Municipal and Industrial Refuse: Compositions and Rates · but the total discarded will increase as...
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Municipal and Industrial Refuse: Compositions and Rates w. R. NIESSEN Bolt Beranek & Newman, Inc. Arlington, Virginia ABSTRACT A useful estimate of the average composition of mu- nicipal solid waste is determined by evaluation of 41 sets of composition data, and a national annual average com- position is projected through the year 2000. The compo- sition and generation rates of industrial wastes are evalu- ated based on data from New Jersey and New York States, and methods are presented for the use of these data in evaluation of regional waste generation quantities. INTRODUCTION Originally, the problems of solid waste were dealt with at the lowest political level-each man took care of his own, usually with a dump at the back of his cave. Subse- quent centuries saw increases in the quantity and diversity of waste; and with increasing urbanization, the problem came to be considered as a municipal solid waste manage- ment matter, its scope confined largely to domestic and commercial waste. Increasingly, however, the scale and cost of solid waste management has further expanded the minimum scope of the effective planner to include industrial waste and to consider waste generation on a regional or statewide basis. In carrying out such broad-scoped evaluations, data is required on all solid waste generators. Also, estimates of future waste composition and generation rate are needed for at least 10 and more often 30-50 years. To prepare such estimates of waste generation, recourse is almost always taken to a local questionnaire and data are assembled and manipulated to produce estimates of 31 9 A. F. ALSOBROOK U. S. Borax & Chemical Co. Knoxville, Tennessee the total present and projected waste management needs of the region. To assist in this process and to provide a means for rapid estimation, we have collected and anal- yzed such data to derive scale factors which may be use- ful for preliminary evaluations. I. MUNI CIPAL WASTES Ruse Composition A previous paper [1] presented an evaluation of 23 ap- parently independent sets of refuse composition data. Since that time, an additional 18 sets have been made available. The new data sets are presented in Table I. The categories into which the refuse is subdivided are generally self-explanatory. The category of textiles refers to both natural and synthetic woven fibers together with spun-bonded synthetics. Synthetic non-wovens, such as PVC coatings used in clothing and apparel, are considered under the plastics category. The yard wastes' category comprises such items as leaves, grass, plants, trimmings and some dirt. The miscellaneous category is comprised primarily of bricks, rocks and dirt. Eliminating the variability in the data due to yard wastes, which are generated on a seasonal basis, and the miscellaneous category, which depends importantly on local acceptance of demolition waste, and estimate of the average refuse composition was made. Statistical anal- ysis of these reduced data (Table 2) show siificant uni- formity and suggest a close similarity of the yard waste and miscellaneous-free fraction of refuse coast-to-coast.
Transcript of Municipal and Industrial Refuse: Compositions and Rates · but the total discarded will increase as...
Municipal and Industrial Refuse: Compositions and Rates
w. R. NIESSEN Bolt Beranek & Newman, Inc.
Arlington, Virginia
ABSTRACT
A useful estimate of the average composition of municipal solid waste is determined by evaluation of 41 sets of composition data, and a national annual average composition is projected through the year 2000. The composition and generation rates of industrial wastes are evaluated based on data from New Jersey and New York States, and methods are presented for the use of these data in evaluation of regional waste generation quantities.
I NTRODUCT ION
Originally, the problems of solid waste were dealt with at the lowest political level-each man took care of his own, usually with a dump at the back of his cave. Subsequent centuries saw increases in the quantity and diversity of waste; and with increasing urbanization, the problem came to be considered as a municipal solid waste management matter, its scope confined largely to domestic and commercial waste. Increasingly, however, the scale and cost of solid waste management has further expanded the minimum scope of the effective planner to include industrial waste and to consider waste generation on a regional or statewide basis.
In carrying out such broad-scoped evaluations, data is required on all solid waste generators. Also, estimates of future waste composition and generation rate are needed for at least 10 and more often 30-50 years. To prepare such estimates of waste generation, recourse is almost always taken to a local questionnaire and data are assembled and manipulated to produce estimates of
319
A. F. ALSOBROOK U. S. Borax & Chemical Co.
Knoxville, Tennessee
the total present and projected waste management needs of the region. To assist in this process and to provide a means for rapid estimation, we have collected and analyzed such data to derive scale factors which may be useful for preliminary evaluations.
I. MUNI CIPAL WASTES
Refuse Composition
A previous paper [1] presented an evaluation of 23 apparently independent sets of refuse composition data. Since that time, an additional 18 sets have been made available. The new data sets are presented in Table I.
The categories into which the refuse is subdivided are generally self-explanatory. The category of textiles refers to both natural and synthetic woven fibers together with spun-bonded synthetics. Synthetic non-wovens, such as PVC coatings used in clothing and apparel, are considered under the plastics category. The yard wastes' category comprises such items as leaves, grass, plants, trimmings and some dirt. The miscellaneous category is comprised primarily of bricks, rocks and dirt.
Eliminating the variability in the data due to yard wastes, which are generated on a seasonal basis, and the miscellaneous category, which depends importantly on local acceptance of demolition waste, and estimate of the average refuse composition was made. Statistical analysis of these reduced data (Table 2) show significant uniformity and suggest a close similarity of the yard waste and miscellaneous-free fraction of refuse coast-to-coast.
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TABLE 2
Statistical Evaluation of National Annual Average Composition Estimate*
Data Mean 95% Samples Weight Mean Standard Confidence ....
Component Utilized Percent (100% Total) Deviation Limits
Food Was tes 38 19.7 19.6 9.08 2.89
Glass 37 10.1 10.1 4.03 1.30
Netal 37 9.9 9.9 2.03 0.65
Paper 38 50.8 50.6 11.04 3.51
Plastics*** 10 1. 70 1.62 1.09 0.69
Leather & Rubber *** 10 1.77 1.68 1. 59 0.98
Plastics, Leather & Rubber (Combined) 32 3.3 3.3 1. 29 0.45
Textiles 33 3.0 3.0 1. 96 0.67
Wood 37 3.5 � 4.98 1.60
Total 100.3 100.0
* Excluding yard-wastes and miscellaneous categories.
* .. We assumed that the data are as i:1 a "normal" distribution. * * * Note that in �any of the data sets, these components are not reported separately.
Source: Arthur D. Little , Inc. estimates
It should be noted, however, that these averages, confidence limits and standard deviations are neither applicable for small samples of refuse as might be characterized by an incinerator bucket load nor for a larger quantity of refuse sampled at any one moment. At best, they can be viewed as providing a rough estimate, of uncertain accuracy, of an average refuse composition. This uncertainty arises, importantly, from our lack of assurance that the data are without bias. Nonetheless, they are a starting point for evaluations where an average composition is needed and no localized data is available.
The next step in "constructing" an average refuse composition is to incorporate data on the "miscellaneous" fraction. Clearly, the quantity of this material is subject to wide variation (Table 3). With re ference to incinerator operation, refuse with a large component demolition waste (which forms the principal part of the "miscellaneous" category) is often not accepted. Thus, for incinerator design application, we selected the 8.7 percent level as a cut-off. This yields an average of 4 percent "miscellaneous" content in yard-waste free refuse (up from 1.7 percent as in Ref. 1).
The yard wastes fraction of municipal refuse can be seen (Table 1) to vary wi th the season. One also can observe a higher winter yard waste fraction in southern states than in the northern states. Data on yard wastes
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in refuse are summarized in Table 4 where seasonality could be determined. Fro m the seasonal averages, the total waste compositions shown in Table 5 were prepared. It should be noted that the results in Table 5 have been adjusted to a moisture level corresponding to the manufactured state of the materials entering the refuse storage bin, i .e ., on an as-discarded basis. The moisture levels presented in Table 6 were used to make this transformation.
