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A waste audit and directions for reduction at the University of British Columbia Felder, Melissa Anne Juanita 1999

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A WASTE AUDIT AND DIRECTIONS FOR REDUCTION AT THE UNIVERSITY OF BRITISH COLUMBIA by MELISSA ANNE JUANITA FELDER B.A. (Hons.), McMaster University, 1996 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUHtEMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES Department of Chemical and Bio-Resource Engineering We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA July 1999 © Melissa Anne Juanita Felder, 1999 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of Clr/£MtCA-L fay B/o- (Zs^-P^e^ £J^>' A A J t-The University of British Columbia Vancouver, Canada Date Juiy 2-b7* 111*1 DE-6 (2/88) A B S T R A C T A solid waste audit is designed to determine the quantity and composition of the waste generated in a specific area. The information provided by an audit can help in evaluating waste management practices; such as the efficiency of recycling programs or the feasibility of composting. The literature revealed that many factors complicate a waste audit; the most significant being that waste varies in space and over time. Methodology was developed to address spatial and temporal variation in waste generated at the University of British Columbia for the 1998 year. This methodology was applied to the campus, and resulting data was analyzed to provide recommendations for achieving campus waste reduction goals. To study spatial variability, the campus was partitioned into sixteen different activity areas. Representative samples of waste were collected a minimum of three times from each activity area. Wherever possible, the number of users of the activity area was enumerated during the sample period. Statistically significant differences were found between the waste-per-user values for the sampled activity areas. Existing data on the number of people frequenting activity areas was employed to estimate total waste loads, as the majority of the activity areas (70%) had smaller variations associated with the calculated waste-per-user data sets than with the original waste data sets. The total waste estimated from the experimental design was compared to documented waste values for the year and was found to vary by -18%. Expected differences were attributed to special events, construction waste, and the omission of some activity areas. Other possibilities included spurious events as found in the sampled waste stream, and the under/overestimation of the amount of users for extrapolation. The University has several options available for waste reduction or diversion targets, including enhancing the current recycling program, source reduction of plastic materials, and diverting organic material to composting. The greatest diversion by weight would be accomplished through the diversion of organic material, as it is estimated to make up a substantial 70% of the calculated annual waste stream. i i T A B L E O F C O N T E N T S Abstract i i Table of Contents i i i List of Appendices vi List of Tables vii List of Figures vii i Acknowledgements ix C H A P T E R O N E : O V E R V I E W 1.1 Introduction 1.2 The University of British Columbia 1.3 General Research Objective 1.4 Specific Research Objectives 1.5 Overview of Thesis C H A P T E R T W O : L I T E R A T U R E R E V I E W 2.1 Introduction 6 2.2 Solid Waste Audits: Definition and Applications 6 2.3 Previous Waste Audit Methodologies 7 2.3.1 The Separation of Solid Waste into Categories 8 2.3.2 The Measurement of Solid Waste - Weighing 10 2.3.3 The Interrelationships Between Waste - Moisture Content 10 2.4 Waste - Spatial & Temporal Representation 11 2.4.1 Waste - Spatial Considerations 11 2.4.2 Waste - Temporal Considerations 14 2.5 Chapter Summary 16 C H A P T E R T H R E E : M E T H O D O L O G Y A N D R A T I O N A L E 3.1 Introduction 18 1 2 3 4 5 i i i 3.2 Outline of Methodology 18 3.3 Activity Areas and Sampling Design 19 3.3.1 Animal Care and Greenhouse Activity Areas 21 3.3.2 Bookstore 23 3.3.3 Classrooms 24 3.3.4 Common Use/Student Activity Areas 25 3.3.5 Food Service Areas 26 3.3.6 Laboratories 28 3.3.7 Library 28 3.3.8 Office Space 30 3.3.9 Residences 30 3.3.10 Other Space: Bathrooms 31 3.3.11 Other Space: Outdoor Bins 32 3.4 Summary of Extrapolation Methodology 32 3.5 Categories Selected 34 3.6 Method of Quantification 34 3.7 Statistical Analysis 35 3.7.1 The Coefficient of Variation 35 3.7.2 Analysis of Variance 36 3.8 Method of Validation 38 C H A P T E R F O U R : R E S U L T S 4.1 Overview of Chapter 39 4.2 Comparison of Waste Values from Different Activity Areas 39 4.3 Extrapolating on Per User 40 4.4 Activity Areas 41 4.4.1 Animal Care Areas 42 4.4.2 Greenhouse Areas 45 4.4.3 Bookstore 47 4.4.4 Classrooms 49 4.4.5 Common Use/Student Activity Areas 50 4.4.6 Food Service Areas 52 4.4.7 Laboratories 55 iv 4.4.8 Libraries 56 4.4.9 O f f i c e Areas 58 4.4.10 Residences 59 (A) 1s t Year Residence - Totem Park Kwakiutl Building 60 (B) 2 n d /3 r d Year Residence 60 (C) 4 t h Year Residence 61 (D) Family Housing 61 4.4.11 Bathrooms 65 4.4.12 Outdoor Bins 66 4.5 Summary of Wastes per Activity Area 68 4.6 Validation of Methodology 69 4.7 Conclusion 70 CHAPTER FIVE : DIRECTIONS FOR REDUCTION 5.1 Percent Composition of Samples 71 5.1.1 Compostable Material in Sampled Waste 71 5.1.2 Recyclable Material in Sampled Waste 73 5.1.3 Residual Material in Sampled Waste 75 5.2 Annual Waste Composition 77 5.3 Compostable Waste Generated Annually 79 5.4 Recyclable Waste Generated Annually 82 5.5 Residual Waste Generated Annually 86 5.6 Annual Waste Generation - Quantity, Composition, and Fluctuation 89 5.7 Recommendations for Reduction 90 5.7.1 Recycling 93 5.7.2 Source Reduction 94 5.7.3 Composting 95 5.8 Conclusion 96 CHAPTER SIX : CONCLUSIONS AND RECOMMENDATIONS 97 References 99 v LIST O F A P P E N D I C E S Appendix A: Activity Area Definitions 105 Appendix B: Private (External) Enterprises at UBC (Not Audited) 107 Appendix C: Waste Categories 108 Appendix D: Animal Care Areas 109 Appendix E: Greenhouse Areas 111 Appendix F: Bookstore 113 Appendix G: Classrooms 114 Appendix H: Common Use/Student Activity Areas 116 Appendix I: Food Service Areas 119 Appendix J: Laboratories 125 Appendix K: Libraries _ 126 Appendix L: Office 128 Appendix M: Residences 130 Appendix N: Other Space: Bathrooms 140 Appendix O: Other Space: Outdoor Bins 142 Appendix P: 1998 Estimated Waste Composition 146 v i LIST O F T A B L E S Table 1 : Summary of Sampling and Extrapolation Methodology for Each Activity Area. 33 Table 2 : Direct Measurement of Daily Waste Material (Waste (g) per Rat Weight (g), %) from UBC Animal Care Centre («=9). 43 Table 3 : Animal Wastage Rates (g/day) for Various Species, Animal Care Centre. 43 Table 4 : Animal Care Facilities : Annual Waste Quantities and Composition (1998). 45 Table 5 : Greenhouse Areas - Waste Parameters. 47 Table 6 : Bookstore - Waste Parameters. 48 Table 7 : Classrooms - Waste Parameters. 50 Table 8 : Common Areas - Waste Parameters. 52 Table 9 : Comparison of the Coefficients of Variation in Samples, Food Service Areas. 53 Table 10 : Food Service Areas - Restaurant Style - Waste Parameters. 54 Table 11 : Food Service Areas - Coffee and Meal Plan Style - Waste Parameters. 55 Table 12 : Library Areas - Waste Parameters. 57 Table 13 : Office Areas - Waste Parameters. 59 Table 14 : Residence Activity Areas - Waste Parameters. 64 Table 15 : Comparison of the Coefficients of Variation in Samples, Bathroom Samples in SUB. 65 Table 16 : Bathroom Areas - Waste Parameters. 65 Table 17 : Outdoor Bins - Waste Parameters. 67 Table 18 : Total and Types of Waste Generated Annually per Activity Area (t). 68 Table 19 : Comparison of Projected Annual Waste to Documented Amounts. 69 Table 20 : Compostable Material Produced Annually in different Activity Areas (t/yr). 81 Table 21 : Recyclable Material Produced Annually in Different Activity Areas (t/yr). 85 Table 22 : Residual Material Produced Annually in Different Activity Areas (t/yr). 88 Table 23 : Total and Types of Waste Generated in Winter per Activity Area (t). 91 Table 24 : Total and Types of Waste Generated in Summer per Activity Area (t). 92 vii LIST O F F I G U R E S Figure 1 : Per User Means Generated in Different Activity Areas. 40 Figure 2 : Average Daily Waste Composition (Horticultural Greenhouse). 46 Figure 3 : Average Daily Waste Composition (Bookstore). 48 Figure 4 : Average Daily Waste Composition (Buchanan A , B200s, D). 49 Figure 5 : Average Daily Waste Composition (SUB Conversation Pits). 51 Figure 6 : Average Daily Waste Composition (The Barn, Yum Yums, Place Vanier Kitchen). 53 Figure 7 : Average Daily Waste Composition (Woodward Library). 56 Figure 8 : Average Daily Waste Composition (Brock Hall). 58 Figure 9 : Average Daily Waste Composition (Totem Park Kwakiutl House). 60 Figure 10 : Average Daily Waste Composition (Gage North & South Towers). 61 Figure 11 : Average Daily Waste Composition (Thunderbird 3000 Block). 62 Figure 12 : Average Daily Waste Composition (Keremeos Court). 62 Figure 13 : Total Residents vs. Total Waste (Totem Park, Gage North & South Towers, Thunderbird 3000 Block, Keremeos Court). 64 Figure 15 : Average Daily Waste Composition (Random Sample, Outdoor Bins). 66 Figure 16 : Percent Compostable Material in Each Waste Sample. 72 Figure 17 : Frequency Distribution of the Percent of Compostable Waste in each Sample. 73 Figure 18 : Percent Recyclable Material in each Waste Sample. 74 Figure 19 : Frequency Distribution of Percent of Recyclable Waste in each Sample. 74 Figure 20 : Percent Residual Waste in each Waste Sample. 75 Figure 21 : Frequency Distribution of Percent of Residual Waste in each Sample. 76 Figure 22 : Composition of Sampled Waste Data. 77 Figure 23 : Composition of Estimated Annual Waste. 78 Figure 24 : Breakdown of Annual Compostable Waste. 79 Figure 25 : Location of Annual Compostable Waste. 80 Figure 26 : Breakdown of Annual Recyclable Waste. 82 Figure 27 : Location of Annual Recyclable Waste. 83 Figure 28 : Breakdown of Annual Residual Waste. 86 Figure 29 : Locations of Annual Residual Waste. 87 Figure 30 : Annual Waste Generation - Quantity and Composition at Different Locations. 89 Figure 31 : Fluctuation in Total Waste Quantities : Winter vs. Summer Terms. 90 v i i i A C K N O W L E D G E M E N T S This project would not have occurred without Mary-Jean O'Donnell, who was originally interested in the project and was responsible for obtaining funding from UBC Waste Management. My principal advisors for the project were Dr. Royann Petrell from Bio-Resource Engineering and Dr. Sheldon Duff from Chemical Engineering. Dr. Petrell and Dr. Duff gave generously of their time and resources throughout project inception, development, and final writing stages. Dr. Petrell helped significantly with developing solutions to address problems with auditing, as well as giving me the initial chance to pursue my project at the University. Dr. Duff lent many a kind ear, eye and suggestion to dreaded seminar preparation and delivery, as well as intervening when things when threatened to spiral out of control (i.e. let's do aGIS of the total waste - should only take a few days!). Of course, thank you to Dr. Emina Krcmar-Nozic for joining in and contributing to the overall clarity and accuracy of the final thesis. Thank you to Dr. James Atwater for important project suggestions. Thank you to UBC Waste Management, especially Shelley Vandenberg, Bernie Dick, Gary Wolfram, Jarnail Sandhu, and Jane Goodlet who took care to treat me as part of the crew. Many thousands of thanks to the people who incidently contributed to this thesis by throwing out waste sometime last year, especially those who threw out waste with low standard deviations. I must include acknowledge the following people who helped me along the way: Dr. Chris Robinson from York University for original project suggestions and framework, Sonya Newenhouse from Wisconsin, Wally Erickson from Simon Fraser University, Chris Underwood from the City of Vancouver, Tim Reeve of Gartner Lee, and Mark Jeffrey from Pollution Prevention in Victoria. From UBC, I would like to thank Janet Land, Alvia Branch, Mark Aston, Sharon Walker, Bob Frampton, Scott Picket, Albert Ng, Heather Keate, Elsie De Bruijn, and Judy Vaz. Especially, thank you to all the wonderful custodians and supervisors who helped substantially in many applications. And to all the very helpful animal care technicians, particularly Gordon, Randy, Karen, Debbie and Jenny for going above and beyond the call of duty. A big and sympathetic thank you to all those people that helped with the worst sort of data collection on the planet: Martina Waldkirch, Patrick Tamkee, Leslie, Pavel Suchanick, Dave Primack, Venkatram Maheudraker, Margaret Wojtarwicz, Royann Petrell (!), and especially Mom. Particular mention must be made of Kevin Lee and Jason Christensen, work study students which helped out significantly with data collection and projects, and of Ricky Leung, Killarney summer student. Thank-you to Schrodinger for disobeying his namesake, and Warren for helping me with coursework (of course not in courses that had grades...). Of course, Mom and Dad, and Al and Sedna, and Herge for Tintin-relaxing times. To anyone else I should have included here and haven't (this thesis is far too long), I'm most sincerely sorry, and thank-you. ix C H A P T E R O N E : O V E R V I E W 1.1 Introduction In September of 1989, all regional districts in British Columbia were assigned to prepare solid waste management plans and submit them to the Ministry of Environment, Lands, and Parks by 1995. These plans were to define strategies by which local government would help achieve the following provincial waste reduction goals: (1) 30% reduction by the end of 1995; and, (2) 50% reduction by the year 2000. Waste reduction progress is measured by comparing the current per capita amount of waste disposed to landfdl to the per capita waste that was generated in 1990, where generated waste includes wastes that are disposed, recycled, and composted (GVRD, 1995. Pers. Comm: C. Underwood. Engineer, City of Vancouver Landfdl). This plan was developed due to public concerns over the environmental safety of landfdl and other waste management options such as incineration. Future population and pollution pressures will lead to even more stringent environmental safeguard requirements, making it difficult to find acceptable sites for landfills. Siting restrictions have implications for the type and amount of waste permitted to go to disposal. Since 1989, more than half of the 207 landfills in British Columbia have closed (Pers. Comm: P. Nell. GVRD Solid Waste Management Programme). In order to comply with current regulations and consider future directions in environmental regulation and sustainability, alternative waste management strategies should be considered for every municipality. Unfortunately, most mandates that include blanket statements like "reduce the waste stream by 40% by the year 2000" include no indications of how exactly this is to be achieved (Snider et al, 1995). Developing available disposal alternatives to landfill and incineration requires knowledge about the material composition of municipal solid waste. Such information can provide local governments with a solid foundation from which to assess solid waste management planning and address mandates for ecological sustainability. 1 1.2 The University of British Columbia In 1991, the University of British Columbia (UBC) contracted Resource Integration Systems Ltd. of Toronto (RIS) to prepare an integrated waste management plan for the campus. The plan evolved from the findings of a 1990 solid waste audit RIS conducted for the University, and focused on outputs that could be recovered via recycling. The 1991 diversion rate to composting and recycling was estimated to be 13%, and nearly 1/2 of this estimate was attributed to grounds waste composting (UBC Waste Reduction Program, 1996). The report indicated UBC has the potential to divert more than 50% of its waste stream, as: 1. The University's waste stream contains a higher percentage of easily recycled materials than that of normal municipal solid waste streams; and, 2. UBC is a closed system, and therefore has the means to implement waste management strategies that may prove more difficult for towns, cities, and regions. (Resource Integration Systems, 1991). UBC began its recycling system in 1988, and has since seen the implementation of waste diversion programs such as Waste Free UBC, the Surplus Equipment Recycling Facility (SERF), and pilot projects such as composting at the Acadia Park residences. These and other initiatives with the exception of SERF were developed by the UBC Waste Reduction Program. The Waste Reduction Program was created in 1991 to "initiate, coordinate and promote waste reduction, reuse, recycling and composting activities at the University of British Columbia" (UBC Waste Reduction Program, 1996). Despite these waste reduction efforts, diversion rates per capita were estimated at 38% in 1998, without including construction and demolition debris (DLC) (UBC Waste Reduction Program, 1998). This diversion rate was calculated by comparing the total waste generated per capita to the waste recycled per capita in 1998 (where recycled waste includes grounds waste composting). The UBC Waste Reduction Program uses current values to estimate diversion rates as information on 1990 per capita waste is scarce (Pers. Comm: J. Metras. Associate Director, UBC Plant Operations. S. Vandenberg. Waste Coordinator, UBC Waste Management). Similar to the Greater Vancouver Regional District (GVRD) and Simon Fraser University, UBC Waste Management categorizes recyclable DLC debris as a separate waste stream as the amount of this material produced (and recycled) is industry-driven and extremely variable (P. Comm: S. Vandenberg. W. 2 Erickson. Waste Coordinator, Simon Fraser University). Levels of D L C debris fluctuate depending on the presence and extent of construction in an area, and do not accurately reflect the per capita recycling/diversion rate. For example, extensive construction one year followed by less construction the next year might indicate that per capita recycling/diversion rates had declined when the opposite may be true. U B C has now less than one year to reach the minimum target of 50% reduction per capita waste generation levels specified as a campus waste reduction goal (UBC Waste Reduction Program, 1998). Researching alternative waste management strategies can also address other environmental initiatives particular to the campus, including mandates developed by the U B C Sustainability Office and the UBC Official Community Plan: ...the University of British Columbia must prepare for the 21st century. UBC aspires to be Canada's leading university. To attain this goal we must be ready to meet the coming social, economic, and environmental challenges and to see opportunities where other would see only obstacles. We have a responsibility to be plan for the future, to be bold— to be visionary. ( U B C Sustainability Office, 1998 Webpage) ... UBC must utilize its land resource to support academic activities and to build on endowments through the development of an integrated community in an environmentally sound fashion, consistent with regional objectives... ( G V R D Strategic Planning Department, 1997 Webpage). In view of the imminent year 2000 goal and environmental benefits possibly afforded by alternative management of solid waste, it seems prudent and desirable to conduct research on alternatives to landfill disposal at the University of British Columbia. 1.3 General Research Objective In order to develop alternative waste management processes, accurate information is required on the amount, location, and availability of the waste material (Yost and Halstead, 1996). This information can be provided by performing a waste assessment of the area in question, or a waste audit. A waste audit is defined as "the study of the generation and the management of waste, not including liquid industrial waste" (GVRD, 1994 and 1995). Waste audits are designed to analyze the quantity and composition of the waste generated in the area of interest. 3 In keeping with waste reduction research, a solid waste audit was proposed for UBC. An audit is often developed with the purpose of assisting institutions, municipalities, and regions to identify opportunities for waste reduction, thereby monitoring environmental progress (Alexander-Hall, 1992). The objectives for the proposed project are twofold: (1) To conduct a representative audit of the solid waste produced by UBC campus that will account for temporal variations, spatial variations, and variables particular to waste; and, (2) Analyze this information to indicate possible reduction strategies for waste currently going to landfill. As the environmental and economic costs of waste disposal increase, it becomes important to have accurate information upon which to base decisions about waste management (Williams and Haines, 1985). These decisions include evaluating current strategies for landfill disposal, assessing the success of recycling programs, as well as researching alternatives to landfill disposal such as composting. This information provides direction for meeting objectives concerning waste reduction and sustainability. 1.4 Specific Research Objectives In order to conduct a representative audit using the time and resources available, and that will address problems with audit methodologies outlined in the literature, it was necessary to develop a novel audit design that would account for the following considerations: (1) Spatial variation, as waste varies geographically across campus, (2) Temporal variation, as waste varies daily, weekly, and with season, (3) The extrapolation to the remainder of campus, as it is desirable to offer directions to reduce this waste on a campus-wide basis; and, (4) The validation of the methodology, as it is crucial to determine the representativeness and scientific validity of the audit design. These objectives were also designed to improve on the audit RIS conducted for UBC in 1991. 4 1.5 Overview of Thesis The following report will investigate previous audit methodologies detailed in the literature and their contribution to the development of the audit design for the University of British Columbia (Chapter 2). Design details, including particularities for different activity areas, are outlined in the methodology (Chapter 3). The results of the audit, the extrapolation of these results to the campus, and the comparison of extrapolated to documented waste generation values are presented and discussed (Chapter 4). Engineering application investigates the meaning of these results for U B C , focussing on potential strategies for reduction (Chapter 5). Conclusions and recommendations arising from the thesis sum up major points from the results and applications chapters (Chapter 6). 5 CHAPTER TWO : LITERATURE R E V I E W 2.1 Introduction The quantification of waste involves many considerations, ranging from category selection for and the measurement of waste, to capturing spatial and temporal variations in the waste. Previous solid waste audit methodologies have included taking visual estimates, using lump sum approaches, and extrapolating measured quantities by factors such as floor space (Resource Integration Systems, 1991; Laidlaw Waste Systems, 1995 and 1997; Robinson, 1993). However, waste varies from day to day, from month to month, and from place to place. This variation makes accurate waste sampling complex. Plans of action for waste must be based on waste realities. Gore and Storrie Ltd. (1991), suggest that waste generation and composition studies are hampered by the following five distortion factors: 1. The nature of the solid waste to be studied 2. The transfer of water between waste constituents that occurred before sampling (moisture transfer) 3. The geographic location of the study area 4. The season of the year when the study was undertaken 5. The year of the study In the present study, all five of these factors were considered in the development of a methodology for characterizing the UBC solid waste stream. The following chapter investigates the methods other waste studies used to address these factors. Geographic location, season of the year, and the year of study are referred to as spatial and temporal considerations. 2.2 Solid Waste Audits: Definition and Applications A solid waste audit is defined as: "the study of the generation and management of waste, not including liquid and hazardous waste" (GVRD, 1994 and 1995). A solid waste audit is designed to analyze the composition of the waste stream by material type (i.e. glass, paper), activity (i.e. demolition waste), or by product type (i.e. glass containers, magazines, cans) to provide valuable information on the waste disposed (Yu and MacLaren, 1995). This is accomplished through the sorting and quantification of waste into defined categories. Audit measurements can assist in evaluating opportunities for reducing waste directed to landfill and in monitoring environmental progress (Alexander-Hall, 1992). As costs of 6 energy, raw materials, and waste disposal increase it becomes important to have accurate information upon which to base decisions about waste management (Williams and Haines, 1985). Waste management can include, but is not limited to, assessing policies for disposal, and research and development of alternative methods to disposal. Knowledge of the waste stream is important for waste management, both for long term (i.e. policy development) and short term planning (i.e. identification of products that have recycling value) (Yu and MacLaren, 1995). Understanding of the quantity and composition of waste is useful for monitoring progress towards achieving waste reduction or waste diversion policy objectives. Determining the composition of the regional solid waste stream is a necessary first step in devising strategies for reducing that waste stream. These waste characterization studies can also contribute to assessing the applicability of a particular resource recovery system for diverting waste (Gartner Lee Ltd., 1991(b); SCS Engineers, 1982). Bird and Hale (1979) conducted the first Canadian attempt to categorize waste according to generation rates and composition on a national scale. The rationale for the study was to identify waste that had high potential value for future resource and energy recovery. 2.3 Previous Waste Audit Methodologies The most widely used methodology for waste audits is "Direct Waste Analysis" (DWA) (Yu and MacLaren, 1995). Also known as "sample and sort", DWA involves the direct examination of waste at the point of generation or disposal. Waste composition may be visually estimated, but this is not recommended unless the waste load is homogenous. Common practice is to sort by hand into predetermined material, product, or activity categories or some combination of these categories. Usually a rough sort is carried out first to separate bulky items. Components can be quantified by volume, number, or weight, with the latter being the most common. Measurements may also be taken of moisture content and other characteristics such as energy content, elemental concentration, and volatility, which help determine suitability of the waste stream for incineration or other processes. The selection of samples for analysis is guided by factors such as the nature of the waste being studied, the geographic location of the study area, and the seasonal variation in the waste (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). 7 2.3.1 The Separation of Solid Waste into Categories Most audits have a category classification prepared for the waste stream prior to the actual sorting of the waste. These range from simple classifications into two broad categories destined for incineration and landfill (Matsuto and Tanaka, 1993), to over sixty categories as in a study on solid waste generated by Metropolitan Toronto (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). The number and type of classes used is dependent upon the information sought by the investigators (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). Bennett et al. (1996) indicate that more numerous categories can identify more precise reduction strategies. Including characterizations such as residual, recyclable, and/or reusable status can offer insight into alternative reduction campaigns. For example, if compostable/reusable food can be differentiated, the potential of alternative disposal schemes such as composting or donating food to soup kitchens can be better assessed. This latter strategy is particularly emphasized by Skaljin (1995), Canadian Restaurant and Food Services Association (CRFA, 1992), and Fishbein and Gleb (1992). Skordilis (1985) noted that distinguishing waste into categories was only possible in consultation with experts. Still, it was concluded that ultimate use should figure prominently in the classification in order to examine the following: (A) The possibility of recovering useful materials from wastes. This can be developed by determining the capacity of local markets to accept recyclables and the capacity of processing facilities (Resource Integration Systems Ltd., 1991; Skordilis, 1985; Reid Crowther & Partners Ltd. et al, 1980). (B) The possibility of disposing of wastes in a way which would be beneficial to the environment. A caveat must be inserted at this point. Waste characterization for future alternatives is based on knowing what part of the waste stream will become important in the future (Pers. Comm.: S. Newenhouse. Waste Consultant). In addition, the Municipal Solid Waste Management Task Force (1989) stated that forecasting waste generation (and also composition) included forecasting the changes in consumer product packaging, changes in societal perception towards waste production, and changes in household income! 