25 06-Oct Field Blank  11-Nov 3.2 44 06-Oct Field Blank 3 11-Nov 3.2 - 06-Oct Lab Blank - 11-Nov 2.2 58 06-Oct Lab Blank - 11-Nov 2.2 63 06-Oct Lab Blank - 11-Nov 2.2 - 06-Oct Trip Blank - 11-Nov 3.3 53 06-Oct Trip Blank - 11-Nov 3.3 35 06-Oct Trip Blank - 11-Nov 3.3 - 06-Oct 2.3 98 11-Nov 2.3 34 06-Oct 2.3 115 11-Nov 2.3 38 06-Oct 2.3 109 11-Nov 2.3 - 11-Nov 5.1 21 16-Dec 1 13 11-Nov 5.1 17 16-Dec 1 18 11-Nov 5.1 - 16-Dec 1 - 11-Nov 5.2 34 16-Dec 2.1 12 11-Nov 5.2 37 16-Dec 2.1 18 11-Nov 5.2 - 16-Dec 2.1 - 11-Nov 5.3 50 16-Dec 2.2 38 11-Nov 5.3 59 16-Dec 2.2 25 11-Nov 5.3 - 16-Dec 2.2 - 11-Nov Lab blank 12 16-Dec 2.3 12 11-Nov Lab blank 11 16-Dec 2.3 6 11-Nov Lab blank - 16-Dec 2.3 -     208  Table 56. Total Organic Carbon - Raw Data - Field Date Site TSS, mg/L Date Site TSS, mg/L 21-Jul Field Blank  06-Oct 3.3 24.807 21-Jul Field Blank  06-Oct 3.3 22.6145 21-Jul Field Blank 0.10725 06-Oct 3.3 21.8105 21-Jul lab blank -0.00215 06-Oct 4.3 11.6075 21-Jul trip blank 6.856 06-Oct 4.3 9.913 21-Jul 6.2 28.916 06-Oct 4.3 9.401 21-Jul 6.2 24.202 20-Oct 2.1 43.26475 21-Jul 6.2 - 20-Oct 2.1 83.9025 21-Jul 6.1 29.178 20-Oct 2.1 - 21-Jul 6.1 24.128 20-Oct 3.1 186.997 21-Jul 6.1 - 20-Oct 3.1 182.698 21-Jul 6.3 29.034 20-Oct 3.1 179.5035 21-Jul 6.3 26.392 20-Oct 4.1 310.2305 21-Jul 6.3 - 20-Oct 4.1 307.4545 08-Sep Field Blank 1.9295 20-Oct 4.1 302.842 08-Sep Field Blank 2.2115 20-Oct 6.1 13.917 08-Sep Field Blank 2.175 20-Oct 6.1 12.955 08-Sep Lab Blank 0.4428 20-Oct 6.1 12.823 08-Sep Lab Blank 0.14175 20-Oct 6.2 148.074 08-Sep Lab Blank - 20-Oct 6.2 146.6555 08-Sep Trip Blank 1.3615 20-Oct 6.2 142.55 08-Sep Trip Blank 1.32 20-Oct 6.3 77.5135 08-Sep Trip Blank 1.125 20-Oct 6.3 77.843 08-Sep 2.1 25.5425 20-Oct 6.3 78.4525 08-Sep 2.1 26.5955 20-Oct Lab Blank 0.2432 08-Sep 2.1 28.1905 20-Oct Lab Blank 0.20775 08-Sep 4.1 82.574 11-Nov 3.1 70.9973 08-Sep 4.1 87.999 11-Nov 3.1 68.1439275 08-Sep 4.1 77.686 11-Nov 3.1 - 08-Sep 6.2 84.679 11-Nov 4.2 56.177737 08-Sep 6.2 79.916 11-Nov 4.2 65.0074645 08-Sep 6.2 81.5505 11-Nov 4.2 - 06-Oct Field Blank 26.5485 11-Nov 3.2 63.35835 06-Oct Field Blank 22.378 11-Nov 3.2 61.956378 06-Oct Field Blank 19.2495 11-Nov 3.2 - 06-Oct Lab Blank - 11-Nov 2.2 59.9747445 06-Oct Lab Blank - 11-Nov 2.2 60.850977 06-Oct Lab Blank - 11-Nov 2.2 - 06-Oct Trip Blank - 11-Nov 3.3 60.8105355 06-Oct Trip Blank - 11-Nov 3.3 61.174509 06-Oct Trip Blank - 11-Nov 3.3 - 06-Oct 2.3 28.792 11-Nov 2.3 59.4984335 06-Oct 2.3 24.4865 11-Nov 2.3 58.307656 06-Oct 2.3 21.214 11-Nov 2.3 - 11-Nov 5.1 60.275809 16-Dec 1 58.5098635 11-Nov 5.1 61.4755735 16-Dec 1 56.914671 11-Nov 5.1 - 16-Dec 1 - 11-Nov 5.2 60.2892895 16-Dec 2.1 57.840332 11-Nov 5.2 59.916329 16-Dec 2.1 59.017629 11-Nov 5.2 - 16-Dec 2.1 - 11-Nov 5.3 59.619758 16-Dec 2.2 59.520901 11-Nov 5.3 57.4583845 16-Dec 2.2 58.5188505 11-Nov 5.3 - 16-Dec 2.2 - 11-Nov Lab blank 0.536478965 16-Dec 2.3 327.1762285 11-Nov Lab blank 0.54721843 16-Dec 2.3 310.5952135 11-Nov Lab blank - 16-Dec 2.3 -    209  Table 57. Metals - Raw Data - Field Samples - Water Date Site As Ag Al B Ba Be Ca Cd Co Cr Cu Fe K Li Mg Mn Mo Na Ni Pb Sb Se Si Sr Ti Tl V Zn   (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) 2015-07-21 6.2  0.011 1.779 0.316 0.065 0.006 92.262 0.009 0.018 0.241 0.060 6.809 2.741 0.026 45.074 0.313 0.016 84.324 0.175 0.023 0.085  10.151 0.397 3.054  0.014 0.886 2015-07-21 6.2  0.010 2.349 0.449 0.069 0.004 131.814 0.009 0.007 0.024 0.264 5.846 2.882 0.026 48.191 0.270 0.017 88.307  0.017 0.004  10.481 0.436 0.067  0.015 2.603 2015-07-21 6.2                             2015-07-21 6.1  0.010 1.607 0.319 0.072 0.005 55.025 0.011 0.019 0.192 0.214 8.607 3.086 0.027 47.381 0.397 0.018 86.301 0.297 0.048 0.040  10.759 0.411 1.890  0.018 0.965 2015-07-21 6.1  0.010 1.630 0.323 0.069 0.004 111.492 0.009 0.007 0.066 0.071 6.062 2.939 0.026 46.823 0.295 0.017 86.864 0.004 0.006 0.002  10.129 0.427 0.059 0.012 0.015 0.646 2015-07-21 6.1                             2015-07-21 6.3  0.010 1.772 0.293 0.064 0.004 50.790 0.008 0.010 0.075 0.062 6.329 2.516 0.025 43.376 0.280 0.015 81.869 0.083 0.022 0.027  10.280 0.366 0.570 0.012 0.018 0.183 2015-07-21 6.3  0.010 2.332 0.348 0.068 0.004 99.471 0.008 0.005 0.033 0.128 6.022 2.525 0.025 45.163 0.261 0.016 83.932  0.009 0.012  10.417 0.402 0.074  0.013 1.498 2015-07-21 6.3                             2015-09-08 2.1   14.720 0.076 0.223  15.710 0.007 0.010 0.113 0.725 17.890 0.394 0.016 5.881 0.323  10.870 0.028 0.106 0.030  23.170 0.111 0.654  0.050 0.751 2015-09-08 2.1                             2015-09-08 2.1                             2015-09-08 4.1   5.310 0.073 0.091  9.494 0.004 0.005 0.035 0.308 7.526 0.284  2.600 0.166  6.777 0.012 0.037   9.706 0.063 0.220  0.024 0.255 2015-09-08 4.1                             2015-09-08 4.1                             2015-09-08 6.2   7.460 0.150 0.069  27.220 0.009 0.005  0.141 16.360 1.373 0.019 22.450 0.440   0.007    15.450 0.231 0.313  0.037 0.035 2015-09-08 6.2                             2015-09-08 6.2                             2015-10-06 2.3   0.104 0.056       0.144 7.322 0.020 0.050 0.269 0.209 0.010 182.921    0.179 15.414     0.077 2015-10-06 2.3                             2015-10-06 2.3                             2015-10-06 3.3   0.960 0.107 0.032  8.922 0.003 0.000  0.062 1.450 0.239  1.548 0.026  15.380 0.002    5.599 0.058 0.031  0.012 0.033 2015-10-06 3.3                             2015-10-06 3.3                             2015-10-06 4.3   0.869 0.083 0.027  8.327 0.003 0.001  0.057 1.340 0.230  1.432 0.020  20.320 0.001    5.267 0.052 0.024  0.011  2015-10-06 4.3                             2015-10-06 4.3                             2015-10-20 2.1   40.970 0.080 0.492  27.340 0.018 0.034 0.238 1.552 52.440 0.619 0.038 14.760 0.756 0.025 12.900 0.073 0.376 0.056  52.950 0.231 1.850  0.127 2.009 2015-10-20 2.1                             2015-10-20 2.1                             2015-10-20 3.1   8.321 0.059 0.125  14.150 0.007 0.006 0.047 0.339 11.190 0.412 0.011 4.152 0.209  10.070 0.012 0.053   15.250 0.095 0.384  0.034 0.360 2015-10-20 3.1                             2015-10-20 3.1                             2015-10-20 4.1   3.545 0.073 0.075  10.890 0.003 0.002 0.014 0.182 4.890 0.231  2.422 0.101  7.332 0.006 0.013   8.898 0.071 0.136  0.020 0.173 2015-10-20 4.1                             2015-10-20 4.1                             2015-10-20 6.1   0.985 0.210 0.043  36.160 0.004 0.001  0.090 2.333 2.136 0.013 37.290 0.178   0.003    4.306 0.351 0.014  0.019 0.027 2015-10-20 6.1                             2015-10-20 6.1                             2015-10-20 6.2   2.884 0.210 0.058  38.330 0.005 0.002  0.082 7.598 2.103 0.016 36.090 0.301   0.003    7.951 0.349 0.090  0.024 0.051 2015-10-20 6.2                             2015-10-20 6.2                             2015-10-20 6.3   0.801 0.200 0.043  36.070 0.004 0.000  0.041 1.593 2.077 0.013 36.580 0.143   0.002    4.089 0.346 0.014  0.019  2015-10-20 6.3                             2015-10-20 6.3                             2015-11-11 3.1   2.033 0.130 0.030  6.787 0.004 0.001  0.114 2.197 0.167  1.518 0.043  5.295 0.004    7.537 0.048 0.092  0.012 0.024 2015-11-11 3.1   1.939 0.081 0.028  6.754 0.003 0.001  0.101 2.053 0.158  1.468 0.040  5.148 0.003    7.030 0.047 0.066  0.010 0.022 2015-11-11 3.1                             2015-11-11 4.2   1.083 0.068 0.011  3.782 0.003 0.001  0.072 1.052 0.093  0.754   2.082 0.002    4.880 0.024 0.023   0.014 2015-11-11 4.2   1.219 0.042 0.016  4.383 0.004 0.003  0.089 1.260 0.096  0.872 0.016  2.243 0.002    5.616 0.028 0.031    2015-11-11 4.2                             2015-11-11 3.2   5.834 0.075 0.015  6.160 0.004 0.002  0.093 1.840 0.150  1.320 0.023  4.536 0.002    7.171 0.041 0.058  0.010 0.012 2015-11-11 3.2   1.986 0.053 0.012  5.281 0.003 0.001  0.080 1.671 0.134  1.147 0.014  3.740 0.001    6.176 0.035 0.049    2015-11-11 3.2                             2015-11-11 2.2   1.485 0.045 0.026  5.971 0.003 0.002  0.065 1.544 0.135  1.214 0.028  3.857 0.001    6.326 0.040 0.042   0.024 2015-11-11 2.2   1.661 0.108 0.029  6.846 0.004 0.002  0.085 1.741 0.146  1.350 0.036  4.521 0.001    7.173 0.046 0.051  0.010 0.020 2015-11-11 2.2                             2015-11-11 3.3   1.435 0.065 0.012  4.321 0.004 0.001  0.090 1.533 0.110  0.948 0.013  2.543 0.000    4.557 0.028 0.044   0.020 2015-11-11 3.3   1.386 0.042 0.011  4.281 0.003 0.000  0.081 1.457 0.110  0.937   2.527 0.000    4.608 0.027 0.044   0.014 2015-11-11 3.3                             2015-11-11 2.3   1.346 0.144 0.015  4.538 0.003 0.002  0.073 1.423 0.127  0.990 0.018  3.255 0.000    5.248 0.030 0.044    2015-11-11 2.3   1.470 0.088 0.020  5.129 0.003 0.000  0.075 1.506 0.134  1.087 0.028  3.652 0.002    6.061 0.034 0.046  0.010  2015-11-11 2.3                             2015-11-11 5.1   0.473 0.080   4.760 0.004 0.002  0.080 0.744 0.112  0.704 0.022  2.494 0.002    4.555 0.028     2015-11-11 5.1   0.714 0.045   4.600 0.004 0.001  0.066 0.658 0.106  0.646 0.016  2.284 0.002    4.161 0.025    0.017 2015-11-11 5.1                             2015-11-11 5.2   0.718 0.082   4.350 0.003 0.001  0.057 1.175 0.114  0.745 0.010  2.541 0.000    3.674 0.026    0.010 2015-11-11 5.2   0.806 0.062 0.010  4.594 0.003 0.000  0.056 1.368 0.116  0.803 0.012  2.623 0.001    3.936 0.029   0.010 0.018 2015-11-11 5.2                             2015-11-11 5.3   0.744 0.073 0.010  5.703 0.003 0.000  0.123 1.216 0.144  0.967   3.516 0.001    4.310 0.035    0.010 2015-11-11 5.3   0.723 0.043   5.407 0.004 0.001  0.100 1.184 0.137  0.937 0.015  3.319 0.001    4.271 0.033     2015-11-11 5.3                             2015-12-16 1.1   0.412 0.123   5.120 0.003 0.002  0.057 0.154 0.114  0.806   3.998 0.003    4.224 0.037     2015-12-16 1.1   0.712 0.098   3.380 0.003 0.001  0.048 0.198 0.095  0.539   2.479 