1-s2.0-0734242X89900554-main

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

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 .

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

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

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


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 .

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


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

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


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 .

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 .


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 .

Clarendon Press, Oxford .

(10) Tjell, J . C ., Elmlund, A . & Hansen, J. A. (1981), Source or central separation of household

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