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An evaluation of the potential for biodegradation of methanol in the Fraser River, BC Bennett, Sharon M. 2005

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A N EVALUATION OF THE POTENTIAL FOR BIODEGRADATION OF METHANOL IN THE FRASER RIVER, BC by SHARON M . BENNETT B.Sc, The University of British Columbia, 2002 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE in THE FACULTY OF GRADUATE STUDIES RESOURCE MANAGEMENT AND ENVIRONMENTAL STUDIES THE UNIVERSITY OF BRITISH COLUMBIA June 2005 © Sharon M . Bennett, 2005 Abstract Increasing concerns over the use of fossil fuels and the decreasing supply of cheap efficient fuel has led a demand for cost effective and sustainable alternative fuels. Methanol is thought to be a promising alternative fuel for use in fuel cell vehicles and as a gasoline additive. With an increase in demand and consequently, an increase in shipping, storage and distribution facilities, the potential for the release of methanol to the environment is high. In the event of a spill, it is important to understand how the natural environment will respond. This study sought to determine the rate and factors contributing to the natural biodegradation of methanol in the Fraser River. Four experimental trials were conducted over a period of a year. Initial methanol concentrations added were 1000 mg/L and 10000 mg/L and measured using gas chromatography. Comparisons were made between samples with and without nutrient amendments. Total bacterial counts and counts of methanol degrading bacteria were also measured using epifluorescence microscopy and the most probable number technique respectively. 1 4 C uptake was also analyzed to measure microbial activity. Results of the trials showed that volatilization played a major role in the loss of methanol. Rates of loss ranged from 6 to 180 mg/L/day. Evidence of biodegradation was found in nutrient amended samples at cooler temperatures with an initial methanol concentration of 1000 mg/L. Little to no evidence of biodegradation was found in the other test conditions. Other contributing factors to the biodegradation rate included temperature, nutrients and competition among natural flora in the river water. Table of Contents Abstract » Table of Contents iii List of Tables vi List of Figures viii Acknowledgements x C H A P T E R 1 - Introduction 1 1.1 The Need for Alternative Energy 2 1.2 Methanol and the Fuel Cell Industry 4 1.3 Properties of Methanol 6 1.4 Toxicity of Methanol 7 1.5 Methanol- Utilizing Bacteria 8 1.6 Mechanisms for Removal of Methanol from Surface Water 9 1.7 Biodegradation of Methanol in the Natural Environment 13 1.8 Fraser River Characteristics 16 1.9 Research Questions 16 C H A P T E R 2 - Methodology 18 2.1 Study Areas 18 2.2 Sample Periods 19 2.3 Experimental Design 20 2.4 Field Sampling 23 2.5 Water Quality Analysis 24 2.6 Methanol Analysis 24 2.7 Bacterial Enumeration 26 iii 2.7.1 Epifluoresence Microscopy 26 2.7.2 Most Probable Number (MPN) Technique 27 2.8 Uptake and Metabolism of 1 4 C labeled Methanol 28 2.9 Data Analysis 30 CHAPTER 3 - Results 32 3.1 Trial One 32 3.1.1 Water Parameters 32 3.1.2 Methanol Loss 33 3.1.3 Bacterial Enumeration 35 3.2 Trial Two 36 3.2.1 Water Parameters 36 3.2.2 Methanol Loss 37 3.2.3 Bacterial Enumeration 39 3.3 Trial Three 40 3.3.1 Water Parameters 41 3.3.2 Methanol Loss 41 3.3.3 Bacterial Enumeration 43 3.3.5 1 4 C Labeled Methanol Uptake Rates 44 3.4 Trial Four 45 3.4.1 Water Parameters 45 3.4.2 Methanol Loss 46 3.4.3 Bacterial Enumeration 47 3.4.5 l 4 C Labeled Methanol Uptake Rates 48 3.5 Comparison of trials 49 CHAPTER 4 - Discussion 55 iv 4.1 Role of Volatilization 57 4.2 Role of Temperature 59 4.3 Role of Nutrients 60 4.4 Role of Biological Interactions 61 4.5 Comparison of Bacterial Enumeration Methods 62 4.6 l 4 C Methanol Analysis 63 C H A P T E R 5 - Conclusions 65 C H A P T E R 6 - Recommendations 67 R E F E R E N C E S 69 Appendix A - Site Pictures 72 Appendix B - Methanol Concentration Calibration 74 Appendix C - Modified Nitrate Mineral Salts Media 75 Appendix D - Methanol Degradation Results 77 Appendix E - Bacterial Enumeration Results 92 Appendix F - l 4 C labeled Methanol Results 105 Appendix G - Water Levels on the Fraser River at Mission 109 Appendix H - Regression Analysis of Methanol Loss Curves 110 v List of Tables Table 1 - 1 Summary of Methanol Properties 6 Table 1 - 2 Summary of water quality parameters monitored at Mission, BC 16 Table 2 - 1 Overview of experimental framework 22 Table 2 - 2 GC Operating conditions 25 Table 3 - 1 Trial 1, summary of water parameters 32 Table 3 - 2 Trial 2, summary of water parameters 37 Table 3 -3 Trial 3, summary of water parameters 41 Table 3 - 4 Trial 3, pH ranges of test conditions at the termination of the experiment 41 Table 3 - 5 Trial 3, 14C labeled methanol uptake rates in ug/L/day 45 Table 3 - 6 Trial 4, summary of water parameters 45 Table 3 - 7 Trial 4, pH ranges of test conditions at the termination of the experiment 45 Table 3 - 8 Trial 4, 14C labeled methanol uptake rates in ug/L/day 49 Table 3-9 Summary of water parameters for Trials 1-4 50 Table 3-10 Comparison of methanol loss rates in mg/L/day 50 Table 3-11 Comparison of number of days for 50% removal 51 Table 3-12 Comparison of methanol loss rates between cool and warm water temperature microcosms in mg/Lday 54 Table 3-13 Comparison of number of days for 50% loss of methanol between cool and warm water temperature microcosms 54 Table D - 1: Trial 1, Site 1 Methanol Concentration Data in mg/L 78 Table D - 2: Trial 1, Site 2 Methanol Degradation Data in mg/L 79 Table D - 3 Trial 2, Site 1 Methanol Degradation Data in mg/L 81 Table D - 4 Trial 2, Site 2 Methanol Degradation Data in mg/L 82 vi Table D - 5 Trial 3, Site 1 Methanol Degradation data in mg/L 84 Table D - 6 Trial 3, Site 2 Methanol Degradation data in mg/L 85 Table D - 7 Trial 3, Methanol Evaporative Loss data in mg/L 87 Table D - 8 Trial 4, Site 1 Methanol Degradation data in mg/L 88 Table D - 9 Trial 4, Site 2 Methanol Degradation data in mg/L 89 Table D - 10 Trial 4, Methanol Evaporative Loss data in mg/L 91 Table E - 1 Trial 1, Site 1 Total Bacteria Counts by Epifluorescenece Microscopy 93 Table E - 2 Trial 1 Site 2 Total Bacteria Counts by Epifluorescence Microscopy 94 Table E - 3 Trial 1 Site 1 and 2 Combined Bacteria Counts by Epifluorescence Microscopy 95 Table E - 4 Trial 2, Site 1 Total Bacteria Counts by Epifluorescence Microscopy 96 Table E - 5 Trial 2, Site 2 Total Bacteria Counts by Epifluorescence Microscopy 97 Table E - 6 Trial 2, Site 1 and 2 Combined Bacteria Counts by Epifluorescence Microscopy....98 Table E - 7 Trial 3, Site 1 Methylotroph Bacteria Counts by MPN 99 Table E - 8 Trial 3, Site 2 Methylotroph Bacteria Counts by MPN 100 Table E - 9 Trial 3, Site 1 and 2 Combined Methylotroph Bacteria Counts by MPN 101 Table E - 10 Trial 4, Site 1 Methylotroph Bacteria Counts by MPN 102 Table E - 11 Trial 4, Site 2 Methylotroph Bacteria counts by MPN 103 Table E - 12 Trial 4, Site 1 and 2 Combined Methylotroph bacteria counts by MPN 104 Table F - 1 Trial 3, Day 46, 1 4 C uptake data 105 Table F - 2 Trial 4, Day , l 4 C uptake data 107 Table H - 1 Trial 1 P values of linear regression analysis using categorical variables 110 Table H - 2 Trial 2 P values for linear regression analysis using categorical variables 111 Table H - 3 Trial 3 P values of linear regression analysis using categorical variables 112 Table H - 4 Trial 4, P values of linear regression analysis using categorical variables 113 List of Figures Figure 2 - 1 Location of study sites within the Lower Mainland of BC 19 Figure 2 - 2 Map of land use patterns adjacent to sample sites near Mission, BC 20 Figure 2 - 3 Schematic of flask set up for 14C uptake analysis 29 Figure 3-1 Trial 1, fraction of methanol remaining over time with an initial concentration of 1000 mg/L 33 Figure 3 - 2 Trial 1, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L 34 Figure 3 -3 Trial 1, total bacteria counts using epifuoresence microscopy 36 Figure 3 - 4 Trial 2, fraction of methanol remaining over time with an initial concentration of 1000 mg/L 38 Figure 3 -5 Trial 2, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L 39 Figure 3 -6 Trial 2, total bacterial counts using epifluorescence microscopy 40 Figure 3 - 7 Trial 3, fraction of methanol remaining over time with an initial concentration of 1000 mg/L 42 Figure 3 -8 Trial 3, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L 43 Figure 3 - 9 Trial 3, methylotroph bacteria counts using MPN 44 Figure 3-10 Trial 4, fraction of methanol remaining over time with an initial concentration of 1000 mg/L 46 Figure 3-11 Trial 4, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L 47 Figure 3-12 Trial 4, methylotroph bacteria enumeration 48 viii Figure 3-13 Comparison of cool and warm water trials with an initial concentration of 1000 mg/L 52 Figure 3-14 Comparison of cool and warm water trials with an initial methanol concentration of 10,000 mg/L 53 Figure A - 1 Site 1, North Shore, saw mill 72 Figure A - 2 Site 1, South Shore, log booms 72 Figure A - 3 Site 2, North Shore 73 Figure A - 4 Site 2, South Shore, gravel pit 73 Figure B - 1 Methanol Calibration Curve 74 Figure G - 1 Water levels on the Fraser River at Mission during the sampling year 109 ix Acknowledgements This project would not have been completed had it not been for a very supportive cast and crew. I owe a great many thanks to those that were a part of this process. A sincere thank you goes to my supervisor, Dr. Sheldon Duff who gave endless guidance and support throughout the project. As well, Dr. Ken Hall, Dr. Les Lavkulich and Dr. Karen Barlett were extremely helpful in sorting out logistics and helping to solve the many mysteries in this project. Funding for the project was gratefully received from the National Sciences and Engineering Research Council and Methanex Corporation. A special thank you goes to Kevin Maloney for having interest in the project and making funding a reality. A big thank you goes to Moira Lemon at the Canadian Wilderness Society for lending us a boat in order to carry out the field work. As well, I am very grateful to Bezhad Imanian for sharing with me his expertise with epifluoresence microscopy. I am very indebted to Shannon, Darren, Ting, Yuehan and Deyanira for their assistance in the field and the lab. Their help allowed me to get out of many binds and obstacles, and if it wasn't for you, I might still be stuck on the river! A massive thank you to my fellow lab mates, Steve and Vivian for being an encyclopedia of help with the GC and other lab knowledge. To the community at RMES and my other friends who were there for emotional, mental and intellectual support. Last, but most importantly, many thanks and hugs go to my parents who have given me all the love and support I have needed along the way. x CHAPTER 1 - Introduction With dwindling oil and gas reserves and an increased urgency to lower vehicle emissions, the quest to find economical and environmentally sound alternative fuels has been intensified. Fuel cell technology is an emerging alternative to the internal combustion engine promising to produce zero emission vehicles. Methanol, though not a new fuel, is one of many options being explored as the next generation of sustainable fuels to be used in fuel cell vehicles. Methanol, or wood alcohol, has been used as a source of fuel as far back as 200 years and is a valuable source of hydrogen needed for fuel cell technology (Perry and Perry 1990). Increasing demand for methanol in the fuel cell industry and as a gasoline additive has increased the potential for methanol release to the environment. Methanol has been ranked third among all chemicals reportedly released into the environment by industry, according to an annual Toxic Release Inventory published in 1992 (Malcolm Pirnie Inc. 1999). Twenty percent of the total amount released into the environment is discharged into soil, groundwater, and surface water (Malcolm Pirnie Inc. 1999). Avenues for release into the environment stem from the distribution, shipping, transportation and storage of methanol. Scenarios of release include accidents involving tank trucks, rail cars, and barges as well as releases which occur during storage or fueling. To date, research on the effects of methanol release into soil and groundwater have been studied; however, the effects of methanol in surface water environments have not. This study will complement a number of other studies aimed at evaluating methanol's impact on the 1 environment and provide valuable information as to the ability for methanol to biodegrade in surface water. This chapter will introduce the topic of study and review the literature on the subject. It will begin with a brief explanation of the need for alternative fuels and in particular the use of methanol as an option for fuel cell technology. Next, it will outline the specific properties and toxicity of methanol. A description of bacteria able to utilize methanol will be given followed by an explanation of the kinetics of methanol degradation and other mechanisms of loss from surface water. Section 1.7 gives a review of previous methanol biodegradation studies in natural environments and the complexities of extrapolating laboratory studies to the field. In section 1.8 a brief description of the Fraser River and water quality characteristics are given. The chapter will conclude with an outline of the scope of the project and the specific research questions addressed in this study. Chapter 2 contains a description of the methods and materials used during the project. Sample site descriptions, field methodology, methanol analysis, bacterial enumeration and 1 4 C methanol experimental procedures are given. Chapters 3 and 4 detail the results and discussion of the results, respectively. Results are categorized according to trial and include methanol degradation curves, bacterial enumeration and water parameters of each trial. Chapter 3 ends in a comparison of cool and warm water trials. Chapter 4 will discuss the findings of the results based on the role of volatilization, nutrients, temperature and bacterial interactions. Chapters 5 and 6 will summarize the thesis with conclusions and recommendations for further study. 1.1 The Need for Alternative Energy 2 The blackout in eastern North America on August 14, 2003 raised alarm bells for North Americans. Effecting 50 million people, the power outage served as a wake up call as to how fragile and important the energy system is for the functioning of our society. Though not caused by a shortage of energy, many questions arose from that incident as to how we should be fueling our societies (Heintzman and Solomon 2003). With an increase in observed effects of fossil fuel consumption, including smog, climate change, and respiratory disease, it is of primary interest that we find a cleaner, more sustainable and cost effective fuel alternative. The WorldWatch Institute has reported that world oil consumption increased 3.4% in 2004 while production has fallen in 33 out of the top 48 oil producing countries (WWI 2005). Alternative energy demand is on the rise and growth is being shown in many sectors. For example, global wind power capacity grew 20% in 2004 (WWI 2005). These current trends provide clear evidence that, as our traditional supplies of fuel decrease and demand increases, the need to find an alternative to fossil fuels has become urgent. Dependency on oil and gas to fuel vehicle transportation is one of the major contributors to our energy crisis, smog and a number of health concerns (WWI 2005). U.S. carbon emissions from motor gasoline in 2002 surpassed those from the entire Japanese economy. There has been an immense amount of debate over what direction society should take in dealing with our transportation issues in the future. From vehicle emissions, road construction and maintenance, to noise pollution, safety concerns and urban gridlock, transportation issues abound. In the State of the World Report 1998, Brown and Mitchell describe ways to build a new economy (1998). The authors are firm in regards to the incompatibility of the automobile with urban centers. They write: The only reasonable alternative to the automobile in urban settings is a combination of state-of-the-art rail passenger-transport systems augmented by other forms of public 3 transportation and bicycles. Whether the goal is mobility, breathable air, the protection of cropland, limits on congestion, or stabilization of climate, the automobile is not the answer (Brown and Mitchell 1998 pi78). With consumer and societal values centered on convenience and comfort, the use of bicycles and public transit may not always be an option. As Geoffrey Ballard, a pioneer of fuel cell technology states, "we have an enduring love affair with our cars...and a deep socio-economic dependence on personal transportation (Ballard 2003 pi09)." Alternatively fueled vehicles such as the fuel cell and other low emission vehicles may offer some relief from the transportation issues facing our societies and may prove to be a stepping stone and a building block to a longer term solution. 1.2 Methanol and the Fuel Cell Industry In the search for an alternative energy source, there has been a movement towards the use of methanol to provide a clean and sustainable source of energy. At present, methanol demand is largely driven by the market for the production of methyl t-butyl ether (MTBE), a gasoline additive designed to reduce air emissions. MTBE is the largest use for methanol and makes up 37% of methanol use (U.S. EPA 1994). Other uses include the production of formaldehyde, acetic acid, chloromethanes and other solvents and automotive chemicals (Methanex 2003). Driven by smog filled air, states such as California, New York and Massachussetts, have implemented strict policy regulations to curb vehicle emissions (Malcolm Pirnie Inc. 1999). These strong emission standards have recently affected the zero emission vehicle market and have provided a strong economic incentive to develop and market fuel cell vehicles (Malcolm Pirnie Inc. 1999). 