P A P E R 05 2005 Characterization Of S O L I D W A S T E Disposed At Columbia Sanitary Landfill In Missouri

Waste Management & Research
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Characterization of solid waste disposed at Columbia Sanitary Landfill in Missouri
Yinghui Zeng, Kathleen M. Trauth, Robert L. Peyton and Shankha K. Banerji
Waste Management Research 2005; 23; 62
DOI: 10.1177/0734242X05050995

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Waste Manage Res 2005: 23: 62–71
Printed in UK – all right reserved
ISSN 0734–242X

Characterization of solid waste disposed at
Columbia Sanitary Landfill in Missouri

Yinghui Zeng
Waste sorts were conducted during each of the four quarters
(or seasons) of 1996 at the City of Columbia Sanitary Landfill. Office of Social Economic Data Analysis, University of Missouri,
Columbia, MO, USA
A detailed physical sampling protocol was outlined. Weight
fractions of 32 waste components were quantified from all geo-
Kathleen M. Trauth
graphic areas that contribute to the Columbia Sanitary Land-
Robert L. Peyton
fill using a two-way stratification method, which accounted
Shankha K. Banerji
for variations in geographical regions and seasons. Compari-
Department of Civil & Environmental Engineering, University
sons of solid waste generated between locations and seasons
of Missouri, Columbia, MO, USA
were conducted at the 80% confidence level. The composi-
tion of the entire waste stream was 41% paper, 21% organic, Keywords: Solid waste characterization, sample survey, inte-
grated solid waste management planning, solid waste composi-
16% plastic, 6% metal, 3% glass and 13% other waste. Paper
tion, wmr 633–8
was the largest composition and glass was the smallest com-
position 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-96-
001, PB96-152 160. 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 1996. 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
Corresponding author: Y. Zeng, Office of Social Economic
national per capita weight for municipal waste reported by Data Analysis, University of Missouri, Columbia, MO 65211,
Tchobanoglous (1993), which included industrial, construc- USA
Tel: +1 (573) 884-9137; fax: +1 (573) 884-4635;
tion and commercial sources. The geographical and seasonal
e-mail: zengyh@umsystem.edu
effects on the waste composition are evaluated and discussed.
Statistical analysis indicates that waste characteristics are dif- DOI: 10.1177/0734242X05050995
ferent among geographical regions and seasons. The potential
for waste recovery and reduction is also discussed. Received 7 January 2003; accepted in revised form 17 November 2004