Refuse Properties and Forecasts
Using the data and methodology presented in Re f. 1, the composition, quantity and combustion parameters for municipal solid waste were forecast through the year 2000. The resul ts are presented in Table 7. It should be noted that these estimates do not provide for the effect of recycle on composition but reflect an estimate of the total waste stream characteristics. The projections show the following trends:
Composition
Paper
Paper, cardboard, and other wood-fiber products will comprise an increasingly dominan t fraction of refuse.
TABLE 3
Data For Percent Miscellaneous in Municipal Refuse
Percent Weight-Percent Percent Miscellaneous Miscellaneous Yard Waste (Yard-Waste-Free Basis)
0.0 21. 1 0.0
0.2 10.8 0.2
0.2 2.3 0.2
0.3 13.5 0.3
0.7* 6.4 0.7
1.0 13.0 1.1
l.0* l.6 l.0
l.3 0.0* 1.3
1. 9* 23.8 2.5
3.2 1.4 3.2
3.2 0.3 3.2
3.4 9.5 3.8
3.4 1.6 3.5 Ave. 4.0%
3.4 2.8 3.5
5.5 3.8 5.7
5.9 4.2 6.2
6.0 5.0 6.3
6.2 0.0 6.2
6.9 9.8 7.6
7.0 0.0 7.0
7.1 9.8 7.9
7.1 5.0 7.5
8.5 0.0 8.5
8.7 0.0 8.7
11. 1 11.1 12.5
12.5 12.1 14.2
12.5 12.1 14.2
14.5 12.0 16.5
16.4 0.0 16.4
23.6 1. 99 24.1
* Obtained from Table 1 by difference.
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This change will decrease refuse bulk density, adversely affecting almost all collection, storage and handling operations associated with disposal facilities. The increase in the portion of refuse accounted for by paper may have a favorable impact on the economics of waste paper recovery, which should become of increasing importance in light of the diminishing availability of new pulp resources forecast for the late 1980's and possible governmental pressure for increased use of reclaimed fiber.
Metal
Our projections show a slight drop in the weight percent of metal (mostly iron) in refuse by the year 2000, but the total weight discarded will increase as the quantity of refuse collected will increase significan tly. The costs for metal recovery will probably remain stable on a unit weight basis. As in the past, tile economic success of metal recovery operations will primarily reflect the market value and capacity for the recovered material. The steel industry recently announced its readiness to accept unincinerated tin can scrap at a price near $15/ ton (thus giving refuse a value of about $l/ton for its steel content alone). A decision regardirlg broad acceptance of unincinerated cans is pending.
Glass
We do not expect the fraction of glass in refuse to change significantly over the next 30 years. Glass will continue to present a residue disposal problem to incinerators or if consistency problems can be solved, a reclamation opportuni ty. This projection could change sharply, however, if very low cost beverage and foodgrade plastic containers were developed. The impact would be even greater if biodegradable plastic containers were developed.
Plastics
The over three-fold growth in the plastics fraction of refuse indicates that operating problems associated with the burning of this waste component can be expected to increase. These include increases in refuse heating value, hydrochloric acid formation (from combustion of chlorinated polymers such as polyvinyl chlOride), and, perhaps, nitrogen oxides formation. Grate plugging may be a problem in small incinerators.
TABLE 4
Data For Weight Percent Yard Waste
In Refuse Delivered To Municipal Incinerators
Recions Summer Spring Winter Fall Yearly Average
No rthern States 33.3 17.9 0.3 6.4
19.0 4.2 0.32
13.0 0 .. 26
26.17
Seasonal Average 22.9 11.1 0.3 6.4 10.2
Middle States 12.1 9.5 1.6
12.3
Seasonal Average 12.2 9.5 1.6
Southern States 21.1 9.2 9.81 5.2
23.8 3.76
Seasonal Average 22.5 9.2 6.8 5.2 10.9
All Regions
Seasonal Average 20.1 10.2 2.9 4.3 9.4
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I ncineration Characteristics
Heating Value and Moisture
We expect the heating value of dry refuse (Btu/lb) to increase at a slow but regular rate. Since an incinerator is, in effect, a heat disposal system, the increase in heating value will result in a corresponding decrease in the effective capacity of furnaces. Moisture content varies widely with refuse storage practices and local wea ther conditions but on average will drop because of diminishing weight fractions of yard and food wastes in refuse. The
indicated drop in average refuse moisture and the increase in heating value will increase flue gas temperatures, resulting in decreases in effective incinerator capacity (from 250 tons/day in 1970 to only 214 tons/day by 2000).
Volatile Carbon
The volatile carbon content of refuse (calculated as total minus fixed carbon) affects the fraction of combustion taking place in the furnace volume above the bed and the rate of soot formation. The slow but steady increase in this value (19 percent over the 30 years) will place an increasing emphasis on obtaining satisfactory overfire air mixing and flue gas residence time prior to quenching to avoid increased emissions of combustible air pollutants (carbon monoxide, soot, and hydrocarbons). This trend will require more sophistication of the designer and improves the outlook for advanced systems; it could lead to down-rating of existing plants.
TABLE 5
Estimated Average Municipal Refuse Composition, 1970
(weight percent, as discarded)
Category Summer Fall Winter* Spring
Paper 31.0 39.9 42.2 36.5
Yard Wastes 27.1 6.2 0.4 14.4
Food Wastes 17.7 22.7 24.1 20.8
Glass 7.5 9.6 10.2 8.8
Metal 7.0 9.1 9.7 8.2
Wood 2.6 3.4 3.6 3.1
Textiles 1.8 2.5 2.7 2.2
Leather & Rubber 1.1 1.4 1.5 1.2
Plastics 1.1 1.2 1.4 1.1
Miscellaneous 3.1 4.0 4.2 3.7
Total 100.0 100.0 100.0 100.0
* The refuse composition in winter for southern states is similar to that shown for fall.
Source: Arthur D. Little, Inc., estimates.
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TABLE 6 Ash Percent Moisture in Refuse on an "As-Discarded"
And "As-Fired" Basis
Component As-Fired As-Discarded
Food Wastes 63.6 70.0
Yard Wastes 37.9 55.3
Miscellaneous 3.0 2.0
Glass 3.0 2.0
Metal 6.6 2.0
Paper 24.3 8.0
Plastics 13.8 2.0
Leather & Rubber 13.8 2.0
Textiles 23.8 10.0
Wood 15.4 15.0
We project a small increase in total ash content during the 1970's, a slight decrease in the 1980's and a larger drop in the 1990's. The problem of ash disposal promotes the use of high-volume-reducing incinerators, but the long-term decline in ash content will lessen the pressure to emphasize volume reduction at the expense of other process characteristics. Also, we expect the ash content of refuse exclusive of glass and metals-the portion which contributes to particulate air pollution-to remain relatively constant. It is noteworthy that both the total and the reduced ash content is higher than that reported in the previous work [I] .
