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This is to certify that the

thesis entitled

WASTE GENERATION IN THE BUSINESS COMMUNITY OF
EAST LANSING, MICHIGAN AND THE POTENTIAL
FOR RECYCLING

presented by

Kenneth Irving Stern

has been accepted towards fulfillment
of the requirements for

M.S. , Resource Development
degree 1n ~

732 J5;

Major professor

 

 

Date August 22, 1990

 

0-7639 MS U is an Affirmative Action/Equal Opportunity Institution

.___....

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"“

 

WASTE GENERATION IN THE BUSINESS COMMUNITY OF
EAST LANSING, MICHIGAN AND THE POTENTIAL
FOR RECYCLING
By

Kenneth Irving Stern

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Resource Development

1990

Printed on Recycled Paper

ABSTRACT

WASTE GENERATION IN THE EAST LANSING, MICHIGAN BUSINESS
COMMUNITY AND THE POTENTIAL FOR RECYCLING

By
Kenneth Irving Stern

At-source sampling was done at 19 retail, restaurant, and printing firms in the
East Lansing, Michigan business community to determine if more accurate and
complete estimates of materials generated can be made by dis-aggregating the
commercial waste stream into sub-sectors. Corrugated cardboard comprised over 40
percent of the retail waste stream, and over 70 percent of printshops’ waste was
paper products. Individual firms averaged as much as 67 percent corrugated and 80
percent paper wastes, respectively. These are significantly higher ratios than are
being reported by disposal-based studies. Overall, 67 percent of the commercial
waste stream may be recyclable paper materials.

At-source examination of the waste stream can provide planners with a more
accurate assessment of the materials available in local waste streams. This will assist
in developing collection programs that maximize recycling and prevent loss of
resources that are missed when the waste stream is incompletely identified and

quantified.

This thesis is dedicated to my father, who came to want thiS--for meufar more than
I did.

and
To the Recyclers of Ingham, Eaton, and Clinton Counties, for a brainstorming

session with staff members in the fall of 1986 found a kernel of an idea that became
this document.

ACKNOWLEDGEMENTS

This list may seem long, but in my quest to develop this project I reached out
far and wide and received assistance from as far away as New York and California
and from Minnesota to Texas. I cannot thank everyone who helped me out, either
nationally or locally, but I do want to acknowledge the assistance I received from
City of East Lansing staff, the local business community--including the many
employees in the 19 participating firms--MSU Students and faculty, and other
recyclers who helped as I developed the project, conducted the field sampling, and
researched this thesis.

I am deeply grateful to my advisor, Tom Edens, for his patience, interest, and
willingness to persevere. He gave freely of his time, and his assistance helped this
document finally become complete. Cynthia Fridgen and Ray Vlasin, committee
members, also were available, and I appreciate their participation. Jon Bartholic,
who started me out in the Department, gets thanks for sending me on my way,
allowing me to present my research at state and national conventions.

My many student helpers made the field sampling possible, coming out early
and late, under all kinds of weather conditions. Extra stars go to Shirley Businski,
Angela Faraci, and Melinda Miller, who went farthest above and beyond the line of
duty. Thanks also to Glenn Barner, Eric Frakes, Dave Frey, Andy Myers, and

iv

Sharon Phillips. Tom Young helped early and Jim Garland helped later on with
computer and writing tasks. Alberto Pulido also offered much needed assistance.

To fellow RD grad students, especially those who came out to help and for
a look-see at field work in action, thanks. Many of you are gone, but you are not
forgotten.

John Czarnecki, former East Lansing mayor, and Tom Dority, East Lansing
city manager, were interested in my ideas and gave the official seal of approval to
this project when it was merely a concept. I hope this research helps in the
development of recycling collection in the commercial sector of my adopted
hometown:

Peter Eberz, Jim Frey, Kurt Outer, and Bryan Weinert were waste
management and recycling professionals who were willing to offer advice. Through
Resource Recycling Systems I was able to watch a waste assessment being done at
a landfill. Your time and attention have been greatly appreciated.

The Recyclers of Ingham, Eaton, and Clinton Counties started me on this
quest, and along the way let me use their truck, trailer, computer, and office.
Thanks folks. You are succeeding in getting recycling expanded throughout the area,
even if the going is Slower than we would like.

My family members, who may have come to doubt that this student Sibling
would ever graduate, were always supportive in the hope they would hasten my
departure from the halls of academia. Thanks mom and dad, for much more than
your interest in this project. Eva, you watched this develop ever so slowly over the

years, but never wavered in your support, concern, or patience. Thanks.

TABLE OF CONTENTS

LIST OF TABLES ........................................... ix
LIST OF FIGURES .......................................... xi
Chapter
I. INTRODUCTION .................................. 1
II. PROBLEM STATEMENT ............................ 5
Introduction .................................... 5
Purpose of Study ................................. 6
The Necessity for Waste Stream Assessments ............ 7
A Systems Approach to the Municipal Solid Waste Stream . . 8
Waste Generation in the Commercial Sector ............ 1 1
limitations of Standard Assessment Practices ............ 12
Consequences of Underestimating the Recyclable Fraction
of the Waste Stream ........................... 14
Summary ...................................... 15
III. REVIEW OF PREVIOUS STUDIES .................... 16
Introduction .................................... 16
Assessing the Waste Stream ................... - ..... 16
Defining the Municipal Solid Waste Stream ............. 18
Sub-Dividing the Municipal Solid Waste Stream .......... 21
Emphasizing the Commercial Sector .................. 23
Comparing Methods of Sampling the Municipal
Solid Waste Stream ............................ 24
The Changing Focus of Waste Stream Assessments ........ 26
Sampling at the Source of Generation ................. 27
Involving Waste Generators in Waste Stream Studies ...... 30
Next Steps: Building on Previous Research Efforts ........ 31

Chapter

IV. HYPOTHESIS AND RESEARCH DESIGN ............... 32

Methodology ................................... 32

Introduction ................................. 32
Why a New Approach for Studying Waste Composition is

Needed .................................... 32

Hypothesis ..................................... 33

Examining Waste at the Point of Generation ............ 34

Program Design as a Part of the Methodology ........... 36

Methods ....................................... 37

Introduction ................................. 37

I. Enlisting Institutional Support .................. 38

11. Choosing Firms ............................ 38

III. Sample Size .............................. 40

IV. Soliciting Firm Participation ................... 40

V. Sampling Period ........................... 43

VI. Tools and Techniques ....................... 44

Introduction .............................. 44

Collecting Materials ........................ 44

Scheduling ............................... 45

Measurement Variables ...................... 46

VII. The Detailed Data Sort ..................... 47

VIII. Other Communications with Participating Firms . . . 49

Testing the Hypothesis ............................ 49
linking the East Lansing and the Westchester County

Studies .................................. 49

Comparing the East Lansing and Westchester County Studies 52

The Westchester County Study ................... 52

Comparing East Lansing and Westchester County Results . . . 54
Comparing Disposal-based Sampling to Sub-Sector

Sampling Based on the Westchester County Study ..... 56
Objectives of Research ............................ 56
Summary ...................................... 57

V. RESULTS AND ANALYSIS ........................... 58
Introduction .................................... 58
Printshops ..................................... 59
The Retail Firms ................................ 63
A Detailed Analysis of the East Lansing Results .......... 67
Comparison of the East Lansing Results with

Westchester County ........................... 67
Evaluation of the Equation: p, > p, .................. 68
Comparing Westchester County’s Commercial Sector with

Disposal-based Commercial Sector Results ........... 68

vii

Chapter

Comparing At-Source Results with Disposal-Based

Sub-Sector Sampling ............................ 72

Evaluating the Hypothesis .......................... 75
Rationale for Higher At-Source Sampling Results ......... 78

Summary ...................................... 80

VI. SUMMARY AND CONCLUSIONS ..................... 82
Summary of the Thesis ............................ 82

Findings ....................................... 84
Implications .................................... 85
Recommendations ................................ 87
Limitations of the Study ........................... 87

Appendix A: Correspondence and Data Sheets Used in Field Sampling . . . 89

Appendix B: Field Sampling Data Tables and Results ............... 101

List of References .......................................... 119

Chapter

VI.

Comparing At-Source Results with Disposal-Based

Sub-Sector Sampling ............................ 72
Evaluating the Hypothesis .......................... 75
Rationale for Higher At-Source Sampling Results ......... 78
Summary ...................................... 80

SUMMARY AND CONCLUSIONS ..................... 82
Summary of the Thesis ............................ 82
Findings ....................................... 84
Implications .................................... 85
Recommendations ................................ 87
Limitations of the Study ........................... 87

Appendix A: Correspondence and Data Sheets Used in Field Sampling . . . 89

Appendix B: Field Sampling Data Tables and Results ............... 101

List of References .......................................... 119

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1 1.
12.
13.
14.
15.
16.
17.
18.
19.

20.

LIST OF TABLES

Waste sectors and generators examined in various studies ........... 22
Rationales underlying protocols for various waste stream assessments

examining commercial sector waste generation ................. 25
Comparing disposal and at-source sampling schemes ............... 33
Criteria for determining firms to include in waste study ............. 39
Demographics of East Lansing, Michigan firms participating in waste

composition study, fall, 1987 .............................. 41
East Lansing and Westchester County sampling parameters . J ........ 52
Recyclable materials in East Lansing and Westchester County ........ 54
Waste composition results, East Lansing printshops, fall, 1987

(in percentages, from weight data) .......................... 62
Percentage of OCC in waste stream, East Lansing retail firms,

fall 1987 (in percentages, from weights) ...................... 66
t- and p- values for mixed papers in the printshop sector,

East Lansing, Michigan (from weights). a = 0.10 ............... 67
t- and p- values for corrugated cardboard from the retail sector,

East Lansing, Michigan (from weights). a =0.10 ................ 67
Disposal based studies (except Westchester County) for commercial

waste composition (weight data) ........................... 70
Commercial waste composition, expressed as a ratio to Westchester

County composition (from weight data) ...................... 71
Waste stream composition, by weight, of three East Lansing

printshops, October 24 - November 21, 1897 (in pounds) ......... 101
Waste stream composition, by volume, of three East Lansing

printshops, October 24 - November 21, 1897 (in cubic feet) ....... 102
Summary statistics from daily sampling, weight data, East

Lansing printshops, fall, 1987, (in pounds) ................... 103
Summary statistics from daily sampling, volume data,

East Lansing printshops, fall, 1987, (in cubic feet) .............. 104
Summary statistics from daily sampling, volume composition data,

East Lansing printshops, fall, 1987, (in percentages) ............ 105

Detailed waste sort, by volume and weight, of printshops participating
in waste stream study, November, 1987, East Lansing, Michigan (in
percentages, cubic feet, and pounds) ....................... 106
Modified waste stream composition, by weight, of ten retail firms,
East Lansing, Michigan, October .23 - November 21, 1987 (in pounds) 107

N
H

29.

. Modified waste stream composition, by volume, of ten retail firms,

East Lansing, Michigan, October 23 - November 21, 1987

(in cubic feet) ......................................... 109
Summary statistics from daily sampling, weight data, East Iansing retail

firms, fall, 1987, (in pounds) ............................. 111
Summary statistics from daily sampling, volume data, East Lansing retail

firms, fall, 1987, (in cubic feet) ........................... 111

Material composition results for East Lansing retail firms participating
in waste stream study, fall, 1987 (in percentages, from volumes) . . . 112
Detailed waste sort, by volume and weight, of retail firms participating
in waste stream study, November, 1987, East Lansing, Michigan (in
percentages, cubic feet, and pounds) ....................... 113

. Waste stream composition, by weight, of six East Lansing

restaurants and bars, October 20 - November 21 and
December 6 - 20, 1987 (in pounds, food wastes not included) ..... 115

. Waste stream composition, by volume, of six East Lansing

restaurants and bars, October 20 - November 21 and

December 6 - 20, 1987 (in cubic feet, food wastes not included) . . . 116
Summary statistics from daily sampling, volume data, East Lansing

restaurants and bars, fall, 1987, (in pounds) .................. 117
Summary statistics from daily sampling, weight data, East Lansing

restaurants and bars, fall, 1987, (in cubic feet) ................ 118

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LIST OF FIGURES

Cost and space estimates for landfilling solid waste in Lansing, Michigan 2
Current and projected waste disposal practices for Michigan’s solid waste 2
Waste flows through the commercial sector with alternative sampling

points S and D marked ..................................

The municipal solid waste stream is comprised of independent sectors . . 19
Sub-sectors of the municipal solid waste stream .................. 19
Commercial to residential waste stream ratios of various
metropolitan areas ..................................... 20
Comparing waste composition of two urban commercial waste streams . '. 20
Alternative locations for measuring waste generation .............. 35
Steps taken to initiate involvement with firms and firm participation . . . 42
Tools and equipment used to conduct field sampling ............... 48
The Westchester County study links East Lansing to disposal-based
studies because both are based on at source Sampling ............ 51
Waste composition from at-source sampling of three East Lansing
printshops, fall, 1987 (pounds) ............................. 60
. Daily waste composition, Printshop-1,East Lansing, Michigan
(fall, 1987, weight data) .................................. 61
Corrugated composition from at-source sampling of ten East Lansing
retail firms, fall 1987 (weight data) ......................... 64
. Daily waste composition, Bookstore-3, East Lansing, Michigan
(fall, 1987, weight data) .................................. 65
. Paper waste composition in the office /printing sector as measured at
firms and by ”pure-load" sampling, for several studies (pounds) ..... 74
. Office / Print sector sampling parameters for several studies .......... 74
. Corrugated paper composition in the retail sector as measured at firms
and by "pure-load" sampling, for several studies (pounds) ......... 76
. Retail sector sampling parameters for several studies .............. 76

. Waste composition measurements in the restaurant sector as measured

at firms and by "pure-10a " sampling, for several studies (pounds) . . . 77
77

. Restaurant sector sampling parameters for several studies ...........

CHAPTERI

INTRODUCTION

The handling and disposal of solid waste is becoming a major problem for
communities throughout the United States. The two most important concerns are
that one third of the nation’s landfills will reach capacity and close by 1991 and that
very few new facilities are being built as replacements (US. EPA 1989). Many other
landfills fail to meet federal or state standards and are listed as environmentally
contaminated Sites (Concern 1988). These factors are causing disposal costs to
escalate on a continuous and long term basis (Figure 1).

The definition of "proper" for handling solid waste has changed: Landfilling,
once the primary method of disposing of solid waste, is now placed last in
governmental policy hierarchy, to be used when other options are exhausted (US.
EPA 1989; MDNR 1988). In Michigan, for example, the state’s Department of
Natural Resources made adoption and implementation of a statewide solid waste
management-policy its top priority for 1988; state legislation required a strategy that
"will reduce land disposal of Solid waste to only ’unusable residuals’ by the year
2005" (MDNR 1988). Michigan’s goal is to minimize landfill needs to a 15 percent
remnant. This is vastly difierent from disposal practices today: Ninety percent of
the state’s solid waste is landfilled, less than 10 percent is recycled through the waste
hauling disposal system (not including beverage containers collected through
deposit), and the remainder is incinerated (Figure 2) (MDNR 1988). Recycling is

Disposal Cost — landfill Space

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Figure 1. Cost and space estimates for landfilling solid waste in Lansing,
Michigan (Adapted from K. Guter, personal communication 1990).

 

 

 

 

 

Waste Reduction [1]] Incineration
Recycling Cl Landfilling
I Composting
1989 2005
25%
10%
..;.:e::r:5:ii:i‘:: 21:. 10%
N ........ 1.5%
40%

 

 

 

 

 

 

 

Figure 2. Current and projected waste disposal practices for Michigan's solid
waste (Adapted from MDNR 1988).

3

intended to supplement and replace waste collection and landfill disposal as the
primary waste management technique as communities meet solid waste diversion
goals set by our governments (MDNR 1988, US. EPA 1989). Recycling strategies
are oriented to the re-direction of used materials back into the manufacturing stream
for processing into new products. There is a long established secondary materials
industry in this country, but it has traditionally been a small sector and has not been
involved with municipal-wide collection of solid waste.

The switch in policies and the creation of programs necessary to develop
alternative waste management strategies will fundamentally change the way waste
is disposed of throughout the United States (MDNR 1988, U.S. EPA 1989). A
completely new physical and social infrastructure will be built as society develops
alternative methods of handling its solid waste. To be successful, recycling programs
will have to collect the majority of readily available materials placed in the waste
stream. If collection programs do not adequately divert materials, recycling goals
will not be reached and recyclables will continue to be landfilled or incinerated in
large quantities.

Proper planning, based on sound, well developed information, is needed to
make this transition in waste management. It is imperative that accurate and useful
data assessing the quantity, composition, and status of waste, as it is generated, are
available. The standard methods for determining waste stream composition are
inadequate for the planning needed for large scale recycling programs. This thesis
focuses on the commercial sector to attempt to Show that current planning for large
scale recycling significantly underestimates that sector’s recycling fraction. Chapter
II will show that little information is available on commercial sector composition,

and that it is not applicable for analyses derived from loads mixed thoroughly

4
together in the process of waste collection. It questions whether the high levels of
recycling called for will be met if Significant portions of materials continue to be
landfilled because they have been undercounted or underestimated. Chapter III
reviews the use of waste stream assessments and their changing status in planning
large scale recycling programs.

This research examines waste production at the micro-level of the individual
firm in the East Lansing, Michigan business community. It estimates sub-sector
contributions and makes a comparison with results based on the standard sampling
techniques commonly used to assess the complete municipal solid waste stream. If
at-source sampling provides more accurate assessments of commercial sector waste
composition than disposal based sampling techniques, it needs to be considered as
a replacement method.

This research project seeks to improve the level of information available by
more accurately documenting materials in the commercial waste stream. A full
accounting of materials available for recycling prevents the significant consequences
of seriously underestimating the amount of materials. The methodology and the
principles underlying it are discussed in Chapter IV.

Chapter V presents the results of the field research and compares the East
Lansing commercial sector waste composition ratios to other disposal based studies
through a Westchester County, New York study that used similar at-source sampling
techniques as the basis for making county wide projections of waste generation and
composition in its commercial sector. Comparisons are also made with studies
where "pure load” packer trucks were used to assess sub-sector waste streams.

Finally, in Chapter VI, the conclusions stemming from both the empirical

work and the comparisons made with disposal based studies will be advanced.

CHAPTERII

PROBLEM STATEMENT

Intrculusztinn

Waste management programming is locally planned, based on assessments of
area or regional conditions. If the level of information is incomplete, deficiencies in
design will occur (Metropolitan Council 1988). The standard methods used for
collecting data (as summarized by the MDNR W
Guidebook, 1986) for planning purposes appear to be inadequate, and do not
accurately or completely estimate waste in the commercial sector (Robinson and
Robinson, 1986). Disposal based sampling methods for assessing waste composition,
generation rates, and quantities are not adequately capturing the complexities of the
non-residential waste Streams. A critical reading of the at-landfill sampling
techniques reveals that representative and complete samples of commercial sector
waste are not being made (Robinson and Robinson 1986, Metropolitan Council 1988,
US. EPA 1988, Cerrato 1989, Kuniholm 1989).

Robinson and Robinson (1986), for example, noted that very little attention
has been paid to this waste stream:

More often than not, the problem of quantifying commercial wastes

cannot be totally resolved . . . . The special problems with commercial

and industrial waste requires knowledgeable approaches that, with few
exceptions, have been woefully lacking and ignored in overall planning

6
W

How can more accurate quantitative and qualitative assessments of the waste
stream be made? Golueke and McGauhey (1969) concluded that disposal based
sampling is "inherent[ly] incomplete . . . ." This chapter will examine some of those
problems. It would appear that sampling early in the process, prior to collection,
would provide a truer picture of waste generation and the potential for recycling.
This hypothesis warrants testing.