0.000    2.668 0.023     2015-12-16 1.1   0.390 0.169   5.084 0.002 0.001  0.076 0.187 0.115  0.796   3.807 0.002    3.945 0.036     2015-12-16 2.1   0.391 0.070 0.012  5.366 0.003 0.002  0.093 0.282 0.134  0.800 0.012  8.377 0.001    3.771 0.036 0.116    2015-12-16 2.1   0.829 0.062 0.011  5.237 0.000 0.001  0.080 0.394 0.121  0.835 0.017  8.188 0.001    4.312 0.034     2015-12-16 2.1                             2015-12-16 2.2   0.510 0.057 0.014  6.160 0.003 0.000  0.054 0.284 0.130  0.944 0.030  8.029 0.002    4.502 0.041     2015-12-16 2.2   0.688 0.046 0.011  5.746 0.003 0.000  0.058 0.410 0.125  0.943 0.028  7.073 0.000    4.487 0.038     2015-12-16 2.2                             2015-12-16 2.3   0.436 0.064 0.011  5.641 0.003 0.006  0.055 1.153 0.132  0.871 0.017  7.695 0.002  0.185  5.114 0.037 2.840  0.010  2015-12-16 2.3   0.542 0.044 0.013  5.888 0.001 0.002  0.071 0.390 0.128  0.928 0.022  8.089 0.000    4.784 0.039     2015-12-16 2.3                                210  Table 58. Metals - Raw Data - Field Samples - Sediment Date Site mg/kg dry weight As Ag Al B Ba Ca Cd Co Cr Cu Fe K Li Mg Mn Mo Na Ni Pb Sb Si Sr Ti V Zn 21-Jul 6.2 Surface - 8.5 17,670.00 31 119.5 12,970.00 - 16.72 42.5 120.5 24,690.00 141.5 22.5 7,415.00 385.5 - 1,177.50 21.56 9 - 5,455.00 69.5 2,532.50 86 218 21-Jul 6.2 Surface - - - - - - - - - - - - - - - - - - - - - - - - - 21-Jul 6.2 Depth - - 14,290.00 25.5 31 13,040.00 5.57 15.55 42.5 93 21,445.00 119 19 7,220.00 340 - 1,216.00 19.41 - - 4,382.00 52 2,121.50 77.5 173.5 21-Jul 6.2 Depth 23.5 - 18,940.00 22.5 36 20,635.00 6.34 14.24 35 73.5 24,510.00 107.5 19.5 7,485.00 404 - 1,559.50 14.97 - - 4,329.50 81 2,034.00 96.5 155.5 08-Sep 2.1 Surface - - 25,180.00 99 136.5 - 6.93 - 74.62 224.17 29,066.02 221 40.49 10,104.30 480.84 - 2,414.50 6.6 33.51 10.27 690 - 3,014.16 - 305.2 08-Sep 2.1 Surface - - 15,795.00 73.5 96 - - - 35.15 185.17 19,420.45 163.5 34.46 4,922.02 365.49 - 1,392.00 4.22 25.25 10.65 693.5 - 1,570.76 - 262.14 08-Sep 2.1 Surface - - 20,955.00 95 128 - - - 69.84 259.43 33,321.79 217.5 41.81 8,993.04 438.81 - 1,612.50 6.45 39.74 17.56 906.5 - 2,463.90 - 306 08-Sep 2.1 Depth - - 21,710.00 97.5 56.5 - - - - 83.35 22,380.85 206 39.3 5,571.59 511.5 - - - - - 908 - 1,611.23 - 27.97 08-Sep 2.1 Depth - - 19,915.00 84 87 - 8.26 - 33.04 74.83 25,256.23 163.5 37.76 7,803.26 461.64 - 1,491.50 5.98 - - 802 - 2,470.66 - 74.22 08-Sep 2.1 Depth - - 18,680.00 88 66 - 5.68 - 45.56 84.88 24,530.92 151.5 38.49 7,180.33 482.88 - 1,670.50 4.56 - - 996 - 2,576.90 - 57.58 08-Sep 2.1 Surface - 9.5 11,440.00 27.5 87 16,780.00 - 16.22 49 262.5 16,900.00 183.5 17 5,410.00 301.5 - 1,279.50 28.84 34.5 - 8,645.00 42.5 1,366.50 58.5 676 08-Sep 2.1 Surface 8 - 21,835.00 48.5 148 29,900.00 5.32 23.76 101.5 408 24,075.00 238 21.5 7,015.00 361 5.5 1,613.50 45.6 72.5 14.5 14,025.00 78 2,195.00 77.5 863.5 08-Sep 2.1 Depth - 9 14,875.00 26.5 137 11,810.00 5.18 16.2 81 108.5 20,275.00 103 14.5 5,925.00 336.5 - 1,430.00 20.05 10.5 - 5,720.00 62 2,006.00 55.5 288 08-Sep 2.1 Depth 17.5 - 21,295.00 17.5 70.5 19,150.00 8.31 20 50.5 130 29,000.00 133 18.5 8,360.00 421 - 1,999.00 28.66 15.5 - 5,735.00 77.5 2,523.00 117.5 368.5 08-Sep 4.1 Surface 14 - 13,350.00 23 115 7,995.00 7.47 15.04 47 135.5 17,605.00 161.5 12 6,405.00 276 - 1,093.00 38.73 26.5 - 7,970.00 55 1,322.00 58.5 379.5 08-Sep 4.1 Surface 23.5 - 13,015.00 43.5 116.5 13,835.00 - 16.49 39 125 18,070.00 161 12.5 6,110.00 284 - 1,417.00 37.08 17 - 9,635.00 58 1,402.00 61 404 08-Sep 4.1 Depth 23.5 - 16,905.00 22 70 13,860.00 - 12.13 28.5 226 20,165.00 140 15 5,815.00 328.5 - 1,309.50 15.9 - - 6,360.00 56.5 1,943.00 65 308.5 08-Sep 4.1 Depth 14 - 16,905.00 18.5 70 11,905.00 - 16.38 73.5 83 21,135.00 135 16 6,935.00 368 - 1,275.00 29.63 - - 6,930.00 63.5 2,206.00 69 233 08-Sep 6.2 Surface 26 - 20,830.00 38.5 60.5 19,780.00 - 17.31 45 67 25,295.00 168 21 7,995.00 433.5 - 1,397.00 27.3 - - 5,540.00 70 2,788.50 80.5 145 08-Sep 6.2 Surface 11 - 18,055.00 26 43 17,565.00 6.15 17.87 37.5 71 23,900.00 148.5 24.5 7,520.00 395.5 - 1,473.00 20.28 - - 5,690.00 73 2,357.00 80.5 162.5 08-Sep 6.2 Depth 17 - 27,145.00 25 67.5 19,615.00 12.06 29.61 37 89.5 37,895.00 177.5 36 13,485.00 662 - 1,650.50 27.36 - - 5,860.00 88 4,022.00 138 202.5 08-Sep 6.2 Depth - - 24,565.00 27 63 18,395.00 11.71 24.84 34.5 84 33,715.00 163 29 11,455.00 609.5 - 1,575.00 26.32 - - 7,320.00 79.5 3,397.50 112.5 209.5 22-Sep 5.1 Surface 18 - 7,890.00 27.5 64.5 6,790.00 - 12.94 23 64 14,890.00 111.5 8.5 3,993.50 213 - 884 27.4 13 - 4,318.50 35.5 924.5 38 235.5 22-Sep 5.1 Surface 18 - 9,475.00 54 76.5 7,400.00 - 14.45 24 88 17,090.00 123.5 9.5 4,566.00 253 - 1,058.00 34.93 19.5 - 5,835.00 41.5 1,009.50 41.5 286 22-Sep 5.1 Depth 23 - 14,725.00 24 83.5 10,865.00 6.42 15.59 23 55.5 19,260.00 153 15.5 7,655.00 373.5 - 1,352.00 30.02 - - 4,882.50 53 1,698.50 61 228.5 22-Sep 5.1 Depth 32.5 - 12,875.00 35.5 60.5 13,685.00 - 15.04 18 93 19,010.00 112 15.5 6,750.00 377 - 1,160.50 23.75 - - 4,665.00 55 1,519.50 56.5 181 22-Sep 5.2 Surface 16.5 - 11,110.00 24.5 - 8,955.00 - 10.64 27 84.5 16,650.00 137 10 4,201.00 - - 1,162.50 24.06 - - 7,835.00 64 1,042.00 44 136.5 22-Sep 5.2 Surface 57.5 - 18,930.00 22 119 - - 11.64 38 99 18,270.00 153 12 4,976.50 414.5 - 1,960.50 19.93 - - - 102.5 1,186.00 54.5 156 22-Sep 5.2 Depth 62 - 16,985.00 10 82.5 11,285.00 - 15.25 27.5 64 23,970.00 243 20 6,975.00 393 - 1,318.50 18.71 - - 7,955.00 71 2,091.00 88.5 120.5 22-Sep 5.2 Depth 10 - 21,800.00 15.5 106 - - 15.25 37.5 81 24,240.00 246 19 7,295.00 399 - 1,125.50 16.55 - - 3,923.00 60.5 1,841.00 88 82 22-Sep 5.3 Surface 8.5 - 11,240.00 38 72 11,725.00 - 12.01 24.5 97 17,430.00 116.5 11.5 6,190.00 319 - 1,258.00 24.67 - - 6,005.00 48.5 1,459.00 49 167 22-Sep 5.3 Surface 17.5 - 11,765.00 26 69.5 - 6.27 12.05 - 84 17,560.00 117 11.5 5,160.00 304.5 - 955 24.63 - - 4,778.50 50 1,342.00 51.5 174.5 22-Sep 5.3 Depth 24 - 10,665.00 14 50 9,885.00 - 10.2 19 68 15,720.00 133.5 11.5 5,600.00 273 - 982 16.41 - - 3,421.50 44 1,374.00 50 56 22-Sep 5.3 Depth - - 10,645.00 14 50 - 5.75 9.66 25.5 98.5 15,900.00 135 12 5,665.00 274.5 - 1,033.00 19.13 - - 3,420.00 46.5 1,381.50 50.5 257.5 06-Oct 3.3 Surface - - 20,915.00 127.5 92 - - - 221.46 90.38 24,170.12 197 37.7 6,844.33 429.34 - 2,596.00 10.33 - 4.16 622.5 - 2,121.70 - 66.52 06-Oct 3.3 Surface - - 18,340.00 126.5 77.5 - - - 183.42 101.75 21,619.81 161 36.61 5,934.29 389.74 - 1,785.00 13.94 - 5.79 850 - 2,247.64 - 88.26 06-Oct 3.3 Surface - 9.5 16,735.00 98 92.5 - - - 190.43 121.03 21,330.53 180 35.81 5,474.86 365.12 - 1,583.00 9.98 6.2 11.7 756 - 2,100.79 - 126.3 06-Oct 3.3 Depth - - 23,475.00 95.5 61 - - - 30.33 67.69 20,449.75 141 35.29 4,860.95 317.38 - 2,066.00 4.45 - 5.88 927 - 1,868.59 - 52.53 06-Oct 3.3 Depth - - 17,125.00 79 58 - - - 67.62 64.73 21,881.17 151.5 38.31 6,614.01 384.57 - 1,613.00 4.77 - - 674 - 1,889.85 - 54.02 06-Oct 3.3 Depth - - 20,105.00 102.5 - - - - 44.03 75.69 24,140.04 169 36.87 6,670.85 411.66 - 2,135.00 3.96 - - 904.5 - 2,176.34 - 72.24 06-Oct Lab Blank Water - - 0.06 0.06 - - - - 0.07 0.09 0.55 0.01 0.05 0.01 0.01 - 0.49 0.04 - - 1.18 - - - 0.1 06-Oct Lab Blank Water - - 0.09 0.03 - - - - 0.03 0.08 0.49 0.01 0.05 0.03 - - 0.4 0.18 - - 1.17 - - - 0.02 06-Oct Lab Blank Water - - 0.03 0.06 - - - - 0.07 0.05 0.88 0.01 0.05 0.01 - - 0.48 0.23 - - 1.12 - 0.6 - - 06-Oct 2.3 Surface - - 15,905.00 13 76 13,040.00 5.58 15.27 52 235 21,955.00 129 18 7,665.00 361 - 1,212.50 15.54 18.5 - 4,610.50 57.5 2,198.00 69 191 06-Oct 2.3 Surface 14.5 - 17,055.00 17 66.5 12,645.00 8.61 15.32 48 160 25,720.00 114 20 8,330.00 397.5 - 1,210.50 15.48 11 - 3,250.00 57 2,357.50 79 164 06-Oct 2.3 Depth 17 - 18,010.00 22 67 15,000.00 7.59 13.47 34 91 22,095.00 125.5 20.5 7,785.00 341.5 - 1,249.00 12.1 - - 2,740.00 56 2,557.00 89.5 82.5 06-Oct 2.3 Depth - - 16,785.00 19 70.5 12,490.00 8.88 16.01 36.5 75.5 24,520.00 149.5 21.5 8,320.00 394 - 1,008.00 13.05 - - 3,873.00 55 2,686.00 77 97 06-Oct 3.3 Surface 21.5 - 15,820.00 14.5 54.5 11,710.00 6.47 13.88 37.5 111.5 23,030.00 121.5 18 8,270.00 386 - 1,074.50 17.79 - - 2,732.50 38.5 2,369.50 70.5 126.5 06-Oct 3.3 Surface - - 17,035.00 - 88.5 11,840.00 10.05 14.53 54.5 119.5 24,535.00 195 20.5 8,665.00 389.5 - 1,418.00 18.47 - - 3,818.50 57.5 2,181.50 75 118 06-Oct 3.3 Depth 9 - 13,425.00 15.5 46 11,190.00 7.05 11.9 21.5 87 19,335.00 - 15.5 - 336 - 1,026.50 14.02 - - 3,046.00 - 1,945.50 55.5 94 06-Oct 3.3 Depth 11 - 16,550.00 12.5 39.5 14,585.00 7.11 13.3 18 82.5 22,685.00 84 20 7,655.00 422.5 - 1,079.50 - - - 3,147.50 69.5 2,040.50 54 102 06-Oct 4.3 Surface - - 12,465.00 10 55 9,565.00 7.11 10.85 45.5 95 20,235.00 104 14 5,605.00 280 - 1,161.00 10.38 - - 2,914.00 45 1,934.50 71 96.5 06-Oct 4.3 Surface 7 - 20,030.00 15.5 133 15,565.00 10.54 17.29 45.5 131 29,390.00 141 21.5 8,980.00 498 - 1,227.00 16.49 10 - 2,877.50 69.5 2,982.00 92.5 157 06-Oct 4.3 Depth - 235.5 25,135.00 9 78.5 17,765.00 13.09 20.49 83.5 74 34,770.00 164 29 13,485.00 594.5 - 1,213.50 32.52 - - 2,656.50 56.5 3,117.50 110 103.5 06-Oct 4.3 Depth - - 12,830.00 12.5 52.5 - - 10.9 45 63.5 19,490.00 140 16.5 6,945.00 308.5 - 978.5 17.53 - - 1,534.50 33 1,711.00 56.5 62 20-Oct 2.1 Surface - - 16,290.00 91.5 109.5 - - - 72.42 103.64 20,787.04 178.5 34.27 6,583.38 389.41 - 1,511.00 7.47 - 8.89 810.5 - - - 103.33 20-Oct 2.1 Surface - - 21,595.00 90.5 86 - - - 57.6 97.75 26,678.58 179 38.23 8,275.44 528.4 - 2,242.00 13.03 8.04 - 829.5 - 2,266.05 - 112.76 20-Oct 2.1 Surface - - 21,965.00 128 83 - - - 77.08 115.49 26,678.92 181.5 35.57 7,786.50 556.96 - 1,856.50 71.29 17.93 7.1 - - 2,208.50 - 147.47 20-Oct 2.1 Depth - - 14,285.00 93.5 68 - - - - 90.01 24,849.56 135.5 35.84 6,737.06 554.73 6.6 990.5 271.05 5.17 5.24 758.5 - 1,740.22 - 68.53 20-Oct 2.1 Depth - - 13,675.00 126 69 - - 7.42 61.9 87.8 16,599.00 131 33.37 4,252.57 298.16 - 1,641.00 25.99 2.96 7.85 1,025.50 - 1,610.90 - 116.02 20-Oct 2.1 Depth - - 17,900.00 81 - - - - 112.98 102.02 24,923.04 328 38.59 7,024.91 442.85 - 1,648.50 61.96 8.77 6.82 984.5 - 2,577.53 - 139.15 20-Oct 2.1 Surface 7 - 19,450.00 19 123 10,525.00 9.01 15.54 52.5 250 26,570.00 159 19.5 9,695.00 441 - 1,386.00 27.6 50 5.5 9,855.00 58.5 2,039.00 78 391 20-Oct 2.1 Surface - - 20,470.00 16.5 104.5 13,820.00 10.05 18.42 37.5 193.5 28,050.00 196 20 10,455.00 501.5 - 1,654.50 25.01 15 - 2,260.50 46 2,648.00 81.5 169 20-Oct 2.1 Depth 22.5 - 13,305.00 14 53.5 10,000.00 - 12.16 50.5 109.5 18,835.00 112 15.5 6,325.00 348.5 - 1,069.00 12.3 10.5 16.5 3,322.50 54.5 1,853.00 62.5 166 20-Oct 2.1 Depth 20 - 17,350.00 10 - 11,630.00 6.83 16.51 41 169 23,800.00 184 21.5 8,350.00 388 - 1,353.50 20.82 21 - 4,542.00 50.5 2,254.00 78 277.5 20-Oct 3.1 Surface 11 - 65.5 14 - 648.5 - - - 23 65 30 - 87 - - 622.5 0.76 - - 434.5 - - - 78 20-Oct 3.1 Surface 17 - 71 12 - 280.5 - - - 26 75 29.5 - 61.5 - - 482 3.9 - - 578.5 - 8.5 - 73 20-Oct 3.1 Depth 8.5 - 5,800.00 28.5 57.5 5,465.00 - 7.71 24.5 169.5 8,055.00 70 - 2,695.00 131 - 836 12.91 14.5 - 6,280.00 31.5 499 21.5 311.5 20-Oct 3.1 Depth 16.5 - 3,721.00 40 52 5,295.00 - - 27.5 138 4,547.00 61 - 1,231.00 70 - 838.5 17.71 13.5 - 5,675.00 28.5 238.5 11.5 292 20-Oct 4.1 Surface - - 19,475.00 27.5 - 14,080.00 10.39 18.42 34 140.5 26,925.00 163.5 24.5 8,910.00 402.5 - 1,090.00 25.76 13.5 - 6,215.00 54.5 2,050.50 75 241 20-Oct 4.1 Surface 55 - 14,065.00 26.5 68 11,450.00 - 15.13 67 122.5 18,690.00 112.5 15 6,810.00 317 - 984 21.68 14.5 - 4,253.00 35 1,866.50 73 167.5 20-Oct 4.1 Depth 10 63.5 10,440.00 32.5 148.5 8,460.00 - 11.96 43 149.5 13,505.00 126.5 11.5 4,815.00 209 - 935.5 32.96 20 - 7,430.00 46 1,173.50 48 227 20-Oct 4.1 Depth 23 - 12,735.00 27.5 138.5 10,405.00 - 13.61 56.5 206.5 15,275.00 155 13 6,455.00 257 - 1,067.00 36.65 30 - 8,670.00 52.5 1,139.00 48.5 390.5 20-Oct 6.1 Surface 15.5 - 10,825.00 33.5 103.5 8,415.00 5.4 11.86 31.5 56.5 14,110.00 143.5 11.5 4,674.50 249.5 - 859 33.22 - - 7,915.00 48.5 1,152.00 52 201.5 20-Oct 6.1 Surface - - 11,710.00 21 97 8,510.00 - 11.7 28.5 48 14,895.00 137.5 12 5,510.00 258 - 991.5 26.22 - - 7,520.00 56 1,368.00 52.5 168.5 20-Oct 6.1 Depth 21 - 13,460.00 22 53.5 9,345.00 6.48 11.38 17 37 20,360.00 143 25 6,440.00 374 - 944 12.53 - - 1,998.00 53 1,731.50 59.5 57 20-Oct 6.1 Depth 23.5 - 15,825.00 19.5 62.5 11,755.00 10.57 18.11 29 44.5 27,805.00 205 28 8,470.00 520 - 1,086.00 16.29 - - 3,681.00 50 2,754.50 80.5 100.5 20-Oct 6.2 Surface 12.5 - 23,890.00 25 43.5 8,675.00 11.03 21.04 27 38 30,970.00 200.5 64 14,880.00 556.5 - 887.5 20.41 - - 5,000.00 52 2,165.00 102.5 69.5 20-Oct 6.2 Surface 34 - 8,075.00 21.5 18 5,725.00 - 7.14 12 23.5 12,005.00 107 21.5 4,555.50 194.5 - 685 11.19 - - 3,954.50 18 1,068.50 38 25 20-Oct 6.2 Depth 116.5 - 13,105.00 36 27.5 8,990.00 - 10.08 68.5 49 19,390.00 116.5 16 7,455.00 351 - 929 22.49 - - 3,618.50 32 1,402.00 55.5 74 20-Oct 6.2 Depth 24 - 78 21 - 1,741.50 - - - 17.5 65 30 - 69.5 - - 516.5 2.74 - - 400.5 - - - 56 20-Oct 6.3 Surface - - 29,000.00 34 51 14,150.00 - 10.5 39.5 126 34,240.00 122.5 66 13,370.00 502 - 1,996.50 17.5 8.5 - 4,153.00 85 1,941.00 98 80.5 20-Oct 6.3 Surface - 7 22,840.00 31.5 58.5 13,550.00 - - 20 129.5 24,995.00 179 62 8,080.00 378.5 - 1,428.50 6 13 - - 60.5 1,817.00 83.5 91.5 20-Oct 6.3 Depth - - 24,450.00 27.5 80.5 19,200.00 - 10.5 45.5 135.5 33,850.00 181.5 62 11,035.00 650 - - 18.5 16 - 6,560.00 95.5 2,849.00 101 217 20-Oct 6.3 Depth - 7.5 17,730.00 34.5 66.5 9,765.00 - - 40.5 126.5 22,150.00 - 50 6,895.00 347 - 1,738.00 8.5 32.5 - 7,040.00 66 1,972.00 65 103 11-Nov 2.1 Surface - - 23,315.00 29.5 - 14,010.00 - 10 35 115.5 32,675.00 180 52.5 10,280.00 527 - 1,498.50 10.5 33.5 - 7,810.00 85 2,693.00 98 98.5 11-Nov 2.1 Surface - 7.5 19,500.00 - 64.5 10,395.00 - 7.5 112.5 682 23,860.00 174.5 45 6,440.00 350.5 11.5 1,443.50 24 127 21 4,351.50 75 1,571.00 63.5 790.5 11-Nov 3.2 Surface - 7.5 - 26.5 191.5 9,390.00 - - 109.5 658.5 22,925.00 189 43.5 6,280.00 312 10.5 1,817.50 22.5 127 17 10,885.00 84.5 1,193.50 58.5 750 11-Nov 3.2 Surface - - 20,140.00 22 207 12,740.00 - 12 173 1,065.50 32,280.00 213.5 48.5 8,515.00 477 21.5 2,788.00 38 183.5 40 19,725.00 98 2,295.50 91 1,220.50 11-Nov 4.2 Surface - - 25,595.00 23 266 10,310.00 - 6.5 124 827 25,795.00 257 44.5 6,950.00 371.5 15.5 1,962.50 26.5 142.5 28 7,825.00 - 1,741.00 69 973 11-Nov 4.2 Surface - 7.5 20,325.00 18 214 12,515.00 - - 108.5 669 23,220.00 220.5 - 6,600.00 341.5 10.5 2,059.50 22.5 101 13 11,555.00 79 1,657.00 63 809 11-Nov 2.2 Surface - 7.5 19,665.00 24 177 5,820.00 - - 58.5 399.5 12,775.00 192 43.5 3,391.50 179 - 2,188.50 8.5 59 - 12,160.00 79 972 31.5 - 11-Nov 2.2 Surface - 10.5 9,885.00 25 97 10,605.00 6.38 - 113.5 750 24,575.00 115.5 40.5 6,665.00 371 14.5 1,495.00 26 138 20.5 7,265.00 46 1,467.50 - 443 11-Nov 3.3 Surface - 7.5 20,500.00 32 238.5 11,545.00 - 5.5 112 710.5 24,770.00 239.5 44.5 6,815.00 377 11 2,948.00 26 140 18 18,215.00 90 - 63.5 883 11-Nov 3.3 Surface - 7 21,645.00 22.5 243.5 10,585.00 - - 92 547.5 21,310.00 239 45 5,475.00 307 8.5 3,050.50 20.5 95.5 6 20,535.00 99 1,465.50 63 921.5 11-Nov 2.3 Surface - 8 16,645.00 28.5 190 - - - - - - 179 41.5 - - - 2,570.50 - - - 16,940.00 89.5 1,164.00 49.5 737.5 11-Nov 2.3 Surface - 13 - 12.5 - 9,505.00 - 5.5 87 621.5 20,200.00 25 32 5,435.00 297 8.5 - - 96 12 - - - - - 11-Nov 5.1 Surface - 8.5 15,995.00 32 - - - - - - - 168 41.5 - 294.5 - 2,283.50 26 - 13.5 12,930.00 78 1,388.00 52 - 11-Nov 5.1 Surface - 8.5 18,165.00 42 - 3,524.50 - - 8 77.5 5,485.00 189.5 41.5 719.5 - 9 2,890.50 20 13.5 - - 91 1,171.50 49.5 208.5 11-Nov 5.2 Surface - 12 2,672.00 45.5 43 487.5 - - - 8 41.5 51.5 33 - 86 - 1,118.00 - - - 3,788.50 26 93 6 208.5 11-Nov 5.2 Surface - 13.5 - 12 - - - - 31 152 - 25.5 32 47.5 - - 641 - - - - - - - - 11-Nov 5.3 Surface - 9 8,925.00 15 123 6,825.00 - - 27 120.5 - 92.5 35 2,484.00 - - 1,033.50 17.5 - - 6,610.00 62.5 456 33 - 11-Nov 5.3 Surface - 10 7,625.00 29 98 8,080.00 - - 20.5 - 13,325.00 82 34.5 1,890.00 224 - 1,394.50 13.5 - - 8,380.00 50 184.5 19.5 300.5 16-Dec 2.1 Surface - 9 9,880.00 25 132 7,145.00 - - 10.5 112 17,590.00 87 35 2,386.00 270 - 1,753.00 20 134 - 10,320.00 63 321 27.5 320.5 16-Dec 2.1 Surface - 9.5 8,115.00 20.5 115.5 9,960.00 - - - 98 16,245.00 71.5 34.5 1,854.50 242 - 1,430.50 16 121 - 7,800.00 57.5 290.5 24 295.5 16-Dec 2.2 Surface - 7.5 14,265.00 18 60.5 - - - 50.5 65 22,455.00 119.5 40 5,070.00 323 - 1,675.00 - 8 - - 71 1,534.00 53.5 - 16-Dec 2.2 Surface - 9 8,575.00 12 - 6,710.00 - - 25 39 15,790.00 88 36 2,926.50 196.5 - 1,266.50 - - - 3,851.00 38 741.5 24.5 100.5 16-Dec 2.3 Surface - 9 14,040.00 15.5 - 7,525.00 - - 67.5 390 17,375.00 161 40.5 4,717.00 245.5 - 1,828.50 13 72.5 16 10,430.00 58 1,185.00 41 492 16-Dec 2.3 Surface - 6 23,745.00 21 245 12,640.00 - 9 112.5 639.5 28,825.00 255.5 48 8,055.00 430 10.5 2,496.50 29.5 119.5 21 14,440.00 95 1,892.00 76.5 759.5   211  Appendix G: Raw Measurements for the Laboratory Column Test Table 59. Determination of the Water Content in Beaver Lake Bog Soil Dec 6 2015 Sample Dish Mi Mf Change Change % Water Content  g g g g % g/g 1 0.9957 14.839 3.3151 10.5282 71% 0.71 2 0.9896 15.829 3.5311 11.3083 71% 0.71 3 1.0012 15.6708 3.4155 11.2541 72% 0.72 Average - - - - 71.4% 0.714 St Dev - - - - 0.4% 0.004  Table 60. Raw Data Recorded for the 2-Week Preliminary Column Study Nov 8, 2015 30 cm sediment, 30 cm water  Depth Surface Time DO pH Temp Cond ORP DO pH Temp Cond ORP Zero 7.64 7.14 16.75 64.12 309.1 8.37 6.5 16.67 53.75 322.7 36 hrs 5.19 6.15 19.2 58.13 384.7 4.69 5.71 19.21 54.97 390.2 96 hrs 5.8 5.49 20.14 66.24 373.5 5.45 5.31 20.31 57.06 378.9 192 hrs 3.99 5.44 20.36 94.33 376.6 5.35 5.48 20.45 49.95 372.6  Table 61. Mass of Soil Added to Each Column for the Column Study Nov 30, 2015 Column Mass (kg) 1 8.18 2 8.16 3 8.36 4 8.6 5 8.46 6 8.82 7 8.36 8 8.9 9 9.24 10 9.84 11 8.38 12 7.6 13 8.46 14 8.56 15 8.82 16 9.08 Average 8.61375 St Dev 0.49597  212  Table 62. Temperature, DO, pH, Conductivity, ORP - Raw Data – Column Log Date Temp DO pH ORP Cond Temp DO pH ORP Cond Distilled Water Column Stormwater Column 01-Dec 17.46 2.3 6.12 328.1 34.31 17.36 2.85 5.7 345.6 60.55 02-Dec 17.64 4.93 4.62 303.3 36.75 16.68 3.76 4.72 295.8 53.15 06-Dec 17.61 4.7 4.75 330.6 48.25 17.71 1.44 4.54 270 61.99 09-Dec 17.81 5.61 4.9 303.4 55.74 17.39 3.4 4.99 312.4 79.52 13-Dec 17.21 3.11 4.91 292.9 67.55 17.61 1.1 4.92 358.4 77.26 16-Dec 17.81 5 4.86 310.1 75.34 17.14 4.98 4.92 350.2 81.22 19-Dec 17.16 1.96 4.96 332.1 74.37 17.59 2.61 5.39 281.1 89.74 23-Dec 17.1 2.57 4.88 285.7 70.04 17.46 1.75 5.24 288.6 90.15 26-Dec 17.58 0.43 5.14 323.7 82.82 17.75 0.31 5.09 299.15 83.58 30-Dec 17.38 0.63 5.26 289.5 86.94 17.63 0.53 5.18 287.3 93.75 02-Jan 17.56 0.41 5.14 285.5 91.66 17.45 0.12 5.4 278.3 88.26 04-Jan 17.62 0.41 5.15 278.9 88.62 17.13 0.57 5.34 284.2 94.3 06-Jan 16.99 1.5 5.38 385.2 81.29 16.85 0.86 4.95 237.7 81.72 08-Jan 16.17 0.54 4 228 100.2 15.84 1.66 4.42 233.2 103.8 11-Jan 16.16 1.33 4.38 210.7 78.1 15.59 0.72 4.23 223 106.3 13-Jan 16.23 0.78 4.5 215.1 89.3 16.01 0.99 4.7 215 103.5 15-Jan 15.94 1.3 4.23 225.6 78.03 15.98 0.93 4.36 260.2 100.3 18-Jan 16.03 0.93 3.68 256.1 128.1 16.03 0.93 3.68 256.1 128.1 20-Jan 16.55 0.57 3.97 223.3 90.74 15.37 1.64 3.77 226.9 111.8 22-Jan 16 0.18 4.11 192.5 118.4 16.01 0.44 4.07 207.9 123.3 25-Jan 16 1.12 4.52 158.4 112.4 16 0.91 4.31 179.4 112.1 27-Jan 16.01 1.17 4.27 203.4 101.9 15.78 1.17 4.27 182.3 111.5 29-Jan 15.35 0.27 4.98 255.1 111.4 15.76 0.2 4.7 234.4 93.1 01-Feb 15.21 0.45 5.03 231.5 111.9 15.61 0.4335 4.5 229 97 03-Feb 16.01 1.09 4.9 226.1 112.9 14.74 1.87 4.62 245.3 103 04-Feb 13.1 0.29 4.89 231.1 111.5 13.1 0.67 4.67 240.5 104.1 05-Feb 11.72 1.77 4.76 269.8 92.74 11.6 0.71 5 244.5 78.94 08-Feb 11.08 1.23 4.82 209.7 110.1 11.1 0.54 4.98 201.4 87.67 09-Feb 11.07 0.28 