8 t Previous investigators have used waste classifications that are composition-related, activity-related, or product-related, or some combination of the former (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). Composition-related describes the composition of the material (i.e. metals, glass, plastic). Activity-related describes the activity that produces the waste (i.e. demolition waste). Product-related describes products found (i.e. plastic tub containers). Sometimes, a product is composed of a single material such as aluminum (i.e. soft drink containers) and thus falls into both composition-related and product-related categories (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). Each type of classification system has disadvantages. A disadvantage of product and composition-related classifications is that large numbers of classifications are required, meaning more time and resources are necessary for sorting (Proctor & Redfern Ltd. and SENES Consultants Ltd. 1991). The main disadvantage of activity-related classification is that other activities can generate the same type of waste. Bird & Hale (1979) also note that if a multi-component item is found in the refuse in sufficiently large quantities, it may be practical to treat it as a separate component. This was the case in the audit conducted for U B C in 1991, where aseptic packaging and diapers were treated as separate categories (Resource Integration Systems Ltd., 1991). Despite the difficulties inherent in developing a list of categories that can address future and classification concerns, background information concerning waste collection and recycling can help to develop the type of categories that would be needed. Alexander-Hall (1992) recorded visual estimates from the waste hauler at the University of Guelph to develop six categories for classification. Proctor & Redfern and SENES Consultants Ltd. (1991), visited landfills and transfer stations operated by the City of Toronto. Observations that were made helped to define waste categories and subsequent development of sorting methodology. Categories can also be further developed qualitatively. Williams and Haines (1985) developed categories from a detailed questionnaire distributed to each state in the research area, and emphasized providing an outline description of waste found in each category of the classification. In short, a comprehensive picture of need is required for different types of facilities, and is essential for the creation of any large-scale regional plan. Category development can be facilitated through direct observation and qualitative information of the study area. 9 2.3.2 The Measurement of Solid Waste - Weighing The waste auditor has three units of measurement available: weight, volume, or number (Robinson, 1993). Solid waste auditing almost always makes use of weight, as solid waste is compacted in most treatments including landfdl disposal, incineration, recycling, and composting. Garbage statisticians have been frustrated by the traditional dependence on weight rather than the space/volume the discards occupy, as "landfills don't close because they are too heavy, they close because they are full" (Rathje, 1993). Even so, weight is the measurement of choice as data is far easier to collect than physical volume, and gives a more accurate indication of resources used (Alexander-Hall, 1992). Garbage trucks are weighed before disposing their loads. Landfills charge by the tonne. Raw commodities from which garbage is produced are also measured in tonnes. In addition, cubic volume cannot be measured accurately until after garbage has been compressed in a landfill (Robinson, 1993). Volume is more useful for liquid waste (i.e. sewage and toxic substances). Numbers of waste items are more useful in specific applications such as reuse of old tires (Robinson, 1993), or for bulky items such as refrigerators ("white" goods). While the attempt to convert weight from an "as received" condition to an "as generated" state is a valid one, this is difficult using predetermined factors as the amount of moisture present in a sample varies (Gartner Lee Ltd., 1991(a)). The rate of absorption and adsorption of water is a function of material, temperature, and other factors. The heterogeneous mixture of wet and dry materials in refuse prevents a simple derivation of moisture transfer between initially wet and initially dry components. Measuring moisture content provides a means to account for differential moistures in samples. 2.3.3 The Interrelationships Between Waste - Moisture Content A drawback of presenting composition results on a percentage basis only is that each category becomes dependent on the other categories (i.e. the addition of a large sofa can "reduce" the presence of paper in the waste stream) (Gartner Lee Ltd., 1991(a)). In addition, waste changes as soon as it is discarded. Paper becomes wet, organic matter dries out, and volumes change with compaction (Department of Facilities Management, 1992). Gartner Lee Ltd. (1991(a)) stress the importance of analyzing both the wet and dry weight percentages of the waste, as when waste is received for sorting it may have accumulated or lost water. Moisture content is a particular concern in audits that measure waste at the point of disposal, as it can change depending on the type of collection (i.e. open/closed refuse bins), time 10 elapsed between collection, weather conditions en route, and interrelationships between wastes. Wastes collected in different seasons or geographic locations can also vary in moisture content (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). Adjusting for moisture content confers better representation of the wastes which tend to accumulate water, such as newspaper. This adjustment allows for accurate identification of management considerations; such as identifying the "actual" amount of recyclables in the waste stream, for example (Gartner Lee Ltd, 1991(a)). Dry weight percent composition can also be used to compare the percent composition of samples taken under different conditions (i.e. before final disposal). If sorted weights are not converted, uncertainty about the amount of moisture in each category can limit accurate representation of generated wastes. Other benefits to measuring moisture content include determining proper controls necessary for composting or digestion of the waste, determining the amount of energy needed if incineration is considered as a disposal option, or to estimate the amount of leachate that a landfill will produce (Gartner Lee Ltd., 1991(a)). Proctor & Redfern and SENES Consultants Ltd. (1991) state that moisture content measurement offers an excellent opportunity at little cost for future planning of alternative waste management disposal and recycling options. 2.4 Waste - Spatial & Temporal Representation Spatial and temporal considerations must be accounted for when conducting a waste audit, as waste varies geographically and according to season (Qdais et al, 1997). A sampling strategy must be designed with these factors in mind in order to represent the waste stream accurately, as a single "snapshot" of waste will prevent extrapolation to any place or time other than that sampled (Gartner Lee Ltd., 1991(b)). 2.4.1 Waste - Spatial Considerations In a study on Industrial, Commercial, and Institutional wastes (ICI), it was observed that no formulas exist that allow quantitative adjustment of waste composition percentages in response to external influences. This is because the "overall composition of solid waste generated in a jurisdiction is a heterogeneous blend of thousands of waste generators, each with its own unique waste composition 11 profile" (CH2M and RIS., 1993). The study by Liberti et al. (1996) on hospital waste required information on current activities within each department to estimate institutional hazardous waste production. Very large differences were found to exist in the amount and the toxicity of waste produced in each department, which in turn depended on the nature of the specialized therapeutic or diagnostic procedures existing at each hospital. This observation, combined with social, geographic and other factors, helps to explain the wide variation in figures reported in the literature for hospital waste production (Liberti et al., 1996). The Municipal Solid Waste Management Task Force (1989) emphasizes the need for source separating areas to better target what areas generate what type of waste. Source separation is particularly useful for management decisions as the feasibility of any one approach to resource recovery is highly site-dependent (SCS Engineers, 1982). This introduces the concept of spatial representation. Robinson (1993) states that waste composition is not homogenous across study areas, consequently diversion or reduction efforts have to be designed keeping that in mind. By identifying the source of a particular type of waste, efficient planning for waste management practice is facilitated (i.e. through optimal truck routing, or location of processing facilities). Spatial representation can be captured in the idea of "activity space", through which waste is sampled to reflect the activity that generates it. Using activity areas to help identify waste composition implies two major assumptions: (1) That waste composition remains constant according to activity; and, (2) That the same activity generates the same type of waste, regardless of location (Robinson, 1993). Sampling by activity space was introduced in the 1991 audit conducted at U B C , but its application is illustrated more thoroughly in an audit conducted by York University (Robinson, 1993; Department of Facilities Management, 1992). In the 1991 audit conducted at UBC, Resource Integration Systems (RIS) grouped university facilities into five generator types. These included: (1) administrative and faculty offices, (2) classrooms and laboratories (including the library), (3) food services areas, (4) residences (including family housing), and (5) other (student union building, bookstore, grad student centre, faculty club, etc.). Buildings that contained more than one type of activity were classified according to the "dominant" type of activity (i.e. a library with a food service area would be classified as (2)). Buildings within each generator class were sampled and a profile was established. In this way, the audit was to provide an estimate of the volume and type of waste generated at each building. 12 The report states: "although the accuracy of this approach is lower than with other types of waste sorting methodologies, it did provide a good overall estimate of waste composition." This disclaimer refers to the almost arbitrary assignation of activities to different buildings, as no clear method was applied to determine when an activity was "dominant". In addition, the generator areas did not remain consistent throughout the report, as it was later stated that buildings were classified into seven general major use categories. These then became: (1) classroom and faculty offices, (2) administrative offices, (3) food service areas, (4) residences, (5) laboratories, (6) libraries, and (7) other. Again, the report states that: "although this classification scheme may have resulted in some error it appears to have worked adequately". In addition to the disparity in activity types from before to after the study, no indication existed in the report that this method was tested for accuracy. The methodology used to sample for and classify different generator classes was unclear, preventing replication of the study. Robinson (1993) in collaboration with the Department of Facilities Management (1992), identified and sampled waste by activity types within buildings at York University (cafeteria, classroom, office, fine arts, recreation, physical plant, library, retail, and residence). An activity space was defined as "the space designated to accommodate one of the many different functions of a university", and was based on the premise that it is the people undertaking the activity that generate the waste. This premise is affirmed by Gore and Storrie Ltd. (1991) in their report, as they based their methodology on the hypothesis that waste generation is a function of people's habits and lifestyles. They found that economic status, type of housing, and cultural background are factors that significantly influence waste generation patterns. This conclusion has been supported by various other studies (Skordilis, 1985; Rathje, 1993; Carruth and Klee, 1969). The type and number of activity space classes should reflect the diverse nature of the study area and yet be general enough to allow for extrapolation to the entire study area. In a study on waste collection, it was found that modeling spatial variation without considering differences between large and small businesses could produce misleading results (Peterson et al., 1995). The error was greatest when activity classes were generalized. Peterson et al. (1995) concluded that i f activity classes are restrictive, the type/amount of waste generated in each class will be more similar. The Ontario Space Use Planning Index for Universities is a document that outlines different space uses common to Canadian universities. These twenty-one categories have been appropriated to U B C planning data and reflect the wide variety of activities that are undertaken at the university (Campus Planning & Development, 1992; Pers. Comm.: 13 A . Inouye, P. Jia. Space Use Analysts, Campus Planning and Development UBC). In addition, these categories have been designed to accommodate the construction of new facilities at U B C . This type of information would allow for a good approximation of waste generation patterns particular to current and future activities at the University. It is important to be able to use sampling results to estimate a waste generation picture for the entire area of interest, as well as assessing the validity of this estimation. In some studies, extrapolation to the rest of the study area has been achieved by using a measure of the activity, such as floor space or the number of people conducting the activity in each building. At York University, floor space data was used to extrapolate the sampled values to other similar activity areas (Dept. of Facilities Management, 1992). Simon Fraser University used total numbers of Full-Time Equivalent (FQE) people to estimate waste generation per capita (Dept. of Facilities Management, 1997 and 1998). Waste estimates from studies conducted by SCS Engineers (1982) were based on the number of persons using the facility, floor space, and the number of paid staff. In order to validate extrapolation methodologies, Reid et al, (1980) state that there are several methods to determine actual waste quantity produced by the area/facility in question. These methods include compiling rate data, permit data, disposal data, transport data, and industry surveys. Which method is used is dependent on the nature of the regulatory structure and associated data records. As a final check for the York study, annual generation estimates derived from the methodology were compared to the waste disposal data documented by Facilities Management. These values were found to differ by 10% (Dept. of Facilities Management, 1992). 2.4.2 Waste - Temporal Considerations The nature of the waste stream varies over time as waste production changes. This variation can be from day to day, from season to season, and from year to year. Daily variation can be a reflection of disposal habits, collection patterns, or any number of variables particular to waste generation. Seasonal change produces broader, more substantial differences in the nature of the waste stream due to climatic fluctuations (i.e. increasing yard waste in the fall is a function of cooler temperatures/less daylight hours stimulating leaf drop in trees). Seasonal variation at a University is also based on the population fluctuations associated with winter, spring, and summer terms. Annual differences can be a function of societal consumptive patterns and changes in the way waste is perceived (i.e. the decline in the amount 14 of recyclables in the waste stream due to the introduction of a recycling program). In short, measuring temporal variation in the waste stream presents a complex problem. To quantify this variation minutely requires substantial data collection and virtually infinite sampling, and even so cannot be typified to every day. Ideally, a sampling program should consider the time and resources required to conduct an audit, as well as recognize the limitations posed by sampling. A generalized extrapolation of the waste stream to times other than those sampled can occur, providing that the methodology underlying this extrapolation can be tested. The RIS audit originally intended to use a full four-quarter sampling approach to account for seasonal variation. However, the results of the first waste sorts correlated "so closely to data compiled by other universities in Canada and the US - it was [therefore] decided that additional waste sorts were unnecessary" (Resource Integration Systems, 1991). The report additionally stated: "although this may obscure the effects of seasonality, other studies have concluded that the contribution which seasonality makes to variation in waste composition data is relatively small..." (Resource Integration Systems, 1991). These studies were not identified, and additional reports like the study conducted by Matsuto and Tanaka, 1993) indicate that solid waste generation is, in fact, susceptible to seasonal variation. Gartner Lee Ltd., (1991(a)) performed surveys twice at each site in an attempt to capture seasonal variation. The comparison of these separate surveys indicated statistically significant differences between the fall and the spring season, including waste quantity, composition, and moisture content. S. Newenhouse (Pers. Comm.: Waste Consultant, Wisconsin State University) stated that only conducting one sample for her study would have skewed waste composition results, as personal experience indicated large variation between even three samples taken of the study area. To further complicate the issue, seasonal events like yard waste production and leaf fall which affect the waste stream in quantity and composition, are in turn influenced by the proportion of mature trees in a particular area (Gore and Storrie Ltd., 1991). For municipal waste management purposes, Gore and Storrie Ltd. (1991) state that the "amortization of the tonnage of leaves and yard waste over the entire year in order to calculate a daily per capita generation rate is very misleading". It is also equally misleading, they conclude, to calculate leaf and yard waste as an annual percentage of an overall waste total. In fact, based on their study, the entire annual tonnage of leaves may be expected to arrive over a period of approximately 3-4 weeks. The arrival rate of leaves is an important factor in formulating alternative management plans for disposal (i.e. composting). In summary, waste generation is the cumulative result of activity that represents a single day of living - these residues can be categorized and 15 quantified but essential waste management requires quantification of residues over generation patterns of more than one day. The use of qualitative information and quantitative information can help target times of differential waste generation (Pers. Comm.: S. Newenhouse). York University extrapolated waste generation according to low and high generation time, which was derived from empirical information, deductive reasoning, objective observation, and qualitative interviews of knowledgeable people (Robinson, 1993; Dept. of Facilities Management, 1992). By surveying the composition of waste loads several times throughout the year, estimates of weekly as well as seasonal variability of wastes are possible (Gartner Lee Ltd., 1991(a) and 1991(b); Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). At the University of British Columbia, the prior separation of yard and leaf waste from the normal waste stream diminishes the impact of seasonal change on waste. However, the change in population and in activities over time has to be accounted for. Despite the difficulties posed in capturing seasonal variation, an important point was mentioned in the RIS report, that, "the marginal benefits of subsequent sampling are not deemed to be of sufficient value to justify additional expense... funds would be more productively utilized on spending time on implementation" (1991). T. Reeve (Pers. Comm.: Waste Consultant, Gartner Lee Ltd.) further states that the purpose of an audit is to "prove" that a certain constituent(s) is/are not present in the waste stream in a certain quantity. To this end, he argues, a single snapshot is sufficient when a clear hypothesis regarding the nature of the waste stream is being tested. The possibility of estimating temporal change in waste using quantitative factors was examined and tested in this study. 2.6 Chapter Summary Audits involve the separation of waste into categories and quantification of waste on a weight basis. The determination of categories is either composition, activity, or product related (Proctor & Redfern Ltd. and SENES Consultants Ltd., 1991). The type of classification system used is dependant on the information sought by the investigator, but should be based on recovering useful materials from waste and potential environmental benefits. Upon category classification, three units are available for measurement: number, volume and weight (Robinson, 1993). Solid waste accounting almost always uses weight measurements as solid waste is compacted in most treatments,, including disposal, composting, and recycling. 16 Waste changes as soon as it is discarded. Paper becomes wet, organic material starts to dry out, and volumes change with compaction (Robinson, 1993). One way to quantify the interrelationships between waste and the change in waste over time is to measure moisture content. Moisture content is useful information for comparing composition results of different loads or different sources (Gartner Lee Ltd., 1991 (a) and (b)). Measuring moisture content is also useful in the planning of alternative waste management disposal options, as parameters like leachate production can then be estimated (Proctor & Redfern Ltd. and SENES Consultants, 1991). Conducting a representative audit is fraught with problems, in particular how to represent the waste stream on a spatial and temporal basis. Heterogeneous solid waste varies from place to place and season to season in both composition and production characteristics (Qdais et al., 1997). It is necessary to carry out a well designed audit that will address spatial/temporal concerns (Charnpratheep et al., 1997), as a simple snapshot of waste will prevent extrapolation to any place or time other than that sampled (Gartner Lee Ltd., 1991(b)). York University has used the concept of activity space to capture spatial variation in the waste stream. An activity space is defined as the space designated to accommodate one of the many different functions of a university (Dept. of Facilities Management, 1992; Robinson, 1993). Sampling for activity space is based on the assumptions that the waste produced is constant within an activity classification, and that it is the people undertaking these activities that generate the waste. Studies have extrapolated activity space samples using data on floor space and population to give total waste estimates. The accuracy of extrapolation results has been assessed by the comparison of estimated to recorded ("actual") information on waste quantity. Lastly, waste varies with the season of the year and can be quantified through seasonal sampling. Comparisons between spring and fall municipal waste composition have shown that variations do exist (Gartner Lee Ltd., 1991(a); Matsuto and Tanaka, 1993). However, the necessity of seasonal sampling diminishes when a clear hypothesis regarding the nature of the waste is being tested. In the present study, a method to estimate temporal variation using quantitative data was developed and tested. 17 C H A P T E R T H R E E : M E T H O D O L O G Y A N D R A T I O N A L E 3.1 Introduction A methodology for auditing the waste produced by U B C campus was developed from investigation into the literature. This methodology is to address the following major objectives for study: 1. Spatial variation, as waste varies geographically, 2. Temporal variation, as waste varies over time of day and with season 3. Appropriate representation and extrapolation to the remainder of campus, as it desirable to offer directions to reduce this waste on a campus-wide basis; and 4. The validation of the methodology, as it is crucial to determine the representativeness and scientific validity of the audit design. These objectives are expected to satisfy requirements for conducting a representative audit and pinpoint directions for waste reduction at the University. 3.2 Outline of Methodology A novel design for auditing waste was developed and applied to U B C campus. The first part of the design involved the division of campus into activity areas as defined by Campus Planning and Development (CP&D, 1997 and 1992). This strategy was based on the study conducted by York University (Depart, of Facilities Management, 1992). On campus, an activity area is defined as an area representing one of the many functions of the University. Activities include, but are not limited to, functions related to residences, food service outlets, and office areas. The literature revealed that waste varies geographically according to activity, so it was decided that dividing the campus into these sections would aid in capturing spatial variation. A minimum of three samples («) of the waste generated in a particular activity area were collected from areas representing said activity. Following collection, sampled waste was separated into pre-determined waste material categories and weighed. Weight quantification and category classifications were selected to facilitate examination of current waste management operations at U B C as well as alternative disposal options. Weight quantification also allowed for the estimation of final design accuracy as outlined below. In addition, the number of people frequenting the activity on the day sampled was, whenever possible, enumerated to study the effect of number of users and activity area on type of waste generated. Two major assumptions that provided the basis for the development of the overall design were: 18 (1) Different activities generate different types and amounts of waste. For example, a food service outlet would be expected to generate a different quantity and composition of waste than a library building. (2) Once the waste loadings in activity areas and users are quantified, then total waste loads can be extrapolated to like activities using the number of total users. As the number of users will fluctuate over time, this methodology will also provide estimates of the temporal change in waste. The audit could, therefore, be used to predict future changes in waste as long as waste types remain constant, addressing concerns with temporal variation. The estimation of total waste loads was then compared to data collected by U B C Waste Management. This data consisted of weigh scale tickets documenting the net weight of waste sent to landfill by the University on daily basis. This comparison allowed for validation of the design methodology. 3.3 Activity Areas and Sampling Design The activity areas were selected based on their incorporation within the Campus Occupational Use (COU) definition of a university activity area, as well as size and resultant estimated contribution to the waste stream. These activity areas include core areas within the university which are provincially funded, and ancillary areas existing on a cost-recovery basis (Pers. Comm: M.J . O'Donnell, S. Vandenberg. Waste Coordinators, past and present, U B C Waste Management). The types of activity areas and their definitions are detailed in Appendix A . The reasoning behind selecting the C O U definition for delineation of activity space was to conform with campus space use planning data. The integration of waste generation data with the campus planning database could help to monitor and predict average waste generation for the university. Private enterprises and external services existing at U B C are not included within the C O U definitions and were not audited. These are listed in Appendix B . Some areas included within the C O U definitions were not audited due to relatively minor representation at the university, sampling redundancy, or association with special events. These areas include the Health Services Facility and Audio-Visual Laboratories, which represent 0.27% and 0.008% respectively of the total campus net assignable square meters. Net assignable square meters is the sum of all areas on all floors of a building assigned for use (Campus Planning and Development, 1998). To reduce redundancy in sampling, some C O U activity areas were not audited, as the waste generated by these areas would be represented through extrapolating other C O U activity areas. For example, the waste from 19 C O U Plant Maintenance and Central Services, which are predominantly office type functions, would be estimated by extrapolating sampled C O U Office Space waste. Lastly, exceptional waste loads including those attributed to special events such as in the Chan Centre and Winter Sports Centre were not audited, as these streams are difficult to capture from an auditing and management perspective. The selection of an area/building to represent a C O U activity area (henceforth activity) was based primarily on the "isolation" of the area/building; as this would ensure that there would be few other activities influencing the waste stream (controlling for activity area). Controlling for activity area minimizes sorting complications and sampling error, questions of which arose with the previous audit conducted at U B C by Resource Integration Systems (1991). For example, the Barn Coffee Shop provides an ideal medium by which to sample a Food Service Activity Area (Coffee Style), as the Barn is an isolated building with a dedicated refuse bin. As such, the chance of "contamination" from another activity waste stream would be reduced. In all cases, precautions were taken to ensure the waste collected was attributable to the activity being sampled. These are detailed in following sections. As Hicks (1993) elaborates, the most obvious way to decrease a population variance is to construct strata from the sampling units. This allows the partitioning of the total variation in such a way that as much as possible can be assigned to the differences among strata. As a result, variation within the strata (or within an activity area) is kept small. For example, i f one were to sample both 1 s t year Residences and Family Housing in an attempt to represent all residence waste on campus, there may be large differences in the type and amount of waste that is generated due to rather different patterns of living within these areas. Not diversifying this activity area could then result in an extremely large variation within this activity class, variation which could be otherwise captured through subdividing the activity area into 1 s t year Residences and Family Housing residences. Therefore, when "sub-activities" were expected to exist within an activity, this activity was broken down accordingly. This was the case with residences, food service activities, and the bathrooms, which were further subdivided into residence year classes, different meal types, and male and female bathrooms, respectively. Each sample of an activity area was to be replicated a minimum of three times to: (1) provide an estimate of experimental error; and to (2) improve the precision of the sample by reducing the standard deviation of the sample mean (average). 20 Generally, as the number of replicates (samples, or n) increases in a sampling design, the estimate of the population mean becomes more precise (i.e. corresponds more closely with the "real" average of the waste produced). The number of replicates of a sample in this study was restricted by the length of time required for sample collection and sorting (sorting alone approximated 400 hours), as well as consideration of the inherent variability existing in the waste stream as detailed in the literature. Studies have indicated that a large number of samples is desirable to determine the "population mean" of waste, however taking large sample weights can help mitigate smaller sample sizes (Qdais et al., 1997, Woodyard and Klee, 1978). Taking large sample weights (twenty-four hours of generated waste) in this study was hoped to compensate for smaller sample sizes. In some areas having smaller sample weights (i.e. Greenhouse), a larger number of samples were taken, however waste was still found to be extremely variable. This is important for waste management planning, as the possible fluctuation in waste should serve to parameterize loading rates for different applications. The variations in design application of sampling and sample extrapolation dictated by some individual activity areas are detailed below. 