United States Office of Air Quality EPA-450/3-78-048
Environmental Protection Planning and Standards September 1978
Agency Research Triangle Park NC 27711
_
<>EPA Emission Density
Zoning Guidebook •>
A Technical Guide
to Maintaining Air
Quality Standards
Through
Land-Use-Based
Emission Limits
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EPA-450/3-78-048
Emission Density Zoning
Guidebook
A Technical Guide to Maintaining
Air Quality Standards Through
Land-Use-Based Emission Limits
by
Norman F. Kron, Jr., Alan S. Cohen, and Linda M. Mele
Technical Editor: Kathryn S. Macal
Energy & Environmental Systems Division
Argonne National Laboratory
9700 South Cass Avenue
Argonne, Illinois 60439
Contract No. EPA-IAG-D7-011 57
EPA Project Officer. John Robson
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air, Noise, and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
September 1978
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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 organizations - in limited quantities - from the
Library Services Office (MD-35), U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina 27711; or, for a fee, from the
National Technical Information Service, 5285 Port Royal Road, Springfield,
Virginia 22161.
This report was furnished to the Environmental Protection Agency by
Energy and Environmental Systems Division, Argonne National Laboratory,
9700 South Cass Avenue, Argonne, Illinois 60439, in fulfillment of Contract
No. EPA-IAG-D7-01157 . The contents of this report are reproduced herein
as received from 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 Environ-
mental Protection Agency.
Publication No. EPA-450/3-78-048
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ACKNOWLEDGMENTS
The authors wish to acknowledge the assistance and guidance provided
by our sponsor and reviewers; their contributions are greatly appreciated.
These individuals are:
Kurt W. Bauer, Southwestern Wisconsin Regional Planning Commission
Frank H. Benesh, GCA Technology Division
Martin Jaffe, American Society of Planning Officials
John Robson, Project Officer, U.S. EPA Land Use Planning Office
Lynne E. Takemato, Metropolitan Council of the Twin Cities Area
Tom Thomas, American Bar Foundation
Raymond W. Thron, Metropolitan Council of the Twin Cities Area
James Wiegold, U.S. EPA Land Use Planning Office
Albert W. Wilmarth, Southwestern Illinois Metropolitan and Regional
Planning Commission
We also would like to acknowledge the secretarial assistance provided
by LaVerne Schneberger and Rosemary Thompson, and the graphics work done by
Barbara Konkel, Jean Korn, Mary Jo Koebl, and Linda Samek.
^^^
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TABLE OF CONTENTS
Introduction 1
Atmospheric Dispersion Modeling and Its Use in EDZ 13
Linear Programming and Its Use in EDZ 17
Step 1: Acquire EDZ Source Data 29
Step 2: Process EDZ Source Data 39
Step 3: Select EDZ Receptor Location Data 57
Step 4: Acquire and Process Meteorological Data 63
Step 5: Acquire and Process Monitoring Station Data 69
Step 6: Acquire and Process Emission Inventory Data ...» 73
Step 7: Estimate Emissions of Exempt Sources . 81
Step 8: Determine Stack Characteristics 87
Step 9: Code and Keypunch Dispersion Model Input Data 91
Step 10: Acquire Dispersion Model 125
Step 11: Install Dispersion Model on Computer and Check . , 127
Step 12: Acquire Linear Programming Package 129
Step 13: Install Linear Programming Package on Computer and Check . . 133
Step 14: Make Air Quality Dispersion Model Runs 135
Step 15: Make Calibration Dispersion Model Run 137
Step 16: Make Right-Hand-Side Dispersion Model Run 139
Step 1": Set Up Linear Program Coefficient Matrix . , 141
Step 18: Estimate Objective Function Coefficients 145
Step 19: Estimate Column and Row Coefficients i49
Step 20: Estimate Right-Hand-Side Coefficients ... 155
Step 21: Estimate Upper and Lower Bound Coefficients 165
Step 22: Code and Keypunch Linear Programming Input Data 171
Step 23: Make Linear Programming Runs 181
Step 24: Analyze Linear Programming Run Results 183
Step 25: Formulate the EDL Strategy 197
APPENDICES
Appendix A: Glossary 205
Appendix B: Abbreviations 217
Appendix C: Metric Units and Conversions 219
Appendix D: Mixing Height Maps 223
Appendix E: UTM Grid Junctions 227
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APPENDICES (Contd.)
Appendix F: Grid Systems other than UTM, , 233
Appendix G: The Use of the Air Quality Display Model 235
Appendix H: The Use of Linear Programming Packages other than
MPSX in EDZ 239
Appendix I: Special Regional Problems 257
Appendix J: Reproducible Worksheet Forms 261
Appendix K: The Cleanair City Test Case 277
REFERENCES 321
ABSTRACT 323
LIST OF FIGURES
Fig. 1. Cleanair City Zoning Map 3
Fig. 2. Hypothetical Cleanair City EDL Map 3
Fig. 3. Flow Diagram of 25-Step Procedure for Setting Emission
Density Limits 7
Fig. 4. Factors in Pollutant Dispersal Modeling ..... 12
Fig. 5. Prerequisites for Step 1 29
Fig. 6. Example of General Regional Map 31
Fig. 7. Examples of US3S 7 1/2" Maps 35
Fig. 8. Sample Form Letter 37
Fig. 9. Prerequisites for Step 2 39
Fig. 10. Grid Axes and 8 km EDZ Grid System 40
Fig. 11. Integral-Multiple Grid Square Sizes 41
Fig. 12. Alternate EDZ Grid Square Arrangements. , 42
Fig. 13. EDZ Grid System for Louisville, Ky 45
Fig. 14. Numbered EDZ Grid System for Louisville, Ky 46
Fig. 15. Worksheet 1 Example • 47
Fig. 16. Example of Complex Zoning „ . 50
Fig. 17. Example of Simplified Zoning for Map in Fig. 16 51
Fig. 18. Construction of an SSU Dot Pattern 52
Fig. 19. Use of an SSU Dot Pattern 54
Fig. 20. Worksheet 2 Example 55
Fig. 21. Prerequisites for Step 3 57
Fig. 22. Receptor Placement in 8 km EDZ Grid Squares Using Center/'
Boundary Technique 59
Fig. 23. Receptor Placement in 16 km EDZ Grid Squares Using Center/
Boundary Technique 59
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LIST OF FIGURES (Contd.)
Fig. 24. Worksheet 3 Example 61
Fig. 25. Prerequisites for Step 4 63
Fig. 26. Worksheet 4 Example 67
Fig. 27. Prerequisites for Step 5 69
Fig. 28. Worksheet 5 Example . „ 71
Fig. 29. Prerequisites for Step 6 73
Fig. 30. U.S. EPA Regional Offices 75
Fig. 31. Worksheet 6 Example 77
Fig. 32. Worksheet 7 Example 78
Fig. 33. Worksheet 8 Example 79
Fig. 34. Prerequisites for Step 7 82
Fig. 35. Worksheet 9 Example 84
Fig. 36. Worksheet 10 Example 85
Fig. 37. Prerequisites for Step 8 87
Fig. 38. Worksheet 11 Example 89
Fig. 39. Prerequisites for Step 9 92
Fig. 40. CDMQC Card Input Sequence for Air Quality Runs 94
Fig. 41. CDMQC Card Input Sequence for Calibration Run 95
Fig. 42. CDMQC Card Input Sequence for Right-Hand-Side Run 96
Fig. 43. Layout of CDMQC Card Explanation Pages 98
Fig. 44. Type 1 Card, All Runs: Run Title 102
Fig. 45. Type 2 Card, Air Quality and Right-Hand-Side Runs: Calibration/
Print Specifications 103
Fig. 46. Type 2 Card, Calibration Run: Calibration/Print
Specifications 104
Fig. 47. Type 3 Card, Air Quality and Right-Hand-Side Runs:
Miscellaneous Control Parameters 105
Fig. 48. Type 3 Card, Calibration Run: Miscellaneous Control
Parameters 106
Fig. 49. Type 4 Card, Air Quality and Right-Hand-Side Runs: EDZ Grid
System Description 107
Fig. 50. Type 4 Card, Calibration Run: Emission Inventory Grid System
Description ..... 108
Fig. 51. Type 5 Card, Light Manufacturing Air Quality Run: Emission
Rates 109
Fig. 52. Type 5 Card, Medium Manufacturing Air Quality Run: Emission
Rates 110
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LIST OF FIGURES (Contd.)
Fig. 53. Type 5 Card, Heavy Manufacturing Air Quality Run: Emission
Rates Ill
Fig. 54. Type 5 Card, Calibration Run: Emission Rates 112
Fig. 55. Type 5 Card, Right-Hand-Side Run: Emission Rates. . . 113
Fig. 56. Type 6 Cards, Air Quality and Right-Hand-Side Runs: Joint.
Frequency Function 114
Fig. 57. Type 6 Cards, Calibration Run: Joint Frequency Function . . . 115
Fig. 58. Type 7 Cards, Calibration Run: Point Source Data 116
Fig. 59. Type 7 Cards, Right-Hand-Side Run: Point Source Data, .... 117
Fig. 60. Type 8 Cards, Light Manufacturing Air Quality Run: Area
Source Data 118
Fig. 61. Type 8 Cards, Medium Manufacturing Air Quality Run: Area
Source Data 119
Fig. 62. Type 8 Cards, Heavy Manufacturing Air Quality Run: Area
Source Data 120
Fig. 63. Type 8 Cards, Calibration Run: Area Source Data 121
Fig. 64. Type 8 Cards, Right-Hand-Side Run: Area Source DaLa 122
Fig. 65. Type 9 Cards, Calibration Run: Monitoring Station Data. . . . 123
Fig. 66. Type 9 Cards, Air Quality and Right-Hand-Side Runs:
Receptor Point Data 124
Fig. 67. Prerequisites for Step 10 125
Fig. 68. Prerequisites for Step 11 127
Fig. 69. Prerequisites for Step 12 129
Fig. 70. Prerequisites for Step 13 133
Fig. 71. Prerequisites for Step 14 135
Fig. 72. Prerequisites for Step 15 137
Fig. 73. Prerequisites for Step 16 139
Fig. 74. Prerequisites for Step 17 142
Fig. 75. Linear Program Coefficient Matrix 143
Fig. 76. Prerequisites for Step 18 145
Fig. 77. Filling in "Explanation of Columns" Row in Coefficient
Matrix 146
Fig. 78. Filling in the Objective Function Row in Coefficient Matrix . 147
Fig. 79. Prerequisites for Step 19 149
Fig. 80. "Column and Row" Section of Coefficient Matrix 150
Fig. 81. Sample CDMOC Output for Area Source Contributions 152
Fig. 82. Filling in the "Column and Row" Section of Coefficient:
Matrix 153
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LIST OF FIGURES (Contd.)
Fig. 83. Prerequisites for Step 20 155
Fig. 84. Worksheet 12 Example 158
Fig. 85. Isopleth Map of Standard Geometric Deviations 159
Fig. 86. Worksheet 13 Example 160
Fig. 87. Filling in RHS Columns of Coefficient Matrix 161
Fig. 88. Calculating Emission Quotas 163
Fig. 89. Prerequisites for Step 21 165
Fig. 90. Probable Effects of No Upper Bound 166
Fig. 91. Sample Data for Illustrating Upper and Lower Bounds 168
Fig. 92. Filling in the Upper and Lower Bounds Rows of the
Coefficient Matrix 169
Fig. 93. Prerequisites for Step 22 171
Fig. 94. MPSX Data Card Input Sequence 173
Fig. 95. MPSX Name Card 175
Fig. 96. MPSX Rows Card 175
Fig. 97. MPSX Objective Function Card I?5
Fig. 98. MPSX Rows Data Cards 176
Fig. 99. MPSX Columns Card 176
Fig. 100. MPSX Columns Data Cards 176
Fig. 101. MPSX RHS Card 177
Fig- 102. MPSX RHS Data Cards 177
Fig. 103. MPSX Bounds Card 177
Fig. 104. MPSX Upper Bounds Cards 178
Fig. 105. MPSX Lower Bounds Cards 178
Fig. 106. MPSX ENDATA Card 179
Fig. 107. Prerequisites for Step 23 181
Fig. 108. Prerequisites for Step 24 184
Fig. 109. MPSX Output Showing EDLs and Other Data 18$
Fig. 110. Identifying an Infeasible Solution 186
Fig. 111. Worksheet 14 Example 187
Fig. 112. Worksheet 15 Example 189
Fig. 113. Three-Dimensional View of an Emission "Valley" 191
Fig. 114. Three-Dimensional View of Emission "Peaks" 193
Fig. 115. Three-Dimensional View of Border Region of Changing Emissions. 193
Fig. 116. Three-Dimensional View of Gradually-Changing EDLs 195
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LIST OF FIGURES (Contd.)
Fig. 117. Prerequisites for Step 25 198
Fig. 118. Floating-Zone-Emission-Quota Situations 202
Fig. 119. Isopleths of Mean Annual Morning Mixing Heights . 224
Fig. 120. Isopleths of Mean Annual Afternoon Mixing Heights 225
Fig. 121. UTM Zone Numbering in the United States and Its Territories . 228
Fig. 122. Producing a Normalized Grid System 230
Fig. 123. Cartographic Point Normalization 231
Fig. 124. Sample Sulfur Dioxide Regression Line 259
Fig. 125. Step 1: General Regional Map for Cleanair. 278
Fig. 126. Step 2: EDZ Grid Origin and 8 km Grids for Cleanair 279
Fig. 127. Step 2: Subdivided EDZ Grid System for Cleanair. . 280
Fig. 128. Step 2: Numbered EDZ Grid System for Cleanair 281
Fig. 129. Step 2: Cleanair City Zoning Maps ........ 282
Fig. 130. Step 2: Gridded Cleanair City Zoning Map with SSU Dot
Pattern 283
Fig. 131. Step 2: Completed Worksheet 1 for Cleanair City 284
Fig. 132. Step 2: Completed Worksheet 2 for Cleanair City 285
Fig. 133. Step 3: Completed Worksheet 3 and Receptor Point Map for
Cleanair City 286
Fig. 134. Step 4: Completed Worksheet 4 for Cleanair City 287
Fig. 135. Step 4: Joint Frequency Function for Cleanair City 288
Fig. 136. Step 5: Completed Worksheet 5 for Cleanair City 291
Fig. 137. Step 6: Completed Worksheet 6 and Point Source Map for
Cleanair City 292
Fig. 138. Step 6: Completed Worksheet 7 and Emission Inventory Grid
System Map for Cleanair City 293
Fig. 139. Step 6: Completed Worksheet 8 for Cleanair City 294
Fig. 140. Step 7: Completed Worksheet 9 for Cleanair City 294
Fig. 141. Step 7: Completed Worksheet 10 for Cleanair City 295
Fig. 142. Step 8: Completed Worksheet 11 for Cleanair City 295
Fig. 143. Step 9: Listing of Five CDMQC Data Decks for Cleanair City . 296
Fig. 144. Step 14: CDMQC Light Manufacturing Air Quality Run Output
for Cleanair City 299
Fig. 145. Step 15: CDMQC Calibration Run Output for Cleanair City. . . 300
Fig. 146. Step 16: CDMQC Right-Hand-Side Run Output for Cleanair City. 302
Fig. 147. Step 20: Completed Worksheet 13 for Cleanair City 304
x
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LIST OF FIGURES (Contd.)
Fig. 148. Steps 18-21: Completed Coefficient Matrix for Cleanair
Fig.
Fig.
Fig.
Fig.
Fig-
Fig.
Fig.
Fig.
Fig.
Fig-
Fig.
Fig.
Fig.
Fig.
Fig.
149.
150.
151.
152.
153a.
153b.
154.
155.
156.
157.
158.
159.
160.
161.
162.
City
Step 22: Listing of MPSX Data Deck for Cleanair City . .
Step 23: MPSX Output for Sulfur Dioxide for Cleanair
City =
Step 23: MPSX Output for Particulates for Cleanair City.
Step 24: Completed Worksheet 14 for Cleanair City. . . .
Step 24: Reallocating Area Source Emissions from Emis-
sions Inventory to EDZ Grid System for Cleanair City -
Part 1
Step 24: Reallocating Area Source Emissions from Emis-
sions Inventory to EDZ Grid System for Cleanair City -
Part 2
Step 24: Completed Worksheet 15 for Cleanair City. . . .
Step 24: Map of Total Potential S02 Emissions for
Cleanair City
Step 24: Map of Total Potential Particulate Emissions
for Cleanair City
Step 24: Map of S02 EDLs for Light Manufacturing in
Cleanair City
Step 24: Map of SOz EDLs for Medium Manufacturing in
Cleanair City
Step 24: Map of SO^ EDLs for Heavy Manufacturing in
Cleanair City
Step 24: Map of Particulate EDLs for Light Manufacturing
in Cleanair City
Step 24: Map of Particulate EDLs for Medium Manufactur-
ing in Cleanair City
Step 24: Map of Particulate EDLs for Heavy Manufacturing
in Cleanair City
305
306
309
310
311
312
313
314
317
317
318
318
319
319
320
320
LIST OF TABLES
Table I. Steps Required to Determine Emission Density Limits. .
Table 2. Resource Requirements for Step 1
Table 3. Resource Requirements for Step 2
Table 4. Resource Requirements for Step 3
Table 5. Resource Requirements for Step 4
Table 6. The Variables Comprising the Joint Frequency Function.
Table 7. Resource Requirements for Step 5
7
30
40
58
64
65
70
x^
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LIST OF TABLES (Contd.)
Table 8. Resource Requirements for Step 6
Table 9. Metric Units Needed for Emission Inventory Data.
Table 10. Resource Requirements for Step 7 .
Table 11. Resource Requirements for Step 8
Table 12. Resource Requirements for Step 9
Table 13. Summary of the Five CDMQC Runs
Table 14. Resource Requirements for Step 10
Table 15. Resource Requirements for Step 11. .......
Table 16. Resource Requirements for Step 12
Table 17. Resource Requirements for Step 13
Table 18. Resource Requirements for Step 14
Table 19. Resource Requirements for Step 15
Table 20. Resource Requirements for Step 16
Table 21. Resource Requirements for Step 17
Table 22. Resource Requirements for Step 18
Table 23. Resource Requirements for Step 19 .
Table 24. Resource Requirements for Step 20.
Table 25. Resource Requirements for Step 21
Table 26. Resource Requirements for Step 22
Table 27. Resource Requirements for Step 23
Table 28. Resource Requirements for Step 24
Table 29. Resource Requirements for Step 25
Table 30. Comparison of Linear Programming Packages. . . .
Table 31. Cleanair City Industry .....
74
77
83
88
93
97
126
128
130
134
136
138
140
143
146
150
156
166
172
182
184
198
240
277
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INTRODUCTION
THE ROLE OF EMISSION DENSITY ZONING
Historically, air pollution control has been on a source-by-source
basis. Polluters were required to avoid being nuisances to their neighbors.
Those who were nuisances were asked to relocate, shut down, or clean up. As
regulations became more sophisticated, limits were placed on the physical
characteristics — such as smoke opacity — of the pollutants emitted by a
source. As long as a pollution source met the emission limitations, it could
be located anywhere in the community, provided of course that the existing
zoning ordinance requirements were followed.
Since the passage of the Clean Air Act Amendments of 1970, source-by-
source air pollution controls have become increasingly popular. Emphasis
has been placed on limiting emissions per unit of production (for example,
grams of sulfur dioxide emitted per joule of energy generated).* These
regulations provide a simple way of turning the social cost of air pollution
into a production cost for the manufacturer (and ultimately a cost to the
consumer) of pollution-causing products.
However, source-oy-source controls have certain limitations. Once a
particular emission threshold is reached, reducing pollutant levels below the
threshold becomes very expensive or even impossible due to the limits of tech-
nological controls. Yet, even with most or all polluters emitting at their
thresholds — with pollution levels of 0-1% of their uncontrolled emissions —
unhealthy pollutant concentrations may occur if several polluters are located
near each other. This is where emission density zoning (EDZ) or other emis-
sion allocation schemes** can be useful. Such schemes are designed to avoid
high pollution levels by placing upper limits on emissions over a geographi-
cal area, as opposed to placing limits on individual pollution sources.
The generally accepted definition of EDZ is: "... a type of air pollution
control regulation in which the maximum legal rate of emission of an air
*Metric units are used throughout this document. Metric/English conversions
are provided in Appendix C.
**For definitions of other emission allocation schemes for which the method-
ology described in this Ruidebook can be applied to estimate emission
quotas, see Brail, et al. (1975b), and Step 25 of this document.
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pollutant Is based on location, land area, land use zoning, and air quality
constraints." (Robson, John, U.S. EPA, personal communication, 1977),
Perhaps the best way to explain how EDZ works is to show its similari-
ties to traditional land use zoning. EDZ may be thought of as an air pollu-
tion control regulation that is superimposed upon an existing zoning ordinance,
All the land use districts and boundary lines contained in the existing zon-
ing ordinance are retained, but there is an additional restraint on how each
hectare of land may be developed. Each hectare is assigned an emission den-
sity limit (EDL), which specifies the maximum emission allowed per unit of
time per unit of land area (for example, grams of particulates per second
per hectare). Any land use permitted by the existing land use zoning
ordinance continues to be allowed; however, with EDZ, the source is not
allowed to emit more pollutants than the limit allowed for the parcel of land
on which it is located.
The concept of emission density limits can be illustrated with an
example involving "Cleanair City," a hypothetical city which uses emission
density zoning to maintain air quality. Bilgewater Industries (BI) would
like to build a slime drying plant in Cleanair City. According to the city's
existing land use zoning ordinance, slime drying is only allowed in heavy
manufacturing (M-3) districts. The zoning map (Fig. 1) shows that the new
BI plant could be located in one of three M-3 districts.
If there were no EDZ regulations, the heavily-polluting slime drying
facility could be built in any of the M-3 areas, provided it met applicable
source-by-source emission reduction requirements and did not violate air
quality standards in Cleanair City. With EDZ in effect, however, there is a
second map. This one has the traditional zoning map as its base, but also
shows the maximum emissions allowed per hectare of land in each zoning class
and each location. Assume that the only important pollutant emitted in slime
drying is sulfur dioxide (SOz); Cleanair City's EDZ ordinance contains a map
of its emission density limits for sulfur dioxide, as shown in Fig. 2. As
can be seen, the three manufacturing areas have identical traditional zoning
classifications, but different EDLs. Some of the factors considered by
Cleanair City officials in setting EDLs were industrial location relative to
population, local meteorological conditions, and pollutant concentrations due
to emission sources already in the area.
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ZONING BOUNDARY
RAILROAD
ZONING CLASSES
OS , PUBLIC OPEN SPACE
R-l TO R-6. RESIDENTIAL USES
r-iro c 3, CBD.COMMERCIAL USES
M-l LIGHT MANUFACTURING
M-2 MEDIUM MANUFACTURING
M-3 HEAVY MANUFACTURING
Fig. 1. Cleanair City Zoning Map
RESIDENTIAL,COMMERCIAL,AND OPEN SPACE CLASSES
WHOSE EDLS ARE IRRELEVANT FOR THE B. SLIME
DRiMNG EXAMPLE
ALLOWABLE so2 EDLS FOR MANUFACTURING CLASSES
M-3 , AREA I , NO MORE THAN 2 0 GRAMS
S02 PER SECOND PER HECTARE
M-3, AREA 2,NO MORE THAN 40 GRAMS
S02 PER SECOND PER HECTARE
M-3 ,AREA 3 , NO MORE THAN 50 GRAMS
SC, PER SECOND PER HECTARE
Fig. 2. Hypothetical Cleanair City EDL Map
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4
Bilgewater Industries projects that the slime drying plant, even after
complying with New Source Performance Standards and all other state and local
emission limitations, will emit 20 grams per second (g/s) of sulfur dioxide.
Therefore, according to the EDLs shown in Fig. 2, BI must buy either 10
hectares (ha) in Area 1 [allowable S02 emissions of 2 (g/s)/ha], 5 hectares
in Area 2 [allowable 862 emissions of 4 (g/s)/ha], or 4 hectares in Area 3
[allowable S02 emissions of 5 (g/s)/ha].* If there is no area where a plant
the size of Bilgewater's can be located, BI will have to search for a plant
site in another zoning jurisdiction. The effect of the varying acreage
requirements is to use up all the land in a given area without overburdening
the capacity of the air to clean itself, thus maintaining a healthy atmos-
phere by meeting the National Ambient Air Quality Standards (NAAQS) esta-
blished by the U.S. EPA.
The preceding example points to the basic reasoning behind EDZ:
• High concentrations of air pollution are harmful to human
health and welfare.
• The air has a certain ability to clean itself of pollutants
(assimilative capacity).
• This assimilative capacity depends on local meteorology and
the types and concentrations of pollutants in the air. If
the concentration of pollutants is too high, the assimila-
tive capacity can be overburdened, possibly resulting in un-
healthy conditions.
• By geographically dispersing sources of air pollution, the
number of areas of high pollutant concentrations will be
minimized.
• EDZ can be used to geographically disperse sources, and
therefore can be used as a tool to keep air pollution con-
centrations at or below acceptable levels.
The U.S. EPA's policy on purchasing land to disperse emissions is
currently in a state of flux. As of this writing,
EPA is taking ever-stronger positions against certain con-
trol practices that utilize the dispersing and self-cleansing
characteristics of the atmosphere. These practices are: (1)
the vertical adjustment of emission points so that pollutants
can become more widely dispersed, (2) the hourly adjustment of
emissions so that favorable dispersion conditions resulting
figures used here do not conform to those used in the Cleanair City
Sample Problem presented in Appendix K.
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from changing weather can be utilized, and (3) procurement
of extra land so that the air above it would not be con-
sidered "ambient" except for standards relating to visi-
bility... (John Robson, U.S. EPA, personal communication,
April 1978).
Given this trend, the authors would like to make it clear to any guidebook
user that it may be possible that, even if the EPA officially disallows the
purchase of extra land to disperse pollutants, such purchase might be per-
mitted under EDZ provided that it did not lead to localized areas of high
pollution concentrations on or off the purchaser's property.