62 Waste Management & Research

Characteristics of solid waste disposed at a landfill in Missouri

Introduction

The percentage and weight of waste components in a solid chosen for this study was to determine a statistically signifi-
waste stream are important data for decision-makers. This cant sampling size, then use physical sampling, separation
information is necessary in order to identify waste compo- and direct measurement to quantify waste characteristics. This
nents to target for source reduction and recycling pro- approach accounts for the unique characteristics of each source
grammes, and to allow technical professionals to design mate- location and the landfill as a whole.
rial recovery facilities (MRF) and waste-to-energy (WTE) The objectives of this study were to: (1) quantify 32 com-
projects. For a MRF, the waste component weight will affect ponents of the waste entering the City of Columbia Sanitary
the sizing of the equipment, tipping floor area, recovered Landfill by source location, sector and season; (2) present a
product storage areas and possible economic benefits from physical sampling protocol to collect the desired number of
the sale of recovered products. The waste component per- solid waste samples determined with the two-way stratifica-
centage will affect the separation and processing configura- tion method for minimizing sampling errors; (3) contribute
tions. For a WTE facility, the weight and composition will data to a state-wide waste characterization study and develop
affect the sizing of the facility and the quantity of energy to be a database that can be used for economic analysis of a mate-
produced. National average values of solid waste composition rial recovery facility and for assessment of existing waste
may not accurately reflect conditions in local communities. reduction programmes within the landfill service area; and
The magnitudes of the variations of waste components are (4) evaluate the potential for waste recovery and reduction.
unknown, but they are needed by solid waste management
planners, local officials and MRF designers.
Methodology
In conducting a study of local conditions, a variety of
The data were collected at the City of Columbia Sanitary
waste characterization methods can be used. Computer mod-
Landfill in Columbia, Missouri during 1996. Thirty-two tar-
els can use national averages for waste generation rates and
geted materials or sorting categories were selected. The compo-
other community features to calculate waste quantities. This
sition was then categorized into six categories: paper, organic,
is a quick method, but it does not account for local waste
plastic, metal, glass and other (Table 1).
characteristics that can vary significantly from national or
Because seasonal variation and geographical variation can
regional averages. An alternative is to use materials-flow sur-
have a significant impact on waste characteristics, sampling
veys based on ‘production data for the materials and products
was designed to be two-way stratified. The first level of strat-
in the waste stream, with adjustments for imports, exports and
ification is seasonal stratification. The second level is geo-
product lifetimes’ (USEPA 1996). Based on production data,
graphical stratification. Sampling was designed to take place
an estimate is made for the total weight of waste generated.
during each of the four quarters of the year. Quarter 1 was
Then an estimate is made of the portion of this generated waste
from 22 February to 29 March 1996, quarter 2 was from 1 May
that is recycled or composted. The remaining waste is defined
to 29 May 1996, quarter 3 was from 5 August to 11 September
as discards. This approach never collects physical samples and
1996 and quarter 4 was from 4 November to 23 December 1996.
is difficult to apply when evaluating waste characteristics at a
The service area of the landfill was subdivided into six ‘regions’
facility such as a landfill or a treatment facility. It is more suit-
consisting of the cities of Centralia, Columbia and Mexico
able for conducting national studies where collecting physi-
and the unincorporated areas of Audrain, Boone and Calla-
cal samples from a rather broad area is difficult. The approach

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.

63

Y. Zeng, K.M. Trauth, R.L. Peyton, S.K. Banerji

Table 2: Component weight of waste entering Columbia Landfill during 1996.

Weight of waste composition category (tonne) Per capita
Geographical source*
(kg year–1)
Paper Plastic Metal Glass Organic Other Total
a
Audrain County 900 300 100 37 600 200 2100 300
b
Boone County 16 800 4800 2900 1300 7700 5700 39 200 900
City of Columbiac 25 600 11 200 3100 1500 13 200 7700 62 300 800
Commercial/industrial 15 300 8100 1900 700 7500 4700 38 200
Residential 8000 2400 1000 700 4800 2300 19 200
University of Missouri 2300 700 200 100 900 700 4900
Callaway Countyd 1300 400 300 200 1000 400 3600 600
e
Centralia 1400 800 200 100 1000 500 4000 1200
Mexicof 1200 500 100 46 500 300 2600 4600
All waste stream 47 200 18 000 6700 3200 24 000 14 800 113 800 800
*Waste came from a50% of population in Audrain County outside of City of Mexico and 5% of Montgomery County; bpopulation from Boone
County excluding City of Columbia, City of Centralia and City of Ashland; cpopulation from City of Columbia and 5% of Osage County; dpopu-
lation in Callaway County outside of City of Fulton and 5% of Osage County and 5% of Montgomery County; epopulation from City of Centralia;
and f5% of population in City of Mexico.

