Characterization of solid waste disposed at Columbia Sanitary Landfill in Missouri

Transcription

1 Waste Manage Res 2005: 23: Printed in UK all right reserved Copyright ISWA 2005 Waste Management & Research ISSN X Characterization of solid waste disposed at Columbia Sanitary Landfill in Missouri Waste sorts were conducted during each of the four quarters (or seasons) of 1996 at the City of Columbia Sanitary Landfill. A detailed physical sampling protocol was outlined. Weight fractions of 32 waste components were quantified from all geographic areas that contribute to the Columbia Sanitary Landfill using a two-way stratification method, which accounted for variations in geographical regions and seasons. Comparisons of solid waste generated between locations and seasons were conducted at the 80% confidence level. The composition of the entire waste stream was 41% paper, 21% organic, 16% plastic, 6% metal, 3% glass and 13% other waste. Paper was the largest composition and glass was the smallest composition for all geographical regions. The result of this study was also compared with a 1987 Columbia, Missouri study conducted by EIERA (1987), with studies conducted in other states such as Minnesota, Wisconsin, Oregon and with national study conducted by the USEPA (USEPA 530-R , PB US Environmental Protection Agency, Office of Solid Waste, Washington, DC). The results of studies from other states are different from this study due to different local conditions, different methodologies and a different scope. There was a small (5%) increase in per capita weight from 1987 to The total per capita weight in the present study was 60% greater than the national per capita weight reported by the USEPA (1996) due to that the USEPA report excluded industrial, construction and certain commercial waste. The total per capita weight agrees with the national per capita weight for municipal waste reported by Tchobanoglous (1993), which included industrial, construction and commercial sources. The geographical and seasonal effects on the waste composition are evaluated and discussed. Statistical analysis indicates that waste characteristics are different among geographical regions and seasons. The potential for waste recovery and reduction is also discussed. Yinghui Zeng Office of Social Economic Data Analysis, University of Missouri, Columbia, MO, USA Kathleen M. Trauth Robert L. Peyton Shankha K. Banerji Department of Civil & Environmental Engineering, University of Missouri, Columbia, MO, USA Keywords: Solid waste characterization, sample survey, integrated solid waste management planning, solid waste composition, wmr Corresponding author: Y. Zeng, Office of Social Economic Data Analysis, University of Missouri, Columbia, MO 65211, USA Tel: +1 (573) ; fax: +1 (573) ; zengyh@umsystem.edu DOI: / X Received 7 January 2003; accepted in revised form 17 November Waste Management & Research

2 Characteristics of solid waste disposed at a landfill in Missouri Introduction The percentage and weight of waste components in a solid waste stream are important data for decision-makers. This information is necessary in order to identify waste components to target for source reduction and recycling programmes, and to allow technical professionals to design material recovery facilities (MRF) and waste-to-energy (WTE) projects. For a MRF, the waste component weight will affect the sizing of the equipment, tipping floor area, recovered product storage areas and possible economic benefits from the sale of recovered products. The waste component percentage will affect the separation and processing configurations. For a WTE facility, the weight and composition will affect the sizing of the facility and the quantity of energy to be produced. National average values of solid waste composition may not accurately reflect conditions in local communities. The magnitudes of the variations of waste components are unknown, but they are needed by solid waste management planners, local officials and MRF designers. In conducting a study of local conditions, a variety of waste characterization methods can be used. Computer models can use national averages for waste generation rates and other community features to calculate waste quantities. This is a quick method, but it does not account for local waste characteristics that can vary significantly from national or regional averages. An alternative is to use materials-flow surveys based on production data for the materials and products in the waste stream, with adjustments for imports, exports and product lifetimes (USEPA 1996). Based on production data, an estimate is made for the total weight of waste generated. Then an estimate is made of the portion of this generated waste that is recycled or composted. The remaining waste is defined as discards. This approach never collects physical samples and is difficult to apply when evaluating waste characteristics at a facility such as a landfill or a treatment facility. It is more suitable for conducting national studies where