GUIDEBOOK FOR SETTING EMISSION DENSITY LIMITS
Use of the Guidebook
This manual, which may be thought of as an EDZ "cookbook," is a
step-by-step guide for formulating emission density limits for a region. An
emission density zoning regulation developed using the method presented here
will ensure maintenance of local and national ambient air quality standards
for sulfur dioxide and particulate matter. The procedure described also
can be used to set emission quotas for a number of air quality maintenance
strategies, such as emission allocation planning, floating zone emission
quotas, district emission zoning (see Brail et al., 1975b), and emission
offset strategies (see Step 20).
This guidebook is primarily designed for use by regional planning com-
missions and air pollution control agencies in metropolitan areas. These two
organizations are the most likely to be responsible for setting EDLs because
their combined expertise provides most of the manpower resources required by
the EDL-setting methodology. In addition, many of the data required are rou-
tinely collected by these organizations.* The regional approach is taken be-
cause interjurisdictional effects of air pollution can be considered at the
regional level.
Although regional planning and air pollution control agencies are
assumed to conduct the analyses needed to sat EDLs, local governmental
Bother organizations may be given the task of implementing the steps for
establishing EDLs but they will most likely have to obtain the cooperation
of the regional planning and air pollution control agency personnel.
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agencies will most likely implement the emission density zoning or other
air quality maintenance regulation that incorporates the EDLs. The region
for which EDLs are developed is assumed to be "typical" in that the local
political jurisdictions within it have enacted zoning ordinaces that are
being followed with at least some concern for good planning.
Another important assumption made in the guidebook is that the "basic
EDZ model" is being used (see Steps 17 and 24). The basic EDZ model assumes
that the metropolitan region using EDZ would like to maximize the emissions
produced in the region while not violating air quality standards. Maximizing
emissions is assumed to be a proxy for maximizing economic growth, or at
least maximizing flexibility in EDZ administration by knowing the maximum
emissions allowed. Flexibility is introduced since a region may select a
lower level of emissions. Goals other than maximizing emissions may be
pursued by using EDZ, as described by Benesh (1977), but maximizing emis-
sions is the simplest and most easily-achieved objective.
The guidebook is heavily dependent on hand calculations, in order to
(1) avoid extensive use of computer programmers and hardware, (2) encourage
agencies with few senior staff to devote to EDZ to try the technique by
using student help, and (3) make the guidebook understandable to a wide aud-
ience. In large regions with high levels of computer expertise, it will
probably be desirable to computerize many of the computations.*
The procedure for setting EDLs presented here consists of 25 steps,
shown in Table 1, which may be performed simultaineously or sequentially, as
shown in Fig. 3. The steps connected horizontally (or diagonally) in the
figure must be conducted sequentially from left to right; steus listed vert-
ically may be completed simultaneously. The 25-step process can be aggre-
gated into five kinds of tasks: data collection and analysis (Steps 1-9),
computer model testing (Steps 10-13), dispersion modeling (Steps 14-16),
linear programming modeling (Steps 17-23), and EDL development (Steps 24-25).
*In recording data and in calculations maintain accuracy of three (or more)
significant figures. To determine three significant figures in a number scan
the number from the left and count the first non-zero digit as the first and
the next two digits as the second and third significant figures. Round the
third digit up if necessary. If at that point you are to the left of the
decimal point, add zeros to complete the number; if you are to the right of
the decimal point simply stop. Example: 4,321,842 would be 4,320,000, and
0.00087634 would be 0.000876 with three significant figures.
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Table 1. Steps Required to Determine Emission Density Limits
Steps
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Action
Acquire EDZ source data
Process EDZ source data
Select EDZ receptor location data
Acquire and process meteorological data
Acquire and process monitoring station data
Acquire and process emission inventory data
Estimate emissions of exempt sources
Determine stack characteristics
Code and keypunch dispersion model input data
Acquire dispersion model
Install dispersion model on computer and check
Acquire linear programming package
Install linear programming package on computer
and check
Make air quality dispersion model runs
Make calibration dispersion model run
Make right-hand-side dispersion model run
Set up linear program coefficient matrix
Estimate objective function coefficients
Estimate column and row coefficients
Estimate right-hand-side coefficients
Estimate upper and lower bound coefficients
Code and keypunch linear programming input data
Make linear programming runs
Analyze linear programming run results
Formulate the EDL strategy
Preliminary
Steps3 Type of Task
None
1
1,2,5
None
None
1 ,2
1,2,6
1,2,6
1-8
None
10
None
12
1-11 |
1-11
1-11 1
1-3,5 |
1-3,5,17
1-11,14,17,18
1-11,15-17
1-3,5,6,17
1-11,14-21
Data
Analysis
Model
Testing
Dispersion
Modeling
Linear
Programming
Modeling
1-22
1-23 1 EDL
1-24 * Development
Prerequisites listed on pnges 8-12 must be completed before work begins on any step.
BEGINNING
PREREQUISITES)—
SATISFIED
5
/
3
17
\\
18
1
2
/
6
10
11
12
13
Fig. 3.
Flow Diagram of 25-Step Procedure for Setting Emission
Density Limits
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Selection of Dispersion Model and Linear Programming Package
The EDL methodology set forth in the guidebook uses the Climatolo-
gical Dispersion Model (version CDMQC)* and the Mathematical Programming
System Extended (MPSX) linear programming package. It must be emphasized
that other dispersion models and linear programming packages may be used
without substantive changes in the results as long as the models have the
features listed below.
A dispersion model other than CDMQC must be able to:
• Compute annual average concentrations of S02 and particu-
lates.
• Print "culpability lists." These are tables for each
receptor that show the pollutant concentration at the
receptor resulting from emissions from each source in
the region. In other words, these tables show the origins
and destinations of the region's air pollutants.
A linear programming package other than MPSX must have:
• A provision to place bounds on the linear programming
variables (i.e. EDLs). If these provisions are not
available, a person with linear programming expertise
should be consulted since the bounds must be incorporated
into the model as two inequality constraints and this re-
quires techniques not discussed in the guidebook.
• The ability to handle problems with a nearly 100% dense
matrix containing x columns and y rows where:
x = a number somewhat less than the number of EDZ grid
squares covering the region times the number of
simplified land use classes (see Step 2). As the
guidebook is written, x will be less than or equal to
1,050. (This means there may be as many as 1,050 columns
in the linear program coefficient matrix.)
y = the number of receptors in the region (see Step 3).
In most cases y will probably be about 200, but there
will be great variability from region to region (this
means about 200 rows in the linear program matrix).
Prerequisites for Using the Guidebook for Setting EDLs
Before the 25-step procedure is begun, the following prerequisite
tasks must be carried out:
-The "QC" in CDMQC indicates the addition to the basic CDM of the capability
to produce source (Q) contribution (C) lists.
-------
1. Decision to Use or Test EDZ. The decision to use or test EDZ or
other emission quota schemes* as air quality maintenance strategies must be
made.
2. Resource Commitment. The financial and manpower resources re-
quired to carry out the procedures described in this guide must be committed
to this purpose. Specifically, the project team will require persons with
the skills listed below; some individuals will satisfy more than one require-
ment .
• Project management — a person to schedule tasks, check the
quality of the work, coordinate efforts of the project staff,
budget expenditures, etc.
• Writing — a person to document the results of the EDL-set-
ting efforts.
• Land use planning/zoning knowledge — a person familiar
with zoning and land use planning techniques.
• Cartographic ability — preferably a person with exper-
tise in preparing zoning or land use maps and knowledge
of the Universal Transverse Mercator (UTM) coordinate
system.
• Computer programming - preferably a person with experience
with linear programming and dispersion modeling.
• Air pollution control - a person familiar with federal,
state, and local air pollution control regulations. Pre-
ferably this person will also be familiar with dispersion
modeling and linear programming.
• Support services — persons with keypunching and secretarial
skills.
• Data analysis and preparation — persons to collect, process,
and code data. College students in the fields of urban and
regional planning and environmental (air pollution) control
may be ideal for carrying out these responsibilities.
The total resources committed to the project will vary from region to region
but should be less than a person-year of effort for professional personnel
and another person-year of effort for support personnel. It should take 6
to 12 months to complete the 25-step procedure for setting EDLs. In
addition, the following materials and facilities must be available: office
space and supplies, cartographic material, and access to a computer.
'-Step 25 must be consulted if you are intending to try any emission quota
scheme other than EDZ.
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10
3. Interagency Cooperation. A cooperative relationship between the
regional planning commission and air pollution control agencies must be
arranged. Possibly the project team will consist of personnel from both or-
ganizations.
4. Literature Review. The literature on emission density zoning,
emission quota schemes, and air pollution legislation must: be reviewed.
Below is a list of documents that should be included in this review.
• Brail, R.K., at al., Emission Density and Allocation Procedures
for Maintaining Air Quality, U.S. EPA Report EPA-450/3-75-075
(June 1975).
• Cosier, P.C. IV, Land Use Based Emission Strategies: Their Promises
and Problems, Planning Comment, 22(2):31-47 (Fall 1976).
• Roberts, J.J. , E.J. Croke, and S. Booras, A Critical Review of
the Effect of Air Pollution Control Regulations on Land Use
Planning, from APCA Critical Reviews #3-Air Quality Management
and Regional Land Use (1975). Originally appeared in the
Journal of the Air Pollution Control Assn., 25(5):500-520
(1975).
• Robson, John, Land Use Planning and Air Pollution, Right of Way,
pp. 40-44 (Oct. 1975).
• Benesh, F.H., et al., A Review of Considerations and Issues in
Emission Density Zoning, U.S. EPA Land Use Planning Office
publication (1977).
5. Legal Analysis. A separate study to examine the legal aspects of
air quality maintenance strategies should be initiated. The results of this
legal study should be a draft of the EDZ or other air quality maintenance
regulation which will incorporate the EDLs developed using the guidebook.
Background material on the legal issues associated with EDZ are discussed in:
Jaffe, M.S., et al., Legal Issues of Emission Density Zoning, prepared by
American Society of Planning Officials for Argonne National Laboratory and the
U.S. EPA, U.S. EPA Report EPA-450/3-78-049 (Sept. 1978).
6. Selection of Sources to be Regulated. In conjunction with the
literature review and legal analysis, a prerequisite political, legal or
planning decision must be made on which sources are to be regulated by EDZ
and which sources are not. Consult legal counsel when making this decision.
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11
In many regions it will be desirable to exempt some sources from the
regulation. An "exempt source" is not controlled by EDZ and is not assigned
an EDL. Small emission sources such as residences and retail businesses may
not require EDZ-type controls because they are relatively clean sources with
predictable emissions. Mobile sources, such as cars and trucks, cannot be
regulated by EDZ. Dust from agricultural lands is not easily controlled with
EDZ. In addition, sources such as electric power plants or municipal inciner-
ators may be exempt because of the small numbers of these sources, the ex-
tensive preconstruction review required, and their public service function.
For legal or political reasons it may be desirable to grant "auto-
matic compliance status" to all existing sources. That is, assuming the
sources were complying with all other relevant air pollution control regula-
tions, their current per-hectare emission densities would be the EDLs as-
signed to the land on which the sources are located. These sources need not
change their emissions to comply \jith EDZ, but may not increase their emis-
sion densities over the assigned EDL. From the standpoint of the methodology
to set EDLs, granting automatic compliance is methodologically the same as
exempting a source from the EDZ regulation. Therefore, any methodological
reference to exempt sources also refers to sources given automatic compliance
status unless specifically stated otherwise.
If you choose to have no exempt sources in your region, you may decide
to treat existing sources as "nonconforming uses." In this case, EDLs would
be set for lands which have sources already built on them, but the EDLs would
be set without regard to the current emissions on the sites. A source on
land with EDLs lower than its current emission densities would be expected to
lower its emissions, buy more land, or in some way come into compliance with-
in a specified time period or change its emissions if the source was enlarged,
sold, or modified. (See Goodman and Freund, 1968.)
Although any particular source will fit into only one of these three
exemption categories, there is no methodological reason why a region cannot
contain any combination of the three categories of exempt sources. In the
EDL-setting procedure "exempt" sources and sources with "automatic com-
pliance status" are treated identically. The guidebook assumes that agri-
cultural, residential, and commercial sources, and all rights of way — i.e.,
sources located in the area source zoning district defined in Step 2 —
-------
12
will be wholly exempt from EDZ. However, even if your region has no exempt
sources, the guidebook can still be used to set EDLs.
7. Study of Guidebook. Each member of the project: team must read the
guidebook (including the appendices) completely before beginning the project.
This will ensure that each staff member has a reasonably clear idea of how
each step of the methodology relates to the other steps. This will avoid
confusion and ensure that outputs from one step are usable as inputs into
the following steps. A meeting of the entire staff to review the method-
ology for establishing EDLs should be held before any steps are initiated.
After these seven prerequisites are fulfilled, you are ready to begin
developing your emission density limits. Good luck!
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13
ATMOSPHERIC DISPERSION MODELING AND ITS USE IN EDZ*
An emission density limit (EDL) specifies the rate of pollutant emis-
sion for a given unit area of land. A primary reason for setting EDLs is
to disperse pollutant sources to avoid "hot spots" where high pollutant con-
centrations occur. But in order to determine where hot spots currently are
in a region or where they might be in the future, one must know where pollu-
tant sources are located, what quantities of pollutants they emit, what type
of stacks they have, and where the pollutants travel after they are released
into the atmosphere. Because of requirements in the Clean Air Act, the
source locations, emission data, and stack characteristics have been col-
lected in state and federal emission inventories. In order to set an EDL,
arid not violate air quality standards now or in the future, the current and
future concentrations of pollutants throughout a region, and the origin of
those pollutants, must be known. To obtain this information, air pollution
specialists, computer programmers, and meteorologists have developed rela-
tively inexpensive computerized simulation models.
Simulation models for air quality analyses are called atmospheric
dispersion (or diffusion) models. These models predict the spatial concen-
trations of pollutants by converting S02 and particulate emissions (measured
on a mass-per-unit-time basis such as grams per second) from a number of
sources to air pollutant concentrations (measured on a mass-per-unit-volume
basis, such as grams per cubic meter) at a number of "receptor points."
Figure 4 illustrates some of the factors that affect the dispersion of air
pollutants. As can be seen, there are quite a few variables that need to be
taken into consideration. Some of the more important of these are discussed
below.
Source Data. Dispersion models calculate the regional air pollution
concentrations that result from point source emissions, area source emissions,
and the combination of these two. Characteristics of point sources needed by
dispersion models include locations, emission rates for each pollutant, stack
dimensions, and exit gas velocities and temperatures. For area sources, the
*If there is a doubt about the definitions of words or phrases, consult the
Glossary in Appendix A.
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14
ODD
ODD
DD
ODD
nan
ctf
CO
B)
•rl
Q
JJ
CO
c
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S-i
o
4-J
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60
•H
-------
15
models require source locations (via emission grid squares), emission rates,
and average stack heights. If future air quality needs to be estimated, the
emissions from point and area sources are projected using U.S. EPA-approved
techniques and then provided to the dispersion model in the same way as data
on current emissions.
Meteorological Data. Meteorological data are highly important to the
dispersion model calculations. The data used are of three types: a joint
frequency function (JFF), annual average temperatures, and mixing heights.
The JFF gives the percentage of the time that a particular combination of
wind speed, wind direction, and atmospheric stability occur. There are six
wind speed classes, 16 wind directions, and six stability classes, which
means that there are 576 possible meteorological situations. Annual average
temperatures and mixing heights for the region are derived from standard
tables.
Receptor Locations. Dispersion models provide answers to the follow-
ing questions: Given air pollutant emissions from sources A and B, and the
meteorology of the region, what will be the concentration of pollutants at
locations X and Y? The locations X and Y are called receptor points, and
may be thought of as a combination of real and imaginary air quality monitor-
ing stations. Real monitoring stations are the region's existing air sampling
stations. Imaginary monitoring stations are points plotted on a regional map
that are used in the dispersion model for simulating air quality measurements
throughout the region. Actually all receptor points are treated identically
in the model, but real monitoring stations have a special significance —
they provide calibration data.
Dispersion Model Runs. Various combinations of the data described
above are required in the five types of dispersion model runs needed for EDZ.
Three of the runs are very similar to each other; one of these is the "light
manufacturing air quality run." This run computes the pollutant concentra-
tions at each receptor that would result from very high pollutant emission
rates in areas zoned for light manufacturing use.* The very large quantities
*Light, medium, and heavy manufacturing land use classes are defined in
Step 2.
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16
are required to ensure that all possible effects of sources are measured.
With lesser quantities, contributions from a source to a receptor may be so
small that the effects on the receptor are smaller than the decimal preci-
sion of the dispersion models. There are two other air quality runs — for
medium manufacturing sources and heavy manufacturing sources -— which are
basically the same as the light manufacturing air quality run but consider
emissions for different zoning classes.
The fourth type of run is the calibration run, which uses monitoring
station data to compute a regression line showing the relationship (corre-
lation) between actual and computer-calculated air pollutant concentrations.
In addition, the calibration run estimates the background, or naturally-
*
causedj pollutant concentrations. The fifth type of run, the right-hand-
side run, calculates, at each receptor, the air pollution resulting from
sources which will not be controlled by EDZ (see Prerequisite 6 in the
Introduction).
The results of the dispersion model runs are input to a linear pro-
gramming package used to determine emission density limits. This procedure
is discussed in the next section.
-'Background concentrations also may be due to man-made pollutants originating
outside your region.
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17
LINEAR PROGRAMMING AND ITS USE IN EDZ*
Linear programming is a technique for finding the best: solution to a
problem when considering a number of constraints. A constraint may be de-
fined as a limit or restriction on what may be done. Common constraints in
EDZ include air quality standards and emission limits. The use of linear
programming in EDZ is best explained through an example.""
Bill Pike, director of the Clean Air Metropolitan Planning (CAMP)
Commission, and Barb Fulton, head of the Task Force on Air Pollution, were
meeting to discuss progress on the region's EDZ plan.
"Good morning, Bill, how are you today?"
"Pretty good, how about yourself?"
"Fine, thanks. I'd like to discuss our EDZ regulation so that you'll
understand how the Task Force came up with the answers we have. I believe
we've kept the public and the business people pretty well informed on what
to expect, so I doubt that there will be very many arguments when the emis-
sion density limits are set. But just in case, I'd like to make sure you
understand what's been happening."
"Fine," Bill began, "Why don't I tell you what I think has been going
on, and you can tell me if I'm wrong... Let me see, as I recall, you went
through a long decision process with the county board chairmen and the Mayors'
Confederation to decide on the region's goals relative to air pollution
control. When all the shouting was over, they decided that they wanted to
maximize their opportunity for economic growth. The EPA said that no matter
what, the region couldn't violate air quality standards. Didn't the EPA say
there were two standards here?"
"Yes," Barb answered, "Center County and Closeby County are Class III
areas but the other four counties are Class II. Because the air quality in
the Class II areas is currently much better than it is in the more developed
areas of our region, the air quality standard there is more restrictive."
"If there is doubt about the definitions of any words or phases, consult the
Glossary in Appendix A.
**This example explains the "basic EDZ model," which assumes that maximizing
emissions throughout the region will maximize opportunity for economic
growth. For more information see Step 17 or Benesh et al. (1977).
-------
18
"OK," he continued, "so your problem was that you had a maximum amount
of emissions that could be spewed into the air but at the same time a desire
for great economic growth. As I recall, the Task Force concluded that the
way to get the best of both worlds would be to maximize the emissions in the
area while not violating standards. To do that, you decided to try EDZ - set
a maximum emission rate per hectare of land."
"Right," Barb said, "and to set the EDZ emission density limits we've
been running a dispersion model and are preparing for some linear programming
analyses."
"Well, Barb, I'm not too sure about the technical details. I think
you'll have to explain this to me."
"OK. The way it works is that we've run the dispersion model a number
of times to generate different kinds of data, all of which are input to the
linear program. First, we did three air quality runs, which told us where
pollutants were transported after emission. Second, we did a calibration
run, which was basically a regression analysis comparing the computed air
quality with the real air quality at each of the region's monitoring stations.
Then we did something called a right-hand-side run that we used to find the
air pollution contributions of sources we wouldn't be covering with EDZ,
specifically, things like cars, homes, and small commercial buildings."
"And then this was input to the linear program?" Bill asked.
"Yes, but there were more data. Do you remember when I came to your
office to collect the region's set of local zoning maps?"
"Yes."
"Well, we took those and calculated the amount of land zoned for
each land use in subareas of the region called grid squares. We took all
these data and put them into a format for the linear program, like this."
She began drawing on a note pad and continued, "This is called the
coefficient matrix. There's one for SOa and another for particulates.*
Each is made up of six parts. Three of these are shown on the first row.
The columns are numbered sequentially and each column represents a land use
class within a grid square. So, for example, column 001 might be for land
*See Step 17 for alternate matrix form.
-------
19
a * *
ftlftftT
FoycnoW
TtoWooi
M
Rowooa
ROW 004
ROW 005
ROUOOt
"taflxw
use class Ml within grid square 001, column 002 might be for class M2 in
square 001, column 003 might be for class M3 in square 004, and so on. The
right-hand-side shows the air quality standard we are trying to achieve
minus air pollution contributions made by exempt sources and background.
Background is the air pollution caused by natural pollutants like dust. The
equation type column shows that the emissions from each grid square to any
receptor must be L (less than) the standard.
"The other three parts of the matrix..."
"Wait just a minute," Bill interrupted, "what about this N up here
in the equation type column ?"
"Oh..." Barb answered, "That just means that that row represents the
objective function of maximizing emissions. In EDZ, the coefficients of the
objective function row are the number of hectares in a zoning class in a
-------
20
grid square. So, if column 001 was for Ml land in grid square 001 and the
zoning survey showed 20 hectares of land in this situation, the objective
function under column 001 would be 20."
"OK, I see. So the objective function row is the fourth part of the
matrix," he said.
"Yes, and the fifth and sixth parts are the receptor rows and the
bounds section. The receptor rows denote each receptor point used in the
dispersion model. So row 001 is for receptor 001, row 002 is for receptor
002 and so on. Here I should explain the columns and rows sect ion.* The
dispersion model plus a few calculations provide the transfer coefficients;
they indicate the air pollution contribution made by each source to each
receptor for a specified emission rate. The columns and rows section is
filled in by multiplying the value in the objective function row times the
transfer coefficient for the particular land use class in each grid square.
"Do you follow me so far?" she ventured.
"I think so," Bill said, "Each receptor point is a place where the com-
puter calculates the concentrations of air pollutants. And each row in the
matrix represents a receptor point,"
"Right."
"And the RHS at the end of each row shows the maximum air pollution
that can be added to each receptor without violating standards. So, if you
knew how much pollution went from each land use to each receptor, then you
would know if the standards at the receptor would be violated."
"Right again," Barb replied, "the way that it is done is to calculate
the transfer coefficients I mentioned earlier. These numbers tell you that
if X amount of pollutant is emitted at location A, then Y concentration of
pollutant occurs at location B."
"I see, so location A is a source and B is a receptor."
"Yes, and the system of grid squares that we set up over the entire
metropolitan area shows the locations of the sources."
"How's that?"
^Sometimes called the "A matrix."
-------
21
Barb sketched the boundary
of the CAMP region and drew a sys-
tem of grid squares over it.
"Here's our region," she said,
"and here are the grid squares.
Now when an individual square con-
tains pollution sources, the pol-
lutants emitted travel according
to our region's topography and
meteorological conditions. In
our air quality dispersion model
run, we treated each grid square
as single source rather than as a
group of little sources.
When each square is treated as a single source, each land use within the
square can be thought of as a contributor to the total pollution coming from
the square."
"Oh, so if the emissions from each source to a receptor are added
across a row, the total must be less than the RHS to avoid violating stan-
dards. "
"Exactly," Barb continued, "that's what the equation type column says.
The air pollution levels at any receptor resulting from all pollution sources
in the region can't be any higher than those allowed by the air quality stan-
dards. If they are higher, then we're violating the pollution regulations.
"The upper and lower bounds show the maximum and minimum emission
rates that can occur per hectare of each land use in each grid square. These
upper and lower limits are calculated from projections, current conditions,
and reasonable expectations.
"The contents of the coefficient matrix are put into the computer,
which then provides us with an optimal solution - the one best way for
setting emission density limits, if there is one."
"What kind of answer will the computer give you?" Bill asked.
-------
22
"It will tell us the maximum emission rates per hectare for each
land use class in each grid square that can be allowed without violating air
quality standards at any receptor. From there we analyze tVie results, see
if they're reasonable, map, and implement."
"That's all?"
"That's all."
"But," Bill asked, "how does the machine pick out the maximum allow-
able emissions, or the maximum or minimum anything for that matter?"
"Imagine this is a four square kilometer grid square and that there
are three land use classes in it like this," Barb said.
Ml a
•* Cured sources
£02 control
"The equation to maximize emissions is called the objective function
and it is :
ZL - 15
SO
"This says that we want to maximize total emissions — that ' s Z —
which are equal to the hectares of Ml land times the emission density limit
for Ml, plus the hectares of M2 land times the emission density limit for
M2."
Bill queried, "The 75 in the equation, is that the hectares of land
in land use class Ml?"
"Yes, and the 50 represents the hectares of land zoned M2."
"But, what happened to the 275 hectares in class A?"
"Oh, I forgot to point out that I assumed those sources were exempt
from EDZ regulation," she explained.
-------
23
"But, why should they be exempt?"
"Most area sources — like residences, retail businesses, and cars
are too small to be worthwhile regulating with EDZ."
"OK, I understand. Go on."
