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Waste Management & Research (1989) 7, 103-114
THE ORIGINS OF MUNICIPAL SOLID WASTE: THE
RELATIONS BETWEEN RESIDUES FROM PACKAGING
MATERIALS AND FOOD
H. Alter*
(Received August 1988)
Data for the composition of municipal solid waste (MSW) from around the world are
used to further examine a previously reported statistical correlation between the
fraction of food residues and the fractions of paper and board, metal, glass and
plastics residues in MSW . For data from many locations, these correlations are
statistically significant ; multiple linear regressions are computed . The fraction of food
waste decreases as the fractions of waste from paper and board, metals and glass
increase .
The situation in the U .S .A . is examined further for just packaging waste. Similar
correlations are established for the fraction of food residues and the fractions of paper
and board and plastics packaging residues for predicted compositions for 1980 to
2000. Similar correlations for the U .K. are not statistically significant . Some reasons
for this are postulated .
The results of the statistical analyses predict that a strategy for decreasing the
fraction of food waste in MSW is to increase the use of food packaging by some
amount, especially plastics and metals, contrary to conventional wisdom .
Key Words --Food waste, paper, plastics, glass, metals, statistical correlations .
1 . Introduction
At least since 1965, conventional wisdom has been that a large contributor to the
quantity of municipal solid waste (MSW) is packaging (such as from paper, board,
plastics, metal and glass) and the obvious way of reducing the amount of MSW is to use
less packaging. As an example, the U .S . Solid Waste Disposal Act of 1965 (Public Law
89-272) . Sec . 202, states
. . The Congress finds . . . that the continuing technological progress and improvement in
methods of manufacture, packaging, and marketing of consumer products has resulted in an
ever-mounting increase, and in a change in the characteristics of the mass of material
discarded . . . "
Texts on modern solid waste management have addressed packaging in wastes ; for
example, Mantel] (1975) . During the 1970s there were U .S. government reports on the
subject (Environmental Protection Agency 1975 ; Office of Technology Assessment 1979)
and there have been more recent symposia and books addressing the same conventional
wisdom (Alter 1980a; Bridgwater & Lidgren 1983) . Bider (1985) restated the premise .
There is merit to this conventional wisdom in that developed countries, with more
sophisticated systems of packaging and distribution, generally have a higher per capita
*U.S . Chamber of Commerce, Washington, D.C. 20062, U .S .A .
0734 242X ;89/020103+
12 $03 .00/0
c l989 ISWA
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1 04
H. Alter
MSW generation than developing countries. This paper questions a different aspect by
examining the relationship between packaging residues and food residues in MSW that
has been, for the most part, overlooked .
In the early 1980s, it was noted that there is a negative correlation between the fraction
of food residues in MSW and the fraction of residues from paper and board, metals and
glass (Alter, 1980h, 1983) . This was expressed as plots of the fraction of food waste in
MSW vs . the residues of paper and board or vs . the sum of the contents of metals, glass
and paper and board . It was also noted that the 17 or so data points from around the
world that were examined fit a linear regression line, but the reason for the linear
correlation was not known .
The early observation of a correlation among constituents in MSW is examined
further here . The number of data points has been increased to 78 and an additional
packaging material, plastics, is examined . The statistical analysis is extended to detailed
waste composition data from the U .S. and the U .K .
2 . Data used for the analysis
2.1 Composition of municipal solid waste
Table I lists the data used for analysis of worldwide trends . It was gathered from the
literature sources cited in the table in an attempt to include many countries . Data for
specific cities or average values are reported, in accord with the cited source .
Table I notes either the year the data were reportedly gathered or, if this was not
included, the year of the publication . If the correlation between food and other residues
in MSW is meaningful, the year should not be important, at least over some reasonable
period of the recent past when food (among other items) has been packaged in the
materials of interest .
Not all of the reports appear to be complete but an effort was made not to include
many with a large amount of missing data . Some reports differentiate between ferrous
metals and aluminum while others report "metals" . In the former cases, the figures for
ferrous metals and aluminum were summed .
