Planning that lays out your step-by-step sampling strategy is absolutely essential prior to stepping foot out into the field. But, even before you work through those steps, by way of background it is useful to review the whys and wherefores of waste sorts.
This chapter [A] recaps the purpose of waste sorts and then [B] explains the steps to develop a study design.
[A] Why do a waste sort?
Essentially, in order to plan solid waste strategies, it is necessary
to first understand something about the waste stream generated by an area's
population. Examples are when you need to know
Sizing requirements for material recovery facilities
Projected sales from recyclables
Proportion and amount of organic material for composting
Effectiveness of mandated recycling programs
Amount of potentially divertable material justifying inclusion in future recycling efforts
Changes in disposal patterns over time
Heat content of the incoming incinerator waste stream
Unfortunately, because waste composition varies so much from place to
place and from time to time, "generic" data from other sources is essentially
useless for answering these questions. The only meaningful composition
data is that which you develop from your own locale.
Since it is obviously impractical to conduct a complete census of the
entire waste stream in your area, it is necessary to estimate the
characteristics of the whole by sampling a small part of the total.
It is the science (and art) of statistics which makes it possible to insure
that your estimates are representative and to know how close those estimates
are likely to be to the correct answer. Otherwise, you will not be able
to use the sample averages in a way that recognizes the possibility of
errors inherent in any sample of the whole. But all this will only work
if you follow the necessary steps.
A good study design begins with asking the right questions and then
following the steps listed below. Before walking through those procedures,
however, you really ought to prep yourself on the on the underlying statistics
and on the physical lay of the land.
First, the Stat /
explains most of what you'll want to know about the underlying theory of
statistics to construct a good study design and conduct a reliable waste
sort. Most people consider a detour through statistics akin to being forced
to eat cold porridge, but the rest of this GUIDE will make more
sense if you do.
Second, to help insure that your plans conform to reality, drive
around for a week to see how solid waste is handled and chat with the major
players in the field. This includes following some collection vehicles
on their routes, watching trucks arrive and dump at the disposal facility,
and interviewing program managers, haulers and facility operators on how
their systems work.
After doing this and perusing the Primer, you will be in a better
position to understand and implement the following discussion of the eight
key steps in a waste sort study design:  the question (p. 9);
 the waste stream (p. 10);  the waste shed (p. 11);
 any groupings by source of waste (p. 12);  the sampling
strategy (p. 17);  the materials sorted (p 20);  time
periods involved (p. 22); and  the desired level of reliability
[Note: If you don't want to wrestle with statistics
but do want to enjoy the fruits of what statistics can provide you may
want to consider purchasing the WasteSort™
software package to automatically handle all of those calculations for
you. An order form is contained on the last page of this GUIDE.]
 State the question being asked
To frame your study design, articulate the question(s) you are trying
to answer, expressed in as clear and precise a manner as possible.
[example: What changes occur in the composition
of waste by weight after the introduction of curbside recycling programs
for metal cans, glass and plastic bottles and newspapers by the city of
[another example: If plastics are diverted from
the waste stream by recycling, will the remaining heat content be sufficient
to sustain economic operation of a waste-to-energy recovery facility?]
 Define the waste stream
Implicit in the questions you are seeking to answer is the type of solid waste in the waste stream that you need to characterize. That is to say, are you only interested in household waste, or do you want to consider distinct industrial types, such as hazardous or construction/demolition debris. One standardized breakdown of waste by type, or, for that matter, by source, is shown on the following table
•Construction and demolition waste
* Sources are indicated by a
number in brackets, the reference for which is found in the appendix: More
Readings, at p. 119.
The first two columns are sometimes referred to as household waste,
the first four columns are sometimes combined under the heading municipal
solid waste, and the last is sometimes referred to as special wastes.
Later, if you chose to only characterize a part of the entire waste
stream, you will need to isolate loads which come from just the types of
waste you are analyzing. Often these different types will come on either
discrete types of trucks (i.e. residential and commercial from packers
and demolition debris from roll-offs) or from certain companies (i.e. Household
Hauler Co. or Axle Demolition Service), making your task easier.
 Define the waste shed
Next, define the "waste shed," the geographic area which encompasses
the source of the waste stream waste which you wish to characterize.
[example: Middlebury County (and the City of
Middletown 35 miles away) is the waste shed for an incinerator whose operator
has a flow control contract with all solid waste generators in the County
and in Middletown and who wants to evaluate the heat content of the incoming
[another example: The City of Axleberg municipal
boundary is the waste shed for a study to find out how effective the city's
new curbside recycling program has been by comparing the proportion of
recyclables in its discards before and after the program began.]
