EPA-450/3-74-028-b
May 1973
AIR POLLUTION/LAND USE
PLANNING PROJECT
VOLUME II. METHODS FOR
PREDICTING AIR POLLUTION
CONCENTRATIONS FROM
LAND USE
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
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EPA-450/3-74-028-b
AIR POLLUTION/LAND USE
PLANNING PROJECT
VOLUME II. METHODS FOR
PREDICTING AIR POLLUTION
CONCENTRATIONS FROM LAND USE
by
A. S. Kennedy, T. E. Baldwin,
K. G. Croke, and J . W . Gudenas
Center for Environmental Studies
Argonne National Laboratory
.' 9700 South Cass Avenue
Argonne, Illinois 60439
Interagency Agreement No. EPA-IAG~0159(D)
! :'
EPA Project Officers:
1
John Robson and David Sanchez
Prepared for
ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Water Programs
Office of Air Quality Planning and Standards
Research Triangle Park, N. C. 27711
May 1973
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This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are
available free of charge to Federal employees , current contractors and
grantees, and nonprofit organi-">tions - as supplies permit - from the
Air Pollution Technical Informa^on Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, or from the
National Technical Information Service, 5285 Port Royal Road, Springfield,
Virginia 22151.
This report was furnished to the Environmental Protection Agency by the
Argonne National Laboratory, Argonne, Illinois 60439, in fulfillment of
Interagency Agreement No. EPA IAG-0159(D) . The contents of this report
are reproduced herein as received from the Argonne National Laboratory.
The opinions, findings, and conclusions expressed are those of the author
and not necessarily those of the Environmental Protection Agency . Mention
of company or product names is not to be considered as an endorsement
by the Environmental Protection Agency.
Publication No. EPA-450/3-74-028~b
11
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TABLE OF CONTENTS
Page
ABSTRACT viii
1.0 INTRODUCTION 1
2.0 AN ANALYSIS OF EMISSION PATTERNS . 3
2.1 CURRENT AIR POLLUTION PROBLEMS IN CHICAGO
STUDY REGION 3
2.2 AN ANALYSIS OF VARIANCE IN MANUFACTURING
EMISSIONS 9
3.0 METHODS FOR ESTIMATING EMISSIONS FROM LAND USE .... 26
3.1 UNIFORM EMISSION DENSITY ESTIMATION BY
MANUFACTURING ZONING CLASS 27
3.2 ANALYSIS OF MANUFACTURING EMISSIONS BY
MAJOR INDUSTRIAL SECTOR (2-digit SIC code) .... 35
3.2.1 SIC 32 Stone, Clay, and Glass Products . . 38
3.2.2 SIC 29 Petroleum Refining anH
Related Industries" ~ 41
3.2.3 SIC 33 Primary Metal Industries 43
3.2.4 SIC 28 Chemicals and Allied Products ... 44
3.2.5 SIC 20 Food and Kindred Products. ... 46
3.2.6 Summary of Analysis of Manufacturing Emissions. 48
3.3 ESTIMATION OF EMISSIONS FROM RESIDENTIAL/
COMMERCIAL LAND 49
4.0 SUMMARY AND CONCLUSIONS 59
APPENDIX A DESCRIPTION OF THE STUDY AREA 63
A. 1 CURRENT MANUFACTURING ACTIVITY IN THE
CHICAGO AREA 63
A. 2 MANUFACTURING GROWTH POTENTIAL IN THE
CHICAGO STUDY REGION 66
A.3 DATA BASE FOR THE STUDY REGION 73
APPENDIX B SUMMARY OF STATE OF ILLINOIS
PARTICULATE EMISSION CONTROL REGULATIONS .... 80
APPENDIX C DERIVATION OF BEST-FIT EMISSION DENSITY
ESTIMATORS BY MANUFACTURING ZONING CLASS ... 87
APPENDIX D CORRELATION AND MULTIPLE LINEAR REGRESSION RESULTS. 93
REFERENCES 118
111
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List of Figures
No. Title Page
2.1 Isopleths of suspended particulates using
unregulated emission inventory. .
2.2 Isopleths of suspended particulates with
Illinois source control regulations applied.
2.3 State of Illinois, Chicago economic planning
and statistical reporting region
2.4 Manufacturing emission sources per square mile in
Chicago study region 10
2.5 Spacial distribution of particulate emissions in
Chicago region 11
2.6 Frequency of suspended particulate emission density
with Illinois point-source regulations applied. ... 12
2.7 Isopleths of suspended particulates using
mean emission density estimates for manufacturing land. . 14
2.8 Frequency of suspended particulate fuel combustion
emission density with Illinois point-source
regulations applied 16
2.9 Frequency of suspended particulate process emission
density with Illinois point-source regulations applied. . 17
2.10 Isopleths of suspended particulates using mean emission
density estimates by manufacturing zoning classification. 22
3.1 Isopleths of suspended particulates using
point-source representation. 29
3.2 Isopleths of suspended particulates using
mean emission density representation. 30
3.3 Isopleths of suspended particulates using
median emission density representation 32
3.4 Isopleths of suspended particulates using
"best fit" emission density representation. .... 33
3.5 Conmercial/institutional building size distribution
in Chicago 50
3.6 Large residential energy use 56
3.7 Large commercial energy use. 58
iv
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List of Figures (Contd.)
No. Title
A.I Total industrial land use in Chicago study region .... 67
A. 2 Manufacturing employment trends Chicago SMSA 69
A. 3 Potential for Manufacturing Land development in area 71
surrounding region at critical concern
B.I State of Illinois allowable emission rate for point-source
control 85
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List of Tables
No. Title Page
2.1 Suspended Particulate Emission Density
(by Heavy and Light Industrial Zoning Class) 19
2.2 Suspended Particulate Fuel Combustion Emission Density
(by Heavy and Light Industrial Zoning Class) 20
2.3 Suspended Particulate Process Emission Density
(by Heavy and Light Industrial Zoning Class) 21
2.4 Suspended Particulate Emission Density
(by 2-digit SIC Code) 23
2.5 Suspended Particulate Fuel Combustion Emission Density
(by 2-digit SIC Code) 24
2.6 Suspended Particulate Process Emission Density
(by 2-digit SIC Code) 25
3.1 Analysis of Variance Test for Difference of Means
Between 12 Largest Polluting Sectors 37
2
3.2 Multiple Regression R and Individual Variable Contributions . 39
3.3 Sample Data for Heavy Residential Energy Use 51
3.4 Sample Data for Heavy Commercial Energy Use 53
A. 1 Manufacturing Output - 1970 § 1971 . 64
A. 2 Summary of Manufacturing Plants and Employment
by Major Industrial Sector - 1970 65
A.3 Growth Factors - Chicago Air Quality Control Region ... 68
A. 4 Industrial Parks Survey • 1970-1972 72
A.5 State of Illinois Emission Inventory File Parameters ... 74
A. 6 Manufacturing Data Summaries by 2-digit SIC Code 76
A.7 Manufacturing Data Percentages by 2-digit SIC Code .... 77
A.8 SIC Classes by Percentage Contributions to Total Emissions . 78
A.9 Activities by Zoning Class 79
VI
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List of Tables (Contd.)
No. Title
B.I Illinois Standards for Existing Process Emission Sources . . 84
D.I Correlation Table - SIC 32 94
D.2 Correlation Table - SIC 29 96
D.3 Correlation Table - SIC 33 98
D.4 Correlation Table - SIC 28 100
D.5 Correlation Table - SIC 20 102
D.6 Correlation Table - SIC 35 .104
D. 7 Correlation Table - SIC 34 106
D.8 Correlation Table - SIC 26 108
D.9 Correlation Table - SIC 30 110
D.10 Correlation Table - SIC 37 112
D.ll Correlation Table - SIC 39 114
D. 12 Correlation Table - SIC 36 116
vn
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ABSTRACT
In order to evaluate or rank land use plans in terms of air quality,
it is necessary for planners to be able to project emission density (mass
of pollutant per unit of land for any specified time period) using only plan-
ning variables, because detailed source characteristics are not available at
the time alternative plans are being developed and evaluated. The objective
of this study is to analyze the utility of various land use parameters in
describing the air quality impacts of land use plans.
Parameters that are tested include land use by zoning class and 2-digit
SIC code, employment dwelling units, and square footage of floor space.
Variables that are to be explained by these parameters include air quality
as represented by the Air Quality Display Model (AQDM), emissions and emis-
sion densities, process weight for industrial sources, and energy consumption.
The basic criterion for evaluating the land-use-based anission estima-
tion methods is the ability of the estimates to reproduce regional air
quality as represented by the AQEM dispersion model, using the best available
point-source inventory information. When data deficiencies prohibit the
application of this criterion, standard statistical measures are applied.
Statistical techniques used are analysis of variance, multiple regression,
and product-moment correlation analysis. Emission inventory and land use
data are drawn from the Chicago metropolitan study area.
Vlll
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1.0 INTRODUCTION
In order to evaluate or rank land use plans in terms of air quality,
it is necessary for planners to be able to project emission density
(mass of pollutant per unit of land for any specified time period) using
only planning variables, because detailed source characteristics are not
available at the time alternative plans are being developed and evaluated.
The planning parameters tested in this study include mean emission densi-
ties by zoning categories or by 2-digit SIC classification, land utiliza-
tion, employment, and building size. The variables to be estimated include
energy and process throughput; these, in turn, determine fuel combustion
and process emissions, respectively. The objectives of the study were
(1) to determine what information routinely collected or available in the
planning process could be used to quantitatively estimate air quality; and
(2) to determine which classification structures or additional parameters
should be used in the planning process in order to carry out air quality
analyses of land use plans. The tests of utility of each type of classi-
fication or each parameter are based on statistical criteria and/or the
resulting air quality representation when inserted in the Air Quality
Display Model (AQDM) dispersion model. Statistical techniques include
analysis of variance, simple correlation, and multiple regression.
It is assumed that manufacturing land is sufficiently distinct in
emission characteristics to be analyzed separately from residential and
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commercial land. Residential and commercial land uses are grouped together
due to their similar emission characteristics.
The Chicago Metropolitan Air Quality Control Region was used as a
study region because of proximity, availability of data, and the large
number of diverse manufacturing sources in the region. Emission inventories
for the Chicago region collected by the City of Chicago Department of
Environmental Control and the State of Illinois Environmental Protection
Agency were used for the study. Using these inventories, we employ a number
of alternative strategies to develop land-use-based emission factors. Sub-
sequently, we apply these factors to presently available Chicago land use
data to evaluate whether the use of these factors can accurately reproduce
estimates of present air quality conditions in the Chicago area.
Section 2 of this report characterizes the emission patterns in the
study region and analyzes the variance in manufacturing emissions.
Section 3 tests various methods for explaining this variance and predicting
emission patterns using the Chicago emission files as a data base. Section 4
summarizes the results of the study. Appendix A describes the Chicago region
in terms of factors influencing present and future emission patterns. The
remaining Appendices, B-D, contain technical detail, data, and statistical
results supporting the text of Section 3.
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2.0 AN ANALYSIS OF EMISSION PATTERNS
Air pollution emission patterns and their air quality effects in the
Chicago region are discussed in the Report Summary, Volume I. This volume
focuses on the stationary source patterns in the Chicago region and, in
particular, on the sources of suspended particulate matter. This limitation
is purely for convenience, and the methods discussed herein are directly
applicable to other pollutant forms emitted from stationary sources.
This section presents a detailed analysis of emission patterns in the
study region by zoning class and major industrial sector. The results of
using mean emission-density estimators by land use classification to pre-
dict pollution concentrations are presented. These results provide the
rationale for exploring other methods of estimation, as discussed in
Section 3.
2.1 CURRENT AIR POLLUTION PROBLEMS IN CHICAGO STUDY REGION
Particulate emissions in an urban area result either from the combus-
tion of fuels containing ash or from industrial plants that produce dust
particles during the manufacturing process. High air pollution concentra-
tions in the Chicago area are due primarily to the intense residential and
commercial land uses surrounding the central business district (CBD or Loop
area of Chicago) and the heavily concentrated industrial areas to the south
and southwest of the CBD. Figure 2.1 shows the suspended particulate iso-
pleths (lines of constant concentrations) and the concentration peaks
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LAKE COUNTY
Figure 2.1. Isopleths of suspended particulates
using unregulated emission inventory.
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resulting from these two intensive land use clusters.
The State of Illinois has enacted emission control regulations
(emission standards) designed to achieve the National Ambient Air Quality
Standard (75 yg/m3 annual geometric mean) by 1975. These regulations are
described in Appendix B. Figure 2.2 shows the forecasted air quality with
the control regulations in effect. Although the control regulations
will have considerable effect in improving air quality, peak areas at or
near the standard will exist. Growth in areas surrounding these peaks will
contribute to the degradation of air quality in the area and threaten ambi-
ent air quality standards. It is for this reason that this study focuses
on the three counties surrounding Chicago; namely, Cook, DuPage, and Will.
This is a subregion of the 8-county Standard Metropolitan Statistical Area
(six counties in Illinois and two in Indiana) and of the 9-county State of
Illinois Economic Planning and Statistical Reporting Region as shown in
Figure 2.3.
This study divides land use into two major categories—Manufacturing
and Residential/Commercial—because of their distinct emission characteris-
tics. Residential and commercial building emissions are a function of the
energy consumed and type of fuel used. Energy consumed is, in turn, a
function of area climatology, building size, and type of construction. The
intense residential/commercial districts of the City of Chicago are rather
unique in that a significant number of buildings are still coal heated. A
rather severe restriction on the sulfur content of fuels (1% limit) has
drastically increased the number of annual conversions from coal to natural
gas or oil in recent years due to the large price differential between low-
and high-sulfur coal in the Chicago area. This trend is expected to con-
tinue to the point where residential/commercial sources will not be
5
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WILL
COUNTY
Figure 2.2. Isopleths of suspended particulates with
Illinois source control regulations applied.
(yg/m3 annual geometric mean)
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CHICAGO REGION
3-County
Study Region
9-County
Study Region
Figure 2.3. State of Illinois, Chicago economic planning
and statistical reporting region.
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significant contributors to the regional air pollution problem given the
availability of low-sulfur fuels. Nonetheless, attempts should be made to
estimate this contribution, and a method for making these estimates is
discussed in Section 3 of this report.
Manufacturing processes and power plants, on the other hand, are now,
and are expected to continue to be, major polluting sources, accounting for
more than 83$ of suspended particulate emissions after source regulation
controls are enforced. Manufacturing emissions can be partitioned into
emissions due to the nature of the production process itself, due to the
combustion of fuels required to carry out the production process, and due
to space heating. Manufacturing fuel combustion emissions will continue to
be a problem because of the large quantities of fuel consumed. Manufac-
turers are typically on the low end of the priority list for receiving clean
fuel supplies, especially natural gas. The current shortage of clean fuel
resources continues to counteract the use of these fuels for manufacturing
purposes, however desirable this may be from an air pollution standpoint.
Coupled with this shortage of clean fuels is the fact that Illinois is rich
in high-sulfur, high-ash bituminous coal reserves and considerable economic
pressure exists to utilize these resources. Thus, the planning of manufac-
turing land use and the location of industrial parks and production facili-
ties that include air pollution considerations are important parts of
maintaining air quality standards in a region such as the Chicago Metropoli-
tan Area. Current manufacturing activity in the study region and potential
for growth in the area is further described in Appendix A.