"Now this gets a little complicated, so let's say there are only two
receptors where air quality is estimated, to simplify things." She wrote
down two more equations right under the first one:
-to . 1$ (o.^E-i *• 5o(o.;0£2 £• SO-3S
"These equations are the air quality constraints for the two recep-
tors. In general, an air quality constraint says that the air pollution at
a receptor caused by sources located in land use class Ml plus the air pol-
lution at the same receptor caused by sources in land use class M2 must be
less than or equal to — or, in compliance with — the air quality standards at
the receptor minus the contribution to air pollution from exempt and back-
ground sources. In this example I used the SOz annual primary standard of
80 micrograms per cubic meter. The 35 and 40 are hypothetical contributions
to air pollution from the exempt sources in land use class A plus any back-
ground concentrations of SOz in the area. Like before, the 75 and 50 are
the number of hectares of land zoned for Ml and M2 . The numbers 0.3, 0,2,
0.4, and 0.1 are hypothetical transfer coefficients."
Bill interjected, "Do you mean for every gram of 862 emitted per
second in each hectare in the Ml area you'd expect air pollution at recep-
tor 1 to increase by 0.3, and at receptor 2 by 0.4 micrograms per cubic
meter?"
"Right."
"So then," he continued, "A gram of emissions per second from an M2
hectare would increase pollution at receptor 1 by 0.2 and receptor 2 by 0.1
micrograms per cubic meter."
"Exactly. Remember the upper and lower bounds?" Bill nodded. "Good,
these were limits on the allowable emission rates per hectare (or the
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24
emission density limits) that reflect technical and possibly political
considerations. Let's say that the allowable emission rate per hectare for
Ml — "Ei " — must be between 0.5 and 3.5 and the limits for the M2 area —
"Ez" — are 2 and 6. The bounds equations are written like this."
2.
Subject -to: ISCe.S^E, •* So(o.:0£5 ^ SO-3S
7S Co. 4^, *- SoCo.Ofct 4. <2o-HO
v!Wv\dts'. o.S ^ L, ^ 3.5
a.o ^= £7. ^ ^-o
"The problem is to find the values of the emission density limits
Ei and £2 — that will maximize the objective function and obey all the
constraint equations. Let's simplify the equations." Barb did a few cal
culations and then wrote:
2. - 75 £v 4-
-to: ;U.S£, -v- 10^ ^
3o £» -H 51^ t HO
o.5 fe. £, ^ 3.S
"OK, I think 1 understand," Bill interrupted, "but how do you find
the solution?"
"This is easiest to explain by looking at this diagram. Now, Bill,
if you'll look at this graph I just drew, you'll see that all the points
between A, B, C, and D will give values that won't violate the air quality
standards or the upper and lower bounds."
"Wait a minute. I lost you."
"Let's look at the first air pollution constraint." Barb pulled out
the paper she'd written the equations on.
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25
h-
i
8
2
O
N
u
upper bound £2,2(0
lounj' bound EI ^ 2
in
VI
uT
2345
OEMblTS LIMIT IM Ml
-------
26
Twenty-two-point-five times EI plus 10 times £2 must be less than or
equal to 45. Here I've drawn the equation 22.5 times EI plus 10 times Ej is
equal to 45," she said as she pointed to the graph.
"O.K., I see that."
"Now, any points below this line will satisfy this constraint. For
example, let's take EI as 0.4 and £2 as 3. As you can see, 22.5 times 0.4
plus 10 times 3 is equal to, let's see, 39, which is definitely less than 45.
But you'll notice that 0.4 and 3 can't be an answer for this problem."
"Why not?"
"Because it's an infeasible point since it doesn't, satisfy the lower
bound constraint for EI; 0.4 is too low."
"I think I see," he hesitated.
Barb continued, "Why don't you take a point on the graph inside the
area A, B, C, and D and convince yourself that all the constraints are satis-
fied. You'll see that every point works. Then, take points outside the
boundary and you'll find that none of those points work. At least one of the
constraints is violated every time."
A few minutes later, Bill muttered, "I see, I see — it works."
"Good, I'm glad you understand now."
"Yes, now I see why the answer must be a point in the area A, B, C,
and D, but how do you find the one that maximizes emissions?"
"Luckily, there is a simple way for finding the optimal solution.
It's called the simplex method. But, rather than trying to explain the
whole method, it will be easier to explain the key property the computer
uses to solve the problem quickly."
"Yes?"
"The optimal solution must be a corner point. If there is more than
one optimal solution, at least two must be corner points which are adjacent
to each other. So, we need only look at the corner points A, B, C, and D
and find the one that maximizes emissions."
"OK, how?"
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27
"Well, let's list the corner point solutions." Barb began to make
more calculations.
"I know what you are doing. You're solving for the values of EI and
£2 at the intersections. . er , the corner points," Bill said proudly.
"I see you have the right idea. Well, here we are."
Poiry-fc \)qlu.e. o£
ft Uv« .S, LI* 3.H") £* 7SC.5 + 50(3.
£* 7
R,iAt D (£i- .1, 8^ A.M^ £ - 75 M) v- SoU.M)--
"As you can see, the optimal solution is at point A since this pro-
duces the maximum value for the objective function."
"Do you mean the emission density limits of 0.5 for the Ml area and
3.4 for the M2 area will result in maximum emissions?"
"Yes. Now if you drew the equation 75 times EI plus 50 times Ea
equals 208 on the graph I drew before, you'd see that it would intersect the
feasible region at point A. Any value of the objective function greater
than 208 will result in a line that does not cross the feasible region, which
means all the points on the line violate one or more constraints."
"I understand now. But if you can do all this figuring on a note pad,
why do you need the computer model?"
"Well, my example has only two land use classes, two receptors, and
one grid square. In our metropolitan area we are using three land use
classes and several hundred receptors and grid squares. The problem is
rather large to be done by hand."
"I see. Is there anything more?"
"Do you see how the technique works?1' she asked.
"Yes, and it looks OK to me."
"Excellent. Then why don't we meet again this time next week and
talk about our progress with the linear programming?"
"That sounds all right to me. See you then."
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29
STEP 1: ACQUIRE EDZ SOURCE DATA
SUMMARY
In Step 1 you will assemble the data needed to produce a list of EDZ
sources and their characteristics. The following substeps are involved in
acquiring EDZ source data:
Step 1A: Acquire or Produce a General Regional Map
Step IB: Identify UTM Coordinates in the Region
Step 1C: Collect Zoning Maps and Ordinances
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
As can be seen in Fig. 5, the only preliminary steps for Step 1 are
the prerequisites discussed in the Introduction. The skills, data, and
hardware required for Step 1 are summarized in Table 2.
STEP 1A: ACQUIRE OR PRODUCE A GENERAL REGIONAL MAP
The product of Step 1A is a wall size regional map showing the bound-
aries of:
the region,
political jurisdictions in the region,
20 : 22 —7 23 24 25
Fig. 5. Prerequisites for Step 1
-------
Necessary Skills
Land-Use Planning
Secretarial-
Cartographic
Necessary Dat.u
Prerequisites
Regional Boundaries
Air Quality Standards
Jurisdictions with Zoning
Powers
r»c,:;i -;o,,ee=
' ••
Ir troductioo
Introduction
In tr eduction
State
>t'i e - sarv
Cart ^graphic
Supplies
First on list directs this step.
• natural physical barriers to development, and
• areas with different air quality standards (for example, Class I,
II, and III air quality maintenance areas).
Detailed descriptions of each of these map features are provided below.
Other information (such as streets and highways) may be included but is not
required. An example of a general regional map is shown in Fig. 6.
Before you begin to draw a map, check to see if a useable map has al-
ready been compiled [by groups such as regional planning agencies, regional
air pollution control authorities, transportation authorities, the U.S.
Geological Survey (USGS), and metropolitan special districts]. Any large-
scale regional map can be used provided it shows regional and political
boundaries, barriers to development, and areas with different air quality
standards. If a base map with only one or two of these items can be located,
then you will have to add the missing information. The most useful base maps
are the USGS 7 1/2 minute (7 1/2") topographic maps, which can be fastened
together to form a single map covering the entire region.
Constructing a Map
A wall-size regional map may be prepared as follows:
Reg-ional Boundaries. Examples of boundaries of the region for imple-
menting EDZ are the Standard Metropolitan Statistical Area (SMSA), Air
Quality Maintenance Area (AQMA), Air Quality Control Region (AQCR), or the
jurisdiction of the regional planning commission. In an ideal situation
these boundaries coincide. Determining the appropriate regional boundary is
a basic political decision which, for the purposes of this guidebook, is
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31
: ;Saruman T»p
MERRY COUNTY
Eowin Twp.
PEREGRIN COUNTY
Gandalf Twp.
LORIENr—' \
PALISADE STATE
DUNES }
PSD AREA ...-•••
I PETES '
V BOG /
SAMPLE
GENERAL REGIONAL MAP
( STEP I)
KILOMETERS
5
10
0123456
MILES
Fig. 6. Example of General Regional Map
-------
32
assumed to be part of the prerequisites to Step 1. If this decision has not
been made, determine the regional boundaries before continuing.
Political Jurisdictions. After the boundaries of the region have been
determined and mapped, the boundaries of each political subdivision that has
zoning powers (municipalities, townships, counties) must be mapped.
Development Constraints. Next, identify areas in which development
is restricted or impossible, such as:
• Waterways: rivers, lakes, reservoirs, swamps, wetlands, and
flood plains of significant land area, where development is
currently restricted.
• Unsuitable terrain: mountains, sloping areas, rock zones,
cemeteries, areas of mudslide potential, etc., where develop-
ment is not allowed.
• Permanent open space: state, county, and local parks; beaches;
federal and state facilities; forest preserves; zoos; etc.
• Other nondevelopable areas: special local conditions may pre-
clude development on certain parcels of land. These areas
should also be mapped.
Areas with Different Air Quality Standards. Certain states and the
federal government have designated some areas as "maintenance areas" and
others as requiring "prevention of significant deterioration" (PSD) in air
quality. U.S. EPA regulations provide for three basic PSD classifications.*
The regulations allow states, federal land managers, and Indian governing
bodies to reclassify areas under their jurisdiction to accommodate the social,
economic, and environmental needs and desires of the local population. A
complete list of the Class I, II and III areas for each state is available
from the state EPA or equivalent agency. Boundaries of Class I, II and III
areas are to be drawn on the regional map, as are those of areas with special
air quality designations established by state or local governments.
*For detailed information on these regulations, see the Clean Air Act Amend-
ments of 1977.
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33
STEP IB: IDENTIFY UTM COORDINATES IN THE REGION
The data to be collected during this substep consist of the Universal
Transverse Mercator (UTM) coordinates that are used in the region. The use
of UTM coordinates is recommended because the locations of point and area
sources in many emission inventories are given in UTM coordinates and the
UTM system is available nationwide.*
The UTM coordinate system, developed by the U.S. Army as a method to
precisely locate any point in the world, is much like the latitude and longi-
tude system. Every point has a unique set of east-west and north-south coor-
dinates, called "eastings" and "northings," respectively. All UTM coordinates
are in metric units (meters).
Unlike latitude and longitude, which have only two "baselines" — the
equator and the central meridian at Greenwich, England — there are many UTM
baselines. In the northern hemisphere, the equator is the baseline for
northings. This means that the northing value at the equator is zero, and
increases as one travels north. But eastings have baselines every 6° of
longitude, so there are baselines at 3°, 9°, 15°, ..., 63°, 69°, 75", 81°,
87°, 93a, 99°, 105", 1110, 117°, 123", 129°, 135°, ..., and 177°.** Each of
these meridians has an easting value of 500,000 m, with points east of the
meridian having easting values greater than 500,000 m, and those west of the
meridian, less than 500,000 m. The 6° grid zones avoid major deformation of
the earth's surface when its shape is transferred from a rounded to a flat
surface. Consult Fig. 121 in Appendix E to determine if there is a UTM grid
junction in your region.
UTM coordinates may be found on U.S. Geological Survey topographic
maps, which are available at a nominal cost from: Branch of Distribution,
U.S. Geological Survey, 1200 S. Eads St., Arlington, VA 22202 (Telephone:
703-557-2751), for areas east of the Mississippi; or Branch of Distribution,
*0ther Cartesian coordinate systems (such as state plane coordinates) may be
used if their use is well established in the region and the origin is
located to the southwest of the region such that all locations within the
region have positive x and y coordinates. Systems such as latitude and
longitude can not be used (see Appendix F).
**Numbers in italics are those relevant to the contiguous 48 states.
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34
U.S. Geological Survey, Box 25286, Federal Center, Denver, CO 80225 (Tele-
phone: 303-234-2351), for areas west of the Mississippi.
The USGS maps will look like the examples in Fig. 7. To read the UTM
coordinates on a USGS topographic 7 1/2" map, find the easting and northing
marks (usually in blue or black print). These are circled on the top map.
The 7 1/2" maps show the location of UTM coordinates every 1000 m with the
numerical values of the coordinates provided in at least one corner of the
map. The easting values are provided on the top and bottom. For example,
in the upper map in Fig. 7, the coordinate labeled 61°°° E is the easting
value of 61,000 m. Similarly, the northing coordinate tt252000 N found on
either side of the map is read as 52,000 m. For the EDZ analysis the small
numbers preceeding the coordinate value may be ignored. On some maps the
small zeros are not printed, as shown on the bottom map; in this instance the
coordinate "* 85 is read as a northing of 85,000 m.
STEP 1C: COLLECT ZONING MAPS AND ORDINANCES
Since EDZ assigns different emission density limits for each land use
class and the intended uses of land are reflected in zoning ordinances or
land use plans, a zoning survey must be conducted.* This survey will pro-
duce a set of maps showing the zoning classes of every (or nearly every)
hectare of land in the region. It is possible that all the maps will be
combined to form a single zoning map covering the entire region. But this
can be time consuming and therefore is not necessarily desirable.
Data Sources
The primary sources of data for a zoning survey are zoning maps and
ordinances. In addition, a large map of the study region with clearly de-
fined landmarks is an essential aid (a copy of the general regional map may
be used here). The addition of roads would increase its usefulness. Zoning
maps can be found in many forms, some of which are listed below in decreasing
order of usefulness:
*For the EDZ analyses zoning ordinances are recommended for use over land
use plans since zoning ordinances are legally adopted, state-sanctioned,
police-power regulations that have force of law whereas the land use plans
typically have minimal legal authority• The discussion of a zoning survey
is adapted from Cohen et al. (1971).
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35
NTFRIOR-G EGLOGICAL SUIVEV WASHINGTON D C -1959-NS
MR 68^4
38°22'30"
0°00'
520 000 FEET
(IOWA)
-^ .. n ^ \ Ji HI .
- "' t 2 820000 FEET (IOWA)
41°22'30"
9 Ml.
\
90°30'
Fig. 7. Examples of USGS 7 1/2" Maps
-------
36
• Composite* zoning map of the entire region.
• Composite zoning map of counties within the region.
• Zoning maps of municipalities including, if applicable, their
extraterritorial jurisdictions.
• Zoning maps of county unincorporated areas.
• Zoning maps of township unincorporated areas.
Of course, the more recent the data, the more likely it is that the data are
accurate and the more accurate, the better.
Recommended Data Collection Methods
The hierarchy of data types discussed above suggests that a systematic
data acquisition procedure can greatly reduce cost and effort.
Composite zoning maps are usually developed for planning purposes.
Therefore, the principal sources of these maps are regional and county plan-
ning agencies.** If composite maps are not available, then the planning
agencies may have a complete or partial file of subregionalt zoning ordinances
and maps. It is obviously desirable to find organizations, such as local or
regional planning agencies, which have compiled much of the information
sought. In all cases, begin by looking for data with regional planning com-
missions, then county agencies, with surveys of individual townships and
municipalities used only as a last resort. In some metropolitan areas,
especially those spanning state boundaries, more than one regional planning
commission may be able to supply data. A few phone calls to all of these may
save much time and effort. The only caution concerning such sources is the
timeliness of the data. In some cases the data files are not updated fre-
quently.
The large map mentioned earlier should be used to note what areas
have composite maps. If the composite maps are recent, data collection for
these areas is complete.
*A "composite" zoning map is defined here as a zoning map covering both
incorporated and unincorporated areas.
**Also called planning commissions or boards. In some cases political sub-
units of county zoning boards are required to file an updated map with the
board. These should be checked.
tHere defined as counties and their primary political and administrative sub-
divisions, including townships, cities, and villages.
-------
37
For each area without composite maps, the survey team should compile
a list of municipalities. This list should be checked against the munici-
pality-zoning map files of the regional and county agencies. If the files
are current, they should be used; if not, the municipalities should be con-
tacted directly to obtain copies of their zoning ordinances and maps. Town-
ship maps may be handled in a similar manner.
A methodology for contacting individual municipalities was proven
successful in the Chicago area zoning survey mentioned previously (Cohen et
al., 1971). In that study, after all regional and county data sources were
exhausted, a form letter (Fig. 8) was sent to each municipality from which
zoning material was desired. A second list of "important" municipalities was
developed. Important municipalities were those known to have heavy industry.
After ten working days, all important municipalities that did not respond
were telephoned. If copies of their maps were not available and a verbal
description was not practical, a personal visit was planned. All five
personal visits required in the Chicago study were made on the same day.
During the personal visit, a street or township map was used to outline areas
of interest.
Dear Sir:
The ... Name of the Organization ... has recently initiated a
project to develop a plan for the maintenance of air pollution stan-
dards. Part of this study involves the compilation of zoning maps
for ... Name of the County(ies)...
The County's Departments of Building and Zoning have already
provided the zoning maps and ordinances for the unincorporated areas
of this region. Since it is impractical to visit personally all the
cities, towns, and villages not zoned by the County, I am asking you
to send me a recent copy of your area's zoning ordinances and map(s).
Your cooperation in this matter will be greatly appreciated.
Sincerely,
Fig. 8. Sample Form Letter
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39
STEP 2: PROCESS EDZ SOURCE DATA
SUMMARY
In Step 2, the data acquired in Step 1 are prepared for coding and key-
punching. This is accomplished through two substeps:
Step 2A: Plot EDZ Source Grid Squares
Step 2B: Determine Zoning in Each Grid Square
Each land use class considered in the analysis located in each grid square is
defined as an "EDZ source" for the dispersion model and linear program. The
output of Step 2 is used in the location of EDZ receptors (Step 3) and the
air quality dispersion model runs (Step 14).
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Before beginning Step 2 you must have acquired the general regional
map, UTM coordinates (see Appendix F if you are not using UTM coordinates),
and zoning ordinances and maps described in Step 1 (see Fig. 9). The
personnel, data, and hardware needed for Step 2 are shown in Table 3.
20 : 22 —7 23 24 25
Fig. 9. Prerequisites for Step 2
-------
40
Table 3. Resource Requirements for Step 2
0
Necessary Skills
Cartographic
Data Analysis
Secretarial
Possibly Land-Use Planning
Necessary Data
Regional Map
UTM Coordinates
Zoning Maps
i Necessary
Data Sources Hardware
Step 1 Cartographic
Step 1 Supplies
See Text
First on list directs this step.
STEP 2A: PLOT EDZ SOURCE GRID SQUARES
Plot Grid Axes and 8 km x 8 km Grid Squares
Place transparent drawing material over the regional map prepared in
Step 1 and locate a point that is at least 4 km west and 4 km south of the
region's boundary and is also the intersection of UTM easting and northing
grid lines. This point will be your EDZ grid origin- record its UTM coor-
dinates and draw a pair of perpendicular lines through it (see Fig. 10). Now
draw an 8 km grid systen, as shown in Fig. 10. The 8 km grid system is recom-
mended for most regions. If your region is smaller than 350 km2, the use of
1 km2 grid squares is recommended.
While it is rarely possible for the EDZ grid system to correspond
exactly with the area source emission grids in the emission inventory used in
Step 6, later steps will be simpler if the two grids share as many common
coordinates as possible. The EDZ grid should not fragment or isolate major
J BOUNDARY OF
( METROPOLITAN
' AREA
FTI METROPOLITAN
8 km
Fig. 10. Grid Axes and 8 km EDZ Grid System
-------
41
population centers, heavy source concentrations, or physical barriers.
Major political boundaries also should be considered.
Modify the 8 km Grid System
Increasing and decreasing the number and sizes of the grid squares in
the 8 km system will provide a grid system that can be used for setting emis-
sion density limits. However, a better system may be derived taking into
account specific characteristics of the region being studied. Therefore, you
may wish to develop your own grid modification scheme. If so, the grid sys-
tem must satisfy the following criteria:
• Areas with the highest population density or growth potential
should be located in the smallest grid squares.
• The most sparsely settled or slowest growing areas should be
in the largest grid squares.
• All grid square sizes must be integral multiples of each
other (see Fig. 11). To ensure this feature, use square
grids with side lengths equal to 1, 2, 4, and 8 km.
The problem in partitioning any area for EDZ source calculations is to choose
the size of grid squares and their spatial arrangement. The number of grid
squares used in the region dictates the cost of each computer run. The
fewer grid squares, the less computer time used and the lower the cost. In
addition, fewer grid squares will involve fewer hand calculations and,
therefore, lower manpower costs. On the other hand, fewer grid squares (for
a given land area) tend to decrease the dispersion model*s precision.
A balance must be struck between cost
and precision. A reasonable compromise
is to have higher precision where popu-
lation and pollution concentrations are
highest, while less precision is toler-
able elsewhere. This is the basis for
the first two criteria listed above for
modifying the 8 km grid square system.
i e The third criterion is a direct require-
ment of the dispersion model suggested
for use in the analysis (CDMQC).
Fig. 11. Integral-Multiple
Square Sizes
-------
42
As a rule of thumb the grid system used should contain about 350
grid squares, to attain adequate precision at a reasonable cost. Both aggre-
gating or subdividing portions of the 8 km grid system ma}' be required to
obtain 350 grid squares. Simple procedures for carving out these tasks are
presented below. Some trial and error will be required before a final grid
system is obtained; this trial and error process should be conducted on
drafting paper rather than on the acetate overlay of the regional map.
Figure 12 shows some examples of grid systems and their expected
effects on the analysis.
Aggregating Grid Squares. If the region has more than 350 8 km grid
squares then some squares should be combined; however, the number of grid
squares larger than 64 km2 should be kept to a minimum.
REGION TO BE GRIDDED
SMALL SQUARES:
HIGH PRECISION, HIGH COST
LARGE SQUARES: MIXED SQUARES:
LOW PRECISION, LOW COST REASONABLE PRECISION AND COST
Fig. 12. Alternate EDZ Grid Square Arrangements
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43
A simple aggregation procedure is:
• Along the boundary of the region enlarge the grid squares
to 16 km on a side provided the population densities in the
areas are relatively low. Most likely these squares will
be in unincorporated areas.
• The most densely populated areas or those of high growth
potential should remain in grids no larger than 8 km on
a side.
• If after this aggregation the number of grid squares still
exceeds 350, some of the 16 km grids in the outlying areas
may be aggregated into 32 km grid squares and neighboring
8 km grids may be aggregated into 16 km grids.
• Repeat the process until the number of grid squares is less
than 350.
After increasing the size of some grid squares it may still be desirable to
reduce the size of other squares in highly populated or growth areas. A
procedure for reducing grid square sizes is presented below.
Su'bd^V^d^ng Grid Squares. The 8 km grid squares may be subdivided to
increase the accuracy of the analysis. The smallest grid square need not be
less than 1 km on a side.
One simple subdivision approach is to leave 8 km grid squares in all
unincorporated areas, and use 1 km grids in all incorporated areas. The main
disadvantage to this system is that it may soon create more than 350 grid
squares. The advantages are that it is fast, fairly simple, and treats all
incorporated and unincorporated areas equally.
Another procedure for subdividing the 8 km squares is a 12-step pro-
cedure suggested by Benesh et al. (1977). A simplified version of his pro-
cedure, based solely on the population characteristics of the region, is that
areas with existing or projected population densities:
• Greater than 2,000 persons/km2 should be contained in grid
squares no larger than 1 km2.
• Between 1,000 and 2,000 persons/km2 should be contained in
grid squares no larger than 4 km .
• Between 200 and 1,000 persons/km2 should be contained in
grid squares no larger than 16 km2.
It is recommended that population projections for EDZ be consistent with
those used for 208, 201, and other planning efforts.
-------
44
Plot Mod-ified Grid System. After a final grid system is determined,
it should be drawn onto the acetate overlay of the regional map by modifying
the 8 x 8 km grid drawn earlier. A new piece of acetate may be needed if a
number of 8 km grids were aggregated into larger grids. A completed grid
system for the Louisville, Ky., area based on decision rules prepared by
Benesh et al. (1977) is provided in Fig. 13.
Number the Grid Squares
Each square in the final grid system is to be given an identifying num-
ber. The numbering system used for the Louisville map in Fig. 13 is shown in
Fig. 14. The numbering system is used only for identification purposes;
therefore, the only requirement is that every square be assigned a number,
starting with 1. It is suggested that the grid squares be numbered from left
to right and top to bottom to facilitate locating them on the map.
List the Coordinates of Each Square's Southwest Corner
Using Worksheet 1* (see Fig. 15), record the UTM coordinates of the
southwest corners of each grid square. The coordinates should be based on the
coordinates of the intersections of the grid axes. If there is a UTM grid
junction in your region, refer to Appendix E before filling out Worksheet 1.
STEP 2B: DETERMINE ZONING IN EACH GRID SQUARE
Although several standard land use zoning coding schemes have been de-
veloped, with few exceptions, none has gained national or even regional
acceptance. You will probably have numerous zoning classes in your region.
It is theoretically pleasing to consider setting unique emission den-
sity limits for as many zoning categories as possible, but it is extremely ex-
pensive and practically impossible to consider every zoning class conceived of
by every planning and zoning authority in a metropolitan area. The EDZ meth-
odology requires that zoning classes between jurisdictions be standardized,
and the number of zoning classes considered for analysis be kept to a minimum.
In Step 2B you will prepare simplified zoning maps that only show uni-
form regionwide zoning classes for use in setting emission density limits.
*Full page, blank worksheets for your use are provided in Appendix J.
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45
CLARK CO
...J
i
[~
I—
I
FLOYD CO.
—t
\
C3
""")
/
Fig. 13. EDZ Grid System for Louisville, Ky. (from Benesh et al., 1977)
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46
1
3
4
5
6
-
7
8
9
10
II J2
14 15
13 16 17
|I9 20
! \~~
18 121 22
24 25
23 26 27
28 29
30 31
32
33
34
35
36
37 ,ZB
39 140 41 42
J44J4t|44J47;4B49
«' [sOJSI MJISJ64) ?6
•57 M 9*j»oUl «2
56 l4JM*8|W|t7M
70 71 72i73J74 7ft
69 7677J7iJT»W> »i
«S MIUJM «7 M
82 |»|»0>l ,«»»».