The purpose of this study is to determine if at-source measuring of waste
provides more accurate and complete estimates of materials generated in the
commercial waste stream. This will be done by comparing composition estimates
of the waste stream from standard disposal based techniques with estimates created
by at source of generation measurements. It develops techniques that effectively and
efficiently measure waste production to reflect the total amounts of material
available for practical capture and the day-to-day dynamics of sub-sector waste
generation.

The heart of the problem in waste management appears to be the
paradigmatical shifting of programming from a disposal-based to a generation-based
orientation. Historically, the profession has assumed collection and focused on
techniques for final disposal. In a recycling based management program, disposal
is handled by the long established scrap and secondary materials industry. The
emphasis now must turn to programs that maximize the collection of waste for its
value as a commodity. This creates a completely new set of dynamics. Part of the
problem is that, until recently, the waste management industry has had little

experience or interest in large scale recycling programs.

7

Another important issue, stakeholder involvement, addresses the problem of
gaining participation from the waste generators once recycling collection programs
are in place. By expanding the stakeholder groups to include local government
personnel as well as commercial sector management and employees, a framework
can be created for program design and implementation after the analysis has been
made.

Chapter IV presents the study‘s hypothesis that more effective waste
management programs can be planned by linking waste generation to its source,

both by geographic location and by type of generator.

Wm

It is ironic that though most persons have a regular, if casual, relationship
with solid waste, little is known about waste management from an academic or
policy perspective. For too long professionals, like most other citizens, have
assumed that the status quo for waste disposal was sufficient. As has been noted by
Golueke and McGauhey (1969):

In the case of solid wastes, where progress toward solution of problems

has been glacier-er in its advance because of man’s inability to see

the problem or his disinclination to recognize it, there is little reason

to fear that the answer will outrace the quest.

Little has changed in the ensuing two decades Since Golueke and McGauhey
(1967) undertook their major, nationally funded, multi-year study for "comprehensive
studies of solid waste management." They observed then that "long experience with
refuse disposal has not produced information on the composition of solid wastes in

the detail now needed for management purposes.” Their list of reasons is also as

accurate as if it was written yesterday. These are (Golueke and McGauhey 1967):

8
1. Traditional concepts of management

2. Scope of concern with solid wastes

3. Dislocation of systems

4. Change in industrial technology

5. Cultural and sociological changes in America

Before solid waste can be ”managed,” local conditions must be analyzed.
Programs and policies depend on knowing waste composition, how much of different
types of materials is available, and where and by whom the waste is generated.
Locally derived data are needed because community characteristics differ from one
area to the next (Metropolitan Council 1988). Waste stream analyses, composition
studies, or characterizations (terms used to define examination of the waste stream)
are conducted as a crucial first step in providing planners and managers with
answers to these questions (Golueke and McGauhey 1967, 1969, U.S. EPA 1975, US
EPA 1979, MDEM 1985, Evans 1985, Savage et a1. 1985, Brunner and Ernst 1986,
MDNR 1986). A major problem arises because analyses of local waste composition

do not adequately reflect the quantity, type, or source of generation of materials.

A Systems Approaoh to the Municipal Solid Waste Stroam

The collection, transport, and disposal system for solid waste is complex, with
a considerable amount of "noise" (variables ranging from source of generation to
final disposal point) that is difficult to filter out in order to study specific waste
sectors that comprise the municipal solid waste stream. In Minnesota, for example,
researchers noted that there is "a complex, interwoven network of waste transport
and disposal between county boundaries, and to a lesser degree, exported outside
the TCMA [Twin Cities Metropolitan Area]." (Metropolitan Council 1988). Figure
3 indicates the various interactions that may take place between the time waste is

generated by a firm within a commercial sector and the time it is finally "disposed."

9
Materials Into)

Flrm
Firm Generates
I 7 Waste /

 

 

t

 

   
   
   
  

Firm Generates
Waste

 

Losses Outside

Disposal Syst
\s/I

Collection for
Recycling

Delivery Other
Leaves Wastesh +— Point Transformations
?

 

 

 

 

 

 

Yes

 

 

 

 

 

 

 

 

 

 

 

   

 

 

V I‘l' I B
e Ices yupgass y.. I Waste Sampled
Procedure /

 

 

 

 

 

 

 

Figure 3. Waste flows through the commercial sector with alternative sampling
points S and D marked.

10
Sampling at the point of generation (at S in Figure 3) could make it more likely that

all available waste would be measured.

The problem with disposal-based sampling is that once material leaves an
establishment a variety of things can happen to it, as depicted by Figure 3. Also,
disposal practices, with the many financial and regulatory dynamics associated with
it, are in a continuous state of flux, and disposal patterns change. As Metropolitan
Council (1988) noted: ”[c]losing landfills and changing tipping fees can create
instantaneous changes in the flow of waste to any facility, thus making it even more
difficult to document waste quantities generated by a particular county."

One important variable to consider is that waste may not be disposed of at
all, because many commercial sector enterprises are already recycling. Westchester
County (1988) found that an estimate of commercial recycling activities could not
be made, because there were not sufficient data to make a determination.

Waste may leave the wasteshed (the geographic area from which all waste is
disposed of in local / shared disposal facilities) and private haulers occasionally
operate in different municipalities and empty their trucks at a facility outside the
wasteshed. The Metropolitan Council study, for example, found that many industrial
loads bypassed the processing facility, where sampling took place (Metropolitan
Council 1988). The waste will almost certainly be mixed and compacted with waste
from other businesses and residential sources. The load may be exclusively from
within a sub-sector (e.g. all office buildings, all restaurants) or it may be completely
mixed, and include wastes from multi-family residential complexes as well as a
variety of businesses (Evans 1985).

If a study is being conducted at a disposal facility (sampling point D, Figure

3), there is no assurance that a representative sample will be taken of the waste that

11
was generated within the wasteshed. Commercial and industrial loads may be
missed, so that a representative sample of the entire municipal solid waste stream
is not taken (Metropolitan Council 1988, MDNR 1989).

Sampling at point S (Figure 3) avoids the "noise” problems that take place
once materials leave the place of generation. While measuring at the source, before
waste enters the complex system, seems intuitively correct, this approach has seldom
been taken in research and planning. Only a few firms, including the environmental
engineering company Malcolm Pirnie, in its 1988 study of Westchester County, New
York (Cerrato 1989, Kuniholm 1989), and Recovery Sciences, in its study for

Sunnyvale, California (Sunnyvale 1986), have taken this approach.

1!! G . . l C . l 5

There is a problem in that little is known about the dynamics of waste
generation. This is especially true in the commercial sector. As late as 1989 the
consulting firm Resource Integration Systems (GBCC et al. 1989) reported that ”few
reliable studies of commercial waste composition have been undertaken.” Cal
Recovery Systems (Metropolitan Council 1988), in conducting a major solid waste
study for the Minneapolis-St. Paul metropolitan region, had difficulty in determining
business waste totals. In their conclusion the concern was raised that:

The quantity of waste recycled by area businesses cannot be accurately

estimated due to the lack of a standard reporting system . . . . very

little waste quantity data have been collected through which one may

identify the geographic and generation source of the waste.

The Metropolitan Council report highlights two major problems: The lack

of data, and (more seriously) the lack of standard and appropriate procedures for

determining business sector waste streams.

12

Locally generated waste is often analyzed by local professionals who have
their own methods of data collection and assessment. Communities typically act in
isolation in determining their needs. There does not appear to be a central
authority or set of procedures that waste management or municipal professionals can
turn to in order to acquire a thorough set of methods for conducting waste stream
analyses. In examining data enhancement priorities for the Metropolitan
Minneapolis area, Metropolitan Council’s first recommendation was to standardize
definitions and test methods for collecting solid waste data. Its second
recommendation was to determine the level of recycling currently taking place

within the commercial and industrial sectors (Metropolitan Council 1988).

I . . i E S I l E E .

Most policies and programs are based on information derived from waste
stream assessments using data collected from sampling waste at landfills, the point
of disposal. Traditionally, characterization studies have focused on the complete
municipal waste stream, but no community of any size has all its rubbish collected
in such a general way. There is a Significant problem with the standard methods
employed in disposal-based sampling if they do not adequately or completely
delineate the complexities or the potential of the non-residential waste streams.
Waste management planners are operating under the burden of methods for
determining waste stream composition that were conceived in the 19405. It is no
longer sufficient to analyze solely for overall composition of materials disposed of
within the wasteshed. More detailed information is needed that reflects how waste

is generated at its source.

13

If these methods are now outdated, they are not adequate to plan for the
scale of recycling being called for and the more sophisticated micro-management
now needed. If the data derived are too general, and even inaccurate, then a false
picture of the dynamics of waste generation is being presented. It is the analysis
of these dynamics that determines waste management policy decisions.

A problem with assessments taken of the overall municipal solid waste stream
is that they can mask the fact that many commercial waste streams are
homogeneous, clean, and have large concentrations of readily recyclable materials
on a consistent basis. As a result, analyses of the at-landfill sampling techniques
lead to the conclusion that representative and complete samples of business sector
waste are not being made (McCamic 1985, Evans 1985). Significant portions of
recyclable materials, chiefly office paper, mixed paper grades, and corrugated
cardboard, are not being counted. The limitations of disposal-based sampling
include:

1. An inability to identify the waste explicitly to the source

2. An inability to prevent contamination of materials

3. An inability to accurately measure weight or volume of materials as generated
because of contamination and compaction inherent in the collection process

4. Limited ability to correlate demographics and social data with the waste
generators

5. Limited ability to design program on the micro-level of individual or sub-sector
generators

6. No involvement of waste generators

7. Orientation to centralized and large scale solutions

These limitations point out why a different system for Studying waste

generation is needed.

 

A lack of a complete accounting of materials results in seriously
underestimating the amount of materials available for recycling. This creates the
problem of having programs incorrectly designed and undersized, causing emphasis
to continue to be placed on standard collection systems. Diversion will then be
incomplete, because equipment, staffing, and routing will be inadequate to handle
all the material that is actually generated. The high levels of recycling that policy
makers are mandating cannot be met if significant portions of commercial sector
materials continue to be landfilled or incinerated. This can cause the related
problem of having unrealistic expectations for the amount of recycling that would be
needed in the residential sector to meet recycling goals (Regional Environmental
Task Force 1989). If the potential for business sector diversion is not accurately
assessed, calculations made for recycling in the residential sector will probably be
artificially high.

An undercounting of the recyclable portion of the waste stream skews the
economics and finances of the local disposal system. This creates the problem of
increased management costs in three major ways:

First, the value of the resource, derived from scrap as a commodity, is lost
when materials are burned or buried. The potential for recovery is lost. Secondly,
programs will not be properly planned (MDEM 1985) and will be under budgeted
and thus unable to handle the entire recycling fraction if it were to be captured.
Equipment and staff will continue to be disproportionally directed toward
landfilling. Programs will target larger waste generators and bypass the many

smaller businesses that, collectively, make up a sizable portion of the commercial

15
sector. Firms will not be prepared to or attempt to separate all materials, since
estimates are lower than actual generation rates. Finally, uncaptured materials sent
to the landfill or incinerator will add to Operating costs of those facilities and shorten
their life spans. Conversely, large quantities of unaccounted for materials that are
captured will results in overcapacity at the processing facilities, which also increases
costs (MDEM 1985, Savage et al. 1985).

Summary

Municipal solid waste is a more complex issue than commonly recognized,
and its dynamics are not well understood. Today’s problem in waste management
appears to be the paradigmatical shifting of programming from a disposal-based to
a generation-based orientation that captures materials for their market value. The
new emphasis in program management is to maximize the collection of waste for its
value as a commodity. This creates a completely new set of dynamics. Since
programs are locally planned, each community and region has to make this
adaptation on its own.

The first need is to analyze waste stream composition so that programs can
be properly sized. Standard, disposal-based sampling methods are not fully assessing
the amount of materials in the commercial waste stream available for recycling.
Most planning continues to be based on sampling done at the point of disposal.
Major new studies done for the states of Michigan (MDNR 1989) and Washington
(WSDE 1988) and the Minneapolis-St. Paul (Metropolitan Council 1988) region
relied on disposal-based sampling. Chapter III, the Literature Review, surveys
various field studies and assesses their results. The literature review will provide
a point of comparison for the standard approach and the alternative presented here

for principles that support at-source measurements of materials.

CHAPTER III

REVIEW OF PREVIOUS STUDIES

Introduction

The first axiom of solid waste management waste planning is to base program
design on an accurate analysis of the waste stream (Robinson and Robinson 1986).
Planners and waste managers agree that data for waste composition must be locally
derived to be useful for developing new program strategies (Golueke and McGauhey
1967, 1969; US. EPA 1979, 1989; Evans 1985; MDNR 1986; Metropolitan Council
1988, are but a few). National estimates provide an overall picture of the condition
of solid waste in the United States, but local dynamics are too variable for these
figures to be useful in the design of specific programs. Studies done prior to the
mid-eighties have been criticized as to the "worth" of the data because of their age,
the mixing of waste streams by sectors, the lack of complete measurement of the
waste stream, and assumptions that were made in conducting on-paper analyses
(Golueke and McGauhey 1967, McCamic 1985, Savage et al. 1985). These issues

remain a concern today.

Assessinuheflastemam
The quantity and composition of solid waste, particularly in the business
sectors, need to be quantitatively evaluated within a community. These factors are

"key considerations in the planning, design, and Operation of solid waste management

16

17
systems," according to the Michigan Department of Natural Resources (1986). In
a guidebook on local planning, composition variables are called:

Major criteria in the collection, transportation, recycling, recovery, and

disposal components of a solid waste management system. The

information produced by a waste stream assessment is used by
designers, financiers, and decision-makers at all level of government.

In their review of alternative methods for analyzing municipal solid waste,
Brunner and Ernst (1986) found:

The importance of reliable information on the composition of

municipal solid wastes (MSW) is emphasized by the following facts: (1)

potentials for recycling . . . or needs for treatment and disposal

capacities . . . can be identified only if information on the amount and
composition of MSW is available; (2) in order to design waste
treatment processes properly, the materials which are to be treated

have to be well characterized; (3) emissions to the environment from

waste management practices can be predicted only if the inputs of

waste treatment are known.

A waste stream assessment is generally defined as any program which
involves a logical and systematic approach to obtaining and analyzing data on one
or more solid waste streams or sub-streams (MDNR 1986). The primary issues in
determining waste composition are the classification system to define waste stream
components (McCamic 1985, MDNR 1986, Metropolitan Council 1988) and the
place of sampling in the flow of the waste stream (MDEM 1985, McCamic 1985,
Kuniholm 1989). Both of these have changed with the new strategies emphasizing
recycling of materials (McCamic 1985, Kuniholm 1989).

Research methods to conduct a project include sampling at the point of
disposal, sampling at the source, and modelling (MDEM 1985, McCamic 1985).
The first two take direct measurements of waste as in the field. The last consists of
on-paper calculations and manipulation of secondary data. These are too far

removed from actual waste generation to be meaningful (Golueke and McGauhey

1967, US. EPA 1979, MDEM 1985, Metropolitan Council 1988).

18

Field sampling in and of itself is not sufficient. The state of Washington
sponsored sampling that was done at 87 sites in 1988, but the sampling period was
of one and two days duration (WSDE 1988). Field measurements taken for the
metropolitan region around Minneapolis, Minnesota were limited to ten days
(Metropolitan Council 1988). Seldom does a study report, as the US EPA (1979)
did, that:

Data generated for this study were developed by limited,on-Site

investigations . . . assumed to be fairly representative of typical waste

generation. However,until detailed waste characterization studies are

done for these sources, the data must be considered an approximation.

These detailed studies could significantly contribute to increasing the
accuracy and utility of the applicability analysis.

The municipal solid waste stream (MSW) is made up of at least six major
component parts: residential, commercial, industrial, institutional, construction and
demolition, and agricultural (Figure 4 and Figure 5) (McCamic 1985, Cerrato 1989,
Kuniholm 1989, Selke 1990). Most programmatic efforts emphasizing recycling, to
date, have gone into the residential sector (Cerrato 1989).

A wide range of estimates exist for the size and material composition of the
commercial-industrial-institutional waste streams. Papke (1989) estimates the range
to be from 10 percent to 75 percent of a community’s total waste stream. Figure 6
shows that communities report widely divergent sizes when determining their
commercial waste streams. The estimates of the material make-up of waste streams
also differ greatly. These composition ratios vary more than local factors would
suggest. Figure 7 compares the commercial sectors of Seattle, Washington and St.
Paul, Minnesota. Waste generation per capita is similar (2.5 pounds vs 2.27 pounds

per day), yet sampling showed that the St. Paul area’s percentage of papers in the

19

 

MUNICIPAL SOLID WASTE STREAM

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Figure 4. The municipal solid waste stream is comprised of independent sectors.

Commercial

Cnstrctn/Demltn Debris

Residential

  

Agricultural

Institutional

Industrial

Figure 5. Sub-sectors of the municipal solid waste stream.

20
I Residential Commercial

Wshtnw Co., Mi. Wstchstr Co., NY E. Lansing, Mi.Rmsy-Wsh'tn Cos, Mn Seattle, Wa

 

Figure 6. Commercial to residential waste stream ratios of various metropolitan areas.

 

Ramsey/Washington Counties 93321 Seattle, Washington

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Figure 7. Comparing waste composition of two urban commercial waste streams.

21
waste stream was 77 percent greater than Seattle’s (Seattle 1989, Metropolitan
Council 1988). While waste generation is determined by local conditions, this

variation is probably greater than the influence of unique circumstances.

Sl-D"li l M "151'!!! 5

Most researchers recognize the unique nature of specific sub waste streams
within the overall municipal solid waste load (St. Paul 1982, Evans 1985, McCamic
1985, Savage et al. 1985, Sunnyvale 1986, Metropolitan Council 1988, MPIRG 1988,
Westchester County 1988, WSDE 1988, Cerrato 1989, Kuniholm 1989, GBCC et a1.
1989, Seattle 1989). These sectors can be divided further along natural divisions of
type of activity, location ("land use" in Evans 1985, and others) and materials
handled (Sunnyvale 1986). The US. EPA (1979) defined four different types of
airport operations and three different sizes of shopping centers.

In the 19805 planners started to use the federal Standard Industrial
Classification (SIC) coding system to define sub-sectors and group firms with similar
waste generation characteristics. Researchers have developed their own sub-sector
classification schemes, as shown in Table 1. Washington state (WSDE 1988) and
Seattle (1989) used a modified SIC based classification, combining groupings to
categorize eight types of commercial generators (and Washington took the same
approach with the industrial sector). The Minnesota Public Interest Research Group
(MPIRG 1988) reduced 33 major SIC groups to four in its survey of the Minneapolis
area. There is no uniform method or standard guidelines currently available. The
use of standardized solid waste definitions and field test methods is the first
recommendation made by the consulting firm CRS (Metropolitan Council 1988) in

its list of priorities to regional authorities for enhancing data collection.

22

Table 1. Haste sectors and generators examined in various studies.