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2.2 AN ANALYSIS OF VARIANCE IN MANUFACTURING EMISSIONS
A factor that complicates the analysis of the air pollution impacts
of land use plans or development projections is the disaggregated nature
of the air pollution problem. Unlike water pollution, there are no cen-
tralized processing or treatment plants for which loads can be estimated
on an aggregated basis. It is the existence of sources on a diverse geo-
graphic plane that constitutes the overall air pollution emission surface
of the urban region. Thus, estimation of emissions on a square-mile or
square-kilometer grid is required to obtain a realistic picture of air
quality.
The spatial distribution of sources from the Chicago emission inven-
tory (see Appendix A) is shown in Figure 2.4, and the resulting particulate
emission pattern is shown in Figure 2.5. It is these emission patterns
that give rise to the particulate concentration surfaces of Figure 2.1.
As can be seen by comparing Figures 2.4 and 2.5, however, it is not the
mere existence of a manufacturing source that gives rise to emissions, but
also the nature and scale of the production process and space heating
requirements. Although high source clusters seem to visually correlate
with high emission areas, further explanation of the spatial variance in
emissions is required to achieve a realistic estimation of emission patterns
in the region.
The need for further analysis can also be viewed statistically as
indicated in Figure 2.6 that shows the frequency distribution of indus-
trial source emission densities in the study region. Not only is the
standard deviation of this distribution quite high in relation to its aver-
age, but the skewness of the distribution causes significant estimation
problems if a figure of 1.17 Ib/hr/acre is used as an emission density
9
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D
n n
>4
Figure 2.4. Manufacturing emission sources per
square mile in Chicago study region.
-------
< 5 (Not Recorded)
5 • < D< 25
25 • < H < 50
50 • < 0 < HIGH
Figure 2.5. Spacial distribution of particulate
emissions in Chicago region.
11
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280
280
260
240
220
200
180
o |60
• . |
UJ
= 140
UJ
£120
100
80
60
40
20
0
_
-
-
-
-
240
200
160
120
80
40
0
-
MEAN = 1.17 (Lbs/Hr/Acre)
_ r\ f\ / •»• i .-. i . .'9 \
ST. OEV. = 5.03
-
-
-
i i ' 1
.1 1.0 10. 100.
TOTAL MANUFACTURING
Sources - 458
SIC 20 - 39
l i l 1 i i ! ™"i ! 1 2
•2 4 6 -8 1.0 1.2 1.4 1.6 1.8 2.0 w Figure 2.6. Frequency of suspended particulat
EMISSION DENSITY, LbS/Hr/Acre emission density with Illinois
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factor in projecting future air quality. The use of this mean emission
density estimate for ranking land use plans was tested by using the AQDM
atmospheric dispersion model. Figure 2.7 shows the calculated air quality
for suspended particulates as derived by applying a 1.17 Ib/hr/acre
2
(9.0 tons/day/mi ) emission density factor to the present industrial land
use pattern in Chicago. Figure 2.2 indicates suspended particulate air
quality estimates based, on the other hand, directly upon the application
of standard emission factors to the Chicago Emission Inventory with Illi-
nois source control regulations applied. Comparison of these two figures
shows that use of the average emission density factor for industrial lands
does produce average air quality estimates that approximate the average
air quality over the entire region. However, due to the bias in the esti-
mation of the average emission density factor and the intense clusters of
manufacturing land use in the area, pockets of very high concentrations
appear in the air quality estimates based upon these factors, as opposed
to those based upon the standard emission factors. Thus, if these estimates
were used in ranking alternative land use plans, or in trying to identify
future potential source clusters in the Chicago area, these average emission
density air quality estimates would lead to the belief that air quality
standards would not be met under the present conditions of Chicago land use
patterns and air quality regulations.
This does not mean that the projections of air quality using these
estimators are not a useful tool in ranking the air quality effects of
alternative land use plans. Due to the bias of the land use emission density
factor estimates, those plans containing a larger percentage of industrial
zoned land will, in all probability, be ranked as being likely to produce
13
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WILL
COUNTY
Figure 2.7. Isopleths of suspended particulates* using mean emission density
estimates for'manufacturing land (mean = 9.0 T/D/Mi2=1.17 Ib/hr/acre)
(*yg/m3 - annual geometric mean)
14
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more significant degradation of air quality than might be justified. We
conclude, therefore, that in using the mean estimators for land-use-based
emission densities, some further methods must be developed to specifically
take into account the skewness and variance of these distributions in
projecting future air quality.
One way to estimate variance is to classify manufacturing sources as
process or fuel combustion, as is currently done for control purposes. The
dominance of process emissions over fuel combustion emissions is shown by
the frequency distributions in Figures 2.8 and 2.9. Examination of the
standard deviation of these distributions, compared with the standard devia-
tion of the frequency distribution of the emission densities for the indus-
trial sector as a whole, indicates that almost the entire variance in the
emission density estimate is due to the variance of emissions in process
sources. Thus, it can be anticipated, that if present industrial land use
projections could be disaggregated into process and fuel combustion sources
the projected air quality estimates would be somewhat improved. This does
not alleviate the need, however, to specifically account for the wide varia-
tion in emission densities for industrial process sources.
We conclude that if mean estimators are to be used, a new process of
classification must be attempted; the process may require planners to obtain
more specific information in order to gain in explanatory power.
A further explanation using mean estimators was attempted; it groups
digit SICs into typical "heavy" and "light" manufacturing land use. A
2
survey of zoning administrators in the Chicago region indicated the
following groupings as predominant:
Heavy SIC 26 - 33
G2 : Light SIC 20-25, 34-39.
A
15
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o\
260
240
220
200
180
IUw
ICf\
>_ loU
o
2 140
S 120
cc
**" 100
80
crv
OU
40
20
«h
-
-
—
-
-
-
-
-
280
200
160
120
80
An
•tU
0
-
1 1 > I ! ! ' 1 . j
MEAN = .048 (Lbs/Hr/Acre)
= .37 (T/D/Mi2)
ST. DEV. = .174
-
-
, , 1
.01 . 1.0 10.
Figure 2.8.
EMISSION DENSITY, Lbs/Hr/Acre
Frequency of suspended particulate fuel
combustion emission density with Illinois
point-source regulations applied.
-------
280
260
240
220
200
180
& 160
z
S HO
o
LU
oe 120
u.
100
80
60
40
20
0
280
240
—
—
_.
—
—
—
—
•
200
160
120
80
40
0
MEAN= .12 (Lbs/Hr/Acre)
= 8.7 (T/O/Mi2)
I ST. DEV. = 5.02
1
1
1
- i
I
— i
,1 1
1 10 100
rr-r^-r^-^-i
.2 .3 .4 .5 ,6 .7 .8 ,9 1.0
EMISSION DENSITY, Lbs/Hr/Acre
Figure 2.9.
Frequency of suspended paniculate
process emission density with Illinois
point-source regulations applied.
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The relevant statistics for this grouping are shown in Tables 2.1, 2.2, and
2.3 for suspended particulate fuel combustion, process, and total manufac-
turing emission densities, respectively. Again, the dominance of process
emission is evident. An analysis of variance between groups indicates that
mean process emission densities are significant at the .05 level, but fuel
combustion emission densities are not. The significance of process emission
densities carries over to total mean emission densities for the two groups.
When the mean estimates are applied to light and heavy manufacturing
land use in the Chicago area, a slightly better air quality representation
is obtained, as shown in Figure 2.10. A comparison with Figure 2.1 indi-
cates that the peak areas are well represented, but the magnitudes of the
peaks remain much too high, indicating a further need for refinement.
A final attempt at mean estimation for manufacturing land was attempted
by using the 2-digit SIC classification. Tables 2.4, 2.5, and 2.6 contain
the relevant statistics for suspended particulate fuel combustion, process,
and manufacturing emission densities, respectively. An analysis of vari-
ance between 2-digit SICs shows no significant explanatory power for the
emission-density variables. From this result, we are tempted to conclude
that knowledge of mean emission densities by 2-digit SIC is of little
assistance in predicting emissions and, hence resultant air quality. Land
use data by 2-digit SIC was not available to test the resulting air quality
representation.
18
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Table 2.1. SUSPENDED PARTIOJLATE EMISSION DENSITY
(Ibs/hr/acre)
si (-6-35)
r2 /zo-zsl
GA \24-59J
,u
bA (255)
G* (203)
TOTAL
ANOV
-
Unregulated Source Inventoiy
Mean
47.60
27.24
38.57
:
*F - F rat
DF - Deere
S - Signi
. Median
Std. Dev.
1
.717
1.04
.92
io
es qf freed
ficance lev
472.65
164.76
_
369.14
4
Skewnes5
15.48
9.63
18.476
• •i ii
/CPE - BetnveenN
fH '• )
\J)FW- Within J
el !
jt(by Heaj/y and l4ght Industrial Z<
i
!
Regulated Source Inventor)'
Mean
1.67
.54
1.17
F* = 5.65
Median
.21
.04
.104
DF* - (454)
|
i
1
!
1
i
1
1
i
1
ming Class)
i
StJ. Dev.
6.29
2.62
5.03
Skcwness
7.63
8.35
9.01
C*C nc-\
p._ ii.y^z._
•
1
:
i
i
19
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Table 2.2. SUSPHNUUU 1'ARTICUl.ATi; RJI:.L COMBUSTION
EMISSION UH.NSITV
(Ibs/hr/acrel
Gj (26-33)
GA £°-25>l
* p-59j
Gj C255)
GJJ (203)
TOTAL (458)
ANOV
Unregulated Source Inventory
Mean
.601
.075
.370
*F - F ral
DF - Degn
NS - Not
f(by Hea
. Median
.ons
.005
.006
in
es of freet
signif ic
vy and L
StJ. Dev.
6.64
.239
4.96
I
I
i
!
..I
I
/fiFBJ- Be
on (
NJFW - IV]
int
1
.ght jlndi
i
I
Skeuiioss
14.13
5.20
18.97
tween\
thin^/
istrial Z
Regulated Source Inventory
Moon
.058
.034
.048
Median
.005
.003
.005
F* =1.34 {jF*= 454)
oning Cl<
1 •
.ss)
!
SLd. Dev.
.208
.120
.174
Skev.-ness
8.53
9.53
9.44
NS
•
20
-------
Table 2.5. SUSPENDED PARTICULATE PROCESS
EMISSION DENSITY
(Ibs/hr/acre)
,t
GJ (26-35)
C2 /20-25\
GA (34-39)
Gj (255)
Gj (203)
TOTAL
ANOV
Unregulated Source Invcntoiy
Mean
46.99
27.16
38.20
. .....
*F - F rat
DF - Degre<
S - Sigfii
ffbv Hea
Median
.666
.998
.773
•
o
s of freedc
icance leve
w and L:
Std. Ucv.
472.64
164.76
369.12
4
~ • ...
/DFB-Be
ml
V^DFW-fti
1
.ght Indi
Skc'.\T.CS5
15.48
9.63
18.48
'
ncen\
hinj
strial Z
Regulated Source Inventory
Mean
1.61
.508
1.121
F* = 5.1A
' ...
Dning Cl«
Median
.129
.012
.040
DP* = U54 )
.ssl
Std. Dcv.
6.23
2.61
5.02
Skevness
7.65 .
8.39
9.008
c* f nr-\
2 (.!"+)
21
-------
50
LAKE COUNTY
Figure 2.10. Isopleths of suspended participates* using mean emission
density estimates by manufacturing zoning classification:
Heavy industry = 13.1 T/D/mi2 = 1.70 Ib/hr/acre
Light industry = 4.2 T/D/mi2 = .55 Ib/hr/acre
*yg/m3 annual geometric mean
-------
Table 2.4. SUSPENDED P.-\RTICUL\TE HUSSION' DENSITY 1"
(Ibs/hr/acre)
2- Digit
SIC (N)
20 (17)
24 (14)
25 (14)
26 (20)
27 (9)
28 (67)
29 (35)
30 (13)
32 (43)
33 (67)
34 (39)
35 (49)
36 (29)
37 (12)
38 (6)
39 (18)
TOTAL (458)
ANOV
Unregulated Source Inventory-
Mean
.74
20.5
8.4
29.6
50.6
8.4
17.8
2.5
196.5
21.2
95.7
7.8
1.7
51.7
3.5
13.1
38.57
f(by 2-c
. Median
.09
3.8
2.0
2.4
6.4
.45
.82
.23
1.81
.65
1.02
1.00
.80
1.6
.26
3.3
Std. Dev.
1.4
39.5
13.0
60.2
106.7
29.9-
50.6
5.3
1141.8
87.7
362.5
34.9
2.5
113.9
5.2
21.7
.92 369.14
ligit SIC
Code)
i
Skev.iiess
2.5
2.4
1.6
2.0
2.1
5.66
3.3
1.9
6.3
6.9
4.2 '
6.5
1.9
2.3
.74 '
2.1
18.48
Regulated Source Inventory
Mean
.48
.13
.08
.80
.27
.95
4.13
.51
2.35
1.36
.63
.88
.21
1.72
.08
.14
1.17
F=1.229
Median
.09
.04
.04
.11
.10
..11
.25
.02
.61
.35
.16
.04
.02
.02
.04
.03
.10
16
DF=439
Std. Dev.
.85
.29
.08
1.88
.44
2.74
14.12
1.2
5.7
3.9
2.2
4.1
.39
5.7
.12
.27
5. nj
Skewiess
2.4
3.2
.62
3.49
2.15
6.28
3.85
2.54
4.2
6.59
5.8
6.3
1.9
T. Q
.1.3
1.9
9.01
NS(.OS)
23
-------
Table 2.5 SUSPr.XULD PACTICULATE FUIIL COMBUSTION
LM1SSIO.V DENSITY "f
(lbs/hr/acre)
_2- Digit
SIC
20 (17)
24 (14)
25 (14)
26 (20)
27 (9)
28 (67)
29 (35)
30 (13)
32 (43)
33 (67)
34 (39)
35 (49)
36 (29)
37 (12)
38 (6)
39 (18)
TOTAL (458)
A.NOV
Unregulated Source Inventory
Mean
.142
.004
.016
1.64
.036
.083
.111
.012
, 2.39
.128
.067
.101
.065
.200
.049
.020
.370
f(by 2-d
i
i
. Median
.022
0.
0.
.01
0.
.011
.023
.002
0.
.006
.003
.004
.007
.065
.036
0.
.006
igit SIC
iStd. Dcv.
.4-17
.009
.026
7.15
.088
.263
.224
.022
15.42
.540
•
.242
.301
.128
.255
.055
.057
4.96
Code)
Skevness
3.7
2.02
1.61
4.13
2.39
4.99
2.77
2.40
6.33
5.59
5.53
3.63
2.23
1.008
.115
3.57 .
18.97
Regulated Source Inventor)'
Mean
.049 •-
.005
.010
.073
.036
.044
.099
.011
.019
.082,
.064
.026
:042
.046
.030
.008
.048
.473
Mcdiaji
.016
0.
0.
.010
0.
.009
.019
.002
.003
.004
.003
.014
0.
0.
.005
16
i)F=439
Std. Dev.
.095
.008
.016
.170
.008
.213
.213
.002
.050
.327
.242
.065
.080
. .065
.047
.014
.174
Skc'.%7iess
5.20
2.27
1.81
3.44
2.39
6.50
2.99
— — ___— — .