9»ji7J»»|»tj«xjio
95 IOl|ialD4<>*KW0
108 [109 110 III
112 113 (114 115
116 117 lie |ii9
248 120 121 122
123
124
125
126
127
128
130 131
129 .132 ;i33
134 135 |I36 >I37
IJlUiHOll*,
l«Z>43JVMMs|'*6 '**7
MII44OO Bl
021541940(156 157
BIB* ISO Bl
!62«IMHai66 167
mmm\i~ \
17*17^ I74ir7! 176 :I77
176 m WO 1*1
»Z»^I»4)U((186 l87
IM^L89 '
190 191 192
193 ,194
195 196 197
198
99
200
2OI I2O2
203 204
205 ! 206
207 ! 208
1
209 210
211 212 !2I3
214 j.215
216 217 218
219 220
221 222^223
224 225
226J227 228
229 230
i
231 232
233
234
235
236
241
242
243
237
238
239
240
244
245
246
247
Fig. 14. Numbered EDZ Grid System for Louisville, Ky. (from Benesh et al.,'1977)
-------
47
WORKSHEET I (FOR STEP 2 ) FILLED OUT BY'
LIST OF SOUTHWEST CORNERS OF EDZ GRID SQUARES
-DATE-
PG.
OF.
EDZ GRID SQUARE NUMBER
GQO&tJ'
OO02.S
0OOM*
^Q£Q&X
OQQS&
OOO23
OOO3O
OOO3I
NORMALIZED
NORTHING IN km
7/££
7&£L
Z/££_
r ws.o
7/6.0
1I$.O
111.O
1I&.Q
NORMALIZED
EASTING IN km
4ULQL
^4±IUL
tuo.o
__^fe/O£
4MO.O
4MLO
W.ILO
<&i&Q
Fig. 15. Worksheet 1 Example
Create Uniform Regionwide Zoning Classes
By far the most important classes for EDZ are the heavily polluting
industrial classes. Facilities located in commercial, residential, and open
space areas usually have small emissions of sulfur dioxide (SOz) and particu-
late matter; therefore, these light polluting classes should be simplified
to one homogeneous class, called Class A (for Area Sources). The recommended
classes to be used in simplifying zoning maps are:
A All residential, commercial, and open spaces except areas
with very large structures (higher than 20 stories). In-
cludes rights-of-way.
Ml Light Manufacturing District. Must have provisions for
assembly, processing, fabrication, and storage plants. No
raw materials should be processed into any of the following
basic products: metals of any kind, glass, plastics, tex-
tiles, leather, paper, petroleum, chemicals, or food. Also
no municipal incineration or motor-freight terminals are
allowed in Ml districts. Ml includes areas with buildings
more than 20 stories tall that were excluded from Class A.
M2 Medium Manufacturing District. Has the same restrictions
as Ml, but allows for municipal incineration and motor-
freight terminals.
M3 Heavy Manufacturing District. No restrictions.
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48
Many municipalities will have business districts t'nat allow indus-
trial processing activities. For the simplified maps, these districts would
most likely be classified as Ml. Similarly, a manufacturing district that
allows food processing, but not the other exceptions, may still be classified
as an Ml district.
One difficulty may arise while classifying manufacturing districts.
Often stone or sand quarries and strip mines are classified as heavy manu-
facturing, and no distinction is made on the zoning map. If it is con-
sidered desirable to create a new class for these types of sources, addi-
tional information is needed to determine what sections of the region are
quarries or mines.
Superimpose the regional EDZ grid system onto the zoning maps and
then reclassify the zoning classes of all the various jurisdictions using
the four classes suggested above (or a reasonable variation). Figures 16
and 17 show a grid square before and after reclassification and simplifica-
tion. The maps resulting from reclassification will be called "simplified
zoning maps" hereafter.
Estimate Areas of Each Land Use Class Within Ea_ch_Grid_S£uare
(Designate EDZ Sources')
The simplified zoning maps must be analyzed to determine the area of
each land use class falling into each of the grid squares napped in Step 2A.
Generally, finding the percentage of a grid square with a particular land
use zoning classification will be easier than finding the area. Therefore,
it will be assumed that percentages will be found first and then changed to
areas (in hectares) for all the land use classes in each grid square.
There are a number of methods for finding the area covered by each
class in each grid square; five of these are reviewed here. While any of the
methods may be used, the simplest and one of the most accurate is the modi-
fied Monte Carlo method. This method has been tested and proven to be
accurate to within 5% by Cohen, et al. (1971). All of the methods des-
cribed below assume the grid squares developed in Step 2A have been super-
imposed upon the simplified zoning maps.
-------
49
The Modified Monte Carlo Technique. This method, recommended for use
in setting emission density limits, employs a dot pattern to generate per-
centages directly from the zoning map. A specially constructed stratified
systematic unaligned (SSU) set of dots is laid on top of a grid square of
the simplified zoning map. The number of dots lying within each zoning
class is counted and then divided by the total number of dots in the grid
square to give a percentage for each zoning class. A dot pattern consisting
of 49 to 100 dots per EDZ grid square will generally work the best; the more
dots per square, the greater the accuracy of the technique.
The recommended modified Monte Carlo procedure steps are:
1. Construct a simplified and gridded regional zoning map.
2. Construct SSU dot pattern on acetate, tracing paper, or
some other transparent overlay material. This process
is illustrated in Fig. 18.
3. Overlay SSU dots on each map grid square (see Fig. 19).
4. Count the number of dots falling into each zoning class (see
Fig. 19).
5. Calculate the percentage of land in each zoning class for
each grid square (see Fig. 19).
6. Tabulate the data using Worksheet 2 (see Fig. 20).
The five-step process presented in Fig. 18 is based on a discussion by
Hadfield (1966). For a further description of SSU dot patterns see Berry
(1962).
Alternate Methods for Est^mat^ng Areas. Five other methods that can
be used in designating EDZ sources are described below.
A planimeter is an instrument that measures the area of a plane figure
by tracing its boundary line. The most common types have a small rubber
wheel that activates a counting device read by the operator. Planimeters
can be extremely useful for measuring subsections of a metropolitan area,
but their use is probably too time consuming for measuring every zoned parcel
in every grid square.
The gravimetric technique uses mass (weight) as a measure of area.
First, the gridded simplified zoning maps must be drawn onto special paper
that, unlike regular paper, is of equal mass throughout its entire area.
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50
CHICAGO ZONING ORDINANCE
SEC 7 T 39N Ri3E
MAP 4-1
r~ii—i i—11—i
RESIDENCE DISTRICTS
Rl SINGLE-FAMILY RESIDENCE DISTRICT Bl-
R2 SINGLE-FAMILY RESIDENCE DISTRICT 82-
R3 GENERAL RESIDENCE DISTRICT 83'
R4 GENERAL RESIDENCE DISTRICT B4-
R5 GENERAL RESIDENCE DISTRICT 85-
R6 GENERAL RESIDENCE DISTRICT 86-
R7 GENERAL RESIDENCE DISTRICT 87-
R8 GENERAL RESIDENCE DISTRICT
^==--=gf USB
BUSINESS DISTRICTS
I TO BI-5 LOCAL RETAIL DISTRICTS
I TO 82-5 RESTRICTED RETAIL DISTRICTS
I TO B3-5 GENERAL RETAIL DISTRICTS
I TO B4-5 RESTRICTED SERVICE DISTRICTS
I TO B5-5 GENERAL SERVICE DISTRICTS
6 AND 86-7 RESTRICTED CENTRAL BUSINESS DiSTRiCTS
5 TO 87-7 GENERAL CENTRAL BUSINESS DISTRICTS
COMMERCIAL DISTRICTS
Cl-l TOCI-5 RESTRICTED COMMERCIAL DISTRICTS
C2-I TO C2-5 GENERAL COMMERCIAL DISTRICTS
C3-I TO C3-7 COMMERCIAL-MANUFACTURING DISTRICTS
C4 MOTOR FREIGHT TERMINAL DISTRICT
MANUFACTURING DISTRICTS
Ml-l TO MI-5 RESTRICTED MANUFACTURING DISTRICTS
M2-I TO M2-5 GENERAL MANUFACTURING DISTRICTS
MJ-I TO M3-5 HEAVY MANUFACTURING DISTRICT
Fig. 16. Example of Complex Zoning (from City of Chicago Zoning Ordinance)
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51
Fig. 17. Example of Simplified Zoning for Map in Fig. 16
Each zoning class would be cut out of the paper, weighed on a highly sensi-
tive balance, and the mass of the cut-outs compared to a "standard" piece of
paper of known area. The area of the cut-outs is then determined by their
mass relative to that of the standard. This technique is somewhat cumber-
some.
Natural or contrived grids also may be superimposed on the map.
Natural grids include grid pattern street layouts and grid utility lines.
These may be used to estimate percent land area per zoning class if the grids
are of kn<~>T,rn area. Since not all sections of a metropolitan area have grid
street patterns, this method would have to be used in conjunction with another
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52
STEP 1
For each size of EDZ grid square, drav;
a square of the same size. Divide it
into smaller squares of equal size.
Each of these small squares has its
own x and y axes, as shown here for
two such squares. The number of
smaller squares will determine the
number of dots and the accuracy of the
procedure. The more dots (squares)
the more accurate the method.
44-
STEP 2
For each small square, add x and y co-
ordinates. Note that the lower left
corner of each square is a new origin.
In all squares in the left-hand column
add dots at points with the same y
coordinates (chosen at random; 4 is
used here) but random x coordinates
(3, 4, 2, and 0 are used here).
01 2340! 23401 23401 2340
STEP 3
squares in the bottom
points with the same
In all remaining
row, add dots at
x coordinates (value determined by the
bottom left square; 3 in this example)
but random y coordinates (0, 2, and 2
are used here) .
0 12340
2340
Fig. 18. Construction of an SSU Dot Pattern (continued on next page)
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53
A t
3
p
Q
4
3
2
1
Q
n
4
(
_
)
•
H
r
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H
)
>
4
L
h
_
5 4
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iT
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STEP 4
Place dots in all remaining squares.
Each dot's x coordinate is taken from
the left-hand column in the same row,
and its y coordinate from the bottom
row in the same column of squares.
In this example the following ooints
would be used:
A(0,0) B(0,2) C(0,2)
D(2,0) E(2,2) F(2,2)
G(4,0) H(4,2) 1(4,2)
D
STEP 5
Remove all lines except the EDZ grid
square boundary, either by erasing or
by tracing the dots and boundary onto
a piece of transparent paper. This
is the completed SSU dot pattern.
Its use is illustrated in Fig. 19.
Fig. 18. Construction of an SSU Dot Pattern (contd)
technique. Contrived grids, which are placed over the zoning maps, are
transparent but have cells (rectangles, triangles, etc.) of known size on
them. The amount of land in a zoning class falling into the square is
totaled, with percent then calculated. Both of these grid methods suffer
from the potential of being overloaded by too much data. The analyst trying
to count city blocks or contrived squares will find the job much bigger than
expected since coverage includes the entire metropolitan area.
Computer-assisted methods are summarily described by Chapin (1972).
Two types are available to the agency fortunate enough to have the facilities.
The first is a method where the analyst traces the outline of the zoned
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54
One Grid
Square of
a Simpli-
fied Zon-
ing flap
SSU Dot
Pattern
Overlay
SSU Dots
Onto
Zoning
Hap
A = 12
m = a
ri2 = i
f13 = 4
Count the
Dots in
Each Zon-
ing Class
A = 1
Ml =
r-12 =
[13 =
2
3
1
4
- 20
- 20
- 20
- 20
= 60%
= 15%
= 5%
= 20%
100%
Percen-
tage of
Total
n/i-t-r-
uots
5
Each grid square is completed
in this manner until the
percentages are known for
each square in the region.
Fig. 19. Use of an SSU Dot Pattern
area with a computer-linked stylus. The computer, functioning much like
a sophisticated planimeter, computes the area of the outlined figures. The
second method can be used only in areas with extensive land-use/zoning in-
ventories that are punched on cards or stored in some other computer-com-
patible manner (disk-tape). Here, if either the area or dimensions of each
zoned parcel are included in the data file, areas would be taken directly
from the cards and aggregated by machine. If extensive computer-compatible
surveys (with areas) are available, the regional zoning map would be
virtually unnecessary. The fortunate planning agency may find such com-
puter-compatible information in the city or county assessor's offices.
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55
WORKSHEET 2 (FOR STFR2) FIl'Fn OUT RY:
PERCENTAGE AND AREA OF EDZ SOURCES
DATE
PG
OF
EDZ
GRID
SQUARE
NUMBER
AREA OF
GRID
SQUARE
(km2)
SIMPLIFIED LAND USE CLASS
Ml
AREA:
% x GRID
SQUARE
AREA (km2j
AREA:
% x GRID
SQUARE
AREA (km2!
M2
AREA:
% « GRID
SQUARE
AREA (km?)
M3
AREA.
%> GRID
SQUARE,
AREA (km2)
T:
Fig. 20. Worksheet 2 Example
"Eyeballing" Is unquestionably the simplest, but unfortunately the
most inaccurate method for determining area. Studies (G.obinson and Sale,
1969) have shown that in judging the area of circles, people average 80%
accuracy, which would lead to questionable results for EDZ purposes. This
method is not recommended.
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56
Number EDZ Sources
Worksheet 2, shown in Fig. 20, is for use in recording the percent
and area of each land use class in each grid square. Each EDZ source must
be given an identifying number. The numbers are to be written directly on
the worksheets. No Class A EDZ sources are to be numbered if they are to be
exempt from EDZ.* If there are no hectares of land in a particular land use
class in a grid square, that class within that grid square is not to be
numbered.
*See discussion of exempt sources in the Introduction and Step 7.
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57
STEP 3: SELECT EDZ RECEPTOR LOCATIONS
SUMMARY
Locations at which the dispersion model will estimate air quality are
selected in this step. These locations are referred to as EDZ receptors.
The following substeps are involved in selecting EDZ receptor locations:
Step 3A: Determine the Selection Technique to be Used
Step 3B: Locate Receptor Points
Step 3C: Record Receptor Locations
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
The gridded map prepared in Step 2 and the locations of monitoring
stations obtained in Step 5 are used to select EDZ receptor locations. The
position of Step 3 in the EDZ methodology is shown in Fig. 21. The personnel,
data, and hardware needed for Step 3 are listed in Table 4.
STEP 3A: DETERMINE THE SELECTION TECHNIQUE TO BE USED
A method must be selected for use in locating receptor points. Since
the cost and accuracy of the analysis increases as the number of receptor
20 ^ 22 —7 23 24 25
Fig. 21. Prerequisites for Step 3
-------
58
Table 4. Resource Requirements for Step 3
Necessary Skills
Land Use Planning
Possibly:
Cartographic
Data Analysis
Necessary Data Data Sources
Gridded Regional Map Step 2
Necessary
Hardware
Cartographic.
Materials
First on list directs this step.
points increases, a trade-off between cost and accuracy similar to the one
found in the regional gridding procedure (see Step 2) must: be made. No more
than 200 receptor points for the entire region can be used per run of
CDMQC.* The receptor locations should satisfy the following criteria:
• Areas of high population densities or growth potential
should have the highest concentration of receptors.
• Some receptor points should be located just outside the
boundary of and extend all the way around the region.
• The locations of monitoring stations may be defined as
EDZ receptor locations.**
A number of alternative methods for selecting receptor points are discussed
below.
Center/Boundary Technique
The simplest way to locate receptor points is to place one in the geo-
graphic center of each grid square (see Fig. 22). In each grid square that
includes the region's boundary line, and is 16 km on a side or larger, an
additional receptor is located within the square but outside the regional
boundary (see Fig. 23).
The advantage of this system is its simplicity. The disadvantages
are: (1) the number of receptors to be considered in the linear program be-
comes very large, therefore increasing the run time and cost; and (2) the
receptors may not be located in the most advantageous positions. If the
*If you are using the AQDM dispersion model, see Apendix G, Sec. 4. If more
than 200 receptor points are needed and you are using CDMQC, see Appendix I.
**If you are using AQDM, see Appendix G, Sees. 4 and 5.
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59
RECEPTORS
REGIONAL
BOUNDARY
REGIONAL GRID BOUNDARY
Fig. 22. Receptor Placement
in 8 km EDZ Grid Squares
Using Center/Boundary
Technique
RECEPTOR POINT PLACED
ADDITIONAL RECEPTOR AT GEOGRAPHIC CENTER
PLACED OUTSIDE REGIONAL
BOUNDARY
Fig. 23. Receptor Placement in 16 km EDZ
Grid Squares Using Center/Bound-
ary Technique
accuracy of the dispersion model is going to be checked against actual mea
sured data, the receptor points and the location of the actual monitoring
stations should be the same. In addition, some locations, such as nursing
homes, may have features that make them more susceptible to air pollution
damage than others and receptors should be placed in the more sensitive
areas. The center/boundary technique makes no allowance for special condi
tions other than the refinements produced by variable-sized grid squares.
Uniform Receptor Grid
Another simple technique is to superimpose a uniform grid of receptors
over the region such that some receptors lie beyond the regional boundaries
and the entire region is covered by receptors. By changing the density of
the receptor grid, the number of receptors can be controlled; however, as
with the center/boundary approach the receptors may not be in the most
advantageous locations.
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60
Activity-Based Technique
This technique begins with the assumption that receptors should be
located in those areas with (1) large populations or (2) populations easily
affected by air pollution. Instead of requiring that each re;ceptor be placed
in the center of the square, this technique provides a nuriber of decision
rules to follow in determining whether a receptor should be placed in a
square and if so, where it should be located. These decision rules are:
• If an existing monitoring station is located within the
square it will be used as the receptor point. If more
than one monitoring station is located in a grid, then
only one should be used.*
• If a single grid square contains areas with different
classifications for significant deterioration require-
ments, then at least one receptor point is to be located
within each area.
• If the population density exceeds 3,000/km2, then the
square should contain a receptor.
• If large hospitals, schools, nursing homes, etc. are con-
sidered places where air quality should be known, they
should receive receptor points. However, the large num-
ber of such institutions may lead to too many receptor
points if care is not exercised.
• If none of the above four rules apply to a square, locate
a receptor in the center of the square.
• As with the center/boundary technique, receptors are to be
placed just outside the regional boundary.
Politically Derived Receptor Locations
Here, every political jurisdiction would receive a receptor point
placed at its population centroid. Receptors are also placed just outside
the regional boundary as done in the center/boundary technique.
STEP 3B: LOCATE RECEPTOR POINTS
Once the decision has been made on how to select receptors, the next
step is to map them onto the gridded regional map. Each receptor must be
numbered. This mapping will allow you to see the distribution of points and
their geographic, political, and population density relationships.
*If you are using AQDM, see Appendix G, Sec. 5.
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61
STEP 3C: RECORD RECEPTOR LOCATIONS
The UTM coordinates of each mapped receptor point are to be recorded
(in kilometers rather than meters) on Worksheet 3 (see Fig. 24).* The
coordinates can easily be determined from the coordinates of the southwest
corner of each grid square.
WORKSHEET 3 (FOR
LIST OF RECEPTOR
STEP 3 )
LOCATIONS
FILLED
OUT
RY
DATF
pr,
OF
RECEPTOR
NUMBER
EASTING
IN km
NORTHING
IN km
NOTES ON THE REASON FOR SELECTING THIS LOCATION
FOR A RECEPTOR (CENTER, SENSITIVE, POLITICAL
JURISDICTION, MONITORING SITE, ETC.)
7/5.S
7/&S
4ML8
£5T*tT£S_
Fig. 24. Worksheet 3 Example
*Full page, blank worksheets for your use are provided in Appendix J.
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63
STEP 4: ACQUIRE AND PROCESS METEOROLOGICAL DATA
SUMMARY
In Step 4 you will collect the meteorological data required for the
dispersion modeling and then put it into the proper form for later coding
and keypunching. Two substeps are involved:
Step 4A: Acquire Meteorological Data
Step 4B: Process Meteorological Data
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Only the prerequisites set forth in the Introduction must be ful-
filled, as shown in Fig. 25. The personnel and data requirements are shown
in Table 5.
STEP 4A: ACQUIRE METEOROLOGICAL DATA
Three types of regional meteorological data are needed for dispersion
modeling:*
20 '22—v 23 24
Fig. 25. Prerequisites for Step 4
*If you are using AQDM, see Appendix G, Sec. 7.
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64
Table 5. Resource Requirements for Step 4
«D
Necessary Skills
Data Analysis
Secretarial
Necessary Data Data Sources
Meteorological Data See Text
Necessary
Hardware
None
First on list directs this step.
If you are using AQDM, see Appendix G, Sec. 7.
• Mean annual atmospheric temperature (°C)
• Mixing heights (meters). Both mean annual afternoon mixing
heights and mean annual nocturnal (or morning) mixing
heights are needed.
• Joint frequency function (% of time/situation); (1) for one
year which matches the monitoring station data, and (2) a 5~
or 10-year average for the region.
The mean atmospheric temperature may be collected from an almanac,
the National Climatic Center, or virtually any source of weather information.
The mixing heights are a measure of the distance from the ground to
a layer of air that will not mix very well with pollutants. The layer repre-
sents a cap on how high pollutants can travel. Mixing heights may be esti-
mated using the maps supplied in Holzworth, G.C., Mixing Heights, Wind Speeds^
and Potential for Urban Air Pollution Throughout the Contiguous United States,
U.S. EPA Report AP-101 (Jan. 1972). These maps are reproduced in Appendix D.*
The joint frequency function (JFF) is the most complex meteorological
input used in dispersion modeling. It consists of 576 weather situations
derived from hourly observations made over a year.** The National Weather
Service calculates the JFF by using Day-Night versions of the STAR computer
program. The Day-Night STAR program takes three variables - wind speed
*The Holzworth book, which is recommended only if you desire more informa-
tion, may be ordered from: Library Services Branch, Mail Drop 35, Environ-
mental Protection Agency, Research Triangle Park, NC 27711, at no charge if
available; or at a nominal charge from Superintendent of Documents, U.S.
Government Printing Office, Washington, DC 20402,
**If you are using AQDM, see Appendix G, Sec. 7.
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65
class, wind direction sector, and stability class - and calculates the per-
centage of time that all three coincide (see Table 6).
The Day-Night STAR program provides the JFF in the exact form needed
for CDMQC, the dispersion model recommended for use in setting emission den-
sity limits. No format changes are required. Card output for the Day-Night
STAR program for your metropolitan area may be obtained from: Tape Division,
National Climatic Center, Federal Building, Asheville, NC 28801 (Telephone
919-672-0203).
You should call the Tape Division to be sure the Day-Night STAR pro-
gram data are available for your region and to make payment arrangements.
The NCC charges a nominal fee to cover its costs. In many cases, data for
more than one year are available. The choice of year is left to your dis-
cretion, but for the calibration run of the dispersion model, the year of the
Table 6. The Variables Comprising the Joint
Frequency Function
Wind Speed
Class
1
2
3
4
5
6
Speed Interval
(knots) (mph)
0-3 0-3.45
4-6 4.6-6.9
7-10 8.05-11.5
11-16 12.65-18.4
17-21 19.55-24.15
>21 >24.15
Stability
Class
1
2
3
4
5
6
Explanation
1 is the most stable;
6 is the least stable.
Stability is determin-
ed by incoming solar
radiation (determined
by cloud cover and
height, and solar
altitude) and wind
speed.
Wind
Direction
Sectors
1
2
3
4
5
6
7
8
9
10
11
1Z
13
14
15
16
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
If you are using AQDM, see Appendix G, Sec. 7.
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66
JFF and the year of the monitoring station data should be the same. The
National Climatic Center can supply 5- to 10-year average data for the Dav-
Night STAR program; this long-term average data should be used in the air
quality and right-hand-side dispersion model runs. If the long-term average
is unavailable, use single year data.
STEP 4B: PROCESS METEOROLOGICAL DATA
Step 4B assures that English-to-metric and metric-to-metric conver-
sions have been completed for: mean annual atmospheric temperature, mean
annual mixing heights, and the joint frequency function.*
The mean annual atmospheric temperature must be in degrees Celsius
(°C);* a conversion formula for changing Fahrenheit to Celsius is provided
in Appendix C. The figure should be rounded to two decimal places and
recorded on Worksheet 4 (see Fig. 26).**
The mean annual mixing heights must be in meters (not kilometers) and
estimated to the nearest meter. Record the value on Worksheet 4.
The joint frequency function data should be received on cards (or tape)
and in the correct format for CDMQC. No changes will be necessary. If the
JFF is not available on cards, the card format is shown in Step 9 for con-
verting printed output to the proper card format.
*If you are using AQDM, see Appendix G, Sec. 7.
**Full page, blank worksheets for your use are provided in Appendix J.
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67
WORKSHEET 4 (
MFTFOROLOGICAL
FOR STEP
DATA
4)
FILLED
OUT
RY
DATF
PC, 1
OF 1
THIS WORKSHEET SHOWS THE EXACT FORMAT THAT THE
METEOROLOGICAL DATA MUST BE IN FOR INPUT TO CDM-QC.
I. MEAN ANNUAL ATMOSPHERIC TEMPERATURE
FOR EXAMPLE, |0|Q|'m8|3| WOULD BE READ BY CDM-QC
AS 13.83° CELSIUS.
PLEASE PLACE YOUR FIGURE HERE, USE NO DECIMAL
POINT. ENTER THIS NUMBER IN COLUMNS 61-66 OF
CARD 4, FOR ALL FIVE CDM-QC RUNS. SEE STEP 9.
2. MEAN ANNUAL MIXING HEIGHTS
FOR EXAMPLE, loioi'Hiom WOULD BE READ BY CDM-QC
AS 14,030 METERS.
