A comparison of two waste stream quantification and characterization methodologies

Waste Management & Research (1995) 13, 343-361

A COMPARISON OF TWO WASTE STREAM QUANTIFICATION AND CHARACTERIZATION METHODOLOGIES Chang-Ching Yu and Virginia Maclaren Department ~/ Geography, University ~/ Toronto. I00 St. Gem:~e Street. Toronto. Ontario M5S IAI. Canada (Received 2 Attgust 1993, accepted in revisedJorm 24 May 1994) This paper compares a traditional engineering approach (direct waste analysis) for collecting waste quantity and waste composition data to a social science approach (questionnaire surveys) for dealing with the same problem. The advantages and disadvantages of these two rnethodologies are discussed, and a comparison made of the results obtained from applying these methodologies to a case study of the industrial~:ommercial institutional waste stream in Metropolitan Toronto. The study shows that while the two approaches produce fairly similar waste quantity estimates. their waste composition estimates are not as close and vary considerably by material. © 1995 ISWA Key Words

Industrial-commercial institutional waste, waste quantification and characterization, waste quantity, composition, direct waste analysis, questionnaire surveys.

1. Introduction An understanding of the present and future characteristics of the waste stream is essential for effective, long-term, waste management planning. Knowledge of waste quantities is important for calculating the need for and size of waste disposal facilities, such as incinerators and landfills. Equally important is the fact that modern waste management practice, with its emphasis on using waste reduction, reuse, recycling and composting for diverting waste from waste disposal facilities, relies on information about the composition of the waste stream ill order to identify products or materials which should be targeted for diversion. Finally, knowledge of both waste quantities and waste composition is useful for monitoring progress towards achieving waste reduction or waste diversion policy objectives. Analysis of the total quantity of waste in tile waste stream, by weight or by volume, is known as waste quantification. Analysis of the composition of the waste stream, by material types (such as glass, paper, metal, etc.) or by product types (such as glass containers, magazines, cans, etc.), is frequently referred to as waste characterization*. The purpose of this paper is to compare two methodologies for quantifying and characterizing waste streams. The two methodologies to be investigated are direct waste analysis (DWA) and questionnaire surveys. Although the methodologies can be used to analyse any type of waste stream, the focus of this study will be their application * Waste characterization also invoh,es several other types of analyses, such as the identification of moisture content, chemical constituents, volatility, and energy content of different components of tile waste stream. Only tile material composition ~,spect of waste characterization studies will be investigated in this paper. 0734 242X/95/040343+ 19 $12.00/0

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to the industrial-commercial-institutional (ICI) waste stream. The advantages and disadvantages of the two methodologies will be discussed, and the results from applying these methodologies to a case study of the ICI waste stream in Metropolitan Toronto, Canada, will be compared. A key objective of the comparison is to determine whether there are statistically significant differences between the results obtained from DWA vs. those results obtained from questionnaire surveys. This information is useful because the two methodologies have different advantages and disadvantages. For example, questionnaire surveys are much less expensive to administer while DWA is generally acknowledged to be capable of obtaining better quality data for a given sampling period. Therefore, the existence of differences in the results obtained by the two methodologies, or the lack thereof, has implications for whether or not it is possible to substitute one methodology for the other in waste quantification and waste characterization studies.

2. Description of the direct waste analysis and questionnaire survey methodologies To date, the most widely used methodology for obtaining waste information has been DWA (Brunner & Ernst 1986). The DWA methodology was developed and refined by engineers in the 1960s and 1970s. Also known as the "sample-and-sort" method, several manuals have been published describing how to conduct a DWA (Gore & Storrie Ltd. 1991b, Wisconsin Bureau of Solid Waste Management 1988, Michigan Department of Natural Resources 1986, SCS Engineers 1979) and there are numerous examples of its application (Gore & Storrie Ltd. 1991a, SWEAP 1991, Matrix Management Group et a/. 1989, 1991, Cal Recovery Systems Inc./Recovery Sciences Inc. 1989, SCS Engineers 1989). Direct waste analysis involves the direct examination of waste set out for collection at point-of-generation (i.e. at the plant, office, store or institution), or of waste delivered to a waste processing facility or to a waste disposal site. The examination may focus on waste quantities or waste composition or both. In some instances, such as at a waste processing or waste disposal facility with a weigh scale, it may be feasible to weigh all of the waste entering the facility, although it is usually difficult to determine the source of such waste in any detail. Most often, waste quantification estimates are based on samples of waste taken directly from the waste streams of interest, and their origins are defined according to the source's Standard Industrial Classification (SIC) code. Sample size and selection is guided by such factors as the geographic scope of the study, the level of disaggregation by SIC code chosen for analysis, and the desired precision of the waste quantity estimates. Waste characterization studies differ from waste quantification studies in that they almost always employ sampling techniques, since it is much more difficult and timeconsuming to determine waste composition than it is to calculate waste quantities. Waste composition may be visually estimated in a DWA, but it is not recommended unless the load is fairly homogeneous (SCS Engineers 1979). Common practice is to sort the waste by hand (or sub-samples of the load if it is not feasible to sort the entire load) into pre-determined material or product categories or a combination of the two. Usually a rough sort is carried out first, in order to separate out large items such as tyres or furniture before proceeding with fine sorting of the remaining material. Characterization of waste by weight is customary. Characterization by volume is possible, though rarely performed (SENES Consultants Ltd. 1992). In addition to waste

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stream weight and composition, measurements may also be taken of the waste sample's moisture content and characteristics of its chemical composition such as energy content, elemental concentration and volatility. These latter measurements are useful if one of the objectives of the DWA is to determine the suitability of the waste stream for incineration. In contrast to DWA, the questionnaire survey methodology is normally restricted to collection of data at point-of-generation, rather than at waste processing or waste disposal facilities. The main steps in the questionnaire survey methodology involve preparation and pre-testing of a questionnaire, sample selection, and administration of the questionnaire to waste generators by means of personal interviews, mail-outs or telephone surveys. Companies are selected for the sample in the same manner as for a DWA, although the number of companies included in the sample can be much larger due to the lower cost of undertaking a questionnaire survey. The survey typically solicits information from managers or other office personnel about their company's waste quantities and characteristics. Respondents may base their answers on waste stream records that are maintained by the company, on visual inspections of waste containers, or simply on their knowledge of the production process and the associated wastes that are likely to result. The main questions posed to respondents during a survey ask about the quantity of waste sent for disposal and the composition of that waste, according to a list of product-based or material-based categories supplied by the surveyor. Numerous other questions can be included in the questionnaire, such as questions about the seasonality of waste generation, the type and quantities of the materials currently being recycled, and waste-related economic information (e.g. employment, floor area, number of working days). The two methodologies are not necessarily exclusive of one another and may be used in tandem. For example, waste composition information requested in a questionnaire survey may be supplied from data collected during a DWA or waste audit conducted by the responding company. Alternatively, a DWA may be supplemented with a short questionnaire in order to collect information on economic activity indicators or on seasonality of production.