collecting physical samples from a rather broad area is difficult. The approach chosen for this study was to determine a statistically significant sampling size, then use physical sampling, separation and direct measurement to quantify waste characteristics. This approach accounts for the unique characteristics of each source location and the landfill as a whole. The objectives of this study were to: (1) quantify 32 components of the waste entering the City of Columbia Sanitary Landfill by source location, sector and season; (2) present a physical sampling protocol to collect the desired number of solid waste samples determined with the two-way stratification method for minimizing sampling errors; (3) contribute data to a state-wide waste characterization study and develop a database that can be used for economic analysis of a material recovery facility and for assessment of existing waste reduction programmes within the landfill service area; and (4) evaluate the potential for waste recovery and reduction. Methodology The data were collected at the City of Columbia Sanitary Landfill in Columbia, Missouri during Thirty-two targeted materials or sorting categories were selected. The composition was then categorized into six categories: paper, organic, plastic, metal, glass and other (Table 1). Because seasonal variation and geographical variation can have a significant impact on waste characteristics, sampling was designed to be two-way stratified. The first level of stratification is seasonal stratification. The second level is geographical stratification. Sampling was designed to take place during each of the four quarters of the year. Quarter 1 was from 22 February to 29 March 1996, quarter 2 was from 1 May to 29 May 1996, quarter 3 was from 5 August to 11 September 1996 and quarter 4 was from 4 November to 23 December The service area of the landfill was subdivided into six regions consisting of the cities of Centralia, Columbia and Mexico and the unincorporated areas of Audrain, Boone and Calla- Table 1: Waste composition category. Waste composition category Waste components Paper Corrugated board, box board, newsprint, magazines, office paper, mixed paper (all paper that does not fit into other category) Organic Food, wood, textiles, manure, other Plastic PET(#1), HDPE(#2), PVC(#3), LDPE(#4), PP(#5), PS(#6), Other Metal Ferrous, non-ferrous, bi-metal, aluminium cans, other aluminium Glass Clear, brown, green, other Other Nappies/sanitary products, banned items*, fines (pass through 63-mm-opening sieve), medical waste, miscellaneous (demolition waste and any other waste) * Missouri State Law bans the acceptance of waste such as yard waste, tyres, batteries and large appliances, motor oil, etc. Waste Management & Research 63

3 Y. Zeng, K.M. Trauth, R.L. Peyton, S.K. Banerji Table 2: Component weight of waste entering Columbia Landfill during Geographical source* Weight of waste composition category (tonne) Paper Plastic Metal Glass Organic Other Total Per capita (kg year 1 ) Audrain County a Boone County b City of Columbia c Commercial/industrial Residential University of Missouri Callaway County d Centralia e Mexico f All waste stream *Waste came from a 50% of population in Audrain County outside of City of Mexico and 5% of Montgomery County; b population from Boone County excluding City of Columbia, City of Centralia and City of Ashland; c population from City of Columbia and 5% of Osage County; d population in Callaway County outside of City of Fulton and 5% of Osage County and 5% of Montgomery County; e population from City of Centralia; and f 5% of population in City of Mexico. way counties. The City of Columbia was further subdivided into three sectors: commercial/industrial, residential and university (Table 2). For the first quarter of the year, no local data were available for estimating the number of samples needed. Twelve waste components in the ASTM International (1992) national data set were used to estimate the required number of samples. For an error, e = 0.05, 0.10 and 0.20, respectively, the number of samples needed were 522, 131 and 34 to achieve an 80% confidence level. One hundred and fifty-one samples corresponding to an error between e = 0.10 and e =0.05 were selected for the present study. During the subsequent sampling periods, due to cost and resource constraints, the number of samples collected was adjusted to 128, 130 and 127, respectively. A two-way stratification method was developed to account for variations between the geographical regions and seasons in calculating waste compositions. Sampling protocol Sample weight The term sample size is sometimes used to refer to two different parameters in solid waste characterization studies. One is the number of sample units to be sorted. The other is the weight of each unit. In this paper, sample size means the number of sample units, and sample weight, means the weight of each unit. The sample weight affects the variability of estimation. Obviously, if a sample weight is too small, it would give