1989 1_/ 1988 2_/ 1988 2_/ 1985 1979 3_/
Hshngtn Ushington In York Ontario U.S EPA
from 9 Ustchstr Canada

commnties County

Firm Type/Category SIC Codes Seattle statewide totals Toronto
1 Construction 150 to 170 xx
2 Hanufacturing 200 to 290 xx xx
300 to 390
3 Transport, Cmmnctns., 400 to 490 xx xx xx xx

Utilities, and
Sanitary Services

4 wholesale 500 to 519 xx xx xx
5 General Retail 520 to 549 xx xx xx xx xx
560 to 579
and 590
6 Automotive Retail 550
7 Restaurant 580 xx xx xx xx
8 Offices xx xx xx xx xx
- finance 600 to 629
- insurance 630 to 649
- real estate 650
- other 660 to 679
- legal services 810
- government 910-970
9 Hotel/Hotel 700 xx xx
10 Major Services 710 to 790 xx xx xx
- social/other 830 to 890
11 Hospitals/Health 800 xx
12 Institutional xx xx xx
-recreational 780 to 799, 840
- educational services 820
- government 910 to 970
13 Apartment Buildings xx xx

1_/ Extrapolated from eight reported categories.
2_/ Extrapolated from seven reported categories.
3_/ Extrapolated from five reported categories.

23

In its study of small-volume waste generators, the US. EPA (1979) identified
five sources of waste streams, with various generators within each category. There
were some published studies for each generator type they examined, but they
recognized an issue that has plagued waste management planners through the
present:

. . . after an extensive literature search, it was determined that

relatively little or no information exists on waste composition and

generation rates from the small operators . . . with the exception of

hospitals, office buildings, and small cities" (U .8 EPA 1979).

However, local communities have always had studies done of their municipal
solid waste to meet their needs. Among the specific commercial sectors that have
been closely examined are St. Paul, Minnesota (1982); Toronto, Ontario (Evans
1985); Sunnyvale, California (1986); Westchester County, New York (1988); Boston
(Greater Boston Chamber of Commerce [GBCC] et al. 1989); and Seattle (1989),
but these reports seldom have been made widely available or put into a publically
available system. They are often proprietary. These studies have utilized
questionnaires, disposal-based sampling, "pure-load” sampling of targeted packer
trucks, and at-source sampling. And, as noted earlier, the composition ratio of these

waste streams vary widely (Figure 6).

Elm]: .15

Planners are turning to commercial wastes in order to divert large amounts
of recyclable materials (U.A. EPA 1979, Evans 1985, MDEM 1985, Sunnyvale 1986,
GBCC et al. 1989, Cerrato 1989). Cerrato (1989) finds the benefits of initiating
recycling programs there include:

1. The commercial waste stream typically consists of a large

fraction of recyclable materials, such as paper, corrugated
cardboard, and wood

24

2. Commercial recyclables are both readily identifiable and easily
separated from the commercial waste stream

3. A significant amount of commercial recycling is usually already
going on and these efforts can be enhanced or used as
examples for other programs

4. Due to increasing disposal and collection costs, commercial
recycling becomes an economically attractive business decision

5. Commercial recycling activity will help municipalities reach
mandated state recycling goals by increasing recycling rates
and therefore reducing the overall commercial waste stream
requiring disposal

The commercial sector may constitute as much as 75 percent of a region’s waste

load (Papke 1989).

oustgg- “‘10. o .1101. J‘ U rye.“ Aer A.” ‘ mug;

While a great many studies have been done on municipal solid waste streams
throughout the country, a uniform set of standards for sampling the solid waste
stream has not been established. Table 2 lists studies recently conducted using
. criteria developed for this research to compare the level of detail used in sampling
the waste stream and any corresponding demographic data that were taken. Most
studies have focused on field sampling of the waste stream at disposal facilities and
transfer stations. The Seattle and Washington State studies conducted additional
"pure load" sampling of targeted sub-sector waste streams. Both these studies also
surveyed businesses using questionnaires to gain demographic data. The similarity
is probably a result of the same consulting firm being used for each project.

The ”pure load" sampling seems to have been done for background purposes
rather than as a basis for finding commercial sector waste quantities. The Seattle
(1989) study explained "an additional 24 ’commercial pure’ samples were sorted . .

. to collect data for specific types of commercial generators . . . . These data were

25

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26

not used to calculate composition estimates." The Washington state study did have
as a ”task” analysis of the data to ”produce waste stream composition and waste
generation rates for each ’pure-load’ category” (WSDE 1988). Neither effort seemed
to use these sampling data to estimate municipal-wide quantities for the commercial
sector.

In contrast, Westchester County’s (1988) field survey "was carried out to test
and qualify the results from . . . studies and surveys that had been completed or

were currently underway . . . .

W

The recent acceptance of recycling as a major solid waste management
technique changes the purpose of waste management. When the primary methods
for disposing of waste were burial or incineration, it was understandable that studies
used for planning solid waste programming were also less detailed (McCamic 1985).

Recycling provides a fundamentally different approach to waste management.
As was pointed out in Chapter II, a paradigm Shift is underway. The new model is
collection-oriented, rather than disposal-based. More specific and different types of
information are needed to define and then handle the "recycling fraction" of the
waste stream. Material composition, rate of generation, collection and processing
methods, and markets must all be considered (McCamic 1985, Kuniholm 1989).
McCamic (1985) stressed "the importance of the point of view in study planning"
in his call for a "meaningful classification system . . . . [that has] categories that
reflect the way that materials are collected and marketed.” Planning for recycling
based waste management must consider the nature of this type of programming

(McCamic 1985).

27

The intent of the project influences the choice of sampling procedures and
approach. Research is done according to the purpose of a proposed project, and the
hoped for outcome will determine the shape of the study (McCamic 1985).
McCamic made a contribution by linking the classification systemnwhich is a
conscious choice-40 "a philosophy of waste management.” He stated:

[t]he expanded classification system implies that the individual

categories are subject to individual handling, and therefore supports

a philosophy of source separation of the individual commodities for

marketing.

McCamic suggested a more specific targeting by classifying discarded items
and materials that can be re-used and repaired.

Part of the evolution of waste characterization studies is to move the
sampling from the point of disposal to the point of generation. This fully recognizes,
in McCamic’s (MDEM 1985) term "the nature of the waste stream." Kuniholm

(1989) uses a flow chart that initially identifies "waste material sources" and follows

waste on two tracks, the recyclable fraction and the disposal fraction.

5 l' l S E G .

McCamic (1985) suggested sampling at the source if the primary interest is
recycling. To maximize recycling, emphasis will be on the point of origin, where
materials are passed from generator to collector. The Massachusetts Department
of Environmental Management [MDEM] (1985) found this to be an advantage
because ”real, rather than Speculative, rates of recyclable separation and collection
may be established." From here, program design will include "not only the total
amount of material to be collected, but also the relative size of bins for different
materials in a multimaterial collection vehicle" (McCamic 1985). Evans (1985) and

Savage et al. (1985) each make the point that the geographic area of waste

28
generation needs to be identified, and McCamic (1985) suggested the use of "well

defined and very comprehensive . . . census data." Finally, several studies (US. EPA
1979, Sunnyvale 1986, Westchester County 1988) are in agreement with the St. Paul
(1982) report, which has an explicit purpose of "education of business managers and
owners regarding the issue of decreasing . . . landfill space.”

More and more consultants are sampling commercial sector waste streams in
this manner to provide a greater level of detail to local communities regarding the
recycling potential (Sunnyvale 1986, Westchester County 1988, Cerrato 1989).
Studies done on a neighborhood level (McCamic 1985) or by "land use" and type of
activity (Evans 1985) have the advantage of identifying waste generation and
composition by sectors of the community. Evans (1985) suggested "investigating
various land uses and their contribution to the overall garbage composition.”
Kuniholm (1989) outlined a materials flow diagram, and targeted the waste material
sources as the location for most accurately estimating the potential percent of
recycling capture. McCamic (1985 ) discussed the "nature of the waste stream" and
suggested "the waste stream may be broadly divided into waste sources, is.
residential, commercial, office, and industrial." The point of generation is the
necessary starting point for managing already-produced waste (US. EPA 1979).

At the end of the 19705 the US. EPA commissioned a ”Small-Scale and Low-
Technology Resource Recovery Study" to examine the applicability of recovering
materials from small volume waste generators (US. EPA 1979). Waste
composition and generation data was estimated by sampling at the source of

generation. It disagreggated the commercial sector and looked at specific "sources,"

or firms.

29

The importance of the EPA report was its recognition of examining ”low
technology" approaches to resource recovery systems to "specific waste Streams of
this nature." Consultants working for cities and counties from New York to
California are now directing their clients toward these sub-municipal level waste
streams. By incorporating this perspective into their protocols, they not only sample
waste more accurately, but the associated level of information (demographics,
attitudes) is also higher. Asking more specific questions brings more useful answers.
Plans can be more efficiently and effectively drawn to maximize capture rates (St.
Paul 1982, Sunnyvale 1986, Westchester County 1988, GBCC et al. 1989).

This approach to sampling is critical; as complex and variable as the
municipal solid waste stream is, it requires a carefully considered procedure to assess
it in a way that produces meaningful results. McCamic (1985) recommended
"Protocols for Recyclers" to provide a framework that will function to gather more
complete information for recycling-oriented programs.

This increase in detail leads to more sophisticated planning and program
design that can maximize recycling. By considering what firms produce (in waste)
based on what they do and how they are set up, recycling programs are made an
integral part of their on-going operations, rather than an after-thought. The US.
EPA (1979) had a limited point of view in assuming that responsibility for proper
waste management rested with individual generators and would be principally an
economic issue for them. They did not focus on social concerns, environmental
aspects, or the potential of "aggregation" and cooperative efforts. Combining all the
firms within a sector together increases the quantities of materials produced, making
recycling more attractive economically. A waste disposal system includes all sub-

sectors and all the members within them. N on-residential waste generators must not

 

30
be looked at as independent, unrelated, and individual firms, but as parts of a whole.
They can be disaggregated momentarily for data collection purposes, but for
program design each firm is an important part of a larger system (Sunnyvale 1986,
Westchester County 1988). Recycling programs will seek to maximize collection
within an entire community, not merely individual firms (Evans 1985, Westchester
County 1986, Cerrato 1989, Kuniholm 1989).

Local sampling allows parameters influencing waste generation to be defined
in greater detail. While number of employees, size, and sales volumes are
commonly included, some unique factors, either by sub-sector (beds in hospitals) or
by an individual study (Recovery Sciences measured dumpster volumes for

businesses in Sunnyvale) can also be considered (US. EPA 1979, Sunnyvale 1986).

In several reports to local governments, consulting firms have emphasized the
need for "acceptance by local firms of the concept . . . of in-house recycling
programs" (Sunnyvale 1986). They suggest that recycling be promoted by the local
government to the business community (Cerrato 1989). The US. EPA (1979) would
involve business managers in a waste characterization study to serve two purposes:

1. Educate decision makers and encourage their interest in solid
waste management in . . . waste reduction . . .
2. Improve the assumptions used in determination of applicability
and thereby enhance decision-makers confidence in pursuing
resource recovery
Two Minnesota studies employing survey techniques had objectives of
determining businesses interest in recycling and of educating owners and managers

on solid waste issues (St. Paul 1982, MPIRG 1988). Gaining the generators’

cooperation and participation in a study will maximize the accuracy of the data,

3 1
make it more precise, and lead to a more sophisticated analysis. Bringing waste
generators into the planning stages offers the advantage of enhancing program
participation, which in turn assists in maximizing material recovery and minimizing

contamination and later processing.

11 S .3.” 12' R Hill

This research offers a new paradigm for solid waste planning in response to the
shift to recycling based program management and the problems presented by
standard procedures as reviewed in this chapter. Sampling at the point of
generation, as indicated by several studies, seems to consistently measure much
higher quantities of recyclable materials in firms’ waste than is measured with point
of disposal sampling. Accurate information is needed if collection of all materials
available for recycling is to occur.

This research examines as its central hypothesis the issue of the amount of
recyclable materials in the waste stream as determined by measurement at the
source of generation. Embedded in its design is the development of a methodology
with a new procedure for field sampling. The methodology depends on stakeholder
involvement to both provide a higher level of detail for planning and to enhance
program participation once implementation is underway. Chapter IV details the

study’s hypothesis and the methodology employed to test it.

CHAPTERIV

HYPOTHESIS AND RESEARCH DESIGN

Msihmlnlm

Introduction

This chapter examines the principles and the sampling methods developed for
this methodology. The procedure employed for analyzing commercial sector solid
waste generation also serves as a framework for organizing recycling practices within
the business community. When properly constructed, these practices can provide a
means for planning recycling-driven collection programs that maximize removal of
materials, for taro reasons: (1) Accurate measurements properly size the amounts of
materials available for recycling, and (2) Involvement of stakeholders in the planning
stages enhances participation once the program is implemented (US. EPA 1979,
McCamic 1985).

.snro. aft 1.1!.'A-..‘ JUIIWL “0"

The limitations outlined in Chapter 11 (page 13) point out the need for a new
approach to waste composition studies. The methodology for this study is based on
the principle that waste management strategies must focus on generation and
collection issues. This research addresses Ackoffs (1962) objective for the

"improvement of the procedure and criteria employed in the conduct of scientific

32

33
research.” The limitations inherent in disposal-based sampling are resolved by
moving the sampling point to the firms’ back door, before collection takes place.

Table 3 compares criteria met by disposal-based and at-source sampling schemes.

 

 

sourcehascd

1. Identify source of waste No Yes
2. Prevent material contamination No Yes
3. Accurately measure weight or

volume of materials as generated No Yes
4. Correlate demographics and social

data with the waste generators No Yes
5. Design program to target individual

or sub-sector generators No Yes
6. Involve waste generators No Yes
When No YcL_

 

The principles that justify the proposed methodology (after Ackoff 1962) are:

1. Waste stream study design will maximize the identification of readily available
recyclable materials

Program design (implementation) is determined by a study’s sampling
approach

Measuring generators’ waste at source allows correlation of demographics
Involving waste generators in the study phase encourages greater involvement
and commitment for the actual program once it is in place

:“P’N

Hypothesis
The approach taken in planning the development of a study influences its

outcome. McCamic (1985) stated that:

[a] classification system implies a philosophy of waste management. .

The expanded classification systems implies that the individual
categories are subject to individual handling, and therefore supports
a philosophy of source separation of the individual commodities for
marketing.

A micro-level emphasis that examines the waste stream by sub-sector and

individual firms supports a structure that can provide greater accuracy and higher

34
levels of detail in the data. Businesses can be grouped by Standard Industrial
Classification (SIC) codes or by location as means of lumping generators of similar
materials. This leads to designing recycling collection programs that are reflective
of the actual quantities of materials in the waste stream (Westchester County 1988).
In light of the limitations inherent in standard waste stream composition
analyses, a different system for studying waste is needed. This study examines the
hypothesis:
H,: Significantly higher quantities of recyclable materials
will be measured when sampling waste at the point of
source generation and by sub-sector than found from
sampling waste aggregated at the municipal level at
the point of disposal.
This tests the statistical hypothesis:
[1,: There is no difference in measurements between
sampling waste at the point of source generation and
sampling waste aggregated at the municipal level at
the point of disposal.
The hypotheses can be represented by the equations:
“NIH—S”: and H1: [‘1 > #2:
where It, represents recyclable materials in the waste stream for at-source sampled
populations and #2 represents recyclable materials in the waste stream for disposal-
based sampled populations.
The significant difference will be shown quantitatively by comparing the

study’s sampling results with results from recent research presented in the literature.

n r i
Figure 8 compares the two sampling options available for measuring
commercial sector waste. Sampling at the diamond, before waste collection will

provide complete accounting of the waste stream by counting materials before they

35

 

 

 

 

 

 

 

 

 

 

     
   
 

 

  

 

 

 

 

 

 

 

 

Waste Generation Waste Generation Waste Generation
Printshop Restaurant/Bar Restaurant/Bar
Sub-Sector Sub- Sector Sub- Sector
Printshop Sub— I
Sector Compoetlon
Percentages Derive
fineetrntlaar Sub- ‘ M i | Materials
Sector Compoetlon )¢__ ater a S Measured
Pereentagee Derive; Measured 7
noun Sub— ‘ 1 N0
Sector Compoatlon k
Percentages Derivejd
fi Materials Collected
and Mixed

 

 

 

     

 

 

  

Delivery
of
Materials

Other
Transformations

  
  
 

Leaves Watershed

    

 

 

 

To
Landfill

1

Materials
Measured

Aggregate
Composition

Percentages
Derived

 

 

 

Figure 8. Alternative locations for measuring waste generation.

36

leave the wasteshed or are salvaged. This prevents mixing and contamination of
materials.

By this approach, the commercial sector is dis-aggregated, down to specific
firms defined by the federal SIC coding system. Waste is sampled on site (before
the first diamond, in Figure 8). It is measured as it is generated and as it would be
available for recycling. More accurate quantitative and qualitative assessments of

the waste stream can be made.

W

The specificity required by the sampling procedure is an important asset
influencing program design and implementation. McCamic (1985 ) found:

Recyclers will . . . want information for planning collection system

specifics, including not only the total amount of material to be col-

lected, but also the relative sizes of bins for different materials . . . .

In two ways, technically and socially, the approach taken by this research prepares
waste generators and management staff for developing follow up recycling programs
once the study is complete.

From a technical perspective, the analysis will show sources of material, rates,
and volumes. Demographic information will indicate locations of firms, types of
businesses, and possible constraints (storage, staffing). Socially, firms will be aware
of waste as an issue and will be prepared to participate, having already assisted with
data collection.

Since materials will have already been picked up in the area, there will be
familiarity with the physical layout of the recycling program. Planning staff will have

to Size equipment and determine schedules, but this will now be based on the

37

knowledge of who the generators are, the quantity and type of materials generated,
and where these are located.

Lee et al. (MPIRG 1988) called this "action research," writing:

This type of research and development has been more extensively used

in the Scandinavian countries than in the United States. The basic

idea is do actually "do something” . . . while generating data--and then

use this data to do more in a more efficient way. Data generation and

analysis is a dynamic process as the project proceeds.

In its report to Westchester County, New York (1988), Malcolm Pirnie, the
environmental consulting firm, suggested ”an evaluation of existing commercial
program infrastructure" as the first step in developing commercial recycling
programs. They began that process by surveying firms in the commercial sector.

A principle of this research methodology is to establish a framework for
recycling practices within the business community as part of its procedure.
Consulting firms are already using this approach. Recovery Sciences, Inc. introduced
its report to the city of Sunnyvale, California (1986) with the statement:

The purpose of this project was to determine the feasibility of

expanding recycling programs to the commercial / industrial sector of

the City of Sunnyvale. The first step in assessing the feasibility of such

a program was to ascertain if there was a sufficient amount of

recoverable materials being discarded by businesses . . . .

A St. Paul, Minnesota study was similarly designed, having as one of its
purposes (St. Paul 1982) ”education of business managers and owners regarding the

issue of decreasing Metropolitan Area landfill space.”

Methods
Introduction
It must be axiomatic that the simpler the method which is designed to solve

a problem, the easier it is to compile the results and the less costly it would be to

38
undertake and implement a program. The key components of this research project
are the quantification of recyclable commercial waste by weight and volume and the
determination of its composition. This was done in specified sub-sectors according
to Standard Industrial Classification (SIC) codes.
This study’ 5 premise is that measurement and identification of recyclable
commercial waste at the source can overcome the limitations of disposal-based

sampling (see Chapter 11, page 13) and enhance the recycling planning process.

I E 1i . 1 . . l 5
Discussions were held with the East Lansing’s City Manager and Mayor to

introduce the project and request official sponsorship in April, 1987. This was
granted. A letter of introduction, access to City staff, and use of City business lists,
as explained below, were the primary items of support provided by the City. Before
field sampling started, a meeting was held with the Police Department to inform
them of the activities that would be taking place in downtown City alleys and
parking lots.

