Replace with appropriate Cover
'?' twi'
12000
(i960
Il970
l^fiBjJmfflfW
I items (in terms of
_^ waste content)
- - _ -n^-ft*
J discarded I
1 materials |
V from LRT items _X
-------
-------
METHODS OF
PREDICTING
SOLID WASTE
CHARACTERISTICS
Environraentai ;.--**.
Region V, Library
230 South Dearborn Street
Chicago, Illinois 6060X
This report (SW-23c) was written by
GAIL B. BOYD and MYRON B. HA WKINS
URS Research Company, SanMateo, California
for the Federal solid waste management program
under Contract No. PH 86-68-98
U.S. ENVIRONMENTAL PROTECTION AGENCY
1971
-------
An environmental protection publication
in the solid waste management series (SW- 23c) .
For sale by the Superintendent of Documents, U.S. Government Printing Office
Washington, D.C. 20402 - Price 40 cents
Stock Number 5502-0048
-------
FOREWORD
INFORMATION ON THE QUANTITY AND COMPOSITION of solid waste is
indispensable in designing, implementating, and operating the solid waste management
systems of today and in forecasting the requirements of the solid waste systems of
tomorrow. Presently, methods of estimating and predicting the quantity and com-
position of solid waste are most commonly based upon direct measures such as
sampling of solid waste itself. There are, however, several disadvantages in the direct
approach: (1) Sampling at disposal sites does not provide adequate information
regarding the source of the waste. (2) It is difficult to recognize and identify solid
wate components in samples. (3) Discard rates for certain solid wastes are too
discontinuous, relative to time, to provide meaningful samples unless the sampling itself
is done over an adequate period of time. (4) Sampling does not provide the insights
necessary to predict future solid waste quantities and compositions.
The project summarized by this report involved a preliminary design of a model for
estimating and predicting the quantity and composition of solid waste and a determina-
tion of its feasibility. The concept employed is, however, a novel one. Rather than
utilize direct measures, the model estimates and predicts on the basis of knowledge of
materials and quantities before they become a part of the solid waste stream, together
with an understanding of the process by which materials become solid waste. Such a
model accomplishes by synthesis what direct measures seek to accomplish by analysis.
The synthetic approach partially or totally overcomes the disadvantages previously
mentioned for direct sampling of solid waste.
The project was directed to a study of residential solid waste, the most complex
area of all in regard to this new technique because of the countless items that enter
the average home, many of which are small or insignificant. Thus, the project elected
to test feasibility in the area most likely to highlight the weaknesses of the technique.
In point of fact, however, the preliminary design performed quite well. Although the
approach involves certain difficulties in implementation and maintenance in the
residential sector at this time, this is not necessarily true for the commercial sector.
Stores deal more in bulk than residences do, and consequently, complexity is reduced.
Further, commercial establishments maintain detailed inventories, something the
homeowner does not do. The model developed is, therefore, especially attractive for
estimating and predicting the quantity and composition of commercial solid waste on a
community-wide basis, hitherto a most intractable problem.
The contributions of the project officer, Albert J. Klee, Bureau of Solid Waste
Management, to this study are gratefully acknowledged, as are those of Professor John
Heer, University of Louisville, and the editors of Life magazine.
-RICHARD D. VAUGHAN
Deputy Assistant Administrator
for Solid Waste Management
in
-------
-------
CONTENTS
METHODS OF PREDICTING SOLID WASTE CHARACTERISTICS
1
SUMMARY 2
DISCUSSION AND GENERAL APPROACH 3
HOUSEHOLD-WASTE PREDICTION TECHNIQUE 8
HOUSE HOLD-WASTE PREDICTION MODEL 12
DESIGN OF MODEL AS USED FOR TEST CASE 17
RESULTS OF TEST OF PROTOTYPE MODEL 20
CONCLUSIONS AND RECOMMENDATIONS 25
REFERENCES 27
FIGURES i
1 A CONCEPTUAL OVERVIEW OF THE BASIC SOLID WASTE PREDICTION MODEL ... 5
2 THE HOUSEHOLD-SOLID-WASTE-PREDICTION MODEL, WHICH WAS EXPANDED
TO ACCOUNT FOR LONG-RESIDENCE-TIME AND SHORT-RESIDENCE-TIME
ITEMS SEPARATELY 7
3 HOUSEHOLD-SOLID-WASTE-PREDICTION MODEL 14
4 SOLID WASTE GENERATION RATES OBSERVED BY UNIVERSITY OF
LOUISVILLE SAMPLING STUDY 23
5 SOLID WASTE GENERATION RATES PREDICTED BY USING URS PROTOTYPE 23
TABLES
1 QUANTITIES OF SELECTED WASTE MATERIALS GENERATED IN TEST
COMMUNITY (AREAS OF JEFFERSON COUNTY, KENTUCKY) 2
2 SIGNIFICANT COMMUNITY ACTIVITY CLASSIFICATIONS 4
3 MAJOR EXPENDITURE CATEGORIES 9
4 HOUSEHOLD CHARACTERISTICS 10
5 MATERIAL CLASSIFICATIONS BY WHICH WASTES ARE DESCRIBED IN
PROTOTYPE MODEL 18
6 PREDICTION OF WASTE GENERATION RATES BY MATERIAL AND
ORIGIN 22
7 COMPARISON OF WASTE GENERATION RATES 23
8 COMPARISON OF OUTPUT DATA FORMATS 24
9 PREDICTION METHOD STATUS AND PROPOSED WORK 26
-------
-------
w^ Jiii*P^ ^^HrJU*
PREDICTING
JL Jl
The design, implementation, and operation of
efficient and economical solid waste collection,
handling, transport, and disposal systems require accu-
rate information on the quantities and characteristics
of the solid waste lo be processed. This information is
needed for the present and for the lifetime of a waste
management system. The overall objective of the study
reported herein was to develop methods that can be
applied to real (or hypothetical) cities, counties, and
regions for estimating the quantity and character of
the solid wastes generated at present and at various
times in the future.
The work was planned to be implemented in two
phases. Phase 1 was the preparation of preliminary
design of the prediction model and a determination of
its feasibility. The second phase was the development
of the model, preparation of the associated computer
programs, assembly of appropriate data banks, and
conduct of test runs for specific areas. This report
describes the work undertaken in the first phase, in-
cluding the results, our conclusions, and our recom-
mendations regarding the concepts and approaches for
the work to be performed in the second phase of the
study.
Within the scope of work as specified in the con-
tract for phase 1, URS Research Company undertook
four major tasks. The first was to develop specifications
for, and to design, the basic waste prediction model.
This necessitated establishing an output information
format pertinent to the anticipated use of the data and
placing particular importance upon the degree of detail
in description of materials, location of waste, and the
time at which waste is produced. Concomitantly,
sources of information on input commodities and
activity characteristics were identified and investigated,
and the type and quality of available information were
defined. Output specifications were compared with
input data availability, and a compatible model was
developed.
The second task was to collect, develop, and formu-
late selected standard data, descriptors, and functions
for input commodities and activities. This involved an
analytical study of materials, commodities, community
activities, and waste production. Although much in-
formation was presumed available, it needed modifi-
cation to the format used in the model. Various
sources were to be evaluated relative to prediction of
the character of commodities in the future. One of the
information items anticipated to be somewhat difficult
to obtain was the "residence time" data. In the initial
study, only a selected spectrum of input commodities
and activities was to be evaluated.
The third task was to test the model manually in a
small community. This test was to be used to evaluate
the general performance of the model. Manual com-
putation was to be used to avoid costly programming
and computer time while the model was in a prelimi-
nary stage of development. A small community or a
segment of a community was to be selected with
information on the quantity of input materials to be
obtained. The waste characteristics would be predicted
and compared with data collected at disposal or other
sites. The basis for the selection of the sample was to
be its capability of providing a significant sample of
the most important prediction parameters.