way counties. The City of Columbia was further subdivided Klee (1980) indicated that the smaller the sample weight,
into three sectors: commercial/industrial, residential and uni- the greater the variance of the waste sample composition.
versity (Table 2). For the first quarter of the year, no local He stated that as sample weights decreased from approxi-
data were available for estimating the number of samples mately 91 kg, the sample variance increased rapidly, but that
needed. Twelve waste components in the ASTM International above approximately 140 kg, the variance increased much
(1992) national data set were used to estimate the required more slowly. He thus recommended a sample weight between
number of samples. For an error, e = 0.05, 0.10 and 0.20, respec- 91 and 140 kg. This recommendation was also adopted by the
tively, the number of samples needed were 522, 131 and 34 to ASTM (1992) standard. Therefore, the target sample weight
achieve an 80% confidence level. One hundred and fifty-one for this study was set at 140 kg.
samples corresponding to an error between e = 0.10 and e = 0.05
Field protocol
were selected for the present study. During the subsequent
Trucks from each geographical area were numbered and ran-
sampling periods, due to cost and resource constraints, the
domly selected from the geographical area during a sampling
number of samples collected was adjusted to 128, 130 and
period. The previously identified truck needed to unload the
127, respectively. A two-way stratification method was devel-
waste at a working area. A landfill worker then used a front-
oped to account for variations between the geographical regions
end loader to mix the waste and collect a 140 kg (within 10%
and seasons in calculating waste compositions.
error) waste sample. The waste sample was identified by a scale
Sampling protocol house ticket with the record of the source of waste, total
weight measured at the scale house, date and time of arrival at
Sample weight the scale house. The identified waste sample was then trans-
The term sample size is sometimes used to refer to two differ- ported to the sorting shed and was deposited onto a high-
ent parameters in solid waste characterization studies. One density polyethylene (HDPE) 60-mil liner for sorting.
is the number of sample units to be sorted. The other is the Sorters removed the large items such as large pieces of
weight of each unit. In this paper, sample size means the corrugated board and put them in an identified container.
number of sample units, and sample weight, means the weight Trash bags were torn open. Portions of the waste were placed
of each unit. The sample weight affects the variability of esti- onto a sorting table and were then sorted by hand and placed
mation. Obviously, if a sample weight is too small, it would into identified containers. An estimate of wetness of the
give inaccurate results, because, for example, a large piece of sample was made and recorded on the data sheet. After the
wood could not physically be included in a small sample. sorting was completed, each container was weighed with an
accuracy of ± 0.23 kg. Standard personnel safety procedures
However, separating and sorting raw solid waste is expensive.
A typical vehicle load of commercial solid waste weighs were followed during the sorting process such as wearing
between 4500 and 9000 kg. It is not practical to separate the gloves, apron/coverall, safety glasses and boots, sorting the
entire vehicle load or a load from an even larger vehicle. nearest item first, etc.



Table 3: Composition of waste entering Columbia Landfill during 1996.

Waste composition(%)
Geographical source
Paper Plastic Metal Glass Organic Other Total

Audrain County 43 14 5 2 29 10 100
Boone County 43 12 7 3 20 15 100
City of Columbia 41 18 5 2 21 12 100
Commercial/industrial 40 21 5 2 20 12 100
Residential 42 13 5 4 25 12 100
University of Missouri 47 14 4 2 18 14 100
Callaway County 36 11 8 6 28 11 100
Centralia 35 20 5 3 25 13 100
Mexico 46 19 4 2 19 12 100
All waste stream 41 16 6 3 21 13 100

Results and discussions Table 4: Percentage error of mean weight fraction of four quarters with
80% confidence level.