PLEASE PLACE YOUR FIGURE HERE; USE NO DECIMAL
POINT. ENTER THIS NUMBER IN COLUMNS 19-24
AND 25-30 OF CARD 4, FOR ALL FIVE CDM-QC
RUNS. SEE STEP 4.
\Q\0\/ \I\1\O\
AFTERNOON NOCTURNAL
(MORNING)
Fig. 26, Worksheet 4 Example
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69
STEP 5: ACQUIRE AND PROCESS MONITORING STATION DATA
SUMMARY
In Step 5, you will obtain (Step 5A) and process (Step 5B) data from
the region's monitoring stations that are needed for the calibration run of
the dispersion model (Step 15). Monitoring station locations may also be
used as EDZ receptors in Step 3.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Only the prerequisites outlined in the Introduction must be fulfilled,
as illustrated in Fig. 27. Minimal resources are required for this step; see
Table 7.
STEP 5A: ACQUIRE MONITORING STATION DATA
Actual air pollutant concentrations, as measured at monitoring stations,
form the basis for the calibration run of the dispersion model. The concen-
trations simulated by the dispersion model are checked against the calibra-
tion data to bring the computed values more in line with reality.
20 22 —> 23 24 25
Fig. 27. Prerequisites for Step 5
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70
Table 7. Resource Requirements for Step 5
0
Necessary Skills
Data Analysis
Secretarial
Necessary
Data
None
Necessary
Hardware
None
First on list direct this step.
The completeness of monitoring station data will vary from region to
region due to availability of local or state-operated monitors. You should
check with both state and local agencies for data.
The most uniform source of information is the U.S. EPA's Storage and
Retrieval of Aerometric Data (SAROAD) system. Each year, the EPA publishes
two reports containing the monitoring station data needed for EDZ: Direc-
tory of Air Quality Monitowng Kites Active in 19 , and Air Quaiity Data
19 j AnnuaL Statistics* Both are published about nine months after the
year in which the data are collected. Single copies of these publications
may be obtained free of charge from: Library Services Branch, Mail Drop 35,
Environmental Protection Agency, Research Triangle Park, NC 27711. A tele-
phone call to the EPA Library at 919-541-2777 (Research Triangle Park, NC)
can be helpful in quickly ordering the books and finding out if they are in
stock. Allow 7-10 days for delivery.
If the latest editions of the books are not available and the latest
data are required, contact your EPA regional office (see Fig. 30 on page 75)
or: Monitoring Data and Analysis Division, National Air Data Branch, En-
vironmental Protection Agency, Mail Drop 14, Research Triangle Park, NC 27711
(telephone: 919-541-5395). The more recent data must be taken from computer
files and will take longer to process. You must ask for the exact data de-
sired. The monitoring data should correspond to the year of the emission
inventory and meteorological data.
The specific data from each monitoring station needed for the calibra-
tion run are:
• UTM coordinates of the monitoring station.
• Measured concentration of SOa in Ug/m3 (annual arithmetic
mean).
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71
Measured concentration of participates in Ug/m (annual
arithmetic mean).
Standard geometric deviation for particulate concentra-
tions.
STEP 5: PROCESS MONITORING STATION DATA
Worksheet 5, shown in Fig. 28, is recommended for use in tabulating
the data.* The data may require two changes before input to the disper-
sion model. English units must be converted to metric equivalents and UTM
coordinates may require normalization. The unit conversions are:
• UTM coordinates must be expressed in kilometers.
• Concentrations (annual arithmetic means) for S02 and parti-
culates must be expressed in micrograms/cubic meter.
• Annual standard geometric deviation for particulates must
be expressed in micrograms/cubic meter.
WORKSHEET K (FOR STEP 5) FILLFD OUT BY:
MONITORING STATION DATA
HATF
PG
OF
NAME
OF
STATION
&r»T£ L/MiV. _
7"$ tivsPlTHL.
CLJLtHJftHl
2."°* CHESTNUT
Lo*l£N tioS/>
STATION
i.D. NUMBER
(FOUR
DIGITS)*
0/00
oaoo
0100
osoo
OLOO
UTM LOCATION
(NORMALIZED) IN
km TO ONE DECIMAL
PLACE XXXX.X
EASTING
HIS.S
1I7.O
li&s
7/Q.4
727. /
723.7
NORTHING
4UO.S
<&m.3
440M
Vi.07.3
4L07.0
4(.iO.<1
MEASURED
CONCENTRATIONS,
ANNUAL ARITHMETIC
MEAN (jig/m3)
so2
3?*0
tt.o
43.5
3S.O
30.0
42.0
PART
62.0
—
7^0
U.O
U.O
77.0
STANDARD 24-HR
GEOMETRIC
DEVIATIONS
(jjg/m3 )
so2
4.02.
[ 3.01
y.37
/.S2.
2.97
3.50
PART
/.Sfe
AS/
^1^L_
-L5QL
/.86
Fig. 28. Worksheet 5 Example
*Full page, blank worksheets for your use are provided in Appendix J.
-------
72
• Four-digit identifying numbers must be attached to each
station to identify the data. The monitoring station
identifying numbers may be changed in Step 3 if monitor-
ing sites are used as EDZ receptors.
Metric-English conversions are provided in Appendix C. Worksheet 5, shown
in Fig. 28, is recommended for use in tabulating the data. If your region
includes a UTM grid junction, see Appendix E. If you are not using UTM
coordinates, see Appendix F.
-------
73
STEP 6: ACQUIRE AND PROCESS EMISSION INVENTORY DATA
SUMMARY
In Step 6 data needed for the dispersion model are acquired from state
or federal emission inventories and processed. Four substeps are involved:
Step 6A: Acquire Point Source Data
Step 6B: Acquire Area Source Data
Step 6C: Make English-to-Metric Conversions for Emission Inventory
Step 6D: Prepare Emission Inventory Data for Use in Step 21
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
For Steps 6A-6C, only the beginning prerequisites need be fulfilled;
data from Steps 1 and 2 are needed for Step 6D. The necessary personnel and
data are shown in Table 8.
STEP 6A: ACQUIRE POINT SOURCE DATA
CDMQC, the dispersion model recommended for use in EDZ, requires the
following information on each point source in the region:*
Fig. 29. Prerequisites for Step 6
*If you are using AQDM, see Appendix G, Sec. 7.
-------
74
Table 8. Resource Requirements for Step 6
0
Necessary Skills
Data Analysis
Cartographic
Secretarial
Possibly:
Air Pollution Control
Expertise
Necessary Data
Data Sources
Simplified Steps 1
Zoning Maps and 2
and General
Regional Map
Necessary
Hardware
None
First on list directs this step.
• Location of the source in UTM or other coordinates.''1
• Emission rates for SOz and particulates.
• Stack height and diameter.
• Exit velocity of stack gases (.or stack gas flow rate) .
• Temperature of stack gases.
Setting emission density limits clearly would be a mammoth undertaking if you
had to collect these data "from scratch." However, the Clean Air Act requires
states to compile comprehensive emission inventories that include this infor-
mation. The state information has been transferred to the. U.S. EPA's Nation-
al Emissions Data System (NEDS), which is updated periodically. With the
data collection already complete, all you need to do is to acquire the data
from your region's state government(s) or the U.S. EPA. It is recommended
that state or local data be used if they are available as they will probably
be updated more quickly than NEDS. State data should be available on com-
puter tape from the state EPA or its equivalent. NEDS data on tape may be
ordered from the U.S. EPA regional offices (see Fig. 30 for phone numbers
and addresses.)
NEDS data are available on cards, computer tape, or printout. It
takes roughly two weeks for an EPA regional office to process a request for
data. The request must be made in writing and explain the exact data re-
quested; there is a nominal charge. Your letter to the EPA regional office
should list the counties in your region, and request, for each point source
in each county, the data listed above.
*See Appendices E and F.
-------
75
STANDARD FEDERAL REGIONS
AND
EPA REGIONAL OFFICES
BOSTOH
NEW YORK
Region
I
II
III
IV
V
VI
VII
VI
II
IX
X
Regional
Office
Tel ephone
(617)
(212)
(215)
(404)
(312)
(214)
(816)
(303)
(415)
(206)
223-7223
264-2515
597-9370
881-3004
353-2000
767-2600
374-5894
837-4905
556-6695
442-1203
NEDS
Contact
Tel ephone
(617)
(212)
(215)
(404)
(312)
(214)
(816)
(303)
(415)
(206)
223-4448
264-9578
597-8133
881-2864
353-2303
749-3837
374-3791
337-4261
556-2326
442-1580
Regional Office,
NEDS Contact
Address
John F. Kennedy Federal Bldg., Boston, MA 02203
26 Federal Plaza, New York, NY 10007
6th & Walnut Sts., Philadelphia, PA 19106
345
230
1201
1735
1850
215
1200
Courtland N
S. Dearborn
•E.,
St
Elm St., Dall
Bal timore
Lincoln St
Freemont St
6th Ave. ,
Ave
, Atlanta, GA 30308
• i
as,
Chicago, IL 60604
TX 75270
. , Kansas City, MO 64108
. , Denver, CO 80203
. ,
San
Seattl
Francisco, CA 941
e, WA 98101
05
Fig. 30. U.S. EPA Regional Offices
-------
76
If the data needed are not available on the emission inventories,
contact state or federal air pollution control agencies or the point source
operators.
STEP 6B: ACQUIRE AREA SOURCE DATA
Area source data are also included in the NEDS file, but are arranged
on a county basis. County data can be used in EDZ, but it is preferable to
have area-source emissions computed over a smaller area. State EPAs and
local pollution control boards have been required to perform extensive air
quality analyses. Part of their work has involved allocating county emis-
sions to subcounty areas. The results of these air quality analyses should
be available from state or local units. The specific data required are:
• UTM coordinates of the emission grid squares used
to allocate emissions to subcounty areas.*
• Emission estimates for SOz and particulates.
• Estimated average stack heights.**
The year for which these calculations were made should be the same as the
year for which the point source and monitoring station data were recorded.
STEP 6C: MAKE ENGLISH-TO-METRIC CONVERSIONS FOR EMISSION INVENTORY
Unit conversions (English-to-metric) are likely to be required to
prepare the emission inventory data collected in Steps 6A and 6B for coding
and keypunching (Step 9). In addition, the coordinate system used in the
emission inventory must be made consistent with the EDZ grid system.
By this time, you should be familiar with the emission inventory data.
If the data are expressed in English units, they must be converted to metric
equivalents (see Table 9).** The conversion factors are given in Appendix C.
The conversions shown in Table 9 must be made for each point and area source
in the inventory. Use Worksheets 6 and 7 (shown in Figs. 31 and 32) to
*These grid squares need not correspond to the EDZ grid system set up in
Step 2. If the emission inventory uses a grid system other than UTM, see
Appendix F.
**If you are using AQDM, see Appendix G, Sec. 8.
-------
77
Table 9. Metric Units Needed for Emission Inventory Data
Variable
Units Most Likely Used
in Emission Inventory
Units Required
for CDMQC3
Point Sources
UTM Location
Emission Rates
Stack Height
Stack Diameter
Exit Velocity of
Stack Gas
Stack Gas Temperature
Area Sources
UTM Location of South-
west Corner of the Area
Source Grid Square
Width of the Area Source
Grid Square
Area Source Emission Rate
(SOz and Particulates)
Estimated Average Stack
Height
V T! r>mot~ ovg
tons/year
feet
feet
flow rate (ft3/min)
degrees Fahrenheit
kilometers or miles
kilometers or miles
tons/year
feet
kilometers
grams/second
meters
meters
meters/second
degrees Celsius
kilometers
meters
grams/second
meters
See Appendix G, Sec. 8, if you are using AQDM.
WORKSHFFT fi (FOR STFP 6 ) Fll 1 FD OUT BY:
EMISSION INVENTORY POINT SOURCES
DATF
Pfi
OF
NAME OF
POINT SOURCE
POINT SOURCE
ID NUMBER
(FIVE DIGITS)
UTM LOCATION
(NORMALIZED)
IN km
EMISSION RATE
'g/sl
S02 PART
STACK
HEIGHT
STACK
DIAMETER
EXIT
VELOCITY
OF STACK
GASES
(m/s)
TEMP.
OF
STACK
GASES
Vzstucci
A 73
2.33
0,0045 38,10
S3.3f
276JL
Q.Q&83
3.881 \&>A
/,83
Fig. 31. Worksheet 6 Example
-------
78
WORKSHEET
EMISSION
7 (FOR STEP 6)
INVENTORY AREA
FILLED OUT
SOURCES
RY:
DATF:
Pfi
OF
AREA SOURCE
ID. NUMBER
FROM
EMISSION
INVENTORY
(5 DIGITS)
UTM LOCATION
(NORMALIZED)
OF S.W. CORNER
OF THE GRID SQUARE
(km)
EASTING
NORTHING
WIDTH
OF THE
GRID SQUARE
(m)
EMISSION
RATE
(g/s)
S02
PART
ESTIMATED
AVERAGE
STACK HEIGHT
(m)
ZlQJtiL
-------
79
WORKSHEET 8 (FOR STEP 6) FILI.FD OUT BY:
EMISSION INVENTORY DATA FOR STEP 24
DATF
PG
OF
POINT SOURCE
NAME
POINT SOURCE
I.D. NUMBER
(FROM
WORKSHEET 6)
SIMPLIFIED
LAND USE
ZONING
CLASS*
EMISSION RATE
OF POINT
SOURCE (A)
IN g/S
S02
PART
LAND AREA
OF POINT
SOURCE (B)
IN ho
EMISSION DENSITY
(4-i-B)
IN (g/s)/ho
S02
PART
JL.QQ
ooooZ,
3£ _
PAIN.
oooo3
£ooo*f
ooooS
HA3
Fig. 33. Worksheet 8 Example
hectares) covered by the source's property in the appropriate column. If the
areas are not in the inventory, find them by using existing maps, tax records,
or calling the operator of the emission source.
Finally, divide each source's emission rate by its land area and record
the results in the right-hand column. These data are to be held for use in
Step 21.*
*If you are using AQDM, see Appendix G, Sec. 8.
-------
81
STEP 7: ESTIMATE EMISSIONS OF EXEMPT SOURCES
SUMMARY
In this step you will estimate the emissions of sources that you have
decided to classify as "exempt sources," "sources granted automatic com-
pliance," or "nonconforming uses." The decision on which sources to so
classify should have been made before the 25-step EDZ methodology was
begun — see the discussion of Prerequisite 6 on pages 10-12 of the guide-
book Introduction.
The contributions to air pollutant concentrations at each receptor of
these sources must be estimated to ensure that air quality standards will not
be violated. The emission estimates made in Step 7 are used in Step 16 to
determine these contributions. If you have decided to exempt some sources
from EDZ, Step 7 must be completed.
If your region has no exempt sources, you may decide to treat existing
sources as nonconforming uses as discussed in the Introduction. In this case
ignore the guidebook references to exempt and automatic compliance sources
and complete the methodology with whatever gaps may exist in Worksheets 10,
13, and 14. The gaps will affect the EDLs determined for the region, but
will not affect your ability to complete the methodology.
The substeps involved in Step 7 are:
Step 7A: Estimate Emissions of Exempt Area Sources
Step 7B: Estimate Emissions of Exempt Power Plants,
Incinerators, and Other Special Sources
Step 7C: Estimate Emissions of Existing Point Sources
Granted Automatic Compliance Status
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
The prerequisites outlined in the Introduction must be fulfilled and
Steps 1, 2, and 6 completed, as illustrated by Fig. 34. The personnel and
data requirements for Step 7 are listed in Table 10.
STEP 7A: ESTIMATE EMISSIONS OF EXEMPT AREA SOURCES
An area source may generally be defined as an aggregation of many
small sources. For example, mobile, residential, commercial, small
-------
82
20 ' 22 -^ 23 24 25
Fig. 34. Prerequisites for Step 7
industrial, and agricultural sources and rights of way may be aggregated into
an equivalent area source. Each individual structure emits very little sul-
fur dioxide and particulate matter, too little to justify a detailed review
or to make EDZ control practical. In aggregate, however, emissions from area
sources can be significant, and therefore their air quality contributions
should be included in setting EDLs.
The air pollution contributions due to area source emissions in some
future year* must be estimated and included in the linear program. The esti-
mates should be made to account for possible changes in emissions due to
growth, changes in environmental control policy, and/or changes in energy
consumption patterns. To make these projections, follow the directions pro-
vided in the EPA reports:
Guidelines for Air Quality Maintenance "Planning and Analysis.
Volime 7: Projecting County Emissions (EPA-450/4-74-008) and
Volume 13: Allocating Projected Emissions to Subcounty Areas
(EPA-450/4-74-014)
These documents may be obtained from: Library Services Branch, Mail Drop 35,
Environmental Protection Agency, Research Triangle Park, NC 2.7711.
*For example, 10 or 20 years into the future may be considered a suitable
time horizon.
-------
83
Table 10. Resource Requirements for Step 7
, ...
0
Necessary Skills
Air Pollution Control
Expertise
Land Use Planning
Data Analysis
•
Necessary Data
Exemptions Decision
Zoning Maps and
Ordinances
Gridded Regional Map
Emission Inventory
EPA Guidebooks
Data Necessary
Sources Hardware
Prerequi- None
sites
Step 2
Step 2
Step 6
See text
First on list directs this step.
The projections generated by these procedures, which are handled in
'Jy the same way as the area source emission inventory data were handled
in Step 6, are to be entered on Worksheet 9 (Fig. 35).* It may be helpful
to photocopy Worksheet 7 since its first four columns are identical to those
on Worksheet 9. Worksheet 9 requires that the average stack characteristics
for future area sources be estimated. The estimation procedure is explained
in Step 8.
STEP 7B: ESTIMATE EMISSIONS FOR EXEMPT POWER PLANTS,
INCINERATORS, AND OTHER SPECIAL SOURCES
If the decision has been made to exempt power plants, incinerators,
and other special point sources, the following steps should be completed to
estimate the emissions from these sources:
1.
2.
3.
4.
Find out the current and approximate future locations and
land areas of these sources (probably from utility company
or municipal records and plans).
Locate the EDZ grid squares that contain the existing and
proposed facilities.
Determine the land use zoning classification(s) of the
facilities.
For each grid square, subtract the quantity of land used
by each facility from the total amount of land in the
same zoning classification. The remaining value will be
considered the amount of land zoned in the zoning class
in the grid square for the linear programming calculations
in Steps 18 - 22; theref'ore, the land areas written into
Worksheet 2 must be changed.
*Full page, blank worksheets for your use are provided in Appendix J.
-------
84
WORKSHEET 9 (FOR STEP 7 ) FILLED OUT BY-'_
AREA SOURCE EXEMPTIONS
.DATE-
PG.
OF.
AREA SOURCE
ID NUMBER*
(5 DIGITS)
00/4$
_. 00/44
00/6$
00/66
_ QO/BT
00*88
00301
0030?
0030S
^00307
UTM LOCATION
(NORMALIZED)
OF S.W. CORNER
OF THE GRID SQUARE
(km)
EASTING
7/0.SS
7/0, $5
7/4. £5
7/V.5S
7/0.55
71&.S5
7*2.55
722,55
722.55
722.55
NORTHING
4fc0#22
460B.ZZ
\ 4tO4J&
4608,ZZ
4&)4,gZ
4&01.&
4604,22.
4&Q6.2SL
4(,08.ZZ
*falO.?&
WIDTH
OF THE
GRID SQUARE
(»)
40QO
4000
{ 4QOQ
400O
4000
4000
&OOO
ZOOQ
ZOOO
ZOQO
FUTURE
EMISSION
RATE
(«/i)
S02
3S1.00
50.00
38,50
55.00
40.00
60.00
I9.-OQ
JT.00
5.00
/o.oo
PART
&5J)0
as. 50
40.00
60.00
$3.00
72.50
43.00
/O.OQ^
7.50
ao.oa
AVERAGE
STACK HEIGHT
(m)
....._7.S
7,5 _
IQ.O
JO^L
10 J)
IQ&
SLQ
5,
-------
85
WORKSHFFTIO (FOR 55TFP 7) Fll i FD OUT BY:
EXISTING POINT SOURCE EXEMPTIONS
DATF
PR
OF
NAME OF
POINT SOURCE
C,L£A &*>#.
POINT SOURCE
ID NUMBER
(FIVE DIGITS)
OOOIO
oaooi
UTM LOCATION
(NORMALIZED!
IN km
EASTING
7/5.
CQOK\S
ooooS
OJOOO
02,000
03000
7/6. S
7t$,0
7/3. S
722.0
NORTHING
4608.0
FUTURE
EMISSION RATE
(g/sj
"soTT PART
2.74
O.QO^S
4.469.3.
l?3£
6*331*
.LtOJ.0
STACK
HEIGHT
(m)
38- 10
M.50
^0,00
Jj4S,Ptf.
STACK
DIAMETER
3,00
A. 83
a./fl.
3.00
EXIT
VELOCITY
OF STACK
GASES
(m/sj
;* 0.000
TEMP
OF
STACK
GASES
&&L&
MIL
SLO?.0
Fig. 36. Worksheet 10 Example
emissions are used, the emission rates are estimated using the two EPA
manuals listed in Step 7A. The following steps need to be completed:
1. Determine the locations and land areas of sources to
be granted automatic compliance status (from the
emission inventory data collected in Step 6).
2. Locate the EDZ grid squares that contain the sources.
3. Determine the land use zoning classification of the
sources.
4. For each grid square, subtract the quantity of land
zoned for each new source from the total amount of
land in the same zoning classification. The remai-ning
value will be Gons-idered the amount of land zoned in
the zoning class in the grid square for> the lineaT
programming calculations in Steps 18 - 22. Therefore,
the land areas on Worksheet 2 must be changed.
5. Determine emission and stack characteristics of the
sources (from the emission inventory of Step 6). If
projected emissions are to be used in setting EDLs,
consult the EPA manuals listed in Step 7A. Enter
the emissions and stack characteristics on Worksheet 10.
-------
87
STEP 8: DETERMINE STACK CHARACTERISTICS
SUMMARY
In Step 8, you will obtain, calculate, or predict individual source
and average stack characteristics for use in the dispersion model runs. Eor
CDMQC, the stack characteristics needed are stack height and diameter, exit
gas velocity, and exit gas temperature.*
Three substeps are involved:
Step 8A: Determine Stack Heights for the Air Quality Runs of
the Dispersion Model
Step 8B: Determine Stack Characteristics for the Calibra-
tion Run of the Dispersion Model
Step 8C: Determine Stack Characteristics for the Right-
Hand-Side Run of the Dispersion Model
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Before you begin Step 8, the prerequisites outlined in the Introduc-
tion must be fulfilled and Steps 1, 2, and 6 completed, as illustrated in
Fig. 37. Personnel and data requirements are listed in Table 11.
Fig. 37. Prerequisites for Step
*If you are using AQDM, see Appendix G, Sec. 8.
-------
Table 11. Resource Requirements for Step
— - — • — " ' — — ^— •
^3
Necessary Skills
Air Pollution Control
Expertise
Land Use Planning
Data Analysis
Necessary Data
Exemptions Decision
Zoning Maps and
Ordinances
Emission Inventory
Data Necessary
Sources Hardware
Prerequi- None
sites
Step 2
Step 6
First on list directs this step.
STEP 8A: DETERMINE STACK HEIGHTS FOR THE AIR QUALITY RUNS
OF THE DISPERSION MODEL
The emission inventory provides the data required to estimate average
stack heights for the light, medium, and heavy manufacturing sources.
Determine which point sources are located in which zoning classes (the
zoning class of each source is listed on Worksheet 8), and list these on
Worksheet 11, shown in Fig. 38.* Sources with the same zoning classification
should be listed together on Worksheet 11. Also list the stack heights on
Worksheet 11. This information should be included in the emission inventory
data collected in Step 6.
Then, after you have identified all the point sources, average the
stack heights within each zoning class. Simple arithmetic means are to be
used; that is, all stack heights within a zoning class are summed and divided
by the number of sources in the class. You should have four numbers when you
complete Step 8A: the average stack heights for light, medium, and heavy
manufacturing sources; and an average for all sources combined.
Contact your local air pollution control authority to determine wheth-
er these average stack heights conform to current building practices and U.S.
EPA policies on stack height allowances for dispersion modeling. If the
averages appear acceptable, use them; if not, the averages should be changed
to conform with EPA policy.
These actual stack height estimates may be assumed to be equal to the
effective stack height for the light, medium, and heavy manufacturing area
sources. This is a conservative assumption — it will result in lower
*Full-page blank worksheets for your use are provided in Appendix J.
-------
89
WORKSHEET 11 (FOR STEP 8 ) FILLED OUT BY'
STACK HEIGHTS FOR AIR QUALITY RUNS
DATE
PG
OF
POINT SOURCE NAME
POINT SOURCE NUMBER
(FROM WORKSHEET 6)
0003
ooo &,
QOQ5
SIMPLIFIED LAND USE
ZONING CLASS
Ml
MV.
re*. H~3
A"-
STACK HEIGHT
FROM WORKSHEETS (m
38.10
VS.1&.
38.JO
—
38 JO
Fig. 38. Worksheet 11 Example
emission density limits than if an actual effective height was used. If a
less conservative average effective stack height estimate is desired, then
for each point source in the emission inventory an average effective stack
height should be calculated. Check with your local air pollution control
agencies or the U.S. EPA to determine an acceptable procedure in your area
for making these estimates.
Then, substituting these effective stack heights for the actual stack
heights in Worksheet 11 and following the procedures described in Step 8A
will result in alternative estimates of stack heights.
STEP 8B: DETERMINE STACK CHARACTERISTICS FOR THE CALIBRATION
RUN OF THE DISPERSION MODEL
The stack characteristics needed for the calibration run are included
in the emission inventory data in Step 6. If all the entries in Worksheet 6
-------
90
have been completed, no further work need be done except that a regional
average (arithmetic mean) stack height must be calculated by summing the
stack heights for all the point sources in the region and dividing by the
total number of point sources. This regional average stack height should be
used as the value for H needed to calculate the SZA value on the Type 5 Card
for the CDMQC calibration run (see Fig. 54 in Step 9).