11 Cl . E'

A 1984 East Lansing City business directory listing 526 firms was used to set
the sampling universe. The business community was evaluated based on the criteria
presented in Table 4. The first criteria created two artificial sub-sectors: 1. firms
that provide "products," such as retail goods, meals, and printing orders, and 2. firms
that provide services, such as banks, hair salons, medical providers, and realtors.
(East Lansing is without a manufacturing sector, and Michigan State University is
considered a separate and independent jurisdiction.) This splits the traditionally

defined service firms in order to separate those which offer material goods from

 

 

. . . l
1. Fits SIC code classifications: "Product” oriented firms
a. Division G.-—retail trade with waste readily
b. Ind. no. 2752-commercial printing separated by a few
c. Ind. no. 7334--services: photocopying employees
2. Fits SIC code classifications: ”Transaction" firms
a. Division H.-financial services eliminated; firms generate
b. Division I.-other services discrete amounts of waste
c. Division J.-government and schools generated by many employees
3. Size of present waste bin Upper limit set to eliminate
largest firms
4. Number of pickups per week Upper limit set to eliminate
largest firms
5. On-Site waste generated primarily Eliminates fast food firms
by employees as part of business
6. Customers create little waste Eliminates fast food firms
on-site
7. Recyclable waste materials commonly Eliminates "exotic firms
Ammonium animaterials

 

 

those that are strict service providers.

The first group became the sample universe, While the second group was
eliminated from consideration. The "product" sub-sector generates large quantities
of readily identifiable types of waste that can be quickly separated out by a few
employees. The ”transaction" sub—sector also produces large quantities of waste,
but this is produced in discrete amounts by many independent employees. Sampling
these types of firms is a more difficult task.

A 1986 East Iansing list of commercial waste collection customers was used
as a guideline to set parameters for size, based on size of container and frequency
of pick ups per week (Table 4). (The City offers waste collection to the business
district.) The sub-sectors selected for sampling included printshops, retail firms,

restaurant and bars.

40
W
A stratified sampling scheme was used, with the nine represented SIC Code
categories combined into three groupings: Printshops, retail stores, and restaurants
and bars (Table 5). Within each grouping, the sample was chosen using a random
process selection, with numbers picked from a book of tables of random numbers.

Thirty-four firms were chosen to be contacted.

BEST" E' E ...

Figure 9 outlines in flow chart form the steps taken to establish relationships
with firms for the conduct of the study.

Once selected, firms were approached and requested to participate. A letter,
co-Signed by East Lansing’s mayor, city manager, and the principal investigator was
sent on City stationary to the 34 chosen firms in early September, 1987 (appendix
A).

Twenty-two firms were contacted through follow up phone calls for
interviews; 18 (82 percent) agreed to meet to discuss the project. Every firm that
was interviewed participated in the study. Table 5 shows firm participation by sub-
sector. R&Bar-1 was chosen (not randomly selected) as a pre-test to test sampling
procedures in the restaurant sub-sector.

The meetings were held to Show the firm managers and owners how carefully
the study would be structured. Overall, the managers were supportive of
investigating waste alternatives and expressed interest in recycling. Among the 10
retail stores, five volunteered to include paper separation as part of the study.

At the meeting summary outlines for the entire project and each sub-sector
were provided (appendix A) and the need for assistance and the role that the firm

and its employees would play was explained. To assure uniformity at these

41

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42

C Letter to Firm)

Phone Call

 

  

 

    

Firm Not Included
in Study

 

 

-Project Outline
Sub-section Outline

1

Interview Form:
-sizelgeneration rate
-employees
-hours open
~sampling times
-types of recyclables

 

I Meeting:

 

 

 

 

  
  

 

  
 

Firm to
Participate

No
Firm Not Included
in Study

 
 
 

 

  

V” Meet with Staff
' to Explain Project

   

Training
7

    

 

 

 

Prepare Firm for Data
Collection:
-signs -schedule
-container

i

Data Collection J

 

 

 

 
   

  

End Field
Sampling

 

 

Figure 9. Steps taken to initiate involvement with firms and firm participation.

43
meetings, an interview form (appendix A) was filled out for each firm. Full
c00peration with staff and management was pledged and the promise made that
sampling would stop at the request of management (only one firm asked). Short
trainings were offered to discuss the project with staff employees; fewer than half the
managers requested these additional meetings.

The interview was used to obtain estimates of the size, composition, rate of
generation of waste, and materials to be sampled. Storage areas and number of
containers (44 gallon cardboard barrels and 30 gallon plastic bags) needed for
materials were agreed upon. Some firms declined the use of barrels. Firms were
given the option of an early morning (8:00 to 10:00) or late evening (9:15 to 10:30)
sampling time. This was set to match opening or closing times of most
establishments; this ensured that a full day’s waste was measured for the firms
sampled daily. The number of sampling stops needed per week was estimated for

each firm and ranged from daily (some on a seven day week) to weekly (Table 5).

j: S 1' E . 1

Sampling started four weeks after the initial interviews. The printshops and
retail stores were sampled in the four week period of October 23 to November 21.
R&B~1 participated as a pre-test for the restaurants and bars concurrently. The
other five restaurants and bars were sampled during the two week period of
December 6 to December 21. This sampling period was shortened because of the
holiday season.

Not all firms met this sampling schedule exactly. Table 5 indicates the length
of participation of every firm. Although the sampling period was considerably

longer than any other study reported in the literature the research was limited to

44
one sampling period. Ideally sampling would be repeated across fall, winter, spring,

and summer to assess seasonal fluctuations in waste generation.

W

W Variables included material composition, rate of generation,
volume, and weight. This was a modified waste stream assessment in that it
measured ”readily recyclable materials” and "rubbish."

W. Material categories were based on sub-sector. Only
recyclable materials frequently generated in the course of doing business were
separated out (see data collection form, appendix A). Though small quantities of
glass, metal, paper, plastic, and other recyclable materials are generated throughout
the commercial sector, these are relatively insignificant in comparison to the
corrugated cardboard, papers, and glass, metal, and plastic food containers used by
the printshops, retails, and restaurants and bars, respectively. The study examined
materials that would be collected in ongoing recycling programs.

Restaurants and bars did not have their entire waste stream measured.
Because rubbish was not sampled, a complete assessment was not possible. The
study was unable to include measurements of food wastes due to logistical and time
constraints. It was, however, recognized that this is a major segment of this sub-
sector’s waste stream (55 percent for two restaurants, Westchester County 1988).

Corrugated cardboard was sampled at all firms. The printshops separated out
white paper, colored paper, and mixed paper. The retail stores segregated materials
into cardboard and rubbish, though five volunteered to separate out office paper,
and the camera store separated out plastic botfles, as well. All restaurants and bars

separated out corrugated, five separated glass, and three also segregated their metal

45
and plastic food containers (Table 5). General rubbish was bagged and left by
employees to be picked up for sampling.

Sohgxlnling. Eight firms were sampled in the morning and six at night during
the October-November sampling period. Printshops were sampled between 8:00 and
9:00 am. Retail firms were given a choice to have early morning pick-up (8:00 to
10:00) or late evening pick-up (9:00 to 10:30) to match the opening or closing time
of the businesses or a more convenient time. The florist’s shop closed at 8:00 pm,
prior to the start of the evening sampling period. Materials were set at the back
door for the evening pick-up. Data collection was done on these two Shifts daily, on
a six day week. On Sunday, sampling was done at 5:30 pm and again at 10:00. In
December, the collection for the restaurants and bars were in the afternoons, from
4:00 to 5:30.

The printshops, three of the four bookstores, and the florist required daily
pick-up. Two of the restaurants and bars also had daily pick-up. All other firms
were able to store materials and were sampled two or three times weekly. The
convenience stores and two restaurants /bars held materials outside. Sampling
collection generally mirrored waste disposal collection scheduling. Table 5 lists the
schedule and other sampling variables.

It took the two person research team less than two hours to collect in the
morning and about 90 minutes in the evening. The number of firms being sampled
on a particular shift determined the length of the sampling period. An attempt was
made to measure the length of time it took to sample per firm. This was not
systematic enough to be able to report results. With drive and set-up and finishing
times, it took approximately 15 to 20 minutes per firm to take measurements. The

entire sampling routine, from arrival to departure, could be done in as little as five

46
minutes, although more time would be needed for larger waste loads. As
anticipated, there was a direct relationship between quantity of waste generated and
length of time needed to take a sample.

Downtown East Lansing has a service alley running parallel to its main street.
Almost all the daily pick-ups were on the alley and as were over half of the
collections made in the October sampling period (Table 5). A 1987 pick-up truck
was used for sampling. At each stop the support stand would be taken out and the
scale set-up adjacent to the firm’s back door.

The sampling routine varied by firm. All the printshops fell into a routine of
having the researchers ”empty the trash." Plastic garbage cans were normally located
by the printing equipment; the research team would enter the work area, remove the
containers, take them out for sampling, and return the containers empty.
Corrugated materials would be placed separately; this, too, would be removed.

The retail stores stored materials by category (corrugated, magazines,
newspaper, office paper, plastic bottles-from the camera store). It was convenient
for everyone to have the researchers enter the firm and carry the material out to the
work station set up in the alley or parking lot. The two convenience stores
generated so much waste that corrugated was stored next to the outside bins and the
waste (mostly bagged) placed in the dumpsters. These bins would be emptied and
the materials measured, then the waste returned to the bins. Corrugated that had
been ”thrown away" was measured as recyclable material.

W5. Weights were taken in pound intervals and volumes
measured in cubic feet. The English measurement system was used since results
would be conveyed to the public (participating firms’ staffs, city staff, trade journals)

as well as to the scientific community. Additionally, recyclables are sold by the

47
pound, and waste disposal prices in the Lansing area are based on cubic feet.
Figure 10 illustrates the tools used for measuring the samples.

Volumes were measured using 44 gallon cardboard barrels calibrated into
quarters. Two printshops had 24 and 32 gallon plastic cans; for these volume was
estimated in eighths of container size. Cardboard was always flattened before
measuring. A 12 foot metallic tape measurer was used to measure height, length
and width dimensions. These were later converted into cubic feet. This approach
guaranteed extremely conservative volume estimates, since most boxes are not
flattened when thrown away. This method was chosen for two reasons: to
accommodate firms that were without adequate storage space to save the material,
and to assure uniformity for comparisons across the sample.

Weight was measured using a hanging spring scale and a 76 inch high metal
stand. Materials were placed in the barrels, the barrels were fitted with a snap-on
ring which had a small plastic ring attached to it, and then the barrel placed on the
scale’s hook. Printshop garbage cans full of paper were weighed directly.
Corrugated that was too bulky to fit in the barrel was placed in a rope sling,
secured, and then weighed.

Most firms bagged their rubbish at the close of their business day. This was
left for sampling and then taken to the dumpster by the study team.

W

Measurement of all waste generated was made once during the sampling
period for the printshops and retail firms. This provided a check on the cooperation
level of the firms by measuring how much recyclable materials were being discarded
in the rubbish. It also allOwed an examination of the entire waste stream. During

the week of November 12th, each printshop and retail firm’s rubbish was set aside

a. Stand and support pole

b. Spring scale (250 lb limit)

c. Forty-gallon calibrated
cardboard barrel

d. Five-gallon calibrated pail

e. Knotted nylon rope sling
with ring and clip

 

 

”I: e:

O
U . .
2.
ea.
e ..
I.-
‘ e
e
..e
..
e...
ee
e...
e
O
e
7'.
Jo ‘
e e
‘b
.’ .

I' e “.5:

 

 

 

 

Figure 10. Tools and equipment used to conduct field sampling.

49
once for a detailed sort. Specific dates for taking the rubbish were picked at
random; the larger waste generators had three trash bags picked at random from
their entire waste load. The bags were marked and stored. They were later sorted
through using a classification system modified from the MDNR’S (1986) waste stream
assessment manual (appendix A). Five gallon plastic buckets, calibrated in thirds,
and the cardboard barrels were used as sorting receptacles. Volumes and weights

were measured as described above.

ni i ' ' ' i
The last week of the survey firm managers were notified by memorandum and
reminded through in-person visits that the sampling period was ending. An initial
summary of results, for each firm, and overall, was given to the managers in

November, 1988 (appendix A).

I . l H l .
___11° .1’ ..- E. 'n 1.10 i' 1‘ ' ' .ru 1"

The analysis of the East Lansing data was contrasted with commercial sector
estimates derived from landfill based-sampling to test the study‘s hypothesis, It, _>_
#2, that more recyclable materials will be identified by sub-sector sampling of the
waste stream. This was done by linking this analysis with a complementary study
done in Westchester County, New York (1988).

To test the hypothesis, it is necessary to establish a relationship between the
empirical results from East Lansing and the field sampling done in Westchester
County. If East Lansing composition rates are similar (or higher) than those

measured for the New York study, it can be assumed that East Lansing’s aggregated

50
commercial waste stream would also measure similarly high (or higher) levels in its
overall composition.

Primary sampling was done to provide a basis for estimating commercial
sector waste generation countywide (Westchester County 1988):

The field survey was carried out to test and qualify the results from

the [secondary] studies and surveys that had been completed . . . .

selected business streams were weighed and separated by component

to estimate waste generation quantities and composition.

Figure 11 represents the East Iansing methods as a sub-set of the system
employed to determine waste composition for Westchester County. The field
sampling done in New York was used as the basis to determine waste composition
for the entire commercial sector. Westchester County has targeted large commercial
generators of recyclable materials as participants in its recycling program based upon
the results of the study (Cerrato 1989). The Westchester County study matches the
research intent of this thesis, using a micro-level examination of waste generators to
extrapolate to the larger municipal solid waste stream.

If the estimates from the two studies are comparable, the East Lansing
methodology and results will validate and reinforce the source sampling done in
Westchester County, New York. The results would then contribute to a growing
knowledge base of primary data based on at-source sampling.

The link with Westchester County’s county-wide estimates is based on the
comparison with the County’s sub-sectors. In order to compare the East Lansing
results with disposal-based analyses, it must be shown that East Lansing

measurements are commensurate to the Westchester County results. If this

relationship can be demonstrated, East Iansing’s results can be compared to other

 
      
  

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Figure 11. The Westchester County study links East Lansing to disposal
based studies because both are based on at-source sampling.

52
disposal-based studies. This provides the link to use this research’s empirical results

to test the hypothesis.

 

This section will summarize the methods employed to estimate the
commercial sector waste stream in Westchester County. The methodology paralleled
the East Lansing project in its focus on field sampling based on at-source waste
generation of specific firms. In disaggregating the commercial sector nine waste
generator ' segments were established. These were later reduced to six groupings,
with selected retail, hotel and motel, and restaurant firms dropped to manage the
data more effectively (Westchester County 1988). Seven SIC code divisions were
included in the 26 firms that comprised the sample frame. Two of these groupings
match the SIC classifications used in the East Lansing study (Table 6).

a. . ‘ ,u. LLL !' 1.1. A - ' - A. J‘ u.!!',-.l' .51.". 'IN

 

 

 

W
SIC # of # of # of # of

Businesslxpe Cm; finns samnlml) films amalgam
Construction 150 to 170 4 1
Industrial 200 to 390 3 1
Printing 2752 3 9-27
Office 600 to 690 3 1
Transp/Cmm/Util 400 to 490 2 1
Whlesle/Warehse 500 to 519 2 1
Retail 520 to 590 10 4-27 7 1
Restaurant/Bar 580 6 1-11 2 1
Public Insttn . 910 to 970 3 1

Totals 19 27 26 1
1. Printshops and retail stores were sampled in a four week period.

Bars (except R&Bar1) were sampled in a two week period.
2. Sample period was one day.

S3

Estimates of waste production by each generator segment were based on the
variable of waste generation rates per square foot of occupied floor space.
Researchers followed the premise that commercial waste production is a function
of the type of business activity and not the number of employees per business. Up
to six separate methods were used to determine generation rates within each
grouping. These included a commercial business telephone survey, a major business
survey, an IBM independent survey (IBM accounts for approximately 13 percent of
all occupied office floor space in Westchester County), a field survey, and the use
of previous studies and national published averages.

The results of the independent surveys were analyzed and integrated into
algorithms developed to determine waste generation rates for each waste generator
segment. The commercial waste load was estimated for each segment. These were
aggregated to determine the county’s commercial waste totals. Commercial sector
composition was determined from the same surveys and published reports and a
breakdown of the commercial waste stream into its principal components was made.

The waste composition estimates obtained from these surveys were revised
based upon the actual field data. The empirical data provided the basis for making
the transformation to the final composition totals. This provided both the estimated
compositional breakdown of the entire commercial waste sector and total tons of
waste for the generators for each material category. These estimates were checked
against other reports and found to compare favorably with 1986 estimates of
commercial tonnage based on haulers records, on estimates of employee waste
generation rates, and against estimates of the non-residential sector being between
60 percent and 100 percent of the residential waste stream (Westchester County

1988).

 

Field sampling done at the source of waste generation provides a primary
basis for developing aggregated commercial sector waste composition estimates for
Westchester County. This is a standard with which to evaluate the East Lansing
results. The major difference between the two studies is that the Westchester
County analysis extrapolates to the larger municipal solid waste stream, while the
East Iansing research does not.

The empirical results from the East Iansing field study will be compared to
comparable Westchester County sub-sector population estimates. Probability testing
will be conducted to assess how the East Lansing data compare with the Westchester
County data. If the East Lansing results are similar, or higher, that will indicate that
the measurements are influenced by the approach taken for sampling (e.g. at point
of generation vs point of disposal). Table 7 lists the percentage composition of
recyclable materials reported in the Westchester County (1988) sub-streams.

The general equation to be tested is: p, > [12, with p, representing the
population mean of materials composition for specific sub-sectors of East Lansing

and u, representing the same population mean for Westchester County’s like sub-

sector.
..I‘ i'al-J‘ "<11. °I 1.1.1.992.“ l‘ 1' ‘ Lu
. East Iansing Westchester County
W W W
Pnntmg ‘
Office " 65 (1)
Retail “ 40 (2)

 

W

‘ = To be determined by probability testing
1. mixed paper

2. old corrugated cardboard

55

The general equation to be tested is: a, > #2, with p, representing the
population mean of materials composition for specific sub-sectors of East Lansing
and p, representing the same population mean for Westchester County‘ 5 like sub-
sector.

This will be tested in the retail and printshop/ office complex sub-sectors.
In each case, the Westchester County study’s population mean for the material
examined will be substituted for u, (Table 7). For the retail sector the equation to
be tested is:

#1 > 40

The Westchester County population mean for corrugated cardboard in the retail
sector is estimated as 40 percent of the overall composition, which is higher than
their field results mean of 34.3 percent. This was derived from sampling seven
stores.

For the printshop/ office sub-sector the equation to be tested is:

”I > 65

The Westchester County population mean for mixed paper in the office sector is
65 percent of overall composition. This estimate is lower than the sample mean of
75 percent, the result of measurements taken at three office buildings.

The alpha level for testing the probability is 0.10, a = 0.10.

If East Lansing’s results test higher than Westchester County’s, the
assumption will then be made that an estimate of East Lansing’s aggregated

commercial waste stream would be similar in composition to Westchester County‘s.

 

The Westchester County aggregated commercial waste stream
estimates will be used to compare material composition make-up with other landfill
based composition studies. An index will be created to compare the percentage of
materials in each study to the like figures in the Westchester County waste stream.
One (1) would be a perfect match of composition percentages. Ratios above one
indicate greater quantities of the materials in the Westchester County waste stream,
while numbers less than one show higher composition for that material in the other
waste stream. Ratios of 1.2 and higher can be considered significant, since they
indicate that Westchester County has estimated that 20 percent more of that
material exists in its waste stream than the study it is being compared to.