4 Due to its physical properties, methanol is considered one of the best hydrogen carriers for use in fuel cell vehicles (Malcolm Pirnie Inc. 1999). Its attractiveness for use in the fuel cell industry derives from its high hydrogen to carbon ratio and other properties such as the fact it is a liquid at room temperature, and ambient pressure and has a relatively low combustion temperature (Malcolm Pirnie Inc. 1999). Hydrogen is the optimal fuel for use in fuel cell vehicles, however its use has many implications with respect to storage and safety. Methanol has a similar energy capacity as hydrogen, though is safer and easier to store (Perry and Perry 1990). The use of methanol as a source of hydrogen may be a promising way to bridge the gap for fuel cell technology to become feasible. Though methanol has promise for use as an alternative fuel, there are barriers to the use of methanol in the fuel cell industry. First, the concern regarding infrastructure is a relatively large one. Many fueling stations are reluctant to implement methanol fueling stations without a strong demand for the fuel. However, without fueling stations, consumers are unlikely to purchase fuel cell vehicles in large numbers (Ballard 2003). Without mass production of vehicles, the cost of the fuel cell vehicle is still too high for the average consumer to demand. Developing the means to distribute fuel in an economically and environmentally sound manner are obstacles many in the industry are facing. Second, the production of methanol is largely based on natural gas. World demand of natural gas is high and supplies are decreasing (Bolger and Isaacs 2003). There are estimates that natural gas production has already reached its peak and will begin to decline in the near future (Bolger and Isaacs 2003). With supplies of non-renewable natural gas dwindling, this source of methanol may not prove to be a sustainable solution. If the current world producers of methanol can begin to find economically and environmentally sound ways to produce methanol from renewable processes, then this alternative fuel will look more promising to those looking for a holistic approach to transportation issues. 5 1.3 Properties of Methanol Some of the properties of methanol have been outlined above, however they will be covered in more detail in this section. Methanol is a simple alcohol with the chemical formula: C H 3 O H . It is a colourless liquid that occurs naturally at low concentrations in wood and volcanic gases. Methanol is also produced from the decomposition of organic material (U.S. EPA 1994). Table 1-1 summarizes methanol's physical and chemical properties. Table 1 - 1 Summary of Methanol Properties (U.S. EPA 1994). Molecular Weight 32.04 Boiling Point 64.7°C Melting Point - 97.8°C Density 0.7915 g/mL Water Solubility Completely miscible in water Henry's Law Constant @25°C 1.087xl0"4atmmJ g"1 mole Vapor Pressure 126 mm Hgat25°C The fate and transport of methanol in the environment has been described in a number of reports (Environ Corporation 1996; Katsumata and Kastenberg 1996; Malcolm Pirnie Inc. 1999; Engen 2001; Helle 2001). Methanol will be photooxidized in the atmosphere with a half life of 3 - 30 days (Howard et al 1991). In soil, methanol has a low adsorptive capacity and in water, is completely miscible. This has implications for high mobility through soil (Engen 2001). The dominant loss mechanism for methanol in soil, surface and groundwater water is thought to be by biodegradation (U.S. EPA 1994). 6 1.4 Toxicity of Methanol There have been a number of toxicity studies done on methanol in a wide array of environments (Environ Corporation 1996; Malcolm Pirnie Inc. 1999; Engen 2001). This section will focus on toxicity studies on humans and on aquatic organisms. In humans, methanol is a natural product of metabolic activity. It is found in the body at levels of 1-2 mg/L concentrations that appear to have no ill effect (Environ Corporation 1996). However, methanol can be toxic to humans in large doses. Methanol can be fatal when ingested and can often lead to blindness and other bodily pains (U.S. EPA 1994). To date, there has been no known link to carcinogenic properties in humans, however high exposure in rats and mice have shown increased cases of malformations and lethality in offspring (Engen 2001). The no-observable effects level in mice has been reported to be 1300 mg/L (Environ Corporation 1996). In aquatic environments, various species have been tested for toxicity of methanol. According to the U.S. EPA (1994), methanol has a low acute toxicity to aquatic organisms and median lethal concentrations (LC50) are found to be greater than 100 mg/L. A study by Hemlstetter (1996) found that the LC50 for Mytilus edulis was 15,200 mg/L and sublethal effects may be reversed (1996). Poirier found that the 96 hr LC50 for rainbow trout was 20,100 mg/L, 29,400 mg/L for the fathead minnow, and 15,400 mg/L for the bluegill. The 96 hr EC50 for rainbow trout was 13,000 mg/L, 28,900 mg/L for the fathead minnow and 12,700 mg/L for the bluegill (Poirier et al 1986). Various blue green algae have shown growth inhibition in a range of EC50's of 20,300 -43,290 mg/L (Engen 2001). Stratton found that the LC50 for blue green algae was 24,701 mg/L . Bringmann and Kuhn found that the toxicity threshold using the cell multiplication inhibition test for blue green alga was 530 mg/L and 8,000 mg/L for green alga. The threshold for Pseudomonas putida is 6,600 mg/L and > 10,000 mg/L for Entosiphon sulcatum protozoa. 7 According to Ingram and Buttke, 10-15% concentrations of alcohol are considered toxic to most microorganisms (1984). Butler reported that a 10,000 fold decrease in microbial population in an aquifer was observed in the presence of 19% methanol. As well, viability of the microbial population was adversely affected at 0.75-1.3% methanol. Inhibition of bacterial growth, or the 16 hr IC50 is >5000 mg/L (Verschueren 1996). Blum (1991) observed that for Nitrosomonas the 24 hr IC50 was 880 mg/L. For methanogens, the 48 hr IC50 was 22,000 mg/L. Aerobic heterotrophic bacteria 15 hr IC50 was 20,000 mg/L (Blum and Speece 1991). 7.5 Methanol- Utilizing Bacteria Methylotrophs, bacteria capable of utilizing C1 compounds as a source of energy are found ubiquitously in nature. Bacteria capable of degrading methanol are found in plant materials, soil, freshwater, marine water and air (Goldberg and Rokem 1991). They are abundant on several plant surfaces and are thought to play a role in protection from pathogenic bacteria. Methylotrophs form a very diverse group of bacteria and are not characterized by a single phylogenetic group. Pink-pigmented facultative methylotrophs are the main group of bacteria that use methanol as an energy source (Goldberg and Rokem 1991). They also grow using a wide range of multi-carbon compounds as sources for carbon and energy. Few are obligate or restricted methylotrophs. Methylotrophs can utilize C1 compounds through the ribulose monophosphate pathway (RuMP) or the serine pathway. Goldberg et al (1976) found that bacteria use CI compounds more efficiently using the RuMP than the serine pathway. Most obligate methanol utilizing bacteria use the RuMP pathway (Goldberg et al 1991). Pseudomethylotrophs are a group of 8 bacteria capable of oxidizing methanol to carbon dioxide using the Calvin cycle (Hanson 1998). Some bacteria also have the capability for denitrification (Goldberg et al 1991). In this study, river water will be examined. Unfortunately, the ecology of bacteria in streams and rivers and in particular, methylotrophs has not been adequately studied (Giller and Malmqvist 1998). However, bacteria in general are thought to be found mainly in biofilms and sediments (Giller and Malmqvist 1998; Naiman and Bilby 1998). For the purposes of this study, only the water matrix will be researched. 1.6 Mechanisms for Removal of Methanol from Surface Water In Section 1.3, mechanisms for methanol loss were briefly discussed. In this section, more detail as to the factors affecting loss and kinetics of methanol degradation will be given. Understanding the factors affecting methanol removal and the kinetics of methanol biodegradation is essential to make predictions regarding the risks methanol may pose to the environment and human health and the need for possible remediation efforts. Biodegradation and volatilization are thought to be major mechanisms of removal of methanol in surface waters (Katsumata and Kastenberg 1996). There are conflicting reports about the role of volatilization of methanol as a removal mechanism. In Katsumata et al (1996), they report that volatilization will play a major role in surface water removal. However, in other reports, volatilization is recorded to be not a prominent mechanism for methanol loss (Malcolm Pirnie Inc. 1999). The controversy arises over the significance of Henry's Law constant which is a ratio of the concentration of a chemical in the air to the concentration in water at equilibrium. In some cases, it is considered a relatively high constant, and in others it is considered low. This creates discrepancies in the evaluation of methanol removal mechanisms. The estimation of volatilization in surface water is complex and involves a number of different parameters. Important variables involved in estimating volatilization are wind speed, current speed, depth, and temperature of surface water (Lyman et al 1982). In a river system, some of these factors change constantly and it is therefore difficult to model a specific rate of volatilization (Pankow et al 1996). In a river system, the influence of dispersion and dilution are also significant factors. A study by Jamali et al (2002) looked at the dispersion of methanol in a river under a series of release scenarios. With an initial concentration of 1000 mg/L methanol was dispersed approximately 50 km downstream in a small river. Initial concentrations of 10,000 mg/L were dispersed approximately 200 km down river (Jamali et al 2002). It was estimated that larger rivers would greatly reduce the downstream concentrations of methanol. In turbulent surface waters, methanol will quickly mix throughout a river or stream in the event of a spill due to its miscibility. Methanol will also float initially on the top of the water as its density is less than water which will increase its rate of volatilization (Katsumata and Kastenberg 1996). Biodegradation is expected to be a main mechanism of loss of methanol in natural environments (Malcolm Pirnie Inc. 1999). The factors affecting the rate of biodegradation of methanol include: Temperature Availability of nutrients « Biomass (amount and type present) Initial methanol concentration Temperature is a significant factor in the growth of microorganisms. According to a sensitivity analysis done by Helle (2001), the rate of biodegradation at 15°C will be twice as fast as surface water at 5°C. Theoretically, this is due to optimal growing conditions for bacteria at a 10 higher temperature. Lower temperatures will impede growth, and therefore slow the rate of biodegradation (Helle 2001). Nutrients may also be a factor which limits the degradation of methanol. Macronutrients such as nitrogen and phosphorus and micronutrients such as iron, magnesium and calcium are essential for bacterial growth. Limitations of nutrients can lead to competition among bacteria and growth inhibition. Micronutrients are usually in adequate supply in natural surface waters. Nitrogen is usually in adequate supply, while phosphorus can be limited in natural freshwater systems (Rheinheimer 1985). The amount and type of biomass present in the surface water will also be a significant contributor the rate of methanol degradation. Under aerobic conditions, methanol biodegradation will occur as (Smith et al 2001): CH 3 OH + 0 2 -> C 0 2 + H 2 0 + biomass In anaerobic conditions (Smith et al. 2001): C H 3 O H + N0 3 " -> C 0 2 + 2H 20 + 1/2 N 2 CH 3 OH + 3/4 S0 4 2" -> C 0 2 + 2H 20 + 3/4 S2" In natural environments, the concentration of biomass may be low. After addition of a substrate such as methanol, there may be a lag time before the onset of biodegradation due to an acclimatization phase or as bacterial numbers increase (Hwang et al 1989; Alexander 1999). Also, organisms capable of biodegrading methanol must also be present in order for biodegradation to occur. Environmental conditions must also be generally favourable to growth in order for significant methanol uptake rates to be observed (Alexander 1999). 11 Initial methanol concentration (substrate, S) is important for two reasons. First, microbial growth rate (and methanol uptake rate) will be a function of the substrate concentration. Generally, microbial growth exhibits saturation kinetics which can be described by the Monod equation: ^ = |J-max*S/S+Ks where: u. = uptake rate of the substrate \xmax = maximum uptake rate of substrate S = substrate concentration Ks = half saturation constant Therefore, it is necessary to evaluate a wide range of starting points to determine the rate of biodegradation. For methanol, estimated values for u.max and Ks are 0.5 mg methanol/ mg biomass/day and 10 mg/L respectively (Helle 2001). Second, methanol may exhibit toxic effects at elevated concentrations, thereby reducing the substrate uptake rate below that predicted by the monod equation. For monod kinetics, at low methanol concentrations (S « K s ) , degradation can be estimated as a first order reaction. At high methanol concentrations, (S»Ks), degradation approximates zero order kinetics (Helle 2001). However, methanol degradation in a mixed microbial community may not follow simple Monod kinetics. In a study by Hwang et al. (1989), methanol biodegradation kinetics in a number of environments was considered. In lake and sea water analysis, methanol degradation was considered multi-phasic (Hwang et al 1989). This indicated that a number of kinetic systems were at work in the system as microbial communities changed in response to addition of the substrate. Therefore, as substrate concentration changed, so too did the kinetic parameters, making it difficult to fit to a simple Monod model. 12 1.7 Biodegradation of Methanol in the Natural Environment Modeling biodegradation for predictive purposes can be difficult due to the dynamic and variable conditions of the natural environment. Often, samples are taken from the natural environment to be studied in laboratory conditions to understand the nature of a chemical's behaviour (Lyman et al. 1982). Inherently, this process can often be flawed and lead to difficulties in linking data obtained in laboratory environments to the natural environment. It is important in the scientific research process to challenge old ideas and theories to make sure untrue assumptions do not perpetuate in the scientific body of knowledge (Ford 2000). Extrapolating data obtained in the lab can lead to a number of misjudgments of the actual behaviour of a chemical or compound in nature (Alexander 1999). In the case of methanol, it is completely miscible in water, making it difficult to track its biodegradability in a natural environment. The level of uncertainty in these conditions is high, making it difficult to form any predictive models. In general, many biodegradation studies are theoretical or based in previous assumptions that may, or may not be correct (Alexander 1999). These assumptions were either based on lab studies that did not correctly account for the number of different physical and biological interactions that occur in nature, or were based on the chemicals behaviour in another medium. Given the pitfalls of using lab studies to model or predict natural environments, they are nonetheless necessary when potentially toxic chemicals are being studied. When doing laboratory studies to predict the behaviour of a chemical, or of an ecological response in a natural setting, sources of uncertainty must be managed and understood. In a study by Eller et al. (2005), a comparison of field and microcosm studies were made to assess the predictive capacity of microcosm experiments. Population numbers and the structure of the bacteria community were analyzed in field rice paddies and greenhouse rice paddies. The study 13 concluded that extrapolations could be made, however careful consideration of population estimates would be needed in order to properly assess ecosystem responses in the field. In the current study, samples from the Fraser River were used for experimental work in the lab. River systems are more dynamic than rice paddies, and therefore more difficult to replicate in a lab setting, however, some generalizations can be made about using information collected in a lab to estimate behaviour in the field. To extrapolate information obtained in the laboratory to a natural setting, overall community structure may be similar in both situations, however assumptions of scale must be carefully considered. The capacity for methanol to be biodegraded has been studied in atmosphere, soil and groundwater studies. The biodegradation rate of methanol in surface water has not been studied experimentally, however there have been models derived to estimate the rate according to known factors. According to Howard et al. (1991), the biodegradative half life (the time it takes to degrade half of the initial concentration) of methanol in surface water ranges from 1 - 7 days. This value was based on estimates of unacclimated grab samples of aerobic soil/water suspensions in aquifers. Aqueous biodegradation half lives in anaerobic environments is expected to be 1-5 days (Howard et al. 1991). This estimate is based on unacclimated grab samples of anaerobic marine water/sediment and soil/water suspensions. In a modeling study, the multimedia compartment model, GEOTOX was used to assess the fate of methanol in different release scenarios. For a release of 5000 gallons of methanol into surface water over a one day period, approximately 35 days was required to remove methanol (Katsumata and Kastenberg 1996). It was deemed that both volatilization and biodegradation were the contributing mechanisms in this study, though the contributions of each were not elaborate on. The removal rate constant for surface water used in the model was 0.2616/day based on the half life values given in Howard et al. (1991). 14 In a doctoral thesis by Goldsmith (1985), methanol biodegradation capability was assessed in subsurface and groundwater matrices in Pennsylvania, New York and Virginia. Test conditions occurred in aerobic and anaerobic environments with a range of water quality parameters. Experiments were conducted in test tube microcosms with initial concentrations of methanol ranging from 1- 1000 mg/L. Initial bacterial populations were counted before experiments began, but were not measured for the duration of the experiments. Total cell counts were assessed using epiflurorescence microscopy and plate counts. Methylotrophs were not isolated in the experiments. General results indicated that methanol was readily biodegraded in all environments. Rates ranged from 0.8 mg/L/day to 20.4 mg/L/day (Goldsmith 1985). Rates were greater in the aqueous saturated zone of anoxic systems. Goldsmith suggests that this could be due to a higher proportion of the microbial community amendable to biodegrading methanol or that bacteria may be more active in that environment. Goldsmith also reports that, in only groundwater microcosms, methanol biodegradation rates were very slow. In 180 days of the experiment, approximately 50 mg/L of methanol had been lost from the initial concentration of 100 mg/L. Goldsmith infers that biodegradation in the water matrix is insignificant. A preliminary surface water study by Spruston (2002)was undertaken in similar conditions as the present study. Two sample sites near Mission, BC were selected for evaluation in the winter of 2002. Experimental design incorporated the use of sacrificial samples (samples were discarded after one use) in sealed flasks. An initial methanol concentration of 1000 mg/L was used and samples were incubated at 15°C and 4°C. Methanol removal results showed little difference in the control compared to raw water samples. Samples with nutrient amendments showed degradation of methanol and followed a first order decay. Nutrient amended flasks took approximately 25 days to remove methanol, while there was little change in the raw water samples at 15°C. A drop in pH was noted in samples with nutrient amendments. Initial microbial growth was observed, however microbial numbers declined after two weeks. 