Finally, the test results were to be evaluated in
order to judge the practicality of the model, the
possible accuracy and precision of the system, and the
limitations to the system.
-------
SUMMARY
The basic concept of the solid waste prediction
model developed in this project is that the waste gen-
erated by a community is derived primarily from the
goods and materials consumed by the community;
therefore waste quantities and characteristics can be
estimated from information concerning the con-
sumption habits of the community and the manner in
which it uses and consumes the material obtained.
Such an approach facilitates the prediction of future
waste characteristics because of the widespread availa-
bility of information concerning the production, use,
and composition of goods and materials in the future.
The study consisted primarily of determining the
availability of usable information and developing a pre-
liminary prediction model for residential-household solid
wastes. This model was restricted to the prediction of
present-day, short-residence-time wastes. Its perfor-
mance was tested by comparing its predictions for a
given locality with the results of an actual study of solid
waste generation in that locality. The areas studied were
in Jefferson County, Kentucky. Pertinent demographic
data were collected and the solid waste quantities of
various materials estimated by manual application of the
prediction method. The results compared favorably with
the measured values (Table 1).
Table 1. Quantities of selected waste
materials generated in test community
(areas of Jefferson County, Kentucky)
(Ib/HOUSEHOLD/wk)
Measured by Univ.
of Louisville study
Material
Paper
Glass
Metal
Low
24.9
3.8
3.9
High
37.8
67
6.0
Estimated by URS
prototype model
23.43
2.54
4.23
It is recommended that additional work be per-
formed to achieve further results. The first objective
would be to complete the development of the
residential-household-prediction model, to modify it to
include institutional and commercial activities, to com-
puterize the model, and to run a test case. The second
objective of the additional work would be to perform
a preliminary development and feasibility test with
methods similai to those used for predicting household
wastes and foi predicting industrial, agricultural, and
other selected wastes.
-------
DISCUSSION AND
GENERAL
APPROACH
An intimate knowledge of the composition of the
waste is needed as a basis for the design of waste
management systems and for the planning of research
and development for solid waste-handling and treat-
ment processes. The technical feasibility of incinera-
tion, composting, and other processes; the operational
practicality of collection, segregation, and secondary-
material recovery systems; and the economic feasibility
of all aspects of waste handling require detailed in-
formation regarding the source, quantity, and quality
of all waste materials. In order to perform such studies
effectively, the changes in characteristics of waste
materials (resulting from population growth, land use
modification, and technological changes in material
properties and industrial processes) must be predicted
for several decades.
The usual method of obtaining data on waste
characteristics and quantity is by physically inventory-
ing the materials delivered to the disposal site. Whereas
such an approach provides information that can be
useful in formulating immediate solutions to current
problems,* its use as the primary basis for defining and
solving long-range problems is suspect for four major
reasons. First, inventorying present and past wastes
cannot serve as a basis for predicting future waste
characteristics or quantity, since it does not account
for any of the technical, social, economic, or other
factors that influence waste generation. Second, the
difficulty of segregating and identifying waste com-
ponents (after they have been stored, compacted,
transported, and repeatedly handled) severely reduces
the accuracy of any quantitative description. Third,
studies conducted at the disposal site generally do not
provide adequate information concerning the sources
of waste. Fourth, the discard rate for many types of
waste is too discontinuous (relative to time) to allow
conventional surveys to obtain sufficiently representa-
tive samples.
The present study is concerned with a waste pre-
diction technique based on the following hypothesis:
that the wastes discarded by a community derive
primarily from the materials purchased, consumed, and
used by that community (i.e., "inputs"); therefore, one
can predict the amount and nature of these waste
"outputs" by identifying what goods and materials
*Data derived from conventional waste measurement and
analyses have been valuable to the present study in that, they
provide baseline waste generation information and indicate
where better definition is needed.
-------
constitute the community's inputs and by knowing
how the community acts upon these inputs.
It is implicit in this hypothesis that studying the
characteristics of commodities, materials, and products
with their "consumption" by society will reveal in-
formation about the resulting waste. Note that the
time required for a given material to pass through the
community is an important consideration and varies
widely for different materials (or products) according
to the uses to which they are put. Some input
materials, such as foods, are received, distributed, and
consumed and become waste in a mattei of hours or
days. Other products, such as those used in construc-
tion, may be resident in the community for half a
century or longer before they become waste.
In a discrete community, the input materials may
be acted on by the community by consuming, process-
ing, or generating (Table 2). The community may
consume them, eventually converting all the input
materials to waste. For example, a case of canned
vegetables (consisting of solid food, liquid food, metal
cans, and a fiberboard box) entering a community will
be converted entirely to waste within a few weeks.*
*For purposes of predicting solid wastes, only the packaging
materials are of interest.
PREDICTING SOLID WASTE CHARACTERISTICS
The community may process them for consumption
elsewhere, with only a fraction's becoming waste in the
community. For example, most of the steel and
plastics shipped to a manufacturing plant will leave the
area, their only trace in the community being the small
amount of production scrap and the few units sold in
the area. The community may generate materials, in
the sense that agricultural crops, vegetation, and live-
stock are grown and resources are obtained from mines
and wells. In most cases, the materials generated within
the area are consumed outside the area, although waste
materials will remain in the area.
Obviously, not all activities are exclusively in one
category or another. For instance, all processing and
generating activities are also consumers of some input
materials but to a lesser extent.
Estimation of the solid waste materials produced by
any given community, through use of the concepts
proposed herein, primarily involves inventorying all
waste-producing activities in the community; compiling
standard information on the materials consumed per
unit size per unit time by each type of activity and
information on which of these materials become solid
wastes; and determining the rate of solid waste pro-
duction from this information.
Table 2. Significant community activity classifications**
Consumption activities
Residential household (single-and multiple-family dwellings)
Financial and business establishments (including government activities)
Institutions
Restaurants
Utilities (including water and waste treatment)
Transportation
Processing activities
Industrial:
Food processing
Manufacturing of durable and nondurable goods
Chemical manufacturing, processing, refining
Construction
Commercial (retail and wholesale marketing)
Generation activities
Agriculture
Mining (including petroleum)
Commercial fishing
Demolition
Miscellaneous (e.g., street sweeping, gardening)
**These classifications relate closely to those of the Bureau of Budget's Standard
Industrial Classification, and to the system proposed by the Urban Renewal
Administration and the Bureau of Public Roads for land use classification, and thus
facilitate the use of the vast amount of data available from the Federal
Government.! ,2
-------
DISCUSSION AND GENERAL APPROACH
Information on the rate of material consumption
(and, therefore, waste generation) by each type of
activity is derived from many sources and is based
largely on descriptions of the materral input-output
characteristics of the processes used by each activity.
Although the actual computations and data bank
formulations vary somewhat, depending on the type of
activity being considered, a basic computational
approach is applicable (Figure 1). The four basic data
banks of the model are described here:
Description of Community. This is a data bank that
provides an inventory of each type of activity in the
study community, as well as their number, size, and
location.* This is actually an inventory of waste
sources.
Description of Standard Activity. This is a data
bank that indicates for each type of activity the
amount of waste items** and materials produced per
unit time per unit size of activity. This data bank
would be of a general nature and would apply to the
study of any area of the country.
"Location refers here to census tract, enumeration district,
county, urban area, or some other segment of the area being
studied.
**In the terminology used herein, "waste item" refers to a
recognizable, discrete object made up of one or more materials.
For example, a discarded television set or sofa is a "waste item."