STEP 8C: DETERMINE STACK CHARACTERISTICS FOR THE RIGHT-HAND-SIDE
RUN OF THE DISPERSION MODEL
The right-hand-side (RHS) run usually involves future emissions of
EDZ exempt sources. In addition to emissions, estimates of the future
stack characteristics of some exempt sources are required. All data described
below are to be placed in the appropriate columns of Worksheets 9 and 10.
Exempt Area Sources. CDMQC only requires average effective stack
heights for area sources. For EDZ, it is assumed that future area source
stack heights will not be significantly different than they are currently.
Area source stack data were collected with the emission inventory data in
Step 6. Assume these are equal to effective stack heights.
Exempt Point Sources (and Point Sources Granted Automat-io Compliance
Status). Except for power plants, it should be assumed that future stack
characteristics will be the same as they are currently unless (1) there is
reason to believe that they will change or (2) current practice does not
conform to the EPA's definition of "good engineering practice." Future power
plant stack characteristics should be estimated based on utility company
records. Stack heights for power plants should be estimated on the assump-
tion that no future stack will be taller than the EPA stack height allow-
ance for setting emission limits, i.e., the EPA's definition of good
engineering practice.*
The average regional stack height calculated in Step 8B should be used
as the H value need for the right-hand-side run Type 5 Card (see Fig. 55).
*If there are stack characteristic restrictions in the land use zoning
ordinance, or local building codes, then these may affect future stack
characteristics. For example, if there are height restrictions in cer-
tain areas of the region (such as near airports), the limitations must
be taken into consideration when heights are estimated.
-------
91
STEP 9: CODE AND KEYPUNCH DISPERSION MODEL INPUT DATA
SUMMARY
In Step 9 the data processed in Steps 2-8 are coded and keypunched
for input to the dispersion model." Since CDMQC is the model recommended for
EDZ, Step 9 applies only to the input sequence for CDMQC. If you are using
AQDM or another dispersion model, Step 9 does not apply; consult the user's
manual for your dispersion model regarding input data format.
The person in charge of Step 9 should reread the section entitled
"Dispersion Modeling and Its Use in EDZ" before beginning this task. Step
9 explains the exact card formats required for the CDMQC runs: the light
manufacturing, medium manufacturing, and heavy manufacturing air quality runs
(performed in Step 14); calibration run (Step 15); and right-hand-side run
(Step 16). Many of the cards input to CDMQC require modifications from run to
run. Therefore, there will be as many as five versions of the same card.**
The substeps involved in Step 9 are:
Step 9A: Code and Keypunch General Data Cards
Step 9B: Code and Keypunch Point Source Cards
Step 9C: Code and Keypunch Area Source Cards
Step 9D: Code and Keypunch Monitoring Station Cards
Step 9E: Code and Keypunch EDZ Receptor Point Cards
Step 9F: Mark and Duplicate Card Decks
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
The data from Steps 2-8 must be collected and processed before you
begin Step 9, as shown in Fig. 39. The personnel, data, and hardware needed
for Step 9 are considerable, as shown in Table 12. The necessary data for
this step include the CDM and CDMQC manuals:f
*Coding is defined here as the act of writing data onto a page (called a
coding form) so that, when keypunched, the data are in the exact form re-
quired for input into a computer. Keypunching is defined as the act of
typing computer cards with the aid of coded data.
**You may need more than five versions of some cards if any of the situations
described in Appendix I apply to your region.
tlf you have completed Step 10, you will have already ordered these manuals.
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92
20 22 —y 23 24 25
Fig. 39. Prerequisites for Step 9
Busse, A.D., and J.R. Zimmerman, User's Guide for the Climatologiaal
Dispersion Model, U.S. EPA Report EPA-R4-73-024, NTIS No. PB-227-346
(Dec. 1973).
Brubaker, K.L., P. Brown, and R.R. Cirillo, Addendum to the User's
Guide for the Climatologioal Dispersion Model, U.S. EPA Report No.
EPA-450/3-77-015 (July 1977).
These manuals are available from:
Modeling Support Section, Mail Drop 14
Source-Receptor Analysis Branch, Monitoring Data and Analysis Div.
Office of Air Quality Planning and Standards
Environmental Protection Agency
Research Triangle Park, NC 27711
RULES AND GENERAL INSTRUCTIONS FOR CODING AND KEYPUNCHING FOR CDMQC
This section is written for individuals with little or no experience
with computer models and, in particular, with CDMQC. Although the guidebook
is written for a wide range of potential users, it is strongly recommended
that an experienced programmer be responsible for this step. Coding and
keypunching data for CDMQC are very mechanical tasks. If the data processed
in Steps 2-8 have been placed on the worksheets and Step 9 is followed
carefully, there should be no problems in coding and keypunching.
-------
93
Table 12. Resource Requirements for Step 9
Necessary Skills
Necessary Data
Data
Sources
Necessary
Hardware
Computer Programming
Data Analysis
Keypunching
EDZ Source Data
EDZ Receptors
Meteorological Data
Monitoring Station Data
Emission Inventory
Exempt Sources
Stack Characteristics
COM and CDMQC Manuals
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Step 8
U.S.
Keypunch
Organizing Files
Coding Forms
EPA
First on list directs this step.
Rules
• Do everything exactly the way the guidebook says to do it.
• Be sure each number, letter, punctuation mark, and symbol
is in the exact column indicated.
• No cards may be added to or removed from the deck.
• Be sure to check each coding form and punched card at least
twice for accuracy. Punched cards should be verified
mechanically.
General Instructions
There are four kinds of data cards used in CDMQC. The first
101 cards, called general data cards, include meteorological
data, the joint frequency function, and miscellaneous control
parameters. The second kind, point source cards, give infor-
mation on up to 200 point sources in the region.* Area source
cards provide data for up to 2500 area sources. Receptor
point cards are used to describe EDZ receptors and monitoring
station data.* These four card groups together make up a "data
deck." There is one data deck for each CDMQC run. Data decks
showing the order that the cards must be placed in for input
into the computer are diagrammed in Figs. 40-42. A summary
of the differences between each CDMQC run is presented in
Table 13.
Figure 43 illustrates the format of the pages used in the
guidebook to explain card format, Each of these figures
(Figs. 44-66) explains a single line to be entered on a cod-
ing form; each line on a coding form is typed on a single
card. The cards must be placed into the deck in the order
given in the guidebook. Be sure to note the oord number and
"If there are more than 200 point sources or receptor points in your region,
or more than 50 monitoring stations, see Appendix I.
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94
EDZ GRID SYS
DESCRIPTION
MISC CONTROL
PARAMETERS .
GENERAL DATA
CARDS
ICALIBRATION/PRINT
SPECIFICATIONS
JL
DIFFERENT SOURCES FOR
DIFFERENT AIR QUALITY
RUNS. FOR EXAMPLE, FOR
THE LIGHT MANUFACTURING
RUN, DATA ONLY FOR LIGHT
MANUFACTURING SOURCES;
AND SIMILARLY FOR THE
MEDIUM AND HEAVY
MANUFACTURING RUNS.
SECONDARY CONT.
LANGUAGE
INITIAL CONTROL
LANGUAGE
Fig. 40. CDMQC Card Input Sequence for Air Quality Runs
-------
95
END-OF-FILE
CARD
MONITORING
STATION DATA
EMISS INV SYS
DESCRIPTION
GENERAL DATA
CARDS
MISC CONTROL
PARAMETERS _
CALIBRATION/PRINT
j SPECIFICATIONS
nr
SECONDARY CONT.
LANGUAGE
INITIAL CONTROL
LANGUAGE
FROM THE
EMISSION INVENTORY
41. CDMQC Card Input Sequence for Calibration Run
-------
96
GENERAL DATA
CARDS
FOR EXEMPT
SOURCES
EDZ GRID SYS
[ DESCRIPTION
MISC CONTROL
PARAMETERS ,.
CALIBRATION/PRINT
I SPECIFICATIONS^
_OC
SECONDARY CONT.
LANGUAGE
INITIAL CONTROL
LANGUAGE
Fig. 42. CDMQC Card Input Sequence for Right-Hand-Side Run
-------
97
Table 13. Summary of the Five CDMQC Runs
LIGHT MANUFACTURING AIR QUALITY RUN
Sources: All light manufacturing land uses within each of the EDZ
grid squares (from Worksheet 2). Stack heights estimated via
instructions in Step 8. Each EDZ grid square with a light
manufacturing zone is given an emission rate of 10,000 g/s.
Receptors: All receptors chosen by the procedure used in Step 3.
MEDIUM MANUFACTURING AIR QUALITY RUN
Sources: All medium manufacturing land uses within each of the EDZ
grid squares (from Worksheet 2). Stack heights estimated
via instructions in Step 8. Each EDZ grid square with a
medium manufacturing zone is given an emission rate of
10,000 g/s.
Receptors: All receptors chosen by the procedure used in Step 3.
HEAVY MANUFACTURING AIR QUALITY RUN
Sources: All heavy manufacturing land uses within each of EDZ grid
squares (from Worksheet 2). Stack heights estimated via
instructions in Step 8. Each EDZ grid square with a heavy
manufacturing zone is given an emission rate of 10,000 g/s.
Receptors: All receptors chosen by the procedure in Step 3.
CALIBRATION RUN
Sources:
Receptors:
All point sources from the federal or state emission inventory,
and all area sources (subcounty) from the state or local
emission inventory or subcounty allocations of federal county
data. Data for both from a given past year.
All monitoring stations from federal, state, or local data,
for the same past year as the point source data.
RIGHT-HAND-SIDE-RUN
Sources: All sources exempt from EDZ control such as area sources.
The projected emission rates, stack characteristics and
other necessary data for these sources is generated by
following the EPA Guidelines Series Volumes 7 and 13 and
Step 8.
Receptors: Same as those for the air quality runs.
-------
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run in which the card is to be used. Do not mix cards
from one run in with cards from another run.
There should be a separate file (or box) to hold the cards
for each CDMQC run.
If you have coding and keypunching problems, consult the
CDM and CDMQC manuals or contact your EPA regional office
(see Fig. 30).
STEP 9A: CODE AND KEYPUNCH GENERAL DATA CARDS (Card Types 1-6)
There are 101 general data cards, and the first five are the most
complex cards in the data decks. The contents of these cards are explained
in Figs. 44-57. Be sure to note each card's position in the data decks and
the run in which it is used.
Type 1 Card. This card titles the output of each of the five runs.
Any titles may be used, but each run should be clearly identified.
Type 2 and Type 3 Cards. These cards provide the computer with
printing and calibration specifications and miscellaneous control parameters.
Note that Columns 1-41 are the same for all Type 2 cards, and Columns 12-80
are the same for all Type 3 cards; the other columns change from run to run.
Type 4 Card. By now you have found that you may have two area source
emission grid systems.* The first is the one used in the emissions inventory
for subcounty allocations of emissions. The second system is the one produced
to define EDZ sources. The emission inventory system will only be used in
the calibration run. The EDZ grid system is used in the air quality and
right-hand-side runs. Since part of the Type 4 card's function is to describe
the grid system, the distinction between the two systems is very important.
Type 5 Card. The decay rates for SOz and particulates (Columns 55-66)
may vary from region to region. The guidebook assumes very long lifetimes
for both pollutants and the same decay rate for both. If you use two dif-
ferent decay rates, two linear program matrices will be required in Step 17
because the transfer coefficients for each pollutant will be different.
Having two decay rates will double the number of calculations to be per-
formed in Step 19, and require that two sets of "columns data" cards be
keypunched in Step 22.
*In some instances these systems will be identical.
-------
100
Type 6 Cards. These 96 cards contain the Joint Frequency Function
data. Single year data are used for the calibration run, while multi-year
average data are used for the air quality and right-hand-side runs.
STEP 9B: CODE AND KEYPUNCH POINT SOURCE CARDS (Type 7 Cards)
The point source data cards for the calibration and right-hand-side
runs are illustrated in Figs. 58 and 59; no point source cards are used in the
air quality runs. Each point source in the emission inventory - to a maxi-
mum of 200 point sources* - will have a point source card describing its
characteristics. Therefore, there may be 1 to 200 point source cards for
each of the two runs. Data for point source cards come from the emission
inventory, as modified by the instructions in Step 6, and from Step 8.
All the point source cards will be used in the calibration run. The
right-hand-side run will have cards only for point sources that are to be
exempt from EDZ (if any). Exempt sources data are optional, as described in
Step 7. The one blank card is to be inserted in the data deck after the
point source cards to signify the end of the point source data., Only a
single blank card will be used in the air quality run in the Type 7 card
position.
STEP 9C: CODE AND KEYPUNCH AREA SOURCE CARDS (Type 8 Cards)
There will be five sets of area source cards, one for each CDMQC run.
As many as 2500 area source cards may be used per run. The cards are illus-
trated in Figs. 60-64. The data for these cards have been generated by Steps
2 and 6-8. Step 2's EDZ source data and Step 8's stack height data are used
in the three air quality runs. Step 6's emission inventory data are used in
the calibration run, with some possibly being used in the right-hand-side run.
Step 7's exempt source projections are used in right-hand-side run.
A blank card is inserted in the deck following the area source cards.
STEP 9D: CODE AND KEYPUNCH MONITORING STATION CARDS (Type 9 Cards for
Calibration Run)
The data from Worksheet 5 should be transferred to coding forms in the
manner shown in Fig. 65. Monitoring station cards are used only in the
*If there are more than 200 point sources in the region, see Appendix I.
-------
101
calibration run. You must have at least three but no more than 50
monitoring stations.*
STEP 9E: CODE AND KEYPUNCH RECEPTOR POINT CARDS (Type 9 Cards for Air
Quality and Right-Hand-Side Runs)
Receptor point cards locate EDZ receptors where the CDMQC program cal-
culates air quality concentrations. The cards, shown in Fig. 66, are used
in the air quality and right-hand-side runs. There may be as many as 200
cards per run.*
STEP 9F: MARK AND DUPLICATE CARD DECKS
After keypunching, each CDMQC card deck should be labeled (by writing
on the edge of the cards) to keep it organized. Since at least five decks
are produced, it is vital to keep the cards separate from each other. A
duplicate of each deck should be made as insurance against accidents.
*If there are more than 50 monitoring stations or 200 receptor points, see
Appendix I.
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STEP 10: ACQUIRE DISPERSION MODEL
SUMMARY
Step 10 is the acquisition of the atmospheric dispersion model used
to calculate air quality.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Only the prerequisites outlined in the Introduction must be ful-
filled, as shown in Fig. 67. Personnel requirements are listed in Table 14.
ACQUIRE DISPERSION MODEL
The Climatological Dispersion Model, version CDMQC, is recommended
for use in establishing emission density limits. Another dispersion model
can be used provided it: (1) can estimate annual average concentrations of
sulfur dioxide and particulate matter, and (2) can estimate the annual
average sulfur dioxide and particulate matter concentrations at each recep-
tor caused by each source, that is, can estimate each source's contribution
to the pollution level at each receptor. (If you are using AQDM, see
Appendix G.)
Fig. 67. Prerequisites for Step 10
-------
126
Table 14. Resource Requirements for
Step 10
Necessary Skills
Computer
Programming
Necessary
Data
None
Necessary
Hardware
None
The CDMQC program is available on cards from:*
Modeling Support Section, Mail Drop 14
Source-Receptor Analysis Branch, Monitoring Data and Analysis Div.
Office of Air Quality Planning and Standards
Environmental Protection Agency
Research Triangle Park, NC 27711
About one to three weeks are required for delivery. In addition to request-
ing the CDMQC program deck, you should ask for the COM and CDMQC manuals
listed below:
Busse, A.D. , and J.R. Zimmerman, User's Guide for the Climatologioal
Dispersion Model, U.S. EPA Report EPA-R4-73-024, NTIS No. PB-227-346
(Dec. 1973).
Brubaker, K.L., P. Brown, and R.R. Cirillo, Addendum to the User's
Guide for the Climatologioal Dispersion Model., U.S. EPA Report No.
EPA-450/3-77-015 (July 1977).
*CDMQC is available on tape through the UNIMAP system, from the National
Technical Information Service, 5285 Port Royal Rd., Springfield, VA 22161.
-------
127
STEP 11: INSTALL DISPERSION MODEL ON COMPUTER AND CHECK
SUMMARY
In Step 11 the dispersion model is checked to make sure it operates
properly. Of the three substeps listed below, the first applies to any dis-
persion model, while the next two are specific to CDMQC:
Step 11A: Install Dispersion Model on Computer
Step 11B: Secure and Keypunch Test Data for CDMQC
Step 11C: Run CDMQC Test Example and Check Results
If you are not using CDMQC, consult the users manual for your dispersion
model for steps similar to 11B and 11C.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Step 10 must be completed —that is, you must have the dispersion
model program and manuals — before beginning Step 11, as shown in Fig. 68,
The major resource required for Step 11 is a computer. Step 11 is best
handled by a programmer. Resource requirements are shown in Table 15.
20 22 —7 23 -— 24 25
Fig. 6f
Prerequisites for Step 11
-------
128
Table 15. Resource Requirements for Step 11
Necessary Skills
Computer
Programming
Necessary Data
CDMQC (or other dis-
persion model)
Program
COM and CDMQC (or
other dispersion
model) Manuals
Data
Sources
Step 10
Step 10
Necessary Hardware
Computer with Fortran
G or H compiler (or
computer with appro-
priate equipment for
the dispersion model
being used) .
STEP 11A: INSTALL DISPERSION MODEL ON COMPUTER*
Installation of the dispersion model acquired in Step 10 is entirely
dependent on the model and the hardware being used. In all cases, installa-
tion must be handled by a programmer familiar with the computer system.
STEP 11B: SECURE AND KEYPUNCH TEST DATA FOR CDMQC
The sample problem in the CDMQC manual, Addendum to the User's Guide
for the Climatological Dispersion Model, should be used to test the CDMQC
package. The input data will be the "TEST CITY Input Data Set" shown in
Fig. C.2 in the CDMQC manual. Format instructions are provided in the manual
and in Step 9.
STEP 11C: RUN CDMQC TEST EXAMPLE AND CHECK RESULTS
After keypunching the data, prepare the card deck, insert the control
language cards appropriate for the computer being used, and run the test
problem. The output from the test run should be compared to the sample
output shown in Fig. C.3 of the CDMQC manual. If the values do not match
check the input data and control language for errors. If there are no key-
punch errors, consult the user's manual or the EPA Modeling Support Section.
*A decision must be made on whether to execute the program from cards or
disk; a disk is easier to handle than a deck of cards. Procedures for trans-
ferring programs from cards to disk vary from computer center to computer
center; consult a programmer familiar with the installation being used if
you want to use a disk. The program may be transferred to disk as a load
module, which eliminates the compiling step and saves time. For simplicity,
this guidebook assumes the dispersion model program is on cards.
-------
129
STEP 12: ACQUIRE LINEAR PROGRAMMING PACKAGE
SUMMARY
Step 12 is the acquisition of a linear programming package suitable
for use in setting emission density limits. Two substeps are involved:
Step 12A: Select a Linear Programming Package
Step 12B: Acquire the Package
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Only the prerequisites discussed in the Introduction must be satisfied,
as shown in Fig. 69. Personnel and information needs are listed in Table 16.
STEP 12A: SELECT A LINEAR PROGRAMMING PACKAGE
Linear programming systems are very similar to one another; they all
use comparable equations and assumptions.
IBM's Mathematical Programming System - Extended (MPSX) is recommended
for use because it has all the required features for EDZ and is readily avail-
able from IBM. However, MPSX may not be usable for all regions implementing
EDZ because it can only be used on an IBM 360 or 370 computer. While the
20 22 —^ 23 24 25
Fig. 69. Prerequisites for Step 12
-------
130
Table 16. Resource Requirements for Step 12
0
Necessary Skills
Computer Pro-
gramming
Secretarial
Necessary Data
Knowledge of
computer
being used
Data
Sources
Local
Necessary
Hardware
None
First on list directs this step.
guidebook assumes the use of MPSX, the data processing steps are basically
the same for all linear programming packages.
Other computer companies and software houses have their own linear
programming packages. Appendix H discusses some of the available linear
programming systems. The linear programming package selected for use in EDZ
must be able to efficiently handle a "large" problem with a nearly 100% dense
coefficient matrix. A "large" problem is one which involves x columns and
y rows, where:
x = a number somewhat smaller than the number of EDZ grid
squares covering your region times the number of simplified
land use classes (see Step 2). If the guidebook methodology
is followed, x will be less than or equal to 1,050 (this
means there will be as many as 1,050 columns in the linear
programming coefficient matrix).
y = the number of receptors in your region (see Step 3) .. In
most cases y will be about 200 (200 rows in the coefficient
matrix), but there will be great variability from region to
region.
MPSX has provisions for upper and lower bounds; if the package you
use does not, the bound constraints discussed in the guidebook will have to
be converted into two inequality constraints, consistent with the format of
the linear programming package.
Consult with personnel at your computer center to determine which
linear programming package is most appropriate for your use.
STEP 12B: ACQUIRE THE PACKAGE
First, contact the personnel at the computer center you will be using
and see if a suitable linear programming system is available. If so, in-
quire about control language and manuals explaining data inputs, and go
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131
on to Step 13. If not, ask about the feasibility of acquiring an outside
package and using it on the computer center's hardware.
If possible, you should contact representatives of the hardware manu-
facturer about acquiring a package and the appropriate supporting informa-
tion. If you are going to use MPSX, obtain IBM Publication No. 5734-XM4,
the MPSX manual: Mathematical Programming System-Extended (MPSX) and
Generalised Upper Boundary (GUB) Program Description. When the materials
arrive, Step 13 may be begun.
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133
STEP 13: INSTALL LINEAR PROGRAMMING PACKAGE ON COMPUTER AND CHECK
SUMMARY
In Step 13 the linear programming package is checked to make sure it
operates properly. Of the three substeps listed below, the first applies
to any such package, while the next two are specific to MPSX:
Step 13A: Install Linear Programming Package on Computer
Step 13B: Secure and Keypunch Test Data for MPSX
Step 13C: Run MPSX Test Example and Check Results
If you are not using MPSX, consult the users manual for your linear program-
ming package for steps similar to 13B and 13C.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Step 12 must be completed — that is, you must have the linear pro-
gramming package and users manual — before beginning Step 13, as shown in
Fig. 70. Resource requirements are listed in Table 17. Step 13 is best
handled bv a programmer.
20 • 22 —7 23 24 25
Fig. 70. Prerequisites for Step 13
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134
Table 17. Resource Requirements for Step 13
Necessary
Skills
Computer
Programming
Necessary Data
Data
Sources
Linear
Programming
Package (MPSX
or other)
Step 12
Necessary Hardware
Computer, Reader/Printer, etc.
required for the particular
program.
If you are using MPSX:
• IBM 360 or 370 computer
• Minimum 86K region
• Printer with >_ 132 positions
• Magnetic tape unit, 2400
or 3400 series
• Eight cylinders space on a
2314 direct access device
STEP 13A: INSTALL LINEAR PROGRAMMING PACKAGE ON COMPUTER
Installation of the linear programming package acquired in Step 12
is entirely dependent on the package and the hardware being used. In all
cases, installation must be handled by a programmer familiar with the com-
puter system.
STEP 13B: SECURE AND KEYPUNCH TEST DATA FOR MPSX
The necessary data are available from Chapter 2 of the MPSX manual,
Mathematical Programming System - Extended (MPSX) and Generalized Upper-
Boundary (GUB) Program Description. They are to be keypunched using the
input format described in Appendix A of the MPSX manual.
STEP 13C: RUN MPSX TEST EXAMPLE AND CHECK RESULTS
The control language program (found in Fig. 3 of the MPSX manual) is
to be keypunched, starting all instructions in card column 10. The package
should be run on the computer and the output compared with Figs. 8 - 14 of
the MPSX manual. If there are differences, check the input data and control
language for keypunch errors. If there are no keypunch errors, consult the
manual or an IBM representative.
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135
STEP 14: MAKE AIR QUALITY DISPERSION MODEL RUNS*
SUMMARY
In Step 14 you make the three air quality runs of CDMQC: for light
manufacturing, medium manufacturing, and heavy manufacturing sources. The
substeps involved are:
Step ISA: Verify Input Sequence for Each Run
Step 18B: Insert Control Language and Run Program
Step 18C: Check Results
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
The cards for the three air quality runs must have been keypunched and
checked for accuracy (Step 9) before you begin Step 14. The CDMQC program
acquired in Step 10 also must have, been tested as described in Step 11. The
position of Step 14 in the EDZ methodology is shown in Fig. 71. The re-
sources required for this step are listed in Table 18.
Fig. 71. Prerequisites for Step 14
*This discussion applies specifically to CDMQC; if you are using another
dispersion model, consult your users manual.
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136
Table 18. Resource Requirements for Step 14
Necessary Skills
Programming
Data Analysis
Necessary
Data
Keypunched
Data
CDMQC Program
Data
Sources
Step 9
Step 11
Necessary Hardware
Computer with a Fortran
G or H compiler
First on list directs this step.
STEP ISA: VERIFY INPUT SEQUENCE FOR EACH RUN
The input sequence for the cards in each of the air quality runs was
illustrated in Fig. 40 on pg. 94. Be sure (1) the cards are in the exact
order shown (or the program will not work) and (2) the proper area source
cards are in each deck (for example, only the light manufacturing area source
cards are in the light manufacturing run deck.)
STEP 18B: INSERT CONTROL LANGUAGE AND RUN PROGRAM
The control language for CDMQC will vary from computer to computer.
Personnel trained in the operation of the computer you are using should be
consulted. It may be possible that the control language from the test case
of Step 11 can be used. The programs should be run using an equivalent com-
puter region of 250K.
STEP 18C: CHECK RESULTS
The printed outputs from each run will be fairly long. They should be
compared with the example CDMQC output provided in Appendix C of the CDMQC
manual. If they are comparable, go to Step 15. If there are discrepancies,
retrace earlier steps to see if the error can be found. Be sure to have a
qualified programmer check the system completion codes and look for execution
errors.