15 1.8 Fraser River Characteristics As mentioned in previous sections, the Fraser River was the sampling site for this project. The Fraser River is BC's largest river system and has the third greatest mean annual flow in Canada (Gray and Tuominen 1998). Its headwaters are located north of Prince George and empties into the Straight of Georgia in the lower mainland of BC. Stretching almost 1400 km in length, the Fraser is the world's most productive salmon river system with five species of salmon (Gray and Tuominen 1998). The river supports a wide range of industrial, forestry, agriculture and mining activities. A summary of the water quality parameters as monitored at the Mission, BC station is found in Table 1-1. Table 1-2 Summary of water quality parameters monitored at Mission, BC. Temperature range 0 -22 °C * Average suspended sediment 165 mg/L * DO 9.3 - 14.7 mg/L** pH 7.3-8.5** orthophospate 4.5 ug/L** nitrate 100 ug/L** Kjeldhal nitrogen 280 ug/L ** * Gray and Tuominen, 1998 ** Hall etal, 1972 1.9 Research Questions To complete a thorough study on the impacts of methanol on the environment, it is important to include quantitative data on the rate of biodegradation of methanol in surface water systems and the factors that affect the biodegradation rate. It is apparent that there is a gap between the theoretical and experimental data regarding the biodegradation of methanol in surface water. This practical knowledge would be useful due to the increasing interest in methanol as a sustainable fuel source. With an increase in demand and consequently, an increase in shipping, storage and distribution facilities, the potential for the release of methanol to the environment is high. The overall scope of this project was to determine the rate of biodegradation of methanol 16 that would occur under natural conditions in the Fraser River. The particular research questions considered in this study are as follows: a) Can indigenous fauna degrade methanol under natural conditions? If so, what is the rate of degradation? b) How is the rate of biodegradation affected by temperature, amount of nutrients, initial bacterial population and initial concentration of methanol? c) How does the microbial community change in response to methanol input? It is anticipated that successful completion of this project will yield valuable information concerning the overall environmental impact study of methanol spills into surface water. As well, an examination of the challenges of extrapolating results obtained in the laboratory to field behaviour will be given. 17 CHAPTER 2 - Methodology Methodology for this project included a combination of field and lab studies. Water samples were obtained from the Fraser River and taken back to the lab for experimental work. Four trials were successfully completed throughout the duration of the project. 2.1 Study Areas Two study areas were selected on the main arm of the Fraser River. Both sites are located within 10 km of the city of Mission, BC. The sites were chosen based on the following criteria: • Non - tidal, freshwater river to complement plume studies done by Methanex (Helle 2001). • Upstream of salt water intrusion Easy boat access • Accessible from the University These sites were also selected for comparison with a preliminary study undertaken in the previous year (Spruston 2002). Site A is located directly across from the city of Mission. Site B is located approximately 9 km upstream from Site A. The approximate GPS coordinates are, N 49 07 736 W 122 17 669 and N 49 07 703 W 122 12 583 for Site A and B, respectively. Figure 2-1 shows the location of Mission within the Lower Mainland of BC. 18 Figure 2 -1 Location of study sites within the Lower Mainland of BC (BC Ministry of Energy and Mines 2005). The city of Mission is surrounded mostly by agriculture and industry. Site A is located across from a saw mill and Site B is located upstream in an agricultural area and near a gravel pit. Figure 2 - 2 illustrates the land use patterns adjacent to the sample sites and shows where each site is located. Pictures of the samples sites are included in Appendix A. 2.2 Sample Periods It was expected that there would be seasonal variations in the rate of biodegradation of methanol by indigenous fauna in the Fraser River. The time of year will have an impact on water temperature, river flow rate, and amount of nutrients and biomass in the river (Giller and Malmqvist 1998). Seasonal variations in river conditions were accounted for in the study by sampling throughout the year. Sampling took place on January 19, March 10, July 15 and September 1 of 2004. The length of each experiment was approximately five to six weeks. 19 Figure 2 - 2 Map of land use patterns adjacent to sample sites near Mission, BC (BC Ministry of Energy and Mines 2005). Dark green represents high upland forested area, light green represents low land agriculture area and purple and pink represent urban or industrial area. 2.3 Experimental Design To summarize the general framework of the study, water samples were collected in the Fraser River and brought back to the laboratory where methanol was added to river water under a variety of test conditions. Two response variables were monitored, methanol concentration and bacterial counts. Field sampling and water quality analysis are discussed in sections 2.4 and 2.5 respectively. Methods for methanol and bacteria analysis are given in sections 2.6 and 2.7 respectively. During each trial, the rate of methanol loss was examined under three conditions. The first set of flasks was a control containing sterilized river water. The second set of flasks 20 contained unaltered river water. The third set of flasks contained river water amended with nitrogen and phosphorus to give additional nutrients to the microcosms. A comparison between the actual river microcosms and nutrient-amended microcosms was undertaken to determine if nutrient availability limited bacterial growth and therefore methanol biodegradation. All three conditions were tested at initial methanol concentrations of 1000 mg/L and 10,000 mg/L. A summary of the general framework is presented in Table 2 - 1. Three replicates of each condition were tested giving a total of thirty six flasks per trial. In the original proposal, three concentrations of methanol were planned as part of the study. In the first run of the project, the lowest concentration of methanol, 100 mg/L, was found to be too low to give reliable results. For the duration of the project, the 100 mg/L methanol concentration was excluded from the study. The differing initial methanol concentrations were included to determine how sensitive the rate of biodegradation was to the initial methanol concentration. In trials 3 and 4, another test was added to assess the rate of volatilization of methanol during the experiment. Sterilized distilled water was used instead of river water in microcosms. Sacrificial samples were used instead of repeated measurements from the same flask to ensure no contamination of the sample occurred. Measurements of methanol concentration were taken less frequently due to the small number of flasks available for sacrificial sampling. 21 Table 2-1 Overview of the experimental framework. Sample Methanol Addition Nitrogen (as (NH 4) 2S0 4) Phosphorus (as K 2 HP0 4 ) Sterilized (Control) 1000 mg/L No Additions No Additions 10 000 mg/L Actual River Conditions (River) 1000 mg/L No Additions No Additions 10000 mg/L Nitrogen and Phosphorus Additions (Nutrient) 1000 mg/L 6.2 mg/100 mg MeOH (Helle 2001)). 1.06 mg/ 100 mg MeOH (Helle 2001)). 10000 mg/L Experimental conditions in the lab were chosen to mimic river conditions as much as possible. Due to the dynamic nature of rivers, it was difficult to control for all variables, however, temperature, turbulence and oxygen requirements were taken into account. Samples were kept in a temperature controlled orbital shaker (Innova 4230 Refrigerated Incubator, New Brunswick Scientific) at the temperature the river was observed to be at the time of sampling. Microcosms were agitated at a shaking rate of 125 rpm and were kept in the dark. 250 mL Erlenmeyer flasks were sterilized in an autoclave at 121°C and 10 psi for 60 minutes. Experimental microcosms were constructed by transferring 100 ml of river water into pre-sterilized flasks topped with a foam plug to allow for oxygen diffusion. Control flasks were sterilized again after river water was added. The determination of the amount of nutrients added were based on a study by Helle (2001). The results of the study concluded that 6.2 mg of nitrogen per 100 mg of methanol and 1.06 mg of phosphorus per 100 mg of methanol were required to produce 53 mg of biomass/100 mg of methanol. These values were used to estimate the amount of nutrients needed to support 22 biomass in the flasks. Nutrient stock solutions were prepared by using (NFLj^SC^ for a nitrogen source and K2HPO4 as a phosphorus source. Concentrations of the stock solutions were 1.77 mg/L and 0.329 mg/L respectively. The stock solution was adjusted to a pH of 6.9 which was approximately the pH of the river at the time of sampling, and was sterilized before use. 2.4 Field Sampling There were a variety of sampling options that were considered to collect water samples in the field. Due to the varying nature of rivers, a number of sample points were taken into account. Factors such as run-off, aquatic plants and rocks near the banks of the river, currents and rate of flow have the potential to influence the amount of biomass present and hence the rate of biodegradation (Kalff 2002). Sampling points within close proximity to shore and at greater depths of the river will differ from sampling points taken in mid-river and at the surface of the water. Two sites were used to account for land use influences. Samples were also collected near the middle of the river to minimize the influence of runoff from land. As bacterial populations are known to be well distributed throughout a river, water samples were taken approximately 10 cm below the surface of the river (Pvheinheimer 1985). Samples were collected in clean, 1 L, sterilized, screw top Nalgene polypropylene bottles. Sample collection was done aseptically, meaning, closed sterilized bottles were immersed to approximately 10 cm below the surface, opened, filled and then recapped. A small airspace was left at the top of the bottle. Sample collection was done facing upriver to reduce potential contamination from the boat motor. Bottles were immersed in crushed ice during transportation back to the lab (Cavanagh et al, 1997). Samples for analysis of water quality parameters were collected aseptically in clean, disposable, sterilized 50 ml centrifuge tubes. Tubes were filled using the same immersion technique to the top leaving no air space and were kept on ice for transport to the lab. These samples were also kept on ice for transport back to the lab. 23 2.5 Water Quality Analysis In the field, pH, and temperature were measured using a waterproof Oaklon pH/conductivity deluxe meter, model # WD - 35630-69, pH/CON 10 Series. A three point calibration of the probe took place in the lab before each field excursion using pH standards. The probe was held at approximately 10 cm below the surface of the water until a stable reading could be taken. Dissolved oxygen readings were taken using a Model 59 dissolved oxygen meter from YSI Incorporated (Yellow Springs, Ohio). Water samples were also subjected to a range of assays in the lab. Parameters measured included dissolved oxygen, total organic carbon, orthophosphate (PO4), nitrate (NO3) and > ammonium (NH/). Total organic carbon samples were analyzed in the UBC Civil Environmental Engineering laboratory using a Dohrmann Phoenix 8000 carbon analyzer and the UV-Persulphate method. Samples were kept in a cold room at 4°C until analysis. Nutrient analysis was performed in the UBC Soils Department laboratory the day after sampling. Samples were kept on ice and then filtered using 0.25um, Whatman #41 filter paper before analysis. Nutrients were measured on a Lachat X Y Z QuickChemAE autoanalyzer using method #10-115-01-1-A for P0 4 (detection limit 0.02 mg/L), method #12-107-04-l-B for N0 3 " (detection limit 0.10 mg/L), and method #10-107-06-2-A for N H 4 + (detection limit 0.1 mg/L). Trials 3 and 4 were not analyzed for nutrients due to lab constraints. 2.6 Methanol Analysis The concentration of methanol in the flasks was monitored throughout the experiment using gas chromatography. An initial methanol concentration reading was taken before the 24 experiment started and another the following day. Subsequently, methanol analysis took place approximately every 3-4 days. To analyze methanol concentration, 1.5 mL of sample was aseptically removed from the microcosm and added to a 1.5 mL conical centrifuge vial. Samples were then centrifuged for five minutes at 6700 xg . One ml of supernatant was added to a clean GC vial. In addition, 0.1 mL of 5g/L 1- butanol solution was added to each vial as an internal standard. Five to six methanol calibration points were included in each set of samples analyzed. A calibration curve was constructed by plotting methanol concentration versus the area ratio of methanol to 1-butanol. The curve was then used to determine the methanol concentration in the sample. A sample calibration curve and calculations are included in Appendix B. Methanol concentrations were determined using a Varian CP - 3800 gas chromatograph. The GC included a Supelco capillary column (Supelcowax-10 TM 24080-U, fused silica 30m x 0.32 i.d. x 0.25 um film thickness). Based on an initial trial, it was found that the accuracy of the GC deteriorated at concentrations of 100 mg/L MeOH. The GC operating conditions are listed in Table 2 - 2 . Table 2 - 2 GC Operating Conditions Carrier Gas Helium @ flow rate of 25 ml /min Flue Gas Hydrogen @ a flow rate of 30 ml/min Air @ flow rate of 300 ml/min Flame Ionization Detector 200°C Injector 150°C Column Oven Temp. 80°C Hold for 2 minutes 200°C @ 20°C/min for 8 min 25 2.7 Bacterial Enumeration Two different bacterial enumeration techniques were used in this study. Total counts by epifluorescent microscopy was initially thought to be the most reliable and suitable method and this method was used in the first two trials of the project (Austin 1988). This method did not select for methanol degrading bacteria, rather counts included all bacteria. Results from the first two trials indicated that a different method was needed to select for methanol degrading bacteria and shorten processing time of samples. Consequently, the Most Probable Number (MPN) technique was used in the last two trials, trials three and four. To obtain a time zero bacterial count, samples were collected from the Fraser River in 50 mL clean and sterilized centrifuge tubes and kept on ice until processing. During the laboratory incubations, samples were taken for bacteria enumeration approximately every seven to ten days. Samples were processed within one day of sampling time. 2.7.1 Epifluoresence Microscopy Filtering and staining of bacteria was done according to the methods described in Porter and Feig (1980). For this method, one of two stains can be used, acridine orange (AO) and 4'6-diamidino-2-pheylindole (DAPI). AO binds to DNA and RNA and fluoresces green and red. Unfortunately, colloidal and detritus floes may pick up this stain which makes counting bacteria cells difficult. DAPI binds only to DNA and fluoresces bright blue. Other material may pick up DAPI but fluoresces a weak yellow colour making it easier to distinguish between cell and non-cell material. DAPI was used in this study because the Fraser River is inherently turbid and therefore this stain would show better quality results. Using DAPI also allows for a longer sample storage time (Porter and Feig 1980). Five mL of sample and 100 uX of 100 ug/L DAPI were added to glass columns attached to a filtration device. A 100 ug/L stock solution of DAPI was made by using double distilled 26 water and was kept in an opaque nalgene polypropylene container in the fridge. Dilutions of the original sample were periodically performed due to the amount of biomass produced during the experiment. The apparatus was covered with a black plastic sheet for five minutes to allow the stain to absorb. The sample was then filtered through a 0.2 um, 25 mm diameter black polycarbonate membrane filter placed on top of a 0.45 um, 25 mm backing filter. Once filtration was complete, a drop of low fluorescence immersion oil was placed on a 25 mm wide glass slide and spread evenly on the surface with a Pasteur pipette. The black filter was carefully placed on the slide and another drop of immersion oil was added before covering the filter with a 25 mm square cover slip. Slides were kept in an opaque storage box in the freezer until counting. Bacteria were counted using a Zeiss epifluorescence microscope and viewed under the lOOx objective using a #1 adsorption filter. Counts were made using a 10 x 10 grid mounted in the left objective to estimate the number of bacteria present in the sample. Ten fields were counted and used to calculate the cell concentration. Equation 2 - 1 was used to calculate the bacterial cell concentration. [Cell] = (total cells counted/total area counted) * (area of sample / volume filtered) (2-1) 2.7.2 Most Probable Number (MPN) Technique During the third and fourth trials, the MPN method was used to determine bacterial growth. The MPN technique is most often used for bacterial indicator studies (i.e. E. coli testing) (Jones 1979). In this method, a series of dilutions of the sample is prepared and aliquots of the dilutions are inoculated into a growth medium. Turbid growth in the tubes is counted as a 27 positive score. The combination of positive and negative scores were observed and compared to MPN tables to determine the number of bacteria in the samples (Oblinger and Koburger 1975). In this method, methylotrophs were selected for growth by using a Modified Nitrate Mineral Salts Medium (Hanson 1998). See Appendix C for full detail of the preparation of the media. Nine mL of media were dispensed into 16 x 150 mm disposable glass culture tubes (Cat no. 14-961-31 Fisher Scientific) and topped with a KIM-KAP autoclavable polypropylene cap (Kimble Products, K7366516, Fisher Scientific). Tubes were sterilized and, after cooling, methanol was added to the media as substrate. One mL of sample was added to the first dilution tube. Five subsequent dilutions of 10"' with three replicates of each sample were performed. Tubes were left to incubate at 37°C for 1 week and then observed for growth. 