A "waste material" is a more specific classification of waste
content, e.g., glass, copper, paper, wood, or textiles.
Standard Waste Composition Data. This signifies a
standard data bank that provides the information
needed to convert a unit of input item into the quanti-
ties of waste materials it will yield. For example, N Ib
of coffee would be converted to X Ib of granular
solids, Y Ib of metal (from the can), Z Ib of plastic
(from the lid).
Local Waste Management Practices. This is a data
bank that provides information on the mariner in
which specific wastes from specific sources are ulti-
mately disposed of by the community (i.e., whether
they are collected as refuse, segregated for salvage,
disposed of on site, or otherwise eliminated). This
information is specific for each community and reflects
both the habits of the residents and the legal restric-
tions on disposal practice.
Two intermediate compilations result from the
various computations (Figure 1).
Total Waste Inventory. This lists the quantity of all
waste items produced in each location within the study
area. It is derived from the first two data banks.
Descriptive Inventory of Solid Wastes. This lists the
quantity of each waste item and waste material likely
to end up as a "solid waste." Solid materials while
airborne 01 waterborne are eliminated from considera-
tion.1 This information is derived by using additional
process data from the Activity Description and from
Waste Composition Data to develop the waste material
inventory.
Standard waste
composition
data
,\
; »
Figure 1. A conceptual overview of the basic solid waste prediction model. The separate models for
predicting the wastes from each type of activity would differ somewhat, but would all share this basic
format. Rectangles represent basic data banks. Irregular octagons represent the intermediate compilations
arising from the various computations. Shaded rectangles are a series of compilations, the final output of
the model.
-------
PREDICTING SOLID WASTE CHARACTERISTICS
The final output of the model is a .series of four
compilations (Figure 1).
Inventory of Wastes Collected as Refuse. This inven-
tory lists the quantities of waste items and materials
normally collected for disposal.
Inventory of Wastes Segregated for Salvage. This
lists the quantities of waste items and materials segre-
gated (by the activity) and eithei recycled within the
process or collected separately for reuse, resource
recovery, or salvage.
Inventory of Wastes Disposed of On Site. This is a
compilation of the quantities of waste items and
materials disposed of by the activity itself. This would
include, for example, materials burned in an incinerator,
ground in a garbage grinder, buried, or otherwise
processed for disposal.
Inventory of Other Solid Wastes. This list includes
those waste items and materials not elsewhere ac-
counted for.
The wastes are tabulated in two ways: by the
amount of each item (e.g., pounds of tin cans, washing
machines) and by the amount of constituent material
(e.g., pounds of paper, ferrous metal, plastic). This
appears to meet the requirements of the initial uses of
the model. It will probably, however, be useful to
introduce further calculations (not shown in Figure 1)
to convert the basic waste inventories so that they
reflect other waste characteristics, such as calorific
value, biochemical degradability, chemical composition,
bulk density, real density, and unit size.
A more specific version of the basic prediction
model applicable to residential households would need
to redefine some data banks in relation to the house-
hold activity function and to incorporate consideration
of two important aspects of the ultimate computa-
tional procedure. The wastes from short-resident-time
(SRT) items must be predicted separately from wastes
from long-residence-time (LRT) items.* Data banks
required for predicting waste quantities and character-
istics at selected times in the future must be included
(Figure 2).
In this study, no specific useful-life duration was
established to differentiate between SRT and LRT
items. Items "consumed" within a year or less are
generally considered, however, to be SRT items. Even
without a rigidl> established criterion, little difficulty
was experienced in assigning items to one category or
the other.
Separating the prediction processes for SRT and
LRT wastes is necessitated by two basic factors. First,
household inventories of LRT items tend to increase;
therefore, a purchase of such an item does not neces-
sarily result in the immediate discard of a like item, as
distinguished from SRT items, which are used essen-
tially immediately and become waste in the same
general period in which they are procured. Second,
because the material characteristics of many LRT items
change with changes in technology, and since those
being discarded today were manufactured 5 to 15
years ago, special consideration must be given to the
material characteristics of the item. The means of pre-
dicting both LRT and SRT wastes are discussed in
detail in a later section.
An important use of the prediction model is related
to the estimation of waste characteristics in the future.
To use the model for such a purpose requires pre-
dictions and forecasts of future changes in the follow-
ing: descriptions of communities; household con-
sumption patterns (and descriptions of standard activi-
ties); composition of SRT and LRT items; household
waste management practices; ownership data on LRT
items; discard rate data.
Generally, the most valid predictions of changes in
materials, products, and technology are those made by
persons having the best knowledge of the specific tech-
nologies and industries involved. Similarly, the most
important predictions of the growth and change of a
given community are those by the responsible author-
ities within the community. The method used (that of
having essentially separate data banks for each pre-
dictive year) was, therefore, chosen partly because it
provides a means of incorporating independent pre-
dictions from different sources.** At present, it is
planned to use 1965 as the base year with 1970, 1980,
and 2000 as projection years. In applying the model,
only 1 year would be considered at a time. Although
forecasts are available for many specific items of in-
formation (e.g., estimates of their anticipated growth
made by communities), it may be possible and desir-
able to develop specialized models (and computer
*LRT items are those that tend to remain in use (or at least
in "storage") in the normal household for a relatively long time
Examples would include furnishings, appliances, books, tools,
and the like. Examples of SRT items would include food,
periodicals, most clothing, and disposable paper and plastic
goods.
**The problem remains, of course, of ensuring that in-
dependent predictions are reasonably compatible. This would be
done at the time the predictions are evaluated and the data
banks prepared.
-------
DISCUSSION AND GENERAL APPROACH
programs) to forecast systematically the growth of
selected activities. The specific means of generating
these forecasts is not within the scope of this study.
During this phase of the study, a detailed diagram
of the household waste prediction model was develop-
ed. It followed the general format already discussed
but employed several minor modifications. This pro-
totype model concentrates on SRT items and considers
but one time period (i.e., it cannot make forecasts of
future wastes).
12000
|1980
11970
1965
Household
comumpr ion
patterns
|?000
11980
|1970
1965
a. community
1
\
J
,
L
V
<
\*
T
4
V
A
P
(2000 1
11980
|W70 1
i965 J
0«scnpt>oo of SRT
waste content)
flnvento-yof ^ \± ' Cescnptiv. ^
> »L.O,en',ia,.os,esj *» \ -^ J ^
^7
|2000 /
, I1980 A\
fl970 Y\\
965 ^H
Household's waste Lj
^nanogemenf 1
practices k
12000 1
1 1 980 1
11970 1
1965 I J
Ownership | ^1
r Study area
households1
data an J »* PI prcs,n, ,nven
LRT items ^_ Of LRT ,,
|I980 1
11970 1
1965 H
Discard rates LJ
For LRT items J
ory j^1
K Study a
d scord
for LRT
|1980 1
|1970
1965 1 LJ
Description of LRT items
(',ri tetms of waste |T~
Campos it on) J
tea's 1 ,
=. J^
Klnvenforyof '
discorded
matena s
From LRT i^emi A
Figure 2. The household-solid-waste-prediction model, which was expanded to account for long-residence-time and
short-residence-time items separately.
-------
TECHNIQU
The household-waste problem was chosen as the
subject for this preliminary development and feasibility
study because in many ways it embodies the most
complex prediction problems. In addition, the col-
lection, transport, and disposal of household waste
represent the most costly and widespread waste man-
agement problems, and their effective solution is
dependent on useful waste characteristic data.
The basic computational model developed for
household-waste prediction has been described (Figure
2). Although the model is large in that it handles a
sizable amount of numerical data concerning expendi-
ture rates, consumer item descriptions, use patterns,
and disposal practices, it is simple in that only basic
arithmetic functions are used in manipulating the data.