2.1 Limitations of'the data
There are several limitations of the data listed in Table 1 . First, the term "food waste" is
ambiguous. Not all reports use this category nor differentiate between food and other
vegetable or organic matter, However, it is judged that for all of the data in Table 1, the
number listed for food waste is mostly that. All of the data suffer from the familiar
uncertainties of determining the composition of MSW : the contents may not have been
determined with good statistical sampling, the report may be the results of only one day
or one season, and there could be inaccuracies in determining the identity of some
materials. The statistical problem is acute when the content of a material is very low,
such as for plastics. There are other problems in determining the content of plastics
because they are used in a wide range of applications, not only packaging . In MSW they
may be present as coatings or as part of a composite (e .g . laminated structures), thus
adding to the error of content determination .
Another shortcoming, and a serious one, is that all of the materials listed are used in
both food packaging and other applications . For example, the paper and board category
could include newsprint and the metals category could include discarded parts of
automobiles .
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Origins of municipal solid waste
105
TABLE I
Composition of municipal solid waste
Country City Year
Paper
and
board Metal Glass
Food
waste Plastics Reference*
Austria Vienna 1975 0.383 0.081 0.092 0.186 0.061 9
Austria Vienna 1982 0.403 0.049 0.081 0.224 0.090 35
Belgium Average 1976 0.300 0.053 0.080 0.400 0.050 9
Bulgaria Sofia 1977 0.100 0.017 0.016 0.540 0.017 9
Columbia Medellin 1979 0.220 0.010 0.020 0.560 0.050 21
Czechoslovakia Prague 1975 0.134 0.062 0.066 0.418 0 .042 9
Denmark Average 1978 0.329 0.041 0.061 0.440 0.068 9
Denmark Average 1970 0.450 0.040 0.080 0.130 24
England Average 1969 0.380 0.097 0.105 0.195 0.014 23
England Average 1935-6 0.143 0.040 0.034 0.137 22
England Average 1963 0.230 0.082 0.086 0.141 23
England Average 1967 0.295 0.080 0.081 0.155 0 .012 23
England Average 1968 0.369 0.089 0.091 0.176 0.011 23
England Doncaster 1985 0.210 0.070 0.060 0.150 0 .050 7
England Doncaster 1982 0.240 0.080 0.080 0.280 0.050 35
England Doncaster 1985 0.280 0.090 0.080 0.200 0.070 7
England London 1980 0.421 0.110 0.117 0 .170 0.040 24
England Stevenage 1979 0.330 0.070 0.090 0.160 0 .030 5
Finland Average 1978 0.550 0.050 0.060 0.200 0.060 9
France Laval 1985 0.340 0.050 0.120 0.300 0.060 7
France Paris 1979 0.340 0.040 0.090 0 .150 0.040 5
Gabon Average 1977 0.060 0.050 0 .090 0.770 0.030 9
Germany (FRG) Aachen 1974 0.308 0.069 0.135 0 .164 0.045 5
Germany (FRG) Aachen 1979 0.310 0.030 0 .130 0.160 0.040 5
Germany (FRG) Berlin 1978 0.218 0.049 0.191 0.314 0.060 10
Germany (FRG) Dusseldorf 1974 0.278 0.044 0.164 0.342 0.062 25
Germany (FRG) Hamburg 1975 0.231 0.045 0.227 0 .300 0.046 5
Germany (FRG) Munich 1974 0.406 0.061 0 .069 0 .075 0.075 9
Germany (FRG) Stuttgart 1974 0.147 0.053 0.099 0 .524 0.062 9
Germany (FRG) Tubingen 1974 0.137 0.047 0.138 0.443 0.076 9
India Calcutta 1976 0.030 0.010 0.080 0.360 0.010 21