2.8 Uptake and Metabolism of14C labeled Methanol During trials 3 and 4, 1 4 C labeled methanol was used to determine the rate of uptake of methanol by bacteria. Tests were conducted using one sample from the control, river and nutrient flasks at 1000 mg/L initial methanol concentration. Experiments were undertaken at, or near the end of the trial period. The design of the method was similar to that found in Pfaender and Bartholomew (1982). Preparation of the labeled methanol involved making a stock solution of 0.13 uCi/ml isotope with a specific activity of 55 mCi/mmol. The stock solution was kept cold at 4°C until use. Based on a trial run, a number of modifications to the original method were necessary to account for the particulars of this study. Samples to be analyzed contained a considerable amount of unlabeled methanol compared to the amount of l 4 C labeled methanol added in the experiment. Therefore, it was necessary to wash the unlabelled methanol from the samples 28 before adding the labeled methanol. One mL of sample taken from the microcosm flasks was fdtered through a 0.45 um glass fibre filter to wash unlabelled methanol from the sample. The filter was then added to 8 mL of distilled water and 1 mL of the isotope in a 25 mL flask. Duplicates were made for each sample. A control for each sample was prepared by adding 0.5 mL of gluteraldehyde to deactivate bacteria in the sample. A 0.45 um, 25 mm cellulose nitrate membrane filter was then folded in quarters and placed into a center well trap suspended in the air space of the flask. Figure 2 - 3 depicts the set up of the flask. Flasks were plugged tightly overnight for approximately 24 hours and left to incubate at room temperature. After incubation, 0.2 mL of phenethylamine was added to the glass fiber filter to trap C O 2 and 0.3 mL of H2SO4 was added to the solution in the flask. The flasks were left to stand for approximately one hour before samples were processed. The solutions were filtered with the same membrane filter originally used and then filters were added to scintillation vials to be measured for l 4 C left in the particulates in the sample. Glass fiber filters were also transferred to scintillation vials to be measured for 1 4 C as C0 2 . Figure 2 - 3 Schematic of flask set up for 14C uptake analysis. 29 Labeled methanol counts were measured using the Beckman LS 6500 multipurpose scintillation counter in the external standard mode to correct for quenching. Comparisons were made between active and dead bacteria samples, and CO2 and particulate samples. 2.9 Data Analysis Analysis of data was conducted using MS Excel. Methanol loss trends, and bacterial counts were analyzed. Methanol loss data were measured and plotted against time. During the experiments, abnormal data points were occasionally observed and found to be unreliable due to inconsistent results from the GC. In these instances, internal standards were inconsistent and therefore these data points were removed from the data set. Data points not used are stated in Appendix D in bold italics. Concentration of methanol data was converted to a percent remaining over time to correct for variable starting concentrations. Graphs were created of the percentage of methanol remaining over time to visually compare test conditions. Linear regressions were used to obtain rates of loss of methanol. Regressions were tested for normalcy and P values were obtained for level of significance. To compare rates of change or trend lines of the different test conditions, regression analysis with categorical variables was used. This method involves creating a dummy variable to obtain P values for slopes and intercepts to determine if the relationship is significant or not. A description of this test is found in more detail in Townend (2002) and Berthouex et al (1994). Using this method for this data set to evaluate significance violates an assumption of the test. Due to the fact that the data in this project is on a time scale, the variables are not independent of each other. However, the main goal of the statistical tests was to compare the slopes of the trend lines and regression analysis using categorical variables was the only way to achieve this. 30 For each test condition, comparisons of site 1 and 2 were conducted in order to determine if there was a significant difference between the two. If there was no highly significant difference, the data was combined from the two sites. Next, the test conditions were compared against the control for evidence of a significant relationship. Rates of change and number of days for 50% of methanol loss were calculated based on these regressions. Trend lines were analyzed for first order reactions by graphing In (Co/Ct) vs time. However, none of the trials exhibited a first order reaction and therefore these were not included in this document. During the experiment, two temperature conditions emerged. Trials 1 and 2 showed cooler temperatures averaging 4.5°C. Trials 3 and 4 showed warmer temperatures averaging 17.7°C. Regression analysis was done to compare if there was a significant difference between the slopes of the two trials in each temperature range. Results were graphed to visually compare the affect of temperature on methanol loss. Graphs were created to plot the enumeration of bacteria throughout the experiments. Averages of the three replicates were plotted and standard deviations calculated. A qualitative analysis was conducted to observe changes over time. 31 CHAPTER 3 - Results This chapter presents the results from the four trials conducted in this study. Each trial is presented separately and then a comparison of the trials is given at the end of the chapter. Raw data for methanol concentrations, bacterial enumeration and l 4 C analysis are given in Appendices, D, E and F respectively. Water levels during the time of sampling are located in Appendix G. Results from statistical tests are given in Appendix H. 3.1 Trial One Field sampling occurred on January 19, 2004 and experimental work began on January 20, 2004 for this trial. 3.1.1 W a t e r Parameters Water parameters monitored during this trial included, temperature, pH, dissolved oxygen, total organic carbon, orthophosphate, nitrate and ammonium. Table 3-1 provides a summary of the parameters. Parameters were within the normal range for the time of sampling (Hall et al 191 A). Table 3 - 1 Trial 1, summary of water parameters. S i t e l Site 2 Average Temp. ( °C) 3.9 3.7 3.8 P H 6.9 6.9 6.9 D O (mg/L) 12.6 12.7 12.65 T O C (mg/L) 2.23 2.21 2.22 ±0.01 P 0 4 - P (ug/L) 12.0 ±0.02 12.0 ±0.02 12.0 ±0.3 N 0 3 - N ( u g / L ) 181.9+1.9 159.9 ±0.9 170.9 ± 12.8 N H 4 - N ( u g / L ) 94.5 ± 12.4 124.4 ±28.3 109.4 ±24.8 32 3.1.2 Methanol Loss Methanol loss was monitored for 43 days in this trial. Figures 3 - 1 and 3 - 2 show the fraction of methanol remaining in the samples over time for each initial concentration of methanol tested, 1000 mg/L and 10,000 mg/L respectively. In each trial, it is noteworthy that methanol loss was observed in each control. It was not known at first if this was due to contamination of the sample, or due to volatilization. This will be explored further in the discussion section of this document. In this trial, highly significant rates of loss of methanol occurred in the nutrient amended samples with an initial concentration of 1000 mg/L compared to the control and the non-amended river samples ( P « 0 . 0 0 1 ) . There was no significant difference between the control and the non-amended river samples (P> 0.05). 1.2 UI c 'E '<5 E a QC I O 0.8 0.6 c o 0.4 0.2 I ! M i . . . C: y = -0.0079x + 1.0297 R2 - 0.87 R: y = -0.0083x + 1.0414 R2 = 0.54 N: y = -0.0271x + 1.0751 R2 = 0.91 A A • t A * » A i A 10 15 20 25 30 35 Day • Control • River A Nutrient 40 45 50 Figure 3 - 1 Trial 1, fraction of methanol remaining over time with an initial concentration of 1000 mg/L (P< 0.01). 33 Results for the initial concentration of 10,000 mg/L of methanol test condition showed similar results to the 1000 mg/L test. A significant difference was observed between the control and the nutrient amended samples (P<0.05). However, there was no significant difference between the control and the non-amended samples and the non-amended samples compared to the nutrient amended samples. Rates of methanol loss in samples with an initial concentration of 1000 mg/L (7-30 mg/L/day) were lower than samples with an initial concentration of 10,000 mg/L (90-140 mg/L/day). 1.4 1.2 c m E © a. x o o 0.8 o c o o 0.6 i i 0.4 0.2 I • • • • • I A • I if - - , • 4. C: y = -0.0113x+ 1.0238 R2 = 0.93 R: y = -0.0111X + 1.106 R2 = 0.56 N: y = -0.0145x + 1.1181 R2 = 0.69 9 A A A 5 10 15 20 25 Day 30 35 40 45 50 • Control • River • Nutrient Figure 3 - 2 Trial 1, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L (P <0.01). In both test conditions, there was a high degree of scatter in some of the test conditions. However, there was a highly significant linear relationship for all trend lines (P«0.001). 34 3.1.3 Bacterial Enumeration In this trial, total numbers of bacteria were counted using the epifluoresence method. Figure 3-3 presents the bacterial enumeration results for Trial 1. Control flasks were counted for bacteria and specs of material were observed. However numbers were insignificant and were not included in the graph. It was assumed that little to no contamination occurred. The observed specs could have been due to particles in the water, due to the turbidity of the water, and not bacteria. Both river water samples showed a decrease in numbers after methanol addition. Numbers rebounded after 16 days and growth continued to just above the initial population numbers. In comparison, the nutrient flasks showed significant growth compared to the non-amended river samples. Nutrient amended samples showed growth after addition of methanol and continued to increase in numbers throughout the experiment. The nutrient amended flasks with an initial concentration of 10,000 mg/L showed steady growth for 30 days and significant growth until the end of the experiment. Visually, growth was observed in all flasks except for control flasks. Nutrient amended flasks showed the most dramatic growth of biomass. Clumping of biomass was also observed. 35 120000000 100000000 80000000 _ l E Rl •c | j 60000000 0 5 10 15 20 25 30 35 40 45 50 Day • River 1000 - - • - - River 10,000 — N u t r i e n t 1000 - - * • - Nutrient 10,000 Figure 3 - 3 Trial 1, total bacteria counts using epifuoresence microscopy. 3.2 Trial Two River sampling occurred on March 10, 2004 for this sample and microcosms began March 11,2004. 3.2.1 W a t e r Parameters Water parameters monitored during this trial included, temperature, pH, dissolved oxygen, total organic carbon, orthophosphate, nitrate and ammonium. Table 3 - 2 provides a summary of the parameters. Parameters were within the normal range for the time of sampling (Hall et al 1974). 36 Table 3 - 2 Trial 2, summary of water parameters. Sitel Site 2 Average Temp. (°C) 5.3 5.0 5.2 PH 6.9 7.0 6.95 DO mg/L 14.5 14.5 14.5 TOC mg/L 2.46 2.53 2.50 ±0.05 P0 4 -P(ug/L) 12.0 ±0.5 12.0 ± 1.0 12.0 ±0.6 N 0 3 - N ( u g / L ) 180.4 ±4.5 176.8 ±21.6 178.6 ±3.8 N H 4 - N (ug/L) 81.0 ± 18.9 182.7 ±65.4 131.87 ±70.6 3.2.2 Methanol Loss Methanol loss was monitored for 42 days in this trial. Figures 3 - 4 and 3 -5 illustrate the percentage of methanol remaining over time. Test conditions for an initial concentration of 1000 mg/L and 10,000 mg/L methanol, respectively, are given. Results from this trial were similar to trial 1. Control flasks again exhibited loss of methanol. Non-amended river samples and nutrient amended samples showed the same trends as in the first trial. There was a highly significant difference between the control and nutrient- amended flasks at an initial concentration of 1000 mg/L (P«0.001). As well, a highly significant difference was observed between the non-amended river samples and nutrient-amended flasks (P«0.001). 37 1.4 0 5 10 15 20 25 30 35 40 45 Day • Control • River • Nutrient Figure 3 - 4 Trial 2, fraction of methanol remaining over time with an initial concentration of 1000 mg/L (PO.01). Highly significant differences (PO.001) were also observed between the control, non-amended river samples and nutrient amended samples with an initial concentration of 10,000 mg/L. Again, rates of loss were higher with an initial concentration of 10,000 mg/L (50- 115 mg/L/day) than in the 1000 mg/L flasks (6 - 22 mg/L/day). All trend lines showed a significant linear relationship although there was some scatter in the data (PO.001). 38 1.2 8 0.8 0.6 0.4 0.2 A A A A A A - A : C: y = -0.0107x +0.9794 R2 = 0.67 R: y = -0.0107x +0.9554 R2 = 0.72 N: y = -0.016X + 1.0507 R2 = 0.71 A ! i i I A I A A 10 15 20 25 Day • Control • River A Nutrient 30 35 40 45 Figure 3 - 5 Trial 2 , fraction of methanol remaining over time with an initial concentration of 10,000 mg/L (P< 0.01). 3.2.3 Bacterial Enumeration In this trial, epifluoresence microscopy was used to determine total counts of bacteria. There was more variation in these samples than in trial 1, particularly in the nutrient amended samples. During the analysis, it was necessary to dilute the nutrient amended samples due to the high density of bacteria on the slides. The dilution may have affected the actual numbers counted in the samples as bacterial clumping was observed visually in the flasks. Clumping of the bacteria may have caused high variability in the samples. Similar trends were observed in this trial compared to the first trial. River water samples with no nutrient amendments showed a drop in numbers after the addition of methanol. Bacterial numbers began to increase after 33 days, however not dramatically. Similarly, nutrient amended 39 samples followed the same trend in this trial as in the first. Bacterial numbers in these flasks were significantly higher than the river water samples with no nutrient amendment. A general increase in numbers of bacteria with nutrient amendments was observed in flasks with an initial concentration of methanol of 10,000 mg/L. The results from this trial had more oscillations in bacteria numbers than the first and could have been caused by the dilution of the samples and clumping in the bacteria. 30000000 25000000 20000000 _i E ro *c % 15000000 ro o 10000000 5000000 0 0 5 10 15 20 25 30 35 40 45 Day — • — R 1000 - B - R 10000 —*— N 1000 - A - - N 10000 Figure 3 - 6 Trial 2, total bacterial counts using epifluorescence microscopy. 3.3 Trial Three The sampling for this trial occurred on July 14, 2004 and microcosms began on July 15, 2004. 40 3.3.1 Water Parameters Water parameters measured in this trial were temperature, pH, dissolved oxygen and total organic carbon and are given in table 3 - 3 . Nutrient data was not available for this trial. pH was measured again after the experiment was terminated and is given in table 3 - 4 . Table 3 - 3 Trial 3, summary of water parameters. Site 1 Site 2 Mean Temperature ( °C) 17.5 17.7 17.6 P H 6.9 6.9 6.9 D O mg/L 10.6 10.9 10.8 T O C mg/L 1.84 1.86 1.85 As shown in table 3 - 3, pH levels at the end of the experiment dropped in the nutrient amended flasks. pH dropped most significantly in flasks amended with nutrients and with an initial concentration of 10,000 mg/L. The control and non-amended river samples were slightly above the initial pH readings. Table 3 - 4 Tria C 1000 7.8-8.1 C 10,000 7.8-8.0 R1000 7.8-8.0 R 10,000 7.8-8.0 N 1000 5.7-7.3 N 10,000 5.1-5.7 3, pH ranges of test conditions at the termination of the experiment. 3.3.2 Methanol Loss Methanol loss was monitored for 46 days in this trial. Figures 3 - 7 and 3 - 8 show the percentage of methanol remaining over time for each test condition, 1000 mg/L and 10,000 mg/L respectively. In this trial, no significant differences were observed between any of the test conditions with an initial methanol concentration of 1000 mg/L. Rates of loss were greater in this trial (15-180 mg/L/day) than in the first two trials (6-140 mg/L/day). Distilled water tests showed loss of 41 methanol as well. However, due to the small numbers of data points, it was not possible to fit the data to a linear trend line. 1.4 1.2 I i i I . . i • ' f C: y = -0.018x + 1.0097 R2 = 0.89 R: y = -0.017x + 1.0147 R2 = 0.89 N: y = -0.0192X + 1.0145 R2 = 0.84 o> 0.8 x o s o 0.6 c o u 2 u. 0.4 0.2 A A I • I f f i a -m t I A ! 0 10 15 20 25 Day 30 35 40 45 50 • Control • River A Nutrient * DW Figure 3 - 7 Trial 3, fraction of methanol remaining over time with an initial concentration of 1000 mg/L (P<0.01). A significant difference was observed between the control and nutrient amended flasks with an initial methanol concentration of 10,000 mg/L (P< 0.01). However, there were no significant differences between the control and non-amended river samples and the non-amended river samples and the nutrient- amended samples. Scatter in data points was small and a significant linear relationship was observed in all trend lines ( P « 0 . 0 0 1 ) . 42 • Control • River A Nutrient • DW Figure 3 - 8 Trial 3, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L (PO.01). 3.3.3 Bacter ia l E n u m e r a t i o n Methylotrophs were selected in this trial for enumeration using the MPN method. At the beginning of the trial only a small number of methlyotrophs were present in the river water. After addition of methanol, all experiments showed an increase in numbers and peaked at approximately 12 days. Methylotroph numbers decreased in all samples after this point and dropped to almost zero. After the drop in population, river water samples with no nutrient amendments recovered somewhat compared to the nutrient amended samples. Flasks with an initial methanol concentration of 1000 mg/L showed the most growth initially compared to the flasks with an initial methanol concentration of 10,000 mg/L. The nutrient amended experiment with an initial methanol concentration of 10,000 mg/L has an outlier at 12 days. An estimation of bacteria concentration was made in order to carry out the 43 MPN technique and the range of concentrations chosen were incorrect giving a skewed number of bacteria at this point. Control flasks were also counted and showed no growth in any samples. 