inaccurate results, because, for example, a large piece of wood could not physically be included in a small sample. However, separating and sorting raw solid waste is expensive. A typical vehicle load of commercial solid waste weighs between 4500 and 9000 kg. It is not practical to separate the entire vehicle load or a load from an even larger vehicle. Klee (1980) indicated that the smaller the sample weight, the greater the variance of the waste sample composition. He stated that as sample weights decreased from approximately 91 kg, the sample variance increased rapidly, but that above approximately 140 kg, the variance increased much more slowly. He thus recommended a sample weight between 91 and 140 kg. This recommendation was also adopted by the ASTM (1992) standard. Therefore, the target sample weight for this study was set at 140 kg. Field protocol Trucks from each geographical area were numbered and randomly selected from the geographical area during a sampling period. The previously identified truck needed to unload the waste at a working area. A landfill worker then used a frontend loader to mix the waste and collect a 140 kg (within 10% error) waste sample. The waste sample was identified by a scale house ticket with the record of the source of waste, total weight measured at the scale house, date and time of arrival at the scale house. The identified waste sample was then transported to the sorting shed and was deposited onto a highdensity polyethylene (HDPE) 60-mil liner for sorting. Sorters removed the large items such as large pieces of corrugated board and put them in an identified container. Trash bags were torn open. Portions of the waste were placed onto a sorting table and were then sorted by hand and placed into identified containers. An estimate of wetness of the sample was made and recorded on the data sheet. After the sorting was completed, each container was weighed with an accuracy of ± 0.23 kg. Standard personnel safety procedures were followed during the sorting process such as wearing gloves, apron/coverall, safety glasses and boots, sorting the nearest item first, etc. 64 Waste Management & Research

4 Characteristics of solid waste disposed at a landfill in Missouri Table 3: Composition of waste entering Columbia Landfill during Geographical source Waste composition(%) Paper Plastic Metal Glass Organic Other Total Audrain County Boone County City of Columbia Commercial/industrial Residential University of Missouri Callaway County Centralia Mexico All waste stream Results and discussions Table 4: Percentage error of mean weight fraction of four quarters with 80% confidence level. Waste component Q1 Q2 Q3 Q4 Total paper Total plastic Total metal Total glass Total organic Total other Weighted average Waste component weight and composition As shown in Table 2, a total of t of waste entered the City of Columbia Sanitary Landfill during 1996 (City of Columbia s Solid Waste Utility, 1996). The City of Columbia contributed the most (55%, t) to the total waste stream entering the landfill in The City of Columbia and the remainder of Boone County contributed approximately 89% ( t) of the waste stream. They contributed the most to each of the waste composition categories. 61% of Columbia waste came from the commercial/industrial waste sector, 31% came from the residential sector and 8% from the University of Missouri. The per capita weight was also calculated using estimated service population of each geographical source. It was estimated that waste came from 50% of the population in Audrain County outside the City of Mexico and 5% of the population in Montgomery County; the population in Boone County excluding that in the City of Columbia, the City of Centralia and the City of Ashland; the population in the City of Columbia and 5% of the population in Osage County; the population in Callaway County outside of the City of Fulton and 5% of the population in both Osage County and Montgomery County; the population in the City of Centralia and 5% of the population in the City of Mexico; and the remaining 95% of the population of the city of Mexico. The percentages of the population were estimated by talking to waste haulers and City of Columbia Solid Waste Utility personnel. As some portion of the waste of some counties or cities goes to different landfills, it is difficult to estimate an accurate service population. However, because all waste in the City of Columbia goes to the City of Columbia Landfill, the per capita weight for the City of Columbia has a higher degree of confidence (820 kg/year, 2.3 kg/day). The waste composition for the entire waste stream and geographical sources in 1996 is shown in Table 3. The percentage composition of waste combined from all locations was 41% paper, 21% organic, 16% plastic, 6% metal, 3% glass and 13% other waste. Paper was the largest composition and glass was the smallest composition for all locations. Table 4 presents the percentage error for the mean weight fractions for four quarters with an 80% confidence level, which