3.3.1 Animal Care and Greenhouse Activity Areas The waste generated in these areas is a function of the animal and plant populations and type of support materials used, which are in turn dependent on the experiments conducted at these facilities. As the numbers and types of experiments fluctuate widely over time, an investigation into modes of operation was required for both activity areas in order to develop the best methodology for quantifying waste. Animal Care Areas After interviews with the key-users of animals on campus, the UBC Animal Care Centre was selected as a sampling area. The Animal Care Centre is the largest and most isolated area for animal research on the U B C campus (Campus Planning & Development, 1997). The Centre provides an ideal vehicle to assess the total amount of waste contributed per animal as well as the total bedding utilized per pen. Samples from this area were collected over a four-week period from May 2 8 m until June 2 5 m , 1998. Data on the existing numbers of animals, pens, and the amount of bedding was obtained. Pens housing different types and sexes of animals were selected for sampling, which included both sexes of pigs, cats, rats, mice, rabbits and sheep («=148). These are the majority of animals used for experimental purpose on U B C Campus (Pers. Comm.: Dr. Love. Director, Animal Care Centre UBC). 21 Two strategies were required to enumerate the amount of waste produced by these areas: (1) the measurement of the amount of waste produced by the animals, and (2) the measurement of the amount of bedding material used by facilities. In order to measure animal waste rates on campus, the following steps were taken. Pens containing each type of animal were cleaned out, and the amount of dry bedding inserted into each pen was weighed. Following a twenty-four hour sample period, the spoiled bedding was removed and weighed in order to approximate the increase in weight (the animal waste added). Defecation/urination averages were developed for the various types of animals housed at Animal Care. These averages were compared to factsheet estimates (Ohio State University Webpage; Ministry of Agriculture, Food and Rural Affairs Webpage), and then applied to remaining UBC facilities using the type(s) and population(s) of housed animal(s) to provide an estimate for generated animal waste. Obtaining measurements of bedding material required a different strategy. The amount of dry bedding added to each pen varies with the experiment, cage size, number of animals, and with the varying lab techniques of different caretakers (Pers. Comm.: G. Gray, R. McGhie, K. Holzmann. Lab Technicians, Animal Care Centre). Invoices on monthly and annual animal bedding purchased by campus facilities were collected to estimate the amount of dry bedding utilized by all the facilities making up this activity area. This data formed part of an extensive report by the author presented to UBC Environment Health & Safety in July of 1998 (UBC Animal Waste Bedding Quantities 1998). Greenhouse Areas Three main areas on campus are classified as Greenhouse Areas: the Horticulture Greenhouse, the UBC Botanical Gardens, and the Plant Operations Nursery. All of these operations divert their organic waste to composting, and as a result do not generate large quantities of waste. The quantity and composition of the remaining non-organic waste fluctuates widely as it depends on chance factors like potting tray breakage. Quantifying the number of trays and other materials purchased was not felt to be properly representative of the waste stream due to the variation in number of trays entering the waste stream. (P. Com: D. Kaplan, R. Rollo. Supervisors, Horticulture Greenhouse, UBC Botanical Gardens). The Horticulture Greenhouse was audited for ten days to estimate average daily waste characteristics for this type of area (January 12-15™, 18m-22nd, 25 m 1999). Average waste was extrapolated by per day of normal operation for the Horticultural Greenhouse. 22 Nursery operation for the U B C Botanical Gardens and for Plant Operations was assumed to be similar to Horticulture as potting trays are also reused in these operations and organic waste is diverted to composting. The average daily waste measured in Horticulture was extrapolated to both U B C Botanical Gardens and the Plant Operations, nursery. Other contributions to the waste stream include large plastic sheets used for insulation of nursery areas in the Botanical Gardens, and rock wool used as a planting medium in the Horticulture Greenhouse. The biannual addition of plastic sheeting from Botanical Gardens Nursery and the monthly addition of rock wool from the Horticultural Greenhouse were added separately to the average waste stream. Waste was extrapolated based on number of days of operation (342). 3.3.2 Bookstore The U B C Bookstore is responsible for the distribution of texts and other materials to the University campus. The Bookstore purchases material depending on historical patterns of customer demand (Pers. Com. D-J. Matias. Financial Director, Bookstore). These patterns of demand are related to the type of textbooks ordered by the faculty and the number of students enrolled in the class, as well as historical data on other supplies such as stationery and computer components. There is a wide variability in the waste produced from Bookstore materials, as 500 students may generate one cardboard box for books as compared to the amount of plastic wrap associated with one student buying a computer. The mass of waste generated by this activity area is small relative to the waste produced by other activity areas. The Bookstore is comprised of three major components: the warehouse, the mezzanine, and the sales floor. Office areas in the Bookstore are included within the Office Activity Area classification. Two peak delivery periods characterize the maximum potential for waste generation emanating from the Bookstore mezzanine and warehouse. These occur prior to the commencement of each major academic term (July/August and November/December). The waste stream from the Bookstore mezzanine, warehouse, and sales floor was sampled during such a peak delivery period (July 2 1 s t -23 r d, 1998). A l l waste receptacles within the warehouse, mezzanine and sales floor were emptied with the aid of custodial staff at seven a.m. of each sample day. After twenty-four hours the generated waste was collected and sorted. This procedure allowed for the capture of the maximum amount of plastics generation that would result due to delivery of purchased items to the warehouse. 23 Much bookstore material is stored within the warehouse and mezzanine until display on sales floor (Pers. Com. D-J. Matias; S. Walker. Warehouse Manager, Bookstore). There is a lag time between delivery to the warehouse and mezzanine areas and display in the Bookstore sales floor. Increased waste from the mezzanine and warehouse during peak delivery periods is compensated by decreased waste in the sales floor due to low purchasing events (Pers. Com.: S. Walker). This within-activity area waste flow was assumed to result in a constant amount of waste being generated from the Bookstore. The extrapolating equation was based on two factors: the number of days the Bookstore is open annually and the average waste generated during three days of operation sampled in July. This resulted in the multiplication of the average waste sampled by 294 days of operation. 3.3.3 Classrooms Preliminary observation of several classroom blocks on campus indicated these areas can generate small amounts of waste regardless of large class sizes and classroom areas. It was decided to sample a building with a great number of frequently used classrooms in order to get as large a sample weight as possible. This would help prevent large sample variation due to the single addition of a few waste items, which would be a consideration in a smaller sample weight. It was also desirable to select a classroom building that was apart from other areas of significant waste generation, such as a food service outlet. Buchanan A , B200s, and D complexes were selected due to their high proportion and usage of classroom space, as they represent 17% of the total classroom floor space on campus (derived from Campus Planning & Development Data, 1997). They also represent among the most frequently used classroom blocks on campus, with up to 14,000 students using classrooms daily during the winter and spring sessions (derived from 1998 Student Information Bookings). Consultation with cleaning staff at Custodial Services was required to address the necessary source separation of classroom waste from other waste in each of these three blocks. This was to ensure that waste was collected from all and only classrooms. The waste generated over a full day (twenty-four hours) was collected at seven a.m. on each of the sampled days from areas agreed upon with the custodial supervisors. Sampled days included Tuesday, April 7 m , Wednesday, April 8 m , and Thursday, April 9 m , 1998. The same areas were sampled again on Thursday, January 15m> 1999, to replace the April 9 t n sample, as it was felt that the influence of the 1998 Arts County Fair might have prevented obtaining average classroom waste generation for that day. 24 Concurrently, data on the number of classrooms in each block, number of courses per classroom, and the number of registrants per course for each of the sampled days was collected and processed to give the maximum number of students in attendance at these blocks during the sample period. In order to determine the overall classroom usage on U B C campus, data was excerpted from the Instructional Space Utilization Report for Winter, Spring, Summer, and Evening Sessions to estimate the total number of students in class per day (Jia, 1997(a) and 1997(b)). This was derived from the total number of campus classrooms and room utilization rate as a percent of forty-five hours per week, and the total number of seats and average seat use within each class. The final extrapolating equation accounted for the average number of students in class for the winter/spring, and summer sessions (including evenings), and number of school days for each respective term (238 in total). The average waste per student as derived from the Buchanan samples would then be multiplied by the entire number of students in class on campus annually. 3.3.4 Common Use/Student Activity Area A preliminary survey of Common Areas indicated a wide variability in the usage and resulting waste in different Common Areas. The Student Union Building (SUB) Conversation Pits 1, 2, and 3 are large and highly frequented common areas, as the SUB represents 43.4% of the total Common Area floor space on campus (derived from Campus Planning and Development, 1997). These areas were selected for sampling on April 3 rd, 7 t n , and 8 t n , 1998. Waste receptacles from each Conversation Pit were emptied and marked bags were placed in the receptacles at seven a.m. of each sampled day. Daily head counts were taken on the half-hour interval for each of the Conversation Pits from seven a.m. to eight p.m. Following the end of the sample period (eight p.m.), waste was collected from the pits, categorized, and weighed. This study occurred with the help of work-study student David Primack. A concrete extrapolating method proved difficult for common areas due to the lack of existing data on users to extrapolate campus waste amounts, such as turnstiles at the entrance of each common area on campus. However, data existing on the amount of floor space particular to common areas at the University does exist, and this factor in combination with the head counts taken at the SUB Building can provide general information on the number of people per square metre in a common area. Resultingly, usage was calibrated based on percent floor space utilized in the SUB Building and extrapolated to other common areas using an adjusted estimated usage of floor space, as follows. 25 Although the amount and type of waste generated per user would be expected to be similar among all common areas, the variability in the usage of different common areas proved to complicate an extrapolation based on the averages from the Conversation Pits. The Conversation Pits represent a "high-end" usage of common area classifications on campus. The Conversation Pits are used throughout an entire day, whereas observation of other common areas (i.e. Chemical and Engineering Lounge) indicated most usage occurred during coffee and lunch periods. As such, the total amounts of waste emanating from the SUB common areas are likely much greater than most common areas. This is due to the large number of people frequenting the activity and the length of use of the activity in these areas. It was assumed that all other common areas with the exception of the SUB Conversation Pits would be used half as much as the SUB. Extrapolating values on floor space were adjusted for this assumption. Summer usage rates were calculated as a 35% decline in use in accordance with the documented decline in purchases from A M S restaurants and in student attendance (Pers. Comm.: N . Toogoode. Food & Beverage Manager, A M S Restaurants; A . Branch. Manager, Scheduling and Administration;. L . Mol . Scheduling Coordinator, Registrar's Office). 3.3.5 Food Service Areas Different types of food service outlets provide different types of meals and utensils. Due to this disparity, several types of food service activities were sampled (representing strata within an activity area). The Food Service Activity classification was broken down into restaurant style, coffee type, and meal plan type outlets. To represent each of these types, Yum Yums Cafeteria, The Barn Coffee Shop, and Place Vanier Kitchen were each sampled three times in February and March of 1998. Details associated with the sampling of each restaurant are subsequently outlined. Each of these restaurants was selected as the waste generated at each could be captured without interference from other activity areas. Yum Yums is a Chinese Food restaurant located within an infrequently used building for other activities (Old Auditorium). Yum Yums style of meal preparation and use of durable utensils is similar to that of Pacific Spirit Park and Trekkers restaurant, areas which can also be classified as restaurant style Food Service areas. However, unlike these restaurants, Yum Yums has no other buildings or activities currently contributing to its refuse bins. The Barn represents an isolated coffee shop type restaurant with its own refuse bin. Of all coffee type areas on campus, the Barn is the largest and most independent (derived from Campus Space Use Planning data, 1997). Place Vanier kitchen is one of two meal plan style restaurants on campus. In Place Vanier kitchen, all kitchen 26 and cafeteria waste are directed to one compactor bin, facilitating ease of sample collection and decreasing error due to waste being added from other areas. External functions performed by Totem Kitchen, the other meal plan style food service outlet, complicate deriving a per capita waste generation rate. Yum Yums, The Barn, and Place Vanier represent a total of 25% of the campus Food Service Area net assignable space. The corresponding eight cubic yard waste bins for Yum Yums and The Barn were emptied at six a.m. the morning of each sample day, prior to the start of daily operation. The Barn was sampled on Monday March 2 n d, Wednesday March 4 m , and Friday March 6 m , 1998. Yum Yums cafeteria was sampled to represent Monday March 2 n d, Tuesday March 3 fd, and Friday March 6 t n , 1998. Due to initial confusion between the report author and waste hauler regarding the bin location for Yum Yums, a two-day sample was taken in an attempt to capture the intended sample days and coordinate with the planned hauling route of the garbage truck (March 2 n d and 3 r c r). After collection from both restaurants, waste was sorted into respective categories and weighed. Place Vanier required a slightly different strategy. Due to safety concerns associated with entering the compactor, an empty eight cubic yard bin was placed alongside the compactor with detailed and reinforced instruction via telephone, site visits, and signage on the bin to place waste in the bin instead of the compactor. The compactor was picked up and emptied on Wednesday, March 4 m at six a.m. and then sealed. Samples were then collected to represent Wednesday March 4 m 5 Thursday March 5 m , and Friday March 6™, 1998. U B C Food Services recently acquired a Customer Count Information System in order to tabulate sales rung through registrars at different outlets (Pers. Comm.: J. Vaz. Director, Food Services UBC). Daily, monthly and annual customer counts can be calibrated for different food service outlets on campus. By deriving the per capita waste generation at sampled sites by using customer count and total waste generated for that day, an average waste generation for that type of food service facility can be derived and applied to other similar locations on campus using customer count information. This required that daily totals for customer counts for the Barn, Place Vanier and Yum Yums be obtained for each of the sampled days. Customer counts for all sixteen Food Service outlets operated by U B C were obtained on a monthly basis. These numbers could then be used as multipliers for the per capita waste derived from the sampled areas to estimate the annual waste produced. 27 3.3.6 Laboratories Laboratory areas presented a particular problem due to their widely varying nature within the activity area classification (from computer and teaching to biology and chemical laboratories). In addition, the possible presence of hazardous material prevented detailed study as hazardous waste material does not fall under the umbrella definition of a solid waste audit. Most hazardous biomedical material is collected by Environmental Health & Safety and currently incinerated at South Campus or collected and treated by the American company, Stericycle (Pers. Comm.: H. Seto, R. Ammodt. Research Technicians, Environmental Services Faculty UBC). However paper toweling waste and some broken glassware (which is sterilized and heavily bagged prior to disposal, is collected by normal waste management operations and taken to landfill. Due to safety concerns associated with handling possibly contaminated toweling waste as well as sharp glass materials, direct contact with this material as would be required through sampling, collection, and sorting was undesirable. In addition, the variation of scheduling of laboratories in terms of weekly scheduled laboratories, canceled laboratories, field trips, rotating laboratories, and other particulars precluded simple extrapolation (Pers. Comm.: L . Mol , A . Branch). As a result, the best approach seemed to be to identify and obtain information on laboratory material purchased by individual U B C laboratories on a monthly basis, under the assumption that purchased supplies reflect demand, usage and disposal of these materials. Again, the solid waste stream would likely see the greatest proportion of non-hazardous waste materials from laboratories consisting of office fine paper - which should go to recycling, and paper toweling waste - amounts which could be captured through the compilation of invoiced materials around campus. This methodology was developed to address possible discrepancies in the extrapolated waste stream that would result if laboratory waste was not incorporated, however the methodology was not developed with the purpose to test overall sample design. It should be mentioned that shop and material laboratories have potential for generating heavy materials, however these laboratories were not included within this methodology. Most of the work involving the derivation of annual laboratory waste was conducted by Jason Christensen, UBC Work Study Student. 3.3.7 Library U B C Campus has several libraries, each of which varies in their usage and construction. For the purpose of deriving per capita information, it was important to sample a library that would allow for an accurate 28 count of the number of visits to that library during the sample period, as each visit holds potential for waste generation. Most of the U B C libraries have entrances constructed around a "wand" system, which often permits as many as ten people to pass through on one revolution. Sampling a library with a turnstile system allows for a more controlled estimate of real users on the day sampled, as turnstiles count in increments of one. Woodward Library was selected as the sample area due to its entrance turnstile construction as opposed to Main or Koerner Library, which have wand systems. Woodward is also one of the more frequently used and larger libraries on campus (Pers. Comm.: H . Keate. Associate University Librarian, Public Services U B C ; E. De Bruijn. Adminstrative Support Librarian, Woodward Library). Woodward represents a total of 13% of Library Activity Area net assignable space (derived from C P & D data, 1997). As Woodward Library shares its refuse bin with other buildings and activities in the area, the best way to sample this activity was to: (1) empty all receptacles in the library prior to sampling (seven a.m.), and (2) return twenty-four hours later and collect accumulated waste from the receptacles. This required extended coordination with Library custodial and circulation staff. Woodward Library was sampled on April 23 rd 27*, and 28 t h , 1998. The lack of uniform turnstile systems in all libraries, as well as the absence of any counting method in some libraries (e.g. David Lam Library) required investigation into another method of extrapolating sampled waste per user data to other libraries on campus. A 1992/1993 census of campus libraries conducted by the Main Library staff consisted of interviews of all patrons of each library to determine whether or not they were from U B C or the community population. The amount of traffic entering each library over two two-week periods for a two-year period was tabulated. These surveys were conducted November and March of 1992 and 1993. It was decided to take the total number of visitors documented through this survey as a percentage of the total U B C population at the time. Under the assumption that library visitation rates remain similar over time, this percentage was applied to the 1998 student population to estimate total daily library usage during the academic term. Daily summer visitation rates were calculated as being 35% lower than in the academic term. External U B C patrons were assumed to remain similar to 1992/1993 levels. The amount of waste generated per user in the Woodward sample was extrapolated to all libraries via estimated annual usage by U B C students, and documented usage by external patrons. 29 3.3.8 Office Space Office areas are scattered throughout the university campus buildings. The offices in Brock Hall, which provides a variety of administrative services for the campus, represent a total of 5% of the Office Area net assignable space and are among the most densely clustered offices on campus (Pers. Comm.: A . Inouye. Space Use Analyst, Campus Planning & Development). Financial Services, the Disability Resource Centre, the Registrar's Office, and the Student Housing Office in Brock Hall were selected for sampling. Data on the amount of waste produced and the number of employees working in each of these areas was collected on July 22°d, 23 f d, and 2 4 m , 1998. In order to capture the waste from these areas only it was necessary to empty all bins in each office prior to each sampling day. This was done with the assistance of custodial staff, in particular Filomena Votas. Sampling from the six cubic yard bins outside of Brock Hall would have resulted in sampling activity areas other than that intended. In order to extrapolate office waste it was necessary to obtain information on the total number of full-time staff at the University, which includes both academic and administrative personnel. Per capita waste as generated by office workers at Brock Hall could then be extrapolated to the remainder of the campus working population. This information was derived from the U B C Factbook, a report which documents the total number of paid U B C staff on an annual basis (Office of Budget & Planning, 1997). Although this would not account for all office staff on campus such as in Forintek and TRIUMF, the majority of staff could be accounted for (Pers. Comm.: Dr. J. Chase. Director, Budget & Planning UBC). External companies like Forintek and TRIUMF were not designated as part of the UBC-controlled activity area as previously detailed in Appendix B. Per capita waste derived from the Brock Hall samples was extrapolated via total numbers of working staff and total number of working days, not including average vacation times and annual holidays. 3.3.9 Residences Similar to food service outlets, the amount and type of waste generated in residence areas could vary depending on the type of residence (i.e. 1 s t Year Student Residences vs. Family Housing). As such, the student residences were divided by year class into 1 s t Year Residences, 2 nd and 3 rd Year Residences, and 4 m Year Residences, representing students with access to meal plans, shared kitchens, and graduate style kitchens, respectively. Sampled areas included: 1 s t Year Residence Totem Park (Kwakiutl House); Gage North and South Towers, which represent a large part of 2 nd/3 rd year residences; and Thunderbird 30 Residence 3000 Block, which is a 4 m Year Residence. These areas were selected as their refuse bins were accessible for sampling and isolated from other activity areas. Totem Park and Thunderbird residences were sampled by emptying dedicated eight and six cubic yard bins at seven a.m. and collecting accumulated waste twenty-four hours later. Gage Towers were sampled by emptying the bins located under each garbage chute at seven a.m. and collecting the accumulated waste twenty-four hours later. The strategy for Gage Towers required coordination with the cleaning staff to prevent garbage from being deposited in the final compactor, with assistance from the head chief-of-staff, Maria Cantafio. These areas were all sampled from March 9 t n to 14 t n , 1998. Family Housing was sampled separately due to the potential differences in its waste stream, and because Family Housing represents a large portion of residence activity areas (Campus Planning & Development, 1997). After several site visits, Keremeos Court was selected for sampling as it was similar to most Family Housing areas and easily accessible for sampling. Keremeos Court was sampled on August 18 m , 19 t h , and 20™, 1998. This required that the six cubic yard bins located in covered areas be rolled out and emptied prior to each sample period (seven a.m.). Collection and sorting of the accumulated waste occurred twenty-four hours later. The sampled residence areas represent a total of 12% of the Residence Area net assignable space. The extrapolation of average per capita waste accounted for the number of beds (and hence students) present in all student residences, as well as considered residence flux times throughout the year, including summer conference visitors. Family Housing was extrapolated on a constant rental basis and average family size occurring within different Family Housing units (Pers. Comm.: R. Simpson. Building Services Manager, Housing & Conferences UBC). 3.3.10 Other Space: Bathrooms The weight of hand paper toweling generated per one visit to a bathroom was obtained through three full day samples taken for ground floor male and female bathrooms at the Student Union Building. Measurements included both weight and count of the number of towels accumulated. Paper towel counts were tabulated as different people tear off varying lengths of toweling, which were thought to possibly result in different weights per user. A concurrent head count was taken of all users entering both bathrooms during the sample period, which ran from July 1 8 m to the 22 nd, 1998. This study occurred with the substantial aid of Ricky Leung (Killarney High School Summer Placement Program). 31 Bathrooms, similar to common areas, posed a particular problem in that no existing method to tabulate users could be identified. As a result, it was decided to utilize number of hours students, faculty, and staff spend on campus in order to derive amount of bathroom visits according to published voiding rates per unit time (Lipschultzky, 1995). Estimated average number of hours on campus for students was calculated from the amount of hours per required number of credits for each of 1 s t , 2 n d, 3 rd, and 4 t n year students from the Faculty of Applied Science, Science, Education and Pharmacy standard timetables. Corresponding summer hours were tabulated based on a decline in student attendance of 35% (Pers. Comm.: L . Mol). Faculty and staff time spent on campus was based on a forty-hour work week, including average scheduled vacation time and annual holidays. Populations of students, faculty, and staff were derived from the U B C Factbook (Office of Budget and Planning, 1997). The average weight of paper towel generated per visit from the SUB bathrooms were multiplied by the estimated number of bathroom visits on campus derived from the previous calculations. 3.3.11 Other Space: Outdoor Bins Eighty-three outdoor waste cans exist on U B C campus. As the total number of generation points are set, it was decided to number all cans and generate a random sample («=10) in order to obtain average waste characteristics that could be extrapolated to all other outdoor cans. In a completely random sample one would not expect any one number to appear more often than another, nor any sequence of numbers more often than another except by chance (Sachs, 1984). The selected cans were marked and emptied prior to commencement of daily activity (six-thirty a.m.). Collection of a day's worth of accumulated waste occurred twenty-four hours later for each can. Outdoor cans were sampled on January 2 5 m , 26th, a n c j 2 7 m , 1999. General locations for bins as determined through randomization included Totem Park, the Bookstore, the SUB, the University Boulevard area, the Museum of Anthropology, Koerner and Main Library, and the Old Administration Building. Extrapolation to the rest of the campus was based on simple multiplication of the average weight and composition of the samples by the total number of cans and number of days in a year, and a 35% decline in the summer. The amount of time and labour required for hauling waste from the outdoor bins is similar for summer, fall, and winter periods (Pers. Comm. J. Martin. Labourer, Plant Operations; J. Metras. Assoc. Director, Plant Operations). 3.4 Summary O f Extrapolation Methodology Table 1 presents a summary of waste measured and intended extrapolating method for sampled waste. 32 o o 3* ft P o ft O c C L O o n p 3 O 3 3" ft GO GO *CT T 3 P o ft CO P. o o 3 GO CO P 3 C L o 3 Q et 3 ft o c 3 a. 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CO K> o o 00 NO 00 CO > O 3 " rr 3 » P § f? 0 0 ft p -1 o 3* co > 8 3 O 5 ' J T 3 0 0 pj § p "1 ft o ft 3 4^ w z a z (/) f—t-3 p qui 3 ft qui 0 0 > ft •-t o 3_ a. o O 3 3 oo GO "a ft als age tude •-t ft 5 CO ft o 3 3 C L 3- D -Flo 0 0 3" ing o —i Cla 3 GO Cla voi "CT 0 0 0 0 voi P o o ft ft oo o e CTQ OC 3 "Hi ST H o 00 re GO w 3 "S-a rt- "i p. tt O T 3 a o 2. W 55 g H CT" GO e 3 3 o •-*» GO 3 "E. S" era O O 3 ft W o a g_ S" s* > > £9 3.5 Categories Selected Following the collection of waste from the activity areas, waste was brought to the Waste Management warehouse for sorting, by hand, into categories. In order to assess the waste materials available for processing, a suitable number and type of categories for waste characterization had to be developed. In the case of U B C campus, thirteen categories were developed in view of current waste management strategies and possibilities for future landfdl diversion. A description and example of each category can be found in Appendix C. Particular focus was placed on those items that would normally be diverted into the U B C recycling stream. These items are: office fine paper, newspaper, beverage glass, pop cans, old corrugated cardboard (OCC), and some plastics (Codes 1, 2, and 5). Determining the type and amounts of recyclable material still present in the landfilled waste stream would aid in improving the current recycling program. Easily compostable material was also considered important for future reduction strategies, as this material has traditionally made up large portions of municipal solid waste. This material includes residual paper, food, wood, and animal bedding/waste. Miscellaneous items, metals, and other plastics (Codes 3, 4, 6, and 7) were classified as residual material. Most categories were further classified (for example, compostable food was divided into raw and processed food), but for ease of interpretation these details were not included within this thesis. 