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137
STEP 15: MAKE CALIBRATION DISPER-' \ MODEL R. V*
SUMMARY
In Step 15 you make the calibration run c- JDMQC. T -ree si->teps are
involved:
Step 15A: Verify Input Sequence for Each \,;n
Step 15B: Insert Control Language and Run Program
Step ISC: Check Results
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Before you begin Step 15, the calibration v\m cards rmst haw been
keypunched and checked for accuracy (Step 9). Tho CDMQC program a^uired in
Step 10 also must have been tested as described in Step 11. The position of
Step 15 in the EDZ methodology is shown in Fig. 7.'. Resources required are
listed in Table 19.
Fig. 72. Prerequisites for Step 15
>This discussion applies specifically to CDMQC; i"
dispersion model, consult your users manual.
you are using an-. -
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138
Table 19. Resource Requirements for Step 15
Necessary Skills
Programming
T>ata Analysis
. ... . „ ... .
Necessary Data
Keypunched Data
CDMOC Program
Data
Sources
Step 9
Step 11
Necessary Hardware
Computer with a Fortran
G or H compiler
ry
First on list directs this step.
STEP 15A: VERIFY INPUT SEQUENCE
The input sequence for the cards in the calibration run was illus-
trated in Fig. 41 on pg. 95. The cards must be in this order or the pro-
gram will not work.
STEP 15B: INSERT CONTROL LANGUAGE AND RUN PROGRAM
The control language for CDMQC will vary from computer to computer.
Personnel trained in the operation of the computer you are using should be
consulted. It may be possible that the control language from the test case
of Step 11 can be used. The program should be run using an equivalent com-
puter region of 250K.
STEP 15C: CHECK RESULTS
The printed output will be fairly long. It should be compared with
the example CDMQC output provided in Appendix C of the CDMQC manual. If they
are comparable, go to Step 16. If there are discrepancies, retrace earlier
steps to see if the error can be found. Be sure to have a qualified program-
mer check the system completion codes and look for execution errors.
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139
STEP 16: MAKE RIGHT-HAND-SIDE DISPERSION MODEL RUN*
SUMMARY
In Step 16 you make the right-hand-side (RHS) run of CDMQC. Three
substeps are involved:
Step 16A: Verify Input Sequence
Step 16B: Insert Control Language and Run Program
Step 16C: Check Results
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
The RHS run cards must have been keypunched and checked for accuracy
(Step 9). The CDMQC program acquired in Step 10 must have been tested as
described in Step 11. The position of Step 16 in the EDZ methodology is
shown in Fig. 73. The resources required are listed in Table 20.
23
24
»
Fig. 73. Prerequisites for Step 16
*This discussion applies specifically to CDMQC; if you are using another
dispersion model, consult your users manual.
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140
Table 20. Resource Requirements for Step 16
Necessary Skills
Programming
Data Analysis
Necessary Data
Keypunched Data
CDMQC Program
Data
Sources
Step 9
Step 11
Necessary Hardware
Computer with a Fortran
G or H compiler
First on list directs this step.
STEP 16A: VERIFY INPUT SEQUENCE
The input sequence for the cards in the right-hand-side run was
illustrated in Fig. 42 on pg. 96. The cards must be in this order or the
program will not work.
STEP 16B: INSERT CONTROL LANGUAGE AND RUN PROGRAM
The control language for CDMQC will vary from computer to computer.
Personnel trained in the operation of the computer you are using should be
consulted. It may be possible that the control language from the test case
of Step 11 can be used. The program should be run using an equivalent com-
puter region of 250K.
STEP 16C: CHECK RESULTS
The printed output will be fairly long. It should be compared with
the example CDMQC output provided in Appendix C of the CDMQC manual. If
they are comparable, go on to Step 17. If there are discrepancies, retrace
earlier steps to see if the error can be found. Be sure to have a qualified
programmer check the system completion codes and look for execution errors.
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141
STEP 17: SET UP LINEAR PROGRAM COEFFICIENT MATRIX
SUMMARY
The dispersion model runs are made to provide data for the linear pro-
gram. In Step 17 you will set up a linear program coefficient matrix to be
used in preparing the inputs to the linear program.
Although the matrix may be used for many formulations of EDZ,* the
guidebook assumes you are following the "basic EDZ model." The basic EDZ
model assumes that you would like to maximize the emissions produced in your
region while not violating air quality standards. Maximizing emissions is
assumed to be a proxy for maximizing economic growth, or at least maximizing
flexibility in EDZ administration by knowing the maximum emissions allowed.
Flexibility is introduced since you may decide on a lower level of emissions
if you so desire. The basic EDZ model also assumes that area sources are
exempt from EDZ.
Many data manipulations are required before the information can be
coded and keypunched for use in linear programming. You will need several
days (or weeks, depending on your staffing) to fill in the matrix by complet-
ing Steps 18-21.**
There are two substeps:
Step 17A: Draw Matrix
Step 17B: Fill in "Equation Type" Column
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Steps 1, 2, 3, and 5 must be completed before Step 17 is begun, as
shown in Fig. 74. Resource requirements are listed in Table 21.
^Several alternatives to the basic model are discussed in Benesh, et al.
(1977).
''"-Many of the procedures in Steps 18 through 21 may be computerized. If you
have the resources for the programming effort required, computerization may
cut down on the time required to fill in the matrix.
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142
20 1 22 —7 23 — 24 25
Fig. 74. Prerequisites for Step 17
STEP 17A: DRAW THE MATRIX
The format of the linear program coefficient matrix is shown in Fig.
75.* The matrix will be very large. The number of columns should equal four
plus t'he number of EDZ sources identified in Worksheet 2. The number of rows
should equal seven plus the number of receptors used in the RHS run of the
dispersion model. Each cell in the matrix should be at least 1.6 cm wide and
1.3 cm high.
STEP 17B: FILL IN THE "EQUATION TYPE" COLUMN
The matrix shown in Fig. 75 shows the equation type column completed
with an "N" in the objective function row and an "L" in ROW 001 to ROW XXX.
Be sure each row has an "L" in the equation type column. The "L" indicates
a less than or equal to (_<) type of equation.
"'This matrix is based on the assumption that you will use equal decay rates
for S02 and particulates. If you use different decay rates, you must use a
separate matrix for each pollutant. In this case, the matrix in Fig. 75 will
change as follows: (1) each matrix will have only one RHS column, and (2)
each matrix will have only one set of upper and lower bounds rows. The other
dimensions will remain unchanged. The guidebook instructions are designed
for use with the Fig. 75 matrix, but can easily be adapted to the two-matrix
situation.
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143
Table 21. Resource Requirements for Step 17
Necessary Skills
Data Analysis
Necessary Data
No. of EDZ Receptors
No. of EDZ Sources
Data Sources
Step 3
Worksheet 2
Necessary
Hardware
Very Large
Piece of Paper
^XCOLUMNS
ROWS^v
EXPLANATION
OF COLUMNS
OBJECTIVE
FUNCTION
ROWOOI
ROW002
ROW003
ROW004
ROW005
ROW006
• • •
ROWXXX
S02 UPPER
BOUNDS
S02 LOWER
BOUNDS
PART UPPER
BOUNDS
PART LOWER
BOUNDS
COLOOOI
COL0002
COL0003
COL0004
• • •
COLYYYY
EQUATION
TYPE
N
L
L
L
L
L
L
• ••
L
RHS
FOR
S02
RHS
FOR
PART
LINEAR PROGRAMMING
COEFFICIENT MATRIX
FOR
S02 AND
PARTICULATES
Fig. 75. Linear Program Coefficient Matrix
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145
STEP 18: ESTIMATE OBJECTIVE FUNCTION COEFFICIENTS
SUMMARY
In Step 18 you will calculate the objective function coefficients
required for the linear program matrix. There are two substeps:
Step 18A: Fill in the "Explanation of Columns" Row
Step 18B: Fill in the "Objective Function" Row
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Step 18 requires data from Step 2 and the matrix drawn in Step 17,
as shown in Fig. 76. Resource requirements are listed in Table 22.
20 22 —-r 23 24 25
Fig. 76. Prerequisites for Step 18
Table 22. Resource Requirements for Step 18
Necessary Skills
Data Analysis
, . — , —
Necessary Data
EDZ Source Data
Data
Source
Step 2
Necessary Hardware
Coefficient matrix
from Step 17
*If you are using different decay rates for SOa and particulates, you will
need two matrices; see Step 17.
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146
STEP ISA: FILL IN THE "EXPLANATION OF COLUMNS" ROW
The "explanation of columns" row is included in the linear program
coefficient matrix to help you read the linear program output and keep
organized. This row shows which EDZ grid square and land use class are
described by each column. The column numbers in the matrix are the same as
the small numbers in the upper right corner of the "AREA" columns in
Worksheet 2 (these are EDZ source numbers). It can be seen on Worksheet 2
that each upper right number identifies both an EDZ grid square and a land
use class. Abbreviations for the square and class also are inserted into the
"explanation of columns" row. See Fig. 77 for an illustration of how to fill
in this matrix row.
EXPLANATION
OF COLUMNS,*!'
VPLIFIED LAND USE CLASS
AREA;
% x GRID
SQUARE
AREA Urn?)
AREA:
% x GRID
QUARE
ARfr* Ufj
%x OR ID
SQUAE
AREA'-
GRID
SQUARE
AREA (km2
0.81^
J.OO
FOR
S02 AND
PARTICIPATES
Fig. 77. Filling in "Explanation of Columns" Row in Coefficient Matrix
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147
STEP 18B: FILL IN THE "OBJECTIVE FUNCTION" ROW
The land areas in square kilometers for each land use class in each
grid square (that is, for each EDZ source) are shown in Worksheet 2 in the
"AREA" columns. Take the areas on Worksheet 2, which are in square kilo-
meters, and multiply them by 100, to convert to hectares. Insert the
hectare values in the appropriate squares in the "objective function" row,
as shown in Fig. 78.
"\COLUMNS
ROWS^v
EXPLANATION
OF COLUMNS
OBJECTIVE
FUNCTION
ROWOOI
ROW002
DA Vif AA ~3
/ORKSHEET 2
'ERCENTAGE /
COLOOOI
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149
STEP 19: ESTIMATE COLUMN AND ROW COEFFICIENTS
SUMMARY
In Step 19, you will calculate the "column and row" coefficients for
the linear program matrix.
PREVIOUS STEPS AND RESOURCE REQUIREMENTS
Step 19 requires data from the dispersion model air quality runs
(Step 14), the matrix drawn in Step 17,* and Step 18's objective function
coefficients. The position of Step 19 in the EDZ methodology is shown in
Fig. 79. Resource requirements are listed in Table 23.
CALCULATE "COLUMN AND ROW" COEFFICIENTS
The "column and row" section of the matrix is shown in Fig. 80. Each
space in the column and row section will be filled with a value derived by
multiplying the objective function coefficient in each column by a "transfer
coefficient."
20 ' 22 —7 23 24 25
Fig. 79. Prerequisites for Step 19
*If you are using different decay rates for S02 and particulates, you will
need two matrices (see Step 17).
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150
Table 23. Resource Requirements for Step 19
Necessary Skills
Data Analysis
!
I
Necessary Data
Disperson Model
Air Quality Run
Objective Function
Data
Source
Step 14
Step 18
~[
Necessary Hardware
Coefficient Matrix
(Step 17)
\COLUMNS
ROWS\.
EXPLANATION
OF COLUMNS
OBJECTIVE
FUNCTION
ROWOOI
ROW002
ROW003 /
ROW004
ROW005
ROW006 \
• • •
ROWXXX
S02 UPPER
BOUNDS
SO;, LOWER
BOUNDS
PART UPPER
' BOUNDS
PART LOWER
BOUNDS
COLOOOI
/
/
\
\
COL0002
^"
--^.
COL0003
COL0004
• * •
•~--.
-^— • ^
COLYYYY
•— ...
\
\
/
s
EQUATION
TYPE
N
L
^ L
\L
\L
IL
/L
'•••
L
RHS
FOR
S02
RHS
FOR
PART
LINEAR PROGRAMMING
COEF
F
FICIENT MATRIX
FOR
S02 AND
ARTICULATES
Fig. 80. "Column and Row" Section of Coefficient Matrix
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151
Secure the output of the three CDMQC air quality runs and find the
pages that list "Individual Source Contributions" for a certain receptor;
under this heading find the columns for "Area Sources" (see Fig. 81).* The
"1A, 2A, 3A,..." designations identify the order in which the area source
cards were put into the computer. Grid square numbers should be written
next to these designations (see Fig. 82). Each grid square would be contri-
buting the amount of pollutant listed in the "micrograms/cu. meter" column
to the receptor if the light, medium, or heavy manufacturing sources in the
square were emitting 10,000 g/s of the pollutant.
To fill in the columns and rows section:
1. For each receptor/source pair, multiply an entry in the
"micrograms/cu. meter" by the appropriate objective
function coefficient. See Fig. 82.
2. Divide the above result by 10,000, and record the result
in the appropriate square in the matrix. See Fig. 82.**
*The same type of information can be obtained from the sections of the AQDM
output labeled "Source Contributions for Five Selected Receptors"; see
Appendix G, Sec. 1, and the AQDM users manual.
**The actual "transfer coefficients" are the entries in the "micrograms/cu-
meter" column divided by 10,000. Conceptually, the transfer coefficients
are multiplied by the objective function coefficients to obtain entries for
the columns and rows section. However, in practice it is simpler to follow
the two-step procedure given above in the text; the two methods of calcula-
tion will, of course, give the same numerical result.
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152
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STEP 20: ESTIMATE RIGHT-HAND-SIDE (RHS) COEFFICIENTS
SUMMARY
In Step 20 you will calculate the right-hand-side (RHS) coefficients
for the linear program matrix. The three substeps are:
Step 20A: Convert Geometric Means to Arithmetic Means
Step 20B: Calibrate Standards and Calculate RHS Coefficients
Step 20C: How to Deal with Negative RHS Coefficients
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
To complete Step 20, you need to know the air quality standard(s)
being applied in your region. These may be the NAAQS, state or local stan-
dards, standards related to PSD, or others. Check with your state air pollu-
tion control agency and EPA regional office as to which standards apply. You
will need the output from the dispersion model calibration and RHS runs and
the coefficient matrix drawn in Step 17.* Figure 83 illustrates the position
of Step 20 in the EDZ methodology. Resource requirements are listed in Table
24.
22 —7 23 24 25
Fig. 83. Prerequisites for Step 20
':If you are using different decay rates for SO? and particulates, you will
need two matrices (see Step 17).
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156
Table 24. Resource Requirements for Step 20
Necessary Skills Necessary Data
" " ' ------
:Data Analysis , Air Quality Standards
. Dispersion Model
Calibration Run
Dispersion Model
RHS Run
Air Quality Projections
Data I
Sources I Neci
Step 1 Coe:
; (St<
Step 15
Step 16
See text •
Necessary Hardware
Coefficient Matrix!
STEP 20A: CONVERT GEOMETRIC MEANS TO ARITHMETIC MEANS
You may have noticed a discrepancy between the CDMQC output for particu-
late concentrations and the annual particulate standard of the NAAQS: CDMQC
provides arithmetic means while the NAAQS are stated in geometric means.* In
order to compare these two types of average concentrations, the output and
standards must be placed on common ground.
An arithmetic mean for a set of "n" values is found by summing the
values and dividing the result by "n." An arithmetic mean is what is com-
monly refered to as an "average." In contrast, a geometric mean for a set
of "n" values is found by multiplying all the values together and taking the
"n " root of the resulting product. However, the difference "between the
two in measuring air quality is generally not very great.
There are at least two ways for resolving this difference. The first
is an accepted practice used in dispersion modeling; it is simple, and is the
method recommended for your use: assume that the geometric mean calculated
by the dispersion model is equal to the arithmetic mean. The second, a more
complex "map and switch" procedure, converts the air quality standard for
particulates at each receptor from an annual geometric mean to an annual
arithmetic mean. Both approaches are discussed below.
Assuming the Means are Equivalent. The difference between the arith-
metic and geometric mean concentrations are fairly small, with the con-
centration predicted by arithmetic means being greater than or equal to that
*If you are using AQDM you will also have this problem.
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157
estimated using the geometric mean. If you simply assume that the arithmetic
mean equals the geometric mean, your calculations will be in error toward
having better air quality than required by the NAAQS. This error will tend
to make the EDLs more restrictive than necessary and thus make the cost of
compliance higher. However, the difference should not be very large.
Using the "Map and Switch" Procedure. To convert the particulate
standards applicable at each receptor to annual arithmetic means, you will
need to know:
< the locations of the EDZ receptor points (mapped in Step 3),
• the locations of the monitoring stations (these are recorded
on Worksheet 5 and should already have been mapped onto the
same map as the receptors; if they are not, do it now),
• the 24-hour standard geometric deviations for each monitoring
station (recorded on Worksheet 5), and
• the air quality standard for particulates applicable at each
EDZ receptor and monitoring station location (mapped in Step 2)
You will also need a scientific calculator and some mapping skills. The pro-
cedure is as follows:*
First, list the air quality standard (yg/m annual geometric mean)
applicable for each receptor in Column A of Worksheet 12 (see Fig. 84).**
Second transfer the 24-hour geometric standard deviations for each monitoring
station listed on -Worksheet 5 to your map of regional monitoring stations, and
draw an isopleth map of the standard geometric deviations, using straight-
line interpolation between monitoring stations where needed (see Fig. 85).t
Third, using the isopleth map, estimate the standard geometric devia-
tions at each EDZ receptor point and record them in Column B on Worksheet 12.
Fourth, perform the calculations indicated in the remaining columns on Work-
sheet 12.tt If these calculations are performed on a calculator or via the
formula listed in the right-hand column of Worksheet 12, the intermediate
columns need not be filled in.
*For more information on the mechanics of this method consult Larsen (1971).
**Full page blank worksheets for your use are provided in Appendix J.
TSee Robinson & Sale, 1969, pp. 151-164; and Harvard University, 1968.
"M"Refer to the CDMQC manual if you have questions about this procedure.
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158
WORKSHEET 12 (FOR STEPPO) FILLED OUT BY= DATE
CONVERTING GEOMETRIC MEANS TO ARITHMETIC MEANS FOR PARTICIPATES
PG^S-
OF^2_
RECEPTOR
NUMBER
-_&QS4_
J
ANNUAL
ARITHMETIC
MEAN,
IN jig/m3
(COL. A X COL.D)
X-- Mgsgln^sg
43JO
6»fc-0
5J.5
[_ s+0
Fig. 84. Worksheet 12 Example
STEP 20B: CALIBRATE STANDARDS AND CALCULATE RHS COEFFICIENTS
The annual arithmetic mean standards for S02 and particulates must be
adjusted by using the calibration coefficients provided by the CDMQC calibra-
tion run. In the output of that run, there are two sections titled "Calibra-
tion Procedure Results: S02" and "Calibration Procedure Results: Part." Under
each of these sections is a set of three parameters labeled "Slope," "Inter-
cept," and "Background." The background value will be zero and is to be
ignored since for the EDZ methodology it is included in the intercept value.
The slope and intercept are to be inserted into the equation below and the
X value calculated repeatedly for both pollutants at all the receptors,*
Place the answer obtained for the X's in Column A of Worksheet 1.3 (shown in
Fig. 86). This equation is sometimes called the "reverse calibration"
equation.
X =
where:
X = calibrated standard at each receptor in yg/m3 (enter in Column A
of Worksheet 13);
-If your region has more than 200 point sources, you will have calculated the
slope and intercept using the method presented in Appendix I.
-------
159
2.00
58.12
1.94
45.51
1.86
2.00
A. = RECEPTOR POINT (•) = MONITORING STATION
57.32 -- MEASURED POLLUTANT CONCENTRATION
ANNUAL GEOMETRIC MEAN
2.48 = MEASURED STANDARD GEOMETRIC DEVIATION
248 = PREDICTED STANDARD GEOMETRIC DEVIATION
Fig. 85. Isopleth Map of Standard Geometric Deviation
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160
WORKSHEET 13 (FOR STEP 20) FILLED OUT BY=.
RIGHT-HAND-SIDE VALUES
DATE:
PG.
OF.
RECEPTOR
NAME
RECEPTOR
NUMBER
(ROW)
AIR POLLUTANT CONCENTRATIONS (;jg/m
CALIBRATED
AIR QUALITY
STANDARD
(A)
AIR QUALITY
IMPACT OF
EXEMPT SOURCES
(B)
RIGHT-HAND-SIDE
VALUE
(A-8)
y = the standard for one pollutant at one receptor point calcu-
lated as an annual arithmetic mean, in yg/m' ;
a = the intercept for the calibration curve; and
b = the slope for the calibration curve.
The RHS run (Step 16) predicted pollution contributions at each recep-
tor from sources not subject to EDZ control. Record these values in Column B
of Worksheet 13. Finally, complete the final two columns on Worksheet 13,
and record the values in the coefficient matrix, as shown in Fig. 87.
STEP 20C: HOW TO DEAL WITH NEGATIVE RHS COEFFICIENTS
You may find that some receptors (rows) have negative values in the
RHS columns. A negative RHS coefficient indicates that either (1) there is
an error in the calculations or (2) your region is currently violating, or
in the near future will violate, air quality standards and needs an air pol-
lution control strategy aimed at attainment.
Check RHS Calculations. Return to the data that were used to generate
the RHS values and check to see that they were copied correctly from their
sources. Next, check the calculations in Worksheet 13. If you find no
errors, your region either has or soon will have an attainment problem.
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161
AIR QUALITY
IMPACT OF
EXEMPT SOURCES
(8)
PART
CALIBRATED
AIR QUALITY
STANDARD
(A)
RIGHT/- HAND-SID
VALUE
(A-B)
"PAST
RECEPTOR
NUMBER
(ROW)
Fig. 87. Filling in RHS Columns of Coefficient Matrix
Attainment Problems. Since State Implementation Plans (SIPs) are
required to contain provisions for meeting air quality standards, you should
consult your state pollution control authority if you have current or future
attainment problems. If the SIP includes a strategy for attainment, it
should include projected pollutant concentrations in the region. These pro-
jections should be substituted for those used to calculate the RHS co-
efficients. If the projections are accurd'ce, all the RHS coefficients should
then be positive.
EDZ can be one part of a strategy aimed at attaining and maintaining
air quality standards. In fact, even if EDZ itself is not implemented, the
EDZ analysis can be used in forming other attainment strategies. For example.
-------
162
a variation of EPA's offset policy may be implemented through your EDZ work.
The EDZ methodology can be used to determine where and to what degree offsets
can be applied in order to attain standards. In addition, the magnitudes of
the required emission reductions would be based on objective criteria;
specifically, the meteorology of your region and the current land use plans.
Following is an explanation of one way that the EDZ guidebook could be altered
to apply the methodology to offsets.
First, all exemptions for area and point sources would be eliminated.
Thus, each RHS value would equal the air quality standard minus the back-
ground concentrations as defined by the intercept of the calibration equa-
tion, divided by the slope of the calibration curve. If any of these new
RHS values are negative, the solution requires an attainment strategy beyond
the scope of the guidebook because the new negative RHS implies that back-
ground concentrations are higher than permitted by the standards. In this
situation, the state and federal EPAs should be contacted for assistance.
Second, assuming the new RHS values are positive, the linear program
should be run exactly as described in Steps 22 and 23.
Third, an emission quota should be calculated for each EDZ grid
square. This is done by taking the EDLs calculated by the linear program
(Step 23) for each land use class and grid square and multiplying these by
the total number of hectares in each land use class in the same grid square
(Worksheet 2 provides the total hectares in each land use class in each grid
square). When these multiplications are finished, they are summed for each
grid square to produce the emission quota for each EDZ grid square. Figure
88 helps explain this procedure.
Fourth, using data from the emission inventory (Step 6) and the gridded
simplified zoning maps (Step 2), determine which point and area sources are
located in each EDZ grid square. Area source emissions should be allocated
from emission inventory grid squares to EDZ grid squares in a statistically
acceptable manner.
Fifth, using an approved method, such as that found in the EPA Guide-
lines for Air Quality Maintenance Planning and Analysis, Vols. 7 and 10, or
a method obtained from the state or local air pollution control authority,
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163
©
093
094
OK
0104
0105
0106 /
0107
OJ5
036
X""
037
036
0108
/GRID SQUARE NUMBER
SAMPLE OF EDZ
GRID SQUARES
0106
A = 50ho
Ml- 20 ha
M2- 10 ha
M3=20ho
REAS OF SIMPLIFIED
LAND USE ZONES
(FROM STEP 2)
0106
Ml - .05(g/s)/ha
M2 = .I0(g/s)/ha
M3 = .50(g/s)/ha
EDLS SET BY
LINEAR PROGRAM
0106
A-IO.Og/s
ISSIONS OF AREA
0106
Ml 20x.05= Ig/s
M2 IOx.lO= Ig/s
M320x.50-IOg/s
0106
Ml
M2
M3
+ A
1
1
10
10
22g/s
SOURCES PROJECTED
Fig,
MULTIPLY (2) x (3),
OR AREAS BY EDLS
'4V ^i OR SUM
ALL EMISSIONS
ORIGINATING FROM
THE SQUARE
Calculating Emission Quotas
estimate the emissions for existing sources (in g/s) as if they were in com-
pliance with all existing emission control regulations.
Sixth, for each EDZ grid square, sum the projected emissions from each
of the sources within its borders. This calculation will give the total pro-
jected emissions for each EDZ grid square. Then for each EDZ grid square,
subtract the total projected emissions from the emission quota. The resulting
values, one for each grid square, will be called increments. The positive
increments are for grid squares whose total emissions could increase by the
incremental value without further violating standards if the squares with
negative increments decreased their total emissions by their increments.
Assuming that no increase in emissions will be allowed in negative
increment squares, the potential for tradeoffs is clear. When a new source
locates, or an old source expands, in a positive increment square, the source
should be encouraged to negotiate offsets with the pollution control authority
and the sources in negative increment squares.
Once the air quality standard is reached, the EDZ strategy used to
attain standards can then be used to maintain the standards.