means that the true mean will lie within the range of the estimated mean ± error associated with the 80% confidence. Because only a limited number of samples of the waste stream were collected for measurement of waste component weight fraction, there is a degree of uncertainty for each mean weight fraction value. This uncertainty is represented by the percentage errors and confidence associated with the errors. The percentage error depends on the number of samples collected: the more samples collected, the lower the error. The percentage error also depends on the variability of the waste stream. The less variable (or more uniform) the waste stream, the lower the errors. The percentage errors tend to be larger for waste components that make up the smaller fractions of the waste stream (e.g., other organics, medical waste, etc.) because with the smaller fraction comes the larger variability. The seasonal and geographical variations in the waste stream and the sample weight of each sample also contribute to the error. The percentage errors tend to be larger for those geographical locations where the fewest samples were collected. Waste Management & Research 65

5 Y. Zeng, K.M. Trauth, R.L. Peyton, S.K. Banerji Table 5: Comparison with other studies. Waste Composition (%) Waste component Columbia 1987 Columbia 1996 EPA 1994 Minnesota 1999 Wisconsin 2001 Oregon 2002 Paper Plastic Metal Glass Wood Textiles Food Inorganic Yard waste Other The number of samples needed to achieve a certain degree of accuracy is affected by seasonal and geographical variations in the local waste stream. For the first quarter of the year, no local data were available for estimating the number of samples needed. One hundred and fifty-one samples, corresponding to an error between e =0.10 and e = 0.05 was selected based on the national data set and simple sampling method (ASTM 1992). The simple sampling method suggests that the 151 samples would generate an error of approximately 10%. After actually collecting 151 samples from the Columbia landfill, and analysing them using the two-way stratified method to calculate the percentage error, the actual error was approximately 20% (Table 4), rather than the 10% predicted by the simple sampling method. This indicates that the use of a national dataset and the simple sampling method did affect the accuracy of the results. To achieve greater accuracy, more samples would be needed. Comparison with other studies A comparison of this study with other studies is presented in Table 5. The waste components analysed in each study were different and regrouped to match the components defined in the national dataset (ASTM 1992). In a Columbia, Missouri 1987 waste characterization study (EIERA 1987), waste sampling was conducted in May and August of There was no reporting of the number of samples taken, the selection criteria, etc. in 1987 EIERA document. Based on an estimation from the City of Columbia Solid Waste Utility (1996), the number of samples collected was less than 10. With the very small number of samples collected, the percentage error associated with the result would be expected to be larger than that generated by the present study. A 1999 Minnesota study (Minnesota Solid Waste Management Coordinating Board 2000) was conducted in the Twin Cities Metropolitan Area. Waste sorts were conducted for five disposal sites through four seasons. The sorts were conducted each season for a 1-week period at each site. A 90 to 180 kg sample was taken from a sample truck. The number of samples collected for each sort was reported as 40 to 60. A total of 1170 samples were sorted. The 2001 Wisconsin study (Cascadia Consulting Group, Inc. 2003) was a state-wide waste characterization study. Samples were collected from 14 landfills during two sampling days. A total of 400 waste samples of 90 to 140 kg were each sorted into 64 categories. The Oregon 2002 study (State of Oregon Department of Environmental Quality 2002) was also a state-wide study. A total of 884 samples were collected for 60 waste substreams and the results were averaged. The USEPA annually publishes a national municipal solid waste characterization report. The USEPA report published in 1996 was most comparable to this study. It presents the results of a study conducted in 1995 which was based on 1994 data. All studies except the USEPA study were based on a physical sampling method, whereas the USEPA study was based on a material flow survey method. The results of studies from other states are different from this study for several reasons. The sampling design and data analysis method for this study was a two-way stratification method, which accounts for variations among seasons and regions when calculating the mean weight fraction. The method used for some of the other studies was a simple sam- 66 Waste Management & Research