The general aspects of the computational model are
discussed here, and the specific application of the
model is described in a later section.
Bases for Household-Waste Model
The fundamental element of the household-solid-
waste prediction model is the data bank entitled
"Description of the Community" (Figure 2). The
number of persons living in each area of the com-
munity is, of course, the community characteristic
most pertinent to the household-waste problem, and
this information is readily available from the Bureau of
Census. Additional descriptors, such as number of
families and income distribution among families,
provide information pertinent to the consumption
habits of the residential sector.
The "Description of the Standard Activity" is a
data bank that indicates the quantitative input-output
characteristics of the activitiesin this case, the resi-
dential household. During the initial phases of the
study, several approaches to determining the "input"
materials to a community's households were investi-
gated, including consideration of the means of inven-
torying the shipments into the community, the ship-
ments to wholesalers and distributors, and so on. From
a review of available information, however, it became
obvious that studies of consumer buying habits would
be the most practical sources of data. Of the various
studies of this type, the most applicable one encoun-
tered was a report entitled "Expenditure patterns of
the American family."3 The report was prepared by
the National Industrial Conference Board in 1965
under the sponsorship of Life magazine, and it will be
referred to herein as the "Life study." Based upon
statistical data collected as part of an unusually com-
prehensive series of surveys of consumer behavior con-
ducted by the Bureau of Labor Statistics, U.S. Depart-
ment of Labor, it contains a vast quantity of data on
the rate at which households spend money on about
700 different goods and services (Table 3). The im-
portant feature of this study is the information on the
manner in which each of these expenditures varies
-------
HOUSEHOLD-WASTE PREDICTION TECHNIQUE
Table 3. Major expenditure categories*
Major expenditure categories
Number of
items listed**
Major expenditure categories
Number of
items listed * *
Food, beverages, and tobacco
Cereals and bakery products 23
Meats, poultry, and fish 18
Milk, cream, and cheese 11
Fruits and vegetables 47
Other foods 56
Alcoholic beverages 5
Tobacco 4
Housing and household operations
Shelter and other real estate 17
Household operations 22
Household supplies 20
House furnishings and equipment
Household textiles 14
Furniture 11
Floor coverings 8
Major appliances 18
Small appliances 4
Housewares 12
Miscellaneous items 11
Clothing and accessories
Clothing (aggregated by sex and age) 142
Clothing materials 1
Clothing upkeep 3
Transportation
Automobile purchase 2
Auto-operating expenses 10
Auto insurance 5
Repairs and parts 1
Other auto expenses 1
Local transportation
Intercity transportation
Other transportation expenses
Medical and personal care
Group plans and insurance
In-hospital care
Professional services
Drugs and medicine
Other medical care
Personal care services
Personal care supplies
Recreation and equipment
TV, radios, and musical instruments
Spectator admissions
Participant sports
Club dues and memberships
Hobbies
Toys and play equipment
Recreation out of home city
Other recreation
All other goods and services
Reading
Education
Other current expenditures
Additional disbursements
1
6
5
3
3
3
6
1
5
14
10
4
3
1
5
7
1
1
6
4
4
7
Total number of items and
aggregations of items listed = 566
*These catagones are handled as separate parameters in the Life study (Ref. 3).
**Some of these "items" are actually aggregations of related individual items. For example, the following aggregation is listed as a
single "item" of men s clothing, suits, sport coats, and trousers.
relative to factors such as family size, family income,
and geographical region (Table 4).
One limitation of the Life study, relative to its use
for waste prediction, is that the results are reported in
dollars expended. Consequently, a considerable portion
of the research effort was directed toward converting
the reported expenditure rates into material input
rates. The Life study states how many dollars are spent
(per household per week) on common consumer items,
such as canned coffee. Consequently, it was necessary
to obtain sufficient additional information to convert
this dollar expenditure figure to pounds of steel can,
pounds of plastic lid, and pounds of spent coffee
grounds (per household per week), since these are the
potential solid wastes.
Other major tasks involved deciding whether or not
these potential wastes actually become waste, how this
occurs, and what form the wastes take. These factors
depend upon how the household processes the "input"
material. For most items, information concerning use
patterns came from a general knowledge of household
operations, rather than from formal data sources. The
details on the means of obtaining and manipulating the
various types of information concerning each input in
-------
10
PREDICTING SOLID WASTE CHARACTERISTICS
Table 4. Household characteristics*
Household characteristics
Number of categories listed
Age of head of household 6
Stage in life cycle (indicates ages of children) 6
Family size 5
Family income 6
Earner composition (identifies breadwinners) 4
Occupation of head of household 6
Geographical region 4
Race 2
Market location (proximity to metropolitan area) 5
Home tenure (owned or rented) 2
Education of head of household 5
Total number of categories listed =
51
*These household characteristics are handled as separate
parameters in the Life study (Ref. 3).
order to obtain final waste generation predictions are
discussed in the following paragraphs.
Prediction of LRT Items. A major problem, as pre-
viously indicated, arises from the necessity of predict-
ing the disposal of items that have a relatively long
useful life. In actuality, today's household waste con-
sists of three major fractions: SRT items, including
their packages and containers, that have been pur-
chased recently; the containers and packaging materials
accompanying the LRT items that have been
purchased recently; LRT items purchased in the past.
Although the Life study data provide information that
can be used as a basis for the predictions of waste
from the first two items, they are not useful for the
prediction of the third, since the purchase of a new
LRT item is not automatically accompanied by the
discard of an older one.
The following characteristics, common to many
LRT items, make prediction of their waste characteris-
tics difficult: their discard rate is difficult to estimate
because of the tendency to increase the per capita
resident inventory of many items; many items dis-
carded by one population group are reused by other
population groups in different locations before finally
being discarded as refuse; technological changes in
manufacturing processes, product design, and materials
have resulted in changes in the composition of many
items.
The first of these characteristics is related to a
number of factors, including: a general increase in
disposable income; a continuing reduction in the base
price of many LRT items; the occurrence of buying
trends based upon status and affluence motivations;
the low salvage value of outmoded but still operable
items.
Television sets are a good example of this tendency.
When television was first introduced, ownership was
quite limited, whereas now virtually all families own a
set, and many own two or more. Television ownership
has risen from 20 percent of all families in 1950 to
more than 93 percent at present.4 Many other items
(e.g., radios, small appliances, boats) also show an
increased per capita inventory.5 The tendency of many
families to maintain more than one residence (e.g.,
vacation homes) also complicates the prediction pro-
cedure.
The fact that many LRT items can be put to use by
someone other than the original purchaser complicates
waste prediction in that it interferes with determining
the location at which the item will ultimately be dis-
carded as waste. This problem also interferes with
estimating the useful life of many items.
The third problem, that of introducing composition
changes into the program, is not overwhelming. Actual-
ly, most of the' significant changes appear to have
occurred rather recently, and their general characteris-
tics can be inferred from trade literature. The problem
is largely related to obtaining sufficiently accurate
"average" composition information for the various
items.
Specific methods of predicting the quantities and
characteristics of yesterday's LRT waste have not been
developed to date. The major problems have, however,
been identified and several concepts have been briefly
explored. The computational procedure is believed to
be conceptually correct (Figure 2). The data bank
"Ownership Data for LRT Items" is used to indicate
the quantities of durable goods used or stored in
typical households of various economic levels. This
type of information is available from the Bureau of
Labor Statistics. Bureau of Census, and various non-
governmental sources, although it must be evaluated
and coordinated before use.
The data bank "description of LRT Items" will
be more difficult to develop. The desired output
"number of each type of items of X years' average age
discarded per household per unit time" can probably
be derived for many items from production and owner-
ship data. The derivation, however, will be somewhat
laborious and t-me consuming unless a computer pro-
gram is developed to assimilate the available data.