India Lucknow 1980 0.020 0.030 0.060 0.800 0.040 21
Indonesia Bandung 1979 0.100 0.020 0 .010 0.720 0.060 21
Indonesia Bandung 1978 0.096 0.022 0.004 0.716 0.055 28
Indonesia Bogor 1985 0.060 0.800 0.040 28
Indonesia Jakarta 1978 0.020 0.040 0.010 0.820 0.030 21
Indonesia Jakarta 1978 0.080 0.014 0 .005 0.795 0.037 28
Indonesia Surabaya 1983 0.020 0.005 0.010 0.940 0.020 28
Iran Teheran 1978 0.172 0.018 0 .021 0 .698 0.038 9
Italy Average 1979 0.310 0.070 0.030 0.360 0.070 5
Italy Milan 1984 0.300 0.030 0.080 0.390 0.100 8
Italy Rome 1980 0.250 0.025 0 .013 0.500 0.060 35
Italy Rome 1979 0.180 0.030 0.040 0.500 0.040 5
Japan Gifu 1985 0.210 0.057 0 .039 0 .500 0.062 30
Japan Mito 1985 0.301 0.015 0.011 0.418 0 .056 30
Japan Sakai (new area) 1985 0.230 0.022 0.053 0.541 0 .081 30
Japan Sakai (old area) 1985 0.295 0.039 0.049 0.404 0.071 30
Japan Tokyo 1972 0.382 0.041 0.071 0.227 0 .073 9
Japan Tokyo 1978 0.436 0.012 0.010 0.340 0.056 6
Japan Utsunomiya 1985 0.249 0.016 0.015 0.502 0 .073 30
Kenya Mombasa 1974 0.122 0.027 0.013 0.426 0 .010 9
Netherlands Amsterdam 1979 0.260 0.030 0.140 0.460 0.060 5
Netherlands Average 1974 0.341 0.036 0.055 0.376 0 .057 10
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1 0 6
H. Alter
* References are listed in the Appendix .
It is believed that the limitations of the data are minimized as much as possible by
using a large number of data points and seeking statistically significant relationships .
3. Data analyses
3.1 Multiple regression
Correlation analyses must be based on reasonable hypotheses of cause and effect . In the
cases examined here, the hypothesis is that the use of packaging materials reduces food
spoilage, hence household waste . This hypothesis assigns positive social and environ-
mental values to the use of packaging materials . Packaging removes husks, peels,
vegetable tops, bones, etc . before the food reaches the consumer . These wastes, which
are diverted at the factory level, can be utilized for example as animal feed .
Statistical analyses were performed using Lotus 1-2-3 (Lotus Development Co.,
Cambridge, Massachusetts) or the STATS+ software system (StatSoft, Tulsa, Okla-
homa) . Before multiple regression analyses, the dependent variables were examined
graphically and by simple regression techniques to establish they were not co-related.
Figure 1 shows the corelation between the fraction of food waste and the sum of the
fractions of paper and board (P&B), metals (M) and glass (GI) . The data points from
TABLE 1-continued
Composition of municipal solid waste
Country City Year
Paper
and
board Metal Glass
Food
waste Plastics Reference*
Netherlands Average 1978 0.222 0.032 0.119 0.500 0.062 10
Netherlands Average 1971 0.223 0.081 0.536 0.068 23
Nigeria Kano 1980 0.170 0.050 0.020 0.430 0.040 21
Nigeria Lagos 0.140 0.040 0.030 0.600 21
Norway Oslo 1985 0.382 0.020 0.075 0.304 0.065 7
Pakistan Lahore 1980 0.040 0.040 0.030 0.490 0.020 20
Philippine Is . Manilla 1978 0.170 0.020 0.050 0.430 0.040 20
Spain Average 1978 0.180 0.040 0.030 0.500 0.0409
Spain Madrid 1979 0.190 0.060 0.030 0.500 0.080 5
Sri Lanka Colombo 1981 0.080 0.010 0.060 0.800 0 .010 21
Sudan Khartoum 1984 0.040 0.030 0 .300 0.026 27