After the relationship between Westchester County and East Lansing is
demonstrated, East Lansing’s results can be compared to other disposal-based
studies through the Westchester County results (Figure 11). This will enable the
thesis’s alternative hypothesis, that sub-sector sampling at the point of generation
will measure greater quantities of recyclable materials compared to sampling mixed

waste at the final point of disposal, to be tested.

Q] . . [B 1
This study’s objectives are to:

1. Explicitly link recyclable materials to sub-sectors and specific firms within each
sub-sector

2. Accurately measure materials for composition, weight and volume at the waste
source, as they would be specified for recycling, with little or no contamination
of materials

3. Require resources for sampling that will be comparable or less than resources
needed for municipal level sampling (budget, equipment, staff, time)

4. Offer conveniences that are comparable or less difficult than that needed for
municipal level sampling

5. Be duplicative and the results replicable

57

6. Compare sub-sector derived estimates for waste composition to disposal-based
estimates of sub-sector composition

Summary
As part of the methodology to support the thesis’s hypothesis that sampling at

the source of generation will measure larger quantities of recyclable materials, a
field sampling procedure was developed. This was tested in the East Lansing
commercial sector in the fall of 1987. The results of this field testing and the

analysis of the data are presented in Chapter V.

CHAPTERV

RESULTS AND ANALYSIS

Introduction

The methods for conducting field measurements were tested during two sampling
periods in the fall of 1987 in East Lansing, Michigan. The sampling data are listed
in tables in appendix B. In this chapter the empirical results will be compared to
results from the Westchester County, New York study to assess the validity of the
field tests. By linking the East Iansing results to the New York study's county wide
estimates, a comparison of composition results obtained by sub-sector sampling can
be made with results from ten disposal-based studies. This relationship is used to
test this thesis’s hypothesis.

A ”one-tailed" t-test was used to statistically determine the probability of the
sample falling within the critical region of the sampling distribution created for the
East Iansing results. The one-tailed test is used to specify if the sampled population
means (#1) are greater than the comparable Westchester County population means
(#2) for the materials measured. If the probability, or significance, is greater than
alpha, a, the statistic is outside of the critical region. The equations [12 > ”I,
signifying that Westchester County’s recyclables are a greater percentage of the
waste stream than East Lansing will be rejected. If the probability is less than alpha,

the test statistic falls within the critical region and the alternative equation, ,4, > [12,

58

59
will be accepted. Such test results would provide a high degree of confidence in
assuming that the East Iansing sub-sectors have higher percentages of the sampled

recyclable materials than the comparable Westchester County waste stream.

Bfintshgns

Over 74 percent of the waste stream of the printshop sub-sector sampled were
paper products (Figure 12). Individually, the total percentage of paper in their waste
loads ranged from 70 percent to 81 percent (Table 8). Every sample contained over
47 percent paper, and loads were at least 75 percent paper 40 times and over 90
percent 10 times (11 = 64) (Table 14, appendix B). Figure 13 shows Printshop-1’s
daily composition totals as one example. Overall, as much as 88 percent of materials
from these printshops is comprised of recyclable papers (white, color, mixed, and
corrugated) (Table 8). These are comparable to figures reported by the U.S. EPA
(87 percent, 1979), Evans (95 percent, 1985), and Westchester County (90 percent,
1988) for total paper generation by office buildings. On a weight basis, large
amounts of materials can be generated daily. As much as 237 pounds of white paper
was discarded by Printshop-2 one day (Table 14, appendix B). By volume, firms
generated as much as 36 cubic feet (1.33 cubic yards) in a single day (Table 15,
appendix B). These results clearly bear out the premise raised in Chapter II:
Segments of commercial waste streams are homogeneous, clean, and comprised of
high levels of recyclable materials.

White paper made up over 30 percent of the load at the two printshops where
this variable was measured. These figures are conservative, because large amounts
of waste paper were observed to be placed in the rubbish daily. The manager of

Printshop-1 estimated that over 95 percent of their paper usage was white paper

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Figure 13. Daily waste composition, Printshop-l , East Lansing, Michigan (fall, 1987, weight data).

62

Table 8.Waste composition results, East Lansing printshops, fall, 1987 (in
W).
n" smple lower upper

 

Printshop-1
mixed paper 27 64.7 96.6 31.9 81.3 9.1 78.5 84.2
cardboard 25 1.5 33.3 31.9 8.5 7.2 6.1 10.8
rubbish 27 1.1 66.7 65.5 12.4 13.0 8.3 16.5
Printshop-2

mixed paper 9 36.3 69.7 33.4 53.5 12.0 47.0 60.1
white paper 13 15.8 68.3 52.5 43.7 18.0 35.5 51.9

color paper 8 7.6 47.2 39.6 27.5 11.2 21.0 34.0
total paper 18 47.3 99.3 52.0 70.6 15.0 64.8 76.4
cardboard 14 0.4 40.0 39.6 7.2 10.7 2.5 11.9
rubbish 15 14.5 50.0 35.5 28.7 10.7 24.2 33.3
Printshop-3

mixed paper 7 12.7 92.6 79.9 56.3 31.4 36.8 75.8
white paper 13 20.8 20.8 0.0 50.7 19.1 41.9 59.4
color paper 9 13.0 60.0 47.0 31.0 15.2 22.7 39.4
total paper 18 59.8 95.7 36 0 78.4 10.7 . 74.2 82.5

cardboard 9 2.6 16.7 14.1 6.2 5.2 3.3 9.1
rubbish 18 4.5 100.0 95.5 22.9 21.7 14.5 31.3
Grand Means

mixed paper 64 48.0

white paper

color paper

total paper 64 75.0

cardboard 64 5.8

_mlzbish 64 19.3

 

(personal communication). Because he declined to have his staff separate paper
into the color and mixed grades, only ”mixed paper" could be sampled. The 81.3
percent mixed paper load for Printshop-1 is probably a close approximate of the
white paper composition ratio (Table 8). The other printshops’ white paper
composition ratio may be at a similarly high level.

The observed white paper data fall between the 66.9 percent estimates made
by Evans (1985) for office towers in Toronto and the 42.9 percent measured by

DeBell in academic and administrative buildings at the University of Colorado

63
(1988). These studies had the advantage of taking samples of the complete waste

stream. Here, the segregated ”rubbish” was bagged and measured as a homogeneous
mass.

The data from the detailed waste sort provides indications that the
measurement of paper products was underestimated due to paper being mixed in
with rubbish. This was confirmed by numerous observations during the sampling
period. When examined, the rubbish content was found to be from 17 percent to
46 percent paper by weight. Corrugated cardboard comprised five to 17 percent of
the samples. Including staff-read newspapers, there was as much as 68 percent
recyclables by weight in the "waste" portion of the material sampled that day (Table
19, appendix B).

In total, the printshop sub-sector waste stream may match the 95 percent

paper content observed by Evans (1985) in his sampling of Toronto office towers.

1] B .1 E'

The retail sector’s waste stream was over 43 percent corrugated cardboard,
by weight (Figure 14). Half of the 10 stores had corrugated cardboard represent
more than 50 percent of their waste, and three measured more than 60 percent OCC
in their waste streams. This included three of the four largest retail waste
generators (Table 9). The amount of corrugated cardboard waste created was
dependent on inventory deliveries, so there were tremendous fluctuations in day to
day generation among the firms. Bookstore-3, as one example, averaged 64.6
percent OCC, but had as little as 14 percent in its waste (Figure 15). By weight and
volume, the convenience stores were the largest steady generators of materials,

discarding up to 157 pounds and 28 cubic feet of OCC on one day. All stores

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Figure 14. Corrugated composition from at-source sampling of ten East Lansing retail firms, fall 1987. (weight data).

"" Rubbish

— Corrugated

65

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Figure 15. Daily waste composition, Bookstore-3, East Lansing, Michigan (fall, 1987, weight data).

66
Table 9. Percentage of OCC 1n waste stream, East Lansing retail

 

 

W).
"n" smple lower upper
samples min maximize mean 5d ed. 9i.
Convnce-l 8 17.0 77.0 60.0 56.2 19.2 45.1 67. 4
Convnce-Z 4 52.5 65.4 12.9 60.2 5.5 55.7 64. 6
Flower-1 11 5. 8 38.6 32.8 25.6 12.2 19.5 31. 6
Book-1 22 10.0 80. 0 70.0 38.0 20.7 30.7 45.2
Book-2 21 2.9 84.2 81.3 31.0 26.9 21.4 40.7

Book-3 25 14.3 91. 7 77.4 64.6 23.4 56.8 72.3
Book-4 7 15. 8 50. 0 34.2 32.7 11.4 25.5 39. 8
Clothing-1 4 53.6 79.6 26.0 64.8 74.0

Camera-1 4 40.0 71.0 31. 0 52.7 13.0 42.0 63.5
Stereo-1 5 3.7 23.4 19.7 11.2 7.3 5.8 16.6

_QngLMQan 110 43.1
showed large fluctuations over the sampling period (Table 24 and Table 25, appendix
B).

The sub-sector mean is significantly above results of Westchester County (34
percent) (1988) and Toronto (16.5 percent) (Evans 1985) firms sampled. These
studies all indicate that corrugated cardboard is a major portion of the retail sector’s
waste stream. Recycling collection programs would have a major impact in reducing
waste loads to landfills.

The detailed waste sort showed good compliance with the sampling methods by
the firms’ employees. This was in contrast to observations over the course of the
study, which indicated that quantities of corrugated cardboard were regularly placed
in the "waste" category by almost every firm. The sample found that five firms had
over 10 percent corrugated in their rubbish (n = 9; the sample for ConvnceStre-l

was lost); the highest percentage was 21.4 percent.

67
EDTIEI'EIEI'BI

This summary has been limited to presenting composition results for the weight
data. Appendix B contains the data tables and tables of statistical analysis for

measures of central tendency for weight and volume sampling of all the firms.

0 no 0 i'-... harlifl'...“ ‘ h 1' 0-5
A one-tailed t—test was used to statistically compare estimates of the East Lansing
population ([11) with the estimates made for Westchester County’s population values
(#2)-
The results of the t-test for the printshop and retail sub-sectors are shown in

Table 10 and Table 11 below. Testing was at the alpha level of 0.10, or a = 0.10.

Table 10. t- and p- values for mixed papers in the printshop sector,

 

 

5W = 010
fntn :1 make significance Arr—um "
All printshops 62 8. 01 <0.0005 * yes
Printshop-1 27 9.38 <0.0005 yes
Printshop-2 18 1.57 0.067 yes
Printshop-3 17 5.17 <0.0005 yes

 

Table 11. t- and p- values for corrugated cardboard from the retail sector,

 

Eaat Lagsjn ngI Michigan (£19m mmi ghts), g- =0. 10.
fi_rn1 n ualne significance ul—W "
All shops 110 1.36 0.089 yes
ConvnceStre-l 8 2.39 . 0.024 yes
ConvnceStre-Z 4 7.38 0.0026 yes
'FlowerShop-l 11 -3.91 4 1.00 no
Bookstore-1 22 -0.46 0.68 no
Bookstore-2 21 -1.53 0.93 no
Bookstore-3 24 5.13 < 0.0005 yes
Bookstore-4 7 -1.70 0.93 no
ClothngStr-l 4 4.44 0.011 yes
CameraStre-l 4 1.96 0.073 yes
StereoSLre-l 5 -8-83 1.00 no

 

Willa.

The equation ”, > p, was set to Westchester County 04,) composition ratios
for the printshop / office complex and retail sub-sectors and was tested (with East
Lansing represented by ”1) as:

printshop / office complex: p, > 65

retail: #1 > 40

The equation for each sub-sector states that the percentage of specific
materials in the East Iansing waste stream is greater than the Westchester County
materials for that sub-sector. For printshops / office complexes, mixed paper is tested
at 65 percent. For retail firms, corrugated cardboard is examined at 40 percent.
Testing was set at the alpha, a, level of 0.10.

The equations can be accepted on the basis of the significance levels shown
in Table 10 and Table 1 1. For the printshop/ office complex sub-sector, the
probability, p, is extremely small and the equation would have been accepted even
if the alpha level had been set at 0.001.

The entire retail sector p-value was below 0.10, indicating a low probability
that East Lansing’ 5 retail sector waste composition percentage for OCC is less than
Westchester County’ 5. This is a result of the high percentages of OCC generated
by the larger retail firms.

Chapter IV explained how the Westchester County study provides a link to
contrast at-source sampling with disposal-based analyses. The probability tests
showed that the East Lansing waste stream results are richer in recyclable materials

than Westchester County. This provide strong evidence that the estimates made for

69
Westchester County’s commercial sector are sound. The East Lansing results
validate the Westchester County figures.

The Westchester County study will now be used as the basis to test the
alternative hypothesis. In this section, the contrast between at-source sampled
studies and those relying on disposal-based measurements will be made. Because
the East Iansing and Westchester County results match so closely, any conclusions
drawn in comparing the Westchester County waste stream with the other studies will
be assumed to correlate to an overall East Lansing waste stream.

Table 12 compares Westchester County’s aggregated commercial sector waste
stream, by percentage of material composition, to ten disposal-based studies of
commercial waste sectors (Seattle 1989, WSDE 1988, Metropolitan Council 1988,
MDEM 1985, Savage et al. 1985, US EPA 1979). The sampling approach is the
primary difference that separates this study from the others.

Westchester County’s waste stream has much higher levels of recyclable paper
than any other study reports. The Westchester County study targeted mixed paper
and corrugated cardboard for recycling, since these two paper products made up 67
percent of the County’s commercial waste stream and offered the ”greatest
probability in terms of physically removing them from the commercial waste stream”
(Westchester County 1988). This analysis will also focus on the paper portion of the
waste stream.

Table 13 makes another comparison of the composition estimates by using
an index to compare Westchester County data to other waste composition
percentages. A ratio (Westchester County/Disposal-based Study) was created for
each study to compare relative levels of materials in the waste stream. The index

shows that Westchester County estimates it waste stream has up to 2.7 times as

70

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72
much corrugated cardboard and as much 4.5 times the composition of mixed paper
as reported for these other communities.

Only the New Jersey Passaic County study lists a higher corrugated cardboard
composition percentage. Westchester County’s load of OCC in the commercial
waste stream is 20 percent to 275 percent higher than the other ten studies. It is
over twice as large for six of the estimates. The County estimates 28 percent of its
waste is OCC, whereas only three of the ten reports list figures over 22 percent.
Westchester County reports higher figures for mixed paper, from 19 percent to 367
percent higher for the six studies reporting this figure (Table 13). The Minnesota
results are the closest; their 32 percent portion of the waste stream is still 18 percent
below the Westchester County composition total. For the composition of total paper
in the waste stream, Westchester County’s estimate is at least 50 percent larger than
reported by seven of the ten studies. Only Minnesota is within 10 percent of
Westchester County’s estimate (including newspaper). Without newspaper, the

Minnesota estimates are 22 percent lower than Westchester County’s (Table 12).

.0 02-11“; - i ,. .' 1‘ . .1 i _' no ...-.3... ‘0. .i 'tro ..r o 3'

Direct comparisons can be made between ”pure load” sampling of packer
trucks done in Seattle (1989), for the State of Washington (WSDE 1988), and in
Toronto (Evans 1985) with at-firm measurements made in Westchester County
(1988), by the U.S. EPA (1979), and for this research (Figures 16, 18, and 20). In
the office / printshop sub-sector, the Toronto office tower was over 95 percent paper,
and contained 66.9 percent white paper, the highest measurement for each category
(Evans 1985). The other two truckloads sampled reported one-third less paper,
overall, and only 16 percent as much high grade papers as Toronto. The East

73
Iansing estimate for white paper was 30 percent lower and was within 19 percent
of total paper, and the Westchester County total paper estimate almost matched, at
94 percent of the Toronto totals (Figure 16).

The great discrepancy in pure load results may be due to local conditions
from time of year sampling and the small sample frames. It is also likely that these
”snapshots” are not typical of the overall waste stream. No more than three samples
were taken from a single site (the Washington State study had 12 samples, but seven
were one-time events and individual data were not reported). In contrast, multi-
week sampling was done in East Lansing. Figure 17 compares sampling parameters
and shows that the East Lansing field work was much more detailed, providing a
view of waste generation over an extended time frame. Total paper composition .
among the three printshops ranged from 70 to 80 percent, a spread of less than 10
percent (Table 8). Designers of waste composition studies stress the need for
sampling periods of at least one week, and suggest seasonal sampling over a year’s
time (MDNR 1986, Robinson and Robinson 1986). Baird ( 1962) has pointed out
that duplication increases the amount of information available and provides
improved reliability in measurements.

Seattle reported 12.24 percent "other paper" and Washington State had 9.5
percent "non-recyclable paper" (Figure 16). It is probable that some of this is paper
that became contaminated when mixed in the packer trucks, thereby decreasing the
recyclable paper fraction measured.

Reports from the retail sector follow this pattern. Corrugated cardboard is
the common material that will be compared here (Figure 18). The statewide
Washington study reported the largest percentage of OCC in the waste stream for

the pure load samples, but their total was only about half estimated by East Lansing,

Percent Materials

I Newspaper
fl Other Paper

Corrugated Paper

100
90

E. Lansing

74

D WhitePaper
I Mixed Paper

W. County

Total Paper

Seattle

Wshingtn

E3 Rubbish/Other

 

Toronto

Figure 16. Paper waste composition in the office/printing sector as measured

Sampling Period (Days) and Samples per Firm

  

8

at firms and by "pure-load" sampling, for several studies (pounds).

9 Sample Frame
Length of Sample Period

 

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Figure 17 . Office/Print sector sampling parameters for several studies.

I Number of Samples (per Firm)

59fl Hoe-3:2

man-"In

75

and was almost matched by Westchester County. The U.S. EPA (1979) reported
OCC composition in the 50 percent range in its examination of three different sized
shopping centers. The same comparison in length of sampling period and number
of samples applies. In East Lansing, 110 samples were taken in a four week period.
Washington State had the most samples of the pure load studies, but only five of the
14 samples were in the same city (Figure 19). Seattle (1989), which reported 9.5
percent ”other paper," also measured 30.6 percent food waste, indicating that grocery
stores were probably a part of the sample frame.

The restaurant sector had more recyclable materials, with OCC dominant.
The two at-source studies measured over 20 percent more corrugated than did
Washington State, which had the next largest component of OCC in its waste. East
Lansing had the greatest percentage of glass, over four times as much as Toronto
and Washington State reported (Figure 20). The same comparison regarding
sampling period and number of samples is true here. East Lansing conducted 46
samples for OCC and 29 samples for glass in its sampling periods of two and four
weeks. Washington State again had 14 samples, with no more than three from a

single city, and no individual data presented in its report (Figure 21).

E l . l 11 l .

The sampling procedures used in this research measured greater amounts of
recyclable materials in the commercial waste stream than are reported with disposal-
based sampling techniques. The null hypothesis, that commercial waste measured
at the source of generation in East Lansing would have smaller or equal quantities
of recyclable materials than the disposal-based studies, is rejected. Instead, it is

probable that East Lansing’s business waste stream has higher percentages of

Percent of Material

§

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76

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Figure 18. Corrugated paper composition in the retail sector as measured

Sampling Period (Days) and Number of Samples

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Figure 19. Retail sector sampling parameters for several studies.

 

 

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77

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and by "pure-load" sampling, for several studies (pounds).

Sample Frame
m Length of Sample Period

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78

recyclable materials than reported by the ten studies employing standard practices
that are compared in this thesis. The commercial waste stream, in general, is
probably significantly higher in its composition of recyclable materials than is
currently being estimated. The alternative hypothesis, that there are larger
percentages of recyclable materials in the commercial waste than standard methods

of sampling suggest, is supported.