I I. I NDUSTR I AL WASTES
Industrial activities covered in this paper are confined to manufacturing establishments (SIC Codes 19-39) whose wastes have a relatively high potential for interaction with municipal wastes during collection and disposal. Excluded are a number of activities which undoubtedly generate large quantities of waste, such as
TABLE 7
Projected Average Generated Refuse Composition,
Heating Value and Quantity, 1970-2000
1970 1975 1980 1990 2000
Composition:
(weight % , a s discarded)
Paper 37.4 39.2 40.1 43.4 48.0
Yard Wastes 13.9 13.3 12.9 12.3 11. 9
Food Wastes 20.0 17 .8 16.1 14.0 12.1
Glass 9.0 9.9 10.2 9.5 E.l
Metal 8.4 8.6 8.9 8.6 7.1
Wood 3.1 2.7 2.4 2.0 1.6
Textiles 2.2 2.3 2.3 2.7 3.1
Leather and Rubber 1.2 1.2 1.2 1.2 1.3
Plastics 1.4 2.1 3.0 3.9 4.7
Miscellaneous 3.4 3.0 2.7 2.4 2.1
(weight %, as burned)
Moisture 25.1 23.3 22.0 20.5 19.9
Volatile Carbon 19.6 20.1 20.6 21. 8 23.4
Total Ash 22.7 23.4 23.9 22.8 20.1
Ash (excluding glass and metal) 6.5 6.2 6.1 6.0 6.0
Relative Heating Value and Quantity: 1
Heating Value (Btu/lb), as fired 1.00 1.02 1.04 1.09 1.17
Heating Value (Btu/lb), dry basis 1.00 1.00 1.00 1.06 1.09
National Population 1.00 1.05 1.10 1. 31 1. 51
Per-Capita Refuse Generation (lb/person/day) 1.00 1.13 1.26 1.44 1.66
Per-Capita Refuse Heat Content (Btu/person/day) 1.00 1.15 1. 31 1.57 1. 94
Total Generated Refuse Quantity (lb) 1.00 1.19 1. 38 1. 89 2.51
Total Refuse Heat Content (Btu) 1.00 1. 23 1. 44 2.05 2.93
1. Ratio relative to 1970 value.
Source: Arthur D. Little, Inc., estimates.
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mining, agriculture and logging, which are often remote from manufacturing centers and commonly dispose of their wastes on-site.
Nature of the Problem
Accurate information on industrial waste generation rates and composition is necessary for the sound planning and management of waste disposal projects, for the choice of the most effective disposal alternative and for the design of adequate physical facilities. The problems in obtaining this information from the manufacturing sector are complex and involve the following factors:
(1) Manufacturing establishments, even those in the same type of business, may differ widely in their waste generating practice.
(2) Most firms are reluctant to reveal production and related statistics for fear of the data being used to the competitive advantage of others.
(3) Some firms are reluctant to provide information on waste volumes and composition for fear of it indicating non-compliance with pollution control regulations. Regardless of their waste's pollution characteristics, there is also a tendency for firms to underestimate its quantity.
(4) Some industrial activities are subject to seasonal fluctuations.
(5) The extent of salvaging, recycling, sale to scrap dealers or other reclamation of wastes differs greatly among manufacturers.
(6) Many firms themselves have little understanding of, and few records on, their waste disposal requirements.
In short, good information on industrial waste generation is usually either unavailable or most difficult to obtain. The objective of this paper is to describe the findings of two recent ADL studies related to this problem, which we hope will help to fill this void.
Approach to a Solution
For determining the waste disposal requirements of the manufacturing sector in a given area there are three approaches that can be taken:
(1) survey the firms, either by direct personal contact or by questionnaires, and obtain their estimates of their waste quantities;
(2) conduct a sampling program for recording wastes collected from the firms and disposed of at incinerator or sanitary landfill sites; and
(3) calculate the quantity of waste generated by relating it to certain economic parameters, such as employment, value added, or production volume.
326
Whereas approach 1 may be practical for small, relatively non-industrialized areas, it is time-consuming and expensive and therefore of improbable practicality for larger, highly industrialized areas. When relying upon questionnaires, there are also several hidden sources of error which can undermine the worth of the entire survey. First, plant or office personnel who are unfamiliar with the subject may fill out the questionnaire, often resulting in a completely unreliable response. Extreme care must be taken to give specific instructions, to present questions in a clear form and to define all terms which may be misunderstood or not understood by the layman. Even so, there will be inevitable inconsistencies in the way firms interpret or choose to answer the questions, leading to a lack of uniformity in their defining and classifying their wastes. Realistic bulk density factors must often be used to convert waste reported in cubic yards into tons, or vice-versa (see Table 8). Second, if all firms are not to be surveyed, bias can inadvertently creep into the selection of a sample group, making it of questionable representativeness. There also may be a question as to whether the sample is large enough to provide a sound statistical base for later projections. Third, erroneous conclusions can result if the aspect of non-response is not considered and analyzed. Bias in sampling and errors connected to nonresponse factors are less of a problem if a stratiform random sample is selected for the survey instead of a uniform sampling of 10 percent or 20 percent of all industrial firms in the study area.
Approach 2 also suffers from a relatively high consumption of time and funds, since personnel are required to conduct the program. A long period of sampling is required, perhaps as much as a year, in order for the data obtained to incorporate seasonal variations and be representative of long-term waste volume and composition. One-week or one-month sampling programs are usually of insufficient duration, except in situations where the industrial mix is such that the waste load varies little over a year's time. In addition, there are problems with securing trained workers for record keeping and with segragating wastes, identifying their respective sources and determining their compositions. If all of these difficulties were to be surmounted, however, a landfill survey would probably represent the most reliable indicator of waste disposal requirements, since it is based upon actual field measurements of waste volumes being disposed of.
Considering the technical difficulties and costs involved in the previous two approaches it is not surprising that many solid waste management studies are based on approach 3, an attempt to calculate waste quantities. By expressing waste generation as a function of some variable economic parameter, calculations of waste loads can
TABLE 8
Sources and Types of Industrial Wastes, Santa Clara County, Calif.9
Cod, s. I.e. CIOUP CLAIIl'ICATIC*
20 rood Ind Undud Productl
WAsn C!NlIATIMC PIOC!SS!.S
Prou .. tna. packl.lna Ind Ihippina
!IPECTEO anenlc WASTES
lULl DlNStTt!S
(lb/c:u yel)
H .... , fa .. , oil" bon .. , off'l v,.'- 1240.0*
UbI .. , frultl. nut. and ,hlla, Ind IIS.e** C I,.11t 1 ---+----------------4-----------------------���------------------r_-
*-**
---W •• ,,1na, Drot ... la. dye1na and .hlDDlna Cloth and fiber 1'111411.1" 22 TutU, M111 Producu
2)
24
2S
21
26
21
28
)0
)1
)2
JJ
)1
11
]I
)t
.ppuII and Ot"", rlallhe4 Product.
Lu"'r 1,,4 V0e4 PrOlllucti
rurnl ture. Woo4
Furniture. Metll
,.,. .. an. "'"'M PrOlllucu
,rthUnl and Publhh""a
Ch.a1ull and "lat,d Product.
htrol.u. &tUnlnl .1Id .. -l.ud 1 ..... H1!LL
LtUMT lad lA.thar Produetl
StOM. Cll,., 'M ell .. Productl
'ab .. lund MIItal h'Mvcta
II.ctrlc.l
Tr.n.ponaUon Iqul ... nt
PTo fa .. 1 nna 1. Icl.ntUlc. Conuolllal lD.tr�.tI
"I.c.lla ... ou . ..... facturi ..
Curtin •• • evin •• 81Unl and pnllln,
Sav.111., .111 work plantl, woodin c(lnt.llna ... nufactur •• and .. nuf.ctu ... of • : c. wood noduct.