6 Characteristics of solid waste disposed at a landfill in Missouri pling method in which a simple arithmetic mean was computed for the weight fraction. The studies for Minnesota, Wisconsin and Oregon were all state-wide studies which included multiple landfills whereas this study only investigated the waste stream entering the City of Columbia landfill. The local conditions such as the type of waste accepted at landfills, climate, economic activities, life styles, recycling and waste management programmes all have a great impact on the waste composition. There was also a lack of a standard definition for waste sorting categories. Thus, each state defined some of their waste categories differently from other states. The results from the USEPA waste characterization study are different from the results of the present study for three reasons. First, the USEPA methodology was different. Instead of using waste sampling data, the USEPA used a materialsflow approach based on production data for the materials and products in the waste stream, with adjustments for imports, exports and product lifetimes. Second, the USEPA characterization study did not cover all materials that enter Subtitle D landfills, such as the City of Columbia Sanitary Landfill. The materials included by the USEPA are defined in their 1996 report (USEPA 1996). This is a different waste stream from that entering the City of Columbia Sanitary Landfill. For instance, the Columbia landfill does accept certain industrial process wastes and construction and demolition debris but does not accept yard trimmings, large appliances, or automobile tyres, whereas the USEPA study did not include wastes from construction and demolition debris, industrial process wastes but did include yard trimmings, large appliances and automobile tyres. Third, the USEPA study is a nationwide study, whereas the study presented here covers a small region. The differences in methodology and definitions described above serve to confound the comparisons. The total waste entering the landfill increased by 40% from 1987 to 1996; most of this increase was due to an increase in the service population. The per capita increase was only 5%. The largest changes in percentage weight among the categories in Table 5 were for plastics, which increased from 7 to 16% (a 129% increase). Inorganic and yard waste both decreased from 7 to 1% (86% decreases). Wood decreased from 15 to 7% (a 53% decrease) and glass decreased from 4 to 3% (a 25% decrease). The large decrease in yard waste composition is due to Missouri Statute RSMo (2004), which mandated that after 1 January 1992, yard waste was not to be allowed to enter landfills. In addition, the decrease in glass composition indicates that recycling for glass was effective. The total recycled glass in 1995 was 200 t (City of Columbia Solid Waste Utility 1996). The composition of waste with respect to corrugated board and newsprint for Columbia landfill decreased from 17 to 13% and 8 to 5%, respectively, from 1987 to This agrees with the 1995 recycling data of 500 t and 400 t, respectively (City of Columbia Solid Waste Utility 1996). However, other un-recycled paper components such as magazines increased. This caused the total paper composition to remain the same from 1987 to A detailed breakdown of the waste category comparisons is shown in Table 6. Among the five categories in Table 6, the weight percentages for paper, plastic and metal were higher at the Columbia landfill (30, 21 and 2% higher, respectively) and glass and other materials were lower at the Columbia landfill (50 and 23% lower, respectively). The per capita weights from Columbia were higher in four of the five categories (not glass). The total per capita weight at the Columbia landfill (800 kg year 1 ) is 60% higher than the national average total per capita weight reported by USEPA (1996), which excluded waste from construction and demolition debris and industrial process waste. A typical per capita value of 1000 kg year 1 for total municipal solid waste generation in US was reported by Tchobanoglous et al. (1993). This value was converted to 800 kg year 1 of discarded waste for total municipal solid waste by using a discard-to-generation ratio of (USEPA 1996). The Columbia per capita weight agrees with the typical national value reported by Tchobanoglous et al. (1993), which included construction and industrial waste that was excluded in the USEPA report (1996). The result with respect to yard waste and waste tyres is rather dramatic: Finally, all individual components had higher per capita weights at the Columbia landfill except for yard trimmings and rubber tyres, which were lower by 88%. This is a clear indication of the effectiveness of the ban on yard waste and tyres at the Columbia landfill (Center for Environmental Technology and Energy Systems and Resources Program 1997). Comparison between locations and sectors Table 7 presents a listing of locations with waste characteristics that are different from each other at the 80% confidence level. It is logical to conclude that annual mean weight fractions that are close to each other may indicate waste characteristics that are similar. Statistical tests were conducted to determine whether there were significant differences between the weight fractions of the waste components at the eight different locations. The tests not only consider the mean value but also consider the extent of spread, or variance, of all measured values about the mean value and consider the number of measured values. Two possibilities were considered. One possibility was that the variances of the two populations, p, were unknown but equal. The other possibility was that the variances of the two populations were unknown and unequal. An F test (Milton Waste Management & Research 67