160000 Outlier 120000 _, 100000 0 5 12 20 26 36 46 Day m— R 1000 • • • • • R 10000 —A—N 1000 - ± - - N 10000 Figure 3 - 9 Trial 3, methylotroph bacteria counts using MPN. 3.3.5 1 4 C Labeled Methanol Uptake Rates On the last day of the trial, day 46, a 1 4 C uptake measurement was taken. Sample 1 from the control, river and nutrient samples with initial methanol concentration of 1000 mg/L was used for the analysis. Table 3 - 5 gives a summary of uptake rates of the three experiments. Results are separated by l 4 C labeled C O 2 and particulates (P). There was little difference in rates of uptake in the particulates of each sample. Measurements for l 4 C labeled C O 2 in the control was not able to be calculated as the sample was 44 unreliable. Nutrient amended samples showed approximately a three fold increase in uptake rates compared to the river samples. Table 3 - 5 Trial 3, 14C labeled methanol uptake rates in ug/L/day. co 2 P Total ( C O z + P) Control NA 2.4 NA River 14.7 1.1 15.8 Nutrients 45.9 1.0 46.9 3.4 Trial Four Sampling occurred on September 1, 2004 and microcosms began on September 2, 2004. 3.4.1 W a t e r Parameters Water parameters measured for this trial included, temperature, pH, dissolved oxygen and total organic carbon and are given in Table 3-6 . Nutrient levels were not able to be measured during this trial. pH levels were measured following the experiment and are given in Table 3 -7. Table 3 - 6 Trial 4, summary of water parameters. S i t e l Site 2 Average Temp. ( °C) 17.8 17.8 17.8 P H 7.1 7.1 7.1 D O mg/L 9.81 9.85 9.83 T O C mg/L 1.79 1.89 1.84 As seen in trial 3, pH levels in trial 4 dropped in the nutrient amended samples. The control and non-amended flasks showed a slight increase in pH. A dramatic drop in pH was observed in the nutrient amended flasks with an initial concentration of 10,000 mg/L. Table 3 - 7 Trial 4, pH ranges of test conditions at the termination of the experiment. C 1000 7.9-8.5 C 10,000 8.0-8.1 R1000 7.5-8.2 R 10,000 7.6-8.1 N1000 6.4-6.8 45 N 10,000 4.8-6.2 3.4.2 Methanol Loss Methanol loss was monitored for 43 days in this trial. Figures 3-10 and 3-11 illustrate the percentage of methanol remaining over time for each initial methanol concentration, 1000 mg/L and 10,000 mg/L respectively. This trial showed similar results as trial 3. In this trial, there was a slight significant difference (P<0.05) between the control and the nutrient amended samples with an initial concentration of 1000 mg/L. There were no significant differences between the control and the non-amended river samples. Also, there was no significant difference between the amended and non-amended samples. Distilled water samples showed loss of methanol, however, due to a small number of data points, a linear relationship was not established. 1.40 1.20 1.00 >i • • I I • 1 2 0.80 x o s 0.60 o 0.40 0.00 C: y = -0.0156x + 0.9551 R 2 = 0.74 R: y = -0.0163x + 0.988 R 2 = 0.89 N: y = -0.0183x + 0.9918 t R 2 = 0.87 J f 10 15 20 25 30 35 40 45 50 Day • Control • River A Nutrient DW Figure 3-10 Trial 4, fraction of methanol remaining over time with an initial concentration of 1000 mg/L (PO.01). 46 Test conditions with an initial concentration of 10,000 mg/L of methanol showed no significant difference between any of the test conditions. Again, distilled water flasks showed loss of methanol concentration, though no linear relationship was established due to low numbers of sample points. 1.20 1.00 I = 080 c '5 E 0) K X ° 0.60 S 4 — o c o I 0.40 0.20 0.00 1 \ J i t. t I ! ! t y = -0.0185x + 0.95 R 2 = 0.89 y = -0.0191x + 0.9592 R 2 = 0.94 y = -0.0179x +0.975 R 2 = 0.87 A A * . i 10 15 20 25 Day 30 35 40 45 Control • River A Nutrient DW 50 Figure 3 - 1 1 Trial 4, fraction of methanol remaining over time with an initial concentration of 10,000 mg/L (PO.01). 3.4.3 Bacterial Enumeration This trial also used the MPN technique to select for methylotrophs in the flasks. A pattern of growth similar to trial 3 was observed. Experimental flasks with an initial concentration of 10,000 mg/L methanol showed less growth than the 1000 mg/L initial methanol concentration flasks. An initial peak at 7 days was observed and bacterial numbers declined in 47 all experimental flasks following this peak. Microbial growth in nutrient amended cultures continued to be low, while the non-amended flasks rebounded in numbers following the decline. Day 36 of the trial showed a great increase in numbers in the river water with an initial concentration of 10,000 mg/L. This point is considered an outlier as there were two flasks showing exceptional growths compared to the others, skewing the averages. No growth was observed in any of the control samples. 25000 10 15 20 25 30 35 40 45 50 Day — • — R 1000 - - R 10000 — A — N 1000 - * - - N 10000 Figure 3 -12 Trial 4, methylotroph bacteria enumeration. 3.4.5 , 4 C Labeled Methanol Uptake Rates The rate of uptake of methanol was monitored on Day 35 of the trial. Samples from Site 1 and 2 were taken from each experimental condition at 1000 mg/L. Rates are given in Table 3 -6. In most cases, Site 2 uptake rates were higher than Site 1. An exception is observed in the nutrient amended sample as the particulate numbers are lower for Site 2 than Site 1. The control 4 8 showed little uptake in comparison with the nutrient amended and non-amended samples. Nutrient amended samples showed higher rates of total uptake than non-amended samples. Table 3 -8 Trial 4, 1 4 C labeled methanol uptake rates in ug/L/day. Shaded columns represent Site 1 data and white columns represent Site 2 data. c o 2 P Total (C0 2 + P) Control NA 40.4 3.4 5.5 NA 45.9 River 24.5 164.2 124.5 159.8 149.1 324.0 Nutrients 176.1 267.1 104.4 76.4 . 280.5 343.5 3.5 Comparison of trials In this section, a comparison of the four trials is given. Each trial was conducted at different times throughout the year in order to account for seasonal differences. Table 3 - 9 gives a summary of water quality parameters throughout the project. Values are an average of sites 1 and 2. After the trials were completed a clear distinction was seen between trials 1 and 2 and trials 3 and 4. Samples for trials 1 and 2 were taken mid-winter and early spring with temperatures averaging 4.5°C. Trials 3 and 4 were conducted in mid-summer and early fall with an average temperature of 17.7°C. There is a slight decrease in TOC in trials 3 and 4 compared to trials 1 and 2. Other water parameters differed slightly between trials, though not dramatically. The four trials can be classified into two categories, cool and warm temperatures. It is worth comparing the two temperature classifications to determine if any trends exist according to temperature. A comparison of the cool water (mean temperature 4.5°C) and warm water (mean temperature 17.7°C) trials will be given at the end of this section. 49 Table 3 - 9 Summary of water parameters for Trials 1 - 4. Trial 1 Trial 2 Trial 3 Trial 4 Temp. (°C) 3.8 5.2 17.6 17.8 PH 6.9 6.9 6.9 7.1 DO (mg/L) 12.7 14.5 10.8 9.8 TOC (mg/L) 2.22 2.5 1.95 1.84 P0 4(ug/L) 12.0 ± 0 . 3 12.0 ± 0 . 6 N A N A N 0 3 (ug/L) 170.9 ± 12.8 178.6 ± 3 . 8 N A N A N H 4 (ug/L) 109.4 ± 2 4 . 8 131.87 ± 7 0 . 6 N A N A A comparison of methanol loss rates are given in table 3-10. Calculations were based on slopes of linear trend lines of concentration over time graphs. In cases where a significant difference existed between the control and the test condition, rates were corrected for volatilization (Rates were corrected by subtracting the rate of loss in the control from the test condition). Corrected rates are given in brackets. Table 3-10 Comparison of methanol loss rates in mg/L/day. Numbers in brackets are corrected rates for volatilization. Trial 1 Trial 2 Trial 3 Trial 4 Control 1000 6.6 5.7 16.1 14.6 River 1000 8.7 6.8 15.4 15.9 Nutrient 1000 30.2 (23.6) 22.1 (16.4) 17.2 17.5 Control 10,000 92.8 52.8 177.8 178.1 River 10,000 109.9 78.0 166.9 177.2 Nutrient 10,000 142.1 (49.3) 114.9 (62.1) 158.4 164.7 Table 3-11 compares the number of days taken for 50% removal of methanol. Results were calculated based on equations of linear regression trend lines of percentage of methanol remaining over time. None of the trials were able to be fit to a first order reaction and therefore half lives were not calculated. 50 Table 3-11 Comparison of number of days for 50% removal. Tr ia l 1 Tr ia l 2 Tr ia l 3 Tr ia l 4 Control 1000 67.0 61.3 28.3 29.2 River 1000 65.2 62.4 30.3 29.9 Nutrient 1000 21.2 20.3 26.8 26.9 Control 10,000 46.5 44.8 22.2 24.3 River 10,000 54.6 42.6 23.1 24.0 Nutrient 10,000 42.6 34.4 24.8 26.5 Figures 3-13 and 3-14 illustrate the percentage of methanol remaining over time for each condition with cool water and warm water temperature trials. In general, cool water test conditions lost methanol at a slower rate than warm water conditions. An exception to this is seen in the trials conducted at cool water temperatures with nutrients amended to the microcosms with an initial methanol concentration of 1000 mg/L. These microcosms had a higher rate of methanol loss than any of the other warm water or cool water trials in this test condition. 51 1.4 1.2 1* i o.s E X o o 0.6 0.4 0.2 * -« -• • A li « ! • J . Warm Water Trend Lines C: y = -0.017x +0.9842 R2 = 0.83 R: y = -0.0167x + 1.0015 R2 = 0.89 N: y = -0.0188x + 1.0035 R2 = 0.85 A A Z • A -A A f • • • * A - A i 4 I I A Cool Water Trend Lines C: y = -0.0087x + 1.0498 R2 = 0.69 R: y = -0.0091x + 1.085 R2 = 0.53 N: y = -0.0271x + 1.0535 • R2 = 0.89 11 • • • • A A A A 10 15 20 • C1000 Cool I R 1000 Warm 25 Day • C1000 Warm AN1000 Cool 30 35 • R 1000 Cool AN 1000 Warm 40 45 50 Figure 3-13 Comparison of cool and warm water trials with an initial concentration of 1000 mg/L. 52 1.4 1.2 0.6 0.4 0.2 I A A A \lfh i.jj i H i Warm Water Trend Lines C: y = -0.0193x +0.9444 R2 = 0.91 R: y = -0.0189x +0.9461 R2 = 0.94 N: y = -0.0176x +0.9513 R2 = 0.88 H : ! i si !i A I Cool Water Trend Lines C: y = -0.0109x +0.9977 R2 = 0.76 R: y = -0.0104x +1.0147 R2 = 0.53 N: y = -0.0152x + 1.0747 R2 = 0.66 I A I . * A * A . ' I I n I A i ! | A I I 10 15 20 25 Day 30 >C10,000 Cool I R10.000 Warm • C10,000 Warm AN10.000 Cool 35 • R10.000 Cool AN10,000 Warm 40 45 50 Figure 3-14 Comparison of cool and warm water trials with an initial methanol concentration of 10,000 mg/L. Tables 3-12 and 3-13 compare methanol loss rates and number of days for 50% loss of methanol for cool water and warm water temperatures. With the exception of the nutrient amended trials, warm water trials lost methanol approximately twice as fast as the cool water trials. Nutrient amended microcosms with an initial methanol concentration of 1000 mg/L showed comparable rates in both cool and warm, though cool water trials were slightly faster. The nutrient amended microcosms with an initial methanol concentration of 10,000 mg/L also showed comparable rates in both cool and warm water trials, though the warm water trials were slightly faster. 53 Table 3-12 Comparison of methanol loss rates between cool and warm water temperature microcosms in mg/Lday. Cool (4.5°C) W a r m (17.7°C) C1000 6.2 ± 0.6 15.4 ± 1.1 R1000 7.8 ± 1.3 15.7±0.4 N1000 26.2 ± 5.7 17.4 ±0.2 0 0 , 0 0 0 72.8 ± 28.3 178.1 ±0.2 R10,000 94 ± 22.6 172.1 ±7.3 N10,000 128.5 + 19.2 161.6 ±4.5 Table 3-13 Comparison of number of days for 50% loss of methanol between cool and warm water temperature microcosms. Cool (4.5°C) W a r m (17.7°C) C1000 64.2 ±4.0 28.8 ±0.6 R1000 63.8 ± 2.0 30.1 ±0.3 N1000 20.8 ± 0.6 26.9 ±0.1 C10,000 45.7 ± 1.2 23.3 ± 1.5 R10,000 48.6 ±8.5 23.6 ±0.6 N10,000 38.5 ±5.8 25.7 ± 1.3 CHAPTER 4 - Discussion The results from this study revealed that only under limited circumstances, did biodegradation of methanol take place. In trials 1 and 2, with an addition of nutrients and an initial concentration of 1000 mg/L, biodegradation of methanol was significant. In all other cases, little to no biodegradation took place. These results did not fit with the theoretical analysis that methanol would be readily biodegraded. It was theorized that the biodegradation of methanol would follow a zero order or first order reaction (Goldsmith 1985; Helle 2001). However, most of the data could not fit within these kinetic models. In samples with an initial methanol concentration of 10,000 mg/L, little to no evidence of biodegradation was observed. Possible explanations for this may be that the concentration was beyond the threshold for bacteria to metabolize the methanol or that their metabolic activity was not significant to produce a detectable decrease in methanol concentration. If there was a spill in the natural environment, methanol concentration will likely be diluted due to the dynamic nature of surface water. This may dilute the concentration to a level that is not inhibitory for methanol degradation to take place. The role of temperature, nutrients, initial methanol concentration and interactions between natural bacterial species were important factors in determining the potential of biodegradation of methanol. Volatilization also became an important contributor to the loss of methanol over time making it more difficult to model the actual biodegradation rate. These factors will be considered in more detail later in this chapter. As was observed in this project, modeling biodegradation in the natural environment is difficult due to the dynamic nature and number of variables involved in the process. In Alexander (1999), a number of factors are outlined that contribute to the difficulty of modeling biodegradation in the natural environment. They include: • 55 a) Other organic chemicals that can be metabolized by methylotrophs may repress or enhance the use of methanol. Total organic carbon measurements were made throughout this project and showed the presence of alternative carbon sources that may have been metabolized by facultative methylotrophs. b) Inorganic nutrients, dissolved oxygen and other growth factors may regulate the process of biodegradation. These factors will be discussed in the following sections of this chapter. c) There may have been a number of different species metabolizing methanol simultaneously and these organisms may have different Ks values. d) Protozoa or other species parasitizing the methylotroph population may have had an impact on the growth, size or activity of methylotrophs. e) Organisms active in the microcosm may have developed into microcolonies and the kinetics of these systems affected by microcolonies is unresolved. In the microcosms there were considerable amounts of clumping observed in all flasks except for the controls. f) An acclimatization period to methanol may exist and most kinetic models ignore the acclimatization period. 56 From this set of factors, it is clear that there is a high level of uncertainty as to how methanol will react in natural conditions. As is explained in Breckling et al (2000), decreasing uncertainty in ecological research is difficult. However, there are ways to handle and manage sources of uncertainty. Among them include, long term data collection, comparison of similar cases, repetition to reduce error, and becoming aware of new emergent properties (Breckling et al 2000). As discussed by Ford (2000), new emergent properties, and therefore new theories, may be missed if challenges to old theories and assumptions are not made. From the literature on methanol biodegradation, there is an assumption that this mechanism is the predominant method of loss in surface water systems. Based on the results of the current study, it is clear that these assumptions may not be well founded and methanol removal involves a more complex set of factors. Longer term data monitoring and increased repetition in experiments are necessary in dynamic systems such as rivers and may reveal new trends. 4.1 Role of Volatilization From the results of the study, it is clear that, aside from biodegradation, another mechanism of methanol loss was responsible for the loss of methanol observed in the control. Volatilization of methanol is thought to have been the other major contributor to methanol loss. Literature estimates noted that volatilization would occur but would not be a major factor in methanol loss (Malcolm Pirnie Inc. 1999). In other literature, methanol is noted to have a medium Henry's law constant and therefore possibly a significant volatilization rate (Katsumata and Kastenberg 1996). In all four trials, loss of methanol occurred in the controls. Indeed, in most of the trials the control lost methanol as quickly as the other experimental tests. If biodegradation was the only removal mechanism then the control should not have had any loss of methanol as it had 57 been sterilized. While it is possible that contamination may have affected uptake in the controls, it is unlikely as bacteria counts were negligible in the controls. Due to the experimental set up, volatilization of methanol during the procedure could have been a significant contributor to methanol loss. Samples were not kept in an air tight flask to allow for aeration and therefore methanol volatilization could have occurred. The contribution of volatilization of methanol could have been avoided if flasks had been completely sealed, however this would have made the flasks an anaerobic environment which was not part of the study design. In trials three and four, the significance of volatilization was quantified by using distilled water microcosms. The distilled water tests were conducted by using sacrificial samples to decrease the chance of contamination from repeated sampling. Therefore, it was assumed that any loss of methanol observed would be due to volatilization. The results indicated that there were significant losses of methanol in the distilled water microcosms (approximately 40-45% over the test period). However, these losses were not as significant as in the other test conditions and did not follow a linear regression. The difference in rates between the distilled water tests and the other test conditions can be attributed to the difference in sampling procedure. The distilled water microcosms were analyzed using sacrificial samples, while the other test conditions were submitted to repeated measurements. Therefore, the frequency of testing in the other test conditions was high compared to the distilled water tests. If any methanol was suspended in the airspace of the flask, this would be released each time the foam plug was removed. Since this occurred repeated times during the other test conditions, the likelihood of any methanol in the vapor phase to be released and lost to volatilization is significant. The removal of methanol through volatilization in the distilled water tests was still significant, though not as significant as the other test conditions because removal of the foam plug happened less frequently. 