2.63
4.32
6.92
5.58
4.15
1.87
1.337
-9
1.38
9.44
NS(.OS)
_
24
-------
Table 2.6. SUSPENDED PARTICULAR: PROCESS
EMISSION' DLNSITY"1"
(lbs/hr/acre)
J.- Digit
SIC (.Y)
20 (17)
24 (14)
25 (14)
26 (20)
27 (9)
28 (67)
29 f3S)
30 (15)
32 (43)
33 (67)
34 (39)
35 (49)
36 (29)
57 (12)
38 (6)
39 (18)
IDTAL (458)
:
-------
3.0 METHODS FOR ESTIMATING EMISSIONS FROM LAND USE
The analysis of emission density variance described in the preceding
section provides the rationale for further investigation into processes and
methods for estimating emissions from land use. The description of the
current state and potential growth of the Chicago region in Appendix A
gives an indication of the parameters that are customarily used and
reported in the planning process for forecasting the rate of urbanization
and change of settlement patterns of the region. These parameters include
rates of change in land use, employment, and productivity for major manu-
facturing sectors; changes in housing stock and population for residential
land; and square footage of floor space for commercial development. This
section analyzes and tests the utility of certain of these parameters in
predicting regional emission and air quality patterns and residential/
commercial land uses. Two criteria were used in evaluating these parameters:
(1) the accuracy of the representation of regional air quality produced
when the parameter estimates were inserted into the AQDM atmospheric
dispersion model, and (2) the reliability of the representation when sub-
mitted to standard statistical analyses such as analysis of variance, product-
moment correlation, and multiple linear regression.
26
-------
3.1 UNIFORM EMISSION-DENSITY ESTIMATION BY MANUFACTURING ZONING CLASS
The previous section indicated some of the difficulties encountered
in using mean emission density estimates by land use class or major indus-
trial sector. The major difficulty stems from the skewness of the emission-
density distribution as shown in Figure 2.6. Some improvement is realized
if mean emission densities by heavy (HI) and light (LI) industry are used.
In order to obtain a direct comparison between the emission-density
approach and the point-source emission factors approach, the AQDM results
for each were compared in the uncalibrated model. This merely means that
results, before fitting to actual air quality data and adding background
concentrations, are to be compared, assuming that the point-source repre-
sentation is the best attainable with current information. The mean relative
error and the standard deviation in the error between air quality concentra-
tions calculated using the point-source representation and those using the
emission-density representation are then used to measure the "goodness" of
the emission-density representation. Thus:
AXi
where
xi
°R = U ^^ " yR
PS ED
^ - Xi
]
= the mean relative error ,
= the standard deviation about
the mean relative error ,
27
-------
PS
ED
the arithmetic mean air quality concentration
calculated at receptor point i using the
point-source file ,
the arithmetic mean air quality concentration
calculated at receptor i using the emission-
density representation ,
i = an index of receptor points ,
N = the total number of receptor points.
Using this criterion and the means for heavy and light industry based
on the 90% largest source sample (see Appendix A, Section A.4), which are:
ED91 = 18.0 T/D/mi2 , (means of sample)
EDLI = 6.0 T/D/mi2
The following are obtained:
yR = -5.86
aD = 1.48
R I
this indicates a severe bias to overprediction. A visual comparison of the
resulting air quality is provided in Figures 3.1 and 3.2.
I
The skewness of the distributions involved would ordinarly argue for
using the median as an estimation instead of the mean. For this sample:
(medians of sample)
ED"
i
1.3 T/D/mi2
EDLI
and
.23 T/D/mi2
.61
JR
.08
28
-------
WILL
COUNTY
20
Figure 3.1. Isopleths of suspended particulates*
using point-source representation.
*(yg/m3 - annual arithmetic mean - uncalbirated model)
29
-------
50
Figure 3.2. Isopleths of suspended particulates*
using mean emission density representation.
(El/11 =18.0 T/D/mi2 - EDLI =6.0 T/D/mi2^
*(yg/m3 - annual arithemetic mean - uncalibrated model)
30
-------
indicating a substantial bias to underprediction. A visual comparison can
be made by simultaneously viewing Figures 3.1 and 3.3.
At this point, it is reasonable to ask if a reasonable estimate of
air quality concentrations can be made using some emission-density estimates
for heavy and light industry. To answer this question, assume that emission-
density estimates are free parameters to be chosen so as to achieve a
"best fit" in the sense that:
mina,, (El)"1, EDLI) (3.3)
K
subject to yR (El)"1, EDLI) = 0 .
That is, emission density estimates, BIT and ED , are sought which yield
the best (least standard deviation), unbiased (yR = 0) comparison with air
quality concentrations as modeled using the point-source information. The
analytic solution to this problem is easily worked out (Appendix C), and
the results yield:
E*!)"1 = 3.53 T/D/mi2 ,
ED11 = .53 T/D/mi2 ,
and
y = 0 (by constraint) ,
K
aR - .20
The resulting concentration isopleth map is shown in Figure 3.4. Thus, the
best fit emission-density representation still leaves a 20% standard devia-
tion in the relative error.
A closer look at the seriousness of this error can be taken if it is
assumed that a large relative error in the lower concentration ranges can
be tolerated, but, hopefully, the peak concentrations are well represented.
31
-------
WILL
COUNTY
Figure 3.3. Isopleths of suspended particulates*
using median emission density representation.
(ED"1 =1.3 T/D/mi2 - EDLI = .23 T/D/mi2)
(yg/m3 - annual arithmetic mean - uncalibrated model)
32
-------
WILL
COUNTY
Figure 3.4. Isopleths of suspended particulates*
using "best fit" emission density representation.
(ElF = 3.53 T/D/mi2 - E*DLI = .53 T/D/mi2)
*(ygm/m3 - annual arithmetic mean - uncalibrated model)
33
-------
Nine receptor points are above 30 yg/m3; this, when coupled with the normal
rural background of 40 yg/m3, can be considered in the critical area of the
standard (75yg/m3). Using the "best fit" emission-density estimates applied
to only these nine points yields:
yR = .22 ,
0R = "^ '
indicating a strong bias to underprediction, with a large standard deviation
of relative error. Thus, it can be concluded that the best fit estimates
actually do worse in predicting the higher peak concentrations than lower
concentration levels; further, the standard deviation of bias is to under-
prediction, an undesirable result for estimating peak levels.
Finally, if "best fit" emission density estimates are generated
using these nine highest receptor points alone, the results are:
E^1 =2.43 T/D/mi2,
EDLI =2.74 T/D/mi2,
where, for the nine highest receptor points,
yR =0 (by constraint).
CFR = .33 ,
and for all receptor points,
yR = .86 ,
0R = '56'
From these results, it can be concluded that several high receptor points
are being influenced by clustered light industrial land use, and even when
34
-------
these nine points are used to determine best-fit emission-density values,
no improvement is observed in the standard deviation of relative error.
All this is at the expense of a substantial bias to overprediction in the
remaining receptor points.
It must be concluded, therefore, that (1) either the land use data
used for this study is severely in error, or (2) that further explanation
is required; e.g., further disaggregation of land use categories, or using
intensity measures such as employment density. The land use data was col-
lected from best available sources and is assumed to be sufficiently reli-
able for purposes of this study. Therefore, the results of this section
are assumed to provide the rationale for further investigations as dis-
cussed in the next section.
3.2 ANALYSIS OF MANUFACTURING EMISSIONS BY
MAJOR INDUSTRIAL SECTOR (2-digit SIC code)
The previous section indicated the need, based on the criteria of air
quality representation, for further explanation of manufacturing emissions.
Manufacturing land use by heavy and light industry failed to give adequate
air quality representation, even when "best fit" emission density estimators
were used. The next level of disaggregation is by 2-digit SIC code; how-
ever, land use data by 2-digit SIC code was not available in the Chicago
area. Therefore, the analysis of this section uses statistical measures to
test the utility of various parameters in predicting emissions.
Even if land use were known by 2-digit SIC, the analysis of variance
of Section 2 yields discouraging results regarding the use of mean emission-
density estimates by the 2-digit classification. This result is reproduced
for a subset of major polluting sectors in the Chicago region as shown in
35
-------
Table 3.1. The variance at the 51 significance level in average emission
densities of industries classified by the 2-digit scheme differs only
marginally, which, in turn, is due to the large variance of emission den-
sity within each 2-digit class. Note, however, that it is the variance
in the land variable that is causing this result, since emissions by
2-digit class are significantly different. Thus, justification is pro-
vided for attempting to estimate emissions within each 2-digit classifica-
tion through the use of certain planning parameters. Average employment
levels, process weight, and energy are particularly important because they
vary significantly among the 2-digit groupings.
In the remainder of this section, the major polluting sectors are
investigated in order, as ranked by total controlled emissions. Each
sector is characterized with respect to its major contribution to air
pollution, process and fuel combustion emission contributions, reductions
in emissions achieved by Illinois source control regulations, and the
degree of explanation of controlled emissions by employment, land use,
process weight, and energy consumption. Descriptions and material pre-
sented in the Standard Industrial CI sification Manual and the
Compilation of Air Pollutant Emission Factors^ are used when necessary
to complete the discussion of each 2-digit classification.
Appendix D contains the results of applying correlation and regression
analysis to the Chicago emission inventory by 2-digit SIC. In addition to
product-moment correlation, four linear regression models are tested for
each 2-digit category; these are:
36
-------
Table 3.1. ANALYSIS OF VARIANCE TEST FOR DIFFERENCE OF
MEANS BETWEEN 12 LARGEST POLLUTING SECTORS
Variable
Land
Employment
Process Weight
Energy
Controlled Emissions
Controlled Emission Density
F
Value
DFB 11
DFW 476
.54
4.6
5.2
2.7
7.2
1.4
Significance
Level
NS
.001
.001
.001
.001
NS
37
-------
General Emission Model
ECT = A'Pw + B'En + OSp + D-Era + E (3.4)
Restricted Emission Model
ECT = A«Sp + B-Em + C (3.5)
Restricted Process Weight Model
Pw = A«Sp + B-Em + C (3.6)
Restricted Energy Model
En = A'Sp + B'Em + C (3.7)
where
CT
E = controlled emissions (Ib/hr)
Pw = process weight flow (t/hr)
En = energy consumption (MBtu/hr)
Sp = space (acres)
Em = employment
and A, B, C, D, and E are linear regression coefficients.
Results of these models are summarized in Table 3.2; the details for the
five major polluting sectors are discussed in the remainder of the section.
These five sectors account for 272, or 50%, of the sources in the emission
inventory file; 761 of the manufacturing land use; 34% of employment; 96.2%
of process material flow; 79.9% of energy consumed; and 85% of controlled
emissions.
3.2.1 SIC 32 Stone, Clay, and Glass Products
Industries in this category manufacture products from materials taken
principally from the earth in the form of stone, clay, and sand; such as
glass products, cement, structural clay products, pottery; and concrete,
38
-------
Table 3.2. MULTIPLE REGRESSION R AND INDIVIDUAL VARIABLE CONTRIBUTIONS
SIC
J?
2?
33
2H
2'l
•55
\}
11,
Vi
77
3:*
36
General Emission Nbdel
RZ
.57
.61
.01
.73
.
-------
gypsum, abrasive and asbestos products. This category accounts for 29% of
controlled particulate emissions in the source file; 97% of these emissions
are due to the manufacturing processes themselves, while only 3% are due to
fuel combustion. This category utilizes 5.5% of the energy consumed or 920
MBtu/hr; of which 833 MBtu/hr are due to the combustion of natural gas.
Thus, process emissions are the major air pollution problem in this category.
Estimated uncontrolled process emission factors for various sub-
categories are shown below:
SIC
32
3211
3229
3241
3251
3273
3274
3275
3281
3291
3295
3295
3296
3297
Description
Stone, Glass and Clay
Flat Glass
Blown Glass
Cement
Brick
Ready Mix Concrete
Lime
Gypsum
Cut Stone
Abrasive Products
Minerals § Earth
Perlite
Mineral Wool
Non-Clay Refractory
Suspended Particulate
Emission Factors
(Ib/ton of finished product)
2.0
60.0
54.0
180.0
0.2
200.0
132.0
31.0
31.0
77.0
21.0
50.0
225.0
Source: Compilation of Air Pollutant Emission Factors (Revised).
U.S. EPA, Office of Air Programs, February 1972.
This category accounts for 61% of process weight flow, amounting to 17,337
tons of material moved per hour. Application of the Illinois control regu-
lations will achieve a reduction in emissions from 88,556 Ib/hr to 1330 Ib/hr,
or 98%. Emissions per ton of process weight will then be .08 Ib/T/hr.
40
-------
Correlation results for this category indicate that controlled emis-
sions are highly correlated (r = .74) with process weight as expected.
Since process weight and land are correlated (r = .66), a high correlation
(r = .58) between emissions and land is also obtained. Energy and employ-
ment are also highly correlated (r = .90).
The general emission model R of .57 is obtained with all five vari-
ables entering the equation. However, process weight dominates the
2
explanation contributing an R of .55, while the remaining variables contrib-
ute the remaining .02. In the restricted emission model, a multiple
2
R of .35 is obtained primarily from the land variable. For the restric-
2
ted process weight model, an R of .43 is obtained, again primarily due to
the land variable. On the other hand, employment accounts for most of the
2
R of .82 in the restricted energy model. These results indicate that
space is the most useful planning variable in predicting controlled emis-
sions in this category.
3.2.2 SIC 29 Petroleum Refining and Related Industries
Industries in this category are engaged in petroleum refining, manu-
facturing of paving and roofing materials, and compounding lubricating
oils and greases from purchased materials. This category accounts for 17%
of controlled particulate emissions; 70% are due to the manufacturing
processes themselves, while 30% are due to fuel combustion. This category
utilizes 36% of the energy consumed, or 6036 MBtu/hr; of which 5115 MBtu/hr
are due to the combustion of natural gas. Thus, process emissions are the
major problem, although fuel combustion must also be considered a problem
due to the high volume of fuel consumed.
41
-------
Estimated uncontrolled process emission factors for the various sub-
categories are shown below:
SIC
Description
Suspended Particulate
Emission Factors
(Ib/ton of finished product)
29
2911
2951
2952
Source: Op cit
Petroleum and Coal
Petroleum Refining
Paving
Asphalt Coating
5.5
45.0
8.5
This category accounts for 171 of the process weight flow amounting to
4755 t/hr. Application of the Illinois control regulations will achieve
a reduction in emissions from 5110 Ib/hr to 767 Ib/hr, or 85%. Emissions
per ton of material moved will then be .16 Ib/t/hr.
Correlation results for this category indicate that controlled emis-
sions are correlated with energy (r = .73), process weight (r = .44),
employment (r = .49), and space (r = .52). Energy is correlated with space
(r = .80) and employment (r = .74), but process weight is not correlated
with either variable.
2
The general emission model R is .61, with energy contributing .53
and process weight contributing .08 to the explanation. The restricted
2
energy model R is .68 with space contributing .64 to the explanation.
Neither land nor employment contribute to the explanation of process weight.
2
In the restricted emission model, the R is .30 with space contributing .28
to the explanation. Thus, space appears to be the most useful planning
variable in explaining emissions from this category.
42
-------
3.2.3 SIC 35 Primary Metal Industries
Industries in this category engage in the smelting and refining of
metals from ore, pig, or scrap; in the rolling, drawing, or alloying of
metals; and in the manufacture of castings, forgings, and other basic metal
products. This category accounts for 16% of controlled particulate emis-
sions; 90% are due to the manufacturing processes themselves, while 10% are
due to fuel combustion. This category utilizes 15.3% of the energy consumed,
or 2575 MBtu/hr, of which 2365 Mbtu/hr are due to the combustion of natural
gas. Thus, process emissions are the major air pollution problem in this
category.