7 Y. Zeng, K.M. Trauth, R.L. Peyton, S.K. Banerji Table 6: Waste composition comparison with national average (USEPA 1996). Component Corrugated board National 1994 (%) a Columbia 1996 (%) Per capita b 1994 (kg year 1 ) Per capita c Columbia 1996 (kg year 1 ) Box board Newsprint Magazines Office paper Mixed paper Total paper PET (#1) HDPE (#2) PVC (#3) LDPE (#4) PP (#5) PS (#6) Other plastic Total plastic Aluminium Ferrous and bimetal Non-ferrous Total metal Total glass Food waste Yard trimmings and rubber tyres d Textiles Wood Other wastes Total other Total a Materials from municipal solid waste that were discarded after materials and compost recovery. b Equal to percentage of materials discarded divided by 100, multiplied by tons total materials discarded in US in 1994, multiplied by 2000 lb/ton, multiplied by 0.45 kg/lb, divided by the July 1994 US population of c Equal to percentage of total weight that entered Columbia landfill in 1996 divided by 100, multiplied by tons total weight that entered Columbia landfill in 1996, multiplied by 2000 lb/ton, multiplied by 0.45 kg/lb, divided by the estimated 1996 total service population of d Assumed equivalent to banned items in present study. & Arnold 1995) was first conducted to compare the variances of the two populations. If the F test indicated that the variances were equal, a test for comparing means with equal variances was performed; otherwise, the test for comparing means with unequal variances was performed. When it was assumed that the variances were equal, a pooled sample variance was computed and a pooled t test was conducted; otherwise, the individual variances were used to conduct the t test (Milton & Arnold 1995). The General Linear Model in SAS (1990) was used to conduct the tests, using a 80% confidence level. The results are summarized below. The weight fractions varied from location to location. Table 7 shows the locations where waste characteristics were different. The letters are the components for which the waste characteristics were different. Every location had at least one waste component that was different from that of other locations. The locations that had the greatest number of different components were Callaway County and the City of Columbia. There were no significant differences between total other waste characteristics at any locations. These results support the notion that waste stream characteristics vary with geographical region and this variation should be taken into consideration when designing a sampling strategy for a waste characterization study. Comparison between seasons Table 8 presents a summary of the annual mean weight fractions of the six waste categories for each of the four quarterly sorts. A statistical test was conducted to determine whether there were significant differences among the waste characteristics between quarters. The method was the same as used to test for significant differences between locations explained in the section entitled Comparison between locations and sectors above. Every quarter had at least one component that was different in other quarters, except that quarter 2 was not different from quarter 3. This lack of difference was probably because of the fact that quarter 2 has an average of 23% of error which is larger than the 20% for other quarters (Table 4). The differences in quarter 3 could be related to special summer events that draw out-of-town visitors and the transition in student population. The differences in quarter 4 could be related to holiday activities. The differences in quarter 1 could probably be due to the lack of special events, transitions and holidays that create the sharp contrasts between quarters 3 and 4. Potential for recovery and reduction Hereafter, the potential for waste recovery and reduction is discussed from the view of market value. Table 9 presents the market values for recyclable materials in the Columbia waste stream. The prices were obtained from Associated Recyclers of the Midwest, The cost of waste collection is not included because it would be incurred whether the waste was recycled or landfilled. 68 Waste Management & Research