58 To calculate the theoretical rate of volatilization in surface water, a number of factors must be considered. Volatilization is heavily dependant on wind speed, current speed, depth of flow and temperature. In nature, these variables can change regularly, thus a standard rate of volatilization is not possible to calculate. However, estimates can be made to generalize the rate. There are a number of different methods to consider in order to estimate the rate of volatilization. Each method is specific to the environment being evaluated. Following the methods in Lyman et al (1982) to estimate the volatilization in surface water, the half life of methanol, with a depth of 8m (approximate depth at sampling area in Mission) and at a temperature of 25°C is 23.4 hrs. The theoretical volatilization half life that would occur in the flasks would be considerably smaller due to the smaller sample size. However, the presence of the foam plug would prevent volatilization from happening more rapidly. Volatilization may play a larger role in methanol loss when concentrations are relatively high and when temperatures are high. Once the methanol concentration is relatively small, biodegradation may take over as the predominant mechanism of loss. 4.2 Role of Temperature At the outset of this study, it was considered that temperature would be a major factor in methanol degradation. Traditionally, it is believed that warmer temperatures allow higher bacteria growth thus a greater rate of methanol biodegradation (Helle 2001). Colder temperatures were thought to inhibit growth of bacteria and therefore biodegradation would also be inhibited (Helle 2001). In this study, the opposite was observed. In relatively colder water temperature with the addition of nutrients, there was a marked difference in biodegradation rates. In warmer water, there was no evidence of significant biodegradation in any of the samples. It is difficult to comment on the relative amounts of growth of bacteria when comparing the cooler and warmer temperature trials. This is because in the first two trials, total bacteria 59 were measured, while in trials 3 and 4 only methylotrophs were counted. However, from visual observation, it was apparent in all four trials that there was abundant growth and considerable clumping in all flasks except the control. This indicates that another population of bacteria, other than methylotrophs was taking advantage of the growth conditions. Based on the results, some inferences can be made about the role of temperature in the biodegradation of methanol. It would appear that in colder temperatures, methylotrophs have a comparative advantage over other bacteria present in the river water. Cooler water may have inhibited growth of other bacteria, thus allowing for methylotrophs to dominate under the conditions in the flasks. In a study by Horz et al (2005) similar results were found. In lower temperatures, type II methanotrophs showed significant growth in a mixed environment. In higher temperatures, type II methanotrophs with abundant methane, did not show more growth compared to other species present in the soil. This would infer that even with an excess of a carbon source, temperature may limit the competitive advantage of some species of bacteria. Though there were differences observed in biodegradation potential with different temperatures, differences were also apparent between nutrient amended and non-amended samples. This factor will be explored in the following section. 4.3 Role of Nutrients The addition of nitrogen and phosphorus amendments was used to determine if nutrients were a limiting factor in the rate of biodegradation. The only case where significant biodegradation took place was in the nutrient amended samples with an initial concentration of 1000 mg/L at cooler temperatures. This would suggest that nutrients may have been a limiting factor in the rate of biodegradation of methanol. However, in most cases, the presence of excess nutrients showed little to no evidence of increased biodegradation and therefore did not positively impact the rate of biodegradation. In Spruston's (2002) study, microcosms with 60 nutrient amendments showed significant biodegradation compared to the control and raw water samples. 94% of the methanol was degraded after 21 days (Spruston 2002). This experiment was also conducted under closed conditions, unlike the conditions found in a natural river system. Periodic inputs of nutrients were not performed in the experiment as would naturally be found in inputs in a river. Conducting the experiment in an open system would offer a more realistic picture of natural conditions, however would have been difficult to do a number of replications. Micronutrients were thought to be a possible limiting factor in this study. However, literature suggests that the levels of micronutrients naturally present in the environment are sufficient for microbial growth (Rheinheimer 1985). As well, mixed cultures are known to not have vitamin deficiencies (Kuenen et al 1982). Therefore, the levels of micronutrients are an unlikely source of limitation of biodegradation. 4.4 Role of Biological Interactions From the results of the bacteria enumeration and methanol loss data, it became apparent that another factor was limiting methylotroph growth. In all four trials, there was considerable growth observed visually, particularly in the nutrient amended flasks. From trials 1 and 2, it was also observed there were large amounts of total bacteria. However, this did not necessarily translate into methanol loss. In trials 3 and 4 it was observed that the methylotroph population increased in the beginning of the experiment, only to decrease rapidly in numbers soon after. These observations were not in accordance to what was predicted by Helle (2001). Helle predicted that in the presence of methanol, methylotrophs would grow rapidly at the expense of other species present and a significant population of methylotrophs would develop. There are a number of different suggestions that could be given to explain why methylotrophs were not successful in degrading the methanol at a significant rate. Grazing by 61 protozoans could have occurred. Once methylotroph bacteria began to die off, their cell products may have been used to sustain growth of other species and eventually, these new species may have out competed the methylotrophs. Running the experiment for a longer period would have been beneficial to observe the oscillations in the microbial community. There have been studies that have looked at the interactions of mixed cultures between methylotrophs and heterotrophs. In general, growth yields of biomass tend to be higher and more stable in mixed cultures than pure cultures. Relationships between methylotrophs and heterotrophs are thought to be commensalistic or mutualistic. In the presence of methanol, heterotrophs are thought to grow on the products of lysis of the primary utilizer. Heterotrophs may have an effect on the total growth yield, though would have little influence over the Ks for methylotrophs. In cases where nitrogen is limiting, this may lead to a higher percentage of heterotrophs in mixed cultures (Kuenen et al 1982). 4.5 Comparison of Bacterial Enumeration Methods During this project, two bacterial enumeration methods were used. As the two methodologies focused on different aspects of the microbial community, it is helpful to compare their results and usefulness in this project. In each trial, there was a high degree of bacterial clumping in some of the samples. For both methods, clumping increased the variability of the bacterial numbers dramatically. Due to the relatively low number of samples, this variability increased the margin of error considerably in some cases. In trials 1 and 2, epifluorescence microscopy was used. For these trials, it was clear that the samples were difficult to count due to the amount of particles in the sample. Though DAPI is thought to be better than acridine orange for excluding inert particles, in cases where highly turbid water is being used, DAPI may still not be adequate. Only total bacteria were counted in 62 this method and it became clear that it would be useful to isolate methylotrophs based on the methanol loss data. Originally, it was assumed that bacterial growth would mainly be attributed to methylotroph growth, and therefore total counts of bacteria would be useful. However, as bacterial growth increased, this did not necessarily translate into increased methanol loss. Using this method allowed for an understanding of how the microbial community as a whole reacted to methanol addition, however it did not allow for determination of how the methylotroph community responded. In trials 3 and 4, the MPN technique was used. In this method, it is necessary to estimate the bacterial numbers in the sample in order to correctly judge the number of dilutions needed. In some cases this was difficult and estimates of dilutions were incorrect creating skewed values for bacteria. In retrospect, it would have been valuable to obtain data for total bacteria counts as well as the counts for the methylotroph population. This data would have been helpful to compare the growth patterns of the heterotroph and methylotroph populations to examine the interactions among these groups of bacteria. 4.6 14C Methanol Analysis Uptake of methanol by bacteria was analyzed using l 4 C labeled methanol. Measurements were taken for trials 3 and 4 near or at the end of the experiments. This experiment showed that nutrient amended samples had a higher rate of uptake of methanol than in the unamended nutrient samples and the control. In contrast, there were higher concentrations of methylotrophs present in the unamended samples than in the samples with excess nutrients. This could be explained by the differences in metabolic activity. Some bacteria may be dormant and not actively utilizing methanol. As well, different bacteria may have different rates of metabolic activity (Yanagita 1990). 63 In both experiments, high counts of disintegrations per minute (DPM) was observed in control samples given gluteraldehyde. This could be explained by experimental error as the filter may accidentally have been splashed with the labeled methanol. Low DPM counts were observed in the control flasks given no gluteraldehyde. This confirms that methanol was not being metabolized in the controls and that no contamination took place. As well, it can be noted that volatilization of methanol could have been taking place in the flasks. This is exemplified by the fact that DPM were observed in all of the gluteraldehyde samples. 64 CHAPTER 5 - Conclusions This study aimed to determine if the natural microbial community in the Fraser River was able to degrade methanol. The project included a number of different experimental parameters including, trials with an initial methanol concentration of 1000 mg/L and 10,000 mg/L and trials with and without nutrient amendments. During this project, a number of unexpected results were observed and further study is warranted to explore the mechanisms behind these results. Future research options are discussed in chapter six. Analysis regarding the role of volatilization, temperature, nutrients and microbial interactions were conducted. Conclusions from this work are as follows: 1. Indigenous bacteria in the Fraser River are not efficient degraders of methanol under natural conditions. Volatilization of methanol is the predominant mechanism of loss. Rates of volatilization were higher in trials conducted at 18°C than at 4°C. Rates of loss ranged from 6-140 mg/L/day in cooler temperatures and 15-180 mg/L/day in warmer temperatures. Most rates were an order of magnitude higher in conditions with an initial methanol concentration of 10,000 mg/L than conditions with an initial methanol concentration of 1000 mg/L. Exceptions occurred in conditions with nutrient amendments at cooler temperatures. 2. Quantifying biodegradation rates as they occur in surface water involve a complex interaction of type of biomass present, temperature and nutrient availability. 3. Bacterial interactions affect the ability of methanol degrading bacteria present in the surface water to degrade methanol. In conditions of lower temperature and excess nutrients, methanol degraders may have an advantage and degrade methanol more 65 readily. Methylotroph populations may initially increase after methanol addition then decline in numbers. In the event of a methanol spill, it is likely that volatilization will be a major mechanism of loss of methanol. Only in conditions where nutrients are in excess and temperatures are low, will methanol degrading bacteria have an advantage over other species. CHAPTER 6 - Recommendations Based on the results of this study, it is apparent that there are still many questions unresolved regarding the rate of methanol biodegradation. This study focused on the biodegradation of methanol in the Fraser River, however there are many other surface water environments that could be studied. With the dynamic nature of surface water, temperature, nutrients and biomass vary greatly from one environment to another. Marine, estuarine, and lake systems would also be interesting to study to understand how these ecosystems would respond to a methanol spill. The likelihood of a spill in these surface water environments is comparable to that in a river system and warrants further study. In light of the number of different variables and environments to test, changes to the experimental design could include: • Running experiments for a longer time • Samples taken at a variety of depths and proximities to shore • Include sediments in microcosm • Comparison of anaerobic and aerobic samples • Monitor evaporation rates of methanol • 1 4 C analysis throughout duration of study An unexpected outcome from this study was the role of competition from other bacterial species in the river potentially having an impact on the rate of biodegradation. There are a number of studies that could be done to determine the interactions of the different species with each other. Studies could be done with a pure culture of methylotrophs to determine at what rate they degrade methanol without external stressors. In addition, a dose-response experiment could be conducted to determine the level of toxicity or inhibition methanol may have on methylotrophs. Identification of bacteria present in the sample would be beneficial to get a better 67 understanding of the dynamics of the system. Finally, enumeration of both total bacteria and methanol degraders would be helpful to know what species are taking advantage in the microcosms. To complement lab studies, a field study undertaken at the time of an actual methanol spill would be valuable to compare to lab results. In the future, it would also be beneficial to build a model to estimate the ecosystem response to a methanol spill. This kind of model would allow managers and scientists to establish the need for and an appropriate bioremediation effort in the event of a methanol spill Despite many advantages that methanol has to offer over oil and gas as a sustainable fuel alternative, there remains one major drawback. Methanol is largely produced from natural gas, which as described in Chapter 1 is a diminishing, non-renewable resource. If methanol is indeed the next promising alternative fuel, producers of methanol would be well advised to expand and develop strategies for harnessing a renewable source of methanol to market the product as truly sustainable. For instance, a study in Ontario has shown that there are great possibilities in using wood waste and municipal solid waste and sewage sludge as a source for methanol manufacture (Mackay and Sutherland 1976). 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Office of Pollution Prevention and Toxics. http:www.epa.gOv/opptintr/chemfact/f methan.txt Verschueren, Kael (1996). Handbook of Environmental Data on Organic Chemicals 3rd Edition. WorldWatch Institute (2005). Vital Statistics. http://www.worldwatch.org/press/news/2005/05/12/ Yanagita, Tomomichi (1990). Natural microbial communities: ecological and physiological features. New York, Springer-Verlag. 