",,,,,,'.nun of houllhoW Ind offiCI furniture, plrtltlon., otfl�. and .ton U.cur .. and .. ttl· .... ,
..... hc tun of hou .. ho Id .D4 or f lc. furniture, locktr., bld.'rl .... , and
,.,.r _n"r.cture. conv.T.lon of ,.ptT • n4 ,..,.rbMTd, .. nuhduu of pa,.r...... 1'1 iwtaa, .nd con .tn'TI
•••• ,. pl:1' publhhln •• printin •• llthos..T�---.Y: .�nvin .nd bookblndirtl.
Cloth and libel'll _tall, p111t1n Ind rubber
SU'ap vood, ahavinla, .avdu.t: tn '0_ in.unca ... tall, pl •• Uc., Ublra, alu.. • .. l.u oatnu and .olvtnt •
Tho.. 111 tid under Cod. 24, and tn addHl!'n cloth and pddtnl rllldu ••
MeUll, pl •• tiCi. utln •• • 1 •••• • ood • TybMr, .dh •• lv ••• cloth, and p.,.r
'I,.r Ind tiber re.ldul •• ch'.ic.lI. paplT coatinl' and Ull.rI, lnka •
u I!WI fl tlaer.
'ap.r, nav.prlnt. clrdboard, _t.l •• ctL�.lcal'-, clo h 11'111.1 .nd s..lu ..
119. )
894.5
464.0
111.0
1611.0
Manufacture Ind pup.rltion o f lnor.anlc Or,lnlc Ind lnor .. nlc ch •• lc.h ... tIll 895.n ch .. teal. (un ••• fro. dru •• Ind .oup. pla.tlc •• rubber. Ila .. , 011., palnt ••
o .. lDt. Ind "uni.h.. Ind n:910tlv •• ) lol'o/.ntl .nd pl __ nt
Manuflctur. of ".vln. Ind rootin.
"�''''h MaMlfac:ture of hbr lcat.d rubb ... . nd olan c lKoduct.
Le.ther tlnnlnl Ind flnhhlnl: .. nuflcture of ltuher belUD. and Dlckle.
Klnufaetur. of flIt .1 .... fabric.tion or foralnl of Ila .. ; _nufaeture of cOftcrati. IYp,u., Ind pla.t.r product. for.lnl and procII.ln. of nonl .nd .toot ,reductl, abra.lv ••• •• be.to., and .l.c. non-.1Mul product.
Maltln •• c •• Un •• forlln •• drlvlnl. rollinl. forainl • • nd •• trudlnl _.0" .... Mtaufactur. of _ul c.n •• h.rwt tooh, •• n.ral hardvetl. nca-Il.ctrlc h •• tlnl .,,. ... tu •• plu.bin. fhtu .... , f.bricat.d .Uuctu ... l preducn. vir •• far ... chlMry
-.... �ul ... U, cMtinl .Dd anrravl1l1 of ... "."f'ctu ... of '�""l .... t fOT coa.t'l\lcttoa, atet.,. .1 ••• tor., ..vIDI .tI 1"""7" C_"7ot'., IMu.ut •• truck •• nall.n, .t'CUrI _chi .. tooh .tc.
Ma.faUurt of .l.ctrlc t .. l,..Dt, • "luM •• , .nd c�icatloft ',perltu •• __ hhiD" drevln •• for.1nl. ""ldlnl. It.., .... . india., pe1nth., plltinl ball na, .nd fir na oDlr.tlon.
Man""fteture of .otor v.hlcl ••• trUCk. and bu. bodta •• IM)tor vlh1cl. plna .nd lCea •• ori •• , lirer.ft and plrtl, .hlp 1M boat bulldlnl .nd rapal .. ln • .otoreyC 1 .. . nd blcye 1 .. Ind parte ttc.
Klnufectun of .DIIM.rla., 1.boratoTY. • nd r ... arch ",.trw.lntl .nd ... oclat.d ,".1_0' ,....f.ct""r. of jew1ry • • Ilvlrvan. ,1"M _1'1. t01" .aI._It . . pa .. Un •• ... "hl.tlc 100II" colt ... noow.ltl .. , :!!:"I ;l:!O::�
I:!�.h ••• • ip •• and
A..phalt IDd uu, feltt, "Mttoa, .PoI�.r cloth IDd fiber
Snap'rubber and pl .. tlc •• la.pbluk curln cOllDound. Ind dvt.
Scrap ll.th.r. thr.ad, d,..., 011 •• aroc ••• ln. and cur In. ca.oound.
C1 •••• ca.nt, clay, c.rl.le •• IYP'ua, ..be.to •• • tona. ,.,er. Ind abr •• lv ••
r.rrou. Ind non-farrou •• t.l. Icrlp. .111 • •• nd. cor ••• patt.rn •• bondlnl
... ta1., c.ra.lc •• •• nd, .lal • • c.1 •• cOltlnl" .olv.nu. lubrlc.Du, plckllnl llquon
Ila .. ..... cor ••• _tal .cr'p, vood. pl •• tlc •• r.lie., rubber. cloth. paint., .0lYe.u. pltrol •• preducu
Mltal .c.rp. c.rboft, 11 •••• a.qUc _t.lI, rubber, pla.tlc •• r •• ln •• Ubtn. cloth re .. dull
Metal .cr.p. lla ••• flber, "ood. rubber. planlc •• cloth. pllnt •• .01.,.nta, petrollu. product • .
Met.ta, pl •• tic •• 1' •• 11'1', .1 .... vood. rubber. flben, .nd abre.l .. .
Metlh, .h ... pl •• UtI. n.lnl. ltathlr, rubber. ca.poe:'tlOft. �t cloth, .u." a4h ....... ,.lnt •• and .ol".nn
***
148.2
***
2601.1
***
185.)
650. )
1�'.1
290. )
562.9
415. )
*# '01' CUnha .......... nln. (ItC '201). *** '0" O,her roo4 hoc ... lnl CltC 20). "Clpt IIC 201_
10 .. t . ... U.b1.
327
be made if measurements of that parameter are available. The two functional relationships most commonly used are the ratios tons/production unit/year (TPUY) and tons/employee/year (TEY). Although the amount of waste produced is probably most closely related to the amount of product produced, particularly in the case of water-borne industrial wastes, it is difficult to obtain reliable production data from industrial plants. Many firms consider such information proprietary; and even if it were readily available the units used to measure production would vary so widely among industries that comparisons and consolidation of data would be plagued with inconsistency.
In the absence of information on product output, the tons/employee/year ratio is used to calculate waste loads. Fortunately, most firms are able and willing to make available relatively accurate data on their employment; usually in the form of total employment. Since the activities of production workers may be largely responsible for the wastes generated in manufactUring, it has been suggested that only the number of production employees should be used in calculating solid waste loads. However, office and other workers are also responsible for significant wastes and should be included, we believe, in total employee talleys. Also available employment data are not usually broken down as to production versus nonproduction components. In Jefferson County, Kentucky [12] , the ratio of industrial production employees to total full-time industrial employees was found to vary from 0.704 to 0.835 and to average approximately 0.77 (for SIC groups 19 through 39). Undoubtedly, this ratio probably exhibits much wider variations within and among industries throughout the U. S.
Individual TEY waste generation coefficients can be established for each type of manufacturing activity, based upon knowledge of the production technology, practices and product mix. The coefficients differ widely for different industries and, depending upon inherent inhomogeneities, may even vary considerably among the members of the same industry, The more similar the operations of the group (e.g., four-digit SIC categories), the more representative of each member firm will be the mean TEY value developed for the group. It is therefore best, if the raw data permit, to develop coefficients for four-digit SIC categories rather than only two-digit designations. Since the calculation of industrial TEY coefficients may be based upon different degrees of knowledge of the operations of various industries, the derived coefficients themselves may correspondingly be of varying quality. Any TEY ratio wiB also reflect the makeup of the industry within the particular geopgraphic area for which it was calculated.