8 Characteristics of solid waste disposed at a landfill in Missouri Table 7: Listing of locations with waste characteristics that are different from the location shown at the top of column, at 80% confidence level. Comparisons among geographical regions a Location name Location number Location number Audrain 1. M, G, O P, M, G P, L, M, G L, O G G, O L, O Boone 2 M, G, O. P, M, G, O P, L, M, O L, M, G M, O P, M L, M, G Callaway 3 P, M, G P, M, G, O. L, M, G P, L, M, G, O P, M, G, O P, M, G, O P, L, M, G, O Centralia 4 P, L, M, G P, L, M, O L, M, G P, G, O P, G P, L, M, O P, G, O the revenues over cost are shown in Table 10 for various facility lives and interest rates. The present worth of the revenues minus O&M cost far exceeds the capital cost, even considering short facility lives and high interest rates. Such present worth would still show the value of recycling even if revenues were less than calculated above or if excess costs associated with bags or bins for recyclable collection were considered. However, this is just a rough estimate, many factors are community specific. The feasibility of an MRF should not depend purely on economics as it did in the past. The USEPA has promulgated regulations for municipal solid waste landfills as required by subtitle D of the Resource Conservation and Recovery Act of 1976 (RCRA 42 U.S.C et seq., 2004), effective October 9, Both existing and new landfills were affected by the statute. Available landfill volume is decreasing because of stringent regulations (The above calculations do not consider the cost of environmental protection features now required for landfills or the cost of groundwater cleanup from potential contamination.). The wastes that used to be discarded need to be recovered and managed in a sustainable way. The USEPA recommends that recycling be the top priority option used in an integrated solid waste man- Columbia- Comm./Ind. 5 L, O L, M, G P, L, M, G, O P, G, O. G, O P, L, M, G L Columbia-Res. 6 G M, O P, M, G, O P, L, G L, G, O. P, M, G, O L, G, O Columbia-MU 7 G, O P, M P, M, G, O P, L, M, O P, L, M, G P, M, G, O. L, M, G Mexico 8 L, O L, M, G P, L, M, G, O P, G, O L G, O L, M, G. a P, total paper; L, total plastics; M, total metal, G, total glass; O, total organics. Table 8: Comparison between seasons. Mean weight fraction Seasons Total paper Total plastic Total metal Total glass Total organic Total other Quarter Quarter Quarter Quarter Annual mean The total weight of recyclables was t (108 t/d). If the recovery rate is assumed to be 80%, the recovered materials are then % = 86 t/d. The revenue is $/d (Table 9). The cost of recycling is the sum of the capital and operating cost (O&M) of the material recovery centre (MRF). Although detailed costs vary by community, the configuration of the MRF and many other factors, one can make preliminary estimates from the general average cost data. The typical unit capital cost for a low-tech MRF is $ per tonne of daily capacity (Tchobanoglous and Kreith, 2002). Thus, the capital cost for an MRF is approximately 1.7 million dollars. The typical O&M cost for a low-tech MRF is 20 $/t. The waste collected from the City of Columbia is 171 t/d ( t/y) (Table 2). Thus, the O&M cost for an MRF would be 3420 $/d. Revenues thus exceed costs by 5580 $/d or $/yr (not considering the time value of money within the year). The revenues from one year of operation would pay for the construction of the MRF. The long-term implications of recycling can be seen from considering the present worth of the value of revenue minus O&M cost. One can convert an annual value to a present worth value using standard economic tables if the life of a facility and an interest rate are specified. Present worth values for Waste Management & Research 69

9 Y. Zeng, K.M. Trauth, R.L. Peyton, S.K. Banerji Table 9: Recyclable materials waste stream entering Columbia landfill in Recovered material 2004 price a (US$ t 1 ) Weight (t) Recovered weight b (t) Market value (US$) Metal Aluminium cans c Other aluminium c Steel cans Ferrous Plastic d HDPE natural HDPE mixed colour HDPE/PET mixed PET clear loose PET mixed colour Paper and paper board Newspaper loose Corrugated loose Office mixed loose Magazines loose Glass Clear Brown Green Total a Price is obtained from Associated Recyclers of the Midwest b Assumes that 90% of the weight is recoverable. c Annual weight was computed as the mean percentage entering the landfill from this source during quarters 3 and 4 times the total annual weight of all waste entering the landfill from this source, since quarters 3 and 4 were the only quarters when measurements were taken for aluminium cans. d Arbitrarily assumes the following distribution: PET (25% clear loose, 25% mixed colour, 50% mixed with HDPE); HDPE (25% natural, 25% mixed colour, 50% mixed with PET). Table 10: Present worth values for excess of revenues over O&M costs (E. Grant et al., 1982). Facility Life (years) Interest Rate (%) Present Worth Factor Present Worth ($) agement system. Economical feasibility is not the only factor that drives the solid waste management system. A sustainable solid waste management system also needs to be environmentally sound and socially acceptable. Therefore, the revenue from recyclables can only be viewed as a supplemental benefit, not as a determining factor. 70 Waste Management & Research