The data bank "Discard Rates for LRT Items" will
provide information on the composition of the dis-
carded LRT items. The format of the information is
-------
HOUSEHOLD-WASTE PREDICTION TECHNIQUE 11
dependent on the output of the "discard rate" data dure for prediction of LRT waste was developed and
and the assumptions concerning the composition of used as part of this study. Its development will be an
LRT items of various sizes, types, ages, and brands. important initial aspect of our subsequent work.
This information will be costly to collect if the accu- Quantities of solid waste derived from SRT house-
racy requirements are demanding. hold items have been estimated by means of a com-
As previously indicated, no computational proce- putational approach.
-------
HOUSEHOLD
PREDICTION
The following discussion examines various portions
of the household-waste prediction model and explains
their content, function, and relationships (Figure 3).
To some extent, the description relates more directly
to the methods used for the manually pei formed case
study, rather than those that wil) be developed for
eventual computer solution.
Prediction of Input Items
Summary of Expenditures. Block c is the result of
the initial computation. It indicates the amount of
money all households in a study area will spend col-
lectively on each of a large number of consumer items.
The expenditures are expressed in terms of "dollars
spent per unit time" and are tabulated on an item-by-
item basis (e.g., expenditure rates for bread, soap, and
floor wax are expressed separately). This expenditure
summary is based upon information regarding the way
an individual household of a particular type spends
money on various items (Consumer Expenditures by
Household, block b) combined with information on
how many households of each type are located within
the study area (descriptive information on households
in study area, block a).
Consumer Expenditures by Household. These data
(block b) were obtained primarily from the Life study
described previously.3 Data are expressed in terms of
"dollars spent per household per unit time" and are
tabulated on an item-by-item basis. The data in the
Life study are presented in the foim of a large matrix,
the row headings being items, the columns being
household characteristics. The body of the matrix itself
consists of the expenditure rates each type of house-
hold will have for each particular item listed (there are
tens of thousands of such rates listed, all given to the
nearest cent per week). In the present study the major
groupings of the Life report row and column headings
were listed. In summary, a wide variety of information
is available, but generally only those data relatable to
income and section of the country were found to be
directly applicable.
The Community Description. Block a provides in-
formation on the demographic and socioeconomic
nature of the sludy area. The following factors were
found to provide the most useful information on the
people and the activities in the study area: population;
population density; average size of household; distribu-
tion of income, number, size, and type of establish-
ments (wholesale and retail trade, selected services,
manufacturers, mineral industries, agriculture). For any
given study area, the most desirable sources of such
information might differ somewhat but would general-
ly include the U.S. Department of Commerce (especial-
ly the Bureau of Census and the Business Defense
Services Administration); the U.S. Department of
12
-------
HOUSEHOLD-WASTE PREDICTION MODEL
13
Labor (Bureau of Labor Statistics); the State depart-
ments of commerce; local chambers of commerce;
various State and local agencies concerned with com-
mercial and industrial development.
Inventory of Input Items. Block / is computed by
multiplying the expenditure per unit time for each
item (from Summary of Expenditures, block c) by the
unit cost of the corresponding item (Price per Unit
Amount Conversion Factors, block d). Thus, block /
gives the number of units of each item entering the
study area per unit time. The amounts are expressed in
terms of whatever units of measure are most appro-
priate for the particular item (e.g., dozens of eggs,
loaves of bread, pounds of coffee, pairs of shoes).
Price/Amount Conversion Factors. These data (block
d) are derived in a subroutine that establishes the average
price per unit amounl, the different sizes and types of
units commonly produced and marketed being taken
into account. This subioutine is also used to estimate the
"average composite package unit," which is used in a
subsequent computation. The data for these subroutines
come from a wide variety of sources, including surveys.
Care must be taken to ensure that costs are taken for a
common year; in addition, regional variations may have
some effect on cost.
The preceding discussion has been limited to that
portion of the model pertaining to the development of
the inventory of input items to the household and is
applicable to all inputs other than LRT consumer
items, i.e., SRT consumer items; the containers and
packaging materials that accompany SRT items; the
containers and packaging materials that accompany
LRT items (Figure 3).
Prediction of Potential Wastes
The following paragraphs are concerned with the
means of using the input inventory to develop waste
predictions.
Block i of Figure 3 represents a tabulation of in-
formation that describes each item in terms of its
material composition (i.e., pounds of glass, steel,
plastic, paper, etc). It is at this point in the model that
each item's consumption rate would be defined in
more detail. For example, W Ib of coffee per week
would be defined as X Ib of granular solids, Y Ib of
tin-plated steel cans, Z Ib of plastic lids. These values
will be the "average" for a composite unit size package
and will be based on the market coverage (i.e., distri-
bution) of sizes and types of packages. For example,
50 Ib of coffee cannot be interpreted as 50 cans of
1-lb size. Rather, it will consist of some mix of 1/2-,
1-, 2-, and 3-lb metal cans, plus some mix of special
paper bags. Since each of these alternative containers
will affect the household waste differently, it was
necessary to devise a simple method to account for
them. The method used here was merely to construct a
hypothetical "pound of coffee," m percent of which
comes in one size and type of container, n percent of
which comes in another size and type, and so on. An
alternative method would be to consider every size and
type available on the market as a separate item, but
the problems of data storage (and availability) rule out
this approach immediately. In this study, descriptive
information was obtained primarily by collecting, dis-
assembling, and weighing a multitude of items and
their associated containers and packaging. The market-
ing information of blocks /, g, and h provides a basis
for assigning weighting factors that reflect the relative
importance of various sizes and types of items available
(i.e., the example of ground coffee being considered
again, marketing information^ indicates that major
emphasis should be placed upon the 1- and 2-lb cans
and only minor emphasis on bags).
Combining the Input Item Inventory (block /) with
the description of each item (block /) yields a Descrip-
tive Inventory (block k) of the material that enters the
household. This inventory indicates, in separate tabula-
tions, both materials and items that enter the house-
hold.
The Waste/Item Conversion Factor (block /) is intro-
duced to indicate the primary fate of materials that
enter the household. The various alternatives are as
follows: solid waste; airborne wastes (e.g., aerosol-type
hair spray, air deodorants); liquid, i.e., sanitary waste
(e.g., cleansers, food, tissue); distributed within resi-
dence so as to be "lost" (e.g., floor wax); distributed
outside residence so as to be "lost" (e.g., fertilizer, pet
food); prolonged "storage."
Those items not entering the solid waste stream
must be carried in the original inventory because of
the need to include their containers as solid waste. It is
anticipated that m the computerized model this func-
tion will be included in block h, the data bank that
gives the composition and content of items. Applying
the waste/item conversion factors of block / to the
inputs (i.e., combining blocks k and /) yields the
Descriptive Inventory of Potential Waste Items and
Materials (block «).*
*Note that the waste materials listed in this inventory are still
keyed to the parent item from which they originated.
-------
14
PREDICTING SOLID WASTE CHARACTERISTICS
-------
HOUSEHOLD-WASTE PREDICTION MODEL
15
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-------
16
PREDICTING SOLID WASTE CHARACTERISTICS
Block m has been included to allow the direct
introduction of data on wastes that are best estimated
outside the model. Examples of wastes handled this
way include: newspapers and periodicals; mail; grocery
bags and cartons used to transport groceries; leaves,
grass, and garden cuttings; floor sweepings. These
wastes are considered separately because their quanti-
ties are not dependent upon the household's expendi-
ture on a particular consumer item.* Newspapers and
mail are estimated on the basis of information from
local publishers and postal authorities; grocery sacks on
the basis of national production figures; leaves, grass,
garden cuttings, and sweepings on the basis of informa-
tion concerning population density, income, and the
degree to which the study area is built up. To date, we
have not been able to develop sufficient information to
allow us to make quantitative predictions of how the
generation of these materials will vary with given
demographic factors, although the experimental studies
of waste composition conducted and sponsored by the
Bureau of Solid Waste Management should be very
useful.