71 Appendix A - Site Pictures Figure A - 3 Site 2, North Shore Figure A - 4 Site 2, South Shore, gravel pit Appendix B - Methanol Concentration Calibration Calculation of methanol concentration is based on the slope of the calibration curve and the ratio of methanol to butanol area. A sample equation is below: Calibration curve equation: MeOH concentration = slope * (Methanol peak area / Butanol peak area) 8000 0 1 2 3 4 5 6 7 8 Area ratio of methanol to 1-butanol Figure B - 1 Methanol Calibration Curve 74 Appendix C - Modified Nitrate Mineral Salts Media Phosphate Solution Add the following per litre distilled water: Na 2HP0 4 3.6g K H 2 P 0 4 1.4g Trace Element Solution Add the following per litre distilled water: Tetrasodium EDTA 100 mg ZnS0 4 • 7H 20 7 mg MnCl 2 • 4H 20 3 mg H3BO3 30 mg CoCl 2 • 6H 20 20 mg CuCl 2 • 2H 20 1 mg NiCl 2 • 6H 20 2 mg Na 2Mo0 4 -2H 2 0 3 mg Ferrous Sulfate Solution Add the following per 100 ml of 10"3 M H 2 S0 4 : FeS04 • 7H 20 0.05 g Magnesium Sulfate Solution Add the following per 100 ml distilled water: MgS0 4 • 7H 20 2 g Calcium Chloride Solution Add the following per 100 ml distilled water: CaCl 2 -2H 2 0 0.2 g Preparation of Media Add the following per 1 L of distilled water: Phosphate solution 20 ml Trace Element solution 10 ml Ferrous Sulfate solution 10 ml Magnesium Sulfate solution 10 ml Calcium Chloride solution 10 ml Potassium Nitrate 1 g Autoclave media. Upon cooling, add 0.2% (vol/vol) Methanol to select for methylotrophs. Appendix D - Methanol Degradation Results Table D - l Trial 1, Site 1 Table D - 2 Trial 1, Site 2 Table D - 3 Trial 2, Site 1 Table D - 4 Trial 2, Site 2 Table D - 5 Trial 3, Site 1 Table D - 6 Trial 3, Site 2 Table D - 7 Trial 3, Evaporative Loss Table D - 8 Trial 4, Site 1 Table D - 9 Trial 4, Site 2 Table D - 10 Trial 4, Evaporative Loss Table D - 1: Trial 1, Site 1 Methanol Concentration Data in mg/L. Day 29 left out of data set due to unreliable numbers. Day 0 6 13 16 20 23 26 34 37 43 CONTROL 1000 1 831.7 819.8 776.8 751.2 715.3 748.1 678.7 654.5 610.7 570.6 1000 2 845.6 835.0 809.4 828.1 784.6 773.0 741.2 736.1 673.2 619.3 1000 3 836.6 835.5 796.9 801.4 743.2 743.2 728.7 695.9 628.3 572.3 AVG 837.9 830.1 794.4 793.5 747.7 754.7 716.2 695.5 637.4 587.4 STD Dev 7.1 9.0 16.4 39.0 34.9 16.0 33.1 40.8 32.2 27.6 10000 1 8024 7636 7294 7065 6633 6427 5882 5223 4779 4698 10000 2 8289 7817 7453 7252 6878 6570 6373 5316 5006 4348 10000 3 8098 7879 7176 7050 6558 6817 6061 5471 4944 4473 AVG 8137 7777 7307 7123 6690 6605 6105 5337 4910 4506 STD Dev 137 126 139 113 167 197 249 125 118 177 RIVER 1000 1 1108.0 1050.4 1012.8 983.3 946.1 878.9 854.5 800.2 690.3 601.0 1000 2 1226.2 1058.0 1020.1 982.7 901.6 858.0 857.1 819.3 716.7 683.0 1000 3 1098.7 1071.7 1018.8 1022.5 985.4 940.1 939.5 913.3 789.8 791.4 AVG 1144.3 1060.0 1017.2 996.2 944.4 892.4 883.7 844.3 732.3 691.8 STD Dev 60.5 5.1 3.7 7.6 25.2 17.3 16.2 22.1 21.2 50.1 10000 1 12551 10553 10135 9812 8918 8559 8408 7552 6568 5872 10000 2 10916 10659 9561 9546 8707 8776 8310 7491 6671 5967 10000 3 10480 10540 9644 9484 8529 8094 7854 6885 6125 5419 AVG 11316 10584 9780 9614 8718 8476 8190 7309 6455 5753 STD Dev 1092 65 310 174 195 348 296 369 290 293 ^ 1 oo Table D - 1 Continued Day 0 6 13 16 20 23 26 34 37 43 NUTRIENT 1000 1 1090.7 1035.9 861.5 854.7 606.3 445.9 249.3 105.8 64.9 0.0 1000 2 1092.1 1046.1 966.9 874.7 684.4 498.8 254.6 27.5 7.2 38.6 1000 3 1065.4 1050.9 975.4 922.7 771.1 638.1 428.7 203.8 132.8 13.9 AVG 1082.7 1044.3 934.6 884.0 687.3 527.6 310.9 112.4 68.3 17.5 STD Dev 15.1 7.7 63.5 35.0 82.5 99.3 102.1 88.3 62.9 19.5 10000 1 10175 10968 10237 9878 9397 9397 8441 7381 6519 6048 10000 2 11191 10534 9505 9322 8372 8302 7371 5766 4892 4182 10000 3 10302 10563 9615 8980 8356 8038 6992 5462 4455 3649 AVG 10556 10689 9786 9393 8708 8579 7602 6203 5289 4626 STD Dev 554 243 395 453 596 721 751 1031 1088 1260 Table D - 2: Trial 1, Site 2 Methanol Degradation Data in mg/L. Bold and italicized numbers are unreliable data points and were Day 0 6 13 16 20 23 26 34 37 43 CONTROL 1000 1 812.1 796.9 742.5 738.1 686.5 659.0 1497.6 581.4 565.2 491.2 1000 2 841.1 808.6 761.0 745.9 715.4 681.7 N D 624.5 556.4 504.1 1000 3 862.8 840.6 793.8 776.3 750.3 694.4 1101.3 667.5 603.3 537.8 AVG 838.7 815.4 765.8 753.4 717.4 678.4 1299.5 624.5 574.9 511.0 STD Dev 25.5 22.6 26.0 20.2 31.9 17.9 280.2 43.0 24.9 24.1 10000 1 8539 8239 7765 7664 7000 6924 6080 6349 5847 5246 10000 2 8438 8031 7348 7111 6503 6358 5731 5375 4819 3746 10000 3 8139 7786 6871 6678 6024 5641 5379 4704 4260 3556 AVG 8372 8019 7328 7151 6509 6308 5730 5476 4976 4183 STD Dev 208 227 447 494 488 643 350 827 805 925 MD Table D - 2 Continued Day 0 6 13 16 20 23 26 34 37 43 RIVER 1000 1 891.6 1095.0 989.0 1014.3 982.4 924.7 1591.5 836.4 800.8 828.2 1000 2 979.6 1028.0 947.3 934.6 851.5 839.6 1361.0 731.0 688.3 802.1 1000 3 985.5 1000.9 935.6 904.9 840.2 813.2 1217.8 742.9 649.9 582.3 AVG 952.3 1041.3 957.3 951.3 891.3 859.1 1390.1 770.1 713.0 737.5 STD Dev 52.6 48.5 28.1 56.6 79.0 58.3 188.5 57.7 78.5 135.1 10000 1 8182 9835 9472 9250 8593 8212 6896 6825 6200 5335 10000 2 8606 10565 9989 9935 9448 8990 8076 8305 7503 6709 10000 3 8617 10414 9141 9024 8316 7786 7414 6750 6072 4555 AVG 8468 10271 9534 9403 8786 8330 7462 7293 6592 5533 STD Dev 248 385 427 474 590 610 591 877 792 1090 NUTRIENT 1000 1 959.4 1420.5 680.5 604.5 368.0 171.8 1092.6 12.6 7.7 0.0 1000 2 1146.2 1283.2 749.4 674.7 501.8 307.1 985.3 63.1 1.6 40.9 1000 3 1127.4 1231.8 740.7 685.5 493.4 282.3 ND 105.3 49.5 0.0 AVG 1077.7 1311.8 723.6 654.9 454.4 253.7 1039.0 60.3 19.6 13.6 STD Dev 102.9 97.5 37.5 44.0 75.0 72.0 75.9 46.4 26.1 23.6 10000 1 10945 9028 9143 8534 7700 6968 ND 5065 4139 4582 10000 2 8105 9799 9610 9377 8663 7992 6488 6343 5121 9489 10000 3 8874 10175 9688 9471 8670 8148 ND 6592 5541 4601 AVG 9308 9668 9480 9127 8344 7702 6488 6000 4933 4591 STD Dev 1469 585 295 516 558 641 * 819 720 14 oo o Table D - 3 Trial 2, Site 1 Methanol Degradation Data in mg/L. Bold and italicized numbers are unreliable data points and were excluded from calculations. Day 0 4 7 11 14 18 21 25 28 33 38 42 CONTROL 1000 1 560.5 495.3 875.2 533.2 596.5 540.8 461.2 145.9 273.2 394.8 392.9 373.8 1000 2 607.6 556.4 603.9 562.9 611.9 639.3 3078.2 530.0 633.2 490.6 488.1 414.3 1000 3 609.3 535.7 743.3 544.7 625.3 527.7 680.0 498.4 538.5 445.6 427.3 361.8 AVG 592.5 529.1 673.6 546.9 611.3 569.3 570.6 514.2 481.6 443.7 436.1 383.3 STD Dev 27.7 31.0 98.6 15.0 14.4 61.0 154.7 22.3 186.6 47.9 48.2 27.5 10000 1 5487 5015 4841 3180 4674 4242 3026 3835 3792 3349 3082 2396 10000 2 5263 5011 5032 3784 4884 4472 4216 4066 4039 3638 3243 2549 10000 3 5266 4893 4702 4604 2409 4185 3190 3631 3607 3501 3139 2531 AVG 5338 4973 4858 3856 3989 4300 3478 3844 3813 3496 3155 2492 STD Dev 128 70 166 715 1372 152 645 218 217 144 81 84 RIVER 1000 1 717.7 721.2 804.5 704.1 936.9 639.8 564.4 620.0 664.4 576.0 541.1 427.9 1000 2 701.1 727.9 801.9 705.5 1036.7 657.7 815.4 610.4 645.4 558.1 509.9 430.2 1000 3 656.1 758.3 692.2 801.6 880.4 699.1 556.0 683.1 1003.4 620.9 586.1 481.4 AVG 691.6 735.8 766.2 737.1 908.7 665.5 645.3 637.8 654.9 585.0 545.7 446.5 STD Dev 31.9 19.8 64.1 55.9 40.0 30.4 147.4 39.5 201.4 32.4 38.3 30.3 10000 1 8089 7230 6746 923 5587 6168 5868 5734 5360 4876 4493 3497 10000 2 8964 6958 6730 5379 6369 6045 5866 5659 5158 4993 4444 3595 10000 3 7537 7081 6366 6689 6347 6007 5797 5570 5487 4865 4398 3520 AVG 8197 7090 6614 6034 6101 6073 5843 5654 5335 4912 4445 3537 STD Dev 719 137 215 927 445 84 40 82 166 71 48 51 00 Table D - 3 Continued Day 0 4 7 11 14 18 21 25 28 33 38 42 NUTRIENT 1000 1 877.4 761.4 791.6 757.7 707.2 408.0 292.6 187.1 194.5 120.0 64.9 6.0 1000 2 942.6 746.8 699.8 601.3 583.1 402.4 1218.5 161.2 907.1 100.0 42.5 0.0 1000 3 796.3 759.3 753.7 587.1 754.9 513.0 347.5 248.1 192.0 120.3 100.3 52.4 AVG 872.1 755.8 748.4 648.7 681.8 441.1 320.0 198.8 431.2 113.4 69.2 19.5 STD Dev 73.3 7.9 46.1 94.7 88.7 62.3 38.9 44.6 412.2 11.6 29.1 28.7 10000 1 6994 7204 6901 6990 6672 6600 6151 5448 5674 4806 4296 3528 10000 2 8868 7110 6704 5122 6112 5555 3489 7479 1498 3675 2891 2129 10000 3 7436 7184 6922 6585 6152 5633 4942 4377 3295 3212 2560 1919 AVG 7766 7166 6842 6232 6312 5930 4861 4912 4484 3898 3249 2525 STD Dev 980 50 120 983 313 582 1333 757 1682 820 922 875 Table D - 4 Trial 2 , Site 2 Methanol Degradation data in mg/L. Day 0 4 7 11 14 18 21 25 28 33 38 42 CONTROL 1000 1 525.8 495.8 644.7 563.0 580.3 485.8 463.4 418.5 434.6 400.6 357.3 296.9 1000 2 548.4 540.6 1882.049 523.3 591.9 361.9 472.5 466.2 440.2 407.7 374.2 309.5 1000 3 594.8 536.1 683.6 595.2 574.6 564.6 500.8 553.6 458.7 441.3 413.0 337.1 AVG 556.3 524.2 664.2 560.5 582.3 470.8 478.9 479.4 444.5 416.6 381.5 314.5 STD Dev 35.2 24.6 27.5 36.0 8.8 102.2 19.5 68.5 12.6 21.7 28.6 20.5 10000 1 5610 5425 5190 5145 5020 4885 4591 4680 4413 4209 3937 3220 10000 2 5250 4989 4909 4804 4488 4297 4090 4208 3782 3436 3188 2441 10000 3 5282 4884 4842 4979 4465 4287 3897 1371 3628 3328 3061 2397 AVG 5381 5099 4981 4976 4658 4490 4193 3420 3941 3658 3396 2686 STD Dev 199 287 185 171 314 342 358 1790 416 481 473 463 Table D - 4 Continued Day 0 4 7 11 14 18 21 25 28 33 38 42 R I V E R 1000 1 718.1 741.6 947.0 737.7 624.0 714.8 702.4 1159.9 626.2 592.7 559.5 451.1 1000 2 523.5 740.3 815.0 743.9 689.4 631.6 660.4 328.9 582.1 561.6 523.1 413.5 1000 3 736.3 734.0 843.5 751.2 703.2 757.5 660.5 822.9 595.0 573.2 539.9 418.9 A V G 659.3 738.7 868.5 744.3 672.2 701.3 674.4 575.9 601.1 575.8 540.8 427.8 S T D Dev 117.9 4.0 69.5 6.7 42.3 64.0 24.2 349.3 22.7 15.7 18.2 20.3 10000 1 7609 7310 6846 5296 6367 6000 5797 5572 5115 4793 4385 3369 10000 2 7319 7383 7289 7123 6819 6535 6313 6301 5964 5665 5470 4260 10000 3 8674 7233 7794 5328 6418 5903 6220 5518 5142 4763 4409 3421 A V G 7867 7309 7310 5916 6535 6146 6110 5797 5407 5074 4755 3683 S T D Dev 714 75 474 1046 247 341 275 437 482 512 619 501 N U T R I E N T 1000 1 845.2 715.4 666.5 634.7 765.7 414.5 234.1 191.7 84.9 2.9 4.6 1.3 1000 2 641.9 614.6 583.6 654.3 738.2 500.3 212.2 280.0 82.8 20.4 21.8 0.0 1000 3 779.2 757.3 671.2 775.7 844.6 422.0 355.1 433.0 173.4 67.3 43.8 0.0 A V G 755.4 695.8 640.4 688.2 751.9 445.6 267.1 301.6 113.7 30.2 23.4 0.4 S T D Dev 103.8 73.3 49.3 76.4 19.4 47.5 77.0 122.1 51.7 33.3 19.6 0.8 10000 1 7740 7135 6306 6508 6264 5754 3823 4273 3856 3140 2554 1787 10000 2 6849 7398 7135 7365 3202 3532 5644 36674 4706 3968 3225 2423 10000 3 6632 7401 7148 7495 6560 6345 6329 5306 4976 4461 3891 2784 A V G 7074 7312 6863 7123 6412 5210 5265 4790 4513 3857 3223 2331 S T D Dev 587 152 483 536 210 1483 1295 730 585 668 668 505 oo Table D - 5 Trial 3, Site 1 Methanol Degradation data in mg/L Day 0 1 5 8 12 15 20 23 26 29 33 36 40 46 Control 1000 1 880.6 809.6 810.4 742.0 965.5 679.2 593.2 550.4 489.4 432.0 365.2 355.2 257.8 192.2 1000 2 872.8 844.4 777.8 775.5 935.5 740.2 665.7 633.7 557.8 519.1 470.3 471.1 395.5 337.4 1000 3 930.1 844.5 825.3 755.9 709.7 664.6 563.5 479.0 427.6 378.3 327.5 310.6 215.9 136.9 AVG 894.5 832.9 804.5 757.8 870.2 694.7 607.4 554.4 491.6 443.1 387.7 379.0 289.7 222.1 STD Dev 31.1 20.1 24.3 16.9 139.8 40.1 52.5 77.4 65.1 71.0 74.0 82.8 94.0 103.6 10000 1 9865 8552 7910 7153 6288 5525 4603 3955 3331 2816 2296 1892 1395 889 10000 2 9110 8849 7901 7274 6929 5789 4736 3999 3467 2979 2455 2070 1592 1041 10000 3 9428 8483 7651 6930 6591 5861 4058 3370 2948 2397 1993 1394 875 AVG 9468 8628 7821 7119 6602 5725 4669 4004 3389 2915 2383 1985 1461 935 STD Dev 379 195 147 174 321 177 94 52 70 86 80 90 114 92 River 1000 1 901.0 865.2 832.6 783.1 1243.4 658.4 558.1 509.8 432.2 390.5 312.9 280.0 227.9 163.6 1000 2 897.4 844.4 809.4 749.9 955.5 688.3 554.7 495.4 444.7 400.5 336.8 291.5 225.1 156.1 1000 3 871.1 856.6 832.3 816.6 840.8 736.0 664.9 639.9 446.0 539.4 479.2 460.3 388.2 306.1 AVG 889.9 855.4 824.7 783.2 1013.3 694.2 592.6 548.4 441.0 443.4 376.3 344.0 280.4 208.6 STD Dev 16.3 10.4 13.3 33.4 207.4 39.2 62.7 79.6 7.6 83.2 89.9 101.0 93.4 84.6 10000 1 8781 8198 7605 6960 6296 5435 4317 3937 3266 2943 2260 1950 1485 978 10000 2 9283 8243 7346 6593 6000 5040 4110 3847 2965 2571 2058 1674 1238 821 10000 3 8872 8145 7564 6626 6358 5685 4465 4135 3274 2912 2440 2033 1580 991 AVG 8978 8195 7505 6726 6218 5386 4297 3973 3168 2809 2253 1885 1434 930 STD Dev 268 49 139 203 191 325 178 148 176 206 191 188 177 94 oo 4^ Table D - 5 Continued Nutrient 1000 1 893.5 820.9 778.5 746.7 1230.1 640.6 539.2 631.4 406.9 363.1 263.8 238.7 149.4 71.0 1000 2 886.6 831.5 774.0 733.7 950.5 592.2 504.2 449.6 364.3 333.6 252.1 226.6 151.6 93.4 1000 3 946.9 808.5 781.4 733.8 795.0 592.5 491.6 461.3 362.1 312.5 238.1 208.2 134.0 34.9 AVG 909.0 820.3 778.0 738.0 991.9 608.4 511.7 514.1 377.7 336.4 251.3 224.5 145.0 66.5 STD Dev 33.0 11.5 3.7 7.5 220.5 27.9 24.7 101.8 25.2 25.4 12.9 15.4 9.6 29.5 10000 1 9436 8186 7985 7519 6592 6459 5725 5286 4513 4212 3743 3304 2772 2244 10000 2 9279 8052 7513 6700 6201 5081 4050 3722 2990 2540 2037 1720 1331 893 10000 3 9215 7929 7285 N D 6004 4891 3818 3519 2773 2394 1869 1539 1179 754 AVG 9310 8055 7595 7110 6266 5477 4531 4176 3425 3049 2550 2188 1761 1297 STD Dev 114 129 357 579 299 856 1041 967 949 1010 1037 971 879 823 Table D - 6 Trial 3, Site 2 Methanol Degradation data in mg/L. Day 0 1 5 8 12 15 20 23 26 29 33 36 40 46 Control 1000 1 902.1 838.0 786.7 N D 863.4 606.6 499.7 764.4 391.2 330.3 259.1 238.5 179.1 121.7 1000 2 882.8 845.8 802.8 755.0 796.2 614.1 512.6 629.1 379.1 332.8 262.8 223.4 174.3 110.2 1000 3 891.4 817.6 808.2 735.1 764.7 643.0 555.8 613.4 460.6 400.1 341.9 323.6 223.0 168.2 AVG 892.1 833.8 799.2 745.0 808.1 621.2 522.7 668.9 410.3 354.4 287.9 261.8 192.1 133.4 STD Dev 9.7 14.5 11.2 14.0 50.4 19.2 29.4 83.0 44.0 39.6 46.8 54.0 26.9 30.7 10000 1 8033 7519 7253 7142 6074 6258 5368 4939 4564 4226 3476 3424 2858 1984 10000 2 9003 8533 7622 7227 6020 5233 4384 3881 3210 2748 2196 1777 1299 817 10000 3 8914 8398 7480 6897 5762 5024 3709 3157 2575 2132 1680 1258 825 471 AVG 8650 8150 7452 7089 5952 5505 4487 3992 3449 3035 2451 2153 1661 1090 STD Dev 536 550 186 171 167 660 835 896 1016 1076 925 1131 1063 793 Table D - 6 Continued R i v e r 1000 1 879.5 840.8 825.7 761.3 1119.8 713.3 582.7 550.8 491.9 459.4 410.5 340.0 297.4 184.9 1000 2 898.2 846.4 754.4 755.1 776.9 682.4 567.3 521.2 470.1 475.4 366.8 308.2 304.1 231.3 1000 3 883.0 832.7 842.5 796.9 709.9 581.1 613.1 572.8 518.8 298.6 456.6 424.8 369.3 322.7 A V G 886.9 840.0 807.5 771.1 868.9 658.9 587.7 548.2 493.6 411.2 411.3 357.7 323.6 246.3 S T D Dev 9.9 6.9 46.8 22.6 219.9 69.2 23.3 25.9 24.4 97.8 44.9 60.3 39.7 70.1 10000 1 8838 8173 7019 7338 6289 6153 5100 4580 4149 3531 3150 2741 2203 1530 10000 2 9007 8399 7773 7548 7232 6812 5960 5351 5022 4571 3983 3732 3257 2382 10000 3 8907 8152 7416 6970 6213 5597 4373 3614 2681 2720 2295 1922 1434 1003 A V G 8917 8241 7403 7285 6578 6188 5144 4515 3950 3607 3143 2798 2298 1638 S T D Dev 85 137 377 293 568 608 794 870 1183 928 844 906 915 696 Nutr ient 1000 1 ' 840.5 892.4 774.8 740.6 1070.7 581.6 498.3 446.8 381.7 342.8 297.0 265.0 235.0 146.5 1000 2 897.9 970.7 821.1 782.3 811.1 694.9 595.0 585.0 514.8 479.6 437.3 397.1 338.6 288.0 1000 3 947.0 836.4 727.2 807.4 749.2 720.3 625.3 570.3 506.3 472.7 425.2 381.2 349.4 262.7 A V G 895.1 899.8 774.4 776.8 877.0 665.6 572.9 534.0 467.6 431.7 386.5 347.8 307.7 232.4 S T D Dev 53.3 67.4 47.0 33.8 170.6 73.8 66.3 75.9 74.5 77.1 77.7 72.1 63.2 75.5 10000 1 9200 8283 7703 7285 6500 5916 4801 4326 3847 3470 3077 2609 2254 1642 10000 2 8954 8214 8198 7669 7490 6663 5933 5519 4953 4667 4328 3737 3410 2881 10000 3 9157 8483 8111 7689 7434 6645 5928 5344 4921 4577 4305 3746 3308 2556 A V G 9103 8327 8004 7547 7141 6408 5554 5063 4574 4238 3903 3364 2990 2360 S T D Dev 131 140 264 228 556 426 652 644 629 666 715 654 639 642 0\ Table D - 7 Trial 3, Methanol Evaporative Loss data in mg/L. Day 0 1 5 20 46 1000 1 891.3 846.4 1000 2 910.5 794.0 1000 3 904.9 505.0 1000 4 888.9 409.0 10000 1 9314 8427 10000 2 8956 8122 10000 3 8954 5160 10000 4 9140 3770 00 —1 Table D - 8 Trial 4, Site 1 Methanol Degradation data in mg/L. Day 0 1 5 8 12 15 19 22 32 36 43 Control 1000 1 1017.5 942.3 888.3 852.9 779.0 742.1 593.9 540.5 454.8 408.7 ND 1000 2 1054.4 983.6 940.8 899.3 854.8 802.7 709.2 700.1 563.8 775.1 1292.3 1000 3 957.3 985.1 901.4 845.0 750.9 709.0 581.2 562.9 405.4 366.4 539.2 AVG 1009.7 970.3 910.2 865.7 794.9 751.3 628.1 601.1 474.7 310.3 915.7 STD Dev 49.0 24.3 27.3 29.3 53.7 47.5 70.5 86.4 81.1 224.7 ND 10000 1 9846 9456 8431 7559 6276 5690 4517 4232 2676 2219 2947 10000 2 9914 9731 8611 7837 6999 6224 5002 4775 2906 2443 3640 10000 3 9873 9478 8399 7616 6626 5993 4731 4453 2859 2116 3390 AVG 9878 9555 8480 7671 6633 5969 4750 4487 2814 1356 1996 STD Dev 34 153 114 147 362 268 243 273 121 167 351 River 1000 1 987.0 933.0 1016.2 820.1 1188.4 712.5 481.8 632.2 399.0 355.1 261.6 1000 2 973.9 914.8 900.7 781.8 979.9 667.7 546.2 489.7 365.4 333.7 216.1 1000 3 1050.1 938.7 905.6 842.7 922.0 752.4 545.9 765.3 519.6 563.1 496.0 AVG 1003.7 928.9 940.8 814.9 1030.1 710.9 524.6 629.1 428.0 250.5 194.8 STD Dev 40.8 12.5 65.3 30.8 140.1 42.4 37.1 137.8 81.1 126.7 150.2 10000 1 9407 9072 8310 7574 6310 6090 4973 4712 3076 2594 1843 10000 2 9667 9156 8252 7501 6350 5734 4501 4391 2689 2205 1679 10000 3 9611 9075 8298 7597 6410 5939 4767 4579 2925 2433 1316 AVG 9562 9101 8286 7558 6357 5921 4747 4561 2897 1447 968 STD Dev 137 48 30 51 50 179 237 161 195 196 270 oo oo Table D - 8 Continued Nutrient 1000 1 955.7 928.5 952.6 800.1 1203.6 653.6 551.8 574.6 356.3 279.3 184.4 1000 2 959.4 918.1 867.0 781.9 974.4 645.9 468.3 573.9 345.2 289.5 187.8 1000 3 985.4 911.2 871.6 806.0 841.6 639.5 552.5 551.8 365.3 339.1 230.7 AVG 966.8 919.3 897.1 796.0 1006.5 646.3 524.2 566.8 355.6 181.7 120.7 STD Dev 16.2 8.7 48.2 12.6 183.1 7.1 48.4 13.0 10.1 32.0 25.8 10000 1 9236 9113 8302 8114 6856 6895 5855 5905 4516 3615 2828 10000 2 9304 8982 7749 6807 5742 4654 3958 3666 2242 1641 947 10000 3 9258 8931 8059 7248 6438 5143 4533 4268 2777 2061 1391 AVG 9266 9009 8036 7390 6345 5564 4782 4613 3178 1464 1033 STD Dev 35 94 277 665 562 1178 973 1158 1189 1040 983 Table D - 9 Trial 4, Site 2 Methanol Degradation data in mg/L. Day 0 1 5 8 12 15 19 22 32 36 43 Control 1000 1 1045.3 980.9 930.2 824.4 1285.2 668.5 483.8 561.8 382.7 547.6 310.1 1000 2 908.3 969.8 906.2 825.0 1063.6 703.2 575.6 565.3 410.7 679.3 223.1 1000 3 1021.0 926.6 893.2 830.1 823.1 349.4 299.7 314.4 230.5 401.8 145.0 AVG 991.6 959.1 909.9 826.5 1057.3 573.7 453.0 480.5 341.3 326.0 135.8 STD Dev 73.1 28.7 18.8 3.1 231.1 195.0 140.5 143.9 97.0 138.8 82.6 10000 1 9864 9628 9174 8513 7953 6390 6170 6174 4809 4413 4067 10000 2 8709 9419 8542 7913 7009 6001 4139 4418 2710 2213 1739 10000 3 9834 9469 8503 7524 6650 5549 4322 4089 2350 2352 1102 AVG 9469 9505 8739 7983 7204 5980 4877 4894 3290 1796 1382 STD Dev 658 109 377 498 673 421 1123 1121 1328 1232 1561 oo Table D - 9 Continued River 1000 1 946.6 923.8 922.0 842.2 950.7 578.2 648.3 675.3 500.4 329.7 342.6 1000 2 963.9 940.3 916.0 828.4 825.8 716.5 503.6 614.3 446.3 326.1 265.7 1000 3 960.4 929.2 916.1 831.4 796.8 709.9 558.2 608.2 486.1 438.6 342.6 AVG 957.0 931.1 918.0 834.0 857.8 668.2 570.0 632.6 477.6 219.0 190.3 STD Dev 9.2 8.4 3.4 7.3 81.8 78.0 73.0 37.1 28.0 63.9 44.4 10000 1 9492 9156 8473 7714 6476 6166 4996 4755 3267 2741 1465 10000 2 9602 9307 8978 8478 7654 7552 6281 6295 4928 4482 2795 10000 3 9380 9219 8197 7497 6705 5954 4820 4408 2809 1550 1572 AVG 9491 9227 8549 7896 6945 6557 5366 5153 3668 1755 1166 STD Dev 111 76 396 516 625 868 797 1004 1115 1474 739 Nutrient 1000 1 915.5 796.0 901.6 645.3 960.4 566.5 451.5 442.4 238.4 342.5 63.6 1000 2 992.0 913.7 961.7 823.1 824.4 729.4 618.1 612.0 465.8 407.5 314.4 1000 3 1002.5 888.5 835.7 834.8 807.0 701.8 558.8 523.6 458.9 356.6 346.1 AVG 970.0 866.1 899.6 767.7 864.0 665.9 542.8 526.0 387.7 221.5 144.9 STD Dev 47.5 61.9 63.1 106.2 84.0 87.2 84.5 84.8 129.3 34.2 154.7 10000 1 9018 9078 8342 7715 6663 6157 4862 4631 2690 2815 1845 10000 2 9270 9225 8948 8605 7800 7336 6214 6269 4580 4664 3928 10000 3 9275 9181 8707 8253 8047 7231 6227 6011 4681 4152 3534 AVG 9187 9161 8666 8191 7503 6908 5768 5637 3984 2326 1862 STD Dev 147 76 305 448 738 652 784 881 1122 955 1107 MD O Table D - 10 Trial 4, Methanol Evaporative Loss data in mg/L. Day 0 5 15 27 43 1000 1 1132.6 823.6 621.4 1000 2 997.8 677.5 1000 3 969.2 331.1 277.3 1000 4 1039.6 665.1 10000 1 9137 8666 6372 10000 2 9865 7850 10000 3 9874 3598.9 2451 10000 4 9816 4702 Appendix E - Bacterial Enumeration Results Table E - 1 Trial 1, Site 1 Table E - 2 Trial 1, Site 2 Table E - 3 Trial 1, Site 1 and 2 Combined Table E - 4 Trial 2, Site 1 Table E - 5 Trial 2, Site 2 Table E - 6 Trial 2, Site 1 and 2 Combined Table E - 7 Trial 3, Site 1 Table E - 8 Trial 3, Site 2 Table E - 9 Trial 3, Site 1 and 2 Combined Table E - 1 0 Trial 4, Site 1 Table E - 11 Trial 4, Site 2 Table E - 12 Trial 4, Site 1 and 2 Combined Table E - 1 Trial 1, Site 1 Total Bacteria Counts by Epifluorescence Microscopy Day 0 8 16 23 29 43 C 1000 1 602916 1370993 916133 1015550 967445 C 1000 2 864554 1167883 1537222 1113898 987756 C 1000 3 487731 1141158 1460254 1357630 1129933 A V G C 1000 0 651734 1226678 1304536 1162359 1028378 S T D Dev 193096 125693 338561 176114 88534 C 10000 1 255135 857873 627656 684160 510448 C 10000 2 239456 967445 853062 1532233 490671 C 10000 3 248543 436954 260569 600422 463233 A V G C 10000 0 247711 754090 580429 938938 488117 S T D Dev 7872 280059 299057 515512 