10 Characteristics of solid waste disposed at a landfill in Missouri Conclusions A total of 536 samples were collected at the City of Columbia Sanitary landfill for 32 waste components in The waste stream was subdivided into two-way stratified sub-levels with the geographical region being the first level of stratification and the season being the second level of stratification. Random samples were collected for each sub-level. A detailed physical sampling protocol was outlined for the sampling scheme determined by the two-way stratification method. It is estimated that a total of t of waste entered the City of Columbia Sanitary Landfill during The City of Columbia contributed the most to the waste stream. The per capita weight for the City of Columbia in 1996 was 2.19 kg person 1 day 1 or 800 kg person 1 year 1 (Table 2). The composition of the waste stream was 41% paper, 21% organic, 16% plastic, 6% metal, 3% glass and 13% other waste. Seven components represented almost 60% of the total waste entering the landfill. Ranked by weight, these were corrugated board, mixed paper, box board, food, miscellaneous other waste (primarily construction-related waste), wood and newsprint. The total weight of waste that entered the landfill increased by 40% from 1987 to Most of this increase was due to an increase in the service population. The per capita increase was only 5%. It was difficult to accurately compare the results of this study with other waste characterization studies because of the lack of consistency in methodology and waste component definitions. For instance, this study sampled all waste entering the landfill. In the national study, the USEPA (1996) excluded certain waste streams from their analysis. The comparisons that were possible suggest variability in waste characteristics by geography and seasons that should be addressed by site-specific sampling for integrated solid waste management. Acknowledgement This project was funded by a grant from the Missouri Department of Natural Resources Solid Waste Management Program. Portions of the information presented in the paper are also documented in the 1997 final project report submitted to the funding agency. R.L.P., the principal investigator and major author of the 1997 report, served as dissertation advisor to Y.Z. during this time. References ASTM International (1992): ASTM D Standard Test Method for Determination of the Composition of Unprocessed Municipal Solid Waste. For referenced ASTM standards, visit the ASTM website, astm.org, or contact ASTM Customer Service at service@astm.org. For the Annual Book of ASTM Standards volume information, refer to the standard's Document Summary page on the ASTM website Associated Recyclers of the Midwest (2004): http@//recyclingcoop.org/market.htm Cascadia Consulting Group, Inc. (2003): Wisconsin Statewide Waste Characterization Study. Cascadia Consulting Group, Inc, Wisconsin. Center for Environmental Technology and Energy Systems and Resources Program. University of Missouri-Columbia (1997): Waste Characterization Study for City of Columbia Sanitary Landfill. Columbia, Missouri. City of Columbia s Solid Waste Utility (1996): Oral conversation with Columbia s solid waste utility. Contact information or other details are on their web site: EIERA (1987): Statewide Resource Recovery Feasibility and Planning Study, Volume II, Solid Waste Characterization Report. Environmental Improvement and Energy Resources Authority, State of Missouri Department of Natural Resources, Jefferson City, Missouri. Eugene L. Grant, W. Grant Ireson, and Richard S. Leavenworth. (1982): Principles of Engineering Economy, Seventh Edition. John Wiley & Sons, Inc., New York. Klee, A.J. (1980): Quantitative Decision Making, Design & Management for Resource Recovery Series, Vol. 3. Ann Arbor Science, Ann Arbor, Michigan. Milton, J.S. & Arnold. J.C. (1995): Introduction to Probability and Statistics, third edition. McGraw-Hill, New York. Minnesota Solid Waste Management Coordinating Board (2000): Statewide MSW Composition Study Final Report. Minnesota Solid Waste Management Coordinating Board, Minnesota. 42 U.S.C et seq. Resource Conservation and Recovery Act of dglbvtz-zsksa&_md5=9a4ce05df4667f0b0c5b81a1dd192fad RSMo (2004): Missouri Revised Statutes. Chapter 260. Environmental Control. Section SAS (1990): SAS/STAT User's Guide: Version 6. 4th edition, v.2. SAS Institute Inc., Cary, NC. SRI International (1992): Data Summary of Municipal Solid Waste Management Alternatives. NREL. State of Oregon Department of Environmental Quality Oregon Solid Waste Characterization and Composition. Sky Valley Associates, Oregon. Tchobanoglous, G., Theisen, H., & Vigil, S. (1993): Integrated Solid Waste Management: Engineering Principles and Management Issues. McGraw- Hill, New York. Tchobanoglous, G. & Kreith, F. (2002): Handbook of Solid Waste Management. 2nd edition. New York. USEPA (1996): Characterization of Municipal Solid Waste in the United States: 1995 Update. USEPA 530-R , PB Waste Management & Research 71