' n f i - ' 1

Clearly, East Iansing and Westchester County are estimating significantly
higher quantities of recyclable papers in their commercial waste streams. This was
true on a direct comparison of samples taken and in the estimates of the entire
commercial sector. The sampling procedures were part of a regime for examining
materials before they can be contaminated. A greater level of detail is possible
when materials are measured in the same condition as for collection for a recycling
program. The mixing, compaction, and contamination that was described in Chapter
II for transported loads is avoided. Because the sampling examined materials as
they would be prepared for recycling, an accurate representation of commercial
wastes for composition, weight, volume, and source of generation was made. This
is the primary reason that the composition percentages of paper products is so much
higher in the East Lansing and Westchester County studies. As Cerrato (1989)
noted: "Commercial recyclables are both readily identifiable and easily separated
from the waste stream."

The longer sampling period and many additional samples taken in East
Iansing were also critical, key differences in comparison to the other studies. The

sampling period could not account for seasonal variation across the year, as

79

suggested by the MDNR (1986) and Robinson and Robinson (1986). These studies
agree that "a longer program can develop more accurate flow rates . . . . a week
long program will not provide reliable data and can be a misleading waste of time"
(Robinson and Robinson 1986). Evaluating these factors is necessary to show the
true potential of this waste stream. The East Iansing results reflect the
consequences of utilizing two to four week sample periods. This longer time frame
provides the opportunity to gain a more accurate picture of waste generation.

The standard collection, transportation, and disposal process alters the
material to a degree that it can be said that a different "product" is measured at
the final disposal site. The many possible transformations discussed in Chapter II
are a series of variables and create a degree of change that cannot reasonably be
factored in to make an accurate assessment of the status of materials at the front-
end of the waste stream. There is a great potential for "loss," either from
contamination or materials being overlooked or leaving the system. At-source
sampling measures materials before they are affected by these artificial changes.
The contamination factor discussed by Golueke and McGauhey (1967) is prevented
from affecting the analysis.

This is true for the ”pure load" samples, also. Sampling by this method is
an attempt to measure recyclables on a sub-sector basis, but it continues the old
paradigm of assuming collection as an inconsequential component. The focus did
not change sufficiently to center on the firms as they generated materials. The
Sunnyvale (1986) study, which examined full dumpsters, found that 26 percent of
the bins it observed contained at least 50 percent recyclables. Sampling is best
done before collection takes place. if the results are to reflect business waste

generation rates.

80

Another clue as to why more paper products are identified in the Westchester
County waste stream may be found in the "other" categories of "Organic N.E.C." and
"All Else N.E.C.” (Not Elsewhere Classified) in the various analyses (Table 12).
These categories are catch alls for materials that are unidentifiable or do not fit into
another classification. Westchester County estimated that four percent of its waste
was this "other." The Buena Vista and Minnesota studies almost match that,
reporting 4.8 percent and 5.3 percent, but no other study is close. The combined
totals for the two categories range from 8.3 percent to 78.9 percent in the other
eight reports. It is probable that much of the organic fraction is contaminated or
unidentifiable paper that is not suitable for being recycled. If this is so, the
contamination is a direct result of the procedure on which the sampling methods are
based. Loads that have been thoroughly mixed, compacted, and transported before
being sampled have a sizable fraction of their materials contaminated. At-source
sampling avoids these problems by providing a detailed representation of waste as

it would generated be for a recycling collection program.

Summary

The correlation of the Michigan and New York results shows a strong
relationship that can be used to compare East Lansing’s results with disposal-based
studies. The East Lansing waste stream is significantly higher in its percentage of
recyclable paper products than commercial waste streams measured by disposal-
based sampling methods are. This supports the rejection of the study’s null
hypothesis, that there is no difference in measurements between sampling waste at
the point of source generation and sampling at the point of disposal. A comparison

with "pure load" sub-sector sampling found similar results.

81
Analyses based on at-source sampling methodologies provide greater details
and make more information available to planners than do disposal-based
methodologies. This creates a waste generator based perspective that will be

necessary in the design and implementation of materials collection programs.

CHAPTER VI

SUMMARY AND CONCLUSIONS

Summantnfmejhesis

Recycling is being developed as a primary technology for handling the nation’s
waste in the solid waste management field. Waste composition studies are a
necessary first task planners and waste management professionals must undertake
in order to quantify the municipal solid waste stream. But the switch to recycling
oriented waste management is hampered by a continued reliance on planning tools
based on the conventional paradigm that depends on centralized disposal
technologies, chiefly landfills.

Recycling in the commercial sector will be a major component of municipal
solid waste management programs. Several empirical studies were cited to show that
there are discrepancies in the estimates of composition and quantities of materials
available for recycling and that sampling methods for assessing the commercial waste
stream are evolving. Composition ratio estimates are not accurate because the
sampling schemes are not detailed enough to account for the many varied factors
that contribute to the complexity of the municipal solid waste stream.

This research presented a new methodology that is based on disaggregating
the commercial sector into sub-sectors and sampling at the point of generation. It

is based on the principle that greater accuracy and a higher level of detail can be

82

83

obtained by sampling at the source of generation. This represents a paradigm shift
that places the emphasis on collection of materials. This requires involvement of the
waste generators, a stakeholder group whose role has previously been assumed.

This study supports McCamic’s (1985) call for a ”new philosophy of waste
management.” Commercial firms need to be defined by the recyclable materials in
their waste streams. Approaching the commercial solid waste system this way will
mirror the recycling programs that will collect materials from these businesses. This
allows the structuring of sampling methods that addresses the ”dilution" effect and
the "noise" problem inherent in assessing the overall municipal waste stream.
Collection programs for recycling are highly discriminatory; the methodology that is
used to develop these plans must also be highly selective. This study analyzed waste
stream composition and volume at the generation source, thus avoided the mixing,
compaction, contamination, and transporting of waste that is inherent in disposal-
based studies. I

The alternative hypothesis, that higher quantities of recyclable materials are
found in the waste stream when measurements are taken at the point of generation,
is supported. Recyclable materials comprised a larger portion of the commercial
waste stream than previously assumed in the printshop, retail, and restaurant subr
sectors in East Lansing, Michigan. As much as four times as much paper wastes
were found in the printshops than in office sector sub-streams analyzed elsewhere.
Over 70 percent of the printshops’ waste consisted of paper products. Corrugated
cardboard (OCC) comprised over 40 percent of the East Lansing retail sector waste
stream; this was also four time more than measured by "pure load” sampling. In the
restaurant and bar sub-sector, corrugated cardboard and glass each totaled over 20

percent of the restaurants’ waste stream. These were also four times the level found

84

in other studies. The findings support Cerrato’s (1989) observation that "commercial
recyclables are both readily identifiable and easily separated from the waste stream.”

The East Lansing results were linked to a study done in Westchester County,
New York, where county wide composition estimates were made of the entire
commercial waste stream. Westchester County estimated that 67 percent of this
waste stream is comprised of recyclable papers (28.4 percent corrugated, 38.4 percent
mixed papers). This analysis is significantly higher than estimates reported by 10
disposal-based studies done around the country. The Westchester estimates were 20

percent to 400 percent higher.

Fin in

1. The printshop sub-sector is extremely rich in high grade, high value paper
wastes. Printshops may have as much as 95 percent paper content available
for recycling

2. Corrugated cardboard (OCC) is the primary packaging material in the retail
sector and represents 43 percent of the waste generated

3. All firms that handle food (convenience in retail, restaurants and bars) are
major generators of OCC, which comprised up to 55 percent of these firms
wastes. Food is primarily packaged in corrugated; since the products are high
turnover items, food providing-firms have inventories of corrugated packaged
products they constantly replenish. This could be as much as 150 pounds once
a week

4. Comprehensive recycling that covers all firms in the commercial sector will have
a major impact, perhaps over 25 percent in reducing waste, because of the
volume reduction that universal participation will achieve

5. Separation of materials into readily recyclable categories before collection links
waste generation to its source. This is a successful strategy for identifying and
quantifying the commercial waste stream

6. Four week sampling periods increases the size of the database and provides
repeatability, improving the estimate of the precision of the measurements
taken (Baird 1962) and reducing the standard deviation in the print samples
to about 12.5 percent. In the retail sector, the standard deviations ranged from
six to 26 percent

7. On a countywide basis, corrugated cardboard may be 28 percent of the
commercial sector’s waste, and mixed papers may comprise 38 percent. These
estimates are four time larger than reported by disposal-based analyses

85
I l' .

There is a distinct advantage to dividing the business community by SIC
codes to examine sub-sector waste streams. It is probable that an examination of
businesses in the service, financial, academic, and government sectors would show
similar high levels of recyclable goods. An SIC code-based sampling scheme can
be very specific and allows firms to be defined by activity and type of waste
- generation. For example, grocery stores and floral shops (two retail codes) could
be grouped together on the basis of the large organic fraction of waste that these
businesses generate. A further refinement might include restaurants and bars (also
in the retail division) and hotels, convention centers, school cafeterias, and other
entities involved in large scale food production and serving activities. Business
activities that generate low amounts of recyclable materials (e.g. barbershops,
laundromats) could be grouped together to ensure a complete representation of the
entire commercial sector and at the same time avoid diluting the estimates of waste
streams that are much richer.

Program planners will gain a more accurate assessment of the materials
available in local waste streams if they do sampling at the source of generation.
Source-based sampling can provide the basis for estimating the aggregated, overall
waste stream. When waste composition is estimated in this way, program plans can
be designed to maximize the amount of recycling possible in the waste stream.

This approach prevents indiscriminate sampling that would place a burden
on developing an analysis to maximize recycling without an appraisal of the dynamics
of the commercial sector. Resource rich sub-sectors are masked when sampling is
based on aggregated loads. The economic potential and cost savings from diverting

the material will continue to .be lost if these waste streams are not completely

86

identified and quantified. Smaller waste generators, such as those represented by
East Lansing businesses, may be considered of secondary importance and not
adequately targeted. Disposal-based sampling will perpetuate centralized and large
scale programming, since that is the conventional planning perspective.

Plans based on the paradigm of disposal orientation tend to conclude that
it is more difficult to achieve the high goals set for waste reduction and recycling
activities because they are inherently undercounting the quantity of material
available and not accurately assessing its source of generation or the condition in
which it would be made available. Malcolm Pirnie, estimated that two-thirds of
Westchester County’s commercial waste stream was paper products and was 41
percent of the municipal solid waste stream, but then advised that only 19 percent
of the commercial waste stream could practically be recovered. The firm suggested
that "32 percent of waste reduction through recycling is optimistic, since it is greater
than that achieved and sustained by any community the size of Westchester County
in the United States” (Westchester County 1988). A shift away from this viewpoint
will also make it easier to assess current levels of commercial sector recycling, which
is not easily analyzed using current methods (Metropolitan Council 1988,
Westchester County 1988).

Because this methodology recognizes the central role business employees
have in maximizing diversion of materials, greater participation in collection
programs will probably ensue. If much higher rates of participation and collection
occur than are typically estimated, waste reduction levels may be as much as 50
percent to 100 percent higher than currently targeted. This could occur
throughout all segments of the commercial sector if emphasis is placed on

separation of recyclables at the point of generation.

87

The sub-sector based assessment is less costly for sampling and analyzing the
waste stream (Westchester County 1988, Cerrato 1989, Kuniholm 1989). This
approach has the potential to make additional resources available and has the
positive feedback effect of providing for larger sampling frames, longer sampling
periods, and sampling across a year’s time to account for seasonal changes. Greater
attention to these variables will increase the accuracy of the estimate of waste
stream composition and quantity for the entire commercial sector. Follow-up
program planning will more accurately size the true recycling potential of the

commercial waste stream.

Recenmiendatiens

1. Divide the commercial waste stream into sub-sectors of well defined SIC code
groupings based on firm activity and type of wastes generated

2. Expand the materials classification system to include film plastic, polystyrene,
polyethylene teraphalate, colored high density polyethylene, and wood

3. Examine the organic fraction in floral and grocery businesses in the retail sector
to determine amount available for composting

4. Sample complete restaurant and bar waste stream to include food wastes

5. Separate glass by color in the restaurant and bar sub-sector

6. Develop plans for sampling financial, service, academic, and government sub-
sectors by having wastes separated within buildings before measurements are
taken

7. Spread sampling into periods throughout the year to assess variability in waste
generation

8. Conduct supplemental surveys for demographics, including number of
employees, square feet in firm, gross sales, hours of operation, materials
currently being recycled, size of bin, and frequency of waste pick-up

9. Assess if collection program participation is greater because stakeholders are
involved during the planning and data gathering stages when this methodology
is employed

I . . . f h S 1
As an empirical study, the analysis was strictly subject to the limitations of the

data. The separation of material was dependent on the cooperation and reliability

of the firms’ employees. Because all materials were not always separated out, more

88
frequent checking of compliance through the use of the detailed waste sort would

strengthen the study.

Only "product"-oriented sub-sectors within the business community were
included in this research, rather than a representative cross-section of the entire
commercial sector population. This prevented a direct extrapolation to the entire
commercial sector from being made.

No direct check was made with waste sampled at the site of disposal for this
business community. Sampling the population both at the source of generation and
the disposal site would have allowed for direct comparison of the two planning
methodologies. This, however, would have large costs associated with it.

Because food wastes were not measured in the restaurant and bar sub-sector,
complete analysis of this waste stream was not possible. A representative sample of
waste generation would have to include the food portion of the waste stream.

As an empirical study, this is an initial attempt to examine some of the causal
factors that determine waste composition analysis. It focused on several relevant
variables that are at the foundation of waste generation, collection, and disposal.
Future research can advance the comparisons made here, continue the evaluation
of the disposal-based paradigm, and further develop and critique the collection-

oriented model suggested as its replacement.

APPENDIX A

CORRESPONDENCE AND DATA SHEETS USED IN FIELD SAMPLING

4uiltuntluna

tel:
cut Iansing. at 45323 [.517 331.1731

 

September 25, 1987

hr. Peter Erdean, General Hanager
Hr. Alec Gores, President
Executive Business Systeas, Inc.
241 East Saginaw, Suite 1

East Lansing, Hichigan 48823

Dear Hr. Erdaan and Hr. Gores,

He are writing to you to ask for your cooperation in a masters research project
being conducted by Kenneth Stern, a student at Hichigan State University. Ken is
conducting a waste stream assessment. an exaeination of the types and aaounts of
garbage generated by commercial fires in East Lansing.

He in the City of East Lansing are supporting Ken in his efforts and are looking
forward to reviewing his results. He believe that this study can lead to the
reduction of waste that must be taken to the landfill. If this is true, there may be
some saving of monies by recycling those eaterials that are readily recyclable.

Ken's project is titled “Recycling as an Alternative Solid Haste Practice for the
Business Coaaunity of East Lansing, Michigan. The attached outline suaaarizes his
project. His preaise is that businesses generate a considerable aeount of 'clean'
waste that can readily be recycled. He believes that the coabination of high
densities of fires, with eany businesses in close proxiaity to one another, and the
high voluae of waste generated by constant turnover by the nature of these
enterprises, will provide sufficient quantities of aaterials to wake a recycling
prograa feasible.

Recycling can have specific benefits to fires in the coanercial sector, as well as

to the larger con-unity. If recycling aaterials brings a substantial reduction in
the amount of waste generated, there ought to be soae reduction in waste disposal
costs, also. Previous research indicates that recyclable aaterials in business

districts wake up to 65 percent of the waste generated, both in voluae and weight.
Reaoving these iteas free the waste streaa can reduce disposal costs to businesses
as well as relieve pressure on area landfills.

Ken's study will quantify the aaount of garbage generated and analyze the data
collected. If the results watch his hypotheses, the Recyclers of Inghaa, Eaton, and
Clinton Counties will work toward establishing a coaaercial pick up route in 1988.

Your fira’s naae has been chosen by a randoe selection process to be a possible
participant in the study. Ken would like to follow up this letter with a seating to
explain his project in greater detail. Your support will enable Ken to wake a pore
thorough exaaination of the waste generated by East Lansing businesses. He hope you
will cooperate with Ken fully as he proceeds with his research.

Please direct any consents or questions to Ken at 351-3751.

Sincerely,
//
l a? /M
John Czarnecki Toe Dorit Kenneth Stern

 

hayor City Hanag Resource Develop-ant

Title:

90
PROJECT OUTLINE

tliecficling as an Alternative Solid Waste Management Practice for
e

usiness Community of East Lansing, Michigan

Purpose: To conduct a waste stream assessment of randomly selected

Objectives:

Primary
Hypothesis:

Timeline:

Sampling
Procedure:

Analysis:

businesses in East Iansing to determine the size, nature of
composition, and frequency of generation of their waste and to
assess the potential for recycling of marketable materials.

1. Develop a process for conducting a waste stream assessment
of busmess on the level of the individual firm.

2. Divide the business community by type and measure the
waste generated by each for its recyc able components:
A. the three sectors are: 1. retail, 2. offices (here

copy centers), and 3. restaurants and bars

B. The recyclable components (by sector) are:
1. retail: corrugated cardboard, newspaper (if sold)
2. offices: card oard, paper white, color and computer)
3. restaurants and bars: card oard, glass, and metal and
plastic food containers

C. Measurements are for weight (in pounds) and volume

3. Correlate waste enerated to firm specific characteristics
and determine ' eneralizations can be made between waste
stream and firm c aracteristics

Can waste stream composition and generation rates be linked
to firm specific characteristics, including

1. type 0 firm

2. 5120 of firm (a. floor space, b. revenues)

3. number of employees

4. time of year

Sampling will be done from mid-Se tember through Mid December.
Nine, week long sampling runs will made

1. Pre-collection interview and survey done to estimate
collection needs, pick up freguency, storage area, and
number of containers neede

. Short (half hour) trainin and discussion with employees to
explain project and emp oyee assistance needed

. Pick up route established according to individual firms
re uirements

. Co lection period starts

. Sampling procedure re-evaluated for modifications as needed;

on 1going communications with firms to assess progress

6. Co ections take place for nine weeks out of twelve

LII-hub)

Study write up January - June, 1988. Additional time as needed

RETAIL

91 Approxieate Pick-up tine

 

Pick-up days

 

PREPARING HATERIALS FOR COLLECTION

1. Separate recyclable aaterials froe “garbage"

2. Recyclable coeponents are: --cardboard --office paper
--other

 

3. Break down cardboard and stack
4. Place paper(s) in container(s): --white --colored

--newspapers and eagazines (if sold)
NO: --CARBONS --NEHSPAPERS (unless separated) --GLOSSY(eagazines)

5. Garbage is: --not readily recyclable eaterials
--please dispose of liquids before adding cups

6. Place garbage in plastic bags; secure with twist-ties

7. I will aeasure (weigh and estieate voluae) both the recyclable eaterials
and the 'trash'

8. Recyclable aaterials will be stored and then taken to brokers
9. I will toss the garbage bags in the duapster

10. I will be back to pick up again on

 

ll. This will continue until eid-Deceeber

Hy Pledge to You:

will coae by regularly, at about the seas tips

will stay out of the way of your business, both custoeers and staff
will not ask for help during ey collection '

will neither create nor leaVe a pass

as very grateful for your assistance

on
O
HHHHH

Project Suesary: Hy thesis, titled, 'Recycling as an Alternative Solid Haste
hanageeent Practice for the Business Coleunity of East Lansing, Michigan,” has as
its field data collection coeponent a waste streae assessaent. Froe aid-October
to aid-Deceeber l5 businesses in East Lansing will have their garbage sorted to
exaaine the recyclable ooeponents. Variables are eaterial type, weight, voluee,
and type of business. the study is being conducted on the unit level of the
individual fire. In house assistance froe eeployees/staff is essential to the
study’s success. Your participation is extreeely vital and valuable. Thank you

for your help.