3. General comparison between DWA and questionnaire surveys A key weakness of the questionnaire survey approach is that very few companies keep records on the amount of waste that their company generates, let alone the composition of that waste. In the absence of records, some researchers claim that estimates made by respondents about their waste characteristics may be no more than an "educated guess" (SENES 1992). If the researcher cannot rely on records being available, then the amount of information that can be requested in the questionnaire about the number and weight of materials in the waste stream is limited by the ability of the respondents to estimate waste composition from visual estimates or their general knowledge of the production process. A typical DWA may disaggregate the waste stream into 40-60 components whereas a typical questionnaire survey might be able to obtain information on less than half that number of categories. The introduction of new legislation such as the Waste Management Act in Ontario, Canada, which enables government agencies to require that companies file annual waste audits (Ontario Legislative Assembly 1992), will facilitate waste characterization studies for the ICI sector in the future, but, at the present time, the effectiveness of questionnaire surveys in collecting waste quantification

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and waste characterization data is hampered by the quality of the data available. For this reason, DWA is generally acknowledged to be a more accurate method for obtaining waste composition and waste quantity estimates, particularly for the period during which the samples are collected. Data availability and data quality problems also contribute to a universal problem of questionnaire surveys known as non-response bias. Although there can be a number of reasons why a company will choose not to respond to a questionnaire, including confidentiality and time constraints, lack of easily accessible data on waste quantities or waste composition can also be an important factor in discouraging responses. Nonresponse bias is bias that occurs in the sample when non-responding companies have significantly different characteristics from responding companies (e.g. differences in the type of economic activity or amount of waste generated). Direct waste analysis is not immune to the problem of non-response, as sampled companies may refuse to take part in the study due to confidentiality concerns, for example. However, DWA is likely to have lower non-response rates than questionnaire surveys because DWA does not require respondents to assemble data, and thus makes it easier for them to participate. Another disadvantage of the questionnaire survey approach is that the accuracy of questionnaire surveys as a tool for obtaining self-reported data has long been a concern in the social sciences (c.f. Warriner et al. 1984). Questionnaire surveys are more susceptible to inaccuracy than the instrument-based measuring techniques that dominate the physical sciences (Belson 1986). Matrix Management Group (MMG) (1988) attempted to test the accuracy of a questionnaire survey used to calculate waste composition estimates for the manufacturing sector in the State of Washington. The survey used telephone interviews to collect data from 259 manufacturing firms and achieved a 61"/,, response rate. After the interviews, 61 of the 159 responding companies were contacted, requesting an on-site visual inspection to verily the accuracy of the waste composition estimates. Approximately 30% of the inspected companies were found to have differences between their survey results and the visual inspections, which were considered "too large" to be explained by monthly/seasonal variation in the type of waste generated. Although these results raise questions about the accuracy of questionnaire surveys for obtaining waste composition data, there are a number of problems with the MMG study. Firstly, the authors fail to provide a definition of what they mean by a discrepancy that is "'too large". Secondly, problems exist in the accuracy of the visual inspection technique itself. A study carried out by SCS Engineers (1975) compared the results of visually estimating the percentage composition, by weight, of 39 waste samples (weighing 67.5 kg each) with the results obtained from sorting and weighing the same waste samples. The visual estimates were made as waste containers were emptied into compactors, so more waste was visible than if only the surface of the waste containers had been observed. The comparison found that the visual analyses yielded results quite close to those developed from manual separation and weighing. However, it found that dense items, such as packets of computer printouts, glass, and cardboard file folders, yielded percentage weights that were up to six times greater than those predicted by visual analysis. SCS engineers (1975) emphasized that the most accurate estimates were obtained when the waste had been mixed thoroughly beforehand. In comparison to the questionnaire survey approach, DWA has three key disadvantages. The most significant disadvantage of DWA is its cost. The high cost of

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DWA derives mainly from the manual sorting procedure required for waste composition investigations. In order to achieve satisfactory confidence levels for the estimates of different materials in the waste stream, several studies have recommended that each sample to be sorted and weighed should weigh approximately 90-180 kg (Lohani & Ko 1988, Musa & Ho 1981, Klee 1980, Bird & Hale Ltd. 1978, Britton 1972, Klee & Carruth 1970). If one were to sample 100 firms, a total of 9000-18 000kg of refuse would have to be sorted and weighed. A recent DWA of the ICI waste stream in Metropolitan Toronto cost an average of CAN $1750 per waste sample (SWEAP 1991), The questionnaire survey conducted in the current study was administered to the same companies that had been covered in the SWEAP DWA study and cost approximately CAN $150 per waste sample. In other words, the unit cost of the DWA study was over 10 times greater than that for the questionnaire survey. The total cost of investigating a highly heterogeneous set of waste generators, such as those found in the ICI sector, can be considerably higher for DWA studies than for questionnaire surveys, since more samples are required to achieve a satisfactory degree of confidence in the results than would be needed for the analysis of a relatively homogeneous waste stream, such as is found in the residential sector (Nemerow 1984). A second disadvantage of DWA is that it usually provides only a snapshot view of the waste stream. DWA samples tend to be collected within a short period of time, such as during a single day or over a period of 1 week, and the short-term sample values are then extrapolated to represent annual waste generation. For a waste stream that may vary depending on the day of the week or by season, such a practice is obviously perilous. A number of DWA studies have conducted multiple sampling during different seasons of the year, and several have identified statistically significant differences in waste generation and composition by season (Kauffman 1990, Cal Recovery Systems Inc./Recovery Sciences Inc. 1989, SCS Engineers 1989, Bird & Hale 1978). Multiple sampling is rare, however, because of the additional costs and time incurred. With a questionnaire survey, questions can be included inquiring about the seasonality of production and this information can be used to modify either the waste quantification or the waste characterization estimates or both, as needed. Although the relative accuracy of a DWA estimate of waste generation and composition for a company during a given sample period is likely to be higher than that of the estimate obtained from a questionnaire survey for the same period, there are a number of factors that can affect the accuracy of DWA in absolute terms. Firstly, since the high cost of DWA usually restricts the number of samples that can be analysed, the resultant statistical precision of such a study is relatively low. Secondly, many waste producers, particularly small businesses, share their waste bins with neighbouring companies. This makes it difficult to identify the separate waste generators, as DeGeare & Ongerth (1971) discovered in their attempt to perform a DWA study of 25-30 companies in each of five ICI sub-sectors, for a total of 125-150 samples. Only 35 samples were actually collected, one of the reasons being that many of the sampled companies were eliminated from the study because they shared their storage containers with others. The Metropolitan Toronto study cited earlier encountered the same problem when sampling shopping malls where food markets, medical clinics, barber shops and other retail activities all shared the same bins (SWEAP 1991). In a DWA study conducted in Long Beach, California, Hinshaw & Braun (1991) found that "'the most difficult task was to obtain pure samples, where office materials would not be mixed with residential wastes, restaurant wastes or wastes from other jurisdictions".