328
It would appear that the quantity of waste produced per employee is functionally related to plant capacity, so that different TEY coefficients should perhaps be developed for large (e.g., over 100 employees) and small establishments (e.g., under 100 employees). It is not clear, however, whether TEY ra tios consisten tly increase or decrease in value with increases in plant size. The generation of ash or clinker wastes from the coal-fueled steam plants of large operations, for example, would seem to influence the ratios upward; however, improved production efficiencies and increased salvage practice justified by high production rates in large plants would seem to influence the ratios downward. The individual TEY values calculated in our surveys for each firm in a given industry group show no discernable pattern when plotted as a function of plant size (number of employees) on arithmetic graph paper. The resulting plots do not prove that the coefficients are independent of plant size, but neither do they suggest what type of functional relationship might exist.
With the TEY rates serving as multipliers, calculations of waste loads can be made, provided that realistic employment statistics and sound projections of future employment levels for each SIC category are used. Such data are commonly available from county or regional planning boards, "County Business Patterns", state departments of labor and industry, the U. S. Department of Labor (Bureau of Labor Statistics) and various other organizations. Since on a micro-economic scale most manufacturing can be considered export-oriented, national trends in employment in industrial groups can be used to indicate employment trends for those groups in smaller areas where local forecasts are unavailable. Table 9 presents growth indices for manufacturing employment in Erie and Niagara Counties, New York. As Standard Metropolitan Statistical Areas grow and become more service-oriented, manufacturing employment becomes a declining fraction of total employment, and manufacturing employment growth becomes less than total employment growth. When relating employment to wastes, seasonality of production rates and employment levels should be considered and operating schedules (i.e., hours/ day, days/year) documen ted to provide a common basis for comparability among resulting TEY ratios.
For projecting future waste requirements, possible changes in the waste/product and waste/employee ratios over time should be accounted for. This requires a consideration of many extremely complex, diverse and interrelated factors, such as changes in productivity, the level of present and future waste and effluent regulations, trends in packaging, and changes in manufacturing practices, in the technology of production processes, in the
nature of raw materials and in the product mix. It is tempting to avoid this maze of influences and hypothesize instead that in the future the effects of factors which tend to increase TEY coefficients (e.g., increased productivity and/or mechanization) will be offset by the effects of factors which tend to decrease them (e.g., increased production efficiency through improved technology and greater waste reclamation and recycling). There may, in fact, be no evidence that unit generation rates for industrial wastes are growing or otherwise changing significantly with time.
In developing TEY coefficients, the fractions of wastes which are utilized in some way (e.g., given away, sold, recycled, treated as by-products, or disposed of on-site) should be subtracted, so that the coefficients correspond to the actual waste quantity requiring ultimate disposal off-site. Many larger manufacturing operations have their
own on-site disposal facilities, such as incinerators, which still generate a residue that must be disposed of in some way (e.g., landfill). Others, such as SIC groups 33, 34 and 35 generate a large amount of scrap metal, most of which is salvaged and does not find its way into the wastes-fordisposal stream.
Derived Compositions and Generation Rates
The ultimate objective of a solid waste survey is to obtain a realistic estimate of waste load quantities for the present and future. To meet this objective we have found it practical to combine approaches 1 and 3 described in the preceding section, i.e., to conduct a survey (usually by carefully prepared questionnaires) of a selected industry sample group and to calculate unsampled waste tonnages based upon TEY coefficients. The ques-
TABLE 9
SIC Code
20 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Source:
Base Year 1969
1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Industrial Employment Growth Rates
Erie and Niagara Counties, New York
1970 1975 1980 1985
0.825 0.772 0.749 0.738 0.908 0.808 0.715 0.631 0.887 0.765 0.661 0.570 1.000 1.000 1.000 1.000 0.935 0.895 0.855 0.810 1.032 1.072 1. 114 1.160 1.046 1. 114 1.180 1.277 0.981 0.959 0.941 0.926 0.949 0.899 0.848 0.797 1.068 1.179 1.261 1.346 1.000 1.000 1.000 1.000 1.016 1.070 1.076 1.033 0.923 0.973 0.962 0.951 1.035 1.079 1.126 1.175 1.033 1.080 1.130 1.184 1.065 1.146 1.233 1. 329 1.020 1.051 1.083 1. 117 1.049 1. 113 1.184 1.258 1.027 1.064 1.104 1.141
1990 1995
0.729 0.720 0.561 0.496 0.491 0.424 1.000 1.000 0.770 0.732 1.210 1.263 1.377 1.480 0.914 0.906 0.747 0.696 1.419 1.489 1.000 1.000 1.039 0.989 0.878 0.828 1.228 1.285 1.241 1.301 1. 431 1.542 1.151 1.185 1.334 1.414 1.182 1.222
Deri ved from Volume 2, "Technical Annexes, Technical Report of the
Buffalo Standard Metropolitan Statistical Area: Economic and
Demographic Analysis and Projections, 1966/67 - 1990", prepared
for the Erie-Niagara Regional Planning Board (New York) by Cornell
Aeronautical Laboratories, Inc. (1969 )
329
2000
0.711 0.439 0.366 1.000 0.695 1.320 1.592 0.901 0.646 1.554 1.000 0.949 0.778 1.345 1.364 1.659 1. 217 1.496 1.261
tionnaires provide estimates and measuremen ts of waste
made by the firms included in the survey sample; and
the raw data on waste generation and employment re
ported in the questionnaires enable TEY ratios to be
derived for each industry group, from which the waste
of firms not included in the survey can be calculated.
The present total waste is calculated according to Eqs. 1 and 2, and projections of future total waste can be
made by multiplying the TEY coefficients corresponding
to the industries in question by the future employment
levels projected for these industries.
where: Tti
--
Tt --
Tsi --
( 1)
(2)
total tonnage of waste for industry group (i) total tonnage of waste in an area the tonnage of waste reported by surveyed
firms in industry group (i) Ts
- total tonnage of waste reported by firms in
the area
T ui = the tonnage of waste calculated for firms not
included in the survey of industry group (i) T u = total tonnage of waste calcula ted for firms
not included in the survey of the area
For all firms within the same industrial activity group
(e.g., two-, three- or four-digit SIC category) an average
TEY coefficient, according to Eq. 3, can be derived from data reported by firms surveyed by questionnaires
or by other means.
where:
Q --
n
� Tti --
1 = 1
n
� Eti --
• 1 1 =
n
. � Tti 1 = 1 Q=---
n
� Eti i = 1
the TEY coefficient in tons-per-em-
ployee-per-year
(3)
the. sum of all wastes for industry group (i) with (n) number of member firms
the sum of all employees for industry group (i)
In this equation, Q represents the weighted average of
individual TEY coefficients for all firms in industry group
(i) and therefore takes into account the effect of plant
330
size on individual coefficients. An arithmetic average, cal
n r. culated according to the equation Q = I /n � E
l ,
i = 1 1 would incorrectly ascribe equal weight to all individual
coefficien ts.