Prediction of Solid Waste Quantity
Block o consists of a series of distribution factors
that indicate the ultimate fate of solid wastes and
reflect the manner in which households typically
handle waste materials. These factors indicate the
portion of each spent item that will be disposed of via
each of the various disposal alternatives. For example,
it might state that, from an input of 1 Ib of ground
coffee, 20 percent of the spent grounds would enter
the sanitary sewer (via the sink, presumably), 80 per-
cent of the grounds would be disposed of via the
garbage can, and 100 percent of both the metal can
and its plastic lid would leave the garbage can. The
influence of other local practices, such as whether
backyard incineration is permitted, would also be
incorporated in this computation. These distribution
factors of block o are based upon the following: in-
formation concerning the waste management practices
of various typical types of households (block p); in-
formation giving the number of households of each
such type located in the study area, as given by the
community descriptors (block a).**
Applying these distribution factors to the inventory
of potential wastes (i.e., combining blocks o and ri)
yields a series of new tabulations (blocks q, q, q",
-------
^(f
MODEL
FOR
The primary objective of this study was to develop
selected parts of the waste prediction concept into an
operable technique and to test its feasibility by apply-
ing it to a real community. In the interest of achieving
these goals as fully as possible within existing time and
funding constraints, it was necessary to assign priorities
to those portions of the model that relate to the field
data against which it was to be compared.
After several possible alternatives were examined, a
study of solid wastes in parts of Jefferson County,
Kentucky, was selected for comparison. This study,
directed by Professor John Heer at the University of
Louisville, was conducted in such a way that residen-
tial-household solid wastes were isolated and measured
independently of wastes from other sources.7 Since the
primary emphasis in the URS study was in the
household-waste-prediction model, it was decided to
concentrate on predicting the equivalent waste com-
ponents.*
The following paragraphs discuss how the particular
functions diagrammed in the basic household-solid-
waste-prediction model were performed in developing
and applying the model for the test case.
Community and Expenditure Data
Data for block a of Figure 3 (the description of
households in study area) were obtained primarily
from Bureau of Census publications. The most impor-
tant information included the population, the average
size of the ho use ho Id unit,** and the general geographi-
cal location.!
Data for block b (Consumer Expenditures by
Household) were obtained from the Life study dis-
cussed previously. For our purposes, not all the detail-
ed data were of value, since a large portion pertained
to expenditures on items that do not contribute signifi-
cantly to household solid wastes. If the Life data are
considered as comprising a matrix, the portion drawn
upon most heavily by our prototype model related to
the following: food, beverages, and tobacco; household
supplies; personal care supplies; clothing and acces-
sories; other SRT items. Some 130 consumer items
were considered in detail to determine how each con-
tributes to waste generation. The major household
characteristic considered in the test case was "geo-
graphical region." Expenditure data for the North
Central region of the United States were adjusted to
reflect the fact that Jefferson County tends to have
somewhat larger households than the national average.
It was originally intended to consider several income
levels independently, but for the reasons discussed on
page 20 , only the average income for all households
was used.
*Exammation of University of Louisville's preliminary results
indicated LRT items do not contribute significantly to resi-
dential waste. For this teason and because of the problems
discussed in a previous section, no attempt was made to estimate
waste derived from these ilems
**The average household size tor Jefferson County is 3.42
persons The national average is 3.36 persons.8 The conversion
factors 3.36/3 42 was applied, to adjust data represented on a
national basis.
tThe portion of the Life study data that applied to the
North Central section of the United States (as defined therein)
was used in this study
17
-------
18
PREDICTING SOLID WASTE CHARACTERISTICS
Material Price and Composition Factors
Data for block d (the price/amount conversion
factors) were generated with information such as that
contained in blocks e, f, and g but did not employ a
formalized mathematical model. Rather, information
concerning the market coverage and prices of various
sizes and types of consumer items were manipulated
by hand to obtain price-versus-amount conversion
factors. Coverage and price information were obtained
from various sources, including production statistics,
wholesale and retail trade statistics, and communica-
tion with persons employed in the appropriate retail
businesses.6'9 The decision to generate the conversion
factors by hand rather than by a mathematical model
was made in the interests of maintaining as much
flexibility as possible during this developmental stage.*
Block i (the description of each item's physical
characteristics and material composition) was generated
with information such as that contained in blocks/ g,
and h. Block h, the descriptions of size and type of
each item, was developed by actually dissecting several
hundred common consumer items and weighing (or
otherwise measuring) their respective component parts
and materials.
Since the present study is concerned primarily with
the development and demonstration of a prediction
technique, it was decided to describe wastes in terms
of material classifications (Table 5).** The particular
classifications (and aggregations) used were selected to
allow the model's predictions to be compared with
measured waste generation data from the University of
Louisville study. The result of this item-by-item analy-
sis is a large and rather comprehensive tabulation of
characteristics (block /).
Data for block / (the waste/item conversion factors)
were developed entirely on the basis of personal exper-
ience, rather than from specific data sources. The
purpose of including these factors is to provide a
means of accounting for the fact that many items are
Table 5. Material classifications by which wastes are
described in prototype model
*Because of their size and complexity, the tabulations of
data and results of computations are not included in this report.
Copies can be made available to authorized requesters.
**The degree of detail with which items are described should
be dictated by the use to which the waste prediction information
will be put. Thus, in a comprehensive survey, pursuant to design-
ing systems for waste collection, transport, treatment, and ulti-
mate disposal, it may be necessary to describe all waste con-
stituents in terms of their material composition as well as their
physical, chemical, and biological properties. In less than
comprehensive studies, it is not necessary to describe wastes m
such detail.
Combustible materials
Paper and cardboard
Plastics
Rubber
Textiles
Leather
Wood
Leaves and grass
Other combustibles (excluding garbage)
Food-derived gaibage
Metals
1-errous metals
Aluminum and Us alloys
Copper and its alloys
Other metals
Glass
Refractory materials (exeluding glass)
purchased in one form but discarded in a very dif-
ferent form. For example firewood is converted to
ashes, fertilizei to garden cuttings, and clothing to lint
and rags.
Extraneous Input Information
Block m (the mechanism for introducing extraneous
information to supplement the model) was most
valuable in accounting for the following newspapers
and periodicals; mail (excluding periodicals); grocery
sacks and other paper bags; and aluminum foil (that
amount not elsewhere accounted for); aerosol-type
metal cans; miscellaneous plastic bottles (those not
elsewhere accounted for); and textile materials. The
advantage of handling the first three entries in this
manner has already been discussed. The remainder
were introduced in Ihis way primarily because they
tend to enter the household in association with a great
variety of consumer items. This approach, that of using
production statistics for the materials themselves, will
provide reliable estimates of their rate of consumption
by the household, provided the materials are used
somewhat uniformly by all classes of households.
In this study, the data for block o (the distribution
factors to reflect household waste management prac-
tices) were developed on an intuitive basis rather than
on the basis of blocks a and p. As mentioned previous-
ly, at the present state of the art, sufficiently detailed
information is not available for predicting how a
household's waste management practices vary relative
to measurable demographic and socioeconomic factors.