Guestions, Problees, Ideas: Call ken, 351-2438 before 8:00 a.e.
evenings till eidnight

355-3414 passages
373-2190 dire eeergencies, only

92

PRE-COLLECTION INTERVIEW SHEET

DATE

 

INTERVIEHER

 

FIRH

 

ADDRESS PHONE8

 

MANAGER/OWNER

 

PRIHARY CONTACT PERSON

 

TYPE OF FIRH

 

HETHODS OF CHARGING FOR COLLECTION SERVICES:

__IN RENT __PROPERTY TAX __CONTRACT wIIH WASTE HAULER __DON’T KNOH

__SHARED ARRANGEMENT (HITH ~____“_”___)

HASTE STREAH
COHPONENT(S)

 

DO YOU RECYCLE NON?

 

HHAT HATERIAL(S)?

 

How nucw HHO PICKS up
(AND RATE) THE HATERIAL? ___

 

LOCATION OF PRESENT BIN

 

HASTE HAULER SIZE OF BIN

 

IS HASTE BIN SHARED? HITH:

 

0 OF PICK‘UPS/HEEK (AND DAYS)

 

 

TIME OF PICK-UP RATE OF ACCUHULATION (SLOk-FAST)

FREQUENCY TRASH TAKEN OUT TIHE TRASH TAKEN OUT

 

STOCKING DAYS

 

COHHENTS

 

 

93

PAGE 2
PRE-COLLECTION FORM (CONTINUED)

NUMBER OF EMPLOYEES INVOLVED IN NASTE COLLECTION AND DISPOSAL:

SIZE OF SHOP (APPROXIMATE SO. FT.):

 

NUMBER OF EMPLOYEES:

 

SUGGESTED FREQUENCY OF PICK UP (DAILY, NEEKLY, ETC):

 

 

TIME (RANGE, IF POSSIBLE) OF PICK UP

 

PRE-PICK UP COLLECTION AREA:

 

NUMBER OF BARRELS NEEDED (ESTIMATE):

 

RACK/OORRAL ron cnnnaonno:'

DATE TO BRING CONTAINERS:

 

DATE FOR TRAINING: TIME

NUMBER OF EMPLOYEES TO BE TRAINED:

 

TIME OF EMPLOYEE TRAINING:

 

COMMENTS

 

 

 

 

 

 

94

 

 

 

 

 

 

 

 

 

 

 

 

DATA COLLECTION FORM
Location on Route Day & Date
Approximate pick-up time Collector(s)
Time of Arrival Weather Conditions
Time Done Pick Up Days (of seven)
Time at Stop Firm Type
Firm Phone #
Address Primary Contact Person

 

1. Cardboard a. (small)

b. (med)
c. (large)
# of boxes

2. Glass a.

b. Plastic

c. Metal
3.
4.
5. Rubbish

TOTALS

Comments / Problems

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Weight in Lbs, Dimensions
No. Gross - Tare = Net Vol = Ft3
NET VOL

 

 

 

Pare

 

firs

95

DITAILID SORT FOR In!!! SYRIAN ISSIBSIIIT

of

 

Type of fire

PROM: VIII OP IOVIIIII IIYI . 2137

set

tare

weisbt - weisbt

[NOS

Total sssple leislt lb.

-. ---. '. " -. '- .- -. '.

 

 

 

 

 

 

 

 

 

 

 

Cosposest

l. leuupaper

1. Office paper

2a. Otter paper (urappisssl

l. Corrusated

Date of Pick up

4. Textiles

 

 

Prepared by

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5. Plastic, retsrsables

t. Plastic, risid

1. Plastic. fils

8. Glass, returnable

9. Class, sou-returnable

10. ferrous

ll. loo-ferrous

ll. lard uaste

ll. Pisst cuttiass

ll. Food uaste

15. Other orsasica

16. Paper food costaisers
lf. ltyrofoas food costaiaera

.ll. Plastic fool coataisers

1!. Otter food costaisers

20. ltyrafaas popcors (pactiasl

2t. Office supplies (peas. etc.)

ll. 'Pactiss' paper

23. Other isorsssics

24. tires

25. lpecial iteas

Totals

 

Cossests

 

96

SUMMARY OF DATA COLLECTED, SELECTED COMMERCIAL
FIRMS, EAST LANSING, MICHIGAN, FALL, 1987

printshops

This is a careful measurement of how much paper three specific printshops
were generating. Most important are the composition ratios: By weight, 81 percent
of the material is readily recyclable; by volume, 69 percent (breakdowns are in the
tables). It is not very refined-~the higher value white paper was not accurately
(completely) sampled. White paper could easily be twice as much as shown here.
Printshopl is a copy center, and almost all of their paper use is on white stock.
With structure and training, reduction at printshops could be on the order of 90
percent. Future studies need: 1. periodic measurements of the "waste” as a check,
and 2. more uniform cooperation by all staffs. Most of what printshopZ calls waste
was "good" paper. Some of that is true for Printshop3, also, though not as much.

On volume, paper falsely boosted the waste figures. All the printshops threw
paper into their "waste" receptacles, and that skewered to the high side their waste
data. Well over 80 percent of your waste is probably readily recyclable materials.
As is, 70 percent of what fits (volume) in your dumpsters could be recycled.

retails

Cardboard predominates in the retail sector. Over 40 percent of the waste
is cardboard by weight; by volume it is about 44 percent. The two convenience
stores show again that cardboard packaging follows food supplies. The data here are
very good, except for the gross nature of the “waste” category not providing any clues
as to what is in it (but again, my assumption was that staff would separate out all
"readily recyclable materials"). Additional samplings of the convenience stores that
did not measure the waste m included in the summaries. These data show
cardboard as a percentage of the complete waste stream. No other type of firm
matched the convenience stores for waste generation, except the flower shop. No
store had as much cardboard, though all were regular generators of some quantity.

Bookstorez reused boxes, storing and taking them back to the warehouse.
Bookstores ”1" and "2" sold newspapers and magazines; an interesting sideline, but
to make picking up those materials make sense, much more would have to be
generated or they would have to be supplementing a residential program. Caution
is needed to generalize from the clothing and camera stores. These seemed to be
typical waste loads. While small, they are regular and steady generators of
cardboard. The stereo shop was probably my one worst participant. They were
remodeling-which happensuand I weighed a lot of floor tiles and cement. Those
data are not representative at all. The flower shop was perhaps the most interesting
firm of all. From 30 to 50 percent of that is flower cuttings. Commercial
composting (with restaurants, hotels, and institutions) may be the most intriguing
future waste management strategy to develop.

97

page 2
East Lansing Commercial Firms
Solid Waste / Recycling Summary

restaurantaudhars

A complete waste assessment was not done, because I did not have the
resources (time, money, staff) to tackle the food portion of the waste stream. That
is an extremely interesting problem to solve, but it will have to wait for someone
else.

The six bars and restaurants, while small in size, were oriented to MSU
students, and large in business. Management and staff cooperated to varying
degrees. The two best firms were R&Bl and R&B3. These both make some
attempt at feeding people, with R&B3 having the larger kitchen. R&BS also
provided good data, but I only could sample for cardboard and glass there. Overall,
by weight of 100 percent of the recyclables, cardboard was 57 percent (with a range
of 33 to 81 percent) and glass was 36 percent (range from 21 to 67 percent, but one
shop with no glass brought down overall average). Using percentages is misleading,
since not all waste was measured and some firms did not always participate. With
the same warnings, the volume numbers show cardboard averaging 80 percent of the
recyclable waste (range of 68 to 92 percent) and glass averaged 12 percent (range
of 6 to 27 percent). The data are too scattered and insufficient to say anything
about plastic or metal food containers, but an observation: All the places were
willing to separate these materials, so potential exists, socially, for recycling all food
containers from the kitchens. The savings here would be more in avoided costs from
volume reduction rather than revenue.

98

lst draft: data summary firm: COPYGRAPH
november 4, 1988

r

In the fall of 1987 a waste stream assessment--an examination of waste
generated--was made of 19 firms in East Lansing. Firms were randomly selected.
This was the field work for Kenneth Stern’s masters program thesis "Waste
Generation and the Potential for Recycling in the Business Community of East
Lansing, Michigan. The study was conducted on the unit level of the individual firm.
Variables measured were composition (material type), weight, and volume. Firms
were divided into the categories of printshops (three participants), retail stores (ten),
and restaurants and bars (six). Sampling for the printshops, retail stores, and one
bar took place from October 23rd through November 19th. The remaining
restaurants and bars were sampled during the two week period of December 6th
through 20th.

Your Eirm

Copygraph generated the second most waste of the three printshops, over
1600 pounds, by weight, and over 300 cubic feet, by volume. Cooperation by staff
and management was good, with support provided throughout. There was some
regular mixing of paper and cardboard into the waste containers and so the numbers
reported here are conservative: Copygraph produces more paper and less "pure"
waste than these figures indicate. Still, 70 percent of this printshop’s "waste," by
weight, was measured to be paper, and 39 percent of that is white paper (again,
lower than actually).

Only 26 percent is genuine waste, and that portion is probably lower, since
paper (41.9 percent, by weight, and 27.6 percent by volume) and cardboard (seven
and 18.4 percent, each) were found in Copygraph’s garbage in the one detailed
sample taken of that printshop’s "trash" in November.

By volume the numbers are 59.7 percent recyclables, at 42 percent, all papers;
19.8 percent mixed papers, 14.1 percent white paper, 6.2 percent color papers, and
18.5 percent cardboard. By volume, 41.3 percent of the material is actual waste, and
again the figure is probably less than that, since paper and cardboard were
sometimes thrown away.

 

The one detailed waste sort found only 51 percent, by weight, and 54 percent,
by volume, of the total discarded materials to be of "trash" components that are not
recyclable.

The average daily weight of the 18 samples was 130.5 lbs. The most waste
generated on a single day was 368 lbs., the least 10 lbs. Nine times over 100 lbs was
generated, and five times that total was over 150 lbs (and three times over 200 lbs).
Paper comprised as much as 99.3 percent of the waste but never went below 47.3

percent, by weight.

By volume, the waste averaged 17.2 cubic feet. The largest load was 36.5
cubic feet, the smallest 2.3 cubic feet. Only four times were less than 10 cubic feet
of materials produced; eight times it totaled more than 20 cubic feet.

copygrph.sum/ 11-4-88

99

”Waste Composition, 3 Printshops (lbs),

East Lansing, Oct. 24» - New. 21. 1987.

 

 

1.9 '-
‘08 -

l

1.6
1.5
1.4
1.3
1.2
1.1

l

111

 

0.9
0.6
0.7
0.6
0.5
0.4
0.3
0.2
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Prntshpl PmtshpZ Prntshp3

pounds
(Thousands)
l

l

 

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70

 

76

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////////.

 

 

 

 

 

 

 

 

 

 

. material categories
m mixed paperES white paper color paper cardboard m waste

Numbers represent percentage of total paper at that printshop

Summary Totals, Three East Lansing
Print-hap- (ft3). Oct 26-Nav 20, 1987.

 

316

 

300-

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material categories ‘
22 mixed papemcardboard color ppr white ppr EX waste
lst number: percentage of total paper
2nd number: percentage of cardboard

100

Waste Composition, PrintshpZ (lbs),

0

 

East Lonaiwp. Oct. 28 - Nov. 21. 1987.

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82

Numbers are total percentage of recyclable papers (without cardboard).

Waste Composition, PrintshopZ (ft3),
Eatt Lansing. Oct ZB-NW 21. 1907.

 

 

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Numbers are total percentages of recyclable papers (without cardboard).

03-May Ob—Nav O7-Nav

31-Oct

20-Oct

as-4
30%
zs-J
20-1

.00. V3.5

Mwhite paper

-mixed paper

m cardboard

ZZ waste

APPENDIX B

FIELD SAMPLING DATA TABLES AND RESULTS

101

Table 14. Haste stress composition, by weight, of three East Lansing printshops, October 24 -
November 21, 1987 (in pounds).

firm: PRINTSHOP 1 PRINTSROP 2 PRINTSHOP 3
and mud whte color TOTL and whte color TOTL
date ppr crdb rbeh TOTL ppr ppr ppr crdb rbsh TOTL PAPR ppr ppr ppr crdb rbsh TOTL PAPR

24-0ct 41 11 3 55

25-0ct 170 4 2 176

26-0ct 20 3 2 25 0 19 3 0 1 23 22
27-0ct 74 7 4 85 0 0 0 0 9 9 0
28-0ct 46 4 15 65 55 0 0 0 26 81 55

29-0ct 60 11 11 82 104 0 0 33 141 104
30-0et 36 7 5 48 0 31 21 0 16 68 52 25 0 0 1 8 34 25
31-Oct 24 4 0 28 6 0 0 4 0 10 6

01-Nov 53 6 3 62

02-Nov 101 0 10 111 0 19 25 1 9 53 44 0 47 25 3 11 86 72
03-Nov 100 3 26 129 48 0 0 1 49 98 48 0 12 7 0 9 28 19
04-May 95 12 40 146 49 85 0 1 0 135 134 7 41 0 0 7 55 48
05-Nov 105 3 24 132 53 12 0 0 11 76 65 107 0 0 14 16 137 107
06-Nov 151 4 6 161 0 87 45 5 0 137 132 0 19 10 0 4 33 29
07-Nov 127 11 22 160 0 83 48 3 39 173 131

08-Nov 132 5 39 176

09-Nov 0 4 8 12

10-lov 154 3 30 187 0 24 20 8 41 93 44 28 67 0 0 64 159 95
11-Nov 67 0 0 10 40 117 67 0 12 20 1 6 39 32
12-Nov 119 17 24 160 28 31 0 4 14 77 59 75 0 0 0 6 81 75
13-uov 71 4 33 108 79 0 0 27.0 39 145 79 0 14 30 0 6 50 44
14-Nov 144 8 14 166 82 27 0 12 41 162 109

15-Nov 137 0 7 144

16-Nov 50 0 4 54 0 20 10 1 5 36 30
17-Nov 155 28 35 218 0 16 6 1 12 35 22
18-Nov 107 6 16 129 0 75 54 0 105 234 129 11 5 0 4 4 24 16
19-Iov 191 3 10 204 0 166 0 1 76 243 166 0 27 0 4 4 35 27
ZO-Nov 112 9 7 128 0 39 23 1 16 79 62 0 9 5 0 6 20 14
21-Nov 113 10 28 151 0 237 28 7 96 368 265

Totals 2688 186 428 3302 467 916 264 85 618 2349 1647 357 308 116 33 210 1024 781

“n“ 27 25 27 9 13 8 14 15 17 7 12 9 9 16 17
Nean* 99.6 7.4 15.9 51.9 70.5 33.0 6.1 41.2 96.9 51.0 23.7 12.9 3.7 11.7 45.9
X Ntrl 81.4 5.6 13.0 19.9 39.0 11.2 3.6 26.3 70.1 34.9 30.1 11.3 3.2 20.5 76.3

nxd ppr s nixed paper, crdb a cardboard, rbsh I rubbish, TOTL a total saterials,
whte ppr 8 white paper, color ppr 8 color paper, TOTL PAPR I all papers combined

* Mean is total weight divided by number of samples taken, not length of sampling period.

102

Table 15. waste stress coposition, by voluue, of three East Lansing printshops, October 24 -
November 21, 1987 (in cubic feet).

PR1NTSHOP-1 PRINTSROP-Z PRINTSHOP-3
word and uhte color TOTL std whte color TOTL
date ppr crdb rbbsh TOTL ppr ppr ppr crdb rbbsh TOTL PAPR ppr ppr ppr crdb rbsh TOTL PAPR

24-0ct 10.0 2.3 0.5 12.8

25-Oct 13.6 1.8 1.2 16.6

26-0ct 6.0 1.3 1.2 8.5 2.0 1 0 0.5 3.5 3.0
27-0ct 12.0 1.5 1.2 14.7 0.0 0.0 0.0 0.0 1.5 1.5 0.0
28-0ct 10.0 3.2 2.5 15.7 7.0 0.0 0.0 O 5.3 12.3 7.0

29-0ct 8.0 3.0 2.4 13.4 12.0 1.4 3.5 16.9 12.0
30-0ct 13.3 9.7 1.2 24.2 0.0 2.0 2.0 0 2.0 6.0 4.0 4.0 0.1 2.8 6.9 4.0
31-0ct 9.0 1.5 1.2 11.7 0.3 0.0 0.0 2.5 0.0 2.8 0.3

01-Nov 14 0 3.1 1.2 18.3

02-Nov 11.0 0.1 2.0 13.1 0.0 2.0 1.0 O 3.0 6.1 3.0 6.0 2.0 2.5 4.0 14.5 8.0
03-May 12.0 1.3 4.8 18.1 8.0 0 8.0 16.4 8.0 2.0 0.5 5.1 7.6 2.5
04-Nov 13.0 8.7 10.6 32.3 6.0 2.7 0.3 9.0 8.7 2.0 4.0 4.3 10.3 6.0
05-Nov 12 5 2.2 3.6 18.3 8.0 1.0 0 4.0 13.0 9.0 8.0 2.2 7.0 17.2 8.0
06'Nov 12 5 1.5 2.4 16.4 12.0 2.0 3 16.5 14.0 3.0 1.0 2.0 6.0 4.0
OT-Nov 11.0 0.4 5.9 17.3 2.6 6.0 2 8.0 18.4 8.6

O8-Nov 12.0 3.6 7.3 22.9

09-Nov 0.0 1.5 0.0 1.5

10-Nov 16.0 1.7 9.5 27.2 2.0 2.0 5 12.0 20.5 4.0 4.0 6.0 25.3 35.3 10.0
11-Nov 8.0 12 16.0 36.5 8.0 1.0 4.0 0.5 1.1 6.6 5.0
12-Nov 9.1 5.0 8.0 22.1 2.0 2.7 4 6.0 14.2. 4.7 10.0 1.7 11.7 10.0
13-Nov 13.5 2.7 9.6 25.8 8.0 13.2 8.5 29.7 8.0 2.0 2.0 3.0 7.0 4.0
14-Nov 12.0 2.3 2.4 16.7 14.0 1.9 8.8 7.0 31.7 15.9

15-Nov 12.5 0 1.2 13.7

16-Nov 8.5 0.8 1.2 10.5 0.6 1.7 2.3 0.0
17-Nov 12.0 6.9 6.0 24.9 4.0 1.0 1.7 5.2 11.9 5.0
18°Nov 12.4 4.3 3.6 20.3 3.2 3.0 3.4 18.3 27.9 6.2 1.0 1.0 0.5 3.0 5.5 2.0
19-Nov 15.2 2.9 3.6 21.7 5.7 0.1 16.0 21.8 5.7 . 4.0 1.3 1.7 7.0 4.0
20-Nov 12.0 4.1 3.6 19.7 2.0 2.0 0.4 2.0 6.4 4.0 2 0 0.5 1.7 4.2 2.5
21-Nov 12.5 3.0 4.1 19.6 3.8 1.2 3.5 12.2 20.7 5.0

Totals 315.6 80.4 102.0 497.9 61.3 43.7 19.2 57.4 128.3 309.9 124.2 41.0 37.0 12.0 10.8 75.1 175.9 90.0
”n” 27 27 27 9 13 8 13 15 17 7 12 9 9 18 23
Mean* 11.7 3.0 3.8 6.8 3.4 2.4 4.4 8.6 7.3 5.9 3.1 1.5 1.2 3.6 5.6
X Ntrl 63.4 16.1 20.5 19.8 14.1 6.2 18.5 41.4 40.1 23.3 21.0 6.8 6.1 42.7 51.2

ud ppr I nixed paper, crcb 8 carcboard, rbbsh 8 rtbbish, TOTL 8 total materials,
whte ppr - white paper, color ppr - color paper, TOTL PAPR a all papers combined

* Mean is total weight divided by lumber of swles taken, not length of sampling period.