Sweden Average 1977 0.500 0.070 0.080 0.150 0.080 9
Sweden Stockholm 1985 0.390 0.050 0.140 0.150 0.080 7
U.S .A . Average 1975 0.289 0.093 0.104 0.178 0 .034 3
U.S .A . Average 1973 0.427 0.092 0.103 0 .146 0.017 34
U.S.A . Berkeley, CA 1967 0.446 0.087 0,113 0 .125 0.019 31
U.S .A . Estimated 1975 0.290 0.091 0.104 0.178 0.034 1
U.S .A . Estimated 1971 0.295 0.091 0.096 0.176 0.034 1
U.S .A . Estimated 1975 0.272 0.153 0.103 0.154 0.032 1
U.S.A . Estimated 1971 0.293 0.155 0.090 0.164 0.026 1
U.S .A . Johnson City, TN 1968 0.349 0.093 0.090 0.3460.034 2
U.S.A . New Orleans, LA 1972 0.394 0.122 0.146 0 .189 0.038 32
U.S .A . N . Little Rock, AK 1978 0.541 0.117 0 .082 0.068 0.087 4
U.S.A . Several 1970 0.442 0.087 0.085 0.166 0.012 33
U.S .A . Wilmington, DE 1973 0.337 0.066 0 .147 0.165 0.033 29
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Table 1 and the linear regression line are shown . This is the same correlation plot
reported earlier (Alter, 1980b) for fewer data points .
For Fig . 1 the equation of the regression line is :
FW = - 1 .0074*(P&B + M + G1)+ 0.7538
R'= 0.6840, n = 78 and p < 0.0000 . (The standard error of the FW estimate is 0 .1216 and
of the coefficient 0 .07854 .)
0 . 9
a)
r
0
o
Lt
0 . 8
a)
N
v
0 .7N
N
0 . 6
w
e3
0 . 5
o
0-4
c
o
0 . 3
0 . 2
0 I
0 . 4
0 . 3
0 . 2
D
U
O
-0-2
-0 . 3
Origins of municipal solid waste
107
0
0 .2
0. 4
0. 6
Sum of residues paper and board, glass and metals
Fig . 1 . Correlation between the fraction of residues from food waste and the sum of the fractions of residues
from paper and board, metals and glass . Data points from Table 1 ; the line shown is Equation l.
0 0 . 2 0 . 4
0 . 6
Reported fraction of food waste
0 . 8
0 .8
(1)
0 . 1
Fig . 2 . Plot of residuals, regression Equation 3, as a test of fit . The average value of the residuals is 0 .003 .
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1 08
H. Alter
Earlier (Alter, 1980b), a correlation was also shown between FW and P&B . Using the
data in Table 1, the regression line is :
F W = - 1 .2490*(P&B) + 0 .6910
(2)
R-'= 0.5759, n = 78 and p < 0 .000. Both Equations I and 2 are close to the regression
correlations reported earlier for fewer data points . (Throughout, the results of statistical
analyses are reported to more decimal places than justified by the data .)
Of perhaps greater interest than these simple regressions is the multiple regression
between the content of food waste and all of the components in Table I . Computing this
regression, and accompanying variance analysis showed that any dependence on the
content of plastics was not statistically significant (for n=66, the significance of
Student's `t' for plastics was 0 .2062), at which point plastics were dropped from the
analysis . Then, the multiple regression is :
FW = - 0.9324*(P&B) - 1 .8877*M - 0.8775*G I + 0 .7742
(3)
n = 75, R2 = 0 .7260 and the 'F' ratio is significant forp < 0.0000. The full regression
results, including the standard errors of the regression coefficients B and values of the
Student `t' are shown in Table 2 .
The related analysis of variance results are shown in Table 3 . In addition, the
significance of the individual regression coefficients wasp < 0 .001 for P&B and metal and
p50.01 for GI .