Waste component weight and composition Waste component Q1 Q2 Q3 Q4
As shown in Table 2, a total of 113 800 t of waste entered the Total paper 9 11 9 8
City of Columbia Sanitary Landfill during 1996 (City of Total plastic 13 12 10 14
Columbia’s Solid Waste Utility, 1996). The City of Columbia Total metal 9 23 12 12
contributed the most (55%, 62 300 t) to the total waste Total glass 10 15 13 17
stream entering the landfill in 1996. The City of Columbia and Total organic 9 9 10 7
the remainder of Boone County contributed approximately Total other 18 24 19 16
89% (101 500 t) of the waste stream. They contributed the Weighted average 19 23 19 18
most to each of the waste composition categories. 61% of
Columbia waste came from the commercial/industrial waste centage composition of waste combined from all locations
sector, 31% came from the residential sector and 8% from the was 41% paper, 21% organic, 16% plastic, 6% metal, 3%
University of Missouri. The per capita weight was also calcu- glass and 13% other waste. Paper was the largest composition
lated using estimated service population of each geographical and glass was the smallest composition for all locations.
source. It was estimated that waste came from 50% of the Table 4 presents the percentage error for the mean weight
population in Audrain County outside the City of Mexico fractions for four quarters with an 80% confidence level, which
and 5% of the population in Montgomery County; the popu- means that the true mean will lie within the range of the esti-
mated mean ± error associated with the 80% confidence.
lation in Boone County excluding that in the City of Colum-
bia, the City of Centralia and the City of Ashland; the popu- Because only a limited number of samples of the waste stream
lation in the City of Columbia and 5% of the population in were collected for measurement of waste component weight
Osage County; the population in Callaway County outside of fraction, there is a degree of uncertainty for each mean weight
the City of Fulton and 5% of the population in both Osage fraction value. This uncertainty is represented by the per-
County and Montgomery County; the population in the City centage errors and confidence associated with the errors. The
of Centralia and 5% of the population in the City of Mexico; percentage error depends on the number of samples collected:
and the remaining 95% of the population of the city of Mex- the more samples collected, the lower the error. The percent-
ico. The percentages of the population were estimated by talk- age error also depends on the variability of the waste stream.
ing to waste haulers and City of Columbia Solid Waste Utility The less variable (or more uniform) the waste stream, the
personnel. As some portion of the waste of some counties or lower the errors. The percentage errors tend to be larger for
cities goes to different landfills, it is difficult to estimate an waste components that make up the smaller fractions of the
accurate service population. However, because all waste in waste stream (e.g., other organics, medical waste, etc.)
the City of Columbia goes to the City of Columbia Landfill, because with the smaller fraction comes the larger variability.
the per capita weight for the City of Columbia has a higher The seasonal and geographical variations in the waste stream
degree of confidence (820 kg/year, 2.3 kg/day). and the sample weight of each sample also contribute to the
The waste composition for the entire waste stream and error. The percentage errors tend to be larger for those geo-
geographical sources in 1996 is shown in Table 3. The per- graphical locations where the fewest samples were collected.

65


Table 5: Comparison with other studies.

Waste Composition (%)

Waste EPA
Columbia 1987 Columbia 1996 Minnesota 1999 Wisconsin 2001 Oregon 2002
component 1994