Estimated uncontrolled process emission factors for the various sub-
categories are shown below:
SIC
33
3312
3313
3321
3323
3331
3332
3333
3334
3341
3341
3341
3341
3341
3352
Description
Primary Metal Industries
Blast Furnace
Electrometallurgical
Gray Iron Foundries
Steel Foundries
Copper Smelting
Lead Smelting
Zinc Smelting
Aluminum Smelting
Brass and Bronze Smelting
Aluminum
Lead
Zinc
Magnesium
Rolling Aluminum
Suspended Particulate
Emission Factors
(Ib/ton of finished product)
200.35
1180.0
17.0
66.0
135.0
162.0
530.0
295.0
50.0
1025.0
110.0
103.0
4.0
135.0
Source: Op cit
43
-------
This category accounts for 3.6% of the process weight flow amounting to 1010
tons of material moved per hour. Application of the Illinois control regula-
tions will achieve a reduction in emissions from 9007 Ib/hr to 733 Ib/hr or
921. Emissions per ton of process weight will then be .73 Ib/T/hr.
Correlation results for this category indicated that controlled emis-
sions are correlated with process weight (r = .73), and somewhat with employ-
ment (r = .35) and energy (.27). However, process weight is poorly correlated
with land (r = .05) and employment (r = .17). Energy is somewhat correlated
with space (r = .28) but poorly correlated with employment (r = .16).
?
The general emission model R if .61 with process weight contributing
.52 to the explanation, the remainder being due to employment (.06) and energy
(.03). Unfortunately, the results for the r^tricted models show that neither
land nor employment is a good predictor of emissions, process weight, or energy.
Some other means for estimating these parameters, particularly process weight,
is required for this category.
3.2.4 SIC 28 Chemicals and Allied Products
Industries in this category produce basic chemicals or products manu-
factured predominantly from chemical processes. Establishments in this group
manufacture three classes of products: (1) basic chemicals; (2) chemical
products to be used in further manufacturing processes; and (3) finished
chemical products to be used in final consumption. This category accounts
for 16% of controlled particulate emissions; 82% are due to the manufacturing
processes themselves, while 18% are due to fuel combustion. This category
utilizes 11.4% of the energy consumed or 1908 MBtu/hr; 758 MBtu/hr are due to
coal use, 146 MBtu/hr are due to the consumption of natural gas. Thus, both
process and fuel combustion emissions pose air pollution problems for this
category.
44
-------
Estimated uncontrolled process emission factors for the various sub-
categories are shown below:
SIC
28
2812
2815
2819
2821
2822
2841
2842
2843
2851
2871
2871
2892
2893
2895
2899
Description
Chemicals and Allied
Alkalis
Dyes
Industrial Inorganic
Chemicals
Plastics
Synthetic Rubber
Soap
Detergents
Surface Acting Agents
Paints
Nitrate Fertilizers
Phosphate Fertilizers
Explosives
Printing Ink
Carbon Black
Chemicals
Suspended Particulate
Emission Factors
(Ib/ton of finished product)
6.0
0.0
20.0
35.0
15.0
90.0
90.0
90.0
2.0
12.9
80.0
36.0
2.0
2300.0
16.0
Source: Op cit
This category accounts for 12.6% of process weight flow amounting to 3569
tons of material moved per hour. Application of the Illinois control regu-
lations will achieve a reduction in emission from 5200 Ib/hr to 702 Ib/hr,
or 86%. Emission per ton of process weight will then be .2 Ib/t/hr.
Correlation results for this category indicate that controlled emis-
sions are correlated with process weight (r = .78) and energy (r = .54), and
somewhat with space (r = .35) and employment (r = .33). However, process
weight is poorly correlated with land (r = 0) and employment (r = .01).
45
-------
Energy, on the other hand, is correlated with both space (r = .52) and
employment ( r = .63), although employment and space are also correlated
( r = .72).
2
The general emission model R is .73, with process weight contribut-
ing .60 and space .12. Since space and process weight are unrelated, a
2
poor R of .13 is obtained in the restricted emission mode. No explana-
tion of process weight is achieved in the restricted process weight
model, but energy is somewhat predictable from employment in the restrict-
ed energy model. These results indicate that some other means of pre-
dicting process weight is required, but employment may be useful in
predicting fuel combustion emissions if fuel use can be estimated.
3.2.5 SIC 20 Food and Kindred Products
Industries in this category manufacture foods and beverages for
human consumption, other food related products such as vegetable and
animal fats and oils, and prepared feeds for animals and fowls. This
category accounts for 1% of controlled particulate emissions; 53% of
these emissions are due to the manufacturing processes, while 471 are
due to fuel combustion. This category utilizes 71 of the energy consumed
or 1945 MBtu/hr; 1084 MBtu/hr are due to coal use, 66 MBtu/hr due to oil
consumption, and 776 MBtu/hr due to the combustion of natural gas. Thus,
both process and fuel combustion emissions pose air pollution problems
for this category.
46
-------
Estimated uncontrolled process emission factors for the various sub-
categories are shown below:
SIC
20
2011
2013
2015
2036
2041
2042
2046
2061
2085
2095
Description
Food and Kindred
Msat Packing Plants
Sausages
Poultry
Fresh Fish
Flour
Animal Feed
Wet Corn Milling
Cane Sugar
Distilled Liquors
Animal Fats
Suspended Particulate
Emission Factors
(Ib/tori of finished product)
0.3
0.3
0.3
0.1
23.0
60.0
8.0
225.0
8.0
9.0
Source: Op cit
This category accounts for 11.6% of process weight flow, amounting to 524
tons of material moved per hour. Application of the Illinois control regu-
lations will achieve a reduction in emissions from 841 Ib/hr to 315 Ib/hr
or 63%. Emissions per ton of process weight will then be .6 Ib/t/hr.
Correlation results for this category indicate that controlled emis-
sions are correlated highly with energy (r = .92) and employment (r = .83);
however, energy and employment are related (r = .58), as are space and
employment (r = .85). Process weight is somewhat related to employment
(r = .44) and space (r = .38).
The general emission model R is .95 with energy contributing .90 and
2
process weight only .05. The restricted emission model R is .80, with
space contributing .77, while employment contributes only .03. The restrict-
2
ed process weight model R is only .19, with employment contributing the
47
-------
entire share of explanation. The restricted energy model R is .81, with
space contributing .78, while employment contributes only .03. These
results indicate that space is the most useful planning parameter in
estimating energy and hence emissions for this category.
3.2.6 Summary of Analysis of Manufacturing Emissions
The previous sections have analyzed the five largest polluting sectors
in the Chicago study region to determine those parameters best explaining
controlled particulate emissions. Contributions to controlled emissions
were assumed functions of process weight and energy. This assumption is
especially true if the Illinois process regulations constrain both process
emissions and emissions due to fuel combustion as described in Appendix B.
In the former case, a non-linear (exponential) relationship holds, while
the latter relationship is indeed linear. Thus, not only were the parameters
of land use and employment tested in a multiple linear model, predicting
emissions along with process weight and energy, but these parameters were
also tested for power in predicting the process weight and energy variables
themselves.
For the five major polluting sectors analyzed in detail, the results
are sporadically encouraging. SICs 32, 33, and 28 are dominated by process
emissions and SICs 29 and 20 are dominated by fuel combustion emissions.
Space is a useful predictor of process weight for SIC 32, and a useful pre-
dictor of energy for SICs 29 and 20. Employment is a useful predictor of
energy for SICs 32 and 28. Unfortunately, neither land nor employment is a
consistently good predictor of process weight flow and further investigations
are required beyond the scope of this study.
Similar results for the remaining seven sectors are left to the reader
to pursue in Table 3.2 and Appendix D.
48
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3. 3 ESTIMATION OF EMISSIONS FROM RESIDENTIAL/COMMERCIAL LAND
Emissions from residential and commercial (R/C) land are due primarily
to fuel combustion for space heating. Therefore, the variance in emissions
can be expected to relate directly to the size and construction of the
building, as well as to the efficiency of the heating unit and the type of
fuel burned. In this study, the size of the building as measured in total
square footage is used to classify commercial buildings, and the number of
dwelling units (DU) is used for residential buildings. The distribution of
commercial buildings in Chicago by floor space is shown in Figure 3.5.
Note that the skewness in this distribution is similar to that which
occurred in the scale of manufacturing sources.
Buildings were classified in two ways for analysis purposes:
1) Light R/C (LRC)
<20 DUs for Residential
<20000 square feet for Commercial
(Data aggregated on a square mile basis.)
2) Heavy R/C (HRC)
>20 DUs for Residential
>20000 square feet for Commercial
(Data retained as point sources.)
Heavy R/C is further divided into intervals of 100 dwelling units or
100,000 sq ft.
It is desirable for planning purposes to know if mean energy use per
3 2
DU or 10 ft is a predictor of energy (and hence emissions, given fuel use)
in each of the classes indicated. To test this hypothesis for HRC, a sample
was drawn from the data for each of the heavy residential (HR) classes, as
shown in Table 3.3. The sample was selected so as to achieve a uniform
sample size in each of the heavy residential building size classes.
Analysis of variance was used to test the significance of variation in
49
-------
tn
O
80
60
Q_
5
1000
BUILDING FLOOR SPACE, 103ft2
Figure 3.5. Conmercial/institutional building size
distribution in Chicago.
-------
Table 3.3 SAMPLE DATA FOR HEAVY RESIDENTIAL ENERGY USE
(Single Source Data > 20 DU/Euilding)
1.
2.
3.
4.
5.
6.
I.
2
3.
4.
s.
f>.
20 - 100 DIJ's
Btu x 106/ Btu x 106/
Dav DU Day/DU
S 21 .38
13 27 .48
IS 43 .38
13 40 .45
47 9.1 .52
19 71 .27
M c a 11 .41
200 - 3UO DIJ's
106 232 .46
181 203 .89
50 250 .20
61 250 .24
72 22!) .32
86 273 .33
Mean .41
100 - 200 DlJ's
Btu x 106/ Btu x 106/
Day DU nay/DU
S3 148 .36
30 144 121
58 187 .31
21 114 .15
27 103 .26
53 190 .28
Mean .26
300 - 400 DU's
162 338 .48
94 364 .26 .
170 , 320 .53
50 312 .16
158 324 .49
170 300 . 57
Mean .37
400 DU's
Btu x 106/ Btu x 106/
Day DU Day/Du
471 1256 .38
370 628 .59
247 550 .45
226 640 .35
130 585 .22
89 413 .21
Mean .37
Grand Mean .37
DFB 4
BSS .11
DFW 25
WSS 1.08
F .64 - (NS)
(J-l
-------
the mean dwelling unit energy consumption of the building size classes of
HR. The results are displayed in Table 3.3 and indicate that means of
dwelling unit energy consumption are not materially different at the .05
significance level. Therefore, for this sample, we can conclude that energy
estimation can be done on a dwelling unit density basis using .37 x 10
Btu/day/DU as an estimator. A similar result is obtained for heavy commer-
•z 7
cial classes (HC) using 10 ft , as shown in Table 3.4 indicating a mean of
.29 MBtu/day/103 ft2 for all buildings greater than 20,000 ft2.
The difference of means between heavy (HR) and light (LR) residential
for the small sample was tested using analyses of variance. The results
are as follows:
LR Mean
No. Sample Pts.
HR Mean
No. Sample Pts
DFB
BSS
DFW
WSS
F
. 53 MBtu/day/103
25.
.37 BMtu/day/103
30.
1
.35
53
1.44
12.96(s)
ft2
ft2
This result indicates that, for this sample, the hypothesis that the same
mean estimator can be used for both light and heavy residential must be
rejected. Therefore, we would use .53 x 10 Btu/day/DU as an estimator of
light residential buildings and conclude that large residential buildings
utilize less heat per dwelling unit than small residential buildings. This
could be partially explained if small residential buildings generally were
higher in square footage of floor space per DU than large residential
buildings, but these data were not available to test this hypothesis.
52
-------
Table 3.4 SAMPLE DATA FOR HEAVY COMMERCIAL ENERGY USE
(Single Source Data 20 x 103 Sq. Ft./Building)
1.
2.
3.
4.
5.
6.
1.
2.
3.
4.
5.
6.
20-100 x 103 sq ft
Btu x 106/ 103 Btu x,106/Day/
Day Sq.Ft. 10 Sq.Ft.
32 87 .37
30 81 .37
14 75 .19
24 54 ' .44
11 45 .24
17 '37 .46
Mean .35
300-400 x 103 sq ft
51 350 .15
92 3SO .24
84 351 . .24
117 312 .38
165 329 .50
61 350 .17
Mean .28
100-200 x 103 sq ft
Btu x 10/6 103 Btu x,10/6/Day/
Day Sq.Ft. 10 Sq.Ft.
46 150 .31
16 110 .14
36 120 .30
21 112 .19 *
17 130 .13
30 150 .20
Mean . 21
>400 x 103 sq ft
130 420 .31
261 768 .34
136 631 .22
115 637 .18
340 510 .67
90 504 .18
Mean . 31
200-300 x 103 sq ft
Btu x 106/ 103 Btu x,106/I)ay/
Day Sq.Ft. 10 Sq.Ft.
51 296 .17
120 275 .44
32 200 . 16
101 240 .42
60 238 .25
77 230 .33
Mean . 30
Grand Mean .29
DFB 4
BSS 105
DBV 25
WSS .42
F .74 - (NS)
en
OJ
-------
A similar result is obtained for commercial buildings, as shown in
the following table:
LC Mean
No. Sample Pts.
HC Mean
No. Sample Pts.
DFB
BSS
DFW
WSS
F
.60 MBtu/day/103
25.
.29 MBtu/day/103
30.
1.
1.33
53.
1.02
66.5
ft2
ft2
54
-------
For planning purposes, it is desirable to have energy use a linear
function of dwelling units and independent of building size. If this assump-
tion is approximately true, then dwelling unit density or floor area ratio
(FAR) can be used. The previous section shows that an average estimator of
energy use per unit is sufficient for the large heavy residential and com-
mercial building classes.
Another way to view this result is that energy for HR use is linear
with dwelling units per building. Figure 3.6 shows the fit of a simple regres-
sion model to the sample data. The result indicates that the regression line
Y = .40X-6.7 ,
where
Y is Btu x 106/day
and X is Dwelling Units
is a good estimator of energy use for the small example of heavy residential
buildings, defined in the previous section.
A simple regression for the entire sample of heavy residential build-
ings for the City of Chicago that included 1103 sample points is also shown in
Figure 3.6. The regression line is given by
Y = .59 x - 11.7 ,
where the units are the same as above. The regression slope for the large
sample has shifted upward significantly, indicating a bias in the small sample
toward low Btu x 10 /day/DU readings.
55
-------
On
600
o
-o
13
-»—
CO
O)
o
e?
cc
LU
400
200
Y = 0.40 X+6J1 SMALL
r2=0.88 /SAMPLE
Y = 0.59 X-H 1.71 LARGE
r* = 0.49 /SAMPLE
1
1
0
400
800 1200
DWELLING UNITS
1600
2000
Figure 3.6. Large residential energy use.
-------
The simple regression results for heavy commercial buildings are
shown in Fig. 3.7. If building size is known, the simple regression model
Y = .SOX - 4.2 ,
where
Y is 106 Btu/day
and X is 10 sq ft ,
can be used as an estimator. The linear fit is displayed in Fig. 3.6.
A sample regression for the entire sample of heavy commercial build-
ings for the City of Chicago that included 1373 sample points is also shown
in Fig. 3.7. The regression, line is given by
Y = .19 x + 24.5
when the units are the same as above.
The regression slope for the large sample has shifted downward some-
what , indicating a possible bias in the small sample toward high
106 Btu/day/103 sq ft readings.
57
-------
600
o
"O
03
<£>
O
400
Y = 0.30 X-4.21 SMALL
r*=0.56 /SAMPLE
- Y = 0.19X^-24.51 LARGE
r2 = 0.26 /SAMPLE
200
LU
UJ
0
200 400 600
THOUSAND SQUARE FEET
800
1000
Figure 3.7. Large commercial energy use.