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An important function of questionnaire surveys to date has been to collect economic data that can be used to determine whether relationships exist between waste quantities produced by a company and the economic characteristics of that company, such as its floor area, its economic output, or the number of employees. If these relationships can be quantified in the form of waste generation multipliers, then forecasts of economic activity in an area can be used to predict waste generation amounts in that same area. Although a number of studies from the 1970s have published information on waste generation multipliers for the ICI sector in North America (Mylnarski & Northrop 1975, Wisconsin Department of Natural Resources 1974, Steiker 1973, Niessen & Alsobrook 1972, Golueke 1971, DeGeare & Ongerth 1971), there have been few attempts to determine the reliability of such multipliers for prediction purposes. Rhyner & Green (1988) examined multipliers in both the 1CI and residential sectors, and found that the accuracy of the multipliers for predicting waste quantities was lower than expected. A possible reason for this may have been that most of the multipliers were taken from studies in which the data had been collected from waste streams dating from the 1970s, while Rhyner & Green applied the multipliers to a waste stream from the 1980s. A recent waste quantification study by Gore & Storrie Ltd. (1991a) found fairly strong, statistically significant relationships between the amount of waste produced by a company and the number of employees in that company, for about one-half of tile 13 two-digit SIC groups surveyed. The authors proposed a number of reasons to explain why the waste generation-employment relationship was poor or absent in the remaining SIC sectors. In some cases, the number of companies sampled within an SIC sector was too small, while in others it was suspected that internal waste management practices, such as waste reduction and recycling, resulted in different generation rates for companies with the same number of employees. Regardless of the evidence concerning the utility of waste generation multipliers, the point is that questionnaire surveys offer the opportunity to not only collect waste quantity and waste composition information, but to simultaneously collect additional relevant economic information that may help in understanding and predicting waste-economy relationships. To summarize, both DWA and questionnaire surveys have their advantages and disadvantages. A decision about which methodology is most appropriate for a given situation will likely depend on the study objectives, the size of the researcher's budget, time available for the study, desired accuracy of the results, and whether or not supplementary information on economic variables and seasonality of waste production is needed. The rest of this paper focuses on one particular aspect of these two methodologies, namely their relative accuracy. Relative accuracy is defined as being the extent to which the waste quantity and composition estimates produced by the two methodologies agree with one another. This definition does not refer to the extent to which the estimates from the two methodologies are representative of the "'true" waste quantity and composition values. Theory suggests that DWA should produce more accurate waste estimates than questionnaire surveys, but if questionnaire survey estimates can be shown to be similar to DWA estimates, then waste studies should be able to substitute the questionnaire survey approach for DWA without suffering a significant loss of accuracy in the resulting estimates. Before proceeding with the empirical analysis, the use of the questionnaire survey data to arrive at waste quantity and composition estimates is described.

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4. Estimating waste generation data from questionnaire surveys Since it is unusual for companies to maintain records on the amount of waste that they generate, indirect methods for obtaining waste quantity data are needed for use in questionnaire surveys. This section presents a method for estimating the amount of waste collected for disposal (i.e. net of the amount of materials recycled), based on the total cost charged by private haulers for waste disposal. Estimating waste quantities by weight was focused upon rather than estimating by volume, because the former measure is more frequently used in practice. The amount of waste requiring disposal by a company is assumed to be a function of the disposal fees paid by that company. The relationship between the amount of waste sent for disposal and a company's disposal charges is hypothesized to be roughly linear in form, and can be estimated by the following equation:

WQ~=tH,

[11

C

where WQ, is the company's annual waste quantity, by weight; t is the proportion of the disposal charge that the average hauler spends on tipping fees; c is the unit cost of tipping fees per tonne; H, is the questionnaire survey data on the annual disposal charge paid by company i to its waste hauler. The parameter representing the waste hauler's disposal costs, t in Equation 1, can be estimated by means of a survey of selected haulers or by obtaining disposal charge fees from waste generators who are charged on a "per tonne" basis, and then calculating the proportion of those charges spent on known tipping fees per tonne. This parameter can be difficult to estimate because of confidentiality problems. The value for the parameter representing tipping fee costs, c in Equation 1, can be estimated from known tipping fee charges at local waste disposal sites. Errors may occur in estimating the tipping fee parameter if different facilities charge different fees and the analyst does not have information on the location of the facility to which each company's waste has been taken.

5. Estimating waste composition data from questionnaire surveys Although waste composition estimates in the literature are typically reported by weight, questionnaire survey respondents may find it easier to provide waste composition estimates by volume. Volume estimates can be translated into weight estimates by using the following equation:

*'i}=*';5 el,

[21

where PI} is the proportion of material j in the waste stream of company i, by weight; PI) is the proportion of material j in the waste stream of company i, by volume; c6 is the density of material j; Di is the average density of company i's waste stream. There are a number of potential errors that can arise in estimating the values of parameters and variables in this equation. For example, if visual inspection techniques are used for estimating waste composition by weight, then the visual perception errors noted by SCS Engineers Ltd. (1975) can lead to over- or under-estimation of waste