23710 R1000 1 1126459 1082363 1770264 2282315 2394560 R 1000 2 1819973 2431975 2890576 3442180 3647428 R 1000 3 1018223 1505152 ND 3442180 2770848 A V G R 1000 2522840 1321551 1673163 2330420 3055558 2937612 S T D Dev 435025 690315 792180 669648 642866 R10000 1 3073375 2741985 2971820 3255105 3279692 R 10000 2 1520653 1186590 1881440 2586980 2454424 R 10000 3 2822160 2260935 3018856 2186105 2557048 A V G R 10000 2522840 2472063 2063170 2624039 2676063 2763721 S T D Dev N 1000 1 5173960 4140593 5439072 22187095 too dense N 1000 2 6547625 ND 7474448 too dense too dense N 1000 3 4986885 4667967 4729256 7782320 too dense A V G N 1000 2522840 5569490 4404280 5880925 14984708 N D S T D Dev 852238 372909 1424937 10185714 N D N10000 1 3094755 33238773 14931792 3875125 48211900 N 10000 2 3853745 29654060 17841610 4420315 103639550 N 10000 3 3105445 12207980 ND 3538390 173872850 A V G N 10000 2522840 3351315 25033604 16386701 3944610 108574767 S T D Dev 435150 11251001 2057552 445049 62975677 Table E - 2 Trial 1, Site 2 Total Bacteria Counts by Epifluorescence Microscopy Day 0 8 16 23 29 43 C 1000 1 300636 863218 416910 472498 327827 C 1000 2 195841 1240040 622158 646745 229835 C 1000 3 594631 1058310 1191935 996308 583674 A V G C 1000 0 363703 1053856 743668 705184 380445 S T D Dev 206740 188451 401546 266750 182694 C 10000 1 302883 1047620 1038712 1079690 1417494 C 10000 2 324976 359897 308585 233653 2561324 C 10000 3 404082 1058310 331390 ND 863752 A V G C 10000 0 343980 821942 559562 656671 1614190 S T D Dev 53209 400179 415112 598239 865710 R1000 1 784379 2378525 1932752 5120510 4778430 R 1000 2 1143830 25799 1676192 4436350 6178820 R 1000 3 1183918 3057340 1697572 3572242 2741985 A V G R 1000 2437320 1037375 1820555 1768839 4376367 4566412 S T D Dev 220016 1590930 142355 775875 1728199 R10000 1 1515308 2832850 3130032 5334310 4970850 R 10000 2 1477893 2357145 1827990 5745875 2656465 R 10000 3 1280128 2399905 1635570 4243930 2843540 A V G R 10000 2437320 1424443 2529967 2197864 5108038 3490285 S T D Dev 126373 263175 812994 776118 1285614 N 1000 1 6333825 7937325 too dense 25656000 49601600 N 1000 2 7621970 8343545 84557900 28809550 25976700 N 1000 3 2512150 13565610 48789160 too dense 35170100 A V G N 1000 2437320 5489315 9948827 66673530 27232775 36916133 S T D Dev 2657529 3138805 25292319 2229897 11908839 N10000 1 27660375 12427125 20866880 22769700 too dense N 10000 2 38238130 19616150 44983520 55374200 117304933 N 10000 3 4735670 7028675 14446771 24854250 122364867 A V G N 10000 2437320 23544725 13023983 26765724 34332717 119834900 S T D Dev 17126227 6314928 16100327 18252242 3577913 Table E - 3 Trial 1, Site 1 and 2 Combined Bacteria Counts by Epifluorescence Microscopy Day 0 8 16 23 29 43 SI C1000 0 651734 1226678 1304536 1162359 1028378 S2C1000 0 363703 1053856 743668 705184 380445 A V G C1000 0 507718 1140267 1024102 933772 704412 S T D Dev 238536 171712 452457 321825 377401 SI C10000 0 247711 754090 580429 938938 488117 S2 CI0000 0 343980 821942 559562 656671 1614190 A V G C10000 0 295846 788016 569995 797805 1051154 S T D Dev 62750 311146 323777 496237 824875 SI R1000 2522840 1321551 1673163 2330420 3055558 2937612 S2R1000 2437320 1037375 1820555 1768839 4376367 4566412 A V G R1000 2480080 1179463 1746859 2049629 3715963 3752012 S T D Dev 60472 345381 1099798 511500 971352 1468290 SI R10000 2522840 2472063 2063170 2624039 2676063 2763721 S2 R10000 2437320 1424443 2529967 2197864 5108038 3490285 A V G R10000 2480080 1948253 2296568 2410951 3892051 3127003 S T D Dev 60472 783266 588840 696081 1460120 948900 SI N1000 2522840 5569490 4404280 5880925 14984708 too dense S2N1000 2437320 5489315 9948827 66673530 27232775 36916133 A V G N1000 2480080 5529403 7176553 36277228 21108741 36916133 S T D Dev 60472 1765627 3766087 35632328 9286843 11908839 SI N10000 2522840 3351315 25033604 16386701 3944610 108574767 S2N10000 2437320 23544725 13023983 26765724 34332717 119834900 A V G N10000 2480080 13448020 19028794 21576212 19138663 114204833 S T D Dev 60472 15483247 10481156 12766592 20257544 44991171 95 Table E - 4 Trial 2, Site 1 Total Bacteria Counts by Epifluorescence Microscopy Day 0 14 21 33 42 C 1000 1 325689 27651 499376 700195 C 1000 2 120441 26226 22093 153936 C 1000 3 86233 19670 28507 58439 A V G 0 177454 24516 183325 304190 S T D Dev 105744 3476 223497 282719 C 10000 1 66991 24801 17104 47036 C 10000 2 108325 64853 96210 173891 C 10000 3 53450 54875 20667 72692 A V G 0 76255 48176 44660 97873 S T D Dev 23341 17023 36480 54764 R1000 1 739748 571151 128993 289452 R 1000 2 810302 323167 177454 783933 R 1000 3 1083966 259411 146097 511784 A V G 6478140 878005 384576 150848 528390 S T D Dev 148457 134472 20067 202212 R10000 1 799612 209524 304309 297538 R 10000 2 696988 267250 243019 409427 R100003 467688 232329 327827 267250 A V G 6478140 654763 236368 291718 324738 S T D Dev 138758 23739 35749 61147 N1000 1 17702640 14666680 9706520 12058320 N 1000 2 16975720 10903800 3753378 5483970 N 1000 3 12379020 10882420 8081640 13191460 A V G 6478140 15685793 12150967 7180513 10244583 S T D Dev 2356999 1778899 2512502 3397899 N10000 1 36645320 5270170 5270170 13725960 N 10000 2 42033080 7172990 8017500 8915460 N 10000 3 2608360 38398480 5932950 30445120 A V G 6478140 27095587 16947213 6406873 17695513 S T D Dev 17454230 15188215 1170586 9226757 Table E - 5 Trial 2, Site 2 Total Bacteria Counts by Epifluorescence Microscopy Day 0 14 21 33 42 C 1000 1 823130 229479 185293 220214 C 1000 2 268675 69841 32070 27081 C 1000 3 146097 92647 17104 33495 A V G 0 412634 130656 78156 93597 S T D Dev 294547 70496 76004 89570 C 10000 1 180305 99061 29932 64853 C 10000 2 126855 64853 33495 22093 C 10000 3 4989 55588 47036 26369 A V G 0 104049 73167 36821 37771 S T D Dev 73367 18696 7368 19229 R 1000 1 3109008 292906 488889 4449713 R 1000 2 1770977 325689 378426 1278524 R 1000 3 3559770 536027 681106 539845 A V G 8594760 2813252 384874 516140 2089361 S T D Dev 759627 107716 125062 1696046 R10000 1 2661810 436954 434014 409596 R 10000 2 1268547 425462 302883 153936 R 10000 3 1133140 385731 272951 258698 A V G 8594760 1687832 416049 336616 274077 S T D Dev 690921 21945 69946 104938 N 1000 1 11010700 3215908 1783092 5483970 N 1000 2 23346960 23133160 2677845 6328480 N 1000 3 13298360 12977660 4243930 3554425 A V G 8594760 15885340 13108909 2901622 5122292 S T D Dev 5358182 8131714 1017018 1161021 N 10000 1 11267260 21593800 35148720 22534520 N 10000 2 24843560 11267260 56742520 8616140 N 10000 3 28349880 18215760 52081680 19840640 A V G 8594760 21486900 17025607 47990973 16997100 S T D Dev 7366788 4298969 9278055 6027416 Table E - 6 Trial 2, Site 1 and 2 Combined Bacteria Counts by Epifluorescence Microscopy. Day 0 14 21 33 42 SI ClOOO 177454 24516 183325 304190 S2 ClOOO 412634 130656 78156 93597 A V G ClOOO 0 295044 77586 130740 198893 S T D Dev 250594 72851 175011 234657 SI C10000 76255 48176 44660 97873 S2 CI0000 104049 73167 36821 37771 A V G C10000 0 90152 60672 40741 67822 S T D Dev 56186 21813 26606 50867 SI R1000 878005 384576 150848 528390 S2 R1000 2813252 384874 516140 2089361 A V G R1000 7536450 1845629 384725 333494 1308875 S T D Dev 1111679 121831 203424 1438015 SI R10000 654763 236368 291718 324738 S2 R10000 1687832 416049 336616 274077 A V G R10000 7536450 1171297 326208 314167 299408 S T D Dev 717719 92703 59910 89538 SI N1000 15685793 12150967 7180513 10244583 S2N1000 15885340 13108909 2901622 5122292 A V G N1000 7536450 15785567 12629938 5041067 7683438 S T D Dev 4140379 5905424 2872407 3606427 SIN10000 27095587 16947213 6406873 17695513 S2N10000 21486900 17025607 47990973 16997100 A V G N10000 7536450 24291243 16986410 27198923 17346307 S T D Dev 13686643 11161677 21818241 7800855 98 Table E - 7 Trial 3, Site 1 Methylotroph Bacteria Counts by MPN Day 0 5 12 20 26 36 46 R1000 1 21000 9300 24000 7300 23 9300 R 1000 2 46000 24000 93000 1500 23 43000 R 1000 3 46000 460000 4300 230 23 150 A V G R 1000 23 37667 164433 40433 3010 23 17483 S T D Dev 14434 256074 46577 3769 0 22567 R 10000 1 24000 1500 73000 2400 73 15000 R 10000 2 4300 75000 1500 110 4 930 R 10000 3 46000 910 360 230 23 43 AVG R 10000 23 24767 25803 24953 913 33 5324 S T D Dev 20861 42607 41614 1289 36 8391 N 1000 1 46000 36000 730 230 23 730 N 1000 2 46000 36000 300 930 93 93 N 1000 3 > 240000 36000 4300 73 93 240 AVG N 1000 23 46000 36000 1777 411 70 354 S T D Dev 0 0 2196 456 40 334 N10000 1 24000 30000 300 30 43 2400 N 10000 2 > 240000 30000 9300 2400 240 2400 N 10000 3 15000 30000 360 430 15 93 AVG N 10000 23 19500 30000 3320 953 99 1631 S T D Dev 6364 0 5179 1269 123 1332 Table E - 8 Trial 3, Site 2 Methylotroph Bacteria Counts by MPN Day 0 5 12 20 26 36 46 R 1000 1 730 2300 2300 230 150 93 R 1000 2 43 300 300 30 150 23 R 1000 3 43 300 360 30 73 23 A V G R 1000 23 272 967 987 97 124 46 S T D Dev 397 1155 1138 115 44 40 R 10000 1 1500 4300 360 91 930 3.6 R 10000 2 240 300 300 36 15 23 R 10000 3 4300 910 1500 30 9.1 9.1 A V G R 10000 23 2013 1837 720 52 318 12 S T D Dev 2078 2155 676 34 530 10 N 1000 1 15000 30000 300 36 73 43 N 1000 2 21000 150000 910 2400 73 43 N 1000 3 4400 91000 300 30 43 43 A V G N 1000 23 13467 90333 503 822 63 43 S T D Dev 8406 60003 352 1367 17 0 N 10000 1 1400 30000 300 30 9.1 9.1 N 10000 2 >240000 750000 24000 1500 430 93 N 10000 3 15000 30000 1500 91 0.3 0.3 A V G N 10000 23 8200 270000 8600 540 146 34 S T D Dev 9617 415692 13350 832 246 51 Table E - 9 Trial 3, Site 1 and 2 Combined Methylotroph Bacteria Counts by MPN Day 0 5 12 20 26 36 46 SI R1000 23 37667 164433 40433 3010 23 17483 S2R 1000 23 272 967 987 97 124 46 A V G R 1000 23 18969 82700 20710 1553 74 8765 S T D Dev 20472 168934 33356 2619 57 15677 SI R10000 23 24767 25803 24953 913 33 5324 S2 R 10000 23 2013 1837 720 52 318 12 A V G R 10000 23 13390 13820 12837 483 176 2668 S T D Dev 16611 27391 26911 860 338 5525 SI N 1000 23 46000 36000 1777 411 70 354 S2N 1000 23 13467 90333 503 822 63 43 A V G N 1000 23 29733 63167 1140 617 66 199 S T D Dev 16801 44024 1433 857 26 248 SI N 10000 23 19500 30000 3320 953 99 1631 S2N 10000 23 8200 270000 8600 540 146 34 A V G N 10000 23 13850 150000 5960 747 123 833 S T D Dev 8073 268328 8679 900 160 1109 Table E - 10 Trial 4, Site 1 Methylotroph Bacteria counts by MPN. Day 0 8 19 36 43 R 1000 1 24000 43 >2400000 >2400000 R 1000 2 24000 43 930 93000 R 1000 3 2400 23 23 240 A V G R 1000 43 16800 36 477 46620 S T D Dev 12471 12 641 65591 R 10000 1 9300 210 43000 15000 R 10000 2 2400 43 93000 16000 R 10000 3 4300 43 1500 29000 A V G R 10000 43 5333 99 45833 20000 S T D Dev 3564 96 45816 7810 N 1000 1 1500 11000 240 43 N 1000 2 43 240 240 43 N 1000 3 23 930 93 23 A V G N 1000 43 522 4057 191 36 S T D Dev 847 6023 85 12 N10000 1 240 1500 430 93 N 10000 2 43 93 93 23 N 10000 3 93 430 93 23 A V G N 10000 43 125 674 205 46 S T D Dev 102 735 195 40 Table E - 11 Trial 4, Site 2 Methylotroph bacteria counts by MPN. Day 0 8 19 36 43 R 1000 1 240 150 43 430 R 1000 2 93 430 43 23 R 1000 3 930 9300 43 23 AVG R 1000 132 421 3293 43 159 S T D Dev 447 5204 0 235 R 10000 1 2400 43 23 23 R 10000 2 240 240 23 23 R 10000 3 43 430 23 23 AVG R 10000 132 894 238 23 23 S T D Dev 1308 194 0 0 N 1000 1 93000 240 430 23 N 1000 2 15000 23 1500 43 N 1000 3 2400 23 43 23 AVG N 1000 132 36800 95 658 30 S T D Dev 49077 125 755 12 N10000 1 2400 93 930 150 N 10000 2 930 240 240 93 N 10000 3 2400 240 240 240 AVG N 10000 132 1910 191 470 161 S T D Dev 849 85 398 74 Table E - 12 Trial 4, Site 1 and 2 Combined Methylotroph bacteria counts by MPN. Day 0 8 19 36 43 SIR1000 43 16800 36 477 46620 S2R 1000 132 421 3293 43 159 A V G R 1000 87 8611 1665 260 23389 S T D Dev 10908 3417 357 37129 SIR10000 43 5333 99 45833 20000 S2 R 10000 132 894 238 23 23 A V G R 10000 109 3114 168 22928 10012 S T D Dev 3119 143 34991 10959 SI N1000 43 522 4057 191 36 S2N 1000 132 36800 95 658 30 A V G N 1000 109 18661 2076 424 33 S T D Dev 980 3979 273 76 SI N 10000 43 125 674 205 46 S2N 10000 132 1910 191 470 161 A V G N 10000 109 1018 433 338 104 S T D Dev 1020 491 288 75 Appendix F - 1 4 C labeled Methanol Results Table F - 1 Trial 3, Day 46, C uptake data. Bold italicized numbers not included in calculations. DPM - Disintegrations/Minute. CO7 - denotes amount of l 4 C labeled as CO2. P -denotes amount of C labelec as particulates in water. D P M Blank 23.8 100 ug/L MeOH 20896.5 100 ug/L MeOH 19361.0 100 ug/L MeOH 19276.0 AVG 19844.5 198 dpm= 1 ug/L Site 1 Control D P M D P M - Blank ug/L/day C 0 2 A 11656.7 11632.9 C O z B 7900.8 7877.0 C 0 2 F 16203.4 16179.6 (AVG A & B) - F -6424.7 P A 1693.4 1669.6 P B 1469.3 1445.5 P F 1105.8 1082.0 (AVG A & B) - F 475.5 2.4 Corrected C 0 2 + P 475.5 Methanol Uptake 2.4 River C 0 2 A 10638.5 10614.7 C 0 2 F 7729.3 7705.5 (AVG A & B) - F 2909.2 14.7 P A 1102.3 1078.5 P F 891.7 867.9 A - F 210.6 1.1 Corrected C O z + P 3119.9 Methanol Uptake 15.8 105 Table F - 1 Continued Nutrient D P M D P M - Blank ug/L/day C 0 2 A 15357.1 15333.3 C 0 2 B 14878.6 14854.8 C 0 2 F 6018.6 5994.8 (AVG A & B) - F 9099.3 45.9 P A 1065.4 1041.6 P B 1023.3 999.5 P F 851.5 827.7 (AVG A & B) - F 192.9 1.0 Corrected C 0 2 + P 9292.1 Methanol Uptake 46.9 Table F - 2 Trial 4, Day , C uptake data. Bold italicized numbers not included in calculations. DPM - Disintegrations/Minute. CO2 - denotes amount of 1 4 C labeled as CO2. P - denotes amount of 1 4 C labeled as particulates in water. D P M Blank 35.2 100 ug/L MeOH 19863.4 100 ug/L MeOH 19641.3 198 DPM = 1 ug/L 100 ug/L MeOH 19931.1 A V G 100 ug/L MeOH 19811.9 Site 1 Site 2 D P M D P M - Blank ug/L/day D P M D P M - Blank ug/L/day Control C 0 2 A 26226.1 26190.9 18906.2 18871.0 C 0 2 B 25949.5 25914.3 21325.2 21290.0 C 0 2 F 54394.3 54359.1 12109.8 12074.6 (AVG A & B) - F -28306.5 8005.9 40.4 P A 2210.7 2175.5 2262.7 2227.5 P B 1145.3 1110.1 1615.2 1580.0 P F 995.1 959.9 849.9 814.7 (AVG A & B) - F 682.9 3.4 1089.1 5.5 Corrected C 0 2 + P 682.9 9095.0 Methanol Uptake 45.9 River C 0 2 A 9658.4 9623.2 48978.0 48942.8 C 0 2 B 24532.3 24497.1 49744.3 49709.1 C 0 2 F 12239.0 12203.8 16849.6 16814.4 (AVG A & B) - F 4856.4 24.5 32511.6 164.2 P A 28823.8 28788.6 29423.5 29388.3 P B 22016.1 21980.9 35859.5 35824.3 P F 762.0 726.8 1010.6 975.4 (AVG A & B) - F 24658.0 124.5 31630.9 159.8 Corrected C 0 2 + P 29514.3 64142.5 Methanol Uptake 149.1 324.0 107 Table F - 2 Continued Site 1 Site 2 D P M D P M - Blank ug/L/day D P M D P M - blank ug/L/day Nutrient C 0 2 A 49854.9 49819.7 45427.9 45392.7 C 0 2 B 51388.3 51353.1 77248.3 77213.1 C 0 2 F 15763.4 15728.2 8453.9 8418.7 (AVG A & B) - F 34858.2 176.1 52884.2 267.1 P A 23939.7 23904.5 16993.5 16958.3 P B 19630.3 19595.1 16632.6 16597.4 P F 1107.1 1071.9 1691.7 1656.5 (AVG A & B) - F 20677.8 104.4 15121.4 76.4 Corrected C 0 2 + P 55536.0 68005.6 Methanol Uptake 280.5 343.5 108 Appendix G - Water Levels on the Fraser River at Mission 4.00 £ 0.00 J -1.00 -\ 1 1 1 1 1 1 1 1 1 ' Dec 1 Jan 4 Feb 7 Mar 13 Apr 16 May 21 Jun24 Jul 28 Sep 1 Oct 5 Date & Time in PST Figure G - 1 Water levels on the Fraser River at Mission during the sampling year. Triangl indicate approximate time of sampling (EC 2005). Appendix H - Regression Analysis of Methanol Loss Curves Table H - 1 Trial 1 P values of linear regression analysis using categorical variables. Bold numbers indicate a significant difference (P < 0.05) and bold italics indicate a highly significant relationship (P < 0.001). L inear regress ion c o m p a r i s o n be tween s i tes 1 and 2 Intercept S l o p e C 1000 0 .490 2.57E-04 R 1000 0 .069 0.490 N 1000 0 .082 0.936 C 10,000 0.591 0.324 R 10,000 4.11E-05 0.684 N 10,000 0 .359 0.849 L inear regress ion c o m p a r i s o n be tween exper iments with a n d initial concent ra t ion of 1000 mg /L Intercept S l o p e C and R 0 .633 0.787 C a n d N 0.221 9.48E-29 R and N 0 .505 5.79E-27 L inear regress ion c o m p a r i s o n be tween exper iments with a n d initial concent ra t ion of 10 ,000 mg /L Intercept S l o p e C and R 0.019 0.896 C a n d N 0.001 0.014 R and N 0 .789 0 .053 L inear regress ion c o m p a r i s o n of initial concent ra t ions of 1000 mg /L and 10,000 mg/L Intercept S l o p e Cont ro l 0 .756 2.69E-07 Rive r 0 .127 0 .094 Nutrient 0 .282 6.43E-11 110 Table H - 2 Trial 2 P values for linear regression analysis using categorical variables. Bold numbers indicate a significant difference (P < 0.05) and bold italics indicate a highly significant relationship (P < 0.001). Linear regression comparison between sites 1 and 2 Intercept Slope C 1000 0.273 0.112 R 1000 0.705 0.463 N 1000 0.103 0.240 C 10,000 0.163 0.655 R 10,000 0.165 0.997 N 10,000 0.127 0.635 Linear regression comparison between experiments with and initial concentration of 1000 mg/L Intercept Slope C and R 0.121 0.768 C a n d N 0.509 3.53E-23 R and N 0.212 2.58E-19 Linear regression comparison between experiments with and initial concentration of 10,000 mg/L Intercept Slope C and R 0.383 0.970 C a n d N 0.059 4.93E-04 R and N 0.007 2.66 E-04 Linear regression comparison of initial concentrations of 1000 mg/L and 10,000 mg/L Intercept Slope Control 0.004 0.247 River 1.00E-04 0.239 Nutrient 0.828 1.14E-18 111 Table H - 3 Trial 3 P values of linear regression analysis using categorical variables. Bold numbers indicate a significant difference (P< 0.05) and bold italics indicate a highly significant relationship (P < 0.001). Linear regression comparison between sites 1 and 2 Intercept Slope C 1000 0.910 0.060 R 1000 0.661 0.394 N 1000 0.979 0.026 C 10,000 0.399 0.728 R 10,000 0.344 0.033 N 10,000 0.039 0.035 Linear regression comparison between experiments with and initial concentration of 1000 mg/L Intercept Slope C and R 0.789 0.331 C a n d N 0.820 0.304 R a n d N 0.100 0.060 Linear regression comparison between experiments with and initial concentration of 10,000 mg/L Intercept Slope C and R 0.906 0.267 C a n d N 0.621 0.009 R a n d N 0.685 0.089 Linear regression comparison of initial concentrations of 1000 mg/L and 10,000 mg/L Intercept Slope Control 0.002 0.089 River 2.00E-04 0.060 Nutrient 0.002 0.080 112 Table H - 4 Trial 4, P values of linear regression analysis using categorical variables. No values were of significant difference (P >0.05) and bold italics indicate a highly significant relationship (P< 0.001). Linear regression comparison between sites 1 and 2 Intercept Slope C 1000 0.938 0.089 R 1000 0.321 0.842 N 1000 0.234 0.243 C 10,000 0.099 0.481 R 10,000 0.166 0.431 N 10,000 0.090 0.079 Linear regression comparison between experiments with and initial concentration of 1000 mg/L Intercept Slope C and R 0.204 0.336 C a n d N 0.188 0.021 R and N 0.896 0.075 Linear regression comparison between experiments with and initial concentration of 10,000 mg/L Intercept Slope C and R 0.680 0.521 C a n d N 0.343 0.599 R and N 0.492 0.222 Linear regression comparison of initial concentrations of 1000 mg/L and 10,000 mg/L Intercept Slope Control 0.964 0.012 River 0.161 0.003 Nutrient 0.546 0.697 113 

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