3 .2Test of the model udequacy
The adequacy of the regression equation may be tested by a so-called residual plot,
although this is not necessarily recommended for multiple linear regression (Montgo-
TABLE 2
Regression weights and errors for Equations 3 and 4
TABLE 3
Analysis of variance for Equation 3
Variable
Regression
coefficient (B) S .E . of B t (n=71) Significance of t
Equation 3
Paper and board -0.9324 0.1188 -7.8510 0.00000
Metals -1.8877 0.4790 -3.9413 0.00040
Glass -0.8775 0.3212 -2.7321 0.00787
Equation 4
Paper and board-0
.9237 0.1017 -9.0806 0.00000
Glass and metals-1
.8736 0.4694-3
.9916 0.00035
Effect Sum of squares d.f. Mean square F
Significance of
F
Regression 2.4192 3 0.8064 62.7235 0 .00000
Residual 0.9128 71 0.01286
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Origins of municipal solid waste
109
mery & Peck 1982). Figure 2 is such a plot to demonstrate the comparison between
predicted vs . actual values for the regression of Equation 3 . The average of the residual
values (predicted -actual) is 0 .003; for an ideal fit, this value is zero .
A "perfect" plot of this sort would have the points evenly distributed about the line
y=0. Figure 2 may have a bias of more points at the higher values of the abscissa . This
might be due to the larger error in measuring the amount of food wastes in this regime ;
i .e ., the counts would include more moisture .
4. The situation in the United States
4.1 Correlations between food and packaging residues
The composition of MSW in the U.S.A . has been computed for the period from 1960 to
2000 by input-output analyses (Franklin Associates 1986) . Such computations are based
on national economic statistics, hence are broad averages . Some of the computations are
retrospective, based on experience; the remainder are prospective, based on assumptions
of future economic activity . As such, there are limitations to the results, including those
that may arise from inherent assumptions of the future . Not all components can be
estimated this way-food wastes, yard wastes and some miscellaneous inorganic wastes
were not . Estimates of these are based on sampling data from as wide a range of sources
as possible, according to the authors, without saying the extent of that sampling .
Despite their limitations, the Franklin results are examined here because they are a
unique set of internally consistent data concerning the composition of the waste and, in
particular, they separate packaging residues of various components from non-packaging
residues in the waste . Table 4 lists some of the results of the analysis for packaging waste
only. It must be noted that not all packaging waste is from food packaging .
Perhaps as an illustration of the effect of inherent assumptions, graphical examination
showed that the variables are probably correlated, hence not suitable for multiple
regression analysis . The correlations may be due to the changes in packaging materials
mix over time, as one type of material displaces another .
TABLE 4
Composition of municipal solid waste in the U .S ., 1960-2000*
Paper and
Year
board
Glass
Steel
Aluminum
Plastics
Food waste
1960
0.144
0.077
0.060
0.002
0.002
0 .147
1965
0.162
0.087
0.051
0.003
0.011
0.131
1970
0.158
0.106
0.048
0.005
0.019
0 .115
1975
0.144
0.108
0.042
0.006
0.024
0.118
1980
0.145
0.105
0.027
0.007
0.034
0 .093
1981
0.151
0.104
0.025
0.006
0.034
0.089
1982
0.144
0.102
0.023
0.006
0.033
0.088
1983
0.149
0.095
0.021
0.007
0.035
0 .085
1984
0.156
0.089
0.021
0.007
0.037
0.081
1990
0.153
0.080
0.019
0.008
0.043
0 .076
1995
0.157
0.074
0.016
0.009
0.048
0.073
2000
0.158
0.068
0.014
0.009
0.052
0.068
* Source: Franklin Associates 1986 .
Packaging material residues
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1 1 0
H. Alter
The data in Table 4 apply only to packaging waste and do not fall near the line shown
in Fig . 1 . (Franklin data for all paper and board, not shown in Table 4, does not fit the
correlation shown in Fig . 1, perhaps because of the high content of other paper and
board in U .S. MSW). The data for paper (and board) packaging residues (PPR) in Table
4 show their own correlation with food waste, as shown in Fig . 3(a). The fraction of PPR
in U .S . waste is changing the time, hence the year for each data point is shown . Figure
3(a) also shows the regression line for the points from 1980 to 2000 . The equation of this
line is :
FW = - l .4065*(PPR) + 0.2949
(4)
R-'=0.7491, the standard error of the FW estimate is 0.0047 and the standard error of
the coefficient is 0.3324 .