3.6 Method of Quantification Upon the separation of waste into categories, waste was weighed using a Libra Mars 100 kg scale, serial # A58, correct to .01 g. Weight was found to be the most simplistic and efficient measurement for quantification in view that most waste management operations, costs, and therefore decisions, are on a per weight basis. Waste is compacted in most applications, making volume measurements irrelevant past the point of generation. For the purposes of this thesis, which was to aid in assessment of current waste generation, wet (as received) weight was considered sufficient to provide directions for waste management and all values are represented as such henceforth. In addition, the nature of the sampling procedure (point of generation) prevented extensive moisture transfer occurring due to weather conditions and to mixing with other waste. These latter conditions are major considerations in studies that document waste at the point of disposal. Qdais et al., (1997) state that climate and residency time will affect waste properties during transportation, storage and collection, and indicate that sampling at the point of generation will most accurately reflect actual waste characteristics. 34 3.7 Statistical Analysis The study outlined by the methodology is an ex-post facto study, which means it is a study where the variables have already acted and the research only measures what has occurred (Hicks, 1993). Ex-post facto research may use statistical methods to analyze data, but is limited by the fact that the research is not really experimental in nature. Two major statistical applications were deemed suitable for overall analysis of the collected waste audit data: (1) the Coefficient of Variation, and (2) Analysis of Variance. Data analysis was conducted using Microsoft Excel 97 spreadsheet and statistical package software. 3.7.1 The Coefficient of Variation The coefficient of variation was used to determine the appropriateness of extrapolating total waste on per user data for activity areas. Data on two variables, waste and number of users, was collected for applicable activity areas. These variables were used to make up two data sets for each activity area: sampled waste data and waste-per-user data. To illustrate, Activity Area 1 was sampled on three days («=3) to give the following values for waste production (first data set): Activity Area Day 1 (kg) Day 2 (kg) Day 3 (kg) 1 10 10 30 On the same days, the number of people that frequented the activity was enumerated. Hypothetically, assume that ten people were counted on Day 1, thirty people on Day 2, and ten people on Day 3 in Activity Area 1. The values in the first data set, sampled waste, would be divided by the respective users to give the following values for waste-per-user (second data set). Activity Area Day 1 (kg/user) Day 2 (kg/user) Day 3 (kg/user) 1 1 0.33 3 These two data sets were then compared to see which was more variable. The numbers used for comparison are called the coefficients of variation (CVar), which are derived by the following formula. CVar(%) •100 35 The mean (average) of the samples is indicated by x. Standard deviation (s), describes how widely sampled values are dispersed from the average. The formula used to calculate standard deviation is as follows, where the number of samples taken for the activity area is indicated by n: As the coefficient of variation value is unitless (%), it permits comparison of data sets of different scale and units of measurement. It was assumed that in most cases, dividing by the number of users would decrease the variation in sampled waste, addressing the suitability of extrapolating on per user (i.e. the coefficient of variation for waste-per-user data would be smaller than that for the sampled waste data). Extrapolating on per user flow over time would then offer a means to address temporal variation in waste. The coefficient of variation can also be used to illustrate the variability of categories within the waste sampled. In this application, categories having comparably large coefficients of variation were identified in each activity area, as it is important to incorporate possible fluctuations in material types for solid waste management planning. 3.7.2 Analysis of Variance An analysis of variance (ANOVA) is used to test claims involving three or more means (averages), and can indicate i f a significant difference exists between these means. The study subject to an A N O V A should satisfy the following criteria: (1) that the populations from which the samples were obtained must be normally or approximately normally distributed; and, (2) that the variances of the populations must be equal (Hicks, 1993). The sample waste was assumed to be normally distributed. A study conducted by Carruth and Klee (1969) indicated that when a component represents 30-70% of the waste stream, waste follows a somewhat normal distribution. This was also the case in a more recent study conducted byQdais et al. (1997). The majority of samples (thirteen of sixteen) in this study contained 50% or more compostable waste. To check for equal variances, the F-statistic (test for equal variances) was used when applicable. The Bartlett test could not be used to test for equal variances in some data sets (as «<5), however (Hicks, 1993) 36 A N O V A was still used, as it is robust to substantial heterogeneity of variances as long as al\n are equal or nearly equal (Zar, 1984). Using A N O V A to determine if there is a difference between waste from activity areas can assess if different activity areas generate different types and quantities of waste. It was decided to run a one way analysis of variance on all the data to see if the average per user waste from sampled activity areas is significantly different (95% confidence). If these are found to be significantly different, this indicates that the initial design of sampling for different activity is justified, as users do produce significantly different amounts of waste depending on the activity. An A N O V A does not indicate where among the means this difference is. Should the researcher wish to see where the difference exists, further tests such as Bonferroni's Comparison, Scheffe's Test, and Orthogonal Contrasts provide methods by which one can identify which groups (activities) differ. In this case, the groups (activities) are fixed and qualitative. Orthogonal contrasts provide an ideal vehicle to compare the means of waste from different activity areas, as they can be logically set up a priori. It was postulated that differences may exist between the per user means of 1st year residences, 2/3 r d year residences, 4 t h year residences, and Family Housing (each «=3). If a comparison of means indicates that no differences exists between these activities, they can be grouped into one activity: residence areas («=12). Similar logic applies to food service areas, which were divided into coffee shop type, meal plan type, and restaurant style, and to bathrooms, which were divided into male/female bathrooms. Should no difference exist between these means, the number of samples taken increases to nine and six respectively. This increases the degrees of freedom for each area, subsequently reducing the confidence intervals around the mean (increasing precision). Confidence intervals (C.Int.) are represented by the formula: where s is the sample standard deviation, n is the number of samples, andc is the confidence level. The t-test value was based on a 95% confidence interval (c=95) and the adjusted degrees of freedom of the sample (w-1). If the number of samples (ri) is 3, then t is equal to 4.303 (Hicks, 1993). Coefficients of variation, standard deviation, and confidence intervals were reported with a maximum of two significant digits. C.Int. = x±t s 2 37 Lastly, for activity areas that have no associated data on per user, an A N O V A can be used to determine if there was a significant difference between sampled days. As these areas would require another method for extrapolation such as number of days of operation, it was necessary to determine if waste varied depending on day of the week. 3.8 Method of Validation U B C Waste Management keeps detailed monthly and annual weigh bills from the City of Vancouver Landfill. These weigh bills accurately account for the net tonnage of waste U B C hauls to landfill daily. The amount of waste that is generated daily, monthly, and annually can be tabulated from these weigh bills. As a result, the total annual waste that is extrapolated from the sampling design can be compared to actual annual amounts that were disposed at U B C in 1998. The final estimation of total waste loads (and standard deviation) from the sampling design would be calculated via the following equation: (Sachs, 1984) 38 CHAPTER FOUR: RESULTS The application of the waste audit methodology to U B C campus produced distinct results for different activity areas. Analysis of the means for per user waste for all activity areas indicated that a significant difference existed, justifying sample design. In some activity areas, it was found that calculating per user values could decrease the variation in waste: Other cases required alternative methods for extrapolation, as per user data had a larger variation than the original data. The total waste generated by U B C in 1998 was estimated to be 2150 tonnes, which differed from documented values by -18%. Directions for reducing this waste are discussed in the next chapter. 4.1 Overview of Chapter The following chapter examines (1) the effect of dividing the campus into activity areas, (2) the effect of of number of users on waste, (3) the specific waste data that was generated and extrapolated for each activity area, and (4) the projected waste stream values vs. the documented weigh bill values for 1998. 4.2 Comparison of Waste Values from Different Activity Areas In order to effectively compare activity areas it was necessary to convert sampled values into comparable units. The per user means generated in applicable activity areas are illustrated in Figure 1. A highly significant difference exists between the per user means generated in activity areas, justifying the design of sampling for different activity areas (95% confidence). Areas where people stay for longer periods of time, such as residences and offices, seemed to result in a considerably larger amount of waste per user than other areas. The difference in waste according to activity is important for waste management practice. If the origin of the waste is not accounted for properly, simple extrapolation of waste based on overall parameters like overall population (Laidlaw, 1995 and 1997) will produce very different and possibly misleading results; as opposed to a more detailed extrapolation by the users of distinct activities. Accounting for spatial variation can help decrease the overall variation in waste, supporting audit methodologies which sample waste at the point of generation instead of at the point of disposal. Sampling at the point of generation has the added benefit of providing valuable information for the best waste management and handling procedure to be adapted. 39 Figure 1 : Per User Means Generated in Different Activity Areas. 600 Activity Area Pre-planned orthogonal contrasts for total waste generated per person found no differences between: 1. the means of 1 s t Year, 2/3™* Year, 4 m Year and Family Housing residences 2. the means of Coffee Type and Meal Plan Food Service areas 3. the means of Male and Female bathrooms Differences were found between the means from the remaining activity areas: Classrooms, Offices, Common Areas, and Libraries. 4.3 Extrapolating on Per User Areas that successfully indicated a decrease in variation by calculating per capita waste values included all of the residences, all of the food services outlets, and the male and female bathrooms (considered as one area) - altogether eight of the eleven activity areas with appropriate values for comparison. Particulars associated with each area are detailed in respective sections. Collecting information on user flow in these areas can be used to extrapolate waste to like areas and to quantify temporal variation in waste without infinite sampling, providing that waste composition remains similar over time. This is most applicable for waste streams not substantially affected by seasonal change. At UBC, yard waste is 40 collected separately from the municipal solid waste and is composted on campus, so seasonal events such as leaf drop in trees would not affect the overall quantity or characteristics of the landfilled waste stream. Other areas that had available values on per user, including classrooms, common areas, and libraries, had larger coefficients of variation for per user data than for original waste data. In order to provide estimates for the total waste loads from these areas, alternatives to extrapolating only on per user had to be developed. These are detailed in respective sections. For some areas data on per user was either not available or not appropriate for extrapolation. These are detailed in the methodology (Animal Care Areas, the Greenhouse, the Bookstore, and Outdoor Bins). 4.4 Activity Areas Subsequent sections display and discuss the individual results of applying the methodology to various activity areas. The details for each activity area are presented according to the following format. A) Presentation of the collected waste data in a bar graph and discussion. This graph illustrates the composition of the sampled waste stream. Each bar represents the average weight (x) of a waste category found in the samples. The standard deviation (s) of each waste material is indicated by the error bars. Categories having the largest coefficients of variation are identified and discussed, as these are the more variable constituents of the waste stream. Waste management strategies have to consider the fluctuation of these materials in any operation. B) The assessment of the extrapolation procedure. This took three forms: • In areas that did not have associated values on usage rates, a one-way analysis of variance was used to determine if any significant difference existed between the waste sampled on different days. These areas included the Animal Care Areas, Greenhouse Areas, the Bookstore, and the Outdoor Cans. If no statistically significant difference was found, the average daily waste was extrapolated to yearly totals based on number of days of operation. • In areas with affiliated values on usage rates, the coefficients of variation for the two data sets (sampled waste data and waste-per-user data) were compared. Areas that had smaller variation for per user values then original waste per day values were deemed suitable for extrapolation on per user. Areas having greater variation for per user values then waste per day values required development of alternative methods for extrapolation. 41 • In residence areas, a scatter diagram of collected data was presented and discussed. This diagram shows the total waste collected per sample vs. the total number of residents tabulated per sample for all residences. It includes the value for the correlation coefficient (r), which indicates i f there is a relationship between resident and waste, and i f so, the strength of that relationship (/2). Scatter diagrams do not accompany all activity areas, as not all areas had sufficient data points to confidently define the shape of the relationship between users and waste (Pers. Comm.: Dr. A. Kozak. Professor of Statistics/Associate Dean Forestry, UBC). C) The extrapolation of the waste from sampled activity areas to estimate yearly totals from all like activity areas. The extrapolation methodology per area varied depending on the results from (B). In cases where the variation increased by incorporating data on number of users, alternative methods to determine annual waste loads were used. These are detailed in respective sections. Alternative extrapolation was necessary to provide comparison with the final yearly waste load as tabulated by U B C Waste Management. Summary data on the waste parameters including confidence intervals of each activity area is presented in a table at the end of each section. Associated ranges for values (+/-) represent confidence intervals of 95%, unless otherwise noted in the text. The tables include information on the following: • The average daily waste generated in the sampled area • The average waste-per-user generated (when applicable) in the sampled area • The coefficient of variation for the sampled waste data • The coefficient of variation for the sampled waste-per-user data (when applicable) • The estimated annual waste produced from all similar areas • The estimated composition of the annual waste produced from all similar areas • The estimated maximum and minimum for total annual waste produced by all similar areas 4.4.1 Animal Care Areas Animal research facilities on U B C campus are estimated to produce 222.8 +/- 25.2 tonnes (3 St.Dev.) of waste annually, 75% of which is bedding material and 25% animal waste. The largest single producer of this waste is the Animal Care Centre (89.7 +/- 21.9 tonnes (3 St.Dev.)). 42 The small projected range (St.Dev.) for estimated annual waste is a result of two major factors: (1) the nature of the sampling procedure for bedding waste, and (2) problems and limitations in sampling animal waste. The procedure for obtaining values for bedding (via tabulating invoices) prevented determining the variation in the bedding waste. Low values for the variation in animal waste was attributable to problems with acquiring values for cats and sheep. Accurate animal waste values could not be obtained from cat populations due to litter being kicked out of the pens. This resulted in negative values for cat waste added. Obtaining waste values for sheep proved difficult as the large soil expanse of the exterior pen currently acts as a natural digester for this waste (Pers. Comm.: G. Gray. Dr. Love. UBC Animal Care Centre). Daily animal wastage values for cats and sheep, as well as uncommon research animals located only in the Zoology Research Centre, were assumed to be 5% of the animals body weight. This percentage was supported in other reports as detailed in the methodology and corroborated by approximating the percent of body weight of animal waste from other species that were sampled. Table 2 illustrates the approximated body percent values calculated from the rat population data. Table 2 : Direct Measurements of Daily Waste Material (Waste (g) per Rat Weight (g), %) from UBC Animal Care Centre («=9). Weight of Rat Sample 1 Sample 2 Sample 3 Average +/-C.Int. 110 grams 5.3 5.7 6.3 5.7 +/-1.3 210 grams 4.7 4.9 4.7 4.8 +/- 0.3 260 grams 4.5 4.1 4.7 4.4 +/- 0.8 Ttl Avg 4.9 +/- 0.5 Estimated and sampled individual animal wastage rates are presented in Table 3. Table 3 : Animal Wastage Rates (g/day) for Various Species, Animal Care Centre. Species Waste Rate C. Int. Comments (g/day) (+/-g/day) Cat 180 n.a. Based on average cat weight of 3.5 kilos* 43 Table 3 (cont'd): Animal Wastage Rates (g/day) for Various Species, Animal Care Centre. Monkey 600 n.a. Based on average primate weight 12 kilos* Mouse 2.1 2.7 Pig 2700 2300 Rabbit 160 190 Rat H O g 6.3 1.4 210 g 10.04 0.60 260 g 11.5 1.7 Sheep 330 n.a. Low averages due to soil digesting waste * Figures for average animal weights for cats and primates were derived in consultation with Animal Care Technicians G . Gray, R. McGh ie Table 4 (overleaf) details the amount of animal and bedding waste produced by different facilities annually. The multiplication of the animal wastage rates provided in Table 3 by the number and type of animals housed at each facility allows annual values for animal waste to be estimated. Although statistically significant differences likely exist between the waste rates of different age animals, little information is recorded in each facility of the age class breakdown of housed animals. The lack of information is due to two main factors: (1) the wide variation in age compositions within cages, and (2) the rapid weight gain in some of the smaller animals. The average animal waste values sampled at the Animal Care Centre were deemed sufficient for extrapolation. The largest producers of animal bedding and waste are the Animal Care Centre, Acute Care, Microbiology, and Pharmaceutical Sciences. For the most part however, animal bedding and waste is widely distributed around campus, and most locations do not produce great amounts of this waste. Should alternatives to current management of animal waste/bedding become feasible, focusing on the largest producers would be the most efficient way to capture the majority of the waste (i.e. Animal Care Centre). Questions of health issues associated with handling of animal waste also have to be addressed in any operation. The report by the author on UBC Animal Waste Bedding Quantities 1998 addresses the quantification of animal waste and issues associated with its disposal in detail. Appendix D provides information on the background calculations for Animal Care Areas. 44 Table 4 : UBC Animal Care Facilities : Annual Waste Quantities and Composition (1998). Facility Animal Waste (t/yr) Bedding Waste (t/yr) Total Waste (t/yr) Animal Waste (+/-3 StDev.) Animal Care Centre 34.4 55.3 89.7 21.9 Acute Care - 34 34 -Anatomy 6.2 4.2 10.4 1.9 Biomedical Research 2.3 11.1 13.4 3.6 Dairy Farm* - - - -Family & Nut. Sci. 0.084 0.019 0.102 0.012 Food Science 0.048 0.210 0.258 0.075 Medical Genetics - 9.7 9.7 -Microbiology 2.3 15.8 18.1 3.6 Neuroscience - 0.74 0.74 -Oral Biology - 2.8 2.8 -Pathology 0.86 4.51 5.37 2.16 Pharmaceutical 2.73 9.15 11.88 0.39 Sciences Pharmacology 0.7 3.4 4.4 1.6 Psychology - 8.3 8.3 -Physiology 0.83 5.72 6.55 1.05 Zoology 4.3 3.3 7.6 1.9 Totals** 54.7 168.0 222.8 25.2 * The Dairy Farm has a separate waste management operation than that for U B C (Pers. Comm. T.Cathkart) * * Totals for standard deviations were calculated via summation formula in the methodology (pg. 38) 4.4.2 Greenhouse Areas Greenhouse areas are estimated to generate 2.35 +/- 0.35 tonnes of waste per year. The majority of this waste originates from the Horticulture Greenhouse, which produces 1.55 +/- 0.12 tonnes of waste per year due to monthly inputs of rock wool (a fabricated medium used to grow plants). The composting of most plant and soil material by the Greenhouse Areas results in a predominance of residual plastic in the Greenhouse waste going to landfill (92%). The composition of the daily waste sampled from the Horticultural Greenhouse is illustrated in Figure 2. 45 Figure 2 : Average Daily Waste Composition (Horticultural Greenhouse). 800.00 | 700.00 4-600.00 I 500.00 •§, 400.00 -3 300.00 + 200.00 + 100.00 0.00 o Waste Category The high variation in values despite the larger number of samples («=10) is due to the small amount of material present in each sample, which illustrates the importance of taking samples of larger weight. Single inputs of a waste material can significantly affect small sample weights. The "uncommon" input of a waste is in fact "common" to this type of activity as the waste stream is determined by seeming random factors such as potting tray breakage. For example, the values derived for recyclable glass are the result of one of ten samples having three recyclable glass bottles weighing 910 g in total. This single data point results in a large coefficient of variation for this material (320%). This input could be the result of a single visitor group unaware of the possibility of directing this recyclable material to another bin (Pers. Comm. D. Kaplan. Supervisor, Horticultural. Greenhouse). Recyclable plastic was also highly variable. No significant difference existed between any of the ten samples of total waste from the Greenhouse (95% confidence). Consequently samples were amalgamated to provide a daily waste generation average. The characteristics of greenhouse waste fluctuate widely as waste is generated by chance factors like potting tray breakage. Multiplying this average by number of operating days for all the greenhouse areas indicated that these areas generate low weights of waste. This is due to two factors: (1) the normal predominance of light residual plastics, and (2) the addition of miscellaneous plastic. Residual plastic, including potting trays, plastic sheets, Styrofoam, and broken pots, made up most of the sampled waste. 46 The amount of residual plastic entering the waste stream is amplified due to biannual inputs of greenhouse sheeting from the Botanical Gardens (-0.3 tonnes annually), as well as the monthly input of residual rock wool from the Horticultural Greenhouse (-1.3 tonnes annually). Parameters estimated from the sample are presented in Table 5. Calculations associated with Greenhouse waste can be found in Appendix E. Table 5 : Greenhouse Areas - Waste Parameters. Greenhouse Activity Area Parameter Horticulture Average Da i ly Waste (g) Coefficient o f Variat ion (%) A l l Greenhouse Areas Estimated Annua l Waste (t) Composit ion (t) Compostable Recyclable Residual Annua l M a x i m u m (t) Annua l M i n i m u m (t) 4.4.3 Bookstore The Bookstore is estimated to produce 3.4 +/- 1.8 tonnes of waste per year. Tonnage is low due to the high proportion of residual plastic materials, and low proportions of heavy recyclable and compostable materials. Residual plastics, being so light, confer poor weight representation on the total waste emanating from the Bookstore as with the Greenhouse. Residual plastic is the largest contributor to the waste stream. Residual material makes up 43% of this waste stream, followed by recyclable material at 30%. The Bookstore is also expected to generate large quantities and volumes of cardboard annually, however this stream is expected to be captured by the recycling program. An average daily breakdown of waste material produced by this area is shown in Figure 3. This figure indicates the average daily waste weight of materials measured in the samples. The largest coefficients 740 +/- 340 65 2.35 +/- 0.35 0.08+/-0.10 (3%) 0.11+/-0.21 (5%) 2.16 +/-0.29 (92%) 2.70 2.00 47 of variation were found in the recyclable categories, namely newspaper, pop cans, and recyclable plastic. It is of note that, as in the Greenhouse, the largest variations are associated with recyclable streams. Figure 3 : Average Daily Waste Composition (Bookstore). 7.0 6.0 5.0 O) i 4.0 O) I 3.0 4-2.0 1.0 0.0 I TJ i? si) w • o o m ex CD O a> en "D 01 •a CD Q o o 7i _ CD 8! S--n 8 (Ii Q_ O CT CD o o Waste Category CD O o O-No significant difference existed between any of the samples of total waste from the Bookstore (95% confidence). An average was calculated to represent daily waste generation and extrapolated on the total number of days of operation. Estimated annual waste and other parameters are detailed in Table 6. Calculations associated with Bookstore waste can be found in Appendix F. Table 6 : Bookstore - Waste Parameters. Bookstore Activity Area Parameter Bookstore Average Daily Waste (kg) 11.6 +/- 6.0 Coefficient of Variation (%) 21 Estimated Annual Waste (t) 3.4 +/- 1.8 Composition (t) Compostable 0.910+/-0.079 (27%) Recyclable 1.04 +/-0.22 (30%) Residual 1.5 +/- 1.6 (43%) Annual Maximum (t) 5.2 Annual Minimum (t) 1.6 48 4.4.4 Classrooms Classrooms are expected to generate 63 +/- 21 tonnes of waste annually. The usage of classrooms for lunching activity results in predominantly compostable waste from these areas, as the samples were made up of mostly food (26%) and residual paper (21%). A l l classroom areas from Buchanan A , B200, and D quadrants generated an average of 63 +/- 21 kg of waste daily. The composition and variation of daily waste generated in these areas is detailed in Figure 4. Utilizing per user data to standardize each sampled day resulted in an increase in the coefficient of variation for total waste, from 13% to 39%. As variation increased by calculating per user weight, the original methodology of extrapolating on user proves problematic for this data set. Exploratory calculations projecting waste as a function of user resulted in large ranges of estimated annual waste values. For example, total annual waste was estimated to range from 0 to 80 tonnes. For this data set, two factors might aid in narrowing the precision in determining the "real" values for waste per-user: (1) gathering more samples; and (2) sampling for a greater range of users. The results thus far likely represent the "noise" around the regression line of waste vs. user. Figure 4 : Average Daily Waste Composition (Buchanan A , 15200s, D Blocks). 25.00 20.00 2 15.00 a 10.00 5.00 0.00 z CD o o o 30 _ CD O o K Q. CD cr CD 50 -n • in Tl o -o O CD 32 f Q) C/l co QL O Q) o o Q-Waste Category 49 In order to extrapolate to the rest of classroom areas on campus, it was decided to project based on overall usage of Buchanan Building in terms of classroom bookings and floor space. Buchanan Building represents a large portion of the classroom bookings and floor space on campus (14% and 17% respectively). No significant difference existed between the total weights of waste in the samples (95% confidence). The average weight of the samples was extrapolated based on a weighted average of usage and floor space to give the annual figures in Table 7. The estimation of annual figures for classroom waste was necessary for comparison of extrapolated waste to tabulated waste loads, however projected figures would likely be most accurate for other classroom areas with similar characteristics to Buchanan Building in terms of usage and floor space. Calculations can be found in Appendix G. Table 7 : Classrooms - Waste Parameters. Classroom Activity Area Parameter Buchanan Building A , B200s, D B lock Average Dai ly Waste (kg) 63 +/-21 Average Per Capita Waste (g) 7.1 +1-6.2, Coefficient o f Variation (%) - Per Day 13 Coefficient o f Variation (%) - Per Capita 39 A l l Classroom Areas Estimated Annual Waste (t) 63 +/-21 Composition (t) Compostable 31 +/- 15 (49%) Recyclable 23.9 +/- 4.6 (38%) Residual 8.6+/-2.0 (13%) Annual Max imum (t) 84 Annual M in imum (t) 42 4.4.5 Common Use/Student Activity Common Use areas are expected to generate 249.8 +/- 20.1 tonnes of waste annually. As common use areas are used primarily for eating activity, generated waste consists of mostly compostable material: residual paper (37%) and food (33%). Figure 5 illustrates the mean composition and variation of the samples taken from the Conversation Pits. 50 Figure 5 : Average Daily Waste Composition (SUB Conversation Pits). 