41 41 33 34 21 21
Paper

7 16 12 11 11 11
Plastic

6 6 6 5 6 8
Metal

4 3 6 3 2 2
Glass

15 7 8 8 14 9
Wood

4 4 4 3 1 2
Textiles

7 9 9 12 11 16
Food

7 1 2 6 2 13
Inorganic

7 1 15 2 1 7
Yard waste

2 12 5 16 31 11
Other

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



pling method in which a simple arithmetic mean was com- to 1996. This agrees with the 1995 recycling data of 500 t
puted for the weight fraction. The studies for Minnesota, Wis- and 400 t, respectively (City of Columbia Solid Waste Util-
consin and Oregon were all state-wide studies which included ity 1996). However, other un-recycled paper components such
multiple landfills whereas this study only investigated the as magazines increased. This caused the total paper composi-
waste stream entering the City of Columbia landfill. The local tion to remain the same from 1987 to 1996.
conditions such as the type of waste accepted at landfills, A detailed breakdown of the waste category comparisons is
climate, economic activities, life styles, recycling and waste shown in Table 6. Among the five categories in Table 6, the
management programmes all have a great impact on the waste weight percentages for paper, plastic and metal were higher at
composition. There was also a lack of a standard definition the Columbia landfill (30, 21 and 2% higher, respectively)
for waste sorting categories. Thus, each state defined some of and glass and other materials were lower at the Columbia
their waste categories differently from other states. landfill (50 and 23% lower, respectively).
The results from the USEPA waste characterization study The per capita weights from Columbia were higher in four
are different from the results of the present study for three of the five categories (not glass). The total per capita weight
at the Columbia landfill (800 kg year–1) is 60% higher than the
reasons. First, the USEPA methodology was different. Instead
national average total per capita weight reported by USEPA
of using waste sampling data, the USEPA used a materials-
(1996), which excluded waste from construction and demoli-
flow approach based on ‘production data for the materials and
tion debris and industrial process waste. A typical per capita
products in the waste stream, with adjustments for imports,
value of 1000 kg year–1 for total municipal solid waste gener-
exports and product lifetimes’. Second, the USEPA charac-
ation in US was reported by Tchobanoglous et al. (1993).
terization study did not cover all materials that enter Subtitle
This value was converted to 800 kg year–1 of discarded waste
D landfills, such as the City of Columbia Sanitary Landfill.
for total municipal solid waste by using a discard-to-genera-
The materials included by the USEPA are defined in their
tion ratio of 0.773 (USEPA 1996). The Columbia per capita
1996 report (USEPA 1996). This is a different waste stream
weight agrees with the typical national value reported by
from that entering the City of Columbia Sanitary Landfill.
Tchobanoglous et al. (1993), which included construction
For instance, the Columbia landfill does accept certain indus-
and industrial waste that was excluded in the USEPA report
trial process wastes and construction and demolition debris
(1996).
but does not accept yard trimmings, large appliances, or auto-
The result with respect to yard waste and waste tyres is rather
mobile tyres, whereas the USEPA study did not include wastes
dramatic: ‘Finally, all individual components had higher per
from construction and demolition debris, industrial process
capita weights at the Columbia landfill except for yard trim-
wastes but did include yard trimmings, large appliances and
mings and rubber tyres, which were lower by 88%. This is a
automobile tyres. Third, the USEPA study is a nationwide
clear indication of the effectiveness of the ban on yard waste
study, whereas the study presented here covers a small region.
and tyres at the Columbia landfill’ (Center for Environmen-
The differences in methodology and definitions described
tal Technology and Energy Systems and Resources Program
above serve to confound the comparisons.
1997).
The total waste entering the landfill increased by 40%
from 1987 to 1996; most of this increase was due to an increase
Comparison between locations and sectors
in the service population. The per capita increase was only
Table 7 presents a listing of locations with waste characteris-
5%. The largest changes in percentage weight among the cat-
tics that are different from each other at the 80% confidence
egories in Table 5 were for plastics, which increased from 7
level. It is logical to conclude that annual mean weight frac-
to 16% (a 129% increase). Inorganic and yard waste both
tions that are close to each other may indicate waste charac-
decreased from 7 to 1% (86% decreases). Wood decreased
teristics that are similar. Statistical tests were conducted to
from 15 to 7% (a 53% decrease) and glass decreased from 4 to
determine whether there were significant differences between
3% (a 25% decrease).
the weight fractions of the waste components at the eight dif-
The large decrease in yard waste composition is due to
ferent locations. The tests not only consider the mean value
Missouri Statute RSMo 260.250 (2004), which mandated
but also consider the extent of spread, or variance, of all meas-
that after 1 January 1992, yard waste was not to be allowed
ured values about the mean value and consider the number of
to enter landfills. In addition, the decrease in glass composi-
measured values.
tion indicates that recycling for glass was effective. The total
Two possibilities were considered. One possibility was that
recycled glass in 1995 was 200 t (City of Columbia Solid
the variances of the two populations, p, were unknown but
Waste Utility 1996). The composition of waste with respect
equal. The other possibility was that the variances of the two
to corrugated board and newsprint for Columbia landfill
populations were unknown and unequal. An F test (Milton
decreased from 17 to 13% and 8 to 5%, respectively, from 1987