The data in Table 4 for ferrous metals and glass do not show the negative correlation
with food waste . Rather, there is a positive slope to the correlations . This may be
explained by the sharp changes in metals and glass contents with time shown in Table 4 .
There is a negative correlation between the fraction of food waste and the fractions of
aluminium and plastics . The range of aluminium fraction in Table 4 is small and no
graph is shown. Figure 3(b) graphs the correlation between the fraction of food waste
and the fraction of plastics packaging residues (PIPR). The linear regression line is
shown; its equation is :
FW= - 1 .6465*(PIPR)+0 .1480
R2=0.9625, the standard error of the coefficient = 0 . 1027 and the standard error of the
estimate of FW=0.0050 . The multiple regression of the data in Table 1 showed a non-
significant correlation between the fractions of food waste and plastics waste . Figure
3(b) shows there is a relationship for the U .S. for packaging residues, suggesting there
might be a relationship for other countries given sufficiently accurate data .
5. Discussion
5 .1The statistical relationship
The data in Table 1 (except for plastics) are represented by the multiple regression model
with a high degree of statistical confidence . From the statistical relationship it may be
concluded that as the use of packaging materials is increased, the fraction of food waste
in MSW decreases over the range examined . It is noteworthy that this correlation holds
for data from many countries, over a considerable range of waste composition, and
perhaps a broad period of time . An alternative explanation is that in less developed
countries, which have less developed food processing and food distribution system, there
is also less home refrigeration, hence more food waste . Any contribution of this latter
point to the statistical model cannot be tested .
The relationship is probably hyperbolic, asymptotic to some limiting values of the
variables . The data fall over part of the hyperbola, hence can be represented linearly .
Higher order correlations were not examined .
The composition of MSW in the U .K. has been reported from 1930 to 1982
(Bridgwater 1986) in terms of kg household - ' week - ' . Of these, the data from 1967 to
1981 were converted to weight fractions and examined for statistical relations between
the fraction of food waste and individually the fractions of paper, glass, metals and
(5)
-
0 .15
0 . 14
0 . 13
0 . 12
0 I I
0 . 1
0 .09
Q
vi
0 .08
0 .07
0.06
0 .12
0 . 11
0 . 1
0 . 09
0 .08
0 .07
0 .06
Origins of municipal solid waste
111
19600
196511
(a)
131980
1983
19810
I
1984 D
19900
19700
1995
20000
0 . 142
0.00
0 . 146 0 . 150 0 . 154 0 . 158 0 . 162
Fraction of paper and board packaging residues (year shown)
0 .060 .02
0 .04
Fraction of plastic packaging residues
Fig. 3 . Correlations for fraction of food waste and fraction of packaging residues, U.S . The year of each data
point is indicated and the regression lines are shown. (a) Paper and board packaging residues; (b) plastics
packaging residues . The equations of the lines are given in the text. El, Data points ; -, regression line .
plastics . (Data for 1971 are missing ; the data for 1982 are incomplete.) None of the
relations was statistically significant ; plastics provided the best fit for 14 points for
R2 = 0.5067 with a positive slope. Regressions for metals and paper had a negative slope .
The contents of the components noted all increased over the time period . The amount of
waste generated per household was about constant. These trends, which are not
understood, may explain why correlations among the constituents in the waste are not
statistically significant .
The statistically significant relationships for the data in Table 1 and for the U .S .
predict that the use of packaging materials reduces the fraction of food waste in MSW .
The magnitude of the reduction can be predicted from the regression coefficients .
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112
H. Alter
Packaging not only reduces spoilage, but also salvages unwanted food residues for
beneficial use .