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composition figures, depending on the density of the component material being estimated. It is not clear whether there is likely to be similar consistency in the nature of errors that may arise when waste composition is estimated solely on the basis of knowledge of the production process and its waste components. Although there have been no studies on the accuracy of visual inspection techniques for estimating composition of waste by volume, it is likely that differences in material densities also affect the ability to visually ascertain composition by volume. Additional errors can be introduced when translating volume-based composition estimates into weight-based composition estimates, since there may be uncertainty about the appropriate density figures to use from the literature for determining the values of individual material densities. There are two main reasons for this uncertainty. First of all, the breakdown of waste composition categories used in questionnaire surveys is usually somewhat limited. This limitation is imposed by the fact that very complex questions can lead to poor response rates. In addition, the rough estimation procedures used by respondents are unlikely to be able to distinguish among materials in the waste stream which exist in very small proportions. Since a given material category (e.g. paper) usually consists of several sub-categories of products (e.g. fine paper, paper packaging, newspaper, magazines, books), each of which can have different densities, the analyst must decide how to average these densities and whether to weight some sub-categories more heavily than others. Another factor that can cause uncertainty about density estimates is the degree of compaction of the waste stream. For example, the density for cardboard that has been mechanically compacted is about 13 times greater than its uncompacted density (Franklin Associates Ltd. 1990). This problem can be minimized by collecting information in the questionnaire survey about whether or not a company has a compactor on-site and, if it does, using density figures for a compacted waste stream rather than for an uncompacted waste stream. To summarize, there are a number of different types of error that can occur when using questionnaire surveys to estimate waste quantity and waste composition data. The cumulated sum of these errors, in combination with some of the methodological differences noted earlier, means that the estimates from DWA studies will more than likely differ fl-om those produced from questionnaire surveys. The objective of the empirical research in this paper is to determine how closely the estimates compare.

6. Data collection methodology The DWA data and questionnaire survey data used in this study were obtained from a sample of ICI companies located in Metropolitan Toronto. The results of a DWA study performed for Toronto's Solid Waste Environmental Assessment Plan (SWEAP 1991) were made available to the authors, and an original questionnaire survey was then administered to the same companies that had been involved in tile DWA study. The DWA study took place between March-August 1990, while the questionnaire survey was conducted shortly after, between July-November 1990. The DWA study used a stratified, random-sampling procedure to select companies from nine economic sectors. The sectors were commercial accommodations, heavy industry, light industry, storage, transportation, retail, office, education and recreation. The number of samples selected from each sector was roughly related to the estimated total quantity and estimated variability of waste production and composition in that sector. The waste stream was divided into 14 categories and 66 sub-categories. A total

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of 111 ICI companies took part in the DWA study and a single collection was made from each one. The waste was sub-sampled before sorting but no information is awfilable on the average weight of the sub-samples, or on the number of sampled companies which refused to participate in the study. Annual waste quantities were estimated from the DWA data using a two-step process. In the first step, the amount of waste collected from an establishment was divided by the time interval during which the waste had been accumulated. This produced an estimate of the amount of waste collected per day. The amount of waste collected per day was then multiplied by the number of working days in the year for that particular establishment, in order to arrive at an annual waste figure. All quantity and composition data were calculated on a wet weight basis. In the questionnaire survey, we successfully interviewed 88 of the 111 DWA companies by means of personal interviews, a response rate of 79'7,,. The questionnaire requested information on waste quantities produced by the company, as well as information on seasonality of waste production and waste composition. The waste composition data was collected for the same material categories and sub-categories that were used in the DWA study. Pre-testing of the questionnaire confirmed that very few companies kept records on the tonnage of waste sent for disposal, and none had kept records on waste composition. Consequently, data was collected on a company's annual waste disposal charges in order to estimate waste quantities with Equation I. Fifty-five of the 88 responding companies had contracts with private haulers and were thus able to provide the needed disposal charge information. The remaining companies were either unwilling to supply disposal charge information, or were relatively small in size and had their waste collected by municipal sanitation departments free of charge or for a nominal fee. These companies were therefore excluded from further analysis.

7. Parameter estimates

The parameters in Equations 1 and 2 were estimated from a variety of sources. The unit cost of tipping fees per tonne, c, was assumed to be CAN $109. This value is the weighted average of the tipping fee charged at landfill sites and transfer stations operated by Metropolitan Toronto. At the time of the survey, Metropolitan Toronto disposal [ilcilities were the primary destinations for ICI waste generated in the region. Two sources ot" information were used to estimate the value of t, the proportion of total disposal charges allocated to tipping fees. The first source was the questionnaire survey itself, where only three companies were able to provide the necessary information. On average, they estimated that approximately three-quarters of the disposal fees charged by waste haulers were for tipping fee expenses. In a supplementary survey, two waste haulers reported that just over two-thirds of their disposal charges were allocated to tipping fees. Although the estimates fi'om these different sources are admittedly crude because of the small sample size, they are relatively close in value and an intermediate value of 0.7 was consequently chosen for t. Table 1 presents the material density figures, d i, that were used in Equation 2. They are based on a literature review of waste material densities by Franklin Associates Ltd. (1990) and experimental work conducted by the same company in conjunction with The Garbage Project of the University of Arizona (Wilson et al. 1989). The results generated from the experimental work were estimated to have confidence ranges of _+20'V,,, at the 95'¼, confidence level.

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Glass Ferrous metal Non-ferrous metal Paper Cardboard Plastics Wood Rubber Textiles Leather (using the textiles density) Food Vegetation (yard) Fines (using the average MSW~ density) Sludge (using the food waste density) Construction waste (using the wood waste density) Special (i.e. chemical containers, etc.) (using the non-ferrous metal density) Others (using the average MSW density)

Trash can density* (kgm -3)

Compactor densityt (kgm 3)

390 120 32 77 26 38 360 102 29 29 300 300 60 300

1293 270 178 350 337 198 444 175 19 I 191 930 720 480 930

360

444

32 60

178 480

* Taken from Franklin Associates Ltd. (1990); f Estimated using Equation 3; ~ MSW= Municipal Solid Waste.

Two points regarding the density figures in Table 1 are in order. The first point is that density estimates were needed for both trash can waste and compacted waste. Most companies surveyed in this study set their waste out in regular waste containers, but a few companies used compactors to reduce waste volumes before collection. Estimates of material density in compactors by material type were not available in the literature. However, Franklin Associates Ltd. (1990) estimate that the average densities for mixed municipal solid waste in trash cans, compactors and landfills are 60 kg m -3, 480 kg m 3 and 660 kg m -3, respectively. The ratio of the difference between the compactor density figure and the trash can density figure to the difference between the landfill and trash can densities is 0.7. This ratio of 0.7 is assumed to hold constant for all materials in the waste stream and was used in Equation 3 for estimating compactor densities by material type.

dcompj = dcanj + 0.7(d/J) - dcanj)

[31

where dcompj, dcanj and d./fj represent the density of material j in compactors, trash cans and landfills, respectively. A second point regarding the data in Table 1 is that for all categories of waste having several sub-categories, except paper and plastics, the calculated density for a category was taken as the average density of all sub-categories. This estimation procedure was adequate for categories in which sub-category densities are relatively similar, but the paper and plastics sub-category densities are quite variable. Therefore, an attempt was

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made to weight these density figures in proportion to the percentage of each subcategory in the waste stream, using the incomplete and admittedly crude composition information from the questionnaire survey.