5.31 186.2 98.7 10.7 0.21 5.07 191.4 90.36 10-Feb 11 1.3 5.1 184.2 98.4 10.79 0.12 5.05 190.17 90.91 11-Feb           12-Feb           15-Feb      11.58 0.9 5.11 311.7 109.3 16-Feb 11.48 0.4 4.88 256.6 127 11.42 0.44 5.13 288.5 98.8 17-Feb 11.41 0.35 4.85 254.3 132.5 9.65 0.76 5.44 301.4 99.9 18-Feb 11.39 0.33 4.85 250.1 134.9 9.78 0.36 5.39 295.7 109.1 19-Feb 10.41 0.34 5.3 270.1 110.8 10.12 0.76 5.4 290.1 110.1 21-Feb 10.35 0.21 5.28 256.2 113 11.1 0.75 5.32 293.2 101.1 23-Feb 10.38 0.81 5.28 261.2 109.7 9.8 0.3 5.4 290.3 144.1 24-Feb 10.3 0.86 5.42 260.6 90.81 10.48 1.02 5.24 282.1 96.99 25-Feb 10.41 0.64 5.16 308.1 100.8 9.78 0.95 5.42 257.2 75.83 26-Feb      10.37 1.96 5.58 352.3 73.93 28-Feb 7.69 1.06 5.45 366 80.38 6.83 0.23 5.23 334.7 71.47 01-Mar 10.51 0.58 5.43 311.8 77.32 10.12 2.84 5.45  66.99 02-Mar 10.64 0.55 5.39 307.5 82.21 9.79 0.57 5.31 341.8 75.88 03-Mar 10.44 0.21 5.5 299.2 74.25 10.47 0.14 5.52 317.1 55.84 04-Mar 6.58 1.26 5.4 322 82.76 6.7 3.23 5.8 372.1 51.97 06-Mar 7.58 2 5.33 331.1 71.04 5.16 0.62 5.63 258.1 45.83 08-Mar 6.92 0.51 5.75 285.2 76.12 5.06 0.24 5.61 252.1 50.62 09-Mar 5.91 0.57 5.67 324.1 55.8 5.08 0.5 5.83 324.1 47.12 10-Mar 6.55 2.32 5.9 345 62.79 5.99 1.9 5.6 339 43.12 11-Mar 6.44 3.13 5.49 334.7 59.49 5.53 1.87 5.73 298.1 45 13-Mar 6.45 1.24 5.6 321 58.13 5.51 1.1 5.69 295.1 46.1 15-Mar 6.43 1.16 5.48 321.1 59.5 5.49 0.97 5.69 281.1 47.1 16-Mar 6.41 1 5.5 320 59.01 5.98 0.95 5.7 282.1 47.1 17-Mar 5.9 0.7 5.39 325.5 60.31 6.1 0.87 5.69 281.1 48.5 18-Mar 4.58 2.66 5.92 331.4 54.37 5.58 3.04 5.89 319.1 51.95 20-Mar 6.47 3.34 5.95 297.8 67.6 6.6 4.1 5.95 245.5 47.54 22-Mar 6.13 3.16 5.83 257 61.16 5.74 2.03 5.85 269.9 46.85 23-Mar 5.94 1.31 5.76 245.3 53.92 5.99 2.26 5.99 263.1 45.63 24-Mar 6.63 1.76 5.83 233.4 50.07 5.93 1.09 5.81 244.3 40.91 25-Mar 6.66 1.94 5.82 233 50.37 6.13 1.14 5.83 225.2 43.48 28-Mar 5.88 0.86 5.87 267.1 67 6.02 0.6 5.9 279.1 51.52    213  Table 63. Temperature, DO, pH, Conductivity, ORP - Raw Data – Column Log Column Date Temp DO pH ORP Cond Distilled Water Columns 1 Dec 4 2015 17.75 0.99 4.52 312.9 83.2 2 Jan 3 2016 17.62 0.41 5.15 278.9 88.62 3 Feb 3 2016 15.21 0.63 5.49 85.11 25.11 4 Mar 3 2016 10.4 0.67 5.68 37.25 331 5 April 1 2016 5.82 0.98 6.23 31.47 266.5 Stormwater Columns 7 Dec 4 2015 17.66 2.16 4.51 330.7 86.43 8 Dec 4 2015 17.63 1.11 4.48 322.4 92.27 9 Jan 3 2016 17.13 0.86 4.95 284.3 94.3 10 Jan 3 2016 17.01 0.97 4.46 297.4 92.1 11 Feb 3 2016 14.91 0.97 5.01 230.1 82.1 12 Feb 3 2016 14.50 0.64 4.93 235.1 83.2 13 Mar 3 2016 10.5 1.69 5.93 345.1 50.59 14 Mar 3 2016 10.41 1.31 5.84 345.7 33.93 15 April 1 2016 6.21 0.40 6.23 263.1 31.01 16 April 1 2016 6.44 1.31 6.23 266.5 31.17   214  Table 64. Turbidity, TSS, COD, TOC - Raw Data - Column Study Date Site Type Turbidity TSS COD TOC NTU mg/L mg/L mg/L Dec 4 2015 Column 1 Water 338.00 576.00 670.00 74.03 Dec 4 2015 Column 1 Water 353.00 506.67 633.00 77.58 Dec 4 2015 Column 1 Water 349.00 546.67 626.00 - Jan 4 2015 Column 2 Water 192.33 347.00 642.50 69.92 Jan 4 2015 Column 2 Water 238.33 221.00 690.50 78.09 Jan 4 2015 Column 2 Water - - - - Feb 4 2016 Column 3 Water 40.00 73.00 167.00 33.77 Feb 4 2016 Column 3 Water 50.67 80.00 174.00 28.00 Mar 4 2016 Column 3 Water - - - - Mar 4 2016 Column 4 Water 43.67 81.00 250.50 69.41 Mar 4 2016 Column 4 Water 32.67 83.00 242.50 98.22 April 3 2016 Column 4 Water 29.67 - - 100.66 April 3 2016 Column 5 Water 72.83 41.00 169.33 41.05 April 3 2016 Column 5 Water 84.17 127.00 171.00 41.64 April 3 2016 Column 5 Water - - - - Dec 4 2015 Column 7 Water 66.70 63.33 273.67 93.03 Dec 4 2015 Column 7 Water 65.90 73.33 193.00 95.49 Dec 4 2015 Column 7 Water 65.30 113.33 177.67 - Dec 4 2015 Column 8 Water 48.10 136.67 149.67 92.92 Dec 4 2015 Column 8 Water 47.80 136.67 148.33 95.92 Dec 4 2015 Column 8 Water 47.30 113.33 152.33 - Jan 4 2015 Column 9 Water 132.33 97.00 464.50 95.31 Jan 4 2015 Column 9 Water 129.33 133.00 447.00 97.99 Jan 4 2015 Column 9 Water - 211.00 - - Jan 4 2015 Column 10 Water 57.33 28.00 203.50 82.18 Jan 4 2015 Column 10 Water 15.33 - 252.00 90.36 Jan 4 2015 Column 10 Water - - - - Feb 4 2016 Column 11 Water 106.67 148.00 277.00 36.54 Feb 4 2016 Column 11 Water 111.00 84.00 268.00 37.42 Feb 4 2016 Column 11 Water - - - - Feb 4 2016 Column 12 Water 118.33 43.00 272.00 33.67 Feb 4 2016 Column 12 Water 124.67 73.00 286.00 34.88 Feb 4 2016 Column 12 Water - - - - Mar 4 2016 Column 13 Water 55.33 46.00 220.00 40.80 Mar 4 2016 Column 13 Water 51.33 33.00 224.50 65.65 Mar 4 2016 Column 13 Water 34.67 - - - Mar 4 2016 Column 14 Water 89.00 29.00 673.50 103.46 Mar 4 2016 Column 14 Water 87.33 78.00 566.00 129.50 Mar 4 2016 Column 14 Water 60.33 - - - April 3 2016 Column 15 Water 35.73 41.00 114.00 20.59 April 3 2016 Column 15 Water 36.93 39.00 108.67 22.00 April 3 2016 Column 15 Water - - - - April 3 2016 Column 16 Water 37.60 44.00 207.67 45.50 April 3 2016 Column 16 Water 39.40 94.00 210.00 44.99 April 3 2016 Column 16 Water - - - - Dec 4 2015 Stormwater Week 1 Water 39.50 169.00 - 54.70 Dec 4 2015 Stormwater Week 1 Water 53.00 162.00 - 54.96 Dec 4 2015 Stormwater Week 1 Water 50.20 201.00 - - Jan 4 2015 Stormwater Week 4 Water 22.33 64.00 178.50 84.16 Jan 4 2015 Stormwater Week 4 Water 20.67 70.00 195.50 84.32 Jan 4 2015 Stormwater Week 4 Water - - - - Feb 4 2016 Stormwater Week 8 Water 25.33 83.00 108.00 5.57 Feb 4 2016 Stormwater Week 8 Water 28.33 85.00 119.00 5.45 Feb 4 2016 Stormwater Week 8 Water - - - - Mar 4 2016 Stormwater Week 12 Water 6.00 406.00 134.50 48.75 Mar 4 2016 Stormwater Week 12 Water 6.00 188.00 143.50 59.87 Mar 4 2016 Stormwater Week 12 Water 4.00 - - - April 3 2016 Stormwater Week 16 Water 91.57 267.00 234.67 3.18 April 3 2016 Stormwater Week 16 Water 92.57 1,491.00 191.00 3.05 April 3 2016 Stormwater Week 16 Water - - - - Dec 4 2015 Blank Week 1 Water 0.19 - - 0.58 Dec 4 2015 Blank Week 1 Water 1.64 1.00 - 0.56 Dec 4 2015 Blank Week 1 Water 1.58 2.00 - - Jan 4 2015 Blank Week 4 Water 0.24 - 3.00 0.56 Jan 4 2015 Blank Week 4 Water 0.13 - 10.00 0.55 Jan 4 2015 Blank Week 4 Water - - - - Feb 4 2016 Blank Week 8 Water 1.30 1.00 11.00 0.92 Feb 4 2016 Blank Week 8 Water 1.00 - 2.00 1.10 Feb 4 2016 Blank Week 8 Water - - - - Mar 4 2016 Blank Week 12 Water - - - 1.04 Mar 4 2016 Blank Week 12 Water - 3.00 - 0.95 Mar 4 2016 Blank Week 12 Water - - - - April 3 2016 Blank Week 16 Water 0.14 - - 0.53 April 3 2016 Blank Week 16 Water 0.13 2.00 - 0.81 April 3 2016 Blank Week 16 Water        215  Table 65. Metals - Raw Data - Column Study - Water Column As Al B Ba Ca Cd Co Cr Cu Fe K Mg Mn Mo Na Ni Pb Sb Se Si Sr Ti V Zn  (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) Column 1  0.96 0.08 0.05     0.07 1.34 0.12  0.11   0.02 0.15 0.01 0.03  0.02  0.01  Column 1  0.91 0.08 0.05     0.17  0.15 1.02 0.16 0.10 8.79 0.13 0.10 0.02 0.05 3.39 0.04 0.04 0.01 0.14 Column 2  0.89 0.07 0.05 5.75      0.15 1.02 0.16  8.58 0.11 0.08 0.02 0.05 3.25 0.04 0.04 0.01 0.14 Column 2  0.88 0.07 0.05 5.67    0.19 2.09 0.14 1.01 0.15 0.09 8.52 0.11 0.07 0.02 0.04  0.03 0.04 0.01 0.14 Column 3  0.78 0.07 0.05 5.39   0.04  2.09 0.14 1.01 0.15 0.09 8.29 0.10 0.07 0.02 0.04 3.25 0.03 0.03 0.01 0.13 Column 3  0.76 0.07 0.05 11.75   0.05 0.14 1.96 0.13 0.97 0.14 0.09 8.15 0.09 0.06 0.02 0.04  0.03 0.03 0.01 0.13 Column 4 0.03 0.72 0.07 0.05 10.47   0.12 0.17 1.74 0.13 0.95 0.14 0.08 7.68 0.07 0.04 0.02 0.04 3.24 0.03 0.03 0.01 0.12 Column 4  0.71 0.07 0.05 8.76   0.17  1.72 0.13 0.93 0.14  7.52 0.07 0.04 0.02 0.04 3.17 0.03 0.03 0.01 0.12 Column 4 0.01 0.57 0.06 0.04 10.22    0.17 1.63 0.13 0.92 0.13  7.28 0.07 0.03 0.02 0.04 3.05 0.03 0.01 0.01 0.11 Column 5  0.52 0.06 0.03 11.33   0.02 0.14 1.56 0.13 0.89 0.13 0.08 7.27 0.06 0.02 0.01 0.04 3.02 0.02 0.01 0.01 0.10 Column 5  0.46 0.05 0.03 10.48   0.05 0.13 1.40 0.13 0.65 0.12 0.08 7.21 0.03 0.02 0.01 0.04 2.81 0.02 0.01 0.01 0.10 Column 7  1.04 0.08 0.06 4.85   0.04  2.58 0.15 1.06 0.17 0.08 13.41 0.14 0.10 0.02 0.05 2.30 0.04 0.04 0.01 0.14 Column 7  1.13 0.08 0.06 5.40   0.05 0.45 2.59 0.16 1.12 0.17 0.07 14.87 0.17 0.11 0.02 0.05 2.03 0.04 0.04 0.01 0.18 Column 8 0.02 1.20 0.09 0.06 3.67  0.01 0.06 0.47 2.63 0.16 1.13 0.18 0.02 14.93 0.20 0.11 0.02 0.05 3.46 0.04 0.04 0.01 0.20 Column 8 0.06 1.21 0.10 0.06 3.48  0.02  0.47 2.75 0.17 1.17 0.19 0.02 16.31 0.21 0.12 0.02 0.05 3.46 0.04 0.04 0.01 0.22 Column 9 0.01 1.28 0.10 0.06 15.44   0.07 0.42 2.81 0.18 1.17 0.21 0.10 16.54 0.22 0.12 0.02 0.05 3.57 0.04 0.05 0.02 0.23 Column 9  1.31 0.10 0.06 14.43   0.07  2.88 0.18 1.20 0.21 0.10 17.04 0.25 0.14 0.02 0.05 3.80 0.05 0.05 0.02 0.25 Column 10  1.35 0.11 0.07 9.90     2.92 0.18 1.20 0.22 0.10 17.81 0.27 0.14 0.02 0.05 3.80 0.05 0.05 0.02 0.25 Column 10  1.59 0.12 0.07 12.36  0.01 0.12 0.48 2.96 0.22 1.23 0.23 0.11 18.88  0.16 0.03 0.05 3.81 0.05 0.06 0.02 0.26 Column 11  1.93 0.12 0.08 7.77    0.51 3.57 0.25 1.27 0.24 0.12 19.90  0.17 0.03 0.05 3.91 0.06 0.06 0.02 0.26 Column 11  2.00 0.12 0.08 8.25   0.12 0.55  0.26 1.27 0.24 0.12 20.16 0.28 0.18 0.03 0.06  0.06 0.07 0.02 0.26 Column 12 0.02 2.05 0.13 0.08 5.64  0.01 0.18 0.55  0.26 1.29 0.28  20.57 0.29 0.18 0.03 0.06 3.98 0.07 0.07 0.02 0.27 Column 12  2.06 0.13 0.09 6.69  0.01 0.22 0.56 3.57 0.26 1.31 0.30 0.12 21.15  0.18 0.03 0.06 4.10 0.07 0.08 0.02 0.28 Column 13  2.06 0.14 0.09 4.90   0.12 0.60 3.85 0.27 1.32 0.30  21.87 0.40 0.18 0.03 0.06 4.57 0.07 0.09 0.02 0.29 Column 13 0.02 2.07 0.17 0.09 4.47   0.12 0.61 3.87 0.27 1.34 0.31 0.13 22.20  0.23 0.03 0.06 4.63 0.07 0.09 0.02 0.30 Column 13  2.14 0.21 0.09 5.30  0.04  0.63 4.06 0.28 1.42 0.31 0.15 22.82 0.43 0.23 0.03 0.06 4.74 0.07 0.09 0.02 0.36 Column 14 0.04 2.16 0.21 0.09 7.76  0.01  0.64 5.91 0.28 1.56 0.32  24.06  0.24 0.04 0.06 4.83 0.08 0.09 0.02 0.38 Column 14  2.24 0.22 0.10 8.06   0.18 0.65 6.01 0.29 1.63 0.37 0.16 24.10 0.53 0.25 0.04 0.07 4.88 0.08 0.09 0.02 0.40 