Note that the area does not have to be composed of contiguous parts.
Especially due to the increasing use of transfer stations, waste can be
economically moved long distances when it makes economic or political sense
to do so. The relevant factors for drawing boundaries can be unrelated
to geographic proximity. Other driving factors are
Economic issues (in terms of costs of long distance hauling),
Political issues (in terms of public acceptance of nearby disposal facilities),
Legal issues (in terms of mutually agreed upon contracts or flow control
ordinances to capture discards from an area).
 Categorize the source of waste generators by type
The fourth step involving grouping the samples by distinct sources is
often done. However, it does not have to always be done. Categorizing waste
by the source which generated it is usually referred to as stratification.
To stratify your samples, it is necessary to be able to
loads by the group which generates the waste; and
Estimate the proportion that each strata bears to the entire waste stream.
(See Table 1 on p. 10 for a list of types of solid waste that
can also relate to sources of waste for stratification.)
Most often, residential and commercial sources are separated into strata.
This division is relatively easy to make because the two have distinct
forms of collection residential waste is collected from individual 30 gallon
cans set out at the curb and commercial waste, in 2-6 cubic yard dumpsters.
For this reason, the two are generally collected in separate trucks with
discrete loads. When construction and demolition debris and other high
volume wastes are important to characterize, they may be split off into
yet another strata. This is usually not difficult to accomplish, because
they are often collected from roll-off or other specialized containers.
Stratification affords three benefits: [a] it provides discrete
information about each strata that you need to answer your questions; [b]
it reduces the number of samples you need to sort to achieve your desired
level of reliability; and [c] it improves the probability of getting
a representative sample.
[a] Discrete information
first reason to stratify by type is to derive separate information about
two or more sub-groups of the waste stream for which discrete data is needed
to answer your question. A recycling program, for example, will usually
be targeted differently for residential and commercial sources. This is
because the two groups are distinct and some policies directed at one would
have little or no applicability for the other. Also, the entity that collects
residential waste (perhaps the municipality itself) will often be a different
one from that which collects commercial loads (most often a private hauler).
This often results in to two separate programs running in tandem.
To generate information for each type of source of waste, it will be
necessary to separately sort the two or more sources and compile their
data distinctly as well.
In the real world, there will usually be a kink in this neat delineation.
Apartments with more than 5 units will typically use the same kind of dumpsters
as commercial establishments and be picked up in the same truck that collects
from stores and factories. Though waste characteristics of apartment dwellers
more closely resembles residents of single family homes than that of grocery
stores or restaurants, in most cases their waste will be hauled on the
same truck as commercial waste.
There is no easy resolution to this quandary. Compromises usually entail
interviews with haulers to exclude trucks which haul from apartment and
commercial dumpsters, but this may inadvertently intrude biases into the
results. The only ironclad way to separate the two when separate apartment
data is required is to use a waste source strategy (see p.
[b] Fewer samples
Another reason to stratify is to reduce the number of samples you need
to achieve a desired level of reliability. Samples often exhibit a good
deal of variability from one to sample to the next compared to their average
proportion, and this results in less reliability.
However, much of the variation can sometimes be explained because the
population actually consists of two or more distinct subgroups, or strata,
One group, such as apartments with garbage disposals, might have little
food waste, while another, such as restaurants, could have quite a lot.
Calculate the average of the two groups, and the percent of food waste
in each group's samples will vary substantially from that combined mean.
As described in the Stat
/ facts primer (see
p. 90), stratification permits you to achieve the same confidence interval
and level with fewer samples. This is done through pooling techniques that
filter out the variation between the strata which is extraneous to the
essential uncertainty you need to worry about surrounding your sample results.
This sampling strategy will work to reduce the number of samples needed
for a desired reliability so long as the variation within your strata
is less than the variation between them.
Figure 1 illustrates for one particular case how the band of uncertainty,
the confidence interval, was narrowed in a typical waste sort by
sorting and then pooling residential and commercial strata separately.
As you can see, had the analyst not stratified in that particular case,
the confidence interval of the combined strata would have been reported
as nearly 30%, instead of less than 15%.
Often, you should also divide your sampling over different seasons (see
p. 17). Although this is primarily done to enhance the representativeness
of your results, if the different seasons are treated as if they are different
strata in the calculations of confidence intervals, the uncertainty band
surrounding the annual averages can be substantially narrowed.