-------
MODEL DESIGN FOR TEST CASE
19
For the purpose of this study, however, such a defi-
ciency appears to be of little significance. In order to
yield results that could be compared with those from
the University of Louisville study (which accounted for
the wastes that were found in home garbage cans), the
assumption was made that wastes would be distributed
throughout the various disposal alternatives in a man-
ner that the investigators assumed was typical, judging
from their own experience. It was assumed that virtual-
ly all disposable packaging materials, small containers,
and paper goods would be placed in the garbage can.*
In addition, it was assumed that the fractions of the
items that could conceivably be segregated and saved
for some other form of disposal (e.g., 15 percent of
returnable glass bottles, 75 percent of old clothing, 50
percent of newspapers) would also be discarded via the
garbage can. These assumptions are only approxima-
tions but must serve until more definitive information
becomes available.
The prototype's output takes the form of a series of
waste inventories (blocks q, q , q", and q'"), one for
each of the major alternative means by which house-
hold wastes are disposed of. The format of these
inventories is the weight of materials corresponding to
each of the material classifications given in Table 5. -
*Some of these disposable materials will not be disposed of
via the home garbage can. The materials associated with lunches
packed in the home but eaten elsewhere are examples of this. In
this study, the only materials for which the quantities discarded
at home were reduced below the total quantities were wax
paper, beverage cans, and "no-deposit, no-return" beverage
bottles.
-------
As indicated in the previous section, the solid waste
generation in residential areas of Louisville was esti-
mated to check the feasibility of the waste prediction
technique.
The University of Louisville (UL) study involved the
periodical collection of the contents of refuse con-
tainers from some 144 households throughout the
study area. The particular households sampled and the
collection schedule employed were both carefully
selected to obtain representative samples of residential
solid waste. These samples were then assayed to
determine both quantity and character, as expressed by
a number of physical and chemical indices.
Even though our study and the UL study were both
directed toward solid waste generation from house-
holds in the same community, certain factors were
expected to interfere with their direct comparison. For
example, although the UL study considers the same
waste items considered in the URS study, it can
account for them only if the householder disposes of
them via the refuse container. That is, if the house-
holder disposed of the waste in some other fashion
(e.g., by separate collection or perhaps on-site com-
posting or burning), it was not counted. Similarly,
wastes disposed of by any route other than the com-
mon refuse container (e.g., those disposed of via the
sink garbage grinder, segregation for salvage, on-site
incineration) would not show up in the UL samples.
These differences between the two studies are cited
primarily to indicate that each method is based upon
simplifying assumptions that impose limitations, some
of which complicate the task of comparing their
respective results.
In many ways Jefferson County was a good test
community: it constitutes part of an SMS A,* an area
for which a rather large amount of statistical data are
readily available; it is neither entirely isolated geo-
graphically nor is it submerged within a megalopolis; it
has had a reasonably stable population growth pattern
and does not, therefore, exhibit some of the diffi-
culties of areas that have "boomed" or are declining in
stature.
A primary goal of the UL study was to determine
the relationship between a household's income and its
waste generation. To accomplish this, the sample
households were divided into three income brackets,
and waste samples were collected and analyzed to
determine the manner in which their amount and con-
tent correlated with income level.
The initial plans for this study included a similar
goal. It had been intended to take advantage of the
fact that our expenditure data include information on
the manner in which household consumption patterns
vary as a function of income level.3 It was planned to
use this information in our model to develop separate
waste predictions for each of three or four income
levels. The preliminary results of the -UL study indi-
cated, however, that, though waste generation is affect-
ed by income level, the statrstical correlation between
the two was barely significant in the Jefferson County
*U.S. Department of Commerce has designated Jefferson
County, Kentucky, and both Clark and Floyd Counties, Indiana,
as the "Louisville, Kentucky-Indiana Standard Metropolitan
Statistical Area (SMSA). The principal cities included are Louis-
ville, Kentucky, and New Albany, Indiana
20
-------
TEST RESULTS OF PROTOTYPE MODEL
21
households considered. For this reason, it was decided
not to spend the limited available time and funding on
a similar effort. Rather it was decided to develop
income-independent predictions.* The basic household
consumption information actually selected was that for
all nonfarm households in the North Central quadrant
of the United States. For the purpose of drawing
comparisons, the UL data for all income groups were
averaged.
URS Results
As described previously, consumer items were
identified as being either SRT or LRT. During the
study the waste content of approximately 120 items
was determined.** The amount of household waste con-
tributed by each of the purchased items was de-
termined for each of the constituent materials. For
example, the amounts of paper, plastic, and aluminum
foil attributable to the consumption of two separate
items, say breakfast cereal and laundry detergent, were
determined and tabulated separately in terms of
pounds of waste material per household per week
(Table 6).
Block m in the model was described earlier as pro-
viding a means of incorporating generation data for
waste components whose amounts are not closely
related to any particular consumer expenditure rate.
The final row of entries in Table 6 consists of totals
calculated for "block m" wastes, such as mail, leaves,
grass, garden cuttings, and miscellaneous wastes.f
*This study of expenditure pattern data has led to the
conclusion that income level is perhaps the most significant
single factor influencing wastes. It is recommended that further
studies be directed toward determining the impact of income on
waste characteristics.
** The list of items comprising Table 3 was pared down by
eliminating LRT items (and the packages for same), items with
insignificant residential solid waste content, and those for which
the expenditure per household was less than $0.05 per week.
fBased on U.S. Post Office statistics, it was estimated that
some 7.1 Ib of paper and cardboard per week enter the typical
Jefferson County household via the mails.10'1' On the basis of
preliminary figures obtained from Bureau of Solid Waste
Management, it is estimated that some t 7 Ib per household per
week of yard wastes and 1.1 Ib per household per week of waste
wood from various sources are to be expected. Circulation in-
formation and newsprint usage estimates were provided by the
publisher of Louisville's principal newspapers.'1 2
University of Louisville Study Results
Because each of the households in the UL study
was sampled several times during the study period, the
observed waste generation data were expressed in terms
of a statistical distribution (Table 7 and Figure 4).
The UL results also differed in format from ours.
They segregated waste materials into 5 categories (con-
sumer items in Figure 4), whereas the present study
employed 15 material categories. The relationship
between the two systems has been indicated (Table 8).
The lack of conflict between the two systems stems
from the fact that we were aware of their format and
designed ours to be merely an expansion of it. The
expanded format is, of course, desirable in that it
provides the degree of flexibility necessary to make the
data applicable to a wide range of uses.
Comparison of Studies
The waste generation rates of Table 6 have been
summed into five categories (see Table 8). which
correspond to those in the UL report, and are listed in
Table 7. Those summations estimated in this study have
been plotted as bold dots in Figure 5 to allow
comparison with the UL results. Note that each of the
predicted values is smaller than the corresponding value
measured by UL. This would be expected, since none of
the predictions are complete to date (i.e., for each, there
are items that have not yet been fully accounted for),
We believe that the performance of the prototype
computation is satisfactory. The discrepancies between
the URS study and the sampling study are well within
the expectation for this preliminary trial of a novel
technique. Furthermore, the discrepancies are ex-
plained by the fact that the present model is based
upon only a minimum amount of information. It was
stated previously that the prototype model used for
the comparison study considers only SRT items and
packaging materials and excludes LRT items. It is be-
lieved that, given the opportunity to include these and
other excluded items and to refine various other pre-
liminary estimates and approximations, the waste
generation rates predicted by the model would progres-
sively approach the actual average rates.*
t The actual average rates are not necessarily equal to those
measured since measurement techniques usually suffer from
sampling and other errois.