Column 14 0.02 2.32 0.25 0.10 8.29   0.22 0.70 6.46 0.29 1.71 0.40 0.23 24.20  0.25 0.04 0.07 4.90 0.09 0.10 0.02 0.40 Column 15  2.57 0.25 0.10 5.92 0.01 0.01 0.25 0.70 6.52 0.31 1.76 0.41 0.24 25.66 0.66 0.26  0.07 5.25 0.09 0.10 0.03 0.43 Column 15 0.08 2.61 0.25 0.11 5.72   0.28 0.70 7.06 0.36 1.76 0.41 0.26 25.87 0.46   0.07 5.32 0.09 0.11 0.03 0.44 Column 16 0.11 2.86 0.29 0.12 5.68   0.29 0.75 8.90 0.37 1.80 0.46 0.27 27.81 0.56   0.07 5.35 0.11 0.55 0.03 0.44 Column 16 0.06 2.87 0.33 0.12 5.68   0.30 0.78 10.04 0.38 2.05 0.46 0.27  0.43   0.07 5.84 0.11  0.03 0.49 Stormwater Week 1  4.57 0.27 0.18 3.44 0.01 0.03  0.77 12.16 0.07 1.32 0.35 0.40 18.25 1.07 0.29 0.05 0.04 8.04 0.03 0.22 0.02 0.82 Stormwater Week 1  4.04 0.17 0.20 3.16 0.01 0.03  0.82 13.19 0.06 1.18 0.34 0.42 16.98 1.20 0.28 0.07 0.05 7.67 0.02 0.18 0.03 0.88 Stormwater Week 4  2.12 0.14 0.10 2.64  0.03  0.56 10.91 0.05 0.74 0.48 0.56 15.13  0.32 0.06 0.05 4.86 0.02 0.11 0.02 0.72 Stormwater Week 4  1.36 0.21 0.10 2.49  0.02  0.48 4.71 0.04 0.52 0.23 0.52 13.23 0.90 0.33 0.05 0.05 4.09 0.01 0.07 0.01 0.74 Stormwater Week 8 0.03 2.25 0.05 0.12 2.36  0.02 0.22 0.51 3.16 0.05 0.77 0.11 0.32 14.02 0.41 0.21 0.05 0.05 4.40 0.01 0.11 0.01 1.01 Stormwater Week 8 0.04 3.52 0.07 0.14 3.98  0.02 0.11 0.59 3.86 0.06 1.17 0.21 0.38 20.24 0.22 0.25 0.04 0.05 7.20 0.02 0.18 0.02 1.11 Stormwater Week 12  7.82 0.05 0.24 4.06  0.01 0.29 0.91 8.36 0.10 2.24 0.14 0.67 15.99 0.25 0.85 0.06 0.04 14.01 0.04 0.38 0.03 1.02 Stormwater Week 12  9.34 0.05 0.28 5.00  0.01 0.28 1.11 9.85 0.12 2.79 0.18 0.75 10.98 0.36 0.99 0.06 0.07 16.55 0.04 0.43 0.03 1.14 Stormwater Week 16 0.04 4.03 0.10 0.21 3.64 0.01  0.13 0.71 4.37 0.06 1.27 0.39 0.54 5.24 0.15 0.67 0.05 0.05 7.72 0.02 0.19 0.02 0.93 Stormwater Week 16 0.09  0.12   0.02 0.02 0.53   0.28  0.37   0.22  0.11 0.05  0.11 1.24 0.07  Blank Week 1  0.11   0.05     2.19  0.02 0.10 0.01     0.05 1.03   0.01 0.04 Blank Week 1  0.09 0.10   0.01    3.40  0.02 0.14 0.01 1.08  0.02  0.05 0.90   0.01 0.04 Blank Week 4   0.10      0.11   0.03 0.09 0.01 1.26  0.01 0.02 0.05 1.09   0.01 0.04 Blank Week 4 0.01 0.13   0.13    0.11 2.48  0.03 0.17 0.01   0.01 0.01 0.08 1.10  0.01 0.01 0.04 Blank Week 8  0.12 0.09  0.20  0.02     0.09 0.07 0.03 1.52  0.05 0.02 0.05 1.22   0.02 0.07 Blank Week 8 0.01 0.09 0.08  0.14   0.02  0.43 0.02 0.08 0.02   0.03 0.05  0.06     0.04 Blank Week 12 0.02 0.06 0.07    0.01  0.06   0.01 0.08 0.01 0.71  0.02 0.01 0.06 0.55   0.01 0.02 Blank Week 12 0.01 0.07 0.08     0.03 0.03 0.17   0.02  0.60 0.08   0.06 0.63    0.02 Blank Week 16  0.05 0.04  0.54   0.07 0.04 0.39  0.12   0.84 0.03  0.01 0.04 0.52    0.04 Blank Week 16  0.06 0.04  0.72   0.14 0.05 0.58  0.15 0.01  1.05 0.08  0.01 0.06 0.70    0.05               216  Table 66. Metals – Raw Data - Column Study - Sediment Date Column Sediment Al B Ba Ca Cd Co Cr Cu Fe K Mg Mn Mo Na Ni Pb Sb Zn    (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) Dec 4 2015 Column 1 Surface 1525.62 29.74 - 14590.41 - - - - 2932.54 23.95 886.21 228.22 9.08 - 34.57 - 7.31 151.05 Dec 4 2015 Column 1 Surface - 29.68 88.67 14299.08 - - - - 2864.73 23.37 885.27 - 8.98 2680.83 28.24 - 7.15 166.81 Dec 4 2015 Column 1 Surface 1525.62 27.47 90.37 13325.11 - - - - 2745.01 22.65 884.75 218.48 8.63 2504.64 22.77 - 6.48 - Jan 4 2016 Column 2 Surface - 27.40 84.42 13222.67 - - - - 2727.06 21.48 914.01 211.59 8.41 2485.42 24.18 - 6.57 - Jan 4 2016 Column 2 Surface 1437.94 26.28 85.09 12623.69 - - - - - 20.32 852.33 - 8.13 2483.48 25.80 - 7.05 142.55 Jan 4 2016 Column 2 Surface 1465.42 - 85.05 11702.02 - - - - 2636.59 21.37 850.52 194.98 8.07 2362.90 26.93 - 6.48 143.28 Feb 4 2016 Column 3 Surface 1343.70 - - 11661.47 - - - - 2717.72 19.12 676.61 190.73 8.01 2083.92 - - - - Feb 4 2016 Column 3 Surface 1285.75 23.42 77.35 11482.28 - - - - 2597.35 19.84 814.80 208.91 7.97 2249.87 - - 6.34 140.13 Feb 4 2016 Column 3 Surface 1209.56 24.64 75.52 11256.94 - - - - 2589.79 18.66 737.67 186.52 7.92 2027.08 27.47 - 6.36 136.80 Mar 4 2016 Column 4 Surface 1177.52 22.92 74.54 10875.86 - - - - 2596.36 18.45 736.13 188.08 7.87 2055.73 - - 6.05 128.40 Mar 4 2016 Column 4 Surface 1174.63 - 73.89 9722.96 - - - - 2552.69 18.55 699.34 172.57 7.68 - - - 6.15 120.76 Mar 4 2016 Column 4 Surface - - 73.89 7393.53 - - - - 2581.85 - 838.04 176.59 7.01 2020.08 22.01 - - 115.20 April 3 2016 Column 5 Surface 1050.22 22.71 68.77 5644.48 - - - - 2446.49 18.05 657.20 158.37 6.92 1908.03 18.62 - 5.83 109.51 April 3 2016 Column 5 Surface 1068.21 22.73 68.48 5040.16 - - - - 2026.57 17.64 587.10 156.94 6.66 1811.39 - - - 107.11 April 3 2016 Column 5 Surface - - - - - - - - 2439.99 18.65 - 168.44 - - - - - - Dec 4 2015 Column 7 Surface 1628.57 41.40 96.94 15328.71 - - - - - 25.08 902.52 230.01 10.33 2916.05 34.88 120.57 7.37 150.94 Dec 4 2015 Column 7 Surface 1635.84 38.40 - 15414.99 - - 20.00 - - 25.02 861.50 253.50 11.45 3038.51 29.14 117.38 7.59 - Dec 4 2015 Column 8 Surface 1646.69 41.40 97.69 15520.93 - - 20.89 - - 25.14 959.89 255.97 11.71 3130.09 40.32 103.04 7.72 168.85 Dec 4 2015 Column 8 Surface 1685.12 43.77 - 15676.16 - - 17.42 - - 26.67 1016.23 222.53 12.05 4021.57 - 98.56 7.90 170.36 Jan 4 2016 Column 9 Surface 1881.78 50.37 - 17966.27 - - 28.24 365.15 - 25.79 999.79 266.29 - 3172.10 42.99 158.78 10.28 170.30 Jan 4 2016 Column 9 Surface 1829.04 49.95 99.35 17073.15 - - 25.71 366.09 3027.24 27.78 1024.05 277.49 12.96 3695.47 - - 9.97 199.66 Jan 4 2016 Column 10 Surface 1841.17 49.27 97.86 16794.02 - - 23.86 374.11 3109.95 30.31 1053.04 278.90 13.01 - 47.81 179.46 9.53 181.42 Jan 4 2016 Column 10 Surface - 48.48 - 16483.31 - - - 379.35 2956.01 29.88 1035.02 276.64 12.50 3512.95 51.07 175.27 9.32 225.84 Feb 4 2016 Column 11 Surface 1903.22 52.40 106.39 21546.60 - 5.24 38.37 401.18 3333.85 29.86 1084.48 280.07 13.69 3407.08 60.74 172.90 8.98 224.26 Feb 4 2016 Column 11 Surface 1991.74 53.96 - 21554.99 - - 42.30 406.44 3250.65 30.36 1117.57 280.71 13.18 3580.71 66.54 175.39 7.90 226.43 Feb 4 2016 Column 12 Surface 2039.44 58.10 117.56 22840.07 - 5.14 40.63 415.07 3401.45 30.75 1167.95 288.74 15.02 3812.54 54.23 181.09 10.57 244.25 Feb 4 2016 Column 12 Surface - 64.51 - 23296.70 - - 39.33 425.91 3439.57 31.55 1124.76 - - - 54.86 189.25 10.72 250.02 Mar 4 2016 Column 13 Surface 2596.64 68.51 - 23780.81 - 5.30 71.16 427.70 3622.37 31.01 - 316.20 19.54 4600.69 62.51 182.55 11.31 318.02 Mar 4 2016 Column 13 Surface 2366.85 71.64 121.18 24041.01 - 5.39 64.35 443.40 3582.91 33.92 1260.08 - 19.70 3338.50 64.51 197.31 11.72 306.55 Mar 4 2016 Column 14 Surface 2457.86 80.76 118.17 25040.65 - 6.13 66.50 445.91 3581.56 42.19 1297.79 - - 5160.07 61.72 - 12.87 323.88 Mar 4 2016 Column 14 Surface 2326.62 81.03 118.55 27966.34 - - 55.14 - - 37.26 1221.63 302.30 23.83 4721.10 81.67 191.93 15.03 323.97 April 3 2016 Column 15 Surface - 91.09 150.59 29033.97 - 9.89 96.09 508.04 4488.20 35.57 1307.80 - - 5561.68 78.61 - 18.14 - April 3 2016 Column 15 Surface 3375.88 91.16 - 31383.00 - 9.54 86.27 525.60 4044.50 48.56 - 345.46 36.21 5731.93 - 203.67 21.29 350.51 April 3 2016 Column 16 Surface 3492.38 104.75 144.31 37912.45 - 10.85 103.87 476.74 4004.22 49.11 1335.00 - 36.64 - 86.01 215.66 25.73 347.20 April 3 2016 Column 16 Surface 3221.18 109.84 - 38138.42 6.04 - 73.87 496.54 4760.73 - - 351.95 - - 75.59 - 27.28 - Dec 4 2015 Column 1 Depth 76.74 31283.32 - 29.85 2886.52 32.63 1182.59 223.90 - 4469.80 - 14.42 - 167.37 76.74 31283.32 - 29.85 Dec 4 2015 Column 1 Depth 84.12 9723.96 29.88 30.08 2616.00 28.19 861.27 231.85 6.44 4077.63 - 13.35 - 154.86 84.12 9723.96 29.88 30.08 Dec 4 2015 Column 1 Depth 56.48 9537.64 15.54 30.08 2605.59 28.95 856.29 243.18 - - - 13.46 5.44 121.86 56.48 9537.64 15.54 30.08 Dec 4 2015 Column 7 Depth 84.52 11237.87 16.71 31.93 2664.30 52.08 1012.19 203.56 8.98 3800.08 25.81 13.84 8.06 173.50 84.52 11237.87 16.71 31.93 Dec 4 2015 Column 7 Depth 85.99 19133.53 50.64 32.22 2924.00 36.88 1123.22 253.45 8.21 4079.25 27.26 - 9.12 178.23 85.99 19133.53 50.64 32.22 Dec 4 2015 Column 8 Depth 87.77 11885.02 35.09 32.99 2853.95 31.21 930.30 290.34 7.52 3891.88 26.00 - 8.17 161.36 87.77 11885.02 35.09 32.99 Dec 4 2015 Column 8 Depth 90.26 19253.47 - 33.56 2741.65 33.02 1076.51 305.57 6.65 - 32.00 16.83 7.95 189.16 90.26 19253.47 - 33.56 Jan 4 2016 Column 2 Depth 65.41 15159.82 - 34.18 3372.13 39.29 1229.41 209.29 - 4735.34 - - - - 65.41 15159.82 - 34.18 Jan 4 2016 Column 2 Depth 70.90 12687.10 30.41 34.34 3064.78 34.72 1093.03 200.08 - 4523.90 - 20.99 5.56 - 70.90 12687.10 30.41 34.34 Jan 4 2016 Column 2 Depth 70.24 26653.80 - - 3735.21 45.35 1447.49 208.33 6.93 - - 19.04 - 100.94 70.24 26653.80 - - Jan 4 2016 Column 9 Depth 91.16 19781.10 47.52 204.77 2650.28 29.97 968.22 189.83 9.36 3071.60 39.75 - 9.98 205.17 91.16 19781.10 47.52 204.77 Jan 4 2016 Column 9 Depth 92.13 15873.45 78.37 205.28 3101.98 34.59 1115.48 264.87 9.83 2786.38 47.06 148.08 11.00 231.44 92.13 15873.45 78.37 205.28 Jan 4 2016 Column 10 Depth 92.49 30073.50 61.07 210.16 3036.34 30.18 1202.91 228.57 7.87 2662.73 32.61 - 7.86 211.81 92.49 30073.50 61.07 210.16 Jan 4 2016 Column 10 Depth 93.80 8946.96 - - 2741.56 29.87 930.86 229.00 7.94 2610.63 27.19 197.09 10.17 214.07 93.80 8946.96 - - Feb 4 2016 Column 3 Depth - 15605.74 - - 3882.58 39.97 1012.65 165.28 - 2440.04 - 15.99 5.81 - - 15605.74 - - Feb 4 2016 Column 3 Depth 68.27 17003.28 17.28 38.22 2173.38 29.20 842.79 161.58 7.61 2845.20 - 17.39 - 106.76 68.27 17003.28 17.28 38.22 Feb 4 2016 Column 3 Depth 72.43 25651.09 36.95 - 2385.57 35.12 993.52 146.12 - - - 18.38 - - 72.43 25651.09 36.95 - Feb 4 2016 Column 11 Depth 94.43 10163.88 66.91 348.27 2103.55 21.56 597.96 152.69 8.25 2368.84 55.45 148.08 7.06 191.69 94.43 10163.88 66.91 348.27 Feb 4 2016 Column 11 Depth 96.34 21657.76 - 363.16 2820.72 30.61 1020.27 188.34 11.15 2848.89 59.60 230.74 9.51 263.52 96.34 21657.76 - 363.16 Feb 4 2016 Column 12 Depth 99.37 24755.68 - 374.56 2909.65 29.24 1178.99 231.63 17.74 - 58.36 171.84 8.22 233.89 99.37 24755.68 - 374.56 Feb 4 2016 Column 12 Depth 100.23 28784.73 65.59 380.11 2135.98 25.41 979.79 163.39 12.13 2709.21 52.35 - 8.64 - 100.23 28784.73 65.59 380.11 Mar 4 2016 Column 4 Depth 72.52 13749.12 - - 2908.03 28.20 857.94 179.26 - 1735.02 - - 5.66 115.43 72.52 13749.12 - - Mar 4 2016 Column 4 Depth 73.07 10596.40 28.63 42.41 2418.43 25.55 682.21 145.23 8.88 1482.77 - 12.83 4.78 134.95 73.07 10596.40 28.63 42.41 Mar 4 2016 Column 4 Depth 75.25 16010.01 - - 2436.11 27.31 772.57 158.08 - 1777.67 - 14.25 - 113.12 75.25 16010.01 - - Mar 4 2016 Column 13 Depth 104.30 7927.01 52.68 - 3579.86 25.97 811.93 191.42 13.11 1742.78 58.50 180.10 11.37 226.52 104.30 7927.01 52.68 - Mar 4 2016 Column 13 Depth - 13392.54 57.38 440.07 2760.56 25.03 817.05 180.16 10.53 1665.96 76.81 - 11.30 205.17 - 13392.54 57.38 440.07 Mar 4 2016 Column 14 Depth 107.52 11349.75 104.61 440.25 5191.54 26.11 807.29 216.55 11.45 1531.56 61.62 169.35 9.88 223.35 107.52 11349.75 104.61 440.25 Mar 4 2016 Column 14 Depth 75.46 9602.39 123.26 446.26 3353.59 20.61 750.49 187.39 12.66 1412.92 56.85 180.93 - 223.77 75.46 9602.39 123.26 446.26 April 3 2016 Column 5 Depth - 8288.39 - - 3175.62 22.49 633.81 165.01 - 1753.51 - 14.65 5.99 158.31 - 8288.39 - - April 3 2016 Column 5 Depth 70.24 14576.27 30.53 - 2261.22 24.49 749.78 159.93 7.88 1329.04 - - - - 70.24 14576.27 30.53 - April 3 2016 Column 5 Depth - 6064.44 - - 1870.92 19.81 578.97 139.76 5.38 1210.18 4.53 14.42 - - - 6064.44 - - April 3 2016 Column 15 Depth - 12179.55 249.06 454.56 2052.15 22.56 665.16 141.68 - 1121.18 74.35 171.85 17.00 289.55 - 12179.55 249.06 454.56 April 3 2016 Column 15 Depth 108.21 6715.62 - 487.31 7096.38 19.25 588.96 279.62 21.81 1451.15 67.71 180.14 10.59 261.12 108.21 6715.62 - 487.31 April 3 2016 Column 16 Depth 110.52 15609.73 262.29 497.15 8616.85 26.34 771.92 299.29 25.46 1834.52 97.05 222.25 10.56 293.51 110.52 15609.73 262.29 497.15 April 3 2016 Column 16 Depth - 11408.66 - 552.65 - 26.04 552.99 366.18 - 1622.96 97.20 195.77 15.53 329.71 - 11408.66 - 552.65   217  Appendix H: Alpha Diversity Field Study Water Samples  Figure 109. Barplot Between Field Site Water Samples for Richness Based on the Chao1 Estimator  Figure 110. Boxplot Between Field Site Water Samples for Richness Based on the Chao1 Estimator  Figure 111. ANOVA Residuals Between Field Site Water Samples for Richness Based on the Chao1 Estimator 218  Table 67. One Way ANOVA Test Result for Richness Comparison of Water Samples by Field Site Based on Chao1 Estimator  Df Sum Sq Mean Sq F value Pr(>F) Length 5 9484682.454 1896936.491 4.225 0.008 Residuals 21 9429328.86 449015.66 NA NA  Table 68. Tukey HSD Test Result for Diversity Comparison of Water Samples by Field Site Based on Chao1 Estimator Comparison p-value Site2-Site1 0.821324 Site3-Site1 0.904872 Site4-Site1 0.70785 Site5-Site1 0.355519 Site6-Site1 0.999981 Site3-Site2 1 Site4-Site2 0.994339 Site5-Site2 0.561553 Site6-Site2 0.089305 Site4-Site3 0.997624 Site5-Site3 0.849941 Site6-Site3 0.498426 Site5-Site4 0.969094 Site6-Site4 0.128793 Site6-Site5 0.004016   Figure 112. Barplot Between Field Site Water Samples for Coverage Based on Good’s Coverage 219   Figure 113. Boxplot Between Field Site Water Samples for Coverage Based on Good’s Coverage   Figure 114. ANOVA Residuals Between Field Site Water Samples for Coverage Based on Good’s Coverage Table 69. One Way ANOVA Test Result for Coverage Comparison of Water Samples by Field Site Based on Good’s Coverage  Df Sum Sq Mean Sq F value Pr(>F) Length 5 0.018 0.004 4.820 0.004 Residuals 21 0.015 0.001 NA NA    220  Table 70. Tukey HSD Test Result for Coverage Comparison of Water Samples by Field Site Based on Good’s Coverage Comparison p-value Site2-Site1 0.827992639 Site3-Site1 0.905485326 Site4-Site1 0.671668950 Site5-Site1 0.336667924 Site6-Site1 0.999753717 Site3-Site2 0.999999986 Site4-Site2 0.985021091 Site5-Site2 0.499809440 Site6-Site2 0.059521831 Site4-Site3 0.994793003 Site5-Site3 0.825560531 Site6-Site3 0.414424802 Site5-Site4 0.975734200 Site6-Site4 0.075627941 Site6-Site5 0.002070515   Figure 115. Barplot Between Field Site Water Samples for Diversity Based on the Inverse Simpson Estimator  Figure 116. Boxplot Between Field Site Water Samples for Diversity Based on the Inverse Simpson Estimator 221   Figure 117. ANOVA Residuals Between Field Site Water Samples for Diversity Based on the Inverse Simpson Estimator Table 71. One Way ANOVA Test Result for Diversity Comparison of Water Samples by Field Site Based on the Inverse Simpson Estimator  Df Sum Sq Mean Sq F value Pr(>F) Length 5 22762.53 4552.507 1.484 0.237 Residuals 21 64442.52 3068.692 NA NA   Figure 118. Barplot Between Field Site Water Samples for Observed OTUs Based on the SOBS Calculation  Figure 119. Boxplot Between Field Site Water Samples for Observed OTUs Based on the SOBS Calculation 222    Figure 120. ANOVA Residuals Between Field Site Water Samples for Observed OTUs Based on the SOBS Calculation Table 72. One Way ANOVA Test Result for Observed OTUs Comparison of Water Samples by Field Site Based on SOBS Calculation  Df Sum Sq Mean Sq F value Pr(>F) Length 5 2356581 471316.3 3.752 0.014 Residuals 21 2638105 125624.1 NA NA  Table 73. Tukey HSD Test Result for Observed OTUs Comparison of Water Samples by Field Site Based on SOBS Calculation Comparison p-value Site2-Site1 0.93985 Site3-Site1 0.983178 Site4-Site1 0.817223 Site5-Site1 0.56575 Site6-Site1 0.998299 Site3-Site2 0.999971 Site4-Site2 0.9829 Site5-Site2 0.637026 Site6-Site2 0.115232 Site4-Site3 0.985022 Site5-Site3 0.832528 Site6-Site3 0.624077 Site5-Site4 0.993807 Site6-Site4 0.118609 Site6-Site5 0.007    223  Surface Sediment Samples  Figure 121. Barplot Between Field Site Surface Sediment Samples for Richness Based on the Chao1 Estimator  Figure 122. Boxplot Between Field Site Surface Sediment Samples for Richness Based on the Chao1 Estimator  Figure 123. ANOVA Residuals Between Field Site Surface Sediment Samples for Richness Based on the Chao1 Estimator   224  Table 74. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on Chao1 Estimator  Df Sum Sq Mean Sq F value Pr(>F) Length 4 2052628 513157 4.261 0.011 Residuals 22 2649487 120431.2 NA NA  Table 75. Tukey HSD Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on Chao1 Estimator Comparison p-value Site3-Site2 0.819704 Site4-Site2 0.938196 Site5-Site2 0.604435 Site6-Site2 0.098849 Site4-Site3 0.519824 Site5-Site3 0.20691 Site6-Site3 0.717862 Site5-Site4 0.987759 Site6-Site4 0.053663 Site6-Site5 0.008423   Figure 124. Barplot Between Field Site Surface Sediment Samples for Coverage Based on Good’s Coverage  Figure 125. Boxplot Between Field Site Surface Sediment Samples for Coverage Based on Good’s Coverage  225   Figure 126. ANOVA Residuals Between Field Site Surface Sediment Samples for Coverage Based on Good’s Coverage Table 76. One Way ANOVA Test Result for Coverage Comparison of Surface Sediment Samples by Field Site Based on Good’s Coverage  Df Sum Sq Mean Sq F value Pr(>F) Length 4 0.003 0.001 4.302 0.010 Residuals 22 0.004 0.000 NA NA  Table 77. Tukey HSD Test Result for Coverage Comparison of Surface Sediment Samples by Field Site Based on Good’s Coverage Comparison p-value Site3-Site2 0.688732 Site4-Site2 0.950255 Site5-Site2 0.764651 Site6-Site2 0.072843 Site4-Site3 0.423353 Site5-Site3 0.207471 Site6-Site3 0.766981 Site5-Site4 0.998012 Site6-Site4 0.044596 Site6-Site5 0.01057   Figure 127. Barplot Between Field Site Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator 226   Figure 128. Boxplot Between Field Site Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator  Figure 129. ANOVA Residuals Between Field Site Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator Table 78. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on the Inverse Simpson Estimator  Df Sum Sq Mean Sq F value Pr(>F) Length 4 61359.32 15339.83 5.558 0.003 Residuals 22 60716.58 2759.844 NA NA    227  Table 79. Tukey HSD Test Result for Diversity Comparison of Surface Sediment Samples by Field Site Based on the Inverse Simpson Estimator Comparison p-value Site3-Site2 0.999893 Site4-Site2 0.197192 Site5-Site2 0.142988 Site6-Site2 0.003011 Site4-Site3 0.383686 Site5-Site3 0.346143 Site6-Site3 0.017658 Site5-Site4 0.999974 Site6-Site4 0.562339 Site6-Site5 0.404104   Figure 130. Barplot Between Field Site Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation  Figure 131. Boxplot Between Field Site Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation 228   Figure 132. ANOVA Residuals Between Field Site Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation Table 80. One Way ANOVA Test Result for Observed OTUs Comparison of Surface Sediment Samples by Field Site Based on SOBS Calculation  Df Sum Sq Mean Sq F value Pr(>F) Length 4 174258.734 43564.683 2.264 0.0948 Residuals 22 423382.085 19244.640 NA NA  10-cm Depth Sediment Samples  Figure 133. Barplot Between Field Site 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator 229   Figure 134. Boxplot Between Field Site 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator  Figure 135. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator  Table 81. One Way ANOVA Test Result for Richness Comparison of 10-cm Depth Sediment Samples by Field Site Based on Chao1 Estimator  Df Sum Sq Mean Sq F value Pr(>F) Length 4 501734.553 125433.638 0.514 0.727 Residuals 11 2685664.111 244151.283 NA NA    230   Figure 136. Barplot Between Field Site 10-cm Depth Sediment Samples for Coverage Based on Good’s Coverage  Figure 137. Boxplot Between Field Site 10-cm Depth Sediment Samples for Coverage Based on Good’s Coverage   Figure 138. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Coverage Based on Good’s Coverage Table 82. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Field Site Based on Good’s Coverage  Df Sum Sq Mean Sq F value Pr(>F) Length 4 0.001 0.000 0.524 0.720 Residuals 11 0.005 0.000 NA NA 231    Figure 139. Barplot Between Field Site 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator  Figure 140. Boxplot Between Field Site 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator  Figure 141. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator   232  Table 83. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Field Site Based on the Inverse Simpson Estimator  Df Sum Sq Mean Sq F value Pr(>F) Length 4 58934.719 14733.680 1.995 0.165 Residuals 11 81253.934 7386.721 NA NA   Figure 142. Barplot Between Field Site 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation  Figure 143. Boxplot Between Field Site 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation  233   Figure 144. ANOVA Residuals Between Field Site 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation Table 84. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Field Site Based on SOBS Calculation  Df Sum Sq Mean Sq F value Pr(>F) Length 4 65168.126 16292.032 0.385 0.815 Residuals 11 464907.031 42264.276 NA NA  Laboratory Study Water Samples  Figure 145. Barplot Between Column Samples for Richness Based on the Chao1 Estimator 234   Figure 146. ANOVA Residuals Between Column Water Samples for Richness Based on the Chao1 Estimator  Table 85. One Way ANOVA Test Result for Richness Comparison of Water Samples by Column Based on the Chao1 Estimator  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 164052.902 164052.902 3.743 0.079 Residuals 11 482161.543 43832.868 NA NA   Figure 147. Barplot Between Column Water Samples for Coverage Based on the Good’s Coverage   235   Figure 148. ANOVA Residuals Between Column Water Samples for Observed OTUs Based on the Good’s Coverage Table 86. One Way ANOVA Test Result for Coverage Comparison of Water Samples by Column Based on the Good’s Coverage  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 0.001 0.001 4.912 0.0486 Residuals 11 0.001 0.000 NA NA  Table 87. Tukey HSD Test Result for Coverage Comparison of Water Samples by Column Based on the Good’s Coverage Comparison p-value Distilled-Storm 0.0486   Figure 149. Barplot Between Column Water Samples for Diversity Based on the Inverse Simpson Estimator   236   Figure 150. ANOVA Residuals Between Column Water Samples for Observed OTUs Based on the Inverse Simpson Estimator Table 88. One Way ANOVA Test Result for Diversity Comparison of Water Samples by Column Based on the Inverse Simpson Estimator  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 871.026 871.027 4.478 0.0580 Residuals 11 2139.510 194.501 NA NA   Figure 151. Barplot Between Column Water Samples for Observed OTUs Based on the SOBS Calculation 237   Figure 152. ANOVA Residuals Between Column Water Samples for Observed OTUs Based on the SOBS Calculation Table 89. One Way ANOVA Test Result for Diversity Comparison of Water Samples by Column Based on SOBS Calculation  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 92725.930 92725.930 2.972 0.113 Residuals 11 343157.497 31196.136 NA NA Surface Sediment Samples  Figure 153. Barplot Between Column Surface Sediment Samples for Richness Based on the Chao1 Estimator   238   Figure 154. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the Chao1 Estimator Table 90. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on the Chao1 Estimator  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 21786.842 21786.842 1.130 0.307 Residuals 13 250705.703 19285.054 NA NA   Figure 155. Barplot Between Column Surface Sediment Samples for Coverage Based on the Good’s Coverage   239   Figure 156. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the Good’s Coverage Table 91. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on the Good’s Coverage  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 7.19E-05 7.19E-05 0.713 0.414 Residuals 13 0.001 0.000 NA NA   Figure 157. Barplot Between Column Surface Sediment Samples for Diversity Based on the Inverse Simpson Estimator   240   Figure 158. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the Inverse Simpson Estimator Table 92. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on the Inverse Simpson Estimator  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 1189.593 1189.593 0.955 0.346 Residuals 13 16194.774 1245.752 NA NA   Figure 159. Barplot Between Column Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation   241   Figure 160. ANOVA Residuals Between Column Surface Sediment Samples for Observed OTUs Based on the SOBS Calculation Table 93. One Way ANOVA Test Result for Diversity Comparison of Surface Sediment Samples by Column Based on SOBS Calculation  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 15856.479 15856.479 2.414 0.144 Residuals 13 85388.725 6568.363 NA NA  10-cm Depth Samples  Figure 161. Barplot Between Column 10-cm Depth Sediment Samples for Richness Based on the Chao1 Estimator 242   Figure 162. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the Chao1 Estimator Table 94. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on Chao1 Estimator  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 35596.312 35596.312 0.496 0.494 Residuals 13 932234.181 71710.322 NA NA   Figure 163. Barplot Between Column 10-cm Depth Sediment Samples for Coverage Based on the Good’s Coverage   243   Figure 164. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the Good’s Coverage Table 95. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on Good’s Coverage  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 0.000 0.000 0.687 0.422 Residuals 13 0.003 0.000 NA NA   Figure 165. Barplot Between Column 10-cm Depth Sediment Samples for Diversity Based on the Inverse Simpson Estimator   244   Figure 166. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the Inverse Simpson Estimator Table 96. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on the Inverse Simpson Estimator  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 7.152 7.152 0.002 0.962 Residuals 13 39758.351 3058.335 NA NA   Figure 167. Barplot Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation   245   Figure 168. ANOVA Residuals Between Column 10-cm Depth Sediment Samples for Observed OTUs Based on the SOBS Calculation Table 97. One Way ANOVA Test Result for Diversity Comparison of 10-cm Depth Sediment Samples by Column Based on SOBS Calculation  Df Sum Sq Mean Sq F value Pr(>F) Exposure 1 6898.043 6898.0429 0.473 0.504 Residuals 13 189753.330 14596.410 NA NA    246  Appendix I: Letters of Permission and Support for the Research Project   247   248  249   250    251     252  Appendix J: Project Management – Timeline and Budget    Plan    Actual    % Complete    Actual (postponed)         PLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETE QUARTERWEEK WEEK(S) WEEK WEEK(S) 01-Feb 08-Feb 15-Feb 22-Feb 01-Mar 08-Mar 15-Mar 22-Mar 29-Mar 05-Apr 12-Apr 19-Apr 26-Apr 03-May 10-May 17-May 24-May 31-MayWeek 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18ResponsibilityNSERC IPS ProposalEstablish KWL's user need 1 4 1 4 100% CJ/PLDetermine project timeline 1 1 1 1 100% JA/JLAssemble project team 4 3 4 3 100% JA/JLTrain graduate student on lab methods 1 18 1 18 100% JA/JLEstablish funding/partner relationship 1 4 1 4 100% JA/CJSubmit proposal to NSERC 12 1 12 1 100% JANSERC review 13 4 13 1 100%Milestone: Project Accepted by NSERCPLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 07-Jun 14-Jun 21-Jun 28-Jun 05-Jul 12-Jul 19-Jul 26-Jul 02-Aug 09-Aug 16-Aug 23-Aug 30-Aug 06-Sep 13-Sep 20-Sep 27-Sep 04-Oct 11-Oct 18-Oct 25-Oct 01-Nov 08-Nov 15-Nov 22-Nov 29-Nov 06-Dec 13-Dec 20-Dec 27-Dec 03-Jan 10-Jan 17-Jan 24-Jan 31-Jan 07-FebWeek 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54ResponsibilityNSERC IPS ProposalGenome BC ProposalConfirm KWL's user need 19 2 19 2 100% CJ/PL/JLFinalize goals, objectives, milestones 19 3 19 3 100% CJ/JAGather project collaborators and supporters19 6 19 6 100% JLEstablish SPES' user need 24 2 24 2 100% JP/JLCalculate detailed budget 25 7 25 7 100% JLManage risks 25 3 25 3 100% JLEstablish project management structure 25 3 25 3 100% JLReview proposal 27 2 27 2 100% JA/SB/CJSubmit proposal to Genome BC 31 1 31 1 100% JA/SB/CJGenome BC review 31 8 31 8 100% Genome  BCModify proposal based on Genome BC review39 2 39 2 100% JA/SB/CJResubmit proposal to Genome BC 41 1 41 1 100% JA/SB/CJGenome BC resubmission review 42 1 42 2 100% Genome BCPrepare for Genome BC project launch 44 4 52 2 100% AllGenome BC project launch meeting 47 3 54 1 100% JA/SB/CJGenome BC project launch 50 1 54 1 100% JA/SB/CJMilestone: Project Accepted by Genome BC253           PLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 07-Jun 14-Jun 21-Jun 28-Jun 05-Jul 12-Jul 19-Jul 26-Jul 02-Aug 09-Aug 16-Aug 23-Aug 30-AugWeek 19 20 21 22 23 24 25 26 27 28 29 30 31ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentSelect research site 19 2 19 2 100% CJObtain site and lab access 19 2 19 2 100% JLDefine research plan 19 13 19 13 100% JLDetermine site characteristics 25 4 25 4 100% JLAssemble lab and field equipment 25 7 25 7 100% JLValidate lab methods 25 4 25 4 100% JLMilestone: Methods and Research Plan Developed and FinalizedACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 01-Nov 08-Nov 15-Nov 22-Nov 29-Nov 06-Dec 13-Dec 20-Dec 27-Dec 03-Jan 10-Jan 17-Jan 24-Jan 31-Jan 07-Feb 14-Feb 21-Feb 28-Feb 06-Mar 13-Mar 20-Mar 27-Mar 03-AprWeek 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentGather and Process SamplesExtract and process field samples 25 20 25 22 100% JLBuild lab study setup 39 1 43 4 100% DV/JLExtract and process lab study samples 39 16 46 17 100% JLMilestone: Field and Lab Samples Analyzed for