[c] More accurate samples
Stratification can help get more representative samples than simple
random selection can achieve in some cases. Random selection, discussed
later (see p. 46), builds upon probabilities. This implies that
most of the time random selection will pick samples that are representative
of discards from the major sources of waste.
However, there will be a small number of times by chance that waste
sorts, done with randomization, are not representative of all sources.
That will be extremely rare with sources that contribute a major fraction
of the waste stream, but rise for sources which are minor. By stratifying
the sub-groups which are a smaller part of the whole, you can insure that
a full quota from those small groups will be randomly picked.
 Strategies to reduce biases
to the statistics you calculate, the likelihood that your study will produce
a reasonable estimate of the correct answer depends upon the success you
had getting representative and unbiased samples. In this regard, four key
points should be kept in mind when doing a waste sort: [a] seasons;
[b] a typical week; [c] hourly and daily differences; and
[d] random selection.
[a] Selection of seasons
In northern climates, it gets cold and snows in the winter. Then comes
the spring and everything comes alive...only to die or get cut down, and
then get thrown into the landfill. Sample for the proportion of organic
material in the waste steam during summer and you will often get a bloated
reading on the annual fraction of green matter. Other materials,
such as aluminum cans, similarly, vary with hot weather.
To reduce those biases, especially in regions with vigorous winters,
you should do sampling at least two seasons (winter and summer). Ideally,
sampling should be done in all four seasons in order to more accurately
reflect the annual distribution of waste.
When seasonal sampling is done, the extent to which each season contributes
a different proportion of waste to the annual waste stream must be determined
so that, when you do your analysis, it can be weighted in computing annual
Incidently, those weights for each seasons that you use in computing
annual averages come in handy in calculating confidence intervals, too.
The so-called "pooling" techniques, discussed for strata (see p.
14), will permit you to narrow the uncertainty bands around the annual
compilation of seasons. For, the only difference between strata
and seasons is that, with strata, the groupings are done
by type of waste generator, and, with seasons by time
of waste generation. Pooling of seasons squeezes out the uncertainty ascribable
to variations between seasons for such things as yard waste, aluminum cans,
etc., just as it does between strata.
[b] Typical Week
For each season that you sample, you will only be going out to sort
the waste stream for one point in time perhaps for a day or, more likely,
for a week. It is important that this point in time typify that season
involved. That time ought not have abnormal characteristics from such things
as holidays, a time when most people move their residence, or from weather.
[c] Hours and days
Trucks that unload early sometimes have different type loads than ones
later. By using a random number generator to select trucks from which to
draw samples (see p. 52), this possible source of bias can be avoided.
Randomization insures that every truck during the day has an equal chance
of being picked.
Variations between days also are theoretically a possible source of
bias. But, although this can be serious when studying the total amount
of waste generated, it is generally considered less so with waste composition
as long as you: (1) avoid holidays and extreme weather; and (2)
randomly select trucks in the proper proportion between residential and
commercial (and any other significant strata's) loads. Otherwise, you'll
need to spread your sampling over several days, and, best yet, over an
[d] Random selection
A study design should contemplate that, whenever a choice must be made
along the way to getting a sample, random selection procedures are used
(for a discussion of that procedure, see p. 27).
Selection should never be based upon judgment calls using visual inspection.
For example, when 20,000 pounds of smelly garbage from a truck is spread
out on the tipping floor, don't eyeball which sample loads "look" representative.
Most people would subconsciously avoid the worst of it, even though the
waste stream consists, in part, of bad smelling stuff. Instead use the
formal random selection procedures that are described in the next chapter
Selecting Samples (see p. 45).
 Materials to sort for
A prime question in a waste composition study is which materials to
sort for. The chief criterion for selection is the nature of the questions
you are seeking to answer with the study.
If the study is intended to develop a recycling strategy, then attention
will typically be centered on things like glass, metal and plastic containers,
newspapers and high grades of paper. If it is to determine the optimal
throughput for a proposed incinerator, then the focus will probably be
to sort all of the important combustible materials, grouped by ones with
different heat and moisture content. On the other hand, if the purpose
is to provide information for mixed waste composting, the organic fraction
and its components become important, etc.