18.00 16.00 14.00 •= 12.00 O ~ 10.00 .S> 8.00 5 6.00 4.00 2.00 0.00 Q i 73 a o 8 | CD 7> -n S n> ^ </) o. °> CD TJ o -o O 2c? (U c/> en ci o o Q-Waste Category The largest variations were found in recyclable materials, in particular old corrugated cardboard and office fine paper. This finding corresponds to those for the Greenhouse and the Bookstore, areas which also had high variations associated with recyclable material. Head counts every half-hour interval were taken in all conversation pits during the sample period. The correlation coefficient for the relation between amount of users and weight of waste was 0.71 (/). This indicates that for these sampled areas and times, a positive relationship exists between the number of users and the amount of waste generated. Approximately 49% of the variation in waste can be attributed to the variation in the number of users (/2). However, as the coefficient of variation increased by calculating values for waste-per-user (from 5% to 7%), the original data set offers more precise parameters for estimating annual waste. No significant difference was found between the total weights of waste produced on each sampled day (95% confidence). Extrapolation of the average sampled waste to other types of common areas on campus used data on floor space, days, and projected use. Projected use was based on two assumptions: 1. The total SUB Building common area space was used to the capacity measured in the conversation pit head counts, and; 2. Other common area spaces on campus were used half as much as in the SUB. 51 Values and calculations for common areas can be found in Appendix H . Table 8 : Common Areas - Waste Parameters. Common/Student Activity Area Parameter S U B Conversation Pits 1, 2, 3 Average Dai ly Waste (kg) 40.7 +/- 4.6 Average Per Capita Waste (g) 71 +/- 12 Coefficient o f Variation (%) - Per Day 5 Coefficient o f Variation (%) - Per Capita 7 A l l Common Use Areas Estimated Annual Waste (t) 250 +/- 28 Composition (t) Compostable 177.05 +/- 11 (71%) Recyclable 34+/-31 (14%) Residual 38+/-15 (15%) Annual Max imum (t) 278 Annual M in imum (t) 221 As an aside, common areas represent areas in which the campus population can converge, converse, and eat, much like the leisure time spent in classrooms. The waste generated in common areas can be said to represent the most typical waste generated at a university, encapsulating students, staff, and faculty as well as capturing the waste stream whether the user brings a lunch or buys lunch on campus. Sampling these areas also yields estimates for the nature of waste generated by customer patronage of the A M S restaurants, as they impose a certain radii of influence on the SUB Building and its Conversation Pits. 4.4.6 Food Service Areas Food Service Areas on campus are expected to generate 39 +/- 11 tonnes of waste from meal plan outlets, 54 +/- 15 tonnes of waste from coffee style outlets, and 44 +/- 27 tonnes from restaurant style outlets per year. Orthogonal contrasts indicated that no significant difference existed between the waste-per-user means from Place Vanier Kitchen and the Barn, permitting meal plan data and coffee style outlet data to be combined. A l l areas contained large amounts of compostable material in sampled waste. 52 Figure 6 illustrates the average waste as derived from samples taken from the Barn, Yum Yums and Place Vanier Kitchen. Although overall composition looks similar, restaurants differed within material categories. For example, the Barn had a greater majority of creamers and disposable dishware in its residual plastic stream than Yum Yums or Place Vanier Kitchen. Most compostable food consisted of post-consumer scraps with the exception of restaurant style areas like Yum Yums, which contained excess pre-consumer scraps in the form of frozen vegetables, pastas, and other base cooking materials. Most of the largest coefficients of variation were associated with the recyclable materials sampled from all restaurants, corresponding with findings from Bookstore, Greenhouse, and Common Areas. Dividing the total waste by the number of users recorded by customer count during the sample period resulted in a decrease in the coefficient of variation in all restaurants sampled (Table 9) . Figure 6 : Average Daily Waste Composition (The Barn, Y u m Yums, Place Vanier Kitchen). • The Barn • Yum Yums • Place Vanier Table 9 : Comparison of the Coefficients of Variation in Samples, Food Service Areas. Food Service Area Sampled Coefficient of Variation (%) Coefficient of Variation (%) Per Day Per Capita The Barn 24 19 Yum Yums 55 7 Place Vanier Kitchen 31 25 80 j 70 _ 60 CD Waste Category 53 Table 9 indicates that incorporating data on customer counts lessened the variation in the sampled waste. An initial comparison of the waste-per-user means from the Barn, Yum Yums, and Place Vanier showed that significant differences exist between these three areas. However, further analysis of the waste-per-user means between only the Barn and Place Vanier indicate no significant difference exists between these two areas. Samples from the Barn and from Place Vanier were amalgamated in order to decrease the size of confidence intervals around the mean by: (1) increasing the sample size; and (2) increasing the degrees of freedom. This means that waste-per-user figures for the Barn and Place Vanier were averaged in order to determine a more precise estimate for extrapolation to like areas on campus. Yum Yums, having a significantly different (and heavier) waste stream, was extrapolated on its original data. This gave a much larger range for estimated waste as the number of samples used to derive this value was small («=2). The change in sample size was due to the amalgamation of two twenty-four hour samples. Parameters for Yum Yums and other restaurant style outlets are provided in Table 10. Table 10 : Food Service Areas - Restaurant Style - Waste Parameters. Food Service Areas - Restaurant Style Parameter Y u m Yum ' s Average Dai ly Waste (kg)* n.a. Average Per Capita Waste (g) 81 +/- 49 Coefficient o f Variation (%) - Original 47 Coefficient o f Variation (%) - Per Capita 7 A l l Restaurant Style Areas Estimated Annual Waste (t) 44 +/- 27 Composition (t) Compostable 34 +/- 34 (78%) Recyclable 3+/-13 (8%) Residual 6.4 +/- 6.1 (14%) Annual Max imum (t) 71 Annual M in imum (t) 18 * Average daily waste was unable to be calibrated as two of the three 24-hour samples were amalgamated. Extrapolation of waste-per-user estimates to like food services outlets and other pertinent information is detailed in Table 11. All calculations for food service outlets can be found in Appendix I. 5 4 Table 11 : Food Service Areas - Coffee and Meal Plan Style - Waste Parameters Food Service Areas - Coffee and Meal Plan Parameter Place Vanier/The Barn Average Per Capita Waste (g) 41 +/- 12 Coefficient o f Variation (%) - Per Capita 27 A l l Coffee and Mea l Plan Outlets Estimated Annual Waste (t) - Coffee 54 +/- 15 Composition (t) Compostable 34.4 +/- 8.3 (64%) Recyclable 6.4 +/- 6.1 (12%) Residual 12.8+/-3.1 (24%) Annual Max imum (t) 69 Annual M in imum (t) 38 Estimated Annual Waste (t) - Meal Plan 39 +/- 11 Composition (t) Compostable 24.8 +/- 6.0 (64%) Recyclable 4.6+/-4.4 (12%) Residua] 9.2 +/- 2.3 (24%) Annual Max imum (t) 50 Annual M in imum 28 It should be noted that the waste generated at food service outlets is not necessarily reflective of the real amount of waste generated on campus by a particular outlet, as each area has a certain "radii of influence" on surrounding activities. This was illustrated in the predominance of materials from McDonald's Restaurant in the nearby outdoor bin that was sampled. Food service areas would be better thought of as the origin for the waste occurring elsewhere on campus. 4.4.7 Laboratories Laboratories generate an estimated 3.9 tonnes of paper towelling waste per year, based on invoice information from laboratory contacts and Central Stores. The majority of laboratory waste, including some glassware and other potentially contaminated material, is collected by Environment Health & Safety for disposal. Appendix J details the locations for the paper towelling waste documented by invoices. The 55 nature of the data gathering (i.e. invoice calculations) obstructed obtaining information on the possible variation in lab waste going to landfill. 4.4.8 Libraries Libraries on campus are expected to generate 44 +/- 25 tonnes of waste annually. Office fine paper makes up the largest portion of this waste stream (31%). Although eating or drinking is not permitted in Library areas, compostable food is the second largest constituent of the annual waste stream (21%). Figure 7 illustrates the breakdown of the waste sampled from Woodward Library. Recyclable materials were the most variable of all sampled materials next to miscellaneous materials, corresponding to findings from other activity areas. Figure 7 : Average Daily Waste Composition (Woodward Library). A o o o _ fl> Oo » tu-rn CD 2 o co Q. £•• 0 1 o cr CD Waste Category TJ O o i 2 i? (U CO W - CL CD 5T Although the number of users varied by up to 200 people during the sample period, the maximum change in waste was seven kilograms. When sample weights were divided by per user weights, the coefficient of variation for total waste increased from 23% to 29%. As variation increased by calculating waste-per-user data, alternative methods were required to calculate library waste. The mean waste generated in the samples was assumed to be representative of the general daily usage of Woodward Library. This mean amount was extrapolated to other library areas using total floor space and the estimated usage rate. The 56 usage rate was calculated by the following steps: (1) Census data was used to estimate the daily number of library patrons in winter and summer terms (i.e. 20,774 people per day in the winter term) (2) The number from (1) was divided by the total library floor space on campus (45,471 m^) to get an estimated usage rate (i.e. 0.46 people/m.2) (3) Since the measured daily usage rate at Woodward Library was less than the estimated daily usage of all libraries (0.23 people/ m^ vs. 0.46 people/m2), the average daily waste from Woodward was multiplied by a factor of 1.98. (4) This final daily average for library areas was then multiplied by the total number of days of winter operation. The extrapolation of the sample data using this methodology was used to generate part of Table 12. Data and calculations for Library Areas can be found in Appendix K . Table 12 : L i b r a r y Areas - Waste Parameters. Library Areas Parameter Woodward Average Dai ly Waste (kg) Average Per Capita Waste (g) Coefficient o f Variation -Per Day (%) Coefficient o f Variation - Per Capita (%) A l l Libraries Estimated Annual Waste (t) Composition (t) Compostable Recyclable Residual Annual Max imum (t) Annual M in imum (t) 57 15.2+/-8.8 11.4+/-8.1 23 29 44 +/- 25 16+/- 13 (37%) 22 +/- 14 (50%) 5.7+/-5.2 (13%) 69 18 4.4.9 Office Areas Office areas are expected to generate 450 +/- 120 tonnes of waste per year. UBC campus has a full time faculty and support staff population of over 5000 people, which in combination with long hours on campus lends to large potential for waste generation. Office waste is especially made up of compostable material (69%): mostly food (41%) and residual paper (28%). Sampling the Brock Hall office areas yielded the following daily average waste stream composition (Figure 8). The majority of the waste stream consisted of food waste and associated disposable materials. Pop cans, metal, and office fine paper categories had the largest coefficients of variation, however categories were less variable in office areas than in other activity areas sampled. Figure 8 : Average Daily Waste Composition (Brock Hall). 3.50 , CD Waste Category Calculating a correlation coefficient for users and waste was irrelevant as the same number of users/workers were present per day (i.e. 21). The variation in waste would be the same for the sampled days as it would be for users. The coefficient of variation for office waste was 11%. However, it was felt per user remained suitable for extrapolation, as waste, like user, did not vary greatly over the sample days. Office Areas present a hidden culprit for heavy waste loads in that they are a large per capita generator of waste, boasting one of the highest weights per user of all activity areas. This is probably due to the 58 stationary nature and duration of the activity (eight-hour working day). Consumption patterns might tend towards bringing food from elsewhere to the office environment, leading to a steady input of waste into the stream over a daily period (i.e. coffee, take-out lunch). The extrapolation of the measured waste to the campus, using total Full Time Employee and Faculty counts, results in the estimated parameters detailed in Table 13. Calculations can be found in Appendix L . Table 13 : Office Areas - Waste Parameters. Office Activity Areas Parameter Brock Hal l Offices Average Dai ly Waste (kg) 7.3 +/- 1.9 Average Per Capita Daily Waste (g) 346 +/- 93 Coefficient o f Variation (%) 11 A l l Office Areas Estimated Annual Waste (t) 450 +/- 120 Composition (t) Compostable 310+/-61 (68%) Recyclable 41 +/-42 (9%) Residual 103 +/- 51 (23%) Annual Max imum (t) 570 Annual M in imum (t) 330 4.4.10 Residences Residences are expected to generate 837.1 +/- 81 (3 St.Dev.) tonnes of waste per year. This waste is mostly compostable (62%>), consisting of food (47%>) and residual paper (15%). A comparison of the per user means for each residence sampled indicated that no statistically significant difference existed between the total waste collected for the residences. A l l of the samples were amalgamated to increase the precision of the extrapolated estimates. The following presents the specific information that was derived for each residence type sampled (Part I), and the extrapolation of this information to other residence areas (Part II). 59 Part I - Sample Composition (A) 1 s t Year Residence - Totem Park Kwakiutl Building Figure 9 illustrates the mean composition of the samples taken from Totem Park Kwakiutl building. A 1 s t Year Residence was expected have less organic waste than other residences due to required Meal Plans for residents, which emphasize cafeteria dining. Still, the majority of the sampled waste was comprised of compostable material (54% = 32% food and 22% paper), and recyclable glass (12%). The largest variation was found in the recyclable categories: old corrugated cardboard, pop cans, office fine paper, and newspaper. Extrapolation of the average waste calculated from all sampled residences resulted in a total of 202 +/- 30. tonnes attributable to 1 s t year residences for 1998. These residences include Place Vanier and Totem Park residences Figure 9 : Average Daily Waste Composition (Totem Park Kwakiut l House). (B) 2"d/3rd Year Residence Sampling Gage North and South Tower twice provided an average daily waste stream as illustrated in Figure 10. The majority of the sample was comprised of compostable material, made up of food (54%), and residual paper (18%). Wood, newspaper and residual plastic materials had large coefficients of variation. The extrapolation of the average waste sampled to the remaining Gage areas yielded a total estimate of 165 +/- 24 tonnes of waste per year from this area. 25.00 , 2 § <T> O » a Waste Category 60 Figure 10 : Average Daily Waste Composition (Gage North & South Towers). 60.00 ; 50.00 40.00 J ! 30.00 '5 20.00 10.00 0.00 -10.00 Waste Category (C) 4 t h Year Residence Figure 11 illustrates the average composition of the daily waste from Thunderbird 3000 Block. Thunderbird had a majority of compostable material in the sample (48%), made up of food (41%) and residual paper (7%). Thunderbird had the least proportion of residual paper waste of all the student residences sampled. The recyclable stream had large coefficients of variation, particularly in old corrugated cardboard, pop cans, office fine paper, and newspaper materials. Fourth Year residences such as Thunderbird and Fairview are expected to generate 183 +/- 27 tonnes of waste per year, based on the extrapolation of the average of all sampled residences. (D) Family Housing Family Housing areas are expected to produce a large amount of waste due to year-round occupation and the tendency for meal preparation and consumption to occur in-house. Values sampled from Keremeos Court (Figure 12) indicate the predominance of compostable material (45% food, 11% residual paper), and total residual plastic (25%). The large amount of residual plastic is due to the addition of diapers and toys to the waste stream. Disposable diapers made up 19% of the residual plastic stream. 61 Figure 11 : Average Daily Waste Composition (Thunderbird 3000 Block). 40.00 35.00 30.00 + °> 25.00 £ 20.00 a 'I 15.00 10.00 5.00 0.00 i . TJ C? 0) CO "S S 2 c ^ 0) O •g 8 n> T I CD S co T3 01 •a CD O O O 33 _ CD G) o 8 1 fD 3) -n <I> °> *S CO Q. ?• » o cr a> Tl o "O O CU 21 0) CO co al o cu o o Q. O o Waste Category Figure 12 : Average Daily Waste Composition (Keremeos Court). The second day sample from Keremeos Court presented odd results in the form of a "failed" compost experiment (i.e. 3 heavy bags of partly decomposed vegetable matter). This resulted in an "extra" seventeen kilograms added to an approximately eighty kilogram sample. As this value fell outside of three standard deviations of the mean (Hicks, 1993), it was dropped from the sample to decrease variation. Other spurious events not related to "normal" resident waste included bags of concrete. 62 Noting such values can aid in illustrating the natural variation that will be inherent with the collection and diversion of this type of waste category (or any waste category for that matter), as these reflect naturally occurring spurious events. A possible explanation for spurious waste is people illegally depositing their heavy waste into the campus garbage dumpsters in lieu of paying for landfill fees. The largest coefficients of variation were calculated for miscellaneous, wood, and old corrugated cardboard categories. The waste generated from Faculty/Staff and Student Family Housing (Acadia Park) is estimated to be 287 +/- 42 tonnes waste a year based on the average per capita waste calculated from all residence samples. Part II - Sample Extrapolation Orthogonal contrasts for the residences (Totem Park, Gage North and South Tower, Thunderbird, and Keremeos Court) indicated no statistically significant differences existed between (95% confidence): (1) The amount of daily per capita waste generated in each residence; and, (2) The amount of daily per capita compostable waste generated in each residence. The plotting of the total waste sampled vs. total residents for all residences and sampled days resulted in the following Figure 13 (overleaf). As the number of residents per residence is assumed to remain constant for each day, the points illustrate variation in a vertical direction only. As correlation for this figure is 0.40 (r), the number of residents is expected to account for 23% of the variation of waste (r^). When the average waste values that were sampled for each residence are compared to the number of residents, the correlation coefficient for the waste (/2) becomes 0.95. What this implies is that in spite of within-residence variation, total waste has a good positive correlation to the number of residents. This result was derived from the amalgamation of thirteen samples (over 400 kilograms of waste). Although some site-specific detail will be lost through the extrapolation of an average for all four types of residences sampled, the decrease in the size of confidence intervals is a desirable benefit. Table 14 illustrates the residence waste parameters resulting from the calculations. Data and calculations for all residences can be found in Appendix M . 63 Figure 13 : Total Residents vs. Total Waste (Totem Park, Gage North & South Towers, Thunderbird 3000 Block, Keremeos Court). Table 14 : Residence Activity Areas - Waste Parameters. Sampled Residences Parameter Average Per Capita Dai ly Waste (g) 365 +/- 54 Coefficient o f Variation (%) 24 Coefficient o f Correlation of Average Total Waste from all Residences vs. No . o f Residents (r2) 0.95 A l l Residences Compostable (t/yr +/- C.I.) Recyclable (t/yr+/-C.I.) Residual (t/yr +/- C I . ) Totals (t/yr +/- C I . ) 1s t Year 128 +/- 25 43 +/- 13 30.9 +/- 8.9 202 +/- 30 2 n d /3 r d Year 105+/- 20 35 +/- 10 25.3 +/- 7.3 165 +/- 24 4 t h Year 116+/-23 39 +/- 12 28.1 +/- 8.1 183+/- 27 Family Housing 182+/-35 61 +/- 18 44+/- 13 287 +/- 42 Ttls (+/- 3 S.D.)* 531 +/- 70. 178 +/- 36 128 +/- 25 837 +/- 84 * Totals for standard deviations were calculated via summation formula in the methodology (pg. 38) 64 4.4.11 Bathrooms Bathrooms are expected to generate 107 +/- 22 tonnes of compostable paper towel waste annually. When the amount of waste measured in each sampled bathroom was divided by the total number of users, the coefficient of variation for all male and female samples declined (Table 15). Table 15 : Comparison of Coefficient of Variation in Samples, Bathroom Samples in SUB. Bathroom Coefficient of Variation (%) Coefficient of Variation (%) Per Day Per Capita Male Bathroom 33 23 Female Bathroom 46 36 As no significant difference existed for sex-specific waste generation, samples were amalgamated («=10). Based on the combined results from SUB male and female bathrooms, one visit to the bathroom generates an average of 7.0 +/- 1.4 grams of paper towelling waste. Using established voiding rates, number of campus users, and estimated average hours on campus, annual bathroom usage is estimated to yield 107 +/- 22 tonnes of paper towelling waste on campus. As one paper towel weighs ~ 2 grams, this total represents the usage of over 50 million paper towels annually. Table 16 details parameters associated with this waste stream. Appendix N provides details of data and calculations. Table 16 : Bathroom Areas - Waste Parameters. Bathroom Areas Parameter S.U.B. Male/Female 1st Floor Bathrooms Per Capita Daily Waste (g) 7.0 +/- 1.4 Coefficient of Variation (%) - Per Day (M) 33 Coefficient of Variation (%) - Per Day (F) 46 Coefficient of Variation (%) - Per Capita (M) 23 Coefficient of Variation (%) - Per Capita (F) 36 All Bathrooms Estimated Annual Waste (t) 107 +/- 22 65 Table 16 (cont'd) : Bathroom Areas - Waste Parameters. Composition (t) Compostable (100%) 107+/- 22 Annual Max imum (t) 129 Annual M in imum (t) 85 4.4.12 Outdoor Bins Outdoor bins are expected to generate 30 +/- 10. tonnes waste annually. Much of this waste is expected to be compostable (58%): residual paper (30%), and food (28%). Residual plastic made up 21% of the outdoor waste bin stream. The average amount of waste generated per outdoor bin as extrapolated from a sample of thirty (ten bins, three sample days) was 1000 +/- 330 g daily. The breakdown of the waste materials found in this area is detailed in Figure 15. Figure 15 : Average Daily Waste Composition (Random Sample, Outdoor Bins). 450 j 400 350 CD Waste Category Compostable material was made up of approximately equal portions residual paper and compostable food. Recyclable materials had the largest coefficients of variation, with the exception of wood and 66 miscellaneous materials. Pop can presence was negligible, perhaps due the efficient scouring of the campus by people in search of extra cash from aluminum can returns. No significant difference was found between sample days for all the bins sampled. This was interesting in that it had been postulated the amount of waste in outdoor bins might increase during nice weather as more people spend time outside. The second sample day, which was sunny and warm compared to the other days, did not have a significantly different amount of waste. The annual amount of waste produced by the outdoor bins was estimated to be 30 +/- 10. tonnes of waste generated annually, when a decline of 35% was considered for summer months. Other major waste parameters associated with the data are shown in Table 17. Calculations and data for Outdoor Bins can be found in Appendix O. Although this methodology was deemed sufficient for this study, further research into waste generated in Outdoor Bins should consider that stratified random sampling might be a superior approach to random sampling. Waste in outdoor bins appears to vary significantly according to location as a large radius of influence was observed on these bins from proximal food service areas. Table 17 : Outdoor Bins - Waste Parameters. Outdoor Bins Parameter Random Sample (10 Bins) Per B in Dai ly Waste (g) 1000 +/- 330 Coefficient o f Variation (%) 88 Estimated Annual Waste (t) 30 +/- 10. Composit ion (t) Compostable 18.2 +/- 6.7 (60%) Recyclable 4.8+/-2.4 (16%) Residual 7.3 +/- 5.2 (24%) Annual Max imum (t) 40 Annual M in imum (t) 20 67 4.5 Summary of Wastes per Activity Area The total amounts of waste generated per activity is presented in the following table (Table 18). Table 18 : Total and Types of Waste generated Annually per Activity Area (t). Activity Area UBC Building Sampled Projected Annual Waste 3 St.Dev.* (t) (t) Animal Area Animal Care Centre 222.8 25.2 Greenhouse Horticulture Annex 2.4 1.4 Bookstore Bookstore 3.4 2.2 Classrooms Buchanan A, B200s, D 63 25 Common Areas SUB Conversation Pits 249 33 Food Service Areas Coffee Type The Barn 54 18 Meal Plan Place Vanier Kitchen 39 13 Restaurant Style Yum Yums Cafeteria 44.4 8.7 Laboratories n.a. 3.9 Library Woodward Library 44 30. Office Space Brock Hall 454 147 Residences 1 s t Year Totem Park -Kwakiutl 201 36 2nd/3rd Year Gage N and S Towers 165 45 4 * year Thunderbird 3000 Block 183 30. Family Housing Keremeos Court 287 51 Bathrooms SUB M/F Ground Floor 107 93 Outdoor Bins Random Generation (10) 30 81 Ttl. Waste (+/- 3 St.Dev.) 2150 +/- 220 (10%) *The standard deviation was required in order to sum the variation for each area, however when evaluating individual areas for waste management, the confidence intervals as presented throughout this thesis should be used at all times. 68 4.6 Validation of Methodology The amount of waste predicted by the methodology was compared to the amount of waste brought to landfill (Table 19). As indicated in the methodology, values were expected to differ in part due to omission of some activity areas or other possibilities for waste generation. These include: 1. Waste generated by special events, such as those in the Chan Centre and Winter Sports Centre, 2. Waste from areas not traditionally associated with the University yet contributing to the annual waste loads (as listed in Appendix B), 3. Waste from activity areas included within the COU definitions that were not audited due to small spatial representation (Audio-Visual labs and the Health Services Facility), 4. The addition of new buildings after implementation of the audit design (i.e. Forestry), and; 5. The addition of "illegal" waste, such as the occasional addition of (very heavy) construction and demolition debris. Other contributing factors may include the disposal of heavy lab materials and equipment into the waste stream. Table 19 : Comparison of Projected Annual Waste to Documented Amounts. Projected Amount Maximum Minimum Amount Brought to O f Waste Estimate Estimate Landfill in 1998 2150 2370 1930 2627 The estimated waste total differed from the documented amount by -18%, or -480 tonnes. Other possible reasons for the discrepancy between these waste amounts include: 1. Spurious events, or other variables affecting the amount of waste generated. The appearance of "strange" waste such as failed compost experiments or cement bags in sampled residence areas illustrate the potential contribution these events could have for the waste stream. 2. The under/overestimation of amount of users for extrapolation. For example, classroom usage was based on the number of bookings and registrants per class, however the number of students in attendance can vary. 69 4.7 Conclusion An analysis of variance of all total per capita waste sampled indicated significant differences existed between per user waste generation quantities for activity areas (95% confidence). Sampling for different areas, or designing appropriate strata for within an area such as food services, can help attribute as much variation as possible to differences between areas or strata. More sampling on particular target groups can help to decrease the total variation that would otherwise be associated with geographic/spatial differences. Failure to account for spatial fluctuation due to different waste-generating activities is a serious consideration, especially when sampling waste at the point of disposal. Sampling waste at point of disposal has been a traditional form of waste auditing in the past and is still in practice today. In some cases, determining the per capita waste decreased sample variation (i.e. all food service areas, all residences, and the SUB bathrooms). Per capita waste has potential to serve as a valid method for extrapolation. This finding is important as incorporating information on per user waste can also address temporal variation in waste without infinite sampling, which is important for regions/facilities that wish to predict their current and future waste stream. Data can and does exist on user flow over time at U B C and possibly in other areas that audits would prove useful. In order to better approximate the true per user waste rate it would be advisable to sample for a wide range in users as well as determining more precisely the number of users in the activity. This would aid in defining a better regression relationship for these two variables. A last observation involves the coefficients of variation for different waste categories. For the majority of the activity areas sampled, it was found that recyclable materials had the largest variation of all categories over time. This could be due to one or both of the following reasons: 1. The greater number of categories, and resulting smaller weights, for the recyclable material stream led to higher variation. Larger and heavier categories may be less affected by fluctuation in waste. 2. The implementation of waste management programmes such as recycling. The presence of these materials in the waste stream might be a reflection of decisions by few individuals instead of the average population. 70 CHAPTER FIVE : DIRECTIONS FOR REDUCTION In order to provide useful information for waste reduction on the U B C campus, in the following sections audit data are manipulated to provide a comprehensive picture of overall waste stream characteristics. In the first section, the sampled waste data is revisited to derive the waste stream breakdown in terms of compostable, recyclable, and residual components. This waste data provides direction for waste management in specific activity areas sampled (i.e. what waste materials can be reduced in Woodward Library). The second section documents estimated annual waste composition and discusses the respective locations and quantities of waste stream constituents. The information from the extrapolated data allows the development of recommendations to reduce the waste stream in compliance with campus waste diversion/reduction goals of 50% per capita. Management recommendations for reaching this goal are presented in order of ease/feasibility of implementation. 5.1 Percent Composition of Samples The amounts of compostable, recyclable, and residual material in the samples were compared to give the waste composition of the sixteen activity areas. The total waste sampled equalled 1832 kilograms, of which 62% was compostable, 19%> recyclable, and 19% residual. Of the sixteen areas sampled: • thirteen of sixteen areas contained 50% or more compostable material, • eleven of sixteen areas had 20%> or less of recyclable material, and; • ten of sixteen areas had 20% or less residual material. The overall sample composition indicates that capturing compostable material would result in the greatest weight diversion. Additionally, as compostable waste composed more than 50% of the sample in most activity areas, reduction of this waste could occur in the majority of these areas on campus. 