103

Table 16. Summary statistics from daily sampling, weight data, East
Lansing printshops, fall, 1987 (in pounds).

total “n" smple lower upper X
mtrls smples min max range mean‘ sd c.i. c.i. mtrls
Printshop-1
mixed paper 2688 27 0 191 191 99.6 46.8 84.8 114.4 81.4
cardboard 186 25 0 28 28 7.4 5.8 5.5 9.4 5.6
rubbish 428 27 0 40 40 15.9 12.4 11.9 19.8 13.0
Printshop-2
mixed paper 467 9 0 82 82 51.9 23.9 38.8 65.0 19.9
white paper 916 13 0 237 237 70.5 65.7 40.5 100.4 39.0
color paper 264 8 0 54 54 33.0 13.7 25.0 41.0 11.2
total papers 1647 18 6 259 253 59.9
cardboard 85 14 0 27 27 6.1 7.0 3.0 9.2 3.6
rubbish 618 15 0 105 105 41.2 29.9 28.5 53.9 26.3
Printshop-3
mixed paper 357 7 0 107 107 51.0 43.3 24.1 77.9 34.9
white paper 308 13 0 67 67 23.7 17.8 15.6 31.8 30.1
color paper 116 9 0 30 30 12.9 9.7 7.6 18.2 11.3
total papers 781 18 0 107 107 43.4 76.3
cardboard 33 9 0 14 14 3.7 4.1 1.4 5.9 3.2
rubbish 210 18 1 64 63 11.7 14.8 6.0 17.5 20.5

ALL PRINTSNWS -- GRAND DEANS

total paper 5116 63 81.2
cardboard 304 48 6.3
rubbish 1256 60 21.0

..........................................................................

* Means are total weights for each category divided by number of samples
taken, not length of sampling period.

104

Table 17. Sumery statistics from daily saspling, volume data, East
Lansing printshops, fall, 1987 (in cubic feet).

total ”n“ * smple lower upper X
mtrls smples min max range mean sd c.i. c.i. mtrls

Printshop-1

mixed paper 315.6 27 6 16.0 10.0 11.7 2.2 11.0 12.4 63.4
cardboard 80.4 27 0.1 9.7 9.6 3.0 2.3 2.3 3 7 16.1
waste 102.0 27 1 10.6 10.1 3.8 3.0 2.9 4 7 20.5
Printshop-2

mixed paper 61.3 9 0.3 14.0 1 6.8 3.9 4.7 8.9 19.8
white paper 43.6 13 1 12.0 11.0 3.4 2.8 2.1 4.6 14.1
color paper 19.2 8 1 6.0 . 2.4 1.6 1.5 3 3 6.2
total paper 124.1 17 7.3 40.0
cardboard 57.7 13 0 13.2 13.1 4.4 4.3 2.5 6.4 18.6
waste 128.3 15 2 18.3 16.3 8.6 5.3 6.3 10.8 41.4
Printshop-3

mixed paper 41.0 7 1 12.0 11.0 5.9 4.2 3.3 8.5 24.7
white paper 37.0 12 1 6.0 5.0 3.1 1.7 2.3 3.9 22.3
color paper 12.0 9 0.5 4.0 3.5 1.5 1.2 0.9 2.1 7.2
total paper 90.0 16 5.6 54.2
cardboard 10.8 9 0.1 2.5 2.4 1.2 0.8 0.7 1.7 6.5
waste 65.1 18 0.5 15.3 14.8 3.6 3.4 2.3 4. 39.2
ALL PRINTSHG’S

total paper 529.7 60 8.8

cardboard 148.9 49 3.0

waste 295.4 60 4.9

* Means are total volumes for each category divided by number of samples
taken, not length of sampling period.

105

Table 18. Summary statistics from daily sampling, volume composition
data, East Lansing printshops, fall, 1987 (in percentages).

“n" smple lower upper
smples min max range mean sd c.i. c.i.
Printshop 1
mixed paper 27 40.3 91.2 51.0 66.5 13.2 62.3 70.7
cardboard 27 0.6 100.0 99.4 18.5 18.3 12.7 24.3
waste 27 3.9 37.2 33.3 18.7 10.2 15.4 21.9
Printshop-2

mixed paper 9 11.8 7 54.9 39.1 21.0 27.6 50.7
white paper 13 6 0 7

color paper 8 5.7 33.3 27.6 19.0 11.6 12.3 25.7
total paper 17 11.8 9 73.0 42.3 21.1 33.9 50.8
cardboard 13 0 0 2 88.2 19.2 18.4 10.8 27.6
waste 15 22 1 4 51.3 44.7 14.5 38.5 50.9

Printshop-3

mixed paper 7 11.3 85.5 74.2 44.2 28.8 26.3 62.1
white paper 12 15.2 57.1 42.0 37.8 14.0 31.1 44.4
color paper 9 0.0 60.6 60.6 19.5 18.1 9.5 29.4
total paper 16 28.3 85.7 57.4 57.2 17.2 50.1 64.3
cardboard 9 1.9 25.8 23.9 12.8 7.2 8.9 16.7
waste 18 14.3 100.0 85.7 42.7 23.4 33.6 51.8

ALL PRINTSHOPS

total paper 60 57.2*
cardboard 49 17.7*
waste 60 32.4*

* timbers do not add to 100 percent due to rounding error.

106

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111

Table 22. Suunary statistics from daily saupling, Height data, East
Lansing retail firms, fall, 1987 (in pounds).

"n'I ------ crdbrd ------
total total samples ----- cardbrd ------- smple lower upper
crdbd Haste crdlust* min max range mean sd c.i. c.i.
Convnce-1 615 661 8/8 16 157 161 76.9 50.9 67.3 106.5
Convnce-Z 359 315 6/6 82 170 88 89.8 31.2 .66.1 115.6
Flower-1 239 736 11/11 5 61 36 11.7 11.1 6.2 17.2
Book-1 167 239 22/29 0 17 17 6.7 6.6 5.2 8.2
Book-2 181 329 21/29 0 26 26 8.6 7.6 5.9 11.3
Book-3 366 179 25/26 0 35 35 16.6 8.9 11.6 17.5
Book-6 30 29 7/8 0 8 8 6.1 2.0 6.9 7.6
Clothing-1 98 66 6/6 15 35 20 26.5 8.6 17.6 31.6
Camera-1 69 56 6/6 12 22 10 17.3 5.5 12.7 21.8
Stereo-1 52 355 5/5 1 18 17 10.6 7.2 5.1 15.7
GRAND NEAN 111 18.5

* First number is for OCC samples, second number is rubbish samples.

Table 23. Summary statistics from daily sampling, volume data, East
Lansing retail firms, fall, 1987 (in cubic feet).
“n“ --------------- cardboard ---------------
total total smples mean] smple louer upper
crdbd Haste crd/us* min max range smple sd c.i. c.i.

Convnce-1 115.0 152.0 8/8 5 28.0 23.0 16.6 7.5 10.0 18.8
Convnce-Z 120.0 89.0 5/5 13 39.0 26.0 26.0 12.8 16.6 33.6
Flower-1 75.0 156.0 11/11 1 19.0 18.0 6.8 5.6 6.1 9.5
Book-1 68.0 79.0 23/26 1 8.0 7.0 3.0 1.9 2.3 3.6
Book-2 36.0 99.0 15/29 1 10.0 9.0 2.3 2.6 1.2 3.3
Book-3 126.0 96.0 26/26 1 12.0 11.0 5.3 3.6 6.1 6.6
Book-6 10.6 11.0 7/8 0.6 2.0 1.6 1.5 0.7 1.1 1.9
Clothing-1 38.0 18.0 6/6 6 17.0 11.0 9.5 5.1 5.3 13.7
Camera-1 26.0 26.5 6/6 5 7.0 2.0 5.2 3.0 2.7 7.7
Stereo-1 20.6 37.0 5/5 5 9.0 6.0 6.1 3.0 1.9 6.3
GRAND MEAN 106 5.9

* First rather is for OCC sauples, second number is ribbish sauples.

112

Table 26. Naterial composition results for East Lansing retail
firms participating in waste stream study, fall, 1987
(in percentages, from vollne).

“nut
smples min
Convnce-1 8/8 26.1
Convnce-Z 5/5 62.8
Flower-1 11/11 7.2
Book-1 23/26 11.6
Book-2 15/29 5.9
Book-3 26/26 21.8
Book-6 7/8 26.5
Clothing-1 6/6 37.6
Camera-1 6/6 33.3
Stereo-1 5/5 6.8

65.8 39.8 66.6 15.9 37.6
91.2 68.6 58.6 19.0 66.6
53.7 66.5 32.3 16.1 26.3
81.1 69.7 62.2 22.7 36.6
65.5 59.6 31.0 17.9 23.3
95.6 73.6 55.1 19.6 68.5
56.3 29.8 37.0 9.3 31.2
68.0 30.6 68.8 13.7 37.5
50.3 17.0 60.6 8.8 33.1
66.2 61.5 28.3 17.1 15.7

*First number is for OCC

samples, the second for rubbish

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117

Table 28. Smary statistics from daily mlina, value
data, East Lansing restaurants and bars, fall, 1987
(in cubic feet).

"n" ample lousr upper
smples min max range moan ad c.i. c.i.

R&Bar-1
cardboard 10 3.8 32 27.7 38.8 28.2 24.2 53.4

glass 11 0.5 7 6.2 45.7 27.4 32.1 59.3

"tin" 3 0.5 1 0.5 7.0 3.6 3.6 10.4

aluninua 1 0.4 0 0.0 1.0 0.0 1.0 1.0

plastic 2 1.1 2 0.9 7.0 5.7 0.4 13.6
R&Bar-Z

cardboard 5 1.7 7 5.5 22.8 5.3 18.9 26.7

“tin“ 2 1.0 3 1.9 9.5 10.6 -2.8 21.8

aluninua 1 0.5 1 0.0 1.0 8.0 -17.6 19.6

plastic 1 4.0 4 0.0 6.0 0.0 6.0 6.0
R&Bar-3

cardboard 6 3.0 11 7.8 33.7 10.7 26.5 40.9

glass 4 1.0 4 3.0 29.0 27.2 6.6 51.4

“tin“ 4 2.0 4 2.0 7.8 2.5 5.7 9.8

plastic 2 0.5 2 1.5 17.0 21.2 -7.7 41.7
R&Bar-4

cardboard 9 17.0 56 39.0 35.4 13.6 28.0 42.9

glass 2 36.0 47 11.0 41.5 7.8 32.5 50.5
R&Bar-S

cardboard 3 14.0 51 37.0 31.0 18.7 13.3 48.7

glass 3 41.0 114 73.0 74.0 37.0 38.9 109.1
R&Bar-6

cardboard 12 8.0 46 38.0 17.8 10.3 12.9 22.7

glass 7 11.0 50 39.0 24.7 13.4 16.4 33.0
GRAND MEANS

cardboard 45 29.5

glass 27 40.6

“tin“ 9 7.9

aluminum 2 0.8

plastic 5 10.8

* leans are the total volume for each category divided by
number of samples taken, not length of sampling period.

118

Table 29. Summary statistics from daily sampling, Height
data, East Lansing restaurants and bars, fall, 1987

(in pounds).

"n" ' smple louer upper

R&Bar-1

cardboard 10 8.0 83.0 75.0 38.8 28.2 24.2 53.4

glass 11 11.0 101.0 90.0 45.7 27.4 32.1 59.3

“tin“ 3 3.0 10.0 7.0 7.0 3.6 3.6 10.4

aluainua 1 1.0 1.0 0.0 1 0 1.0 1.0

plastic 2 3.0 11.0 8.0 7 6 0.4 13.6
R&Bar-Z ,

cardboard 5 14.0 27.0 13.0 22.8 5.3 18.9 26.7

“tin“ 2 2.0 17.0 15.0 9.5 10.6 -2.8 21.8

aluninua 1 1.0 1.0 0.0 1.0 8.0 -12.2 14.2

plastic 1 6.0 6.0 0.0 6.0 0.0 6.0 6.0
R&Bar-3

cardboard 6 18.0 47.0 29.0 33.7 10.7 26.5 40.9

glass 4 3.0 67.0 64.0 29.0 27.2 6.6 51.4

"tin'I 4 5.0 11.0 6.0 7.8 2.5 5.7 9.8

plastic 2 2.0 32.0 30.0 17.0 21.2 -7.7 41.7
R&Bar-4

cardboard 9 17.0 56.0 39.0 35.4 13.6 28.0 42.9

glass 2 36.0 47.0 11.0 41.5 7.8 32.5 50.5
R&Bar-S

cardboard 3 14.0 51.0 37.0 31.0 18.7 13.3 48.7

glass 3 41.0 114.0 73.0 74.0 37.0 38.9 109.1
R&Bar-6

cardboard 12 8.0 46.0 38.0 17.8 10.3 12.9 22.7

glass 7 11.0 50.0 39.0 24.7 13.4 16.4 33.0
GRAND HEANS

cardboard 45 29.5

glass 27 40.6

“tin" 9 7.9

aluminum 2 1.0

plastic 5 10.8

* Neans are the total weight for each category divided by
number of samples taken, not length of sampling period.

LIST OF REFERENCES

LIST OF REFERENCES

Ackoff. RL 1962. MW Wiley.
New York, New York. 464 pp.

Baird, D. C. 1962. - ° , ' .
W. Prentice-Hall, Inc. Englewood Cliffs, New Jersey. 198 pp.

 

Brunner, PH. and W.R. Ernst. 1986. Alternative methods for the analysis of
municipal solid waste. WW. 4:147-160.

Cerrato, D.S. 1989. Estimating recyclables in the commercial waste stream. Paper
presented at the New England Resource Recovery Conference and
Exposition, Newport Rhode Island, June 1989. 8 pp.

Concern, Inc. 1988. MW. Concern, Inc., Washington,
DC. 30 pp.

Evans, B.W. 1985. How garbage analysis can reduce collection costs. BieCyele,
Emmaus, Pennsylvania, March, 1985, pp 30-33.

Gale, D. and T. Croll. 1989. Caping with Seattle’s solid waste crisis. Eagle
We. Volume 2, Number 2:10-16.

Golueke, CG. and RH. McGauhey. 1969. MW

management, seeend anneal repen. Sanitary Engineering Research
Laboratory College of Engineering and School of Public Health, Berkeley,
California.

. 1967. 0mr0'1' i ' rcl' 0. Hli .... ‘ l!e-!1"!l’!. 1 141,1.
repent. Sanitary Engineering Research Laboratory College of Engineering and
School of Public Health, Berkeley, California. 202 pp.

Greater Boston Chamber of Commerce, City of Boston, and Commonwealth of
Massachusetts. 1989. 1i
Prepared by Resource Integration Systems / Resource Conservation
Consultants and Earthworm, Inc. Boston, Massachusetts. 119 pp.

119

120

Guter, Kurt. Granger Land Development Company, Lansing, Michigan. Interview,
July, 1990.

Kincaid, Kevin. Kinko’s Copies, East Iansing Michigan. Interview, October, 1987.

Kuniholm, P. F. 1989. Guide to estimating recycling capture rates. Paper presented
at the New England Resource Recovery Conference and Exposition, Newport
Rhode Island, June 1989. 8 pp.

Lansing Regional Environmental Task Force. 1989. Position paper for regional
environmental task force, November 6. 1989. Lansing Regional Environmental
Task Force. 5 pp.

Massachusetts Department of Environmental Management. 1985. Waste
WWW Prepared for the

Massachusetts Department of Environmental Management, Bureau of Solid
Waste Disposal, Boston, Massachusetts by F .W. McCamic, 68 pp.

McCamic, F.W. 1985. Waste Composition Studies: Protocols for Recyclers. Journal
of Resource Management and Technology. Vol 14, 4:229-234.

Metropolitan Council. 1988. ' i ' ° '
n r . Prepared for Metropolitan Council, St. Paul,
Minnesota by Cal Recovery Systems, Inc. 44 pp.

. 1988. ' ' ' _
generatinn. Prepared for Metropolitan Council, St. Paul, Minnesota by Cal
Recovery Systems, Inc. 48 pp.

 

.1988. W
Cnmpnsitien. Prepared for Metropolitan Council, St. Paul, Minnesota by Cal
Recovery Systems, Inc. 66 pp.

..1988 I... ' °' ° in arm i0 l! ol ' -
management. Prepared for Metropolitan Council, St. Paul, Minnesota by Cal
Recovery Systems, Inc. 48 pp.

Michigan Department of Natural Resources. 1989. WWW
. Prepared by Franklin Associates, Ltd. and Resource

Recycling Systems. Lansing, Michigan.

. 1988. Michigan solid waste policy. Prepared by Resource Recovery
Section, Waste Management Division. Lansing Michigan. 38 pp.

. 1986. W Iansing. Michigan
55 pp.

121
Minnesota Public Interest Research Group. 1988. W

1 51 hr --n°-r-x..-1 -1-'.-’mmrr 1 11111
W ByM lee.W..D Nynas,D Ware
M. Brownrigg, T. Super, J. Britton, and S. Zobbie. Minneapolis, Minnesota.
182 pp.

Papke, C. 1988. Getting real with recycling. Solid Waste & Power, Kansas City,
Missouri. April, 1988. pp 28-32.

Robinson, D. G. and W. D. Robinson. 1986. Raw material quantity and composition:
a final check. In W: W D Robinson,
ed. John Wiley and Sons, New York, New York. pp 507-530.

Savage, G. M., LF. Diaz, and C. G. Golueke. 1985. Solid Waste Characterization.
We, Emmaus, Pennsylvania, November, 1985, pp 35-37.

Seattle. Washington- 1989- WWWEmfl
Repeat. Prepared for the Department of Engineering Solid Waste Utith by
Matrix Management Group, Herrera Environmental Consultants, R.W. Beck
and Associates, Ferndes Associates, and Gilmore Research Group. Seattle,
Washington. 120 pp.

Selke, S.E.M. 1990. Baelgaging_and_the_engimnment. Technomic Publishing
Company, Inc., Lancaster, Pennsylvania. 179 pp.

St. Paul. 1982. WWW Prepared by Brian
Golub and the Solid Waste and Energy Division of the Department of Public
Works, St. Paul, Minnesota. 69 pp.

Sunnyvale, California. 1986. ' i

W
manners'ialandindnstrialateasxolumel Prepared for Sunnyvale California
by Recovery Sciences, Inc. 78 pp.

United States Environmental Protection Agency. 1989. mm
an_agenda_f9_r_aeti_o_n. U.S. E.P.A. Office of Solid Waste and Emergency
Response. 70 pp.

..1988 .11. {~11'.'-_'__11111.”-..111 1°«0W '1 1' ._- '11
___—W. U. S. E. P .A. Office of Solid waste and Emergency
Response. 31 pp.

.1979. Wm Municipal
Environmental Research Laboratory. 254 pp.

University of Colorado. 1988. WWW!- Prepared for the
University Colorado at Boulder by J. DeBell. Boulder, Colorodla. 4 pp.

 

122

Washington State. 1988. 3. ' . .1. o. , ' ' 1 ’ 1 ' ' 1 1 t ' 11
W Prepared for the Office of Waste
Reduction and Recycling, Washington State Department of Ecology, Seattle,

Washington, by Matrix Management Group, R.W. Beck and Associates, and
Gilmore Research Group.

Westchester County. 1988. , ' u .. ' '
Prepared for Westchester County Department of Public WOrks Solid Waste
Management Division, White Plains, New York, by Malcolm Pirnie, Inc.

 

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