-------
4.0 SUMMARY AND CONCLUSIONS
Comprehensive planning as a control mechanism to maintain regional
air quality depends on: (1) the applicability of the plan over time;
(2) the ability of public administrators to implement the plan; and
(3) the ability of planners to forecast the air quality effects of land
use decisions and policies and to rank land use or effects-assessment
plans. The latter element has been addressed in this study.
The basic criterion for evaluating the land-use-based emission esti-
mation methods was the ability of the estimates to reproduce regional air
quality as represented by the AQEM dispersion model, using the best
available point-source inventory information. When data deficiencies
prohibited the application of this criterion, standard statistical
measures were applied. Statistical techniques used were analysis of
variance, multiple regression, and product-moment correlation analysis.
Emission inventory and land use data were drawn from the Chicago
metropolitan study area as described in Appendix A.
The following conclusions are drawn from this study:
(1) The major problem with the air quality prediction on the basis
of manufacturing land use data is in the wide variance and
skewness in emission density distributions; severe distortions
in air quality representations occur when mean and median
estimates based on land use are employed in the AQEM model.
59
-------
(2) The use of mean and median estimates in representing air
quality through dispersion modeling showed that results are
highly sensitive to these estimates, particularly in the
critical "hot spot" areas. A derivation of best-fit (minimum
variance in relative error) emission density estimates by
light and heavy industrial land use classes showed that the
least standard deviation in relative error was 20%, with most
of the contribution to the error occurring in hot spot regions.
From this result, it was concluded that uniform emission
density estimates by zoned land use class were insufficient
by themselves to adequately represent air quality degradation
due to manufacturing emission; measures of use intensity are
also required.
(3) Further attempts to account for the variance in manufacturing
emission patterns were made by disaggregating manufacturing
land into major industrial sectors by 2-digit SIC categories.
Since land use or spatially distributed employment data on this
level were not available, the air quality representation of
resulting estimates could not be computed. Rather, statistical
measures were used to judge the utility of various parameters
in estimating emissions. The results showed that land use and
employment were sporadically successful in explaining emissions,
process weight flow, and energy consumption. However, although
process weight frequently explained controlled emissions by
2-digit SIC class (logically, since the Illinois process control
regulation is of the Bay Area Curve Class), land use and
60
-------
employment were poor predictors of process weight. Therefore,
it can be concluded that other parameters for estimating process
weight flow need to be incorporated into the analyses, perhaps
measures of capital intensity; such measures were not available
in the data inventory used for this study.
(4) Studies of residential and commercial energy use by building
size class in the Chicago area indicated that dwelling unit
density and floor area ratio are potentially useful parameters
in estimating unit energy consumption. It was noted that a
significant difference in unit energy consumption exists between
large (high rise) buildings of greater than 20 DUs or 200,000
sq ft and small (low rise) buildings of less than that amount,
the former being more efficient. No significant difference in
unit energy consumption was observed between size classes of
high rise buildings.
61
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APPENDIX A
DESCRIPTION OF THE STUDY AREA
62
-------
APPENDIX A
DESCRIPTION OF THE STUDY AREA
The purpose of this appendix material is to characterize the Chicago
study area in terms of parameters that influence the air quality of the
region, both present and future. This description provides a rationale
for testing the utility of these parameters in estimating air pollutant
emissions from land use, since they are commonly used for forecasting
growth and development in the region. The first section characterizes
current manufacturing land use in the region, and the second estimates
development potential in the next decade. Finally, the data base used in
the study is briefly described.
A.1 CURRENT MANUFACTURING ACTIVITY IN THE CHICAGO REGION
Chicago has traditionally been a large diverse and basically stable
major industrial center as reflected by gross manufactures sales shown in
Table A.I. In 1970, Chicago's share of the Gross National Product amounted
to 5.281 or $51.4 billion. Current employment patterns in the major indus-
trial sectors of the region and in the study subregion are shown in Table
A. 2. The study subregion comprises approximately 90% of the manufacturing
employment of the region and 64% of the manufacturing employment of the
State of Illinois. The Chicago area is one of the largest electrical equip-
ment manufacturing areas in the nation and the largest manufacturer of
household electrical equipment and appliances. This industry is the largest
employer in the area with 145,000, but third in total sales volume behind
the Primary Metals and Food and Kindred Industries.
63
-------
TABLE A. 1-MANUFACTURING OUTPUT - 1970 & 1971 - METROPOLITAN CHICAGO
(In Millions)
Gross Manufacturers Sales
Value Added by Manufacture
TOTAL
Primary Metal Industries
Food & Kindred Products
Electrical Equipment & Supplies
Fabricated Metal Products
Machinery, Except Electrical
Chemicals & Allied Products
Printing and Publishing
Petroleum and Coal Products
Transportation Equipment
Paper and Allied Products •
Instruments & Related Products
Rubber & Plastic Products
Stone, Clay & Glass Products
Apparel & Related Products
Furniture & Fixtures
Lumber & Wood Products
Leather & Leather Products
Textile Mill Products
Miscellaneous
1971
$37,989
6,514
5,378
4,174
3,835
3,641
3,042
2,428
1,598
1,383
1,148
891
871
805
532
521
193
.145
103
787
1970
$37,299
6,539
5,159
4,194
3,844
0 724
2,884
2,448
1,478
1,303
1,110
894
799
723
518
521
165
145
101
750
1971 • --
$18,308
2,585
2,162
2,075
2,014
1,999
1,651
1,572
420
639
553
543
494
422
249
275
95
84
42
434
1970
$18,024
2,595
2,074
2,086
2,019
2,045
1,565
1,585
388
602
535
556
453
379
246
275
81
84
42
414
Source: Chicago Association of Commerce and Industry (CACI)
The Year-end Statistical Roundup for 1971.
-------
Table A. 2. SUMMARY OF MANUFACTURING PLANTS AND EMPLOYMENT BY MAJOR INDUSTRIAL SECTOR - 1970
2-Digit
SIC
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Adm.
Total Mfg
%/State
VRegion
STATE
Emp . Units
12561 16
118787 1441
(D)* 3
4937 80
34813 670
12000 517
23281 490
42305 484
110083 2623
57775 830
10135 99
38427 533
12387 132
36143 761
108487 606
142188 2289
214792 2985
190831 927
46476 256
41556 358
35332 756
83857 690
1377471 17548
100 100
REGION
Emp. Units
(D) 10
80352 829
(D) 3
3493 67
22266 538
7614 277
(D) 395
33746 398
91013 1947
44043 660
4996 71
(D) 451
5690 98
19311 383
67063 425
105926 1874
(D) 2239
144991 786
30323 177
(D) 303
29580 614
72157 550
96730 13097
70.4 74.9
100 100
SUB- REGION
Cook County
Emp. Units
1034 9
71344 734
CD) 3
3270 57
20367 499
6527 222
13315 346
28117 336
83210 1711
32663 567
2594 58
20715 347
5431 94
13315 270
58289 349
93960 1603
91956 1816
123635 644
26247 140
29421 271
26936 549
65311 483
817985 11110
59.4 63.4
84.3 84.7
DuPage County
Emp. Units
2218 19
179 14
716 13
1276 13
2411 89
614 23
2293 43
414 21
1276 20
2998 102
3014 156
5592 52
218 8
1170 12
259 23
2757 24
27570 645
2.0 4.2
2.8 4.9
Will County
Erap. Units
(D) 1
893 19
453 6
126 7
136 6
1319 13
1141 26
1654 17
2027 7
(D) 4
1165 20
2163 9
1831 22
CD) 47
757 7
221 8
CD) 6
365 7
28934 233
2.1 1.3
3.0 1.8
ON
Cn
*D - Denotes figures withheld to avoid disclosure of operations of individual reporting units.
Source: County Business Patterns, Illinois, 1970.
-------
Current manufacturing land use for the study area is shown in Figure
A.I. The subregion contains a total of 100 square miles of manufacturing
land or approximately 5% of the 2130 square miles of surface area.
Approximately 41 square miles is devoted to heavy industrial use, while
the remainder is devoted to light and general manufacturing uses.
A.2. MANUFACTURING GROWTH POTENTIAL IN THE CHICAGO STUDY REGION
Total manufacturing activity in the Chicago region is expected to
increase at a stable rate over the 10-year period from 1970 to 1980.
The Department of Health, Education and Welfare sponsored research on
economic projections for air quality control regions throughout the
country. This research was conducted by the U.S. Department of Commerce,
Office of Business Economics, Regional Economics Division, and resulted
in the publication, "Economic Projections for Air Quality Control Regions."
Table A.3 shows the resulting productive growth factors for the Chicago
region through 1980. A base year of 1967 is used with projections made
for 1970, 1975, and 1980. "Growth factors" for each economic activity
are given. For example, the growth factor in Chicago for Food and Kindred
Products in 1975 is 114.8 which means that 1975 production will be 1.148
as great as the 1967 production levels in the region.
The Chicago region will continue to dominate manufacturing employment
in the State, increasing approximately 11% and accounting for 80% of the
statewide increase in manufacturing employment according to the state
Office of Planning and Analysis projections. Figure A.2 shows recent
manufacturing employment changes in the Chicago region and forecasted
employment for 1985 by the Northeastern Illinois Planning Commission.
66
-------
0 • < D < 10 (NOT RECORDED)
10 • < H < 40
40 • < 0 < 100
Figure A.I. Total industrial land use
in Chicago study region.
67
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Table A. 3
Growth Factors - Chicago^ Air Qua 1 ity Ccnt.ro 1 Rsgion*
(1967 = 100.0)
Item 1970 1975 1980
Manufacturing 108.5 128.5 152.4
Food * Kindred Products 102.5 114.8 128.6
Textile Mill Products 104.1 115.1 127.2
Apparel + Other Textiles 104.2 116.7 130.6
Printing + Publishing 105.2 122.8 143.4
Chemicals •«• Allied Products 114.7 140.5 172.1
Umber + Furniture 115.2 134.0 155.8
Machinery, All 108.0 131.1 159.3
Machinery-, Excl. Electrical 106.0 123.4 143.6
Electrical Equipment + 109.8 138.3 174.1
Supplies
Transportation Equipment 122.4 145.8 173.9
Motor Vehicles + Equipment 147.4 180.2 220.2
Transportation Equipment, 107.3 125.3 146.2
Excl.
Other Manufacturing 108.7 128.1 151.2
Paper + Allied Products 109.5 135.2 166.9
Petroleum Refining 131.2 145.6 161.7
Primary Metals 108.9 123.6 140.2
Fabricated Metals + Ordinance 102.0 121.3 144.6
Miscellaneous Manufacturing 112.3 137.2 167.6
Stone, Clay and Glass 108.5 125.7 145.7
Other Misc. Manufacturing 113.3 140.0 173.0
•Source: ^^0^ Projections for Air Quality Control Regions. A report to
the National Air Pollution Control Administration, ffiW, prepared by
the U.S. Dept. of Commerce, Office of Business Economics, June 1970.
68
-------
1000 h
200
100
0
• TOTAL SIX COUNTY SMSA
o TOTAL THREE COUNTY
D COOK COUNTY
X DuPAGE COUNTY
+ WILL COUNTY
1968
1969
1970
Sources: County Business Patterns. U.S. Bureau of Census: 1968-1971.
Northeastern Illinois Planning Commission Planning Paper
No. 10. Revised, 1972.
Figure A.2. Manufacturing employment trends Chicago SMSA.
69
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Although it is difficult to project the fraction of growth that will
result in new development or precisely where this new development will
locate, some indications can be derived from land availability in the
region. Presumably, this reflects regional planning for public and private
transportation facilities, wastewater treatment systems, utilities, etc.,
as well as other locational advantages for manufacturers inclined to
locate in the Chicago region.
Land zoned for manufacturing use in the study region totals approxi-
mately 267 square miles. In a ring surrounding the current high peaks of
air pollution in the area, 19 square miles of land are currently used for
manufacturing, while 84 square miles are zoned for manufacturing use
(Figure A. 3), a potential increase of 342%.
While the area of land zoned reflects potential manufacturing develop-
ment as currently planned, no indication is given of the rate at which
development is actually taking place. The Chicago Association of Commerce
and Industry conducts an annual survey of industrial parks and districts
in the metropolitan Chicago area. The summary table of this survey for
1971-72 is shown in Table A.4. This table indicates Will County as the
most rapidly developing county in the study region having developed approxi-
mately 1 square mile of industrial land in the year under consideration and
opening up approximately 1-1/2 square miles in new industrial districts.
Suburban Cook County leads in total acreage of industrial development, but
a significant withdrawal of lands from industrial use has occurred princip-
ally in the southern portion of the County. DuPage County leads in lands
available to manufacturing, but is not realizing the rapid industrial
growth that is occurring in Will County.
70
-------
.2
Current Mfg. Land Use - 18.78 mi
Zoned Mfg. Land Use - 83.86 mi'
Growth Potential Factor - 342%
Figure A.3. Potential for Manufacturing Land development in
area surrounding region at critical concern.
71
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TABLE A.4. INDUSTRIAL PARKS SURVEY • 1971-1972*
Number ot
City of Chicago
Suburban Cook Co., III.
North Cook
West Cook
South Cook
DuPage Co., III.
Kane Co III
Lake Co.. III.
McHenry Co., III.
Will Co., III.
Lake Co., Ind.
Porter Co., Ind.
Chicago metropolitan
area
industrial
1972
37
149
79
31
39
48
26
19
1
23
13
2
318
parks
1971
36
156
83
29
41
49
27
21
1
18
12
2
319
Total no. of acres
of land
1972
2.826
14,732
8.640
1,735
4.377
10,325
4.123
2,642
250
7,502
1,111
770
44,301
in parks
1971
3,064
17,190
8.720
1.654
6.416
10.560
•3,955
2,846
260
6,766
1,092-
770
46,103
No. of acres sold
and leased
1972
2,416
8.295
5.274-
1.483.
1.542
2.983
943
414
0
1.687
225
144
17,116
through
1971
2,355.
8,302 .
4,891
1.402
2,089
3.035
1,041
701
0
1,088-
257
144
16,923
Number ot acres
available
1972
410
6,437
3.366
252
2,835
7.342
3,175
2.228
250
5,815-
886
626
27,185
for industry
1971
709
8.888
3,829
252
4.407
7,525"
2,914
2,145
260
5.6/8
835
626
29,183
•June to June
Source: Chicago Association of Commerce and Industry
72
-------
A.3 DATA BASE FOR THE STUDY REGION
The data base for this study consists of source inventories, land use
information by square mile and a permitted-use zoning policy. The data is
used for testing the estimation procedures as described in the body of this
report. A description and summaries of the data are included here to
further characterize the source patterns of the study region.
The regional source inventory file consists of source identification,
fuel combustion, process emission, and stack data. A description of the
data as recorded in the inventory can be seen in Table A.5. The data were
collected as part of the Illinois State Implementation Planning Program
during the summer of 1971 by a team of students, who, under the supervision
of the Argonne Center for Environmental Studies, surveyed the entire state
for manufacturing source information. The Census Bureau publications,
"County Business Patterns in Illinois" and the "Directory of Manufacturers,"
were used to guide the information collection operations. The Illinois
State emission inventory contains planning parameters such as land use,
employment, energy consumption by type of fuel, and process output data,
in addition to emission information.
Emission factor information was used to derive total emissions from
data surveyed. No direct emission testing was performed in collecting
these data. Information was obtained by secondary source review, telephone
contact, or site visit. It should be noted that the City of Chicago sup-
plied combustion information to the State directly, in their own format.
Therefore, fuel combustion from manufacturing sources within the City proper
was not collected in the survey. The emission factors utilized in the con-
version equations were obtained from a report by the U.S. Environmental
Protection Agency.