67


Table 6: Waste composition comparison with national average variances were equal, a test for comparing means with equal
(USEPA 1996).
variances was performed; otherwise, the test for comparing
means with unequal variances was performed. When it was
Per capitac
b
National Columbia Per capita Columbia assumed that the variances were equal, a pooled sample vari-
1994 1996 1994 1996
Component ance was computed and a pooled t test was conducted; other-
(%)a (kg year–1) (kg year–1)
(%)
wise, the individual variances were used to conduct the t test
Corrugated 8 13 40 100
(Milton & Arnold 1995). The General Linear Model in SAS
board
(1990) was used to conduct the tests, using a 80% confi-
Box board 5 9 30 70
dence level. The results are summarized below.
Newsprint 5 5 30 40
The weight fractions varied from location to location.
Magazines 1 3 5 30
Table 7 shows the locations where waste characteristics were
Office paper 2 3 10 20
different. The letters are the components for which the waste
Mixed paper 12 10 70 80
characteristics were different. Every location had at least one
Total paper 33 43 200 300
waste component that was different from that of other loca-
tions. The locations that had the greatest number of different
PET (#1) 1 2 3 10
components were Callaway County and the City of Colum-
HDPE (#2) 2 2 10 10
bia. There were no significant differences between total ‘other
PVC (#3) 1 2 5 20
waste’ characteristics at any locations. These results support the
LDPE (#4) 4 5 20 40
notion that waste stream characteristics vary with geographi-
PP (#5) 2 2 8 10
cal region and this variation should be taken into considera-
PS (#6) 2 2 8 20
tion when designing a sampling strategy for a waste charac-
Other plastic 2 2 9 20
terization study.
Total plastic 14 17 60 100

Comparison between seasons
Aluminium 1 1 7 7
Table 8 presents a summary of the annual mean weight frac-
Ferrous and bi- 5 5 30 40
metal tions of the six waste categories for each of the four quarterly
Non-ferrous 0 1 2 4 sorts. A statistical test was conducted to determine whether
Total metal 6 7 40 50 there were significant differences among the waste character-
istics between quarters. The method was the same as used to
Total glass 6 3 40 20 test for significant differences between locations explained in
the section entitled ‘Comparison between locations and sec-
Food waste 9 9 50 70 tors’ above. Every quarter had at least one component that
Yard trimmings 17 1 90 10 was different in other quarters, except that quarter 2 was not
and rubber tyresd
different from quarter 3. This lack of difference was probably
Textiles 4 4 20 30
because of the fact that quarter 2 has an average of 23% of error
Wood 8 7 50 60
which is larger than the 20% for other quarters (Table 4). The
Other wastes 6 13 30 100
differences in quarter 3 could be related to special summer
Total other 44 34 200 300
events that draw out-of-town visitors and the transition in stu-
dent population. The differences in quarter 4 could be related
Total 100 100 500 800
to holiday activities. The differences in quarter 1 could prob-
a
Materials from ‘municipal solid waste’ that were discarded after
ably be due to the lack of special events, transitions and hol-
materials and compost recovery. bEqual to percentage of materials
discarded divided by 100, multiplied by 159 760 000 tons total idays that create the sharp contrasts between quarters 3 and 4.
materials discarded in US in 1994, multiplied by 2000 lb/ton, multi-
plied by 0.45 kg/lb, divided by the July 1994 US population of
Potential for recovery and reduction
260 372 174. cEqual to percentage of total weight that entered
Columbia landfill in 1996 divided by 100, multiplied by 125 790 Hereafter, the potential for waste recovery and reduction is
tons total weight that entered Columbia landfill in 1996, multiplied by
discussed from the view of market value. Table 9 presents the
2000 lb/ton, multiplied by 0.45 kg/lb, divided by the estimated
1996 total service population of 138 341. dAssumed equivalent to market values for recyclable materials in the Columbia waste
banned items in present study.
stream. The prices were obtained from Associated Recyclers
of the Midwest, 2004. The cost of waste collection is not
& Arnold 1995) was first conducted to compare the vari- included because it would be incurred whether the waste was
ances of the two populations. If the F test indicated that the recycled or landfilled.