-------
22
PREDICTING SOLID WASTE CHARACTERISTICS
2 £ ~
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-------
TEST RESULTS OF PROTOTYPE MODEL
23
Table 7. Comparison of waste generation rates (Ib/HOUSEHOLD/wk)
Material
Paper
Garbage
Glass
Metal
Minerals
Low
249
4.8
3 8
3.9
0
Measured by Univ. '
of Louisville study
Medium
29.7
9.9
53
4.7
0.7
High
37.8
12.6
6.7
6.0
3.1
Estimated by URS
prototype model
23.43
Not estimated
2.54
4.23
Not estimated
PAPER
GARBAGE
GLASS
METAL
MINERALS
10
20
PAPER
GLASS
METAL
10
20
30
40
30
40 HOUSEHOLD SOLID WASTE GENERATION RATES (Ib/HOUSEHOLD/wk)
HOUSEHOLD SOLID WASTE GENERATION RATES (Ib/HOUSEHOLD/wk)
| | RANGE OF ALL MEASURED VALUES
* MEAN
VALUE PREDICTED BY URS PROTOTYPE MODEL
] RANGE OF ALL MEASURED VALUES
* MEAN
Figure 4. Solid waste generation rates observed by Figure 5. Solid waste generation rates predicted by
University of Louisville sampling study. using URS prototype. URS results are superimposed
over University of Louisville results.
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24
PREDICTING SOLID WASTE CHARACTERISTICS
Table 8. Comparison of output data formats
University of Louisville URS prototype
waste categories waste categories
Paper fS^^""
Garbage =dl____
Metal <^^
Paper and
cardboard
Plastics
Rubber
Textiles
Leather
Wood
Leaves and
grass
Other combustibles
(excluding garbage)
F'ood-denved
i garbage
Ferrous metals
Aluminum and
its alloys
Copper and
its alloys
\ Other Metals
Glass -~=^r^H^ Glass
,. . (~ Refractory materials
Minerals -=dl~_~ , , , ,
1 (excluding glass)
-------
CONCLUSIONS
^m mHli^ffi ^H wH^V^
The solid waste prediction method investigated in
this project is technically feasible and appears to offer
a unique and effective means of estimating waste quan-
tities and characteristics. In addition, it provides a
framework for the prediction of future wastes. Table 9
indicates the relation of the progress to date (phase 1)
to the overall requirements of the program and sug-
gests that subsequent work be undertaken in two
phases. Phase 2, the next logical step, would be
devoted to completing the capability to predict the
solid wastes from residential, commercial, and institu-
tional activities in the community. These wastes make
up a large fraction of the solid wastes arriving at
disposal sites and are very susceptible to changes (as a
result of changing technology) that affect waste-
handling and disposal operations. Moreover, pertinent
input data are generally available concerning these
wastes. For these reasons, it appears desirable to give
priority to these aspects of the work. In phase 2,
however, the problems of predicting solid wastes from
industrial, agricultural, arid other sources would also be
investigated, at least through the stage of a test of the
approaches. The major and final development of pre-
diction methods for wastes from these sources would
constitute the third phase of the work. There are two
reasons for relegating these to phase 3. First, current
work (being done by other contractors) as well as
planned studies concerning industrial and agricultural
wastes should be used as a basis for the proposed
prediction methods. The results will not be available to
an appreciable extent during the phase 2 period.
Second, the requirements for a computerized solid
waste estimation procedure for industrial, agricultural,
and possibly other wastes have not been defined or
delineated. Both of these deficiencies would be re-
solved by the preliminary studies in phase 2, and
decisions concerning the need for phase 3 and specific
approaches to be taken therein would thereby be per-
mitted.
The major tasks that should be undertaken in phase
2 are the following:
1. Complete the development of the residential-
household-waste-generation model, including those
aspects related to LRT items and the prediction of
future wastes.
2. Adapt the model to handle the wastes gen-
erated by commercial, institutional, and other activities
that are found to be major contributors.
3. Perform preliminary model design and conduct
a feasibility study for prediction of industrial, agricul-
tural, and other wastes.
4. Establish computation specifications; reevaluate
preliminary residential model; relate desired model
specifications to computer capacity and operating cost;
firm up computer model specifications; establish input,
output, and function formats.
5. Develop computer program; prepare flow dia-
grams and design overall program; prepare and debug
programs; verify running times.
6. Expand standard data banks; collect, evaluate,
and collate as many standard activity and commodity
descriptions as are pertinent to the test area (see task
7); put data in computer format.
25
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26
PREDICTING SOLID WASTE CHARACTERISTICS
Table 9. Prediction method status and proposed work
Waste types
Residential, SRT
Residential, LRT *
Residential, future
Commercial
Institutional
Industrial
Agricultural
Other
Development stages
Conceptual
design
Preliminary
data collection
Phase 1 (complet
Manual
test
ed)
Final design
program for
computer
Phase 2
Complete
data collection
Phase 3
Computer
test
*SRT-short-residence-time items
LRT-long-residence-time items
7. Select test area; develop test area data; collect,
evaluate, and compile input commodity and activities
inventories and schedules for area, including popula-
tion, land use, and other changes; put data in com-
puter format.
8. Run waste prediction for test area; compute
several pertinent predictions for a range of conditions.
9. Evaluate results; compare predictions with
independent estimates or surveys; prepare report.
-------
REFERENCES
1. Office of Statistical Standards, U.S. Bureau of the Budget. Standard industrial
classification manual. 1967. Rev. ed. Washington, D.C.. 1967. 615 p.
2. Urban Renewal Administration, Housing and Home Finance Agency, and Bureau of
Public Roads, U.S. Department of Commerce. Standard land use coding manual.
1st ed. Washington, U.S. Government Printing Office, 1965. 111 p.
3. Linden, F., ed. Expenditure patterns of the American family. New York, National
Industrial Conference Board, 1966. 175 p.
4. Sindlinger and Company, Inc. 1967 brand inventory of household durables in
Delaware Valley, U.S.A. Philadelphia Inquirer, June 1967. 30 p.
5. Automobile Manufacturers Association. 1968 automobile facts and figures.
[Detroit], 1968. 70 p.
6. Personal communication. T. Healy, Safeway Stores, Inc., to G. B. Boyd, URS
Research Company, July 1968.
7. University of Louisville. Louisville, Ky. -Ind. metropolitan region solid waste
disposal study: interim report on a solid waste demonstration project; volume I:
Jefferson County, Kentucky. [Cincinnati]. U.S. Department of Health,
Education, and Welfare, 1970. 205 p.
8. Sales management's survey of buying power. Sales Management, 98 (12): 1,3,5...
15. June 10, 1967.
9. Bureau of Census, U.S. Department of Commerce. Special report on household
ownership and purchases of automobiles and selected household durables,
1960-1967. Current Population Reports Series P-65: Consumer Buying
Indicators, No. 18. Washington, U.S. Government Printing Office, Aug. 11,
1967. 24 p.
*Perhaps one of the most valuable results of performing the present study was the collection and
evaluation of a large body of statistical information concerning community inputs and activities.
More specifically, this information consists of industrial and agricultural production rates,
commercial sales activity, personal consumption patterns, and the like. Whereas the amount and
detail of information are not readily describable, some feeling may be obtained by considering that
more than 200 documents constitute the basic bibliography (all of these contain information that
has direct bearing on predicting waste generation). For simplicity, however, only those sources of
information that precisely document the text of this report have been cited herein.
27
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10. Personal communication. B. Ehrler, Postmaster, Louisville, Ky., to G. B. Boyd, URS
Research Company, Oct. 1968.
11. Personal communication. R. R. Germain, U.S. Post Office Department, San
Francisco, to G. B. Boyd, URS Research Company, July 1968.
12. Personal communication. L. A. Kern, Traffic Manager, Louisville Times and
Courier-Journal, .o G, B. Boyd, URS Research Company, July 1968.
14
28
II. s. (,OYJ KTxMl NT PIUNTD.f. PI-FICK IH71 O - 447- 2if4
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