8. Comparison of waste quantity estimates In this section, a test is used to assess the existence of a statistically significant relationship between the waste quantity estimates derived from DWA and those derived from the questionnaire survey. The relationship is examined at three different levels of aggregation. At the first level, the two sets of waste quantity estimates for individual companies are compared. At the second level of aggregation, individual companies are allocated to their appropriate sectors (industrial, commercial or institutional) and the two sets of waste quantity estimates for individual companies within those sectorst are compared. For the third level of aggregation, the overall mean of the DWA waste quantity estimates is compared with the overall mean of the questionnaire estimates. One reason for exploring different levels of aggregation is that the estimates for some sectors may be better than those for others. A second reason relates to the researcher's purpose in collecting waste generation data, If the data is to be used for estimating sectoral generation rates, then the researcher will only be interested in whether differences exist in the estimates produced by the two waste quantification methodologies at the sectoral level. Similarly, if the researcher needs an estimate of the average waste generation rate for the entire economy, then it does not matter whether differences exist between the two methodologies for sector estimates as long as there are no statistically significant differences between the two sets of estimates when the data have been aggregated up from the sectoral level. For the individual company comparisons, the Pearson product moment correlation coefficient, r, was used to measure the strength and direction of the relationship between the two sets of waste quantity estimates. A second method used for measuring the closeness of the quantity estimates was to regress one set of estimates on the other and then calculate the values of the regression coefficients, b0 and b~, as in Equation 4. If the two methodologies produce identical waste quantity estimates, then the slope of the regression line, b,, should be equal to 1.0 and the intercept, b0, should be zero. WDi = bo + bl WQi + ei

[41

where WDi is the DWA estimate of the amount of waste disposed of annually by c o m p a n y i; WQi is the questionnaire survey estimate of the amount of waste disposed of annually by c o m p a n y i; ei is the regression residual or error of the i'th sampled company.

t Companies in the manufacturing, transportation and storage sub-sectors were allocated to the industrial sector. Companies in the retail, wholesale, restaurant and wholesale sub-sectors were allocated to the commercial sector. Companies in the government, financial, business services, education and health services sub-sectors were allocated to the institutional sector. This classification of sub-sectors into sectors is somewhat different from the conventional SIC classification which allocates financial and business services to the commercial sector rather than the institutional sector. We have used this particular classification scheme because it creates a grouping in the institutional sector that is dominated by officeestablishments producing similar types of wastes.

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1200 1000 0

800 600 400

2OO

200 400 600 Questionnaire survey estimates (1000 kg)

800

Fig. 1. Comparison of waste quantity estimates--DWA vs. questionnaire survey.

TABLE 2 Correlation of DWA and questionnaire survey waste quantity estimates Industrial

Commercial

Institutional

Total sample

Unadjusted estimates

r=0.69 (0.000)* h~=0.68 (0.016)* b.=33749 (0.338) n=33

r=0.84 (0.002)* h~ = 1.17 (0.440) ho= 13263 (0.830) n= 14

r=0.85 (0.007)* bl = 1.27 (0.426) b~= -26626 (0.706) n= 8

Seasonally adjusted estimates

r=0.75 (0.000)* b~=0.68 (0.009)* b.=28588 (0.284) n=30

r=0.72 (0.004)* b~ = 1.02 (0.943) b,,=40542 (0.620) n= 14

1"=0.88 (0.004)* r=0.71 (0.000)* bl = 1.41 (0.231) b~=0.82 (0.062) ho= -8467 (0.902) b0=37974 (0.217) n=8 n=52

r=0.74 (0.000)* bl =0.85 (0.169) b.=27033 (0.350) n = 55

* Statistically significant for ~=0.05.

Figure 1 plots the DWA estimates against the questionnaire survey estimates for the total sample, and the first row of Table 2 shows the results of the correlation and regression analyses for both the total sample and the sectoral data sets. The figures in brackets in Table 2 represent the probability that the sample statistic is equal to zero for r and b(,, and equal to 1.0 for b~. The results are deemed statistically significant if P_<0.05. The correlation between the DWA and questionnaire survey estimates is fairly good for the total sample, with a statistically significant correlation coefficient of 0.74. In addition, the hypothesis that the slope of the regression line is 1.0 cannot be rejected, nor can the hypothesis that the intercept of the regression line runs through the origin. Since the number of observations on the right hand side of Fig. 1 is rather sparse, Cook's Distance Test (Cook 1977) was applied to detect influential points. Three influential cases were identified in the 10% confidence region. All three points are associated with large firms generating over 500 kg of waste annually. This result suggests

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that the conclusions obtained should be treated with caution when referring to waste quantity estimates for larger firms, as our sample of large firms is fairly small. At the sectoral level, the correlations between DWA and the questionnaire survey estimates for companies in the industrial (r=0.69), commercial (r=0.84) and institutional sectors ( r = 0.85) are all statistically significant and are again fairly high. The hypothesis that the intercept of the regression line runs through the origin cannot be rejected for any of the three sectors, but the slope of the regression line is significantly different from 1.0 for the industrial sub-sector. Since the value of the regression coefficient for the industrial sector is 0.69, the questionnaire survey estimates tend to be higher than the DWA estimates for companies in this sector. A possible reason for this is that waste production in the industrial sector may have greater seasonal variation than that in the other sectors, but the estimates have not yet been adjusted for seasonality. A t-test was used to determine whether there was a statistically significant difference between the overall mean of the DWA waste quantity estimates (,%= 183.6 metric tonnes, n = 55) and the mean calculated from the questionnaire survey (,'~ = 184.6 metric tonnes). There is no statistically significant difference in the mean estimates generated by the two methods (t=0.05, P=0.963). Overall, the above results suggest that there is a fairly good correlation between the DWA waste quantity estimates and the questionnaire survey estimates across the total sample and within the three sectors. DWA and the questionnaire survey produce statistically identical results when the average waste quantity estimates from the two methodologies are compared.