Using the TEY ratio assumes that the relationship between the numerator and denominator is linear. How
ever, individual ratios calculated and compared for firms
included in our surveys have shown that, although the
ratios may be a useful function, the relationship between
tons of waste and numbers of employees is not strictly
linear. It may be that some other, perhaps more complex iteration of the TEY ratio may describe a more nearly
linear relationship between the components of the ratio
and lead to more valid use of it in calculating waste
q uan ti ties. A comparison of TEY ratios derived in several pub
lished solid waste studies suggests that ratios for the same industry group vary markedly for different regions
of the U. S. and even within a relatively small geographic area. This may be due to actual diversity among the operations, processes, efficiencies and/or management of firms
within the group or it may be due to differences in the
calculating the coefficients, in the definitions of economic parameters (e.g., waste, employees) and/or in the
degree of reliability or confidence ascribed to the ratios. For this reason, the use of coefficients developed for one area should be used carefully when calculating waste quantities for a different area. It should also be recog
nized that the TEY ratios for sludges and liqUids in Table
10 may not be as realistic as are those for solid wastes, since most firms are more reluctant to reveal much about
sludge and liquid wastes in a questionnaire-type survey
than they are about solid wastes.
Table 9 presents TEY coefficients for 1 9 SIC groups which we developed from a consolidation of employment and waste generation data reported during recent surveys
in Erie and Niagara Counties, New York, and in north
eastern New Jersey (Essex, Bergen, Hudson, Passaic and Union Counties). The combining of data from two sepa
rate geographical areas to derive composite ratios can be justified by two considerations. First, the combined data
represen t a larger sampling of all firms belonging to a
given SIC industry group than does either body of data by itself. This increase in the statistical base increases the level of confidence attached to the coefficients derived
from the data. Second, although individual firms within a particular SIC group in Erie-Niagara may have different waste-generating characteristics or practices than similar
firms in northeastern New Jersey, it is likely, since both
areas are highly industrialized, that differences in the in
dustry mix of each SIC group are not so significant as to
TABLE 1 0
I ndustrial Waste Coefficients
95% S I C DATA AV E RAGE STANDARD CON F I DENCE CODE I NDUSTRY POI NTS TEY DEV I AT I ON L I M ITS
20 Food Processing So l ids 31 7 . 949 1 0 . 45 1 1 . 877 Liquids 1 1 0 . 001 0 . 036 0 . 025 S l udges 1 0 . 400
22 Textil e Mi 1 1 Products So l ids 1 6 2 . 1 60 1 . 854 0 . 464 Liq u ids 1 5 0 . 1 07 0 . 233 0 . 1 35 S l udges 1 1 . 508
23 Apparel So l ids 20 2 . 1 92 6 . 1 97 1 . 461 Liq u ids 0 S l udges 0
24 Wood Products 'So l ids 1 0 8 . 531 7 . 648 2 . 4 1 9 Liq u ids 0 S l udges 0
25 Furni ture So l ids 7 2 . 783 3 . 578 1 . 352 Li qui ds 0 S l udges 0
26 Paper and Al l ied Products So l ids 2 1 3 . 987 8 . 267 1 . 804 Liq ui ds 9 0 . 01 0 0 . 026 0 . 01 3 �l udges 8 0 . 0 1 2 0 . 073 0 . 052
27 Printing , Pub l ishing Sol ids 24 5 . 835 5 . 958 1 . 242 Liquids 1 2 0 . 01 3 0 . 000 0 . 000 S l udges 0
28 Chemical s and Al l ied Products So l ids 39 8 . 862 7 . 437 1 . 1 9 1 Liq uids 23 2 . 599 4 . 504 1 . 593 S l udges 28 2 . 554 5 . 944 2 . 1 02
,
29 Petro l eum So l ids 4 1 . 594 2 . 75 1 1 . 376 Li qui ds 1 0 . 041 S l udges 1 0 . 003
30 R ubber , P l astics So l ids 1 3 9 . 835 9 . 1 63 2 . 54 1 Liq uids 8 0 . 072 0 . 1 00 0 . 07 1 S l udges 1 0 . 084
33 1
TABLE 1 0 (Cont.) Industrial Waste Coefficients (Cont'd . )
S I C CODE I N DUSTRY
3 1 Leather Sol i ds L i q u i ds S l udges
32 S tone , C l ay Sol i d s L i qu i ds S l udges
3 3 Pri ma ry Meta l s So l i ds L i q u i ds Sl udges
34 F a b r i ca ted Me ta l s Sol i ds L i q u i d s S l udges
35 Non - E l ectri c a l Machi nery Sol i ds L i q u i ds S l udges
36 E l ectri c a l Machi nery Sol i ds L i q u i ds S l udges
37 Transporta t i o n E q u i pment Sol i ds L i q u i ds S l udges
38 P rofe s s i onal and Sci . I ns t ruments
Sol i ds L i q u i d s S l udges
39 M i s c e l l aneous Manufactu ri ng S o l i ds L i q u i d s S l udges
Source : Arth u r D . L i ttl e , I nc .
DATA PO I NTS
2 o o
1 8 1 7
1 3 5 1
42 22 23
47 2 1 1 8
2 1 1 5
0
8 4 6
7 5 0
2 5 0 0
332
AV E RAGE TEY
8 . 989 -
-
6 . 4 1 2 0 . 005 0 . 0 1 1
3 . 1 84 1 . 397 0 . 423
6 . 832 0 . 01 4 0 . 055
3 . 1 89 0 . 258 2 . 453
2 . 94 1 0 . 1 72
-
2 . 562 0 . 31 9 0 . 1 9 1
1 . 76 9 0 . 074
-
1 . 603 -
-
STANDARD DEV I AT I ON
6 . 986 -
-
1 5 . 300 -
0 . 024
8 . 2 1 0 1 2 . 06 7
-
9 . 1 80 0 . 024 2 . 268
1 . 448 0 . 1 37 2 . 36 1
7 . 009 0 . 077
-
4 . 097 0 . 1 83 0 . 1 24
2 '. 06 1 0 . 088
-
1 . 883 -
--
95% CON F I DENCE
L I M I TS
4 . 941 -
-
3 . 606 -
0 . 0 1 7
2 . 277 8 . 534
-
1 . 4 1 6 0 . 009 1 . 309
0 . 2 1 1 0 . 052 1 . 363
1 . 529 0 . 039
-
1 . 449 0 . 1 29 0 . 880
0 . 779 0 . 062
-
0 . 3 77 -
-
-
SIC
20 Da
ta P
oin
ts
Aver
age
Stan
dard
Dev
iati
on
Conf
iden
ce L
imit
s*
22 Da
ta P
oint
s Av
erag
e St
anda
rd
Devi
atio
n Co
nfid
ence
Lim
its*
23
Data
Poi
nts
Aver
age
w
Stan
dard
Dev
iati
on
w
w
Conf
iden
ce L
imit
s*
24 Da
ta P
oint
s Av
erag
e St
anda
rd
Devi
atio
n Co
nfid
ence
Lim
its*
25
Data
Poi
nts
Aver
age
Stan
dard
Dev
iati
on
Conf
iden
ce L
imit
s*
26 Da
ta P
oint
s Av
erag
e St
anda
rd D
evia
tion
Co
nfid
ence
Lim
its*
27
Da ta
Poi
nts
Av
erag
e St
anda
rd D
evia
tion
Co
nfid
ence
Lim
its*
Pae
er
Wood
Le
athe
r
30
30
30
52.3
7
.7
32.7
10
.9
11.7
3
.9
18
18
18
45.5
0
40.3
0
18.6
17
17
17
55.9
0
37.4
0
17.8
9 9
9 16
.7
71. 6
0
33.6
34
.8
0 22
.0
22.7
7 7
7 24
.7
42.1
0
12.3
16
.2
0 9
.1
12.0
20
20
20
56.3
11
.3
0 8
.7
15.5
0
3.8
6
.8
26
26
26
84.9
5
.5
5.8
12
.3
2.2
4
.7
TA
BL
E 1
1
Ind
ust
rial
Was
te C
om
po
siti
on
Come
onen
t (W
eig
ht %
)
Rubb
er
Plas
tics
Me
tals
Gl
ass
----
30
30
30
30
. 9
8.2
4
.9
.4
3.7
2
.8
.1
1.3
1.