73
-------
TABLE A.5. STATE OF ILLINOIS EMISSION INVENTORY FILE PARAMETERS
Source Identification Fuel Combustion Process Emission Source
Stack Data
Source identification
number
Source name
Source street address
Boiler capacity Emission factor table code Height (ft)
(106 BTU/hr)
City
Zip code
Geocode
X-Coord inate (Km)
Y-Coordinate (Km)
Stanc
das?
ard land use
ification number
Lot size (acres)
Employees
Zoning
Coal (tons/year)
Oil (105 gal/
year)
Oil grade
Gas (106 ft3/
year)
Heat content:
Process quantity
Process weight rate
(Ib/hr)
Process name
Emission factor
Coal (103 BTU/lb) Emissions (Ib/hr)
Oil (103 BTU/gal)
Gas (BTU/ft3)
Percent ash coal
Particulate
emission factor
Emissions (Ib/hr)
Inside diameter (ft)
Temperature (°F)
Velocity (ft/sec)
Gas volume (acf/sec)
Number of units
-------
Tables A.6 and A.7 summarize the source file data by 2-digit SIC
code for those classifications for which data existed in the source file.
The SIC classes were ranked according to their percentage contribution to
total emissions as shown in Table A.8. The top 12 ranking classes were
then selected for analysis, accounting for 99% of emissions and 90% of the
sources in the file.
In addition to point-source information, the estimation methodology
requires land use information by zoning classification. For purposes of
this study, manufacturing land use was divided into two categories—heavy
industrial CHI) and light industrial (LI). Current land use information
for the Chicago region was obtained from the regional planning body, the
Northeastern Illinois Planning Commission (NIPC). The land use inventory
was collected on a square-mile basis for the Chicago region and computerized
as fractions of land area for each land use class•
Finally, the methodology requires that a permitted-use zoning policy
be established. This was accomplished by a survey of county zoning adminis-
trators. The results indicate that heavy and light industrial activities
are most commonly defined as shown in Table A. 9.
75
-------
TABLE A.6. MANUFACTURING DATA StMWRIES
by 2-digit SIC code
SIC
Codo
Process
Land Employ- Weight Energy Coal Oil
(acros) moiit (t/iir) (MBtu/hr) (MEtu/iir) p-Ctu/hr)
UC
Qis Emissions
Q-Btu/lir) (Ib/hr)
CT \ \ \ %
Emissions UC UC CT CT »
flb/hr) PR PC PR FC Sources
27. 0
?"> 7
?."•• Z
?«,. If
?7. M
?•«.
239&!61_
775.72
10*16
5^.76
2365.65
1211.33
32(1.31
il'82
?5«17
12*97.9* 119269
8*0.61
76.S3
0.68
1293.88
1981.72
52"1.27
.36
.32
.56
.79
.53
.71
.77
.21
.26
.50
1.85
2
88563
2138
179S
*2*
699
28
312
31*.9*
*.?!
17103
5.31
127.pp
9.8*
767.37
59.27
2.32
133*.62
732*86
160.68
*1.37
5,1.1,2
5.*3
*9.72
29
0
99
99
*9
59
9*
98
0
98
98
98
5*
93
36
72
98
9*
70
103
1
1
50
0
5
1
101)
2
1
2
6
6*
27
2
5
53
9*
e
88
67
8(1
9B
82
69
89
e
63
65
71
70
12
96
*6
6
100
11
33
19
If
17
10
3
9
36
35
28
88
3
17
21
*
1
16
16
25
12
79
15
1
5f
83
56
56
31
12
6
it
-------
TABLE A.7. MANUFACTURING DATA PERCENTAGES
by 2-digit SIC code
SIC
Code
20
22
23
24
2S
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Land
2.25
0.05
0.13
1.22
0.55
1.89
0.10
41.13
12.43
2.11
0.10
15.06
5.17
6.29
5.64
2.11
2.87
O.S7
0.34
Employ-
ment
4.93
0.21
0.25
1.24
2.14
3.31
3.19
10.48
2.72
2.19
0.15
3.60
12.61
10.12
17.52
12.24
8.74
1.69
2.68
Process
Weight
1.85
0.01
0.00
0.49
0.00
1.99
0.01
12.62
16.81
0.37
0.00
61.30
3.57
0.24
0.43
0.05
0.07
0.00
0.19
Energy
11.57
0.08
0.03
0.15
0.39
2.29
0.32
11.35
35.91
1.52
0.24
5.47
15.32
7.91
4.20
1.38
1.32
0.34
0.21
.Coal
45.25
0.00
0.00
0.00
0.00
6.29
0.00
31.62
0.00
0.00
0.00
1.54
0.00
0.19
9.49
0.22
5.20
0.00
0.21
Oil
3.65
0.00
0.25
0.78
0.39
2.97
0.00
8.04
48.72
1.36
0.65
2.54
10.89
5.94
8.36
2.23
0.54
2.46
0.25
Gas
6.21
0.11
0.00
0.08
0.47
1.44
0.43
7.94
40.92
1.84
0.22
6.67
18.93
9.69
2.56
1.48
0.70
0.09
0.20
UC
Emissions
0.70
0.06
0.00
0.24
0.18
1.08
1.66
4.36
4.28
0.41
0.00
74.25
7. 55
2.46
1.51
0.36
0.59
0.02
0.26
CT
Emissions
6.97
0.09
0.01
0.38
0.12
2.81
0.22
15.52
16.97
1.31
0.05
29.43
16.21
3.11
3. 55
0.92
1.12
0.12
1.10
I
Sources
4
1
0
3
3
5
2
15
7
3
0
9'
15
10
10
6
2
1
4
-------
Table A.8. SIC CLASSES BY PERCENTAGE CONTRIBUTIONS TO TOTAL EMISSIONS
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Other
2 -Digit
SIC Code
32
29
33
28
20
35
34
26
30
37
39
36
24
27
25
38
22
31
23
-
2 of
Controlled
SP* Ends.
29.4
17.0
16.2
15.5
7.0
3.6
3.1
2.8
1.3
- i
1..
.9
.4
.2
.1
.1
.1
.05
.01
.04
Cumulative
% of Cont.
SP Emis.
29.4
46.4
62.6
78.1
85.1
88.7
91.8
94.6
95.9
97.0
98.1
99.0
99.4
99.6
99.7
99.8
99.9
99.95
99.96
100.00
No. of
Sources
50
40
83
79
21
56
56
25
15
12
20
31
16
12
16
6
4
1
1
-
1 of
Sources
9
7
15
14
4
10
10
5
3
2
4
6
3
2
3
1
1
0
0
0
Cumulative
1 of
Sources
9
16
31
45
49
59
69
74
77
79
83
89
92
94
97
98
99
99
99
99
CO
-------
Table A.9. ACTIVITIES BY ZONING CLASS
Heavy Industry
Light Industry
SIC
Description
SIC
Description
26 Paper and Allied Products
27 Printing, Publishing, and
Allied Industries
28 Chemicals and Allied Products
29 Petroleum Refining and
Related Industries
30 Rubber and Miscellaneous
Plastic Products
31 Leather and Leather Products
32 Stone, Clay $ Glass Products
33 Primary Metal Industries
20 Food and Kindred Products
21 Tobacco Manufactures
22 Textile Mill Products
23 Apparel § Other Finished Products
Made from Fabrics § Similar
Materials
24 Lumber § Wood Products,
Except Furniture
25 Furniture and Fixtures
34 Fabricated Metal Products, Except
Ordnance, Machinery, § Trans-
portation Equipment
s
35 Machinery, Except Electrical
36 Electrical Machinery, Equipment,
and Supplies
37 Transportation Equipment
38 Professional, Scientific, and
Controlling Instruments; Photo-
graphic and Optical Goods;
Watches and Clocks
39 Miscellaneous Manufacturing Industries
79
-------
APPENDIX B
SUMMRY OF STATE OF ILLINOIS
PARTICULATE EMISSION CONTROL REGULATIONS
80
-------
APPENDIX B
SUMMARY OF STATE OF ILLINOIS
PARTICULATE EMISSION CONTROL REGULATIONS
In implementing the federal guidelines for the State of Illinois, the
Illinois Pollution Control Board adopted a set of comprehensive air pollu-
tion control regulations designed to limit emissions of sulfur dioxide,
participate matter, nitrogen oxides, carbon monoxide, and hydrocarbons
from stationary sources throughout Illinois.
An additional provision that would have effectively banned coal for
residential or commercial use in the Chicago area by mid-1975 was not
included in the package due to a temporary restraining order. This order
was entered against the Board by a Cook County circuit court judge, who
termed the ban unconstitutional as presently structured.
The new regulations represent a major effort by the state to control
the air contaminants, and to form the heart of the Illinois program for
meeting federal standards and combatting air pollution. Except for con-
trols on particulate matter, the state previously did not have emission
limits on these air pollutants.
Specifically, in regard to particulate air contaminants, the
program:
1) Significantly tightens limits on the emission of particulate
matter from such operations as steel mills, oil refineries,
81
-------
electric power plants, cement plants, and corn wet-milling
facilities.
2) For the first time, requires sophisticated new equipment to
control emissions from coke ovens.
3) Greatly strengthens existing standards for emissions from
incinerators.
4) Adopts a statewide nondegradation standard to prevent the
unnecessary deterioration of air that is now clean, and to
prevent new sources of pollution from being located in
inappropriate places.
5) Institutes a statewide requirement of operating permits
for all pollution sources as an aid to enforcement.
6) Requires sources to monitor their emissions, to keep detailed
records, to adequately maintain their equipment, and to make
regular reports to the state.
7) Specified participate emission standards and limitations for
new and existing emission sources, for incinerators, and for
fuel combustion emission sources.
The air pollution regulations are designed to enable the state to meet
the national ambient air quality standard by 1975.
In the case of Illinois manufacturing sources, emission standards are
divided into fuel combustion and process regulations. Fuel emission regu-
lations in the Chicago major metropolitan area require that no person
shall cause or allow the emission of particulate matter into the atmo-
sphere from any existing fuel combustion source to exceed 0.1 pound of
particulate matter per million Btu of actual heat input in any one-hour
period.
82
-------
For process emission sources, no person shall cause or allow the
emission of participate matter into the atmosphere in any one-hour period
from any existing process emission source in excess of the allowable
emission rates specified in Table B.I, either alone or in combination
with the emission of participate matter from all other similar new or
existing process emission sources at a plant or premises. Interpolated
and extrapolated values of the numbers in Table B.I for process weight
rates up to 30 tons per hour shall be determined by using the equation:
E = 4.10 (P)0'67 (B.I)
and interpolated and extrapolated values of the data for process weight
rates in excess of 30 tons per hour shall be determined by using the
equation:
E = [55.0 (P)0'11] - 40.0 , (B.2)
where E = allowable emission rate in pounds per hour
and P = process weight rate in tons per hour.
The process weight regulation in the Illinois Implementation Plan was
modeled after the Bar Area Curve developed by the Bay Area Pollution Con-
trol District in San Francisco. This process weight regulation was based
on well-controlled process industries found there. The Bay Area Curve
rises to an allowable emission of 40 pounds per hour with increasing size
of operation, and then allowable emissions increase at a reduced rate
above 40 pounds per hour with increasing size of operation. The Bay Area
Curve, as applied to the State of Illinois regulation, can be seen in
Figure B.I. The Bay Area regulation is quite stringent for sources with
a combination of large process weight rate and large emission factors,
83
-------
TABLE B.I
Illinois Standards for Existing
Process Emission Sources
Process Weight Rate
Pounds Per Hour
100
200
400
600
800
1,000
1,500
2.000
4,000
6,000
8,000
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
Process Weight Rate
Tons Per Hour
0.05
0.10
0.20
0.30
0.40
0.50
0.75
1.00
2.00
3.00
4.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
500.00
Allowable
Emission Rate
Pounds Per Hour
0.55
0.87
1.40
1.83
2.22
2.58
3.38
4.10 .
6.52
8.56
10.40
12.00
19.20
25.20
30.50
35.40
40.00
41.30
42.50
43.60
44.60
51.20
55.40
58.60
61.00
63.10
64.90
66.20
67.70
69.00
84
-------
100.0
OO
en
o:
LU
O-
t/1
o
a.
oo
oo
UJ
DD
et
PROCESS WEIGHT RATE: POUNDS PER HOUR
Figure B.I. State of Illinois allowable emission rate for point-source control
-------
such as the SIC class 32 (stone, clay, and glass industries). It is
noticeably lenient for sources with small emission factors and large
process weight, such as SIC 28 (chemicals and allied) and SIC 29
(petroleum).
86
-------
APPENDIX C
DERIVATION OF BEST-FIT EMISSION-DENSITY
ESTIMATORS BY MANUFACTURING ZONING CLASS
87
-------
APPENDIX C
DERIVATION OF BEST-FIT EMISSION-DENSITY
ESTIMATORS BY MANUFACTURING ZONING CLASS
This appendix provides the derivation of the equations for deter-
mining those emission-density estimates by manufacturing zoning class
that best represents air quality as calculated by the Air Quality Display
Model (AQDM) dispersion model, using best available point-source emission
inventory information in the Chicago region. For purposes of calculation
with AQDM, the air quality is determined at 237 receptor locations in the
region, yielding 237 points at which to compare the representation
achieved by using detailed point-source information with that achieved
using an emission-density representation by land use class.
For purposes of this study, two manufacturing classes are considered,
heavy industry (HI) and light industry (LI). Two estimates of emission
density are sought, EET and ED , that best represent air quality in the
sense of minimizing the standard deviation in the relative error in cal-
culated air quality between the point-source (PS) representation and the
emission-density (ED) representation, and simultaneously achieve a zero
mean relative error (unbiased). In this appendix, the emission-density
formulation of the AQDM dispersion model is derived, after which the
best-fit equations are displayed.
88
-------
If it is assumed that the region is divided into geographic grid
squares indexed by £, that k is an index of receptor points, and that
m is an index of land use or zoning class, then the pollutant concen-
tration X, at receptor point k, is given by:
where X, is the pollutant concentration at receptor point k,
a •. is the dispersion model transfer coefficient describing
the contribution of a unit emission from land use class m
in grid square a to the concentration at location k
(assumed independent of emissions) ,
EB7 represents the expected emission density in tons/day/acre
for land use class m in grid square a,
A™ is the specified percentage of land designated for land
use class m in grid square £,
If it is further assumed that the emission density, ED1?, is a vari-
Xr
able to be estimated uniformly over the entire region by land use class m,
then the following emission-density formulation of the dispersion equation
results :
or =< (C.2)
m £ £K £
or X, = I ED1" Zc£kA£ (C.3)
m £
where X. = I P£ ED111
m
89
-------
For the case where m = HI, LI, we have:
Xk = P-El) + P-ED . (C.5)
PS
Let XT, be the calculated concentration at reception
point k using the point- source formulation
ED
and Xv be the calculated concentration at reception
point k using the emission-density formulation
(eq. B.5).
Then the relative error is given by
PS
xk
PS
xk
1 -
ED
PS
*k
-fp^1 • ED I + pF-ED1"1 1
PS
X
k
/ri°\ HT /PLT'" TT
1 ' PTT - 1 K ' FT)
: CJJ i CJJ
1 PS / 1 PS /
V 1 . \ x /
k k
where JHI
_ k
"PS
xk
and DLI
pLI _ Pk
Pk ."