5.1.1 Compostable Material in Sampled Waste Compostable material represented 62% of all sampled waste, or 1141 of 1832 kilograms. This material includes residual paper, compostable food, animal waste, animal bedding, and/or wood. Figure 16 shows the percent of total waste that compostable material made up in each sampled area. The highest proportions of compostable material were found in the SUB bathrooms and Animal Care Centre (100%). Material from these areas was mostly wood-derived, in the form of paper towelling in the SUB bathrooms, and wood chip/sawdust bedding from Animal Care. 71 Figure 16 : Percent Compostable Material in Each Waste Sample. • % Other • % Compostable Activity Area Yum Yums Food Service Restaurant, Gage North and South Towers, and the SUB Conversation Pits also had high proportions of compostable constituents; at 79%, 72%, and 71 %, respectively. Material in these areas consisted of mostly post-consumer scraps such as fruit peelings and left-over food (i.e. french fries, spaghetti, bread), and pre-consumer scraps (i.e. dough and uncooked vegetables). Areas like the Bookstore, Horticultural Greenhouse, and Woodward Library generated much lower percentages of compostable waste (-24% on average). A frequency distribution describes how the data is dispersed across a range of values; for example, how many of the samples have more than 50% of compostable materials in the waste. Plotting the frequency distribution of compostable waste from the samples showed the waste followed a somewhat normal distribution. According to Figure 17, thirteen of the sixteen samples contained 50% or morecompostable waste. Although approximating normal distributions was not a focus of the statistical analysis on audit data, this finding agrees with research conducted byCarruth and Klee (1969). They found that individual components do not follow a normal distribution except when they represent 30-70% of that stream. This statistic supports the observation that the sampled waste stream has a weighty compostable fraction. 72 Figure 17 : Frequency Distribution of the Percent of Compostable Waste in each Sample. 4.5 4 3.5 3 2.5 2 + 1.5 1 0.5 0 4 4 % Compostable 5.1.2 Recyclable Material in Sampled Waste. Recyclable material made up 19% of all sampled waste, or 344 of 1832 kilograms. This material includes office fine paper, newspaper, OCC, recyclable glass and plastic, and/or pop cans. Figure 18 illustrates the percent of total waste that recyclable material made up in each of the sixteen activity areas. Woodward Library had the largest average proportion of recyclable materials in its samples (-50%), followed by Buchanan Block A, B, D (-38%), the Bookstore (-31%), and Totem Park 1 s t year Residence (-29%). The following lists the major recyclable components in these areas. • Woodward Library: office fine paper (31%), recyclable glass (12%) • Buchanan Classrooms: recyclable glass (19%), newspaper (11%>) • The Bookstore: office fine paper (19%), old corrugated cardboard (5%) • Totem Park: recyclable glass (12%), office fine paper (6%), newspaper (5%) Besides the SUB Bathrooms and Animal Care Centre, places with the least proportions of recyclable materials included the Brock Hall Offices, Place Vanier Kitchen, and Yum Yum's Restaurant (9%, 8%, 7% respectively). 73 Figure 18 : Percent Recyclable Material in each Waste Sample. o O) 2 c <t> o 100% T 90% 80% 70% 60% 50% 40% 30% 20% 10% -0% 0 31 38 14 15 8 7 15 50 9 16 29 17 21 15 • % Other • % Recyclable CD CD O O O TD -X> TO ^ 3 Activity Area Figure 19 : Frequency Distribution of Percent of Recyclable Waste in each Sample. 7 6 5 + i 4 o I 3 IL 2 4-1 0 10 20 30 40 50 60 % Recyclable 0 70 0 80 90 100 The frequency distribution of the percent recyclable material in the sampled waste stream was non-normal (Figure 19). As the total amount of recyclable material in the sampled waste is less than 30%, this finding corresponds with observations by Carruth and Klee (1969). The majority of samples, (eleven of 74 sixteen, or 69%), fall within the bracket of having 0-20% recyclable material in the waste stream. Prior to recycling programs, most municipal and university solid waste streams were estimated to consist of 50-55% recyclable materials (Rathje, 1993; Department of Facilities Management, 1992; Resource Integration Systems, 1991). Applying this assumption to U B C indicates that there has been at minimum a 64%> diversion of recyclable material in most of the particular areas sampled. 5.1.3 Residual Material in Sampled Waste Residual material made up 19% of the total sampled waste: 346 of 1831 kilograms. Residual material is made up of residual plastic, metal, and/or other miscellaneous materials. It should be noted that this value closely approximated the amount of recyclable material in the sampled waste (344 kilograms). Figure 20 presents the percent of the total waste that residual material made up in each of the activity areas sampled. Figure 20 : Percent Residual Waste in each Waste Sample. High proportions of residual waste can be considered "ideal" for landfdl disposal (on a weight basis), as: (1) Compostable/presently recyclable material is not generated. (2) Compostable/presently recyclable material is already diverted, as in the Horticultural Greenhouse. 75 (3) Costs to manage this waste are low as the weight associated with large volumes of residual plastic is comparably light. Waste management costs are currently measured on a weight basis as landfills charge by the tonne for disposal. Areas that indicated the highest amounts of residual waste include the Horticulture Greenhouse (75%), the Bookstore (49%), and Kwakiutl Family Housing (27%). The Greenhouse areas have extensive compost operations and produce the most residual waste, thereby are among the lightest waste generators of all areas sampled. The following lists the breakdown of residual material in areas of greatest production: • The Greenhouse: residual plastic - potting trays (52%), miscellaneous textiles (24%) • The Bookstore: residual plastic wrapping (32%) • Family Housing: residual plastic wrapping (7%), metal food tins (as metal is not currently included within the U B C recycling program) (4%) Besides the SUB Bathrooms and the Animal Care Centre, areas with low proportions of residual waste included Buchanan (13.5%), the Library (12.9%), and 2 /3 r d Year Gage Residence (11.6%). Figure 21 : Frequency Distribution of Percent of Residual Waste in each Sample. 2 + 1 0 10 20 30 n 40 50 60 70 % Residual 80 90 100 The frequency distribution of the percent residual material in the sampled waste stream was non-normal (Figure 21), also in agreement with findings by Carruth andKlee (1969). The majority of samples (ten of 76 sixteen) fall within the bracket of having 0-20% residual material in the waste stream. Should all presently recyclable and compostable material be diverted, the waste stream in most of the sampled activity areas would decline by a minimum of 80%. 5.2 Annual Waste Composition The sample data that was previously outlined can help to develop specific waste management projects for each of the sixteen areas sampled. The overall composition of the sampled waste (Figure 22), would indicate that the majority of the university waste stream is compostable, by mass. However, a small activity area (i.e. greenhouse) would have a greater influence on the sampled waste stream than it does on the actual waste stream. This potential inaccuracy can be prevented by extrapolating the sample data to give the "real" waste composition picture. Extrapolating the sampled waste represents each activity area in proportion to its influence (as per the methodology). The estimated waste composition takes on a different configuration when derived annually (Figure 23). This indicates that results from audit methodologies using more generalized factors (i.e. total student population) instead of more detailed study (i.e. the flow of people through activity areas) might be misleading (Department of Facilities Management, 1998, 1997, and 1992). Figure 22 : Composition of Sampled Waste. Residual 77 Figure 23 : Composition of Estimated Annual Waste. Residual 15% Recyclable 15% Compostable 70% According to extrapolation results, compostable material represents 70% of the waste material produced by U B C annually, followed in equal portions by recyclable and residual material at 15%. A breakdown of these proportions by particular constituents can be found in Appendix P. Compostable material has increased by 8% from the total waste composition of the samples, and recyclable and residual materials have decreased by 3%. It is interesting that this estimate corresponds almost exactly with estimates of municipal solid waste composition in British Columbia. The Ministry estimated that 70% of the solid waste generated by the province in 1998 was compostable, however this estimate includes recyclable office paper (Ministry of Environment, Lands, and Parks, Webpage). The following sections examine the particulars of the annual compostable, recyclable, and residual waste streams via: (1) Presentation of waste materials making up each of the waste types (i.e. what percent does wood make up of the annual compostable waste?) (2) The locations of waste materials (i.e. where on campus does wood originate?) (3) The quantities of waste materials (i.e. how much wood is produced annually from this location?) 78 This information wil l help shape efficient strategies for current and alternative waste management. For example, should composting become a desirable option for the university, an important consideration would include parameters on the type/quantity of material collected for processing. Depending on the desired characteristics of the end product, the University may wish to target an area producing large quantities of residual paper/bedding material for bulking matter, and co-ordinate these amounts with pre-planned portions of food material. Both inputs require knowledge of the composition, quantity, and location of compostable material on campus. 5.3 Compostable Waste Generated Annually Compostable material generated on campus is estimated to total -1510 tonnes annually. This total is made up of the material types and proportions as in Figure 24. Cloth was not examined due to its low representation in the waste stream (-0.06 tonnes/yr.). Figure 24 : Breakdown of Annual Compostable Waste. Compostable material is distributed among the different activity areas as seen in Figure 25. Which areas would be selected for implementing a composting program would depend on the characteristics of the area as well as the quantity and type of compostable waste it produces. 79 Figure 25 : Location of Annual Compostable Waste. 350 300 -ials (t/yr] 250 ibleMater 200 ibleMater 150 Compost: 100 50 A Activity Area Selecting an area for a diversion program should consider: (1) the feasibility of collecting the waste, and (2) who wil l be responsible for diverting the waste. These factors differ for areas that generate large amounts of compostables, such as Office Areas, Common Use areas, and Family Housing. For example, offices and common use areas produce large amounts of compostable waste, but the wide and variable distribution of these activity types around campus would hinder centralized collection facilities. Family Housing and other similar residence areas might be more suitable for the placement of collection facilities as buildings are concentrated and space exists for additional bins. However, the success of a composting program in these areas would depend on the extensive instruction of residents and their positive participation in such a program. Given rapid resident turnovers as in student residences, education programs would need to be offered on a continual basis. Other areas could be considered for pilot programs, such as Food Service outlets. Food Service outlets, despite having lower quantities of compostable waste, might provide an ideal area for pilot diversion programs as diversion strategies could be incorporated within employee duties. In addition, many food service outlets currently pay U B C for the disposal of their waste to landfdl. Decreasing waste management costs for these areas might provide additional incentive for diverting waste. Lastly, some areas should not be considered for initial program design. These include Classrooms, Outdoor Bins, and Libraries. The decentralized nature and low generation of compostable waste in areas 80 including classrooms and outdoor bins would not favour a cost-effective composting program. Library areas should enforce already established regulations prohibiting eating and drinking. This would minimize the generation of compostable waste altogether in these areas. The following Table 20 provides quantitative information on the quantity and type of compostable material produced in each activity area annually. As compostable material on campus is made up of several waste types, a variety of possibilities exist for producing excellent quality compost. Which areas would be incorporated within a composting program must be carefully evaluated for the type of input they would provide in addition to their characteristics. For example, collectingcompostable waste from only animal care facilities would provide large quantities of potentially compostable material, but the quality of resulting compost would suffer as feedstock has insufficient nitrogen for the process. Table 20 : Compostable Material Produced Annually in Different Activity Areas (t/yr). Activity Area Compostable Food Residual Paper Animal Bedding & Waste Wood Material Total Compostable Material Animal Areas 223 +/- 25 222.7 +/- 25.2* Greenhouse 0.08+/-0.10 0.08+/-0.10 Bookstore 0.31 +/-0.22 0.60+/-0.21 0.91 +/-0.08 Classrooms 17+/- 13 13.2+/-5.5 0.8+/- 1.7 31 +/- 15 Common Use 83 +/- 20. 89.9 +/- 9.4 4.7 +/- 4.7 177+/- 11 Food Service Coffee 22.5 +/- 5.5 11.7+/-2.9 0.20+/-0.17 34.4 +/- 8.3 Meal Plan 16.2+/-3.9 8.5+/-2.1 0.15+/-0.12 24.8 +/- 5.9 Restaurant 24 +/- 28 10.1 +/-5.2 0.71 +/-0.49 35 +/- 34 Laboratories 3.9 3.9 Library 9.4 +/- 4.9 6.9 +/- 8.3 16+/- 13 Office Areas 186+/-23 125 +/- 39 311 +/-61 Residence 1 s t Year 93 +/-21 31.2+/-9.1 3.9+/-5.8 128 +/- 25 2 n d /3 r d Year 76 +/- 17 25.5 +/- 7.4 3.2+/-4.7 105 +/-20. • 4 t h Year 84+/- 19 28.3 +/- 8.2 3.5 +/- 5.3 116+/-23 Family 132+/- 30. 44+/- 13 5.6 +/- 8.3 182+/-35 81 Table 20 (cont'd) : Compostable Material Produced Annually in Different Activity Areas (t/yr). Bathrooms Outdoor Bins 8.4+/-3.4 107+/- 22 9.0+/-3.6 0.7 +/- 1.4 107+/- 22 18.1 +/- 6.7 Totals (+/- 3 St.Dev.) 751 +/- 78 520+/-110 223+/-25 24+/- 21 1510** +/-150 * The error term for animal waste is represented by 3 St.Dev. * * Not including cloth ~ 0.06 tonnes annually. 5.4 Recyclable Waste Generated Annually Total recyclable material generated on campus is estimated to total -320 tonnes annually. The recyclable waste stream emitted by the campus consists of the material types and proportions in Figure 26. The recyclable waste stream is dominated by recyclable fine paper (-120 tonnes) and glass (-100 tonnes). As office fine paper is quite light respective to other materials like glass, this finding indicates that a large volume of paper is still entering the waste stream. Figure 26 : Breakdown of Annual Recyclable Waste. Pop Cans Recyclable Plastic 4% 4% 3% Newspaper 19% 82 Recyclable material is distributed among the different activity areas as seen in Figure 27. Which areas are pinpointed for further education, regulation and possible bin additions would primarily depend on the characteristics of those areas and the quantity and type of recyclable waste they produce. Figure 27 : Location of Annual Recyclable Waste. 70 n 60 -(t/yr) 50 n .2 o 40 -™ £ 0 30 -n JS u >. u 20 -o X 10 -0 - f_La Activity Area Emphasizing the recycling program in select areas should consider: (1) the ease of collecting the recyclables, (2) the population responsible for their diversion; and (3) the type of material to be collected. These factors will influence the degree of success of encouraging recycling in a specific area. Family Housing, 1 s t Year Residences, and Offices produce the largest amounts of recyclable material, however greater recycling success might be more easily realized in regulated areas such as the library. In addition, targeting specific materials would be more efficient than a campaign targeting all recyclable material. Family Housing and 1 s t Year residences are estimated to produce the largest annual amounts of recyclable material, at ~61 and ~43 tonnes, respectively. This material is mostly made up of fine paper and recyclable glass. Although the recycling program is emphasized annually and many bins are available for collection in these areas, the success of most residential programs is dependent on individual participation (Pers. Comm.: M-J . O'Donnell, S. Vandenberg. U B C Waste Management Coordinator. Past and current, respectively). The constant resident turnover in these areas makes annual education programs necessary; however successful, waste diversion programs remain difficult to establish. 83 Office areas were also estimated to produce a large amount of recyclable waste (-42 tonnes), yet on perusal of the sample data, office areas had one of smallest proportions of recyclable materials in the waste stream (-7.8%). This illustrates how the sheer number of participants in an activity area can influence a waste stream despite otherwise good participation in a recycling program. Office waste has one of the highest per capita generation rates of all activity areas, particularly of fine paper. Conversely, although Woodward library had the largest proportion of recyclables in its sampled waste stream (-50%), libraries rank lower than most areas in terms of overall recyclable waste generation (-22 tonnes a year). Table 21 (overleaf) provides information on the type and amount of recyclable material produced in each activity area annually. As the recycling program on campus currently includes different waste types, which waste type would be most effective to pinpoint should be considered. For example, although office paper waste is present in the greatest quantity, greater recycling success in terms of weight might result from targeting areas producing large amounts of recyclable glass. One beverage glass container is weight equivalent to -50 sheets of o f f i c e paper. Targeting Family Housing or 1 s t Year Residences for recycling glass containers could well be equivalent to targeting office, common, and classroom areas for recycling office fine paper, as well as being less labour and cost intensive. Another consideration is the impact of the new British Columbian Bottle Deposit regulation on the amount of glass going to landfill, as this regulation came into effect after this present audit was conducted. Resource Integration Systems (RIS) originally estimated the University waste stream to consist of 55% recyclable materials in 1991 (RIS, 1991). This was prior to the full-fledged instigation of the recycling program. Although the RIS study was questionable in terms of audit results, other studies have found that prior to recycling efforts, paper and glass make up 50-55% of a municipal waste stream (Rathje, 1993; Robinson, 1993; Bennett et al., 1996). According to the results of the current UBC study, recyclable material represents approximately 15% of the waste going to landfill in 1998. It is likely that the recycling program has been responsible for a minimum diversion of 70%> of recyclable material from landfill. It must be noted that this percentage, although high and to be commended, could be indicative of the difficulty in achieving 100% diversion rate without added incentives. In example, pop cans represent one of the lowest percentages in the recyclable (-4%), and overall (0.04%) waste stream, even in comparison to other light recyclable materials such as plastics and office fine paper. An explanation for the diversion 84 oo + H o b re o o & CD <T> O 03 5^  T) » n e 5" </) & g T3 v< o (a S o 5 ro to •< LO •< ft B. ft s •< s ft __ ft O t-1 r s ' S 2 » « 8, ^ o o o n 03 o sr O S T3 ft ft 3. pa ft B o > rt a g > ft ft p NO O N N O + O O + bo + • to 4^ ^ U) tO (Ji N O O N U ) >• + + + o O N y\ In io to ~j ~j oo N O io bo + + + i i i W U) * i io U i w o o o U> LO o © © ',—4 O N O N 4^ O N 00 1—» to N O 4^ ; f ;+ + ; f + +^  + LO o © p O N o o 1 . © N O to Ln to O N N O O N o o o o >-* 4i. O N O N © © to Ln N O 4^ + + + + + + + + o o to o o O N N O Ln to © N O Ln 4^ L>> O N <-/> bo + + + + I I I I O N bo o 0.53 0.74 0.16 1 + i + i + • • oo o o 4^ o bo In to o to o LO 42. to U) y\ 4^ © bo O N O N + + + + + i + + + + i 4i. • 4^ i N O 4^ 4 .^ • 4i. Lo 1^ to to to bo bo X. N O Ui Co + + + + 42. to to to + + CO + O O to P LO £^ 4^ 4^  O N + I 4^  O N 4^  + o 1^ o to p o o to o o bo o o O N bo O O O N N O N O to to N O 4^  1—' + + + + + +^  +^  +^  + + + 1 O S p to o to o to p o Co Lo O N N O bo O N o oo O O o to p o o p o O o o o o O N 4^  Lo 4^  bo to o o o o Ux to oo oo Lf> + +^  +^  + + + + ^ I + + + + + p p O o o o o p o to o o Lo to to to O N o O N o 4^  O N o Lf> 4^  00 to to o^ + + H - O o ^-+ + j-. to to °^ to — S9 n T3 ft a ft •a ft Q . g ° C £= on 3. £ re a. JO re w) rt re a-o 13 o S3 H o 65 JO 63 re re ?T to fB »" -r re o c e re re a • ES a e 5' O re re re re . 1 . success of this material is the further "processing" of this material by people collecting pop cans from garbage bins after others have disposed of them. Further steps such as those accompanying the diversion success of pop cans might be required to achieve capture rates greater than 85%. This corresponds with a comprehensive study conducted by Angus Reid (1996) on regional participation rates in recycling programs. The results from their study indicated that regardless of current education campaigns, accessibility to facilities, and other factors, an estimated 8% of any population will always neglect to participate in recycling and other waste management programs («=2600). 5.5 Residual Waste Generated Annually Residual material generated at U B C is estimated to total -320 tonnes annually. This total is made up of the materials and proportions in Figure 28. No current or future management plan for diverting this material has been devised, except with the possible replacement of disposable dishware by durable materials in some food service outlets. Given that residual plastic is the third largest constituent in the overall annual waste stream (-12%), generating approximately 270 tonnes of waste annually, perhaps attention should also be turned to this material stream. Questions regarding the sustainability of residual plastic vs. other options such as durable dishware must also be addressed. Figure 28 : Breakdown of Annual Residual Waste. Miscellaneous 86 Residual material is distributed among the different activity areas as seen in Figure 29. Which areas are pinpointed for reduction programs would primarily depend on the characteristics of those areas, besides the quantity and type of residual waste produced. Figure 29 : Locations of Annual Residual Waste. 120 100 ! 8° CO S 3 60 40 20 X L Activity Area Office areas and residence areas produce the largest amount of residual waste (-100 t, 120 t), followed by the common areas (-40 t). Although these areas produce the most residual waste, food service areas may well be the best areas to initiate diversion programs due to the following characteristics: (1) Food service outlets are the main providers of the residual plastic that is brought to an office area, common area, or via take-out in residence areas; and, (2) Centralized purchasing decisions for materials. A "blanket order" purchase system has been in place at U B C for a number of years. This system provides U B C employees with a list of vendors with whom U B C has specialized contracts for different materials. These materials include disposable plastics materials (Pers. Comm.: S. Martin, D. Graham, U B C Purchasing). Targeting the availability of residual plastic in certain activities could affect its presence in the waste stream without the need for elaborate infrastructure changes or intensive educational programs. The report by RIS (1991) states that: "purchasing practices at U B C dictate how many and what kind of materials are used... Clearly purchasing provides the first opportunity for U B C to reduce its waste...". 87 The following table provides information on composition and quantity of residual material produced in each activity area annually (Table 22). Residual material as classified represents few waste types, as other materials were allocated to either compostable or recyclable waste streams. In order to further divert the residual waste stream, attention must be paid to the composition and locations of respective materials. As mentioned previously, food service areas would be a good area for exploring source reduction possibilities via purchasing decisions for disposable. Other considerations exist, however, such as the energy costs that would be associated with washing durable dishware. Another possibility is expanding the recycling program by including metal food cans and tins, as these made up 2.4% of the annual stream. However, given the distribution of this stream on campus (i.e. not more than 12 tonnes is found in any one activity area), the cost of program implementation may well exceed current benefits of diversion. Table 22 : Residual Material Produced Annually in different Activity Areas (t/yr). Activity Area Residual Plastic Miscellaneous Metals Total Residual Material Animal Areas Greenhouse 1.98+/-0.25 0.18+/-0.28 2.16+/-0.29 Bookstore 1.2+/- 1.4 0.19+/-0.50 0.08+/-0.18 1.5+/- 1.6 Classrooms 1.1+1- 1.6 0.85 +/- 0.37 8.6+/-1.9 Common Use 32.1 +1-1.6 6.2 +/- 7.5 38+/- 15 Food Service Coffee 11.4+/-2.5 0.7+/- 1.8 0.72 +/- 0.59 12.8+/-3.1 Mea l Plan 8.2+/- 1.8 0.5+/- 1.3 0.52 +/- 0.43 9.2 +/- 2.3 Restaurant 5.6 +/- 6.8 0.82 +/- 0.70 6.4+/-6.1 Laboratories Library 5.3 +/- 5.3 0.07 +/- 0.29 0.36+/-0.17 5.7 +/- 5.2 Office Areas 96 +/- 48 6.6 +/- 7.3 103+/-51 Residence 1 s t Year 22.8 +/- 8.0 8.2+/-3.2 30.9 +/- 8.9 2 n d /3 r d Year 18.7+/-6.6 6.7 +/- 2.6 25.3 +/- 7.3 4* Year 20.7 +/- 7.3 7.4 +/- 2.9 28.1 +/-8.1 Family 32.5+/- 11.5 11.6+/-4.5 44 +/- 13 Bathrooms Outdoor Bins 6.5 +/- 4.8 0.01 +/-0.03 0.85 +/- 0.65 7.3 +/- 5.0 Totals 271 +/- 74 1.7 +/- 3.0 51 +/-16 323 +/- 80. (+/- 3 St.Dev.) 88 5.6 Annual Waste Generation - Quantity, Composition, and Fluctuation The following figure summarizes the information presented in previous tables (Figure 30). It provides a general illustration of materials making up the annual waste emanating from each activity area. Figure 30 : Annual Waste Generation - Quantity and Composition at Different Locations. 500 • Residual • Recyclable • Compostable Activity Area As waste fluctuates significantly from winter to summer term on campus and within activity areas, Figure 31 illustrates the different quantities generated in winter compared to summer terms. Winter term extends from September to April, and summer term from May until August. Waste decreases in summer due to two major factors: (1) Decreased time span - summer term is half as long as winter term, and; (2) Decreased usage levels in different activity areas, since there are less students present. Not all areas decline by similar percentages. The large reduction in waste in some areas (70%+) was attributable to the summer decline in the populations using these areas. These include meal plan Food Service areas, 1st Year Residences, Classrooms, Common Areas, and 2nd and 3rd Year Residences. Waste declined by an average of 70%, however this average ranges from 42%> as in the libraries to virtually 100% as in the restaurant meal plan facilities. The overall compositions of the winter, summer, and the annual waste stream remain remarkably similar to each other, as the respective percentages are: compostable material (70-71 %), recyclable material (14-15%), and residual material (15%). 89 Figure 31 : Fluctuation in Total Waste Quantities : Winter vs. Summer Terms. Management programs must consider what happens to waste in different areas in the summer. In a composting operation, selecting a meal plan style kitchen for inputs of food waste would mean the system would operate at less than full capacity in the summer as meal plan outputs decline significantly. In this case, another activity area should be incorporated in the program that could replace this diminished supply of organics in the summer months. Tables 23 and 24 (overleaf) provide data on summer and winter waste compositions and quantities from different activity areas. 5.7 Recommendations for Reduction In 1997/1998 UBC disposed of 2627 tonnes of garbage to landfill and 1546 tonnes to recycling. In total, UBC generated 4173 tonnes of waste, 36% of which was diverted due to recycling and yard waste composting efforts. The percent reduction in waste per capita is currently estimated at 38% (UBC Waste Reduction Program 1998). Using current parameters on population and total waste generated, UBC must divert a minimum of an additional 500 tonnes of waste from landfill in order to reach campus goals of 50% reduction/diversion in waste per capita. Even should the recycling program achieve a capture rate of 100%, estimates from the audit indicate that not enough recyclable material is present in thelandfilled waste stream to achieve this goal (-320 tonnes). Reaching the 50% reduction goal will require either (1) a reduction in the total waste produced by the campus or (2) the diversion of this waste from landfill. 90 H o o O W> b re N O 00 <7) rt b re in Ui b re 7a >o ?d 0 0 0 p*. Cfl 00 vi on re re to H - w S. ° i ^ D -O O 00 5' o r r o o 3 o 2 « S . S T § I 1-1 c re •rl *rl 0 0 0 0 0 0 D . o. a. w w w re re re . . g O O 03 CO > 2 ST 2 S. » o' rt' w O S3 £r rt >—1  re % re o re O o S> re re oo 3 O <* B3 re P to £ ! j !-j> to — w £ v^ . S-* --> 00 ON I n — H - t o u> 00 •O. - O Ui ^ NO tO t O ( - ^ O N N O h _ . N - ' H - H -u ' v o ^ j L n - t ^ g o o o N u !o M W W W u NO • • NO tO ON • ON NO ~J !— U i ON tO O - 0 J N L OO OO . ^ ^ — — tO NO w OO ON 1° ON 00 _ O OO Ji. 4i. (0 P : • NO j i . NO O O to 00 to to to ^ M o 00 r ft ^ j 00 - J O N U I ~J © •f^  ON NO OO ON ON O0 Ui Ui Ui tO tO O Ui U i u i o * w to ^ M M to o ui Ui ui o •< r re 69 ; o S I re 50 re re > '< ; ft 58 re H as a to re a H o » s a H re o o re s re » re D. 5" 3 re »f •a re • •1 re The following suggestions for waste management are presented in order of ease of implementation, from enforcing already existing programs to implementing novel management regimes. Suggestions are presented in two parts: (1) mention of a notable material stream, areas largely responsible for its origin, and estimated possible diversion from these areas, (2) a brief discussion of reduction possibilities. Estimated diversions (conservative) associated with different strategies were calculated by summing the annual quantities from referred areas. Any action taken by the University should consider aspects of environmental sustainability, including: (1) carbon dioxide emissions (i.e. via on-site composting), (2) energy costs associated with durable material replacing disposable material (i.e. washwater vs. compostable materials), (3) potential effects of water contamination (i.e. compost leachate, washwater), (4) detrimental effects of novel technology (i.e. increased fuel costs for transportation/collection, increased types/number of bins). 