0
18
18
18
18
4.7
10
.7
4.9
17
17
17
17
0 0
0 0
0 0
9 9
9 9
0 0
0 0
7 7
7 7 0 0
20
20
20
20
0 9
.4
0 18
.2
8.0
26
26
26
26
a 0
0 0
�---
�
Text
iles
Fo
od
Mi s
c.
Tons
/Em p
loy
ee/Y
ear
30
30
30
30
0 16
.7
9.2
7
.949
0
29.9
21
.1
8.7
60
10.7
7
.5
3.13
18
18
18
18
26.8
2
.160
38
.1
1.90
0 17
.6
0.8
6
17
17
17
17
36.5
1.
35
-2
.192
37
. 3
2.8
6
.211
17
.7
1.3
2
.94
9 9
9 9
0 0
7.8
8
.531
0
0 19
.7
7.6
48
12.9
4
.97
7 7
7 7 2.7
83
3.5
78
2.6
4
20
20
20
20
14.0
3
.987
27
.5
8.3
30
12.1
3
.64
26
26
26
26
5.8
35
12.5
75
4.8
2
TA
BLE
11
(Con
t.)
Indu
stri
al W
aste
Com
posi
tion
(C
ont.
)
Comp
onen
t (W
eig
ht %
)
SIC
Pa
pe
r Wo
od
Leat
her
Rubb
er
Pla
stic
s Me
tals
Gl
ass
Text
ile
s Fo
od
Mis
c.
Ton
s/Em
plo
yee
/Ye
ar
28 Da
ta P
oint
s 48
48
48
48
48
48
48
48
48
48
48
A
vera
ge
55.0
4 .
5 -
-9.
3 7.
2 2.
2 -
-19
.7
8.86
2 St
anda
rd
Devi
atio
n 34
.0
6 •. 2
--
17.0
13
.9
4.2
--
32.8
10
.999
Co
nfid
ence
Lim
its*
9
•.
6 1
.7
4.8
29
3 .9
1.2
9.
3 3.
09
Data
Po
int
s 5
5 5
5 5
5 5
5 5
5 5
Ave
rage
72
. 1 6.
8 0
0 15
.3
4.4
0 0
-1.
0 1.
594
Stan
dard
Dev
iati
on
35.7
4.
4 0
0 30
.7
5.2
0 0
-1.
3 2.
751
Conf
iden
ce L
imit
s·
31.4
3 .
9 27
.0
4.6
1.1
2.41
30
-
Data
Po
ints
13
13
13
13
13
13
13
13
13
13
13
A
vera
ge
56.3
5.
2 0
9.2
13.5
-
0 -
--
9.83
5 S
tand
ard
Devi
atio
n 31
. 5
6.2
0 20
.3
20.7
-
0 -
--
9.16
3 Co
nfid
ence
Lim
its·
17
.2
3.4
11.0
11
.3
4.97
w
31
w
�
Data
Poi
nts
3 3
3 3
3 3
3 3
3 3
3 A
vera
ge
6.0
3 .9
53. 3
-
-13
.5
-0
0 -
8.98
9 St
anda
rdDe
vi a
tio
n 4.
2 5 .
4 47
.3
--
19.2
-
0 0
-6.
986
Conf
iden
ce L
imit
s·
4.7
6.1
53.6
21
.7
7.89
32
Data
Poi
nts
16
16
16
16
16
16
16
16
16
16
16
Ave
rage
33
.8
4.3
0 -
-8.
1 12
.8
-0
40.0
6.
412
Stan
dard
Devi
atio
n 37
.5
8.4
0 -
-24
.8
29.6
-
0 44
.8
15.3
00
Conf
iden
ce L
imit
s·
18.4
4.
1 12
.2
14.5
22
.0
7.48
33
Data
Poi
nts
12
12
12
12
12
12
12
12
12
12
12
Ave
rage
41
.0
11.6
0
-5 .
4 5.
5 2.
0 0
-29
.0
3.18
4 St
anda
rd D
evi
atio
n 27
.4
12.4
0
-9.
8 7.
8 4.
3 0
-40
.0
15.7
96
Conf
iden
ce
Lim
its·
15
.5
7.0
5.5
4.4
2.4
22.7
8.
93
34
-Da
ta P
oin
ts
36
36
36
36
36
36
36
36
36
36
36
Ave
rage
44
.6
10.3
0
--
23.2
-
--
12.2
6.
832
Stan
dard
De
viat
ion
37. 7
20
.8
0 -
-34
.5
--
-31
. � �:
��8
Conf
iden
ce L
imit
s·
12.3
6.
8 11
. 3
10.
w
w
u.
SIC
35 Da
ta P
oint
s Av
erag
e St
anda
rd D
evia
tion
Co
nfid
ence
Lim
its*
36
Data
Poi
nts
Aver
age
Stan
dard
Dev
iati
on
Conf
i den
ce L
,i mi
ts*
37
Data
Poi
nts
Aver
age
Stan
dard
Dev
iati
on
Conf
iden
ce L
imit
s*
38 Da
ta P
oint
s Av
erag
e St
anda
rd D
evia
tion
Co
nfid
ence
Lim
its*
39
Data
Poi
nts
Aver
age
Stan
dard
Dev
iati
on
Conf
iden
ce L
imit
s*
PaE
er
Wood
48
48
43.1
11
.4
34. 3
19
.5
9. 7
5
.5
19
19
73. 3
8
.3
24.4
10
.1
11.0
4
.5
8 8
50.9
9
.4
34.2
6
.3
23.8
4
.4
8 8
44.8
2
.3
34.0
3
.6
23.6
2
.5
20
20
54.6
13
.0
38.7
23
.7
17.0
10
.4
* 95
% Co
nfid
ence
Lim
its
Sour
ce:
Arth
ur D
. L
ittl
e,
Inc.
TA
BL
E 1
1 (
Co
nt.
)
Ind
ust
rial
Was
te C
om
po
siti
on
(C
on
t.)
ComE
onen
t {W
eig
ht %
l
Leat
her
Rubb
er
Plas
tics
Me
tals
Gl
ass
Tex�
iles
48
48
48
48
48
48
2.5
23
.7
0 6
.8
30.8
0
1.9
8.7
19
19
19
19
19
19
0 3
.5
2.3
0
0 7
.0
3.5
0
3.1
1.
6
8 8
8 8
8 8
0 1.
4 2
.1
0 0
1.5
2.9
0
1.0
2.0
8 8
8 8
8 8
0 0
6.0
8
.4
0 0
0 6
.4
17.2
0
4.4
11
.9
20
20
20
20
20
20
11. 9
5
.0
22.2
10
.3
9.7
4
. 5
Food
Mi
sc.
----�
48
48
19
19
1.2
2.4
1.
1
8 8
19.5
3 3
. 3
23
.1
8 8
20
20
8.1
14
.0
6.1
,.ons/
Empl
oyee
/Yea
r
48
3.1
89
1.43
8 0
.39
19
2.9
41
7.00
9 3
.2
8 2.56
2 4
.097
2
.84
8 1.76
9 2
.061
1
.43
20
1.60
3 1.
901
0.8
2
make the derived TEY ra tio unrepre sentative of the group in each area.