(C.6)
90
-------
crid the ^.ean relative error is:
, EDHI pHI EDLI „ -LI
j,, = 1 - irr- E - -
„
K
irr- , TT- -
N „ k N K
and the standard deviation o£ the re 1 stive error is
1/2
,? (C.8)
t'c select the best-fit uribiassd errJ.ss ion-density estijnators,
stftUT **LI
EiJ and ED , the following minimization problem must be solved:
min OR (C.9)
s.t. PR = 0 .
Tliis can be solved explicitly using the Lagrange multiplier technique to
give the following result :
IBHI = -N ZP" Z P?1^1
**I I
ED =
D
N z ft1 z ]
SHI 2
- 1_
,, K ,
K K
p
k 'k k k M<
(C.ll)
91
-------
where
D = EP" EPJ:1 E
k K k K k
E P E
k k k
k
2
92
-------
APPENDIX D
CORRELATION AND MULTIPLE
LINEAR REGRESSION RESULTS
93
-------
Table D.I. CORRELATION TABLE - SIC 32 Stone, Clay $ Glass Products
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.13
-
Process
Weight
.66
-.10
-
Energy
.04
.90
-.14
-
Coal
.15
.82
-.06
.62
-
Controlled
Emissions
.58
-.004
.74
-.05
-.01
-
-------
COu-LL *',0
AMONG EMISSIONS AND
B5/M3/73
PAGE 17
vA«IiHLE
CASES
STO OEV
SIC 32 Stone, Clay 5 Glass
ProJiKts
£u?fc5;» qfl 9J».cl59 2*»?.9510
'•<"S-i 5 &* 1 3 S . q ', 0 3 27R.8376
>-;•£<, •;? 3*6.7 >68 P69.3699
-•//«, «>1 M.7 172 5.1955
= K/7 S.1 1771. 2M? 49B5.0156
:.-/f.o «!« 2h.(-l23 29.8296
OEt-F'.OEMT VA»IA8LE.. VAOCHH
SUMMARY TABLE
0.56858
.»-;-. 6 "'75^62 0.57(196
.!»••.»; M.?^*^*! 0.57162
iCr,-.- T»NT I
to
tn
', ( PF •.•;!'. I V&^ 1 A'H *-.. VAP'JOB
SUMMAKY lAHLt
.•A'lAH.IF MULfll'Lt R K SIJinKK
W' M 0.5U37T 0.1«!)ri
1'.' •.•.!/•. e )
SUMMA.KY tABLt
VfJ|f..;|t MIILTII'LC K K SUUAKt-
VA'01? 0.6S/52 0.^3234
Vi-' )3 0.6H2SO O.'tbSRl
icr •, ,r/.'. ri
bpacc (acres)
.. limployncnt
Process Weiclit (t/l>r)
l-Jiorgy (Mlltii/hr)
Coal (MUu/)lr)
Uncontrolled Emissions (Ibs/hr)
ControllcJ Ijnissioiis (ll>s/lir)
RSO CHANGE SIMPLE R a
0.55119 B.7<(2»2 iJ.iJ22.i5
fi.H16l0 (1.58377 ?. iM<))»
0«/0238 •0.01?25 -i".61915
0.1^0^66 -3.0*769 -^.^STim
15.7J919
KS!J CHANbt SI^PLt K H
d-UOfibl -O.()l)il!«HO
KS'J CHANGt SIKPLt K «
(I.U33*/ -U. 09611 -O.'iBlSl
9FTA
BtIA
-0. >t)l J 1
BtIA
SUMMARY lAULb
MIIL I IPLK K K iOHAKI-
0.81 ? I'i
CHANlib SIMPI y K
O.SO<|32
U.IO/St
U.U1 I IV
O.UObSV
O.I 3<>
-------
Table D.2. CORRELATION TABLE - SIC 29 Petroleum Refining and
Related Industries
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.76
-
,
Process
Weight
.01
.02
-
Energy
.80
.74 *
.24.
-
••
Coal
-
-
-
-
-
Controlled
Emissions
.52
.49
.44
.73
-
-
-------
i-.D HET.PES AMOM3 EMISSIONS AND PREDICTORS
CO»Prr. (C«E»Tlr;N OATF « HS/03/71)
VAH.AHLE CASES MEAN
t'•/!.? *« 97.575?
Off.* 451 11H.«66£
a.p
137.
19.181,1
«5/(53/73
PAGE 22
STD OEV
237.4664
193.6956
1»5.2731
«. n
375,»899
3i.95la
SIC 29 Petroleum Refining and
Related Industries
Space (acres)
l-JHl)lo)llK-nt _
Process WciRlit (t/hr)
Coal (MBtu/hr)
Uicontrollcil Hmissions (Ibs/lir)
Controlled linissious (Ibs/hr)
M V»°I»HL£«. VAfcBHfi
i I. ~ • i1
ICTi'iTt'.T l
MULTIPLE REGRESSION
SUMMARY TABLE
•UjLTIPLE R R SQUARE RSO CHANGE SIMPLE R
0.53355
PU7H16* fl,61096 0.07741
f.7a!7«i 0.61113 B.»OiH7 0.48836
(""78192 0.61140 0.0HP127 0«5?6»6
6.88699
'l'l".T v«"IAh|f.. VA'O'II
^ 1 iti
I'.' •.'. f/-M I
0( >'•.•'.( '. r //.:' |.»"i.l . .
SimMAKY TABLb
MILFIPLt K K SUUAKh KSU CHANUb
0.52646
O.i41H')
SICfHt K
U.U1U6 f
BtIA
U.2UV66
VA-IAfcl r
•H-'^i jf
(Crv.UM
SU1MAKY FAHLb
lUlTIPLI: R K SOIJAKb KSt) CHANWb
U.IJUO^O
0.00145
O.OU19S
SUMMAHY IAIILL
f'ULTU'Lb K K SUUAKk .
-------
Table D.3. CORRELATION TABLE - SIC 33 Primary Metal Industries
10
oo
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.28
-
Process
Weight
.05 •
.17
-
Energy
.10
.16
.07.
-
Coal
-
-
-
-
-
Controlled
Emissions
.14
.35
.72
.27
-
-
-------
10
ID
»'<0 frrOBtS AMONrj EMISSIONS 4Nn PREDICTOR!
CO("-E'. (CWE4TION DATE . "5
"
t-IABLE CASTS
Wf.? R3
l-!f/>-\ f\
* '/, '.'. 4 R T
..-//S »l
:7V.f> f.T
^ '•'••/• J SI
•~7.Jt. 81
19
?9b
1 p
11
B
IdH
8
Ml
.rv
.11
. 1 '
,V
.f
.5
.R:
M
AN
.-54
("7
55
9?
48
97
U L T
STD
51.
68H.
45.
13B.
0.
539.
14.
I P L E R
DEV
6155
4U93
8586
1583
0
7397
8433
E G
•Jt?- '.CE'.T VAMAiiLE.. VAP008
llr" , 2
IC'J'.-TINT 1
05/03/73 PAGE 27
SIC 33 Primary Metal Industries
Space (acres)
Process Weight (t/lir)
Ilicrsy (MIUii/lir)
Coal (HBtu/hr)
Uncontrolled Ijnissions (Ibs/Iir)
Controlled tinissions (Ibs/hr)
SUMMARY TABLE
MULTIPLE R R SQUARE RSO CHANGE SIMPLE R
0.52241 0.52P41 0.72378
0,571)35 0.05584 0.35494
0.61397 0.U3472 0.26951
0.61401 0.00103 0*14314.
8
0.21824
^..'3356
4.194349
V1 ' 'i') ?
-------
Table D.4. CORRELATION TABLE - SIC 28 Chemicals and Allied Products
o
o
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.72
-
Process
Weight
-0
.01
-
Energy
.52
.63
.34.
-
Coal
.61
.77
.40
.83
-
Controlled
Emissions
.35
.33
.78
.54
.61
-
-------
Nf> fcE'j'tS AMON.-, EMISSION1; AND PREDICTORS
'•**?.'< 'CREATION RATE •
VA=-|t>ttE CASfl
05/03/73
PAGE 32
STO otv
SIC 28 Chemicals and Allied
Products
f--7t,f 79
A P / / 4 79
AW?.:'» 79
4K/>if7 79
IU7.1.H 79
«-"•.'«•.««» V«.«
«»-I«lM.F
/*,/.?
/ A * ,- . *
(Cfi'-'-TA-T 1
":""'"
'. /. •- '; -i l
jt^f •<"'. r VA'I AtLf. . VA'-'UO'.
VA'- 1 .'.':! r
VA- )0?
"r^'.',';i 'it v/1" [A:>i.r .. VA"OO^>
••'_ --
vA^i^ir •'.,
Vi-103
vi> D7
lf.3.*l!',0 11?7.777H
R57.Rl.79 763.19*9
1S.1RH1 22?. 5915
SUMMARY TABLE
f ;LTIpL£ R H SQUARE
"«7(i76'i 0.58929
tf.Ri.213 0.70918
H.RS'.IC' 0.72918
. 0.85525 0.73115
O.RB512 0.73171
SUMMARY TAHLH
M 11.1 IPlt K R SUUARt;
0.3HH
o.oovaa o.si<422
B
0.00362
f- 05526
0.^2865'
1.10266
H
O.O0383
U.UO'.'.S
/.04H23
»
o.ooro*
-0.0031S
H
O.D1H22
13.111U
BET*
J. 03157
.'..••33111
Ht 1 A
0.1/16)
Btl A
-0.01 f*3
Hbl A
0.11310
-------
Table D.5. CORRELATION TABLE - SIC 20 Food and Kindred Products
o
Is)
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.85
-
Process
Weight
.38
.44
-
»
Energy
.88
.84 *
.23
-
Coal
.90
.83
.23
.96
-
Controlled
Emissions
.88
.83
.43
.92
.95
-
-------
nCO«»-Et AID «EQRtS ArONO EMISSIONS ANO PREDICTORS
FILE COB»EO (CREATION'DATE - ""i/flS/TS)
05/03/73. PAGE 37
SIC 20 Food and Kindred 'Products
Vta?*7 21
Df.PE'DENT VARIABLE" VAR00g
i -;•/<•
A >• '/. / ?
(CO'-'-'TA^T)
33. s-1! 2 68.3397
lF6.Cf»' 0 758.7631.
P1.Q1..7 7*. 93*1
10.02 1 111.B615
1».S9 1 37.7325
SUMMARY TABLE
MULTIPLE R R SQUARE
0.9*80] 0.89873
P<972<>2 0.9<|560
H« 97377 0. 91628
.. 0.97299. 0.91670
0.97310 0.91693
Space
linployment
Process Weight (tfhry~~ "~
IJierijv (MBtii/hr)
~ Coal"
Uncontrolletl Fjnissions (IhsArl
Controlled Dnisslons (Ibs/hr)
RSO CHANGE SIMPLE R
». 89873 0.91801
0.0d*6R 0.831*0
0.0(9012 . 0.91830
0.00W23 0.88056
B BETA
0.1227!! *. 21367
8.01232 . J..»S19S
-8.82119 -7.73885
5.31777
-------
Table D.6. CORRELATION TABLE - SIC 35 Machinery, Except Electrical
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.81
-
Process
Weight
-.07
-'.07
-
Energy
.89
.90
-.05.
-
Coal
.85
.83
-.04
.95
-
Controlled
Emissions
.36
.41
.83
.44
.42
-
-------
3'. L
o
Cn
fl|_t
-5 AH'Jt.'i FMISSION3
IC»E»TIoN DATE -
PofOlCTORS
05/03/73
PAQE »2
SIC 35 Machinery, Except
Electrical
•/ii'It'lUE CASrs
- -xt 56
J & '« '* f. b S 6
ILWf.l, *,<,
/*-.:;; 54
C/El-E'.rF^T YAR[AQLEi« VAPf«08
/AbKRi.f
/»- • 5
<»..' 1
• i-:' 6
ICV.sTiM 1
tlP.-.M-l »»-l*Ml.. V«'IO»
VA-M A M f
(f'i'. tf Ml
Of "t'.'JI-M VAUAHLF.. VA"0')4
V*M*«IP
if. .". .rti.M
•,r,,v,r-.T v.»i*,if.. VAO-IOS
VA^I tHI c
VA'-'-')>
ME»N STD DEV
Sl.'KOSS 66.6H23
607.0Q28 1091.9629
32.1167 96.2852
2.8693 7.9*79
SUMMARY TABLE
MjLTIPLE R R SQUARE RSO CHANGE
0>83191 0.69211 0.69211
0*96^66 0.92287 0.23076
I?. 96567 0.93251 0.0011*5
0.96574 0.93265 0.0001*
SUMMARY TAHLt
MILIIPLE R R souuRb KSU CHANGE
0.41200 O.IH141 0.00212
SUMMARY lAULt
HfLIiPLt C R SOU4RK RSU CHANGfc
0.0/S67 O.OO^fJ O.OOS73
0.07S33 0.00629 O.OOOif
SUMMARY TABLH
fJLT IPI.fi R K SlJIIAHe KSU CHANGt
O.'»;)()r>2 0.81094 0.810V4
n.'I'iC'li O.UB460 O.U/ttih
^X^A_k 1 X\.UX
Space (acres)
Employment
Process Keipht (t/Iir)
Energy (N3(tu/l\r)
Coal (MBtu/lir)
Uncontrolled Emission
Controlled Emissions
SIMPLE R
0*83193
0.**187
0.41023
0*42875
0*36038
SIMPLt R
0.'. 102
O.HV026
s (Ibs/lir) ...
(Ibs/hr)
B
0*566?5
0.0*605
0*00169
0.02772
-0.003fl9
0.01368
B
0.0025?
O. J0942
B
-0.00/68
-O.OOO4<>
B
O.O186I
BETA
».?2587
?. 23272
.'..-•72P7
-.'.^2591
BtIA
0.34^94
(I.U/-JU6
Htl A
-0.0423B
-0.04OVi
BtIA
0.4663!)
-------
Table D.7. CORRELATION TABLE -
DIC 34 Fabricated Metal Products, Ex-
cept Ordnance, Machinery,
and Transportation Equipment
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.01
-
Process
Weight
.84
-.02
-
Energy
.23
.05
.19
-
Coal
.98
-.03
.86
.20
-
Controlled
Emissions
.68
.04
..86
.48
.67
-
-------
A'.O WE'jf
fILE
AMO'.'O EMISSIONS AND PoEDlCTORR
OATE • 85/03/731
MEAN
05/03/73
PAGE *7
STO OEv
-SIC 34 Fabricated httal Products,
Except Ordnance, Madiinery
5 Transportation Equipment
Vf-SMl S<,
VAU7KR 56
••si'-><:.' Cl'-T VARIABLE.. VAWUH8
,*M«b..F
1 •• .' , *
*-/. •>
ICC'-hT*'. Tl
ntcf •;;/'. i.i v/i'- LAM »-.. vARooti
VA-IABir
VA<0"! J
ICO« j!AM)
.';fcfVjl '. T //. "1 All r. .. VAl'OOH
VAM»».lf
VAiiOO?