Environmental Parameters and Extracted/Preserved for DNA AnalysisACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 01-Nov 08-Nov 15-Nov 22-Nov 29-Nov 06-Dec 13-Dec 20-Dec 27-Dec 03-Jan 10-Jan 17-Jan 24-Jan 31-Jan 07-Feb 14-Feb 21-Feb 28-Feb 06-Mar 13-Mar 20-Mar 27-Mar 03-Apr 10-Apr 17-Apr 24-Apr 01-May 08-May 15-May 22-May 29-May 05-Jun 12-Jun 19-JunWeek 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentGather and Process SamplesSequencingTrain graduate student 42 12 42 24 100% SB/JLPrepare field study samples for MiSeq 40 12 40 18 100% JLMiSeq field study samples 46 8 58 6 100% JLReview field study results 54 2 64 2 100% BM/SBPrepare lab study samples for MiSeq 45 16 45 18 100% JLMiSeq lab study samples 61 4 64 2 100% JLReview lab study results 65 2 66 2 100% BM/SBPrepare samples for HiSeq 62 2 62 4 100% JLHiSeq samples 64 8 66 4 100% JLReview results 72 2 70 2 100% BM/SBMilestone: MiSeq and HiSeq Data Received and Reviewed254           PLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 27-Dec 03-Jan 10-Jan 17-Jan 24-Jan 31-Jan 07-Feb 14-Feb 21-Feb 28-Feb 06-Mar 13-Mar 20-Mar 27-Mar 03-Apr 10-Apr 17-Apr 24-Apr 01-May 08-May 15-May 22-May 29-May 05-Jun 12-Jun 19-Jun 26-Jun 03-Jul 10-Jul 17-Jul 24-Jul 31-Jul 07-Aug 14-Aug 21-Aug 28-Aug 04-Sep 11-Sep 18-Sep 25-Sep 02-Oct 09-Oct 16-Oct 23-Oct 30-Oct 06-Nov 13-Nov 20-Nov 27-Nov 04-Dec 11-Dec 18-Dec 25-DecWeek 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 95 96 96 97 97ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentGather and Process SamplesSequencingBioinformaticsTrain graduate student 48 16 48 16 100% SB/BM/JLAssemble and filter MiSeq data for field samples 54 2 64 4 100% SB/JLPerform pipeline and statistical approaches using field results 56 2 66 8 100% JLReview field study results 58 2 74 2 100% BM/SBAssemble and filter MiSeq data for lab samples 67 2 76 1 100% JLPerform pipeline and statistical approaches using lab results 67 2 77 1 100% JLReview lab study results 69 2 78 1 100% BM/SBAssemble and filter HiSeq data 67 2 80 4 100% JLPerform pipeline and statistical approaches using field and lab results 67 6 85 11 100% JLReview Metagenome results 69 2 96 2 100% BM/SBMilestone: Data Filtered, Analyzed and ReviewedPLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 24-Jul 31-Jul 07-Aug 14-Aug 21-Aug 28-Aug 04-Sep 11-Sep 18-Sep 25-Sep 02-Oct 09-Oct 16-Oct 23-Oct 30-Oct 06-Nov 13-Nov 20-Nov 27-NovWeek 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 95ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentGather and Process SamplesSequencingBioinformaticsTool DevelopmentCompare field and lab study results 78 2 94 2 100% JLCompare study results with literature 78 2 94 2 100% JLReview results, observations, and conclusions 78 2 94 2 100% SB/JADiscuss commercialization opportunities 80 1 94 2 100% JA/JLDiscuss limitations and shortcomings 80 1 94 2 100% JA/JLWrite methodology for follow up 83 2 94 2 100% JA/CJ/JLMilestone: Observations Drawn and Suggestions Given for Follow UpPLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 31-Jan 07-Feb 14-Feb 21-Feb 28-Feb 06-Mar 13-Mar 20-Mar 27-Mar 03-Apr 10-Apr 17-Apr 24-Apr 01-May 08-May 15-May 22-May 29-May 05-Jun 12-Jun 19-Jun 26-Jun 03-Jul 10-Jul 17-Jul 24-Jul 31-Jul 07-Aug 14-Aug 21-Aug 28-Aug 04-Sep 11-Sep 18-Sep 25-Sep 02-Oct 09-Oct 16-Oct 23-Oct 30-Oct 06-Nov 13-Nov 20-Nov 27-Nov 04-Dec 11-Dec 18-Dec 25-DecWeek 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 95 96 96 97 97ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentGather and Process SamplesSequencingBioinformaticsTool DevelopmentReporting and WritingWrite graduate thesis 53 40 65 32 100% JLReview and revise graduate thesis 82 12 80 18 100% DV/SB/JA/JLWrite journal article 82 8 94 2 100% JLReview journal article 90 4 96 2 100% DV/SB/JAWrite SPES publication 82 1 96 1 100% JP/JLReview SPES publication 83 1 97 1 100% CJ/PLPrepare conference presentations - i.e. BCWWA, WEST, other 68 8 64 9 100% AllMilestone: Graduate Thesis, Journal Article, Publications, and Presentation Material Prepared255    Table 98. Project Budget and Finances Item Budgeted Spent Difference Description 1 400.00 165.00 235.00 Sampling disposables 2 10.00 20.00 -10.00 Renting sediment samplers 3 80.00 80.49 -0.49 Supplies for sampling equipment 4 110.00 0.00 110.00 Supplies for column test      Subtotal 600.00 265.49 334.51 Field and lab study execution      5 10200.00 1431.87 8768.13 DNA Preparation 6 900.00 829.00 71.00 DNA Consumables 8 8400.00 4000.00 4400.00 Environmental lab 9 3150.00 14033.25 -10883.3 MiSeq 10 6250.00 8929.66 -2679.66 HiSeq      Subtotal 28900.00 29223.78 -323.78 Sequencing and preparation      11 10000.00 10000.00 - Graduate student stipend 12 12000.00 12000.00 - Graduate student stipend      13 2500.00 2500.00 - Bioinformatics 14 2000.00 2000.00 - Printing, publishing      Total 54000.00 53989.27 10.73  PLAN PLAN ACTUAL ACTUAL PERCENTACTIVITY START DURATION START DURATION COMPLETEWEEK WEEK(S) WEEK WEEK(S) 21-Aug 28-Aug 04-Sep 11-Sep 18-Sep 25-Sep 02-Oct 09-Oct 16-Oct 23-Oct 30-Oct 06-Nov 13-Nov 20-Nov 27-Nov 04-Dec 11-Dec 18-Dec 25-DecWeek 82 83 84 85 86 87 88 89 90 91 92 93 94 95 95 96 96 97 97ResponsibilityNSERC IPS ProposalGenome BC ProposalMethod DevelopmentGather and Process SamplesSequencingBioinformaticsTool DevelopmentReporting and WritingSharing and PublishingPresent User Partners with results and recommend follow up 82 8 96 2 100% AllSubmit article to relevant journals for publishing 92 4 96 2 100%Publish graduate thesis in UBC CiRcle 92 4 96 2 100% AllInclude publication in SPES annual report 88 8 96 2 100%Add raw data to repositories 88 8 96 2 100%Milestone: Data Available to Advise Follow on PhasesMilestone: Written Material Submitted for Publishing256  Appendix K: Independent Statistical Review by UBC Applied Statistics and Data Science Group Upon completion of this thesis, an independent review was conducted by the University of British Columbia Applied Statistics and Data Science Group. The following is the report on statistical limitations and potential opportunities for future exploration.  257    258   259     260    261  Appendix L: Reflections on the Work Here, I (Jessica LeNoble), present a personal reflection on the work that was performed over the duration of this research project and for the preparation of this thesis. This narrative was inspired by my reading of a past graduate student’s thesis, who studied under my supervisor. During my graduate degree, I learned that there is a great deal of education that takes place beyond the classroom and beyond the design of a research plan and the achievement of one’s intended (or unintended) results and conclusions. Some examples include:  I learned to adapt: when I arrived at UBC, my original research goals involved testing a very different set of hypotheses for the mining sector. At the time of this research, the finances and desire for a student-led project did not exist within the local mining community but there was desire for a similar project to be conducted for stormwater treatment systems. I struggled at first but was ultimately able to design a project that met my desire to learn a new skill in genomics and to experiment with metal-contaminated sites, which was the area for which I was most passionate.   I learned to think quickly: with field work, there is really no end to the unexpected troubles one can encounter. Especially while working in a public park, thinking quickly or creatively is an essential trait that can only be acquired through experience. My experiences in the wetland will not be forgotten. Highlights include, blowing up my research vessel (dinghy) without a pump (i.e. with my mouth), fending off wild animals (racoons) who were enticed by my tin foil, fixing equipment with packing tape, and working around the general public who walk the gravel path that boarders Lost Lagoon.  Finally, I learned to appreciate the research community: while at UBC, I experienced an incredible level of kindness and guidance. Everyone I worked with wanted to see me succeed with my project and I observed a fantastic level of passion for pollution control and conservation among my colleagues and supervisors. I have taken the opportunity to share my experiences with others through presentations in several forums. First, while studying at UBC, I worked for a program called engcite where I shared my experience of studying and researching in engineering with hundreds of girls between the age of 8 and 18, encouraging them to consider entering a technical discipline. I think back to the privilege that I experienced at a young age where I was exposed to the environmental field by a teacher in grade 9 and I hope that this outreach may have the same effect for someone else. I also presented my research as a narrative on my experiences in graduate studies at two conferences – the Water and Environment Student Talks Conference and at the young professionals’ reception during the British Columbia Water and Waste Association Annual 262  Conference. In both presentations, rather than focusing on my research outcomes, I focused on the three learning outcomes that I have listed above. I believe that while our research conclusions shape our presentations and publications, it is ultimately our research experiences that shape our futures. It was my goal with these presentations to inspire others to pursue research in the area where they are most passionate so that they too may produce positive changes in the fields of their interests. I have been incredibly fortunate to work with an excellent team and to have contributed a useful resource to fields of growing importance, namely pollution control, waste management, stormwater treatment, microbiology, and environmental conservation.   

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