9. Accounting for seasonality effects The waste streams of companies included in the DWA study were sampled over a 1week period and no attempt was made to take samples at other times in the year. Therefore, the DWA estimates do not account for the possible impact of seasonal variation in waste production. The questionnaire survey was used to collect information on the existence and extent of seasonal variation, and this information was then used to modify the original DWA waste quantity estimates. Respondents to the survey were asked to estimate the percentage of their company's annual waste that was set out for collection in the winter, spring, summer and fall seasons and these percentages were then inserted into the following seasonal adjustment equation:

W D A i: ~ WDi S i '/,,

[5]

where W D A i is the seasonally-adjusted DWA estimate of annual disposed waste quantity for company i; WDi is the unadjusted DWA estimate of annual disposed waste quantity for company i; 5',. is the estimated percentage of the waste produced in the season during which the DWA waste samples were collected from company i. Just under half of the respondents in the survey reported some seasonality effects. The question now is whether or not the seasonally-adjusted estimates, WDAi, achieve a better fit with the questionnaire survey estimates than the unadjusted estimates, WD~. Table 2 shows that the adjusted (r=0.71) and unadjusted (r=0.74) correlation coefficients for the total sample are very close. The fact that the correlation between

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the DWA estimates and the questionnaire survey estimates does not improve substantially when using seasonally-adjusted DWA estimates is unexpected. This result is probably due to some of the potential waste quantity estimation errors described earlier, and to a lack of precision in the method chosen for making seasonal adjustments to the data. A single seasonal adjustment factor was applied to a company's entire waste stream rather than using separate adjustment factors for each component of the waste stream, and it is possible that each component may be subject to different seasonal cycles. For example, the percentage of office paper and newspaper waste may remain relatively constant during the year while production process wastes, such as scrap metal and packaging, may exhibit considerable seasonal variation. An analysis of correlation coefficient changes by sector reveals that the correlations between the two sets of estimates have improved slightly in the industrial and institutional sectors, but declined from r = 0 . 8 4 to r = 0 . 7 2 in the commercial sector. The reason for this decrease is not entirely clear. It may simply be that respondents in the commercial sector were less familiar with the nature of seasonal variation in waste generation for their establishments. In summary, the preliminary efforts made in this study to improve the fit between DWA and questionnaire survey estimates by adjusting for seasonality were less effective than expected. Correlations between the two sets of estimates increased slightly in most cases but actually declined in one case. This result, however, does not necessarily mean that the need for seasonality adjustments should be dismissed. Future studies of this sort should focus on collecting more accurate information on seasonal variations of individual waste components, and on developing a better model of seasonal adjustment.

10. Comparison of waste composition estimates It is more difficult to obtain reliable information on waste composition from questionnaire surveys than it is to obtain information on waste quantities. Therefore, it was anticipated that the fit between the DWA waste composition estimates and the questionnaire survey-based waste composition estimates would be poorer than the fit found for the waste quantity estimates. As with the waste quantity estimates, the differences between the two sets of waste composition estimates were tested at three different levels of aggregation. Over 90'V,, of the respondents chose to provide waste composition estimates by volume and these had to be transformed into estimates by weight using Equation 2. Firstly, the values of separate correlation coefficients were calculated across the total sample for each material in the waste stream. Eight of the 15 material categories had statistically significant correlation coefficients, but all of them were rather low, ranging from 0.30 for wood waste to 0.59 for plastics (Table 3). Using the DWA estimates as the dependent variable and the questionnaire estimates as the independent variable, the regression coefficient, b~, was found to be significantly different from 1.0 for seven of these eight materials, indicating that there are statistically significant differences between the two sets of estimates. At the sub-sector level, the same eight material categories had at least one sub-sector correlation coefficient that was statistically significant. There was a wider variation in the values of the correlation coefficients at the sub-sector level, ranging from a low of 0.34 for paperboard in the industrial sector, to a high of 0.80 for glass in the institutional sector. Eleven of the 14 sub-sector regression coefficients for the above eight materials were significantly different from

r=0.71 (0.000)* b, = 1.49 (0.059) b~,= 5.93 (0.088) r = 0 . 4 5 (0.005)* b t = 0.22 (0.000)* bo=0.55 (0.651) r = 0 . 5 0 (0.002)* bl = 3.47 (0.022)* bo = 3.51 (0.182) r = 0 . 3 9 (0.018)* bl =0.33 (0.000)* bo = 5.71 (0.083) r = 0 . 5 8 (0.000)* bt =0.54 (0.001)* b, = 3.17 (0.045)* r=0.51 (0.001)* bl = 0.39 (0.000)* bo = 0.39 (0.871 )

i-=0.34 (0.042)* bt = 0.51 (0.055)t b . = 18.98 (0.000)*

r=0.61 (0.001)* bl = 0.48 (0.000)* h, = 4.52 (0.392)

r = 0 . 5 8 (0.002)* h b= 0.16 (0.000)* bo= 1.90 (0.003)* r = 0 . 6 5 (0.000)* hi =2.23 (0.032)* b, = 1.35 (0.082)

r = 0 . 4 4 (0.024)* hi = 0.24 (0.000)* h . = 11.03 (0.007)* r=0.61 (0.001)* bL = 0.86 (0.540) bc~= 13.49 (0.004)* r = 0 . 5 3 (0.006)* b~ =0.50 (0.006)* bu = 0.52 (0.074)

Commercial ( n = 26)

* Statistically significant for :~=0.05; ]'The null hypothesis is that b~= 1.0.

Food

Construction materials

Wood

Textiles

Glass

Plastics

Non-ferrous metals

Paperboard

Paper

(n = 37)

Industrial

r = 0 . 8 0 (0.000)* bl = 0.22 (0.000)* bo= 1.05 (0.21 I)

Institutional (11= 1 5 )

r = 0 . 5 9 (0.000)* bl = 1.09 (0.589) bo = 5.75 {0.004)* i-=0.51 (0.000)* bl = 0.21 (0.000)* bo= 1.04 (0.098) r = 0 . 5 3 (0.000)* bt =3.45 (0.000)* bo = 2.03 (0.093) r = 0 . 3 0 (0.007)* bl =0.24 (0.000)* bo = 5.03 (0.006)* r = 0 . 3 2 (0.005)* bl =0.37 (0.000)* bo = 3.78 (0.002)* r=0.51 (0.000)* bt = 0.39 (0.000)* bo = 3.10 (0.192)

r = 0 . 3 3 (0.003)* bl =0.24 (0.000)* b0= 16.65 (0.000)* r = 0 . 4 3 (0.000)* bl = 0.64 (0.022)* b0= 16.57 (0.000)*

Total sample 01 = 78)

TABLE 3 Correlation of DWA and questionnaire survey waste composition estimates: statistically significant results

taa

-,n.

e~

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C-C Yu & I,: Maclaren

TABLE 4 Comparison of DWA and questionnaire survey (QS) aggregate waste composition estimates (n = 78)