((.( ••', TAKT i
r-l?fri."M VAC. IA"H (•.. VAH005
VA-. IABI [
VA°"'
3b.?*R6 16?. 03*7 Space (acres)
351.^356 97?. 555* . Employment
1. pjifl 3.7*97 Process Weight (t/hr)
23.7?7S 87.6151 PnerSX,,PI13tX1}r} - - - —
fl.fg J!9 0.6U53 G3al (MBtu/h>)
•52.47K3 252.7663 Uncontrolled Emissions Clbs/l'^i-
2."SP!7l 5.1106 Controlled Emissions (Ibs/hr)
SUMMARY TABLE
ful-TIPLE R R SQUARE RSQ CHANGE SIMPLE It B
i"Hf,?lH K.7*335 0.7*335 0.86218 1 .5091*5
r.92j«<>0 0.8*71* 0. 10379 0.*7983 0.01883
7.9374R 0.87887 0.0317* VI. 66539 -6-»6685
S-.941H7 0.88713. 0.00X25 B.679** .. 0t013jl
^•9*216 0.88767 0.^0(45* 0.0**47 0.00012
0.23685
MIMMAKY lAULt
"ULIIPLH K K VJUAKt KSU tHANGfc SIMPLfc K 0
0.671'<4 0.46163 0.4blb3 0.6/94A (1.02142
0.6804f 0.4630S O.U0141 O.O444' U.()(UI2O
S1IMHAKY FAIlLb
Hit IIMIF K K SUUAKb KbJ tMANOb SIKl'lb K B
J.R41M O.TOB61 0. /CS61 0.841/9 O.H1949
•).H4/2!> 0./0292 0.2.100* 0.1241b
0.2341^ 0.0^483 O.OU190 0.0*^96 O.nn39)
BETA
IM«"721
J. 32287
•0.76599
^.#2377
HtIA
0.61905
BblA
O.B420'
t)tl A
0.22961
U.O436)
-------
Table D.8. CORRELATION TABLE - SIC 26 Paper and Allied Products
o
oo
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.14
-
Process
Weight
.11
.72
-
Energy
-.02
.40 s
.16
-
Coal
-.02
.30
-.05
.88
-
Controlled
Emissions
.03
.74
.90
.35
.19
-
-------
»SO orr.fc-es AMo'-'G EMISSIONS AMD PPEDICTORS
CrJuuLr, (CSEATION DATE • H5/03/73I
VAOtABLE CASF<5
„*?,, 2S
•/A'-//* ?S
VA''7/-7 2S
VA»-//^ 3^
"
22
15
6
51
b
MFAN
'""
.4631
,*079
.1»373
.75S1
. CR08
M U 1
STO DEV
*B.983*
101 .S3R8
31.932*
3H. 1360
1*2.9213
11.212*
TIPLE REG(
05/03/73 PAGE 52
SIC 26 Paper and Allied Products
Space (acres)
liiiployiiiLMit
Process Weight (t/hr)
Fincrgy (MBtu/hr)
Coal (MUtu/hr)
Uncontrolled Bnissions (Ibs/hr)
Controlled Ijmssions (Ibs/hr)
/A j.' J.SL€
( O.-Ti'.TI
SUMMARY TABLE
Hll-TlPLE R R SQUARE RSQ CHANGE
".951038 0.81068 0.81*68
0.86595
"•93262 0.86979
f.93312 (1,87073
"•93337 0.87180
SIMPLE R
0.90038
0.193*1
0.03127
0.02093 0.73719
0.3*927
B
0.09833
-e.
2.22^23
BETA
.-•»38S
,'393*
VAV..',^
{(.< >,<,!/.•;r)
SUMMARY TAHLE
'ULIIi'LE R R SQUAKh RSlJ LHANlit
0.7 »f 10
0. /
U.UU4M1
SICPLfi K
U./JU4
O.U3li!/
-U.Olbl)^
Btl A
-O.O/OOO
VA>I t hi. e
Vt'" V) 1
1C'.'. jft
SUMMARY TAbLk
lULUPLt R R SUlMRt KSU CHANOt St«PLt R
0.a53H O.ill73 0.511T8 O.M53H
O.J3HB1
-6*.
Bt 1»
O.T1S38
VA> I A HI I
VA.OTX
(CC',-,TA'(T)
lIPMMAKY lAULt
F R R SQUARt
O.'iOOl?
O.'.O/I)'.
0.16(10'*
0. Ifcbbrt
SIM^Lt R
U.4UOU
(KOMI 2
-U .O
HM A
0.41UJ*>
-------
Table D.9. CORRELATION TABLE - SIC 30 Rubber § Miscellaneous
Plastic Products
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.71
-
Process
Weight
.17
. .28
-
Energy
.63
.31
.35
-
Coal
-
-
-
-
-
Controlled
Emissions
-.09
-.16
.30
.67
-
-
-------
COB»EG
i *MO»!0 EMISSIONS ANO
(CREATION DATE • H5/03/73)
05/03/73
PAGE 57
Vt»IABt£
VtP?K3
* t. '•> •• / i
1 & '" ?. 7 4»
•/A "/US
V A R 7 / ft
v A " z it 7
CASFS
IS
is
1 5
15
IS
15
MEAN
'.'..lA'.H
283.5132
6.1SP7
17.B2P0
0.0
32.3573
3.9513
STO OEv
70.6698
*H5. 7395
16.206*
31.5517
P • 0
9?.anai
9.6«17
R E G 1
•,»>f -.r,f 'iT
.. A u /, , ^
/ * - j
lC<"'.-TAS1 *
VACUUM
SIC 30 Rubber G Misc.
Plastic Products
Sp;icc (ncrcs)
lin^lovincnt
Process Weight (t/hr)
Energy ^D!tu/hr)
Coal (Ml!tu/hr)
Uncontrolled Emissions (Ibs/hr)
Controlled Emissions (Ibs/hr)
SUMMARY TABLE
.TIPLE R H SQUARE RSO CHANGE SIMPLE R
>->.66565 P.**309 0.**309 tl.66565
t'.9?7<>7 0.86020 0.1(1712 -«l. 0S526
0.00819 -0«15536
-0.12P91
iJ. 03311
2.U55B
BF. TA
,1.1315?
.- I/.M I
SUMMARY TAHLt:
MILT1PLI- M R SOUAKK KSg CHANUk
0.1SS36 0.02*1'.
O.!1)")!!
U.OUI18
SIPI'Lfc H
-0.1->!>36
-O.n«!>2<>
-O.DO'.'.'J
O.O.If'i'3
<•••«. IV*
Hbl A
' i A':t f
V»*' It''.I I
C'j'.ilt'iF 1
SUMMARY FAIILt
fULrtPLt R R SgUARfc RSO CHANGE SIMPlt R
0.20063 Oiniafi O.OtUl'y 0.20063
0.28302 0.08010
0.0013!)
0.0126F
BtlA
0.31 f20
-O.OtllB*
ICIA'.Tt'HI
SUMMARY TAUlh
lUl.TlPLfc R K SOUAKh KSQ LHANGt
0.627^2
O.t>5
-------
Table D.10. CORRELATION TABLE - SIC 37 Transportation Equipment
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.45
-
Process
Weight
-.22
-.25
-
Energy
.19
. .51
-.16
-
Coal
.28
.60
-.12
.93
-
Controlled
Emissions
-.13
-.03
.94
.19
.23
-
-------
VA-'/A
< A ••:'!• 7
/ A i :• , *
v * a / . 6
.*-:•. f
<*<•'. 3
ICV-iTlNT 1
srOBES AMofT, EMISSIONS AND PREDICTORS
'•>{•-> (CHEiTInN OATf. « •> 3
IC'-'.M*'.')
VAKOOfl
SUMMAP.Y TAHLh
MlILF It'll: R R bUliaKh KS'J CHANUt
D.U0113
SIMPLb K
-0.131Z4
-O. '.11 I 44
O.'JOOZl
4./6281.
8HA
-O.MBJ'S
31 r>t •, •;( i;T yfiu | All f .
(C'fi'.IA'MI
SUMMARY lAbLt
MlLflPI-E K K SUUARb KSQ CHANOh
SII^PLh K
0.06040
U.UhO'tU
0.01^14
-0.00064
-o. )o6 r ••
-0.1S3Z8
-0.13813
{:£Cf'l')l'if VA"IA1lt.. VAJT05
•4tli I ifcr C
VA •",';'}
(Ct.NSTAM)
SUMMARY TABLk
MJLIIPlb R K S(WARt KSO LHANOt
U.Sb',',1
U.266M
0.190/346
-------
Table D.ll. CORRELATION TABLE - SIC 39 Miscellaneous Manufacturing
Industries
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
- -
Employment
.28
-
Process
Weight
-.24
-.07
-
Energy
.55
.10 '
-.10
-
Coal
.57
-.0
-.05
.28.
-
Controlled
Emissions
-.22
-.07
.99
-.09
-.03
-
-------
CO*":_L A'.O PC-,«E3 AMO'.r> EMISSIONS AND PCEDICTORS
FILE CCi'-l't') ICREATION DATE • H4/03/73I
MEAN
S.?735
STO DEV
5.2509
?.7630
i.iaas
29.5590
I(l.fl567
H5/C3/73 PAGE 67
SIC 39 Misc. Manufacturing Industries
Space
Employment
Process Weight ft/lirl
lincrgy (MBtii/hrJ
Coal (MBtu/hr)
Uncontrolled Emissions. (Ibs/hr)
Controlled Emissions (Ibs/hr)
-X, <,
- '/ 'j
-'. S
-'•'<;
O'-'-TASI )
<» v/.'- 1 Ai.L*
VAKOOO
M U I TIPLE REGRESSION
SUMMARY TABLE
I u'-T'PI-E R R SQUARE RSO CHANGE
0.99897
Hi 99967
•.99969
0t99969
('•99969
H. 99938
0.99939
0.99939
0.00.137
0.0C1H00
SUMMAHY TAULt
IIUTII'Ct K K SUUHKt KSU CHANUfc
SIMPLE R
{1.999*9
-0.03291
•fl. 09*13
-0.06667...
-0.222*2
SIM'lb K
-0.222*2
a
0.8*573
0.1*4*8
«. 019,15
0.00^1^
0.00*17
0.09052
H
-0.42SSB
BFTA
J..1.M57
J.t'lbi-l
,'.?.'533
f'flZl*
f'ffilt
ttt-l A
-0.222*2
If.',!.', I^Stl
VAPOO'<
SUMMARY lABLt
^JLTIHLE K K SUUAMt HSU CHANlit
0.23/lf 0.0562O O.OS026
OtIA
-0.23719
VA^IA-'tl t ., VAR005
-l Aftl F
SUMMARY TAULt
MJLTIPIK R K SIJUAKt KSU (.HANGh
O.iMBI
0.30U2
0.3U4SO
0. J01W
0.00307
O. 1UIIJB
-u .ii'iufa
0.31.478
HM A
-------
Table D.12. CORRELATION TABLE - SIC 36 Electrical Machinery, Equip-
ment and Supplies
c\
Space
Employment
Process
Weight
Energy
Coal
Controlled
Emissions
Space
-
Employment
.15
-
Process
Weight
-.11
.14
-
Energy
.08
.68
.21
-
Coal
.11
.13
-.07
.47
-
Controlled
Emissions
.01
.24
.81
.27
.20
-
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A'.O PFr,(.C3 AMO»;r, EMISSIONS AND PREDICTORS
CT^ME'f (CWEiTI^N DATE « ^b/03/73)
CASrs UFAN
05/03/73
PAGE
STD OEv
SIC 36 Electrical Machinery, Equipment,
and Supplies
"*/« 31
V »//J M
C.Ei-5 '-t/t '(T VA-I»lLE.» VAMJ1H8
. * - ! 1. h i F
, t '•• •'.•-,
ic-- .;.t»..Ti
•jf:'< •.';' M /«•'IA(!t^.. VAfno i
','FWF-V.lff.r V4-.IAKlt.. VA?'jn4
Vt>|A»lJ.
VA^'J-jl
(C( V.UMI
i,H-f. N'JI'.» VA^.IAHLe., VAR005
/^lABII
VAOC-J3
/»=•.•: j^
0.*719 1.375H
0.17(10 Ci.9*65
13.7002 21.6211
1.33*5 2.5*88
SUMMARY TABLE
MlJ TIpLE R R SQUARE RSO CHANGE
»HlC461 0*65708 0.6S70R
• Hi,'**? 0.72161 0.*>6*53
.«Si,«l 0.73M7* H.PIH013
."65** 0.7*898 0.3182*
SUMMARY TAHLt
MULIIPLS R R SUUARt RSU CMANUt
C. 2406V 0. 05/93 O.OOO/^
SUMMARY TABLt
MUt riPLt V. R SOUAKH RSU tHANUt
(.135UO 0.01844 0.01044
C.l"'23<> 0.03f01 0.0185?
1
SUMMARY IABLE
MUI tIPLfc R R SOUARK MSJ tHANUfc
( .67^82 0.46215 0.46215
(.68015 0.462AO O.OOO45
Space
anploymcnt
'roccss Weight (t/hrj
Coal ^Btu/lir)
Jncontrolled Hmissions (Ihs/lir)
Controlled Emissions (Ibs/hr)
SIMPLE R 8
0>8»i'61 1.713S*
0.19773 0.87121,
0.2390* «.ia,V73
0.27151 -0.0*23?
0.00777 a.«061?
SIHPLF R H
O.23V04 O.'JOUSl
O.UI)/rf -O.J(.)323
0.rH34/
SIK"Lb K 8
0.13580 0.0)026
-0.11453 -0.00fa2
0.4*0(1*
SIMPLb K M
u.r>r.3?35?
^.05395
rtt,»
U.2432B
-0.028*6
Kt 1 *
0.15633
-0.13(91
BtIA
0.683JI
-0.021*5
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REFERENCES
1. TRW Systems Group. Air Quality Display Model (AQEM).
Contract No. PH 22-68-60. November 1969.
2. A. S. Cohen, et al. Evaluation of Emission Control Strategies with
Emphasis on Residential/Commercial Space Heating for S02 and
Participates in the CMAQCR. Argonne National Laboratory Center for
Environmental Studies Report, IIPP-4. March 1971.
3. Executive Office of the President, Bureau of the Budget.
Standard Industrial Classification Manual. Prepared by The Technical
Committee on Industrial Classification, Office of Statistical
Standards. 1957.
4. U.S. Environmental Protection Agency. Compilation of Air Pollutant
Emission Factors (Revised). Research Triangle Park, North Carolina.
February 1972.
5. Office of Planning and Analysis, Executive Office of the Governor.
Occupational Manpower Projections: 1975-1980. Springfield, Illinois.
February 1973.
6. County Business Patterns. U.S. Bureau of Census: 1968-1971.
Northeastern Illinois Planning Commission Planning Paper No. 10.
Revised, 1972.
118
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-450/3-74-028-b
2.
3. RECIPIENT'S ACCESSIOC+NO.
4. TITLE AND SUBTITLE
Air Pollution/Land Use Planning Project Volume II.
Methods for Predicting Air Pollution Concentrations
from Land Use
5. REPORT DATE
May 1973
6. PERFORMING ORGANIZATION CODE
iUTHOR(S)
A,S. Kennedy, I.E. Baldwin, K.G. Croke, J.W. Gudenas
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Argonne National Laboratory
Energy and Environmental Studies Division
9700 South Cass Avenue
Argonne, Illinois 60439
10. PROGRAM ELEMENT NO.
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EPA-IAG-0159(D)
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Transportation and Land Use Planning Branch
Office of Air Quality Planning and Standards
Environmental Protection Agency
Research Triangle Park, North Carolina 27711
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Final
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
In order to evaluate or rank land use plans in terms of air quality, it
is necessary for planners to be able to project emission density (mass of
pollutant per unit of land for any specified time period) using only planning
variables, because detailed source characteristics are not available at the
time alternative plans are being developed and evaluated. The objective of
this study is to analyze the L- ility of various land use paramters in
describing the aiV quality impacts of land use plans.
Parameters that are tested include land use by zoning class and 2-digit
SIC code, employment dwelling units, and square footage of floor space.
Variables that are to be explained by these parameters include air quality
as represented by the Air Quality Display Model (AQDM), emissions and emission
densities, process weight for industrial sources, and energy consumption.
17.
KEY WORDS AND DOCUMENT ANALYSIS
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Planning and Zoning
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Area Emission Allocations
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