Competing against these considerations are logistical concerns when
a list is expanded. True, the bulk of the expense will be incurred setting
up and evaluating the sorting process. The cost of adding materials to
the list usually is minor in relation to total expenditures. However, errors
in measurement are another matter. At some point it becomes cumbersome
for the sorters in the field to keep track of all of the materials being
separated, and mistakes increase soar. After 30 materials, you should be
cautious about further additions unless you have budgeted for careful hiring
of qualified sorters, supervising and sorting each sample.
Table 2 is an illustrative list of materials for a waste sort
 Time periods to sample
Sometimes the questions being asked will relate to how the waste composition
changes over time (perhaps in order to project the future) or in response
to an event.
[example: How effective will a new recycling
ordinance be at diverting material from the landfill.]
[another example: Will the changing ratio of
metals to plastic over time significantly affect the heat content of the
incoming trash to an incinerator.]
Taking sets of samples at different points in time, either over the
course of each of several years (called a time series) or before-and-after
an event can help provide the data to answer these questions.
A before and after research design sorts before the event in question
and then another afterwards. Looking ahead to the analysis chapter,
a particular type of statistical test, called the significance of the
difference between two means test, can be used to interpret the before
and after data to determine whether apparent differences are really significant.
 Degree of reliability needed
The big ticket in waste composition studies is how many samples that are costly to sort are needed. That, in turn, will depend upon how much uncertainty you are willing to accept around the averages you calculate from your samples Defining that is an important part of the study design. To figure that out, there are two questions to ask
"How much will it cost if a properly sized system today becomes missized
in the future because the composition of the waste stream changes?"
Surveys of waste system operators have generally concluded that estimates
of waste quantities need to be very close to the correct answer.
However, that need has been found less important in waste composition
studies for at least two reasons.
First, the uses to which waste quantity data is put can be critical
to the success of the projects it is used for. Bond repayments in the solid
waste industry come from tipping fees at capital intensive waste facilities
that have been sized based upon this type of study. In turn, those tipping
fees are also calculated from the projections of waste quantities. A wrong
guess here could send the whole program into bankruptcy if revenues generated
from those tipping fees are inadequate to pay off creditors.
With waste composition, on the other hand, misestimation of relative
proportions is often less directly related to the project's financial survival.
To a significant extent, the bottom line for a materials recovery facility
or incinerator will be more closely tied to the total incoming flows, for
which a tipping fee is levied, rather than from sales of recyclables or
energy. Similarly, were you to incorrectly estimate the proportion of one
material, another material's fraction will be overestimated because the
total in percentages remains 100%. Many times these offsets in the relative
relationship of different materials will come close to netting out to zero
in the sales value of the entire stream. This is one reason why the high
cost of reliability is usually more difficult to justify for waste sorts.
Second, in waste sorts the costs of a wrong guess are spread
over the period that a facility is used. Over that time, the relative relationship
of many of the materials in the waste stream are likely change from that
which you carefully sorted today. This can be due to changes in product
container designs, buying habits, and so on. For example, plastic substitutes
for glass, and paperboard for some plastic. "Light weighting", or making
thinner walled containers, reduces the fraction of all one-way consumer
containers. A meticulous breakdown of today's waste stream cannot guard
against these fluctuations.
That is the reason why the precision of a single snapshot in time is
not of overwhelming importance. Overall, a larger degree of uncertainty
is acceptable in garbage than in rigorous scientific studies where, for
example, maximum safe dosages of drugs are being determined in studies
which describe relationships of dose to health that are constant over time.
The following table typifies how a desire for greater reliability can
translate into drastically more samples to sort. Compare, for example,
the number of samples needed in an illustrative case for a wide uncertainty
band of ±20 and a low confidence level of 90% here 37 samples to
the 844 samples needed for a narrower ±5 ban at the 95% level.
as % of
[cross-reference: For details on how to estimate
the number of samples needed to achieve a desired level of reliability,
see p. 31.]
In light of these considerations, a reasonable decision would be to
use a 90% confidence level and, if funding permits, a ±10% confidence
interval. If funding is tight, a ±20% interval could be substituted.
All of this has focused on how to randomly select enough samples to
get a precise answer. There are other issues interrelated with these.
Among them is the physical act in the field of accurately sorting
and weighing the sampled materials. For a discussion of good field techniques,
see the sorting samples chapter at p. 55. (For a technical discussion
of the difference between the statistical concepts of precision
and accuracy that underlie the more common term reliability
used in the text, see the Stat
/ facts Primer at
[note: A tear out Study Design Planning Worksheet is included in the appendix. See p. 121.]