Paper Paperboard Ferrous metal Non-ferrot, s metal Plastics Glass Rubber Leather Textiles Wood Vegetation Fine Special Construction materials Food

DWA

QS

s.l=.,

24.7 22.3 5.9 0.9 13.3 2.8 0.4 0.0 4.5 7.5 1.4 0.3 0.6 4.6 10.7

33.2 9.0 3.3 0.7 6.9 8.4 0.5 0.0 0.7 10.3 0.4 2.2 0.7 2.2 20.9

3.101 2.275 1.342 0.188 1.537 1.247 0.270 0.008 1.202 2.149 0.410 0.360 0.295 1.318 2.477

t-Value 2.73 - 5.86 - 2.00 - 1.07 -4.16 4.45 0.30 - 1.00 - 3.16 1.29 - 2.38 5.28 0.02 - 1.81 3.57

2-TailedP 0.008* 0.000" 0.049* 0.289 0.000" 0.000" 0.762 0.320 0.002* 0.203 0.020* 0.000" 0.982 0.073 0.001 *

* Statistically significant for :~= 0.05.

1.0. These results show that the strength of tile relationship between tile DWA and questionnaire survey estimates is, for the most part, considerably lower for waste composition estimates than for waste quantity estimates. Table 4 presents the results of the t-tests applied to the comparison of overall means for waste composition by material category. It illustrates that, for six out of 15 waste material components compared, there is no statistically significant difl'erence between the DWA and questionnaire survey estimates. These materials are non-ferrous metal, rubber, leather, wood, special wastes, and construction wastes. There is a consistent pattern in the results in that these materials are among those comprising the smallest proportions of the waste stream. Unfortunately, this is not a very useful result in practice since it means that the two waste characterization methodologies do not produce similar estimates of waste composition for the largest, and therefore most important, components of the waste stream.

!1. Conclusions

Two waste quantification and characterization methodologies were compared and evaluated in this paper. Both methodologies have distinct advantages and disadvantages. A major advantage of the questionnaire survey is its relatively low cost, while the major advantage for DWA is its superior accuracy over a given sampling period. No attempt was made to test tile "'true" accuracy of the two methodologies, because obtaining that information through an extensive year-round sampling of a sufficiently large number of companies was beyond the resources of this study. Instead, the focus of this study was to examine the closeness of the estimates generated by the two methodologies, by comparing the results of the two approaches when they were applied to an identical

Waste stream quant([ication and characterization

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sample of IC1 establishments. If the two methodologies produce results that are consistent with one another, then it can be concluded that neither methodology is more accurate than the other and that other factors, such as cost and the desire to collect supplementary information on economic variables or on seasonality of production, favour the use of a questionnaire survey approach over DWA. The results of the study show that there is a relatively high correlation between the two methodologies for waste quantity estimates across the combined industrial, commercial and institutional sectors and within each of these sectors. These results suggest that the use of a questionnaire survey in a waste quantification study is a relatively good substitute for DWA. Seasonally adjusting the DWA estimates of waste quantity produced mixed results in terms of its impact on the correlation between DWA and the questionnaire survey estimates, and was not considered an effective procedure. Further research is needed to improve the seasonal adjustment model and the data upon which it is based. With respect to waste composition, the correlation between the two methodologies is not as good overall as that found for waste quantities either within the three sectors or across the total sample. This lack of consistency in the correlation results raises questions about the appropriateness of substituting a questionnaire survey for DWA when making waste composition estimates. Differences were tested in the mean waste composition values generated by the two methodologies, and revealed that almost all of the means for the highest percentage composition materials (paper, paperboard, ferrous metal, plastics, textiles, glass) had statistically significant differences. It is concluded that DWA and questionnaire survey estimates of waste composition cannot be readily substituted for one another at the present time. As more ICI companies begin to implement waste auditing procedures, either voluntarily or in response to environmental regulations, it is anticipated that the quality of waste composition data that can be collected by questionnaire surveys will improve substantially, and that the fit between the two waste characterization methodologies can also be expected to improve. To summarize, the similarity between the results generated by the application of the two waste quantification and characterization methodologies is stronger for waste quantity estimates than it is for waste composition estimates. The similarity varies by level of data aggregation as well. Therefore, decisions about whether a questionnaire survey approach can be substituted for DWA will depend on whether waste quantity or waste composition data is of interest, and on the level of aggregation for which these data are needed. The selection of a particular waste study methodology will also be influenced by other factors. For example, if the main objective of the study is to investigate the percentages of some specific waste components in the waste stream (e.g. office paper, cardboard, or used tyres) for the purpose of assessing their recycling potential, and if it can be assumed that there is no problem with seasonality, then DWA is the recommended methodology as it can sort and weigh materials at any level of detail. On the other hand, if the objective is to investigate waste quantity and recycling behaviour in relation to economic factors, then the questionnaire survey may be more appropriate. Finally, it should be stressed that the above conclusions are inferred from a relatively small sample set, particularly those which relate to the sectoral estimates. The validity of these conclusions therefore requires further testing and investigation. Future research should focus on the impact of improving the accuracy of parameter estimates, refining