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 regionsa

Location number
Location
Location name
number
1 2 3 4 5 6 7 8

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
Columbia- 5 L, O L, M, G P, L, M, G, O P, G, O . G, O P, L, M, G L
Comm./Ind.
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 1 0.391 0.156 0.059 0.027 0.240 0.127
Quarter 2 0.429 0.173 0.064 0.026 0.190 0.118
Quarter 3 0.383 0.150 0.063 0.030 0.210 0.164
Quarter 4 0.448 0.152 0.055 0.030 0.209 0.106
Annual mean 0.412 0.158 0.061 0.029 0.212 0.128

The total weight of recyclables was 39 300 t (108 t/d). If the revenues over cost are shown in Table 10 for various
the recovery rate is assumed to be 80%, the recovered mate- facility lives and interest rates.
rials are then 108 × 80% = 86 t/d. The revenue is 9 000 $/d The present worth of the revenues minus O&M cost far
(Table 9). The cost of recycling is the sum of the capital and exceeds the capital cost, even considering short facility lives
operating cost (O&M) of the material recovery centre (MRF). and high interest rates. Such present worth would still show
Although detailed costs vary by community, the configura- the value of recycling even if revenues were less than calcu-
tion of the MRF and many other factors, one can make pre- lated above or if excess costs associated with bags or bins for
liminary estimates from the general average cost data. The recyclable collection were considered.
typical unit capital cost for a low-tech MRF is $10 000 per However, this is just a rough estimate, many factors are com-
tonne of daily capacity (Tchobanoglous and Kreith, 2002). munity specific. The feasibility of an MRF should not depend
Thus, the capital cost for an MRF is approximately 1.7 mil- purely on economics as it did in the past. The USEPA has
lion dollars. The typical O&M cost for a low-tech MRF is promulgated regulations for municipal solid waste landfills as
20 $/t. The waste collected from the City of Columbia is required by subtitle D of the Resource Conservation and
171 t/d (62 300 t/y) (Table 2). Thus, the O&M cost for an Recovery Act of 1976 (RCRA 42 U.S.C. 6901 et seq., 2004),
MRF would be 3420 $/d. Revenues thus exceed costs by effective October 9, 1993. Both existing and new landfills
5580 $/d or 2 031 120 $/yr (not considering the time value were affected by the statute. Available landfill volume is
of money within the year). The revenues from one year of decreasing because of stringent regulations (The above calcu-
operation would pay for the construction of the MRF. The lations do not consider the cost of environmental protection
long-term implications of recycling can be seen from consid- features now required for landfills or the cost of groundwater
ering the present worth of the value of revenue minus O&M cleanup from potential contamination.). The wastes that
cost. One can convert an annual value to a present worth used to be discarded need to be recovered and managed in a
value using standard economic tables if the life of a facility sustainable way. The USEPA recommends that recycling be
and an interest rate are specified. Present worth values for the top priority option used in an integrated solid waste man-

69


Table 9: Recyclable materials waste stream entering Columbia landfill in 1996.

2004 pricea Recovered weightb
Recovered material Weight Market value
(t) (US$)
(US$ t–1) (t)

Metal
Aluminium cansc 772 600 480 370560
c
Other aluminium 639 400 320 204480
Steel cans 72 2400 1920 138240
Ferrous 72 3000 2400 172800

Plasticd
HDPE natural 507 500 400 202800
HDPE mixed colour 265 500 400 106000
HDPE/PET mixed 22 1800 1440 31680
PET clear loose 11 400 320 3520
PET mixed colour 11 400 320 3520