5.7.1 Recycling • Concentrate on emphasizing recycling programs of materials that will maximize diversion weights. For example, recyclable glass is a heavy material. Focusing on areas throwing out large amounts of recyclable glass will maximize the amount of weight diverted from landfdl with less effort than would be required to achieve the same weight diversion for office fine paper. Family Housing, Common Areas, Student Residences, and Classrooms produce large amounts of recyclable glass. Possible diversion resulting from concentrating on these areas is estimated to be -90 tonnes annually. • Focus on the material that represents the majority of recyclable waste being disposed. Office Fine Paper results in at least -120 tonnes of this material going to landfill a year. As paper is light, 120 tonnes would represent a much larger volume of paper than of recyclable glass. Although areas like offices seem to participate well in the recycling program, the input of few sheets by few people can still result in large cumulative amounts when the extent of the campus population is taken into account. Offices, Family Housing, Student Residences, and the Libraries produce large amounts of Office Fine Paper annually. The maximum possible diversion from these areas is estimated to be -110 tonnes annually. • Pinpoint other recyclables present in significant amounts. Newspapers represent an additional -60 tonnes going to landfill a year. The majority of newspaper emanates from Common Areas, Family Housing, the Student Residences, and Classrooms. Maximum possible diversion from these areas is estimated at -50 tonnes. Further initiatives for recycling Old Corrugated Cardboard, Pop Cans, and 93 Recyclable Plastic are not deemed necessary nor cost-effective, as they approximate -35 tonnes annually of the waste going to landfdl. Targeting significant recyclable materials in mentioned areas can range from source reduction initiatives and additional recycling education to well-planned bin addition and placement. Observation of the lack of recycling bins in some highly frequented classroom and common areas suggests the latter strategy could most easily influence program success. Ensuring that enough receptacles are placed to provide alternatives for disposal presents the consumer with an immediate choice instead of the attractive convenience of the nearest receptacle. In the case of McMaster University, rearranging the constellation of bins so that recycling and garbage bins were always found side by side had a most singular effect on recycling success (Pers. Comm.: M . Jeffrey. Pollution Prevention Program, Ministry of Environment). The success of bin types and locations can be evaluated through instigating a pilot project in Library areas. These areas, which still produce significant amounts of office fine paper in their waste receptacles, can initiate an "all-or-nothing" strategy by providing only bins for recycling. This strategy would have a dual purpose - enforcing both recycling and prohibitions on eating in these areas. Capture rates associated with the recycling program could also be an indication of the maximum potential capture rate associated with voluntary participation in a recycling program. In this case, other people retrieving pop cans from the waste bins (which now represent 0.04% of annual garbage stream) might serve as illustration of the benefits of "additional enforcement" on recycling programs. This type of incentive might be beneficial in residence areas, which already have excellent exposure to education campaigns and recycling facilities. Current capture rates in these areas might be reflective of the maximum possible participation of a continually changing resident population. 5.7.2 Source Reduction • Total residual plastic produced on campus is estimated to contribute -270 tonnes to landfill annually. Residual plastic is mostly associated with food and its consumption, including condensed and expanded plastic wrap, bags, cups, and cutlery. Areas generating the most residual plastic include Office Areas, Common Areas, and Residence Areas. Maximum possible diversion from these areas is estimated at -220 tonnes annually. 94 Focusing on purchasing decisions offers a centralized method by which activity areas responsible for the generation of residual plastic can be influenced a priori. This could take the shape of making take-out plastic materials prohibitively expensive for consumers, encouraging food and drink consumption "in-house" at food service outlets. Changing the nature of take-out would have substantial trickle down effects on the amount of residual plastic entering the waste stream in many other areas including office areas, common areas, and student residences. Although more intensive than promoting an already established recycling program, modifying purchasing decisions offers an alternative to waste diversion from landfill. Source reduction decreases the amount of waste generated originally, efficiently addressing waste reduction goals. 5.7.3 Composting • Residual paper and compostable food make up the largest component of the annual waste stream, at 70%, or -1300 tonnes annually. Many areas on campus could provide compostable material. Suggested areas to target would include: (a) Family Housing due to large quantities of quality compostable waste, possible areas for bin placement, and centralized facilities; (b) 1st- 4 t h Year Residences, for similar reasons; and (c) Food Service outlets, as directions for diversion can be incorporated into employee duties. In addition, bulking agents such as the bedding material produced by the Animal Care Centre could help derive a good compost mixture. The total materials that could be diverted from solely these areas are approximated at -700 tonnes annually. Decreased production of organics in the summer, particularly in the student residences and the meal plan outlets have to be considered in any operation. Lastly, given recycling capture rates, the responsibility for diversion in residence areas might benefit from enforcement of the program via hired staff. The most significant waste diversion can result from implementing a composting program at UBC, either based on campus or contracted to an outside company. Although the cost of implementing a campus wide composting program would be substantial for either alternative, the current cost to the University to landfill its waste is considerable (-$16,000.00 monthly in tipping fees alone). The possibility of reusing or selling compost produced on campus has promise for: (a) Reducing costs associated with ongoing and exhaustive soil remediation projects on degraded lands at U B C (Pers. Comm.: Dr. Bomke. U B C Soil Science Dept.; L . Ferrari. Groundskeeping, Plant Operations); or, (b) Revenues resulting from selling compost to an industrial, agricultural, or even a municipal market. The University may also wish to consider contracting the collection of organic waste to an external company, however in view of previous 95 problems with composting pilot projects considerations should include the expected longevity of the operation, the stability of the contract costs, and the desirability of paying to dispose of quality compost. 5.8 Conclusion In order to achieve a campus waste reduction goal 50% per capita diversion/reduction success by the year 2000, other initiatives besides emphasis of the recycling program are required. Not enough recyclable material is currently brought to landfdl to achieve the necessary weight diversion. Two additional possibilities have been suggested: source reduction of residual plastic, and composting of residual paper, food, and some animal bedding. Source reduction of residual plastic must be paired with intensification of the recycling program or composting to result in the necessary weight diversion. Other environmental costs should also be evaluating, such as the energy and washwater needs for durable dishware to replace disposable residual plastics. Composting would require extensive planning, management modifications, and significant capital outlays; however such an operation will readily capture the required amount of material to reach 50% capita diversion. Composting should also be evaluated for environmental concerns including odour, leachate, and carbon dioxide emissions. Given the rapid approach of the year 2000 and the substantial weight diversion required, it is suggested that the University focus on one of these two options to meet campus waste reduction targets. 96 C H A P T E R SIX : C O N C L U S I O N S A N D R E C O M M E N D A T I O N S A representative audit of the solid waste produced by University of British Columbia campus was conducted. Main objectives for the audit included capturing potential spatial and temporal variation in waste, assessing the validity of the methodology, and providing recommendations to achieve campus waste reduction goals of 50% diversion per capita. Spatial variation in waste was addressed by sampling activity areas particular to the University campus. Wherever possible, the number of users of an activity area was enumerated when waste was sampled. Statistically significant differences were found between waste-per-user amounts for different areas. Accounting for spatial variation can help to decrease the overall variation in waste. This finding advocates developing waste audit methodologies which sample waste at the point of generation instead of disposal, as sampling at the point of generation will provide more precise estimates for waste. Waste-per-user values also provided a means to address temporal variation in some activity areas. Calculating waste-per-user values decreased sample variation for all sampled food service areas, residences, and the SUB bathrooms. This finding is important as collecting information on user flow can then help quantify temporal variation in waste without infinite sampling, providing that the waste stream is not significantly affected by natural seasonal events (i.e. leaf drop in trees). Data can and does exist on user flow over time and space at U B C and possibly in other areas for which audits would prove useful. When waste amounts had a good association with the number of users in an activity area, the estimated flow of users was used to project the annual amount of waste generated by all similar activity areas. Other areas required additional information for extrapolation, such as floor space. The estimated annual waste amount was then compared to the documented amount of waste sent to landfill, and was found to differ by -18%. Expected reasons for the discrepancy included the omission of: some activity areas (i.e. Forintek, TRIUMF); special event waste (i.e. Chan Centre events); and illegally disposed construction and demolition debris. Other reasons included spurious events (i.e. "strange wastes" - such as failed composting experiments), and the possible under or overestimation of the number of users for extrapolation. At least 500 additional tonnes of waste must be diverted from landfill to meet campus goals of year 2000, 50% per capita reduction. The annual amount of waste produced by U B C in 1998 was estimated to be 2150 metric tonnes. The composition of this total was projected to be 70% compostable material, 15% 97 recyclable material, and 15% residual material. This composition changed marginally between the winter and summer terms. Directions for reducing this waste on a campus-wide basis were developed by analyzing the characteristics of projected values. Two options were recommended in order to meet campus waste reduction goals: 1. Intensification of the recycling program and source reduction of residual plastic. Focusing on recyclable glass, office fine paper, and newspapers in Family Housing, Student Residences, Common Areas, Classrooms, and the Libraries has potential for diverting -250 tonnes of recyclables from landfill. Source reduction of residual plastic from Food Service outlets has possibilities for reducing -220 tonnes of waste due to trickle-down effects to Office Areas, Common Areas, and Residence Areas. 2. Diverting material to composting has potential to reduce the landfilled waste steam by 700 tonnes by capturing food and residual paper materials from Family Housing, l s t -4 , h year Residences, and Food Service outlets, and collecting animal bedding from the Animal Care Centre. Further study should focus on obtaining precise per-user estimates for residence areas, as the extrapolation to other residences in this study was limited to areas of similar size and resident populations. Additionally, larger samples for Office Areas in terms of weight would help substantiate present estimates, as the total annual weight generated by Office Areas was estimated to be very large. Further investigation into the waste generated by Outdoor Bins should consider the potentially significant difference in waste due to bin location. In this case a stratified random sampling protocol might improve on random sampling strategy that was utilized. Pinpointing the exact amount of waste that is generated by a Food Service Outlet would require that studies account for the radii of influence a food service outlet has on surrounding activities. In this study the influence of Food Service Outlets on the campus waste stream was not well-represented by the waste that was collected in these areas, as much waste leaves these areas in take-out form. Overall recommendations for future research into waste management would include the integration of the waste audit data that was collected with campus planning data. 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Reinke (1995) "Modeling Spatial Variation in the Demand for a Waste Collection Service" In Waste Management and Research (13), pp. 55-66. Proctor & Redfern Ltd., SENES Consultants Ltd. (1991) Metropolitan Toronto Solid Waste Composition Study - Solid Waste Environmental Assessment Plan (SWEAP) Metro Works Department, Toronto. Qdais H. , M . Hamoda, J. Newham (1997) "Analysis of Residential Solid Waste at Generation 100 Sites" In Waste Management and Research (15), pp. 395-406. Rathje(1993) Rubbish! The Archaeology of Garbage Harper-Collins, New York. Reid, Crowther & Partners Ltd. Bissel & Assoc. SCS Engineers. (1980) Hazardous Wastes in Northern and Western Canada: Assessment of Need Volume lToronto, Ontario. Resource Integration Systems, Ltd. (1991) Building a Sustainable Community. An Integrated Solid Waste Management Programme Planning Element RIS, Vancouver. Robinson, C. (1993) "Accounting, Auditing, and Ethics Lessons from a Waste Audit" Presented at Canadian Academic Accounting Association Education Conference Montreal; October 1-3 1993. Sachs, L. (1984) Applied Statistics Springer-Verlag, New York. SCS Engineers. (1982) Systems Technology Corporation. "Small Scale Resource Recovery Programs"In Pollution Technology Review, No. 89 Noyes Data Corporation, New Jersey. Skaljin, M.(1995) McMaster University Waste Audit McMaster University, Hamilton. Snider, M . , G. Matar, A . Gibson (1995) "Campus Source Reduction" In Resource Recycling (8), pp.75-78. Skordilis A . (1985) "Household Waste Analysis in the Greater Athens Region using Generally Acceptable Statistical Methods" In Sorting of Household Waste and Thermal Treatment of Waste Elsevier Applied Science Publishers, London, pp. 49-71. U B C Waste Reduction Program (1996) 1995-1996 Annual Report Department of Plant Operations, University of British Columbia. U B C Waste Reduction Program (1998) 1997-1998 Annual Report Department of Plant Operations, University of British Columbia. Williams H.E., R.C. Haines (1985) "Sampling and Analysis of Household Waste: Contract Coordination Activity" In Sorting of Household Waste and Thermal Treatment of Waste Elsevier Applied Science Publishers, London, pp. 36-48. Yost P., J. Halstead (1996) " A Methodology for Quantifying the Volume of Construction Waste" In Waste Management and Research (14), pp. 453-461. Y u C - C , V . MacLaren (1995) "Comparison of Two Waste Stream Quantification and Characterization Methodologies" In Waste Management and Research (13), pp. 343-361. Zar, J.H.(1984) Biostatistical Analysis. Prentice-Hall. Englewood Cliffs, New Jersey, pp. 718. 101 W O R L D W I D E W E B R E F E R E N C E S G V R D Strategic Planning Department, 1997 Webpage. Ministry of Agriculture, Food and Rural Affairs. Ontario FactSheet on Livestock Manure. Ministry of Environment, Lands, and Parks. 1998 Source G V R D Solid Waste Operation. Ohio State University Extension. Department of Horticulture and Crop Science - Livestock Manure Management and Wastewater. U B C Sustainability Office, 1998 Webpage. 102 P E R S O N A L C O M M U N I C A T I O N Aamodt, Ron. Research Technician. Environmental Service Faculty, U B C Bomke, Dr. Art. Acting Head. Soil Science Department, U B C Branch, Alvia. Manager. Scheduling and Administration, U B C Cathcart, Edward. Manager. South Campus Farm, U B C Chase, Dr. John. Director. Budget & Planning, U B C De Bruijn, Elsie. Adminstrative Suppport Librarian. Woodward Library, U B C Erickson, Wally. Waste Coordinator. Plant Operation, Simon Fraser University Ferrari, Lee. Grounds Supervisor. Plant Operations, U B C Graham, Don. Manager. Purchasing Department, U B C Gray, Gordon. Lab Techician. Animal Care Centre, U B C Holzmann, Karen. Lab Technician. Animal Care Centre, U B C Inouye, Akahito. Space Use Analyst. Campus Planning & Development, U B C Jeffrey, Mark. Consultant. Ministry of Environment, Victoria Jia, Peter. Space Use Analyst. Campus Planning & Development, U B C Kaplan, David. Supervisor. Horticulture Greenhouse, U B C Keate, Heather. Associate University Librarian. Public Services, U B C Kozak, Dr. Antal. Professor of Statistics/Assoc. Dean Forestry. Forest Resource Management, UBC Holzmann, Karen. Lab Technician. Animal Care Centre, U B C Love, Dr. Arthur. Director. Animal Care Centre, U B C Martin, John. Grounds Labourer. Plant Operations, U B C Martin, Shirley. Enquiries Clerk. Purchasing Department, U B C Matias, Debbi-Joe. Financial Director. Bookstore, U B C McGhie , Randy. Lab Technician, Animal Care Centre, U B C Metras, John. Associate Director. Plant Operations, U B C 103 Mol, Louise. Scheduling Coordinator. Registrar's Office, U B C Nell, Pamela. Recycling Coordinator. G V R D Solid Waste Management Programs Newenhouse, Sonya. Waste Management Consultant, Wisconsin, U.S.A. O'Donnell, Mary-Jean. Waste Management Consultant, formerly U B C Waste Management Coordinator Reeve, Tim. Waste Management Consultant. Gartner Lee Ltd. Vancouver Rollo, Ron. Supervisor. Botanical Gardens Nursery, UBC Seto, Howard. Technician. Environmental Programs, U B C Simpson, Rosemary. Building Services Manager. Housing & Conferences, U B C Toogoode, Nancy. Food & Beverage Manager, former. SUB A M S Restaurants, U B C Underwood, Chris. Engineer. Burns Bog Landfill, City of Vancouver Vandenberg, Shelley. U B C Waste Management Coordinator. Plant Operations, U B C Vaz, Judy. Director. Food Services, U B C Walker, Sharon. Warehouse Manager. Bookstore, U B C 104 APPENDIX A : ACTIVITY AREA DEFINITIONS (OS A P P E N D I X A : A C T I V I T Y A R E A DEFINITIONS Activity Space Animal/Greenhouse Assembly/Exhibit 1 " Athletics/ Recreation* Audio Visual Labs* Bookstore Central Services* Classrooms Clinical & Health Services* Common Use/ Student Activity Food Service Areas Laboratories Library Def in i t ion Animal/Plant facilities in overall support of teaching or research and not normally integrated with or under the control of academic departments. A room or group of room intended to serve the general university population and to be used for dramatic, musical, or devotional activities, or for exhibition purposes, and rooms directly serving these facilities. All indoor areas used by students, staff, or the public for athletic activities, either of recreational or competitive purposes, and rooms directly serving these facilities A room or group of rooms used in the production, distribution and storage of non-print instructional media, and rooms directly serving these facilities, providing campus-wide rather than exclusively departmental services. A room or group of rooms used to sell products or services, exclusively or primarily for the university population, and rooms directly serving these facilities. A room or group of rooms used to provide campus-wide services for both academic and non-academic sections of the university, and rooms directly serving these facilities. A room primarily used for scheduled teaching purposes which does not require special equipment. A room or group of rooms intended to supply health services primarily to the general university population, and rooms directly serving these physical and wellness service facilities. OR Space owned and operated by the university that is used in direct support of instruction, research, and service for clinical health science disciplines. A room or group of rooms accessible to the general university population intended for recreation, rest, or relaxation, and rooms directly serving these facilities. A room or group of rooms used for preparing or eating food or which directly serve these facilities including central facilities located in residences and faculty clubs, whether operated by the university or an external company. A room used for instruction of undergraduate students, which requires special purpose equipment or is so arranged that use is restricted to a particular field of study, and rooms directly serving these facilities. Activity in these facilities would include student participation, experiments, observation, or practice in a field of study. OR A room used for laboratory applications, research or training in research methodology which requires special-purpose equipment for staff or graduate student experimentation, observation, preparation, or service and other rooms directly serving these facilities. A room or group of rooms used for the acquisition, processing, storage, circulation, study, or use of books, periodicals, manuscripts or other media of published information generally under the administration of the university 1 0 5 A Plant Maintenance* Residences Other Space library system and accessible to the university population at large, and rooms directly serving these facilities. A room usually assigned to one or more individuals on a permanent basis, containing office-type equipment and used by faculty, departmental administrative and support staff, and students, or a room directly serving these facilities. OR A room usually assigned to one or more individuals on a permanent basis, containing office-type equipment and used by administrative and support staff, • and students, or a room directly serving these facilities. I Space associated with the operation and maintenance of university buildings, • grounds, vehicles, and other elements of the physical plant. | A room or rooms used to accommodate one or more individual and the I ancillary areas in direct support of such rooms. j Any room or category not included hereto. I In the audit, this included Bathrooms and Outdoor Cans. Not audited (Adapted from Campus Planning & Development, 1992) 106 APPENDIX B : PRIVATE (EXTERNAL) EXTERNAL ENTERPRISES AT UBC (NOT AUDITED) 101 APPENDIX B : PRIVATE (EXTERNAL) ENTERPRISES AT UBC (NOT AUDITED) Alumni Association Association of Administration and Professional Staff (AAPS) B C Research Inc. BC Transit (Bus Loop) Biomedical Research Centre (BRC) Carey Hall CUPE 116 Daikin US Comtec Lab Discovery Parks (South Campus/Hampton Place) Environment Canada Faculty Association Faculty Club Faculty Women's Club Forest Research Institute of Canada (FERIC) Forintek Graduate Student Centre (GSC) Hillel House INPFC G. McGavin Building National Defense National Research Council Nordion Norman McKenzie House Pan Hellenic House Paprican Point Grey Research Silviculture Institute St. Andrew's St. Mark's Super Power Technologies Inc. Tri-University Meson Facility (TRIUMF) TRIUMF House University Hospital Vancouver School of Theology (VST) Ward Laboratory 107 A A P P E N D I X C : W A S T E C A T E G O R I E S 102 o 00 * c -a CD - 1 CO r+ 5' 3 3 3 " fD T J C cr o o 3 cn 5. n ft cn c 65 fD cn oJ c Co 2 2 «" a O Co ft — i o e cn ^ 2 IB cn CO cr T3 ft o o O O CL o o o O O P a a f t a -g * u o g O 5 a- — E2. 3 r T J o o J f 3 *•<. cn — o O 3 T J O -a o_ >5 fD r^  3 " fD 3 fD a. a , 3 3 O 3 5" C L fC a. 3" o S3" f t a a 2. 55' 3 T J o cn co c r 00 WJ P o cr o X fD cr ^ 3 fD f t fD cr o 3 T o T J o fD o o co cr co CA ft O a I ST T ) co T J ft fD O 2_ Co cr 70 fD O co c r Q 1 ° £ a. 3 m sr cyi i ^ 3 o T J i-i O T J IT 3 ft DQ ST a <D 3 cn 3' T3 o_ v: fD r - f ST fD 3 fD ft o r g T J 3 sr O"3 3- ft 2" ft 3 C3 ft R ft g T3 a. O Z ft co ft T J 3 41 3 ft ft "i T ) ft "1 g 2 o S ST P s fD T J r< g tz) T J o -2 o s o P o T3 0Q = 3> £ C/3 o c a S m o era o M 3 CO 3' ft ?r o !-( 3 i-l g-OO £ 2. o 3 cn fD cn cr § C L 0Q 3 CO OQ Co N 5' jn ft % ft 6" T3 f t cn o o 3 T3 O cn r+ § cr fD o o C L 73 fD cn C L 3 E L T J CO T J f t o ft o T J O I ft) n o o C L o c:. c, 3-3 3 CO CO CO cn rf ft o C£ C L fD C L ffi Q L < O 0Q 3 3 T JT J T J 2 Q fD 3 n ' T3 Co ft 5 CL O o - + i ft ft f t CL T3 fD _ C L o S ° 8 C L C L tV5 CL 3 O o C L o sr T J CO ft C L a. 5 0Q O S3* rS o ^ T J Q cn ^ t?. T J co H T ) T co 3 T J 3 ft R - i cn B n o 3 K ft S+s o o co 3 r+ ft o C L ft sr a, 3 cn ft i i . ft cn o c T J T J | f t O « 'S iZ- ft cr ft* cn cn O 3 ft ft co VI ft ft T J cn O 3 ft 3 CO 3 0Q 0Q a" n P *-3^  no « cn et ft P cr P 0Q VI o >5 H d 05 5 ft r 05 ft > 05 H W ft > H W ft o M 05 A P P E N D I X D : A N I M A L C A R E A R E A S o VO < m IB X CO ^ Q . CD Q . cr ST o CO CO co' 2 ? » 3 o £ CD ? U ~ ^ CO 2 cB CD CD " S 3J OJ To To M O O cn o o w w -» O l\> JO O W l» CQ CQ CQ 3 11 3 3 O "ID o o SJ. 2! CO 3T r+ CD CD <J> CO % 2 o o O --4 O Ol O O ^| O) o CO _k 3 3 g L>. iu iu o ^ g *. S g N 5 ° -i cn to oo ^ bo > w cn . . -N CO cu cu <9. > a < CU CQ >< • CQ (/> S 5 <8. o ~ o cu co CD O- W 3 CD CU CA 7J 73 "0 3 =• aj.flJ.c5o 5 « M J ff CO JT 7 3 K o o ~ * • Ol o o 1 I • W l\» -» O M JO o cn cn CQ (Q CQ o O o o o Ol 00 o b b b b b CD a> O) a> a> CD CD CD CD CD CO CO 00 00 00 00 00 b 00 o o o _L cn ho CO 00 00 00 cn CD 00 b b b b b b Ol cn cn cn a> CD CD CD CD CO CO 00 00 00 00 00 00 o 2 S. 5 C» J 2 Ol O ^ O 00 M Ol CD CD Ol CD O o in CO b Ol CD 00 o M o o o CL o 3 -CD CD o P °> K oi S 0B !° cn -± co co <P io 2 o OJ S co 5 3 cu 3 -CO •o CD o 5' ca CA 0) 3 •a. CD a. _ cn ca BJ & I *< CD _ 00 CO £> I t I< <D _ CO CQ SJ I t 3 £ co > cu 3 m 1 » > 5- ? 3. m 3 O SL > o S 2 3 CD > 0 r-1 I co —I m CD CD ANIMAL AREAS - ANNUAL EXTRAPOLATED WASTE (t) Sample Calculation (((58cats)*(175g/cat/day)*(365 days/yr))/100,000g/t)=3.71 cat waste/yr Facility Species # Waste +/- Per Bedding Total +/- 3 StDev Added (t/yr) Facility Added (t/yr) (t/yr) Animal Care Centre Cat 58 3.7 n.a. 34 55 90 22 Monkey 9 2.0 n.a. Mouse 450 0.34 0.44 Pig 20 20 17 Rabbit 108 5.6 7.0 Rat 850 2.89 0.38 Sheep 2 0.2 n.a. Acute Care Facility n.a. n.a. n.a. n.a. n.a. 34 34 n.a. Anatomy Mouse 1500 1.1 1.5 6.2 4.2 10.4 1.9 Rat 1500 5.09 0.68 Biomedical Research Mouse 3000 2.3 3.0 2.3 11.1 13.4 3.6 Dairy Farm n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. Fam. & Nut. Science Rat 90 0.084 0.011 0.084 0.019 0.102 0.012 Food Science Mouse 64 0.048 0.063 0.048 0.210 0.258 0.075 Medical Genetics n.a. n.a. n.a. n.a. n.a. 9.7 9.7 n.a. Microbiology Mouse 3000 2.3 3.0 2.3 15.8 18.1 3.6 Neuroscience n.a. n.a. n.a. n.a. n.a. 0.74 0.74 n.a. Oral Biology n.a. n.a. n.a. n.a. n.a. 2.8 2.8 n.a. Pathology Gerbil 72 0.054 0.071 0.9 4.5 5.4 2.2 Hamster 8 0.0602 0.0079 Rat 20 0.0679 0.0090 Mouse 900 0.68 0.89 Pharm. Science Cat 4 0.26 0.00 2.73 9.15 11.88 0.39 Rat 730 2.48 0.33 Pharmacalogy Guinea Pig 12 0.18 0.18 0.7 3.7 4.4 1.6 Mouse 27 0.020 0.027 Rabbit 8 0.42 0.52 Rat 27 0.092 0.012 Psychology n.a. n.a. n.a. n.a. n.a. 8.3 8.3 n.a. Physiology Mouse 200 0.15 0.20 0.8 5.7 6.5 1.1 Rat 200 0.679 0.090 Zoology Cormorant 17 0.0 n.a. 4.3 3.3 7.6 1.9 Guinea Pig 24 0.4 n.a. Mouse 750 0.56 0.74 Rat 200 0.679 0.090 Seabird 20 0.5 n.a. Small Bird 68 1.2 n.a. r Squirrel 100 0.9 n.a. Totals Animal Waste 55 Bedding Waste 168 Total Waste 223 +/- t/yr 25 (3 St.Dev.) Values for uncommon animals were based on 5% body weight wastage values. Body weight values were derived in consultation with Animal Care Technicians. Values for facilities were summed based on the individual animal standard deviations (Sachs, 1984). Values were rounded based on original values to two significant digits for the error terms, and corresponding precision for the averages and measurements. 110 A P P E N D I X E : G R E E N H O U S E A R E A S /// o o o p+ rt> B JB JU 71 X O n> CD o (A O 3 s "S I 3 0) CD * CT 3 CD 01 _ CO O CO (O ho —* o o o •• CO N> O 00 O g o o o 00 o o NJ o Ol -» Ol o CO.. o o o ST CO ho (n o o o -J o o o 4* o 4* OS o o Ol o -o oo o 0 > rt- CD CD 01 W W 01 01 3 3 TO "O CD CD C Q . CO 5 CD S CO CD o m m z X o c (A m • w > m D i w D > < 0> CD o 3 —* o ST Q. 01 CU CO CD cn S CD S o •o CD e. CD CL CD CL CD 01 ^ B ~ CO CD O T3 CD cr CD CO CD o 00 b ° 2. o = 2. CD •D o 3 CL g. co' w ED oi CD 01 01 3. 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Residual Paper 2.09 1.75 2 32 6.16 2.05 0.29 0 71 Compostable Food 0.89 1.39 0.86 3.14 1.05 0.30 0.73 Office Fine Paper 1.93 2.02 2 59 6.55 2.18 0 36 0 89 Newspaper 0.36 0.05 0.02 0.43 0.14 0.19 0.47 occ 0.48 0.84 0.30 1.61 0.54 0 28 0 69 Recyclable Glass 0.46 0.43 0.61 1.50 0.50 0.10 0.25 Recyclable Plastic 0.00 0.11 0.32 0 43 0.14 0 16 0 40 Pop Cans 0.02 0.11 0.00 0.14 0.05 0.06 0.15 Residual Plastic 4.0 2.3 ,5.9 12.1 4.0 1.8 4.6 Metal 0.50 0.32 0.00 0.82 0.27 0.25 0.63 Wood 0.00 • 0.00 0 00 0.00 0.00 0 00 0 00 Misc 0.1 0.5 1.4 2.0 0.7 0.7 1.7 Totals 10.8 9.7 14.4 34.9 11.6 2.4 6.0 Total Compostable Material 2.98 3.14 3.18 . 9.30 3.10 0.11 0 27 Total Recyclable Material 3.25 3.57 3.84 10.66 3.55 0.30 0 73 Total Residual Material 4.5 3.0 7.3 14.9 5.0 2.2 5.4 BOOKSTORE - TOTAL ANNUAL GENERATION Sample Calculation (((2.05 kg Residual Paper/day)*294 days/year)/1000 kg/t)=0.60 t ResPaper/yr Category Avg. Clnt. CVar (%) t/year +/- t/yr Max. Min. Residual Paper 2 05 0.71 14 0.60 0.21 0.81 0 39 Compostable Food 1.05 0.73 28 0.31 0.22 0.52 0.09 Office Fine Paper 2.18 0.89 16 0.64 0.26 0.90 0.38 Newspaper 0.14 0.47 130 0.04 0.14 0.18 0.00 occ 0.54 0.69 52 0.16 0.20 0 36 0.00 Recyclable Glass 0.50 0.25 20 0.147 0.072 0.219 0.075 Recyclable Plastic: 0.14 0.40 110 0.04 0.12 0 16 0 00 Pop Cans 0.05 0.15 130 0.013 0.044 0.057 0.000 Residual Plastic 4.0 4.6 46 1.3 2 5 00 Metal 0.27 0.63 93 0.08 0.18 0.27 0.00 Wood 0 00 0.00 0 0.00 0.00 0.00 0 00 Misc 0.7 1.7 100 0.19 0.50 0.69 0.00 Totals 11.6 6.0 21 3.4 1.8 5.2 1.6 Total Compostable Material ...... 3 1 Q 0.27 3 0.910 0.079 0.989 0 832 Total Recyclable Material 3.55 0.73 8 1.04 0.22 1.26 0 83 Total Residual Material 5.0 5.4 44 1.5 1.6 3.1 00 Values were rounded to correspond with two sig. digits for error terms, based on original values. 113 A A P P E N D I X G : C L A S S R O O M S o o o £f ET ST 7J JJ O CD CD O CO O 3 f l l ,. 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(£> CO CO jvj P> w K r* N P b o ^ ho Ol o o ST in ro u b o o D > C CD CD 01 co co 01 01 3 3 •a -o CD CD a. a. > < ca O O > 70 m > co i co > m o I co H m o > COMMON AREAS - WASTE PER CAPITA DATA (g) Number of Users 601 518 614 1733 (total) Sample 1 Sample 2 Sample 3 Avg. StDev C Int. CVar (%) Residual Paper 23.3 28.3 24.8 25.5 2.6 64 10 Compostable Food 24 27 19 23 4 10 17 Office Fine Paper 1.2 0 1 0.9 07 1.7 74 Newspaper 4.4 1.3 3.1 2.9 1.6 3.9 53 occ 1.0 0.0 0.3 0.4 0 5 1.3 123 Recyclable Glass 4.7 4.6 3.7 4.3 0.6 1.4 13 Recyclable Plastic 0.83 0.35 0.41 0.53 0 26 0.65 50 Pop Cans 0.76 0.44 0.33 0.51 0.22 0.55 43 Residual Plastic 8.70 9.13 9.33 9.05 0 32 0.80 4 Metal 1.5 1.1 2.5 1.7 0.7 1.8 42 Total Wood 0.8 1.4 1.8 1.3 0 5 1.2 37 Misc. 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 71 75 66 71 5 12 7 Total Compostable:Material : 48 .57 46 50 6 15 12 Total Recyclable Material 13.1 7.8 8.0 9.6 3.0 7.5 31 Total Residual Material 10.2 10.3 11.8 10.8 0.9 2.3 9 As variation was greater in the per user data, alternative extrapolation was required. New calculations were based on estimated usage of floor space and differences in registration. Other common areas were assumed to be used 1/2 as much as the SUB common areas. Values have been rounded to correspond with two significant figures for the error terms, based on original values. 117 < 01 CD 8 « =! o CD m ' co S 0 i 1 f =• sr ? -• s § CO • CQ* CD 3 CT 01 CO CD Q . TJ TJ g CD CD 3 CO O T J c 2. co a n> ST — 2 «x 3 * » I 3 3 CD D) sr ft) 3 CD o> O l g N> CD ~ IS): O l : * bo to to ho bo oi m m m to bo to Ol CO CO CO CO CO CO Ol <5 o o> o to co Ol O l ^ CO —* bo '-J co *k CO a> o sr CO 4*. in m • o o hO to o CO CO to O l o to oo hO 00 o 01 ::: CD CQ O •2 •5? > S CQ CQ 3 to s 0) •< 3 ° I 3 •S 5 -—- t-* CD p . 