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the method for making seasonal adjustments, and on obtaining additional observations for large quantity generators. Acknowledgements The authors are grateful to the Metropolitan Toronto Works Department for providing the financial support for this study and the raw data from the DWA study. The first author also wishes to thank his thesis supervisor, Dr Joseph Whitney, for his guidance throughout this research and his comments on a draft version of this paper. References Bird & Hale Ltd. (1978) Municipal Re/itse Statisti~w lbr Canadian Communities ql Over I00,000 (1976-1977). Belson. W. A. (1986) Validit.r in Survey Research. Aldershot, U.K.: Gower Publishing Company Ltd. Britton, P. W. (1972) Improving Manual Solid Waste Separation Studies. Journal q/the SanitaJ:v Enghwerhsg Division. ASCE 98. No. SA5. Brunner, R H. & Ernst, W. R. (1986) Alternative Methods for the Analysis of Municipal Solid Wastes. Waste Mamtgement & Research 4, 147 160. Cal Recovery Systems Inc./Recovery Sciences Inc. (1989) Waste Characteri-ation Study .[br Berkek:v. Cal(/ornia. Prepared for the Department of Public Works, City of Berkeley, California. U.S.A. Cook, R. D. (1977) Detection of Influential Obserwltion in Linear Regression. Tec/mometrics 19. 15-18. DeGeare, T. V. Jr. & Ongerth. J. E. (1971) Empirical Analysis of Conamercial Solid Waste Generation. Journal ~!/ the Sattitarr Enghleering Divishm. ASCE 97 (SA6), 843-850. Franklin Associates Ltd. (1990) Esthmtles o[ the I'bhtme qf MS I,V aml Selected Components in Trash Cans and Land.fills. Kansas, U.S.A.: Franklin Associates Ltd. Golueke. C. G. (1971) Comprehensil'e Studies ql'S, lid Wasle Management. Third Annual Report, National Technical Information Service, NTIS Report PB 218-265. Washington, D.C., U.S.A.: U.S. Department of Commerce. Gore & Storrie Ltd. (1991a) Ontario II~tste Composition Study. Iq)htme 11. Commercial I,l~lste Composition Study. Toronto, Canada: Ontario Ministry of the Environment. Gore & Storrie Ltd. (1991h) Ontario H'aste Composition Study. I'o/ttme IlL" Proce~ho'es l]'ldlllld~ Toronto, Canada: Ontario Ministry of the Environment. Hinshaw, J. & Braun. I. (1991) Targeting Commercial Businesses for Recycling. Resottrce Recvclhlg. November, 27 32. Kaufl)nan, C. R. (1990) Quantity am/Compo.~'ilhm o.f Hou.s'e/iokl So~k~ I,l,'ustes Col/ecled in Kuala Lumpto; Mah(vsia. Master of Applied Science thesis, University of Toronto, Canada. Klee, A. J. (1980) Design & Management./or Resources Recto'err l"oL 3. Quantitative Decishm Makhlg. Ann Arbor. MI, U.S.A.: Ann Arbor Science Publishers, Inc./The Butterworth Group. Klee. A. J. & Carruth. D. (1970) Sample Weights in Solid Waste Composition Studies. Jmo'nal qf the Sanitary Eng#werhlg Division. ASCE 96 (SA4), Proc. Paper 7469, 945 954. Lohani. B. N. & Ko, S. M. (1988) Optimal Sampling of Domestic Solid Waste. Journal of Environnwntal Engineering. 114(61, 1479- 1483. Matrix Management Group. R. W. Beck and Associates & Gilmore Research Group (1988) Best Management Practices Analysis for Solid H/aste. l~dunw One. 1987 Reo'cling and Waste Stream Sttrvey. Office of Waste Reduction and Recycling, Washington State Department of Ecology, U.S.A. Matrix Management Group, Herrera Environmental Consultants, R. W. Beck and Associates, Fernandes Associates, and Gilmore Research Group (1989) 1988]1989 Waste Sll'e~tm Composition Study. Final Report. City of Seattle, Department of Engineering, Solid Waste Utility, U.S.A. Matrix Management Group, Herrera Environmental Consultants, R. W. Beck and Associates,

Waste stream quant(fication and characterization

361

Fernandes Associates, C-~S2 Group, Inc. and Elway Research, Inc. (1991) 1990 Waste Stream Composition Study, Final Report. City of Seattle, Department of Engineering, Solid Waste Utility, U.S.A. Michigan Department of Natural Resources (1986) Solid Waste Stream Assessment Guidebook. Michigan Department of Natural Resources, Community Assistance Division, Lansing, Michigan, U.S.A. Musa, E. & Ho, G. E. (1981) Optimum Sample Size in Refuse Analysis. Journal of the Sanita O, Engh~eering Division. ASCE 107, No. EEH, 1247-1259. Mylnarski, H. D. & Northrop, G. M. (1975) Derivation of Residual Coeffi'cientsfor Typical Polluting hldustries in New England. National Technical Information Service, NTIS Report PB 258-996. Washington, D.C., U.S.A.: U.S. Department of Commerce. Nemerow, N. L. (1984) hutustrial Solid Wastes. Cambridge, Massachusetts, U.S.A.: Ballinger Publishing Company. Niessen, W. R. & Alsobrook, A. F. (1972) Municipal and Industrial Refuse: Composition and Rates. Proceedings: 1970 National hwinerator Conference. New York, U.S.A.: American Society of Mechanical Engineers, 319-337. Ontario Legislative Assembly (1992) Bill 143: An Act RespeethTg the Management of Waste h7 the Greater Toronto Area and to Amend the Environmental Protection Act. Toronto, Canada: Queen's Printer for Ontario. Rhyner, C. R. & Green, B. (1988) The Predictive Accuracy of Published Solid Waste Generation Factors. Woste Management & Research 6, 329-338. SCS Engineers (1979) Municipal Solkl Waste Survey Protocol. Municipal Environmental Research Laboratory Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, U.S.A. SCS Engineers (1975) Quantities and Composition of Solid Wastes Generated hy U.S. Envilvnmental Pivtectio, Agency O[h'ces. Washhlgton Mall, Washington D.C.. 4014 Long Beach Boulevard, Long Beach, California 90807, U.S.A. SCS Engineers (1989) Metlv Waste Characterization Study 1989190. Sort No. I Spring Season. Prepared for the Portland Metropolitan Service District, Portland, Oregon, U.S.A. SEN ES Consultants Limited (1992) Waste Stream Quantification and Characterization Methodology Stud); Fimll Report. Volume I--Report. Prepared for: Solid Waste Management Division, Environment Canada, Ottawa, Canada. Steiker, G. (1973) Solid Waste Generation Co¢:[fi'cients: Mamtfacturing Sectors. Regional Science Research Institute, Discussion Paper Series No. 70, Philadelphia, PA, U.S.A. SWEAP (Solid Waste Environmental Assessment Plan) (1991) Waste Composition Studj: Metropolitan Toronto Department of Works. Discussion Paper No. 4.3, Toronto, Canada. Warriner, G. K., McDougall. G. H. G. & Claxton, J. D. (1984) Any Data or None at all'? Living With Inaccuracies in Self-reports of Residential Energy Consumption. Envitvnment and Behaviour 16, 503-526. Wilson, D. W., Rathje, W. L. & Huges, W. W. (1989) ~dtane ¢;f Solid Wastes Under D~f./erhTg Landfill Coml)ositio,s. Compaction E.vperhT~ents on Fresh and Landfill ReJitse Ji'om Tucson, Arizona. The Garbage Project. U.S.A.: Franklin Associates Ltd. Wisconsin Bureau of Solid Waste Management (1988) Guide to ConducthTg Waste Characteri-ation Studies. Madison, WI. U.S.A.: Bureau of Solid Waste Management. Wisconsin Department of Natural Resources (1974) Report on the State of Wisconsin Solid Waste Management Plan. Madison, WI, U.S.A.: Department of Natural Resources.