The noted effects of packaging on food waste, and particular for those cases where the
regression coefficients are greater than one, are contrary to conventional wisdom that
packaging materials just add to the waste . Thus, a strategy for reduction of the amount
of MSW is to increase food packaging . Certainly this strategy has practical limitations
which must be determined .
References
Alter, H . (Ed .) (1980a), Papers presented at the EC Congress: Packaging, Recovery and Re-use,
Utrecht, October 23, 24, 1979 . Resource Recovery Conservation, 5, 1-1 15 .
Alter, H . (1980b), Resource recovery : technical and economic risks . Resource Recovery Conserva-
tion, 5, 39 .
Alter, H . (1983), Materials Recovery, from Municipal Waste . Unit Operations in Practice . pp . 2-3
Marcel Dekker, New York .
Bider, W . L . (1985), Packaging today, solid waste tomorrow? Where does it go? Paper presented at
the 4th International Conference on Packaging, Lansing, Michigan .
Bridgwater, A . V . (1986), Refuse composition projections and recycling technology . Resource
Conservation, 12 (3&4), 159 .
Bridgwater, A . V . & Lidgren, K . (Eds) (1983), Energy , in Packaging and Waste . Van Nostrand
Reinhold, Wokingham .
Environmental Protection Agency (1975), Resource recovery and waste reduction . Third Report
to Congress . Report SW-161 . U.S. Environmental Protection Agency, Washington .
Franklin Associates (1986), Characterization of municipal solid waste in the United States, 1960 to
2000 . Final report to U .S . Environmental Protection Agency, Office of Solid Waste and
Emergency Response, Washington . Franklin Associates, Prairie Village . Kansas .
Mantell, C. L . (Ed) (1975), Solid Wastes: Origin, Collection, Processing and Disposal, Ch . 1 .3 . John
Wiley, New York .
Montgomery, D . C . & Peck, E. A . (1982), Introduction to Linear Regression Anult'sis . John Wiles .
New York .
Office of Technology Assessment (1979), Materials and Energy from Municipal Waste . Office of'
Technology Assessment, Congress of the United States, Washington .
Appendix
References for Table 1
(1) Doggett, R . M ., O'Farrell, M . K. & Watson, A . L . (1980). Forecasts of the quantity and
composition of solid waste . EPA-600/5-80-001, p . 53. U.S. Environmental Protection Agency .
Cincinnati .
(2) Clemons, C . (1975), Composting at Johnson City . Final Report on Joint USEPA-TVA
Composting Project . EPA/530/SW-31r.2. U.S . Environmental Protection Agency . Cincinnati .
(3) U .S. Environmental Protection Agency (1977), Fourth Report to Congress . EPA-SW-600 .
Washington .
(4) Resource Recovery Today and Tomorrow (1980), Proceedings, National Waste Processing
Conference, p . 74. American Society of Mechanical Engineers . New York .
(5) Commission of the European Communities (1979), Raw Materials. Studies on Secondary Raw
Materials, I. Household Waste Sorting Systems . Brussels .
(6) Gotoh, S . (1981), Recent developments in resource recovery from municipal solid waste in
Japan. In, Materials and Energy from Refuse . Proceedings of the 2nd Symposium, Antwerp,
Belgium, 20-22 October 1981 . Buekens, A . (Ed .) Koninklijke Vlaamse Ingenieursvereniging
V. Z. W .
(7) Barton, J. R ., Poll, A . J ., Webb, M . & Whalley, L . (1985), Waste Sorting and RDF Production
in Europe, eh. 1, Elsevier Applied Science, London .
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Origins of municipal solid waste
113
(8) Casci, C. & Cassitto, L . (1984), Rifiuti urbanie industriali, (urban and industrial waste) .
Appunti Sulla Transformazione Energetica, p . 20 . Clup, Milano .
(9) Wilson, D. C . (1981), Waste Management, Planning, Evaluation, Technologies. pp. 6-7 .
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