Table 1 1 presents a breakdown and statistical analysis of the composition of industrial solid wastes (i.e., excluding sludges and liquids), based upon data developed during our survey in northeastern New Jersey. The table illustrates that a large percentage of industrial waste is of uniform character and relatively independent of the industry involved. This waste consists predominantly of shipping waste, plant trash and office waste. Some activities, such as food processing and che micals, are characterized by wastes peculiar to the processes involved.
Significance of Derived Data
The TEY coefficient s reported by individual firms surveyed in the same industry group represent data points that show varying degrees of dispersion, depending upon the technological homogeneity within the group. The dispersion is large for a heterogeneous group in which the operations of member firms are quite differen t and have different waste/product ratios, such as the food processing industry (SIC Code 20). Smaller but nevertheless significant dispersion is obtained in the case of a homogeneous group, such as the saw mill business (S IC Code 242 1).
In a 1 969 report Combustion Engineering concluded that the observed dispersions in waste/employee ratios are caused by a number of component dispersions in productivi ty , in hours worked per year per employee and in waste/product ratios attributable to differences in manufacturing techniques and efficiencies and to errors in the definitions and estimates of waste stream quantities [ 1 3 ] . They found that dispersions of TEY coefficients
for individual firms within nearly all manufacturing industrie s are log-normally distributed. The sample coefficients appear to come from populations of random . variables (i.e., random coefficients), such that the total population of coefficients has a predictable statistical distribution. The greater the number in the sa mple , the more closely the sample dispersion approximate s a lognormal distribution. Combustion Engineering concluded that with this relationship it is possible to calculate from a small number of samples the mean TEY ratio for a given industry and , therefore, to predict within reasonable confidence limits the total waste generated by that industry.
Statistical analysis of the individual TEY coefficients reported by firms sampled recently in our surveys yields the standard deviation s from the mean, or average , TEY ratios shown in Table 9. The confidence limit s calculated with an assumed 95 % confidence coefficient indicate the degree of certainty with which we know the mean. In other words, there is a 95 % certainty that the mean of the sampled coefficients lies within the confidence limits
336
given in the table ; however, if we had access to coefficients for all firms in the entire population (industry group), the true mean could be somewhat greater or less than that calculated from our sample data.
As an alternative to the confidence level approach, relative error can be u sed to evaluate the certainty of waste quantities calculated fro m mean TEY coefficients. In fact, relative error analysis may be preferred, since it is likely that error in the calculation of wastes (T u ) by TEY coefficients is much greater than error due to sampling variation. Confidence limits can be best applied to analyze sampled data where error is due solely to sampling variation. In the case of waste quantity calculation, however, error can also be due to bias in the sample , nonresponse factors, lack of uniformity in defining and measuring industrial wastes, and the possibility that the TEY coefficient itself may not be independent of the size of operation, as repre sented by employment.
Variance in the total waste tonnage estimated for a particular industry group can be calculated according to Eq. 4 .
where : Vi
n = Ui =
N Vi = (e i T si)2 + [ ( L Ei)2 . (Ui)2 ] (4)
N -n
variance in Ti due to lack of confidence in sampled data an assumed error (e.g., 20 percent) in Tsi total number of firms in industry group (i) number of firms included in survey of group (i) the standard deviation of the mean TEY coefficien t ca1cula ted from the individual coefficien ts for each firm sampled in group (i)
The relative error (REi) in the total waste quantity (Ti) estimated for a particular industry group is expressed as follows :
(5 )
For co mbining waste estimates for all manufacturing establishments ( 1 9 in our surveys) the following equations apply :
1 9 T = L Ti
I
1 9 V = L Vi
1
(6)
(7)
RE = ,;v T
(8)
One significant conclusion drawn from statistical analyses carried out in our two recent industrial waste surveys is that any error associated with the reported waste component Ts is usually insignificant when compared with error associated with the unsampled waste Tu calculated from TEY coefficients and employment figures. Since data on employment are usually relatively accurate and easy to obtain, the error in the total waste quantiy Tt is, therefore, largely due to uncertainty in the TEY ratio used to calculate T u . Comparisons of the V values corresponding to 0 percent and 20 percent error in T s and of the RE values corresponding to 0 percent and 20 percent error in Ts reveal only insignificant differences, supporting the conclusion that the principal source of error in T t is involved in the calculation of T u ·
This critical significance of the Tu component in estimated quantities of industrial waste points to the need for caution in using TEY coefficients deveroped for firms in one area in the calculation of waste loads for another area. To insure realism in the estimates, TEY coefficients can be relied upon when derived from and used to supplement a survey of manufacturing activity in the area in question.
ACKNOWLEDGMENT
The authors wish to acknowledge the Hackensack Meadowlands Development Commission; the ErieNiagara County, New York Planning Commission; the State of New York; the firm of Camp, Dresser and McKee, Inc. ; and Arthur D. Little, Inc. (the authors' previous affiliation), which sponsored the several studies underlying this paper. We wish to particularly express our appreciation to Miss Elaine Coyne, Miss Carol
Saraceno and Paul Marneffe of Arthur D. Little Inc., who spent many hours in solicitation, tabulation and treatment of the data.
RE FERE N CES
[ 1 ) W. R . Niessen and S. H. Chansky, "The Nature of Refuse", Proceedings of the 1 9 70 National Incinera tor Conference, ASME, New York, N. Y., 1970.
(2) L. E. Daniels, "A report on the Dekalb County Incinerator Study", Report SW-3 1ts, USDHEW, PHS, BSWM ( 1 970).
( 3 ) J. L. Hahn, "Study of the Delaware County No. 3
Incinerator in Broomall, Pennsylvania", Report TO 3 . 1 .010/0,
USDHEW, PHS, BSWM ( 1 970).
(4) 1. L. Hahn, "A Study of the New Orleans East Incinerator", Report TSR/01 .38/9, USDHEW, PHS, BSWM ( 1970).
( 5 ) W. C. Achinger, "Study Report on a Pilot Plant Con-
ical Incinerator", Report, SW-14ts, USDHEW, PHS, BSWM ( 1 970). ( 6 ) L. E. Daniels, "A Report on the Hartsfield Incinerator
Study", Report SW-30ts, USDHEW, PHS, BSWM ( 1 970).
( 7 ) W. R. Niessen et. al., "Systems Study of Air Pollution
From Municipal Incineration", Report to NAPCA under Contract CPA 22-69-23 by Arthur D. Little, Inc., Cambridge, Mass., 1 970.
( 8 ) E. R. Kaiser and A. A. Caroti, "Municipal Incineration of Refuse with 2% and 4% Additions of Four Plastics", Report to the Society of the Plastics Industry ( 1971).
(9) C. G. Golueke and P. H . McGauhey, "Comprehensive Studies of Solid Waste Management", First and Second Annual Report-Research Grant No. EC-00260, USDHEW, PHS,
BSWM ( 1970). ( 10) Proceedings of Mecar Symposium-Incineration of
Solid Wastes, March 1 967.
( 1 1 ) "Master Plan for Solid Waste Collection and Disposal,
Tri-Parish Metropolitan Area of New Orleans", Albert Switzger
& Associates, lnc., USDHEW, BSWM ( 1 969). ( 1 2 ) "Louisville, Ky.-Ind. Metropolitan Region Solid
Waste Disposal Study: Interim Report on a Solid Waste Demonstration Project. V. l - 1efferson County Ky., U. of Louisville, vol. 1
[ 1 3 ) "Technical-Economic Study of Solid Waste Needs and Practices," 4 vols., Combustion Engineering, Inc., Winsor,
Connecticut, 1 Nov. 1 967.
337