00 o Ol CO 3- ~J to r f Ol to Z z c c tl) 3 3 CT CT c CD CD oo -1 -1 o o 70 TJ CD CD CQ CQ > CO' co' CO c •S 3 ~~ 3 CD —i H CD i 3 c o1 T J 01 3 3 CD CD 3 -t» -tv Ol Ol •0. CO o C O O CO = 01 <-> <J c S» 3 3. 3 <5 "SS Ol Q . g -01 •< co co < oi c S 3 3 a •a g CD CD CD " \ 0 Z, CD 1 3 3 oi 5' 3 o o Ol O 00 Ol o -tv b -4 -J CO Ol o CQ CO 7T CQ 3 i to 3 0) S 2 , 01 CD ;< —i Reg.)/( Ol "S Reg.)/( -J a. 0) Ol 4» CO •< Ol o CO CO 3 ho to hO o CO Win o o c ro CD "S. TJ n CD - j o CO CO b -0 * -4 7T Ol CQ '— 3 to o 5' < CO o al 0) a cg_ —i 01 CD * 4» 3 Ol 4» CO CO -* Ol 0) 3 •< CO hO > '1000 ICam CQ TJ C CO II II Ol O o 4». o CO o 5' 7T CQ CO ex c 0) 3 *< 3 CD H CD it* cg_ a! 01 >< TJ CD CO CL c SL T J 0) 13 > > < < CO CO 1 1 CD j? =" 3 M tVJ s> » 3 3 CD S Q. CD CQ' CD 3: => Q. CO > CD TJ CD CO TJ CD o CQ -ft co' O at Si 3 a 0 to 5 S CO CD "* Q . o > "* < D CQ 0) •< TJ m CD O a? ? 3 H TJ CD S 3 co TJ O —h D 01 >< co o 3 3 CD > CD I. l < CD o u o c > TJ m > CO o 3 in O H > > Z Z c > I— a m z m 5 A P P E N D I X I : F O O D S E R V I C E O U T L E T S FOOD SERVICES COFFEE STYLE - SAMPLED WASTE DATA (kg) Area Sampled: The Barn Date Sampled: 02.03.98 05.03.98 06.03.98 Total Avg. StDev C. Int. CVar (%) Residual Paper 87 10.7 10.2 29 5 9.8 1.0 2.5 10 Compostable Food 16.7 21.1 16.5 54.3 18.1 2.6 6.5 14 Office Fine Paper 0 041 0.000 0 068 0.109 , 0 036 0.034 0 085 94 Newspaper 0.1 1.9 0.3 2.2 0.7 1.0 2.5 140 OCC i i l S l l - i l i i : ! 0.0 0.0 0.4 0.6 1.5 170 Recyclable Glass 2 11 1 15 5 6 14 110 Recyclable Plastic 1.3 0.0 0.7 2 0 0.7 • 0.7 1.7 100 Pop Cans 0.07 0.34 0.07 0.48 0.16 0.16 0.39 99 Residual Plastic 9.4 9.7 83 27.4 9.1 0.7 1.8 8 Metal 0.2 0.0 1.0 1.3 0.4 0.6 1.4 130 Wood 0.00 0.23 0.23 0.45 0 15 0 13 0.33 87 Misc 0.0 3.2 0.0 3.2 1.1 1.9 4.6 170 Totals 40 58 39 137 46 11 27 24 Ttl. Compostable Material 25.4 32.0 26.9 84.3 28.1 3.5 8.6 12 Total Recyclable Material 5 13 . : v 2. 20 7 6 14 86 Total Residual Material 9.6 12.9 9.3 31.9 10.6 2 0 4.9 19 FOOD SERVICE COFFEE STYLE - WASTE PER CAPITA DATA (g) Number of Users 895 995 932 2822 (total) Category Sample 1 Sample 2 Sample 3 Total Avg. StDev C. Int. CVar (%) Residual Paper 9.7 10.7 10.9 31.3 10.4 0.6 1.6 6 Compostable Food 18.6 21.2 17.8 57.6 19.2 1.8 4.5 9 Office Fine Paper 0.046 0.000 0 073 0.119 0.040 0.037 0 092 93 Newspaper 0.1 1.9 0.3 2.2 0.7 1.0 2.5 130 OCC 1 2 00 0.0 1.2 0.4 0.7 1.7 170 Recyclable Glass 2 11 1 15 5 5 14 110 Recyclable Plastic 0.0 0.7 2.2 0.7 07 1.8 100 Pop Cans 0.08 0.34 0.07 0.49 0.16 0.15 0.38 95 Residual Plastic 105 9.7 8.9 29.2 9.7 0.8 2.0 8 Metal 0.2 0.0 1.1 1.4 0.5 0.6 1.5 130 Total Wood 0.00 0.23 0.24 0.47 0.16 0.14 0.34 87 Misc. 0.0 3.2 0.0 3.2 1.1 1.9 4.7 170 Totals 44 59 41 144 48 9 23 19 Ttl. Compostable Material 28 3 32.2 28.9 89.4 29.8 ::::2Nr|::; 5.1 7 Total Recyclable Material 5 13 3 21 7 6 14 81 Total Residual Material 10.8 13.0 10.0 33.7 11.2 1.5 3.8 14 Values have been rounded to correspond with two significant digits for the error, based on original values. 119 A ! " CD" NJ O cn n> ST ° i ° O 3 Q. OJ ST CO CD cn • » u Q. m 3 CD * * 8 I I & •q » <S -CD j- CD & Q . 5 a a S «. -a ® - - co -a -• CD c -» K o CD o> ""• T J < o> a> S 0 ) o 0 ) 3 s 0 ) cn CD T J Q . cn CD O CD 3 . CQ CD CD ^- 0) CD — 0) °-Q. Q) 3 CD CB •o 0) CO O C Q CD I f < < 0 ) C Q •2 CD 3-O Q. CO CD 3 0 O tt £ £ o TJ TJ 2 CD CD 3 CO O T J 1 1 8 a & I' 3 <D » 1 3 a =• If 5 DJ 3 CD £L 5" C O C D . . . U ^ CD co '*> b M k O ) A "Nl CO N ) . C O U l C O 0 0 C O 0 5 0 0 — ^ — * C O Ul N J N J C7> C O o sr C O 2 co a 0) CD C Q O •3 C Q C O > C D < 3 C Q CD t S 3 £ CD < •5 * "5 3 x TJ D J 5 3 C D 5! s | c c co cn CD CD CO -L C D C D C O C O C O C O co 0) 3 CD o u o c O O D CO m 1 o m co o O •n u m m co « » m TJ TJ CD CD § 5- c =• cn cn 3. CD CD N J O J o o N J C O C O Ul N J cn C CO 3 ° C Q <e. i s ! T J C D cn T J 0 ) T J CD CO TJ CD CD o 5! > z z c > r-o m z m FOOD SERVICES RESTAURANT STYLE - SAMPLED WASTE DATA (kg) Area Sampled: Yum Yum's Date Sampled: 02/ 03.03.98 05.03.98 Total Avg. StDev C. Int. CVar (%) Residual Paper 27 12 39 19 10 94 54 Compostable Food 70 30 90 50 30 250 60 Office Fine Paper 0.3 0.0 0.3 02 02 1.9 140 Newspaper 2.3 1.8 4.0 2.0 0.4 3.2 18 OCC 0 2 2 1 11 140 Recyclable Glass 2.0 1.7 3.7 1.8 0.2 1.4 8 Recyclable Plastic 0.9 0.0 0.9 0.4 06 5.4 140 Pop Cans 0.6 0.3 0.8 0.4 0.2 1.9 51 Residual Plastic 16 6 22 11 7 59 61 Metal 1.9 1.1 3.0 1.5 0.6 5.5 41 Wood 0.8 2.7 0.8 6.8 56 Cloth 0.3 0.0 0.3 0.2 0.2 2.2 140 Totals 120 60 180 90 50 430 55 Ttl. Compostable Material 100 40 140. 70 : 42 360 61 Total Recyclable Material 5.9 55 11.4 5.7 0.3 3.0 6 Total Residual Material 18 7 25 12 7 65 58 FOOD SERVICES RESTAURANT STYLE - WASTE PER USER DATA (g) Number of Users 1384 670 2054 (total) Category Sample 1 Sample 2 Total Avg. StDev C. Int. CVar (%) (g/user) (g/user) (g/user) (g/user) (g/user) (g/user) Residual Paper 19.3 17.8 37.2 186 1.1 9.5 6 Compostable Food 47 39 87 43 6 51 13 Office Fine Paper 0.2 0.0 0.2 0.1 0.2 ..:i.4K^; 140 Newspaper 1.6 2.6 4.3 2.1 0.7 6.4 33 OCC 0 3 3 1 . .• 2 16 140 Recyclable Glass 1.4 2.6 4.0 2.0 0.8 7.5 42 Recyclable Plastic 0.6 0.0 0:6 0.3 0.4 3.9 140 Pop Cans 0.412 0.403 0.815 0.407 0.006 0.056 2 Residual Plastic 11 9 21 10 1 12 13 Metal 1.4 1.6 3.0 1.5 0.1 1.3 10 Total Wood 1.37 1.22 2.59 1.29 0.10 0.90 8 Cloth 0.2 0.0 0.2 0.1 0.2 1.6 140 Totals 85 78 163 81 5 49 7 Ttl. Compostable Material 68 58 127 63 7 • 63 11 Total Recyclable Material 4 8 12 6 3 25 44 Total Residual Material 13 11 24 12 ....I- 1 11 11 Values have been rounded to correspond with two significant digits for the error, based on original values. 121 < "o d o o o o. o £ o CA ca' a. co' o o a. cn CD i o CD z; c/i 0) cr 01 CA 2. <a s * 53 CA o — S o 73 § CD 3 O TJ P. CA 0) 17 CD 3 01 CD ro CJ) Ol oi Ol' -:co 00 M CJl in ^ «° O E cn 4» CO it Nl O 0) CD CQ O •3 C <D 2 CQ ~- CD CO CO :'p". ' if 1 1 -l V; <a. 5-B I 2 2 2 CA CD CO c 3 ~ 3 CD 5 O *<:• . I-*-•3 SL 5 i "5 3 -i X TJ 01 £0 3 CD CD 3 -»• CO CO Ol co ro Ol CO CO CO -"• 3 3 cr cr CD CD CO 0) 3 •o CD O 0) cn cn oo 00 co co 73 CD CA 3 a. 3 2 CD „ CO $ 3 00 5L ?! TJ -o TJ Tl CD CD C C 2 2 CA CA -*• CO 00 Ol c CA CD C O § I I CD •< ~5 O . O O O P o o o o CO O •9. ¥ Ol I  2 73 CD 7) CD u CA 01 "° "2 01 CD TJ ""• CD 5' w 3. | 2 3 CD o o D CO m 1 o m 73 m co H > 5 co 3 O -1 > z c > o m z m M FOOD SERVICES MEAL PLAN - SAMPLED WASTE DATA (kg) Area Sampled: Place Vanier Kitchen Date Sampled: 04.03.98 05.03.98 06.03.98 Total Avg. StDev C Int. CVar (%) Residual Paper 21 11 49 16 5 13 33 Compostable Food 32 47 21 99 33 13 32 39 Office Fine Paper 0.00 0.71 0.18 0.89 0.30 0.37 0.91 120 Newspaper 0.0 0.8 0.8 1.6 0.5 0.5 1.1 87 occ 0.6 1.9 2.0 4.6 1.5 0.8 2.0 52 Recyclable Glass 0.0 2.8 0.7 3.4 1.1 1.4 3.6 127 Recyclable Plastic 1.4 4.3 6.8 2.3 1.8 4 5 79 Pop Cans 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Residual Plastic 21 19 10 50 .:;-jl7j :; ;:|: 6 14 34 Metal 0.8 2.2 1.2 4.2 1.4 0.7 1.7 50 Wood 0.41 0.61 0.00 1.02 0.34 0.31 0 78 92 Misc 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 73 97 51 221 74 23 57 31 Ttl. Compostable Material 49 68 32 149 50 18 46 37 Total Recyclable Material 2.0 7.3 8:0 17.3 5.8 3.3 8.2 57 Total Residual Material 22 21 11 54 18 6 14 32 FOOD SERVICES MEAL PLAN - WASTE PER CAPITA DATA (g) Number of users 2290 2249 1926 6465.0 (total) Category Sample 1 Sample 2 Sample 3 Total Avg. StDev C Int. CVar (%) Residual Paper 7.4 9.5 5.6 22.5 7.5 2.0 4 9 26 Compostable Food 14 21 11 46 15 5 12 33 Office Fine Paper 0.00 0.31 0.09 0.41 0.14 0.16 0.40 120 Newspaper 0.00 0.34 0.41 0.76 0.25 0.22 0.55 88 occ 0.3 0.9 1.1 2.2 0.7 0.4 1.0 57 Recyclable Glass 0.0 1.2 0.3 1.6 0.5 0.6 1.6 120 Recyclable Plastic 0.6 0.5 2.3 3.3 1.1 1.0 2.5 89 Pop Cans 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Residual Plastic 9.1 8.4 5.3 22.9 7.6 2.0 5.0 26 Metal 0.37 0.96 0.60 1.93 0.64 0.30 0.74 46 Total Wood 0.18 0.27 0.00 0.45 0.15: 0.14 0.34 92 Misc. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 32 43 27 102 34 8 21 25 Ttl. Compostable Material 22 30 16 68 23 7 18 31 Total Recyclable Material: 0.9 ' 3.2 4.2 8.3 2.8 1.7 4.2 62 Total Residual Material 9.5 94 5.9 24.8 8.3 2.0 5.0 24 Values have been rounded to correspond with two significant digits for the error, based on original values. 123 < CO o o CQ < co. c CD 3 * H IB £ £ J I S o § 3 CU » CO CO Q . C Q -3 o co o 3. 3 £ if CD " S 2 cn 3 CD Q  s> » S c? 2 cn co < CQ 3 BL = 3 c 5i •= CD o co cn co CD CD o a CO o TJ cn = CD CO CL CO " Z 8 o o c? CD 3 3 CD o o 0 . cn CD 2 —I —I H 0 0 a £ £ o 73 73 2 CD CD =1 cn o -a f I 8 £ o> ST I 5 tr S CD CD 3 3 p *> 8) 00 co ro .: 05 co 4^  b ro Ji ai js» "~J CO CD *. g CD ~ O CO O CD • 1-* CD CQ O •3 CQ g cn > co < : 3 CQ i ^ S 5 —' CD: cn 2 c7 •5 3 •< co C 3 TJ co £U 3 CD CD •D> rO •vl O co cu 3 •a CD a to o c CO o 3 73 CD CD ho --4 O O O O II 00 CJl 73 CD o o o co m 1 o m co m > 1-TJ 5 o > Z C > r-o m z m A P P E N D I X J : L A B O R A T O R I E S LABORATORIES - TOTAL ANNUAL GENERATION* Sample Calculation ((1.0 Box/Mth)*(10.4 Kg/Box))*(8 Mth/Winter)=83.2 Kg in Winter (((1.0 Box/Mth)*(10.4 Kg/Box))/8.3)*(4 Mth/Summer)=5.0 kg in Summer Parameters 10.4 Weight of Box (kg) 8.3 Decline in Lab Usage (Summer vs. Winter) Laboratory # Boxes/Mth Weight/Mth Winter Summer Total (kg) (kg) (kg) (kg) Archaeology 1.0 10.4 83.2 5.0 88.2 Aquatic Centre 0.0 0.0 0.0 0.0 0.0 Biology 6.0 62.4 499.2 30 1 529 3 Bio-Resource Engineering 0.7 6.9 55.5 3.3 58.8 Botany: Zoology 6 5 67.6 540.8 32 6 573 4 Chemical Engineering 1.0 10.4 83.2 5.0 88.2 Chemistry 2.0 20.8 166.4 100 176.4 Civil Engineering (Rusty Hut) 1.0 10.4 83.2 5.0 88.2 Centre for Integrated Computer Systems 0.0 0.0 0.0 0.0 0.0 Physiology (DH Copp) 3.3 34.7 277.3 16.7 294.0 Food Sciences 3.3 34.7 277.3 16.7 294 0 Forestry (McMillan&New) 0.0 0.0 0.0 0.0 0.0 Metals & Materials 1.0 10.4 83.2 5.0 88.2 Mining & Mineral Processing 1.5 15.6 124.8 7.5 132.3 Earth and Ocean Sciences 1.5 15.6 124.8 7.5 132.3 Fine Arts (Lassere) 0.0 0.0 0.0 0.0 0.0 Freidman (Anatomy) 2.0 20.8 166.4 10.0 176.4 G. Cunningham (Pharmacy) 5.0 52.0 416.0 25.1 441.1 Physics (Hebb) 0.0 0.0 0.0 0 0 00 Physics (Hennings) 1.0 10.4 83.2 5.0 88.2 Math 0.0 0.0 0.0 0 0 0.0 Electrical Engineering (Macleod) 0.0 0.0 0.0 0.0 0.0 Scarfe 0.0 0.0 0.0 0.0 0.0 (Pharmacology) Medical Sciences Blk C 2.0 20.8 166.4 10.0 176.4 Dentistry (J MacDonald) 1.5 15.6 124.8 7.5 132.3 Music 0.0 0.0 0.0 0.0 0.0 Pulp and Paper 0.5 5.2 41.6 2.5 44.1 Osborne Gym 0.0 0.0 0.0 0.0 0.0 War Memorial (Human Kinetics) 0.0 0.0 0.0 0.0 0.0 Family and Nutritional Sciences 1.0 10.4 83.2 5.0 88.2 Network For Ctrs. of Excellence (Hennings) 0.5 5.2 41.6 2.5 44.1 Old Comp Sci Building (Stats, Math) 0.0 0.0 0.0 0.0 0.0 Rehabilitation Sciences 0.0 0.0 0.0 0.0 0.0 Mechanical Engineering 0.5 5.2 41.6 2.5 44.1 Commerce and Business (Angus) 0.0 0.0 0.0 0.0 00 Zoology 0.0 0.0 0.0 0.0 0.0 Biochemistry (DH Copp) 15.6 124.8 7.5 132.3 Totals (kg) 461.1 3688.5 222.2 3910.7 Totals (t) 0.5 3.7 0.2 3.9 *AII lab waste accounted for consisted of residual paper towelling material. Values for some areas were unobtainable. Summer lab waste accounted for the decline in scheduled labs during this time (Factor of 8.3). This decline was calibrated from the Space Utilization Reports for all sessions (Jia, 1997). 125 4 A P P E N D I X K : L I B R A R I E S LIBRARY - SAMPLED WASTE DATA (kg) Area Sampled: Woodward Library Date Sampled: 23.04.98 27.04.98 28.04.98 Total Avg. StDev C. Int. CVar (%) Residual Paper 2.0 1.5 3.7 7.2 2 4 1 2 2 9 48 Compostable Food 3.4 2.5 3.9 9.8 3.3 0.7 1.7 21 Office Fine Paper 2.3 7.0 4.9 142 4 7 2.3 57 49 Newspaper 0.1 1.0 0.1 1.1 0.4 0.5 1.2 130 OCC 0.00 0.00 0.00 0 00 0.00 0 00 0 00 0 Recyclable Glass 2.0 0.4 3.0 5.3 1.8 1.3 3.2 73 Recyclable Plastic 0.50 0.23 0.40 1.13 0.38 0 14 0.34 36 Pop Cans 0.36 0.20 0.35 0.91 0.30 0.09 0.22 30 Residual Plastic 1 0 2.1 2.4 5.5 1 8 0.7 1.8 41 Metal 0.110 0.110 0.150 0.370 0.123 0.023 0.057 19 Wood 0.00 0.00 : 0.00 0.00 0.00 0.00 0.00 0 Misc 0.07 0.00 0.00 0.07 0.02 0.04 0.10 170 Totals 11.8 14.9 18.9 45.5 15.2 3.6 8.8 23 Total Compostable Material 5.4 4.0 7,6 17.0 5.7 1.8 4.5 32 Total Recyclable Material 5.2 8.7 8.7 22.7 7.6 2.0 5.0 27 Total Residual Material 1.2 , 2.2 2.6 5.9 2.0 0.7 1.8 37 LIBRARY - WASTE PER USER DATA (g) Number of Users 1305 1489 1248 4042 (total) Category Sample 1 Sample 2 Sample 3 Total Avg. StDev C. Int. CVar (%) Residual Paper 1.5 3 0 5.5 1 8 1.0 2.5 55 Compostable Food 2.6 1.7 3.1 7.4 2.5 0.7 1.8 29 Office Fine Paper 1.8 4 7 3.9 10.4 3.5 :;;!!!&;••:'. 3.7 43 Newspaper 0.07 0.64 0.08 0.79 0.26 0.33 0.81 120 OCC 0.00 0.00 0.00 0.00 0.00: 0.00 0.00 0 Recyclable Glass 1.5 0.3 2.4 4.1 1.4 1.1 2.6 77 Recyclable Plastic 0.38 ... 0.15 0.32 0.86 0.29. 0.12 0.29 41 Pop Cans 0.28 0.13 0.28 0.69 0.23 0.08 0.21 36 Residual Plastic 0.8 : ; i . i ; ; . 4 \ 1.9 4.1 1.4 06 1 5 43 Metal 0.084 0.074 0.120 0.278 0.093 0.024 0.060 26 Total Wood 0 00 0 00 0.00 0.00 0.00 0.00 0.00 0 Misc. 0.054 0.000 0.000 0.054 0.018 0.018 0.044 100 Totals 9.0 10.0 15.1 34.1 11.4 3.3 8.1 29 Total Compostable Material 4.1 2.7 6.1 12.9 4.3 1.7 4.2 40 Total Recyclable Material 4.0 5.9 7.0 16.8 5.6 37 27 Total Residual;Material 0.9 1.5 2.0 4.4 0.6 1.4 39 Values have been rounded to correspond with two significant digits for error terms, based on original data. 126 A Si o o ST ST T J o a o 2. ^ £ ° 3 CD DJ 3. S- 3 rr m f" DJ M -J Ol O CD '-J o O N J —* s 6 oo cn i» b oi CO CO CO l:w!:° "la Ko | M CO CD b b CO CO CO Ol to M O) Lio^ ii^ iito O CO N J s » <° o cn O sr 01 CO cn oo bo cn kj cn co O DJ CO o CQ > | CQ J P £ =• i? :CD i 1 — co c i3 3 CD i 3 £ rf 1 sr C 3 v< 01 A x TJ Ui 01 01 3 3 TJ CD (D CD o 0) o c 0) rf o 3 Ol . *i N J K 5 co "5 "S o cn Ol CO 3 T J T J ? , CD CD N J o O •a TJ_ CD CD £ £ 01 01 *< >< to > c cn § • •o » C JO cn CD 51 0 o -• o co Q -01 01 > 2 § I 0) Q. CQ S CD S 0 a 01 -n CD </) a 1 _ _ N J O O o co fo -~i o OJ ; J T J T J T J T J CD CD CD CD O O O O TJ_ TJ_ TJ_ TJ_ CD CD CD CD £ £ £ £ C H C cn 01 co CD cn » CQ Si co * s T J = T J S oi 3 oj 3. CD 3 CD ,— < cn 3 » a 9 3 CD CD cn ui 2 CO 00 CL Q . 0) N J CO y CO P> N) u, CO CD g CD CQ 3f CQ Q- > CL 0) 0) N J -Zl — ^ 3 o i CO 00 " * t » 2 « 3 3 CD T J D w i g CD S - 1- ° o C o o cn o 0 <g iff eg" » S N J P° *. Ol o °> *r CQ 5 <2. - S Q . o Si •*> "0 o> 0) _ T J ? ro o TJ Q. 0) CD >< N J o £ 8 o> 3. M a N J CD Ol c 51 ® CD cn i!; TJ 3 o> o 1 a •8 a CD" £5. 3 N J £ 0) >< c cn 0) CQ CD T J o> CD o o Q . S 0) 3. "If ' N J Ol Q. 0) •< c cn 0) * s 73 A a 3 3 S NJ B CO T J * • CD cn o *. TJ_ CD £ > o> — •< o 0) 3 TJ C CO 0) CQ CD _ T J II 0) s s 5 00 O 7T O CQ s a < Q) 0) << s > Tl O CO 0) f 3 CD TJ CD c co r-Q . Q~ CO C 3 3 CD If ^  3 a i 5 rf * ? 3 3 > > < < CO CO CO CO o> o> 3 3 T J TJ_ CD CD * * l l 3 =r CD sr 2 % 3 V 3 2? CD CD T J  CQ O o - oi i i o c b 8 oi - 1 •< CO » T J I s —. > c < cn CQ 0) • CQ T J 3. s * ° ? o L? 3 w o o 5' Ui TJ M 0) CD 2 = S 3 c cn 01 CQ CD o 0) cn J^i cn o —» 0) 1= 3 3 CD —I CD 3 3 0) rf <' CD o a o c 5 O > c > r-O m z m H o z A P P E N D I X L : O F F I C E A R E A S 121 OFFICE - SAMPLED WASTE DATA (kg) Area Sampled: Brock Hall Offices Date Sampled: 22.07.98 23.07.98 24.07.98 Total Avg. StDev C. Int. CVar(%) Residual Paper 2.14 1.71 2.16 6.00 2.00 0 26 0 64 13 Compostable Food 3.11 2.82 3.00 8.93 2.98 0.15 0.37 5 Office Fine Paper 0.80 0 55 0.36 1 70 0.57 0.22 0 54 38 Newspaper 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 OCC 0.00 0.00 0.00 0.00 0.00 0 00 0 00 0 Recyclable Glass 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Plastic 0.00 0.00 0.00 0.00 0.00 0.00 0 00 0 Pop Cans 0.11 0.14 0.00 0.25 0.08 0.07 0.18 88 Residual Plastic 1.73 1.18 1 71 4.61 1.54 0.31 0.77 20 Metal 0.16 0.09 0.07 0.32 0.11 0.05 0.12 45 Wood 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Misc 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 8.0 6.5 7.3 21.8 7.3 0.8 1.9 11 Total Compostable Material 5.25 4.52 5.16 14.93 : 4.98 0.40 0.98 8 Total Compostable Material 0.91 0.68 0.36 1.95 0.65 0.27 0.68 42 Total Residual Material 1.89 1.27 1.77 4.93 1 64 0.33 0.81 20 OFFICES - WASTE PER USER DATA (g) Number of Users 21 Category Sample 1 Sample 2 Sample 3 ' Total Avg. StDev C. Int. CVar (g/user) (g/user) (g/user) (g/user) (g/user) (g/user) (g/user) (%) Residual Paper 102 81 103 286 95 12 30 13 Compostable Food 148 134 143 425 142 7 18 5 Office Fine Paper 38 26 ' 17 81 27 10 26 38 Newspaper 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 OCC 0 00 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Glass 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Plastic 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Pop Cans 5.4 6.5 0.0 11.9 4.0 3.5 8.6 88 Residual Plastic 82 56 81 220 73 15 36 20 Metal 7.6 4.3 3.2 15.1 5.0 2.3 5.6 45 Total Wood 0.00 0.00 0.00 0 00 0.00 0.00 0.00 0 Misc. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 383 308 347 1039 346 37 93 11 Total Compostable Material 250 215 246 711 237 19 47 8 Total Recyclable Material 43 32 17 93 31 13 32 42 Total Residual Material 90 61 . • 84 235 78 16 39 20 Values have been rounded to correspond to two significant digits for the error terms, based on original data. 128 A to o o ST ST 70 O CD O o 3. "< -2 2. 7i » 8 ? I 3 ra 0) _ 2* If —. r» Or <D: 5' w « * co ro -j CO o cn *. o _ i ro _x cn g " co ro o *-CD o ST lit CO *. cn cn o cn o co to o O P 0) : if CQ O «3 S CQ CQ co S ! <a. o c •— CD 5 o sr 5 4 H 3 ><v 01 X 5 3 TJ 0) ~i 01 3 CD •f CD 3 8 S 8 CO CO ->J ™ ro ro S W T > C 0) O T3 O X TJ E. S O -(Q 4 ^ o w o> o> cn ^* co 0) 3 •o CD O fl)_ o c o 3 CO cn ro CQ TJ CD C cn CD O o> -J ro 00 co CO c o o o o o o 5 o Tl 31 o m o > I -> > O m z m I A P P E N D I X M : RESDDENCES 130 1st YEAR STUDENT RESIDENCES - SAMPLED WASTE DATA (kg) Area Sampled: Totem Park Date Sampled: 09.03.98 10.03.98 13.03.98 Total Avg. StDev C. Int. CVar (%) Residual Paper 11.3 15.6 14.7 41.6 139 2.3 57 17 Compostable Food 22.8 19.9 18.3 61.0 20.3 2.3 5.7 11 Office Fine Paper 5.3 4.7 1.8 11.8 3.9 1 9 4.7 48 Newspaper 5.4 2.3 2.8 10.5 3.5 1.7 4.1 47 OCC 1.7 0.3 1.9 39 1.3 0.9 2.1 66 Recyclable Glass 10.1 7.2 6.3 23.7 7.9 2.0 4.9 25 Recyclable Plastic 1.77 1.80 1 52 5.09 1.70 0.15 0 38 9 Pop Cans 0.11 0.34 0.16 0.61 0.20 0.12 0.30 59 Residual Plastic 11.0 9.4 8.2 28.6 9.5 1.4 3.5 15 Metal 2.2 0.5 2.5 5.2 1.7 1.1 2.7 63 Wood 0.00 0 00 0.00 0.00 0.00 0.00 0.00 0 Totals 72 62 58 192 64 7 17 11 Ttl Com postable Material 34.1 35.5 33.0 102.6 34.2 • r:?:;K||g:f:::::. 3.1 4 Total Recyclable Material 24 17 15 56 19 5 28 Total Residual Material 132 9.9 10.6 33.7 : 11.2 1.8 4.4 16 1ST YEAR RESIDENCES - WASTE PER CAPITA DATA (g) Number of Residents 164 (constant) Sample 1 Sample 2 Sample 3 Total Avg. StDev C. Int. CVar (%) Residual Paper 69 95 90 253 84 14 35 17 Compostable Food 139 121 112 372 124 14 35 11 Office Fine Paper 32 29 11 72 24 11 28 48 Newspaper 33 14 17 64 21 10 25 47 OCC 10 2 12 • 24 8 5 13 66 Recyclable Glass 62 44 39 144 48 12 30 25 Recyclable: Plastic 10.8 11.0 . 9.3 31.0 10.3 0.9 2.3 9 Pop Cans 0.7 2.1 1.0 3.7 1.2 0.7 1.8 59 Residual Plastic 67 57 , 50 58 9 21 15 Metal 14 3 15 31 10 7 16 63 Wood 0.00 0.00 0.00 0.00 0 00 0.00 0 00 0 Totals 440 380 360 1170 390 40 110 11 Ttl Compostable Material 208 216 201 626 209 8 19 4 Total Recyclable Material 149 102 89 339 113 31 78 28 Total Residual Material 81 60 65 206 69 27 16 As no significant difference was found in the total waste per user values for all sampled residences, the average of all residences was used for extrapolation. Values were rounded to correspond with two significant digits for error terms, based on original data values. 130 A < O 01 o lue nfe cn en CD O CD CD < 3 cn C 3 O Q. CD cn C L CD O TJ O cB O cn CD res 3 TJ O o 00 Q. cn s c ith two mmer < cn| CO 3 at —»i o' o' CD 3 3 O CL in he CD 3- cn C L CD CD 3 3 8 CD 3 cn CO CD ba 00 cn CD O. O 3 o in 3* 9L Q. SJ. 5T < c CD cn H —I H O O CS r+ r> _ B a n TJ TJ | CD CD 5 £• -8 o i - 2. a S oi OJ E E S CD CD % 3 3 CD Q) Q) fij (D CD 00 9° — * N O _ • . N O 00 M U ^ 00 . ° w K CD 00 00 - j . No (D CO Ol !» s u 00 0 1 CO —i CO o CA CO Ol Ol -4 CO NO Ol M o Nl O 01 i-r CD CQ O •2 1* X CD ca in i'Zr '-.if eg. O c • cn ^ CD 3-"5 3 01 T x 01 S 3 CD CD 3 CO ->• HI IO CD M U CO o J> cn N O oo o ro CD N O O D TJ O 01 CD 3 »< cn <? » a! 3 O <" 3 o> IT < CD 3 Cn _ < CD" CO £Z 3 3 CD TJ o D TJ o 01 CD 3 •< C A 3" C A C L renc 5' ents/ C D 3 o Visi CD DO * < o H 5' C A o H CD o To 3 tern CD TJ tern 3 01 TJ 01 Pai H > 3 5" Sui 3 3 CD CD TJ CD Tl o> TJ CD O o CD C A + CO Ol < O o C cn CD o o o o o o Ol 00 CO £Z 3 3 CD CO 01 3 •o CD O tu o c 01 5' 3 Ol Ol 7? 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ST CD CD - i - i . iii' B> 3 s -v iK>- : J V Ol CO CJl 1 0 1 0 - > 0 0 0 0 O CD -O l 0 0 b b) "-i oi ° co bo cji 0 1 S o CO CJl CO O ; O o CO u O l CJl CO -4 ro 4> o tu ff CQ O •3 C <D <D C Q — CD CQ co 5 P — 3 •5 i "5 3 tu 3. x TJ CO _ 3 CD CD a ro 0 0 J -to o o 3 CD" s Z3 O CD < D tu >< CD CD CO 3 CQ 3 CD tu ^ C O O 3 i s 3 CD w c 3 3 CD co 01 3 CD o o c Q. 01 *< 73 CD CO. TJ CO - 4 0 0 -4 o 01 CQ CD O o 3 C cn CD o o o "o o o II CO CO . — . . — . oS "cji -J Ol CO 4*. CO CJl CQ CQ C cn CO CD r/d ro 01 >< to 71 Q. CD 01 cn ys/ idu tu 3' TJ 01 CD TJ CD * O o CJI o 7T CQ O 01 CQ to CD 7 ^ C cn CO CD .-»- <a 3 ay) 3 CD . o o o CQ 7T CQ 0 0 - 4 CO CO 7T CQ 71 CD cn TJ 01 TJ CD CL 01 •< D 6» CO 73 D -< m > 73 CO H C O m 73 m co D m z o m co H o > > Z z c > I -o m z m 5 4TH YEAR STUDENT RESIDENCES - SAMPLED WASTE DATA (kg) Area Sampled: Thunderbird, Block D Date Sampled: 09.03.98 11.03.98 12.03.98 Total Avg. StDev C. Int. CVar(%) Residual Paper 4.20 3.50 4.00 11.70 3.90 0.36 0.90 9 Compostable Food 16 17 39 72 24 13 32 54 Office Fine Paper 4 14 4 22 ' 7 .. 6 15 85 Newspaper 0.0 2.7 5.1 7.8 2.6 2.5 6.3 98 OCC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Glass 1.0 3.5 2.7 7.2 2.4 1.3 3.1 53 Recyclable Plastic 0 00 0.00 0.00 0.00 0.00 0.00 0.00 0 Pop Cans 0.00 0.20 0.25 0.45 0.15 0.13 0.33 88 Residual Plastic 10.5 6.5 12.6 29.6 9.9 3.1 7.7 31 Metal 3.2 6.4 2.6 12.2 4.1 2.0 5.0 50 Wood/Cloth 4 1 10 15 5 • 4 11 91 Totals 43 55 79 177 59 19 46 32 Ttl Compostable Material 25 21 52 98 33 17 43 53 Total Recyclable Material 5 V: 21 .'/••' y i 2 ' r ; • 37 12 8 20 65 Total Residual Material 13.7 12.9 15.2 41.7 139 1.2 3.0 9 4TH YEAR STUDENT RESIDENCES - WASTE PER USER DATA (g) Number of Residents 160 (constant) Sample 1 Sample 2 Sample 3 Total Avg. StDev C. Int. CVar (%) Residual Paper 26 3 21.9 25.0 73.1 244 2.3 5.6 9 Compostable Food 100 110 240 450 150 80 200 54 Office Fine Paper 23 90 24 137 46 39 96 , 85 Newspaper 0 17 32 48 16 16 39 98 OCC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Glass 6 22 17 45 15 8 19 53 Recyclable Plastic 0.00 0.00 0.00 . : 0.00 0.00 0.00 . 0:00 0 Pop Cans 0.0 1.3 1.6 2.8 0.9 0.8 2.1 88 Residual Plastic 65 41 79 . 185 62 19 48 31 Metal 20 40 16 76 25 13 31 50 Total Wood 28 4 59 91 30 28 69 91 Totals 270 340 500 1100 370 120 290 32 Ttl Compostable Material 130 330 640 200 110 270 53 Total Recyclable Material 30 130 70 230 80 50 130 65 Total Residual Material 85 80 95 261 87 7 18 9 Values were rounded to correspond with two significant digits for error terms, based on original data values. 135 < ID C CD 01 i CD O cn co' ON o o =t ST ET o TJ 73 | CD CD = 5"S O & - sr £ co co S 2 3 CD CD D) =» ^ J? 3 s CD fl) CO 5' CD CD Si 5; M -N 01 CO Ol N N _ —i to Co C D •b Ol Ol 00 ^ bo -i ro oi CO S CO 0 0 £ ^ ^ <° oi o -> ro _k ro oo P> S w _k C D o ro C D g ^ * o CO 2 CO CO O l ro o O CO CD CQ O 3 1^ CQ CO i 5 <9. 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CD CD CD O o c 01 o 3 co 7± co ro oi S * co oi °° - o o 01 *< 01 o —* > < CD cu CQ CD T J CD (0 CL (D 3 O * < c c c 01 CO 01 CD CD CD cn 01 CO 3 " 5' 5' Tl TJ "0 01 3 - 3 " 3 CO 01 01 01 *<" CD CD Hou X X o sin >us usi CO ing 6u O l O l CO T J CD CO CL c 92. TJ 01 TJ CD C 01 CD ro co o + CO - b O l 0 0 O l C CO CD Tl > -< X o c co z o T J m co o m z o m co H o > z z c > r o m z m o o o II • b J > CO A P P E N D I X N : O T H E R S P A C E : B A T H R O O M S BATHROOM - SAMPLED WASTE DATA (kg)* Area Sampled: Student Union Building, 1st Floor Male & Female Bathrooms Date Sampled: 18.06.98, 19.06.98, 22.06.98 Male kg No. Males Female kg No. Females Sample 1 1.15 151 0.74 98 Sample 2 1.15 132 1.24 110 Sample 3 1.46 290 1.18 263 Sample 4 0.56 81 0.32 55 Sample 5 1.53 281 1.42 212 Total 5.85 4.90 Averages StDeviation C. Intervals CVar (%) 1.17 0.38 0.47 33 0.98 0.45 0.56 46 *AII sampled waste consisted of residual paper products. BATHROOM - WASTE PER USER DATA (g) Male Female The variation went down by tabulating per user values for Sample 1 7.6 7.6 both male and female bathrooms. Sample 2 8.7 11.3 Sample 3 5.0 4.5 An analysis of variance indicated Sample 4 7.0 5.7 no significant difference existed Sample 5 55 6.7 in the sex-specific waste per capita generated. Total 33.7 35.8 Values were rounded to Averages 67 7.2 correspond to significant digits for StDeviation 1.5 2.6 error terms, based on original data. C. Intervals 1.9 32 CVar (%) 23 36 140 A 3 3 H =• tu o 2. x sr i i o •n T] O C 0) C L g - CD oi (3 = : (D 01 CO O l I co cn co - - -o -O l ^ p p p p CO O O ) O l o 3 3 CD CO -* J - 0 - J - o b b b b N M M M ro oi A ro A ^ ^ p p o CO b C D co Tl <» CD S: - i {u f < sr I to CQ CD ~ C to CD 1 * 3 3-2 in S sr 5 o 3 3 CD to 00 U l s u co £ * ff a c 3 C L 01 C O —* CO O l 0 0 O J CO CO - O U l N J N J C D p p p p O) OJ b bj p-l -O, - v l b b b b CO CO CO C O OJ o OJ OJ U l A t> A « -» ro ro b b b ^ 3 c ? c? I - i 01 !< 5T cn CQ CD ~ c to CD 2. » CD » 5 ° 3. st •5 TJ OJ 3 3 CD CD 3 b m < > cn to < — 3 . C O 3 K. < S- o' a? CD 3 CO = = I c < TJ 3 . < - CD ST C CD CO 2. TJ S C O 2 3 8- 3 T J c < o Q. 0) sr CO 0) 3 •o CD o SL o c CD > -I X TJ O O 3 co O H > > Z Z c > r -I co H m CD m z m H m o TJ Z. 3 A CD " = ! 3 _ l A P P E N D I X O : O T H E R S P A C E : O U T D O O R BINS OUTDOOR BINS - SAMPLED WASTE DATA (g) Area Sampled: Random Selection - Bins # 38, 9, 6, 55, 69, 64, 81, 54, 67, 27 Date Sampled: 25.01.99 (Day1) Bin Sampled: TP - Park TP - Com. Bkstore SUB McD. Mus. 1 Mus. 2 Old Adm Residual Paper 0 40 610 480 170 0 0 40 Compostable Food 250 80 230 160 270 0 0 230 Office Fine Paper 0 0 0 0 0 0 0 0 Newspaper 0 0 0 0 0 0 0 0 OCC 0.00 0.00 0.00 o:oo 0.00 0.00 0 00 0 00 Recyclable Glass 0 227 136 227 0 0 0 0 Recyclable Plastic 0.0 22.7 0.0 0.0 0.0 0 0 00 00 Pop Cans 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Residual Plastic 10 3.E+00 820 70 70 3.E+00 0 10 Metal 0 18 295 55 18 0 0 45 Wood 0.0 0.0 0.0 7.2 0.0 0 0 0.0 00 Misc 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Totals 260 390 2090 1000 530 3.E+00 0 320 Ttl. Compostable Material 250 120 840 650 440 0 0 270 Total Recyclable Material 0 250 136 227 0 0 0 0 Total Residual Material 10 20 1120 130 90 3.E+00 0 60 Bin Sampled: Main Koerner Total Avg. StDev C. Int. CVar (%) Residual Paper 300 960 2590 260 330 230 130 Compostable Food 890 860 2970 300 320 230 110 Office Fine Paper 68 0 68 7 22 15 320 Newspaper 0 68 68 7 22 15 320 OCC 0.00 0.00 0.00 0.00 0.00 0 00 0 Recyclable Glass 227 0 818 82 109 78 130 Recyclable Plastic 0.0 22.7 45.5 4.5 9.6 6.8 210 Pop Cans 0.00 0.00 0.00 0.00 0.00 0.00 0 Residual Plastic 100 210 1290 130 . 250 180 190 Metal 9 23 464 46 90 64 190 Wood 0.0 0.0 7.2 0.0 2.3 1.6 0 Misc 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 1591 2140 8320 830 830 590 99 Ttl. Compostable Material 1180 1820 5560 560 590 420 105 Total Recyclable Material 295 91 1000 100 119 85 119 Total Residual Material 110 230 1760 180 340 240 192 NB. 3.E+00 means 3 in Scientific Notation. Values were rounded to correspond with two significant digits for the error terms, based on original data. 142 A OUTDOOR BINS - SAMPLED WASTE DATA (g) Area Sampled: Random Selection - Bins # 38, 9, 6, 55, 69, 64, 81, 54, 67, 27 Date Sampled: 26.01.99 (Day 2) Bin Sampled: TP - Park TP - Com. Bkstore SUB McD. Mus. 1 Mus. 2 Old Adm. Residual;Paper: .: 0 40 460 840 250 0 180 100 Compostable Food 0 90 500 590 340 110 0 230 Office Fine Paper 0 45 159 91 0 5 0 0 Newspaper 0 0 0 500 430 500 0 0 OCC 0.00 0.00 ' 0.00 0.00 0.00 0.00 0.00 0.00 Recyclable Glass 0 0 0 0 0 0 0 0 Recyclable: Plastic: 0 4 0 23 0 25 0 72 Pop Cans 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Residual Plastic 0 6 110 280 390 30 20 80 Metal 0 14 23 23 0 3 0 45 Wood 0 0 0 0 ; 659 0 0 0 Misc 0.00 0.00 12.20 0.00 0.00 0.00 0.00 0.00 Totals 0 200 1260 2350 2070 670 200 530 Ttl. Compostable Material 0 130 960 1430 1250 110 180 330 Total: Recyclable Material 0 50 160 610 430 530 0 70 Total Residual Material 0 20 150 300 390 30 20 130 Bin Sampled: Main Koerner Total Avg. StDev C. Int. CVar (%) Residual Paper 640 930 3430 340 350 250 100 Compostable Food 640 1050 3550 360 340 240 95 Office Fine Paper 0 91 391 39 56 40 140 Newspaper 0 0 1430 140 230 170 160 OCC 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Glass 0 227 227 23 72 51 320 Recyclable Plastic 23 23 169 17 22 16 130 Pop Cans 0.00 0.00 0.00 0.00 0.00 0.00 0 Residual Plastic 180 500 1590 160 180 130 110 Metal 7 136 252 25 42 30 170 Wood 0 0 659 66 208 149 320 Misc 0.00 0.00 12.20 1.22 3.86 2.76 320 Totals 1480 2950 11710 1170 1020 730 87 Ttl. Compostable Material 1270 1980 7640 760 700 500 91 Total Recyclable Material: 20 340 2220 220 240 170 110 Total Residual Material 190 630 1850 •": 190 200 . 150 110 Values were rounded to correspond with two significant digits for the error terms, based on original data. 143 OUTDOOR BINS - SAMPLED WASTE DATA (g) Area Sampled: Random Selection - Bins # 38, 9, 6, 55, 69, 64, 81, 54, 67, 27 Date Sampled: 27.01.99 (Day 3) Bin Sampled: TP - Park TP - Com. Bkstore SUB McD. Mus 1 Mus. 2 Old Adm. Residual Paper 0 10 480 660 430 20 0 140 Compostable Food 0 50 360 300 160 0 0 0 Office Fine Paper 0 182 45 0 0 0 0 Newspaper 0 0 0 364 0 0 0 0 OCC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 00 Recyclable Glass 0 0 0 80 660 0 0 0 Recyclable Plastic 0.0 0.0 0.0 11.4 18.2 0 0 00 22 7 Pop Cans 0 15 0 11 68 0 0 0 Residual Plastic 0 20 610 180 40 10 . 2190 20 Metal 0.0 0.0 22.7 38.6 5.2 10.5 0.0 3.9 Wood 0 00 0 00 000 0.00 0.00 0.00 0.00 0.00 Misc 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Totals 0 190 1660 1690 1380 40 2190 190 Ttl. Compostable Material 0 60 840 960 590 20 0 140 Total Recyclable Material 0 110 180 510 •' 750 0 0 20 Total Residual Material 0 20 630 220 50 20 2190 20 Bin Sampled: Main Koerner Total Avg. StDev C. Int. CVar (%) Residual Paper 340 810 2890 290 300 210 100 Compostable Food 250 710 1820 180 230 160 130 Office Fine Paper 0 9 327 33 60 43 180 Newspaper 0 0 364 36 115 82 320 OCC 0.00 0.00 0.00 0.00 0.00 0.00 0 Recyclable Glass 0 0 740 70 210 150 300 Recyclable Plastic 0.0 0.0 52.3 5.2 8.8 6.3 170 Pop Cans 0 0 94 9 21 15 230 Residual Plastic 160 290 3520 350 670 480 190 Metal 22.7 22.7 126.3 12.6 13.3 9.5 110 Wood 0 00 0.00 0.00 0.00 0.00 0.00 0 Misc 0.00 0.00 0.00 0.00 0.00 0.00 0 Totals 770 1840 9940 990 850 610 86 Ttl. Compostable Material 590 1510 4710 470 520 370 110 Total Recyclable Material 0 10 1580 160 260 190 170 Total Residual Material 180 310 3650 370 670 480 180 Values were rounded to correspond with two significant digits for the error terms, based on original data. 144 KB 0 o s±i st st o 73 73 2 CD CD 3 M O T ] 1 a S a & g| i f * • sr 3 3 - * a 2 CD to: :cn cn o o o , o -t* to cn ->• o o co o ->• to ?j S s o o 0 0 r* *• co cn co to cn tn g A bo g cn to cn KJ -> ,^ Iv) I O N J l CO I O CO C D o o o 09 09 o CO o> in p o> co cn co o o ho o o o sj sr Sj toi (Q to o 6 0) CD 3 <:: CQ O :o '— SJ 3 2 S 3 CD -i H CD ~i T 3 co 3 3 CD i 1 3. E „ + C T-3 «<•: "3 3 •< D> A xDJ 3 3 CD rf CD ul CA 01 3 •a CD O a o c O c H D O o 73 CD 0 O 01 c rf « 9-C 8 CO - i CD C T S- co o 1 3 —I o I-> Z ~ z c > I -O m z m 2 N J C O -t* C Q 73 CD CO TJ CO T J CD O DJ 3 6 CO •s. 00 CO o CO O l cn O co •< o o o b o o o o APPENDIX P : 1998 ESTIMATED WASTE COMPOSITION Estimated 1998 Waste Composition, University o f British Columbia Ref: Felder, M . (1999) A Waste Audit and Directions for Reduction at the University o f British Columbia 146 /{ 

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