Paper and paper board
Newspaper loose 61 5400 4320 263520
Corrugated loose 110 14500 11600 1276000
Office mixed loose 50 2900 2320 116000
Magazines loose 110 3600 2880 316800

Glass
Clear 44 1900 1520 66880
Brown 33 700 560 18480
Green 22 300 240 5280

Total 39300 31440 3296560
a b
Price is obtained from Associated Recyclers of the Midwest http://recyclingcoop.org/market.htm. Assumes that 90% of the weight is recovera-
ble. cAnnual 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 alu-
minium cans. dArbitrarily assumes the following distribution: PET (25% clear loose, 25% mixed colour, 50% mixed with HDPE); HDPE (25% natu-
ral, 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 Interest Rate Present Worth Factor Present Worth
(years) (%) ($)

20 20 4.870 9 891 554
20 10 8.514 17 292 956
20 5 12.462 25 311 817
20 2 16.351 33 210 843
10 20 4.192 8 514 455
10 10 6.144 12 479 201
10 5 7.722 15 684 309
10 2 8.983 18 245 551

agement system. Economical feasibility is not the only factor tally sound and socially acceptable. Therefore, the revenue
that drives the solid waste management system. A sustainable from recyclables can only be viewed as a supplemental bene-
solid waste management system also needs to be environmen- fit, not as a determining factor.



Conclusions to 1996. Most of this increase was due to an increase in the
service population. The per capita increase was only 5%.
A total of 536 samples were collected at the City of Columbia It was difficult to accurately compare the results of this
Sanitary landfill for 32 waste components in 1996. The waste study with other waste characterization studies because of
stream was subdivided into two-way stratified sub-levels with the lack of consistency in methodology and waste compo-
the geographical region being the first level of stratification nent definitions. For instance, this study sampled all waste
and the season being the second level of stratification. Random entering the landfill. In the national study, the USEPA
samples were collected for each sub-level. A detailed physical (1996) excluded certain waste streams from their analysis.
sampling protocol was outlined for the sampling scheme The comparisons that were possible suggest variability in
determined by the two-way stratification method. It is esti- waste characteristics by geography and seasons that should
mated that a total of 113 800 t of waste entered the City of be addressed by site-specific sampling for integrated solid
Columbia Sanitary Landfill during 1996. The City of Colum- waste management.
bia contributed the most to the waste stream. The per capita
weight for the City of Columbia in 1996 was 2.19 kg person–1
Acknowledgement
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% This project was funded by a grant from the Missouri Depart-
metal, 3% glass and 13% other waste. Seven components rep- ment of Natural Resources Solid Waste Management Pro-
resented almost 60% of the total waste entering the landfill. gram. Portions of the information presented in the paper are
Ranked by weight, these were corrugated board, mixed paper, also documented in the 1997 final project report submitted
box board, food, miscellaneous other waste (primarily con- to the funding agency. R.L.P., the principal investigator and
struction-related waste), wood and newsprint. The total weight major author of the 1997 report, served as dissertation advi-
of waste that entered the landfill increased by 40% from 1987 sor to Y.Z. during this time.

References

ASTM International (1992): ASTM D 5231–92 Standard Test Method for Milton, J.S. & Arnold. J.C. (1995): Introduction to Probability and Statistics,
Determination of the Composition of Unprocessed Municipal Solid Waste. third edition. McGraw-Hill, New York.
For referenced ASTM standards, visit the ASTM website, http://www. Minnesota Solid Waste Management Coordinating Board (2000): Statewide
astm.org, or contact ASTM Customer Service at service@astm.org. MSW Composition Study Final Report. Minnesota Solid Waste Manage-
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to the standard's Document Summary page on the ASTM website 42 U.S.C. 6901 et seq. Resource Conservation and Recovery Act of 1976.
Associated Recyclers of the Midwest (2004): http@//recyclingcoop.org/mar- http://www4.law.cornell.edu/uscode/42/ch82.html. http://web.lexisn-
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