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165
STEP 21: ESTIMATE UPPER AND LOWER BOUND COEFFICIENTS
SUMMARY
In Step 21 you will estimate the upper and lower bound coefficients
for the linear program matrix.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Step 21 requires data from the emission inventory (Step 6) and the
matrix drawn in Step 17; see Fig. 89. Worksheet 8 (Step 6) must have been
completed. Personnel and data requirements are listed in Table 25.
SETTING UPPER AND LOWER BOUNDS
The Need for Bounds
Setting upper and lower bounds ensures that the resulting EDLs are
technically and politically reasonable. The upper bound represents the
maximum emission density for sources in a particular land use class. By
setting the upper bound, you avoid the chance that the linear programming
20 ' 22 —^ 23 24 2
Fig. 89. Prerequisites for Step 21
-------
166
Table 25. Resource Requirements tor Step 21
Necessary Skills'
Necessarv Data
Hat a
Sources
Land Use Planning
Data Analysis
(Possibly
Air Pollution
Control Expertise
Zoning Maps and
Ordinances ctep 2
Worksheet 8 St ep 6
Emission Inventory Step 6
None
_J
_l
First on list directs this step.
package will place an absurdly high EDL anywhere in the region. Figure 90
graphically shows the possible consequences of not having an upper bound.
Lower bounds, on the other hand, represent the lowest feasible emis-
sions that could come from a hectare in a particular land use class. The
purpose for lower bounds is to ensure that some growth can be allowed in
each grid square. Lower bounds are optional, but if they are not set, the
linear programming package may arrive at a solution where no increase in emis-
sions will be allowed in some of the squares.
PROBABLE SOLUTION
WITH UPPER BOUND
PROBABLE SOLUTION
WITHOUT UPPER BOUND
Fig. 90. Probable Effects of No Upper Bound (three-dimensional view)
-------
167
The basic EDZ model assumes that upper and lower bounds will be con-
stant throughout the region for a single land use class. However, in some
instances upper and lower bounds may vary by both land use class and square.
For example, if some grid squares are already highly developed, a lower
bound of zero may be used to allow the option of restricting emission in-
creases in these squares while still allowing for growth in all other squares
in the region. If upper bounds varied by grid squares, the squares in the
central or older parts of the region could be allowed higher EDLs to account
for higher density of development and, therefore, less land for reducing
emission densities. On the other hand, high EDLs in the outlying areas may
be desirable to alleviate clustering in the central or core areas.
Thus, for each column in the matrix, there must be an upper bound; a
lower bound may be included if you desire. Accurately setting these bounds
is important since the linear program may be expected to find a solution
where many land use classes will have permitted emissions set at the upper
or lower bound. Bounds units are grams of emissions per second per hectare
[(g/s)/ha].* The rest of the guidebook is written assuming that lower bounds
have been set, but not setting lower bounds will not be a disadvantage later.
Finding the Bounds Coefficients
Upper and lower bounds coefficients are found with the aid of Work-
sheet 8, which was completed as part of Step 6. This worksheet plus a few
calculations will reveal the upper and lower bounds for each land use class
in the region. Since three manufacturing land uses are being considered
(light, medium, and heavy), there will be three distinct upper bounds and, if
used, three lower bounds. The bounds are calculated as follows:
Check with your state or local pollution control board to see if the
sources listed on Worksheet 8 are currently complying with all present and
known future emission control regulations. For those sources that are not
in compliance, you must consult with the source operators and/or the state or
local pollution control authority to estimate the emissions which will occur
after compliance. These estimated emissions are to be in grams/second. The
estimated emissions for sources not in compliance are to be divided by the
*For a further discussion of bounds, see Benesh et al. (1977).
-------
168
area of the sources (in hectares) and substituted for the emission densi-
ties previously listed in the final column of Worksheet 8.
The highest emission density for any one source within i land ase
class is the upper bound for that class. The upper bound for any one land
use class is to be placed in each column of the linear program's coefficient
matrix that describes lands belonging to the land use class. Tor example,
assume that all the data for heavy manufacturing sources in a region are
shown in Fig. 91 (Worksheet 8). The highest: emission density For SO? in the
right-hand column is the 37.50 (g/s)/ha for source 0014. This 37.50 repre-
sents the highest SOj emission rate per hectare that may be expected for a
heavy manufacturing source. Therefore, for every column in the linear pro-
gram matrix that describes heavy manufacturing lands, the S0? upper bound
would be 37.50 (g/s)/ha. This process is repeated for light and medium manu-
facturing classes, and for all three classes for particulates. Figure 92
shows where to insert the upper bounds into the matrix.
If lower bounds are set, the lowest emission density for any one
source within a land use class is the lower bound for the class. The lower
bounds are to be handled much like the upper bounds. For example, if the
data in Fig. 91 were for all the heavy manufacturing sources in. the region,
then the SO? lower bound for all columns describing heavy manufacturing
sources would be 15.0 (g/s)/ha. This figure would be inserted into the
linear program coefficient matrix as shown in Fig. 92. If you decide not
WORKSHEET 8 (FOR STEP 6) FILLED 0
EMISSION INVENTORY DATA FOR STE
JT BY: DATE PG
P 24 OF.
POINT SOURCE
NAME
Etr&Attf fJAi+1
f=~AverT~/r
WA/NE
POINT SOURCE
I.D. NUMBER
(FROM ^
WORKSHEET 6f
&Q/*£
0095
QI2Z
*£S£M7KZ> rl£*.K I
SIMPLIFIED
LAND USE
ZONING
CLASS
^3
tf3
o rfor co*wc
EMISSION RATE
OF POINT
SOURCE (A)
IN g/S
S02
1&0
£&°
fo*j£> ro rH:
PART
30.0
2CL-O
: vmtuK coc,
LAND AREA
OF POINT
SOURCE(B)
IN ha
S..Q
fo-o
.. 3-0
X&IKfTtB ft,
EMISSION DENSITY
(A-f B)
IN (g/s)/ ha
S02
^7. S
iS'.O
11.3,
sierra® £>
PART
h /S-0
3.0
Fig. 91. Sample Data for Illustrating Upper and Lower Bounds
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169
^XCOLUMNS
ROWS^v
EXPLANATION
OF COLUMNS
OBJECTIVE
FUNCTION
ROWOOI
ROW002
ROW003
ROW004
ROW005
ROW 006
• ••
ROWXXX
ISOplWPTR
SOUNDS
/>02 LOWER
J BOUNDS
&ART UPPER I
^ROUNDS
PARrfcUWER
BOUNDST-"-^
[COLOOOI
.. —
-J
COL0002
•==
COL0003
•••^•^•mvM
COL0004
— —
«•«—•——•
• ••
• .
. • •
COLYYYY
— -~»,
•"-^
EQUATION
TYPE
N
L
L
L
L
L
--
L
• • •
L ]
UMW^H^VH^V.
RHS
FOR
S02
"
~
1
I
1
J
1
RHS
FOR
PART
' I
~~1
1
~~1
~~1
NINEAR PROGRAMMING 1
COEFFICIENT MATRIX 1
1 FOR
I S02 AND 1
/ PARTICULATES 1
—J
Clipper and lower bounds entered here, according to instructions in text.
Fig. 92. Filling in the Upper and Lower Bounds Rows of the
Coefficient Matrix
to use lower bounds, you may either insert zeros or allow MPSX to auto-
matically set the lower bounds at zero.
The upper and lower bounds you have calculated should now be checked
to determine if they reflect current development practices. If past practice
is little different from what can be expected in the future, the bounds
should not be altered. But, if future sources are expected to have different
per hectare emission densities, then changes must be made. For example, newly
built sources may have distinctly different emission densities from older
sources due to factors such as the trend toward single-level factories and
-------
170
larger lots. In addition, there may be significant differences between regu-
lations designed for existing and new sources. If the effect of such trends
and new regulations on emission density limits can be estimated, then the
bounds must be adjusted to bring them in line with changing conditions.
You may wish to consider varying the upper and lower bounds by land
use class and grid square. This variation might be desirable for reasons
such as local differences in emission regulations. In this case, the upper
and lower bounds would be found in the same manner as discussed above, except
that instead of finding the highest and lowest emission densities per land
use class for the entire region, the highest and lowest emission densities
per land use class per grid square would be used. Thus, each column in the
linear program matrix would have a different set of bounds.
-------
171
STEP 22: CODE AND KEYPUNCH LINEAR PROGRAMMING INPUT DATA
SUMMARY
The input data for the MPSX linear programming package are coded and
keypunched in Step 22. The card formats shown here are for use only in MPSX.
(If you are using another linear programming package consult your user's
manual to determine proper card format.) The data deck assembled in this
step will be used in two linear programming runs, as explained in Step 23.
There are two substeps:
Step 25A: Code and Keypunch MPSX Data
Step 25B: Check Card Order
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
All the data required for keypunching are in the linear program
coefficient matrix; before beginning Step 22, you must have completed Steps
1-11 and 14-21. See Fig. 93. Resource requirements are listed in Table
26.
Fig. 93. Prerequisites for Step 22
-------
172
Table 26. Resource Requirements for Step
a
Necessary Skills
Data Analysis
Keypunching
Possibly Programming
Necessary Data
Completed LP Matrix
Data I Necessary
Source hardware
Steps 17 Keypunch
11
-" ^ -L j
i
',
First on list directs this step.
STEP 25A: CODE AND KEYPUNCH MPSX DATA
The MPSX card formats presented here must be followed; a complete
card deck is illustrated in Fig. 94. All cards shown must be present and in
the right order. The card formats, illustrated in Figs. 95 - 106, are
discussed below.
Name Card. The name card, placed at the start of the data deck,
simply labels the output. Its format is shown in Fig. 95.
Single Word Card1. Rows. Each set of data cards is preceded by a card
with a single word on it that tells the machine what: type of cards to expect.
There are five of these cards; the first one is the "rows card," shown in
Fig. 96.
Rows Data Cards. The "rows data cards" indicate the type of constraint
being applied to each row and the row names. The first rows data card de-
cribes the objective function row in the linear program coefficient matrix,
as shown in Fig. 97. Each remaining rows data card describes one row (i.e.,
one receptor) in the matrix; see Fig. 98.
Single Word Card: Columns. Insert this card after the last rows data
card; see Fig. 99 for format.
Columns Data Cards. The "columns data cards" provide the computer
with values for each of the column and row coefficients in the. matrix. See
Fig. 100.
-------
173
ENOATA CARD
BOUNDS DATA
POLLUTANT 2
BOUNDS DATA
POLLUTANT I
SOUNDS CARD
RHS DATA FOR
POLLUTANT?
RHS DATA FOR
POLLUTANT I
RHS CARD
COLUMNS DATA
COLUMNS CARD
ROWS DATA
ROWS CARD
NAME CARD
Fig. 94.
liPSX Data Card Input Sequence
(if you have two matrices,
you will need two decks; see
Step 17)
Single Word Card: RHS. Insert this card after the last columns data
card; see Fig. 101 for format.
Right-Hand-Side Data Cards. These cards provide the computer with the
right-hand-side values for SO? or particulates for each row (receptor).
There are two sets of RHS data cards — one for SC>2 and one for particulates.
Each set contains one card for each row in the matrix. Be sure to give each
-------
174
set a different "RHS identifier" in Columns 5 - 12 of each data care (see
Fig. 102). The two sets of RHS data cards snould be next to one another in
the deck.
Single Word Card: Boundsi Insert this card after the last RHS data
card; see Fig. 103 for format.
Bounds Data Cards. There are two sets of "bounds data cards" -- one
for SOj and one for particulates. The formats for upper and lower bounds
data cards are shown in Figs. 104 and 105, respectively. Place all the SO;
bounds cards together, and all the particulate bounds cards together. These
two sets of cards should be next to each other in the deck.
Single Word Card: ENDATA. This is the last card in the data deck.
See Fig. 106.
STEP 25B: CHECK CARD ORDER
Check the deck tc make sure it contains all the necessary cards in the
correct order. Then proceed to Step 23.
-------
175
COLUMN
NUMBER
EXPLANATION
REQUIRED
ENTRIES
TYPICAL
COMPLETED
CARD
1-4
—
NAME
NAME
5-14
Leave
Blank
15-22
Eight-letter
name supplied
by user.
(user-specified)
EDZLPRUN
23-80
Leave
Blank
Fig. 95. MPSX Name Card
COLUMN
NUMBER
EXPLANATION
REQUIRED
CARD
FORMAT
1-4
Identifies rows
data cards
RCiWS
5-80
Leave Blank
Fig. 96. MPSX Rows Card
COLUMN
NUMBER
EXPLANATION
REQUIRED
CARD
FORMAT
1-2
Leave
Blank
3
Equation type
N
4
Leave
Blank
5-12
Matrix row name
0BJFUNCT
13-80
Leave
Blank
Fig. 97. MPSX Objective Function Card
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176
COLUMN
NUMBER
EXPLANATION
REQUIRED
ENTRIES
TYPICAL
COMPLETED
CARD
1-2
Leave
Blank
>-^
j
Equation type
L
L
A
Leave
Blank
5-12
Matrix row name.
Start in Column 5.
(different on each
card) .
R0W001
13-80
Leave
Blank
Fig. 98. MPSX Rows Data Cards
COLUMN
NUMBER
EXPLANATION
REQUIRED
CARD
FORMAT
1-7
Identifies columns
data cards
C0LUMNS
8-80
Leave Blank
Fig. 99. MPSX Columns Card
COLUMN
NUMBER
EXPLANATION
REQUIRED
ENTRIES
TYPICAL
COMPLETED
CARD
1-4
Leave
Blank
5-12
Matrix
column
name.
Start in
Col. 5.
(varies)
C0L001
13-14
Leave
Blank
15-22
Matrix
row
name.
Start in
Col. 15.
(varies)
R0W001
23-24
Leave
Blank
25-36
The coefficient found
at the intersection
of the matrix column
and row listed. This
is a 12-digit number
(including decimal
point). If this
value is zero, you do
not need a card for
that column-row pair.
(varies)
0000201.9631
37-80 i
1
Leave
Blank
Fig. 100. MPSX Columns Data Cards
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177
COLUMN
NUitBER
EXPLANATION
REQUIRED
CARD
FORMAT
1-3
Identifies RHS
data cards
RHS
4-80
Leave Blank
Fig. 101. RHS Card
COLUMN
NUMBER
EXPLANATION
.
REQUIRED
ENTRIES
TYPICAL
COMPLETED
CARD
1-4
Leave
Blank
5-12
RHS identi-
fier. Must be
different for
S02 and part.
Start in
Col. 5.
(varies)
RHSS02
13-14
Leave
Blank
15-22
Matrix
row
name .
Start in
Col. 15.
(varies)
R0W001
23-24
Leave
Blank
25-36
Value in RHS
column in
matrix for
row and
pollutant-
listed.
(varies)
000000003.12
37-80
Leave
Blank
Fig. 102. MPSX RHS Data Cards
COLUMN
NUMBER
EXPLANATION
REQUIRED
CARD
FORMAT
1-6
Identifies bounds
data cards.
B0UNDS
7-80
Leave Blank
Fig. 103. MPSX Bounds Card
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178
COLUMN
NUMBER
EXPLANATION
REQUIRED
ENTRIES
TYPICAL
COMPLETED
CARD
1
Leave
Blank
2-3
Type
of
bound .
UP
UP
4
Leave
Blank
5-12
Identi-
fies
pollu-
tant.
Start
in
Col. 5.
(user
speci-
fied)
BNDS02
13-14
Leave
Blank
15-22
Identi-
fies
matrix
column.
Start
in
Col. IS.
(varies)
C0L001 '
23-24
Leave
Blank
2.5-36
Value of
upper bound
for pollu-
tant: and
matrix
column
listed
(12-digit
number) .
(varies)
0000431.0301
37-80
Leave
Blank
Fig. 104. MPSX Upper Bounds Cards
COLUMN
NUMBER
EXPLANATION
REQUIRED
ENTRIES
TYPICAL
COMPLETED
CARD
1
Leave
Blank
2-3
Type
of
bound .
L0
L0
4
Leave
Blank
5-12
Identi-
fies
pollu-
tant.
Start
in
Col. 5.
(user-
speci-
fied)
BNDS02
13-14
Leave
Blank
15-22
Identi-
fies
matrix
column.
Start
in
Col. 15,
(varies)
C0L001
23-24
Leave
Blank
25-36
Value of
lower bound
for pollu-
tant and
matrix
column
listed
(12-digit
number) .
(varies)
000000001.00
37-80
Leave
Blank
Fig. 105. MPSX Lower Bounds Cards
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179
COLUMN
NUMBER
EXPLANATION
REQUIRED
CARD
FORMAT
1-6
Tells computer data
deck is finished.
ENDATA
7-80
Leave Blank
Fig. 106. ENDATA Card
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181
STEP 23: MAKE LINEAR PROGRAMMING RUNS
SUMMARY
In Step 23 you will run the linear programming package. The instruc-
tions given here apply specifically to MPSX. If you are not using MPSX,
consult your users manual.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Steps 1-22 must be completed before Step 23 is begun; see Fig. 107,
Resource requirements are listed in Table 27.
RUN MPSX PACKAGE
The MPSX package needs to be run twice — once to obtain SOj emission
density limits and once to obtain particulate EDLs (both runs use the data
deck assembled in Step 22). The "MPSX control language" that precedes the
data deck determines which RHS and bounds data (that is, SOg or participates)
are used in each computer run. An experienced programmer familiar with the
computer being used should be consulted to set up both the MPSX control
language and the job control language. If you have £• small linear
Fig. 107. Prerequisites for Step 23
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182
Table 27. "Resource Requirements for Step 23
Necessary Skills .
Necessary Data
Data i Necessary \
S ourc e s i v a r dwar e
Programming
i MPSX Package Step 12 j IBM ?60/37
i Input Data Step 22 I
1 Job Control Language Local |
programming problem or wish to include an S02-particulates feasibility con-
straint as discussed in Benesh et al. (1977), have a linear programming
expert reformulate the problem so that you can combine the two runs into
one.
The MPSX package may only be run on IBM 360 and 370 hardware.
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183
STEP 24: ANALYZE LINEAR PROGRAMMING RUN RESULTS
SUMMARY
In Step 24 you will check the reasonableness of the EDLs for S02 and
participates set by the linear programming package. The discussion in Step 24
assumes the use of the basic EDZ model, whose approach is to maximize emis-
sions throughout the metropolitan area.
Four substeps are involved:
Step 24A: Read EDLs on MPSX Output
Step 24B: Identify Any Infeasible Solutions
Step 24C: Map Linear Program Results
Step 24D: Check Maps for Reasonableness
Each substep in Step 24 must be done for both SOz and particulates,
using the output of the two linear programming runs.
When Step 24D is completed, you will have set EDLs for each hectare of
manufacturing-zoned land in your region. Emissions will have been maximized
and air quality standards will not be violated. Therefore, the EDZ strategy
described in the Introduction will have been completed, with implementation
as the final step. If per-hectare EDLs are desired in your region, then
Step 24 is the final step you need to follow. If you wish to use another
emission quota strategy, such as floating zone emission quotas or jurisdic-
tional emission quotas, then Step 25 must be followed.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
Steps 1-23 must be completed before you begin Step 24, as shown in
Fig. 108. Resource requirements are listed in Table 28.
STEP 24A: READ EDLs ON MPSX* OUTPUT
The linear program output is fairly long and not particularly easy to
read for those with little experience in linear programming. The key to
':The following discussion applies to MPSX: consult your users manual if you
are using a different linear programming package.
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184
/^HL*H
25
Fig. 108. Prerequisites for Step 24
reading the output is to avoid being distracted by the "extra" information
that the computer prints. To find the emission density limits [stated in
terms of (g/s)/ha for each pollutant for each land use class in each grid
square]:
1. Find the section of the MPSX output titled: SECTION 2 - COLUMNS
(see Fig. 109).
2. Under SECTION 2 - COLUMNS, you will find a number of headings.
Under the one labeled .COLUMN, are listed the column names
used in the linear programming coefficient matrix.
Table 28. Resource Requirements for Step 24
Necessary Skills
Land Use Planning
Data Analysis
Cartographic
Necessary Data
Gridded General
Regional Map
Worksheet 2
Worksheets 9 and 10
Completed Linear Program
Coefficient Matrix
Linear Program Output
Data Necessary
Sources Hardware
Steps 1 None
and 2
Step 2
Step 7
Steps 18
- 21
Step 23
First on list directs this step.
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185
SECTION
NfJMBEfi
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
2 - CCLUK
.COLUMN*,
CCL0001
CCL0002
CCL0003
COI0004
COL0005
COL0006
CCL0007
CCL0008
CCLOC09
CCL0010
CCL0011
CCLOC12
CCL0013
CCL0014
CCL0015
NS
AT
BS
UL
UL
LL
UL
LL
UL
UL
UL
UL
UL
UL
UL
UL
UL
norm-it...
.29509
. 13500
.35430
a
.35430
0
.,35430
.35430
. 13500
.13500
.00330
.00330
.00330
.00330
0OQ330
. „ INPUT COST* .
31.25000
87.52000
18.72000
12. 48000
37.44000
18.75000
6.25000
12.50000
43,75000
12.50000
12.50000
75.00000
6.25000
37.50000
25.00000
Fig. 109. MPSX Output Showing EDLs and Other Data
Under SECTION 2 - COLUMNS, you will also find a column labeled
...ACTIVITY... The values in this column are the per-hectare
EDLs for each of the columns in the linear program coefficient
matrix (remember that each matrix column represents one land
use class within a grid square).
STEP 24B: IDENTIFY ANY INFEASIBLE SOLUTIONS
A linear programming problem, such as the one developed for EDZ, may
have no feasible solutions. In EDZ, an infeasible solution occurs when the
computer cannot find a configuration of EDLs that xvill not violate air
quality standards. If you have an infeasible solution, you will find the
words SOLUTION (INFEASIBLE) on one of the pages of the output. (This is
illustrated in Fig. 110.)
Since Step 20 required that all right-hand-side coefficients be posi-
tive, there can be only one reason for the infeasible solution; the lower
bounds are set too high. Infeasibility resulted from the computer setting
all EDLs at the lower bound and yet still not being able to avoid violating
standards at some receptors.
To solve this problem, decrease or eliminate at least some of the
lower bounds. Decreasing the bounds will cut the chances for growth in
-------
186
EXECU'IOP. MPS/360 V^.~'AB
SOLUTION (INFEASIBLE)
TIME = 0.03 MINS. ITERATION" NUMBER - 15
•.,.NAM£«.. ...ACTIVITY... L'EFINE3
FUNGI10UAL 445.61722 UBJI
RESTRAINTS S02X
BOUNDS.... S02X9.00
'"±*. 11"'. Identifying an Infeasible Solution
emissions in some land use zoning classes and some areas, tut this outcome
is necessary if air quality standards are to be maintained. An experienced
analyst familiar with linear programming can suggest ways of efficiently
altering lower bounds.
STEP 24C: MAP LINEAR PROGRAM RESULTS
Map Total Potential Emissions*
Once the EDLs have been found, there will be a great temptation to
map them. While EDL maps are important for transferring data from one office
to another** (for example, from the regional group that set the EDLs to the
local group that will administer them), and are important in political deci-
sion-making, they are not truly useful for finding out whether the linear
program found a technically reasonable solution.
The reason why comparison of EDLs would not reveal whether the linear
program arrived at a reasonable solution is that EDLs are dependent upon the
number of hectares zoned for each land use class and the number of exempt
sources in each grid square. The important technical consideration is wheth-
er there are large unexplainable fluctuations in total potential emissions
per square kilometer. For example, the total potential emissions allowed in
two adjacent grid squares may be identical, however, one square may have
*Total potential emissions are those that would occur if all the land within
an area had emissions that were at the maximum quantity allowed by the EDL
plus the emissions from EDZ-exempt sources.
**Instructions for making EDL maps are in the second part of Step 24C.
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187
twice as much land area zoned in a particular zoning class as the other —
which may result in one square having an EDL for the zoning class that is
onlv half as large as the other. Tn this case, EDLs would vary widely while
total potential emissions fneacu grid square would be the .same.
The comparison of total potential emissions on a square kilometer
basis will show the gradual differences and jumps in emissions between equal
si?;e areas that are caused by meteorological conditions, current emissions
and exempt sources rather than by differences in zoning practice. To pro-
duce a map of total potential emissions, follow the procedure discussed
below.
Complete Worksheet 14
The first step in mapping total potential emissions is to calculate
the emissions of future EDZ sources located in each EDZ grid square that
would result if future construction used all of the allocated EDL. This is
done by completing Worksheet 14 (see Fig. 111).*
WORKSHEET 14 (FOR STEP 24) FILLED OUT BY:
FINDING THE POTENTIAL EMISSIONS OF EDZ SOURCES PER km2
DATE:
PG
OF
COLUMN
NUMBERS
FROM LP
OUTPUT OR
MATRIX
EXPLANATION
OF COLUMNS"
ACTIVITIES X INPUT
COST = EMISSIONS
PER EDZ GRID SQUARE
PER LAND USE CLASS
(g/s)b
S0
PART
EMISSIONS OF FUTURE EDZ
SOURCES LOCATED IN EACH EDZ
GRID SQUARE IF FUTURE
CONSTRUCTION USES ALL OF
THE EDL ALLOCATED
(g/s)c
S0
PART
QQCU
QQC2.
9.22Z
/i.r/c.
4A04S
.388
9,422.
oooS
ooo^
17.551
Q
132.132
O
25.
Q.Q
0.0 _.
2S.BJ4
Fig. 111. Worksheet 14 Example
"Full-page, blank worksheets for your use are provided in Appendix J.
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188
The two left-hand columns of Worksheet 14 art completed by copying
the column numbers and the corresponding entries i~\ the "e:xpl ;• -iac Ion ol
columns" row from the linear niogranming matrix, TV fil."t i ri the "Ac :1\ i t Le:~
>: Input Cos;:" columns in WorKsreet 14, refer to the linear nf'.. £/am output
discussed earlier (Fig. 109"). Note, rha t the entries in rlie . TPLUMN. column
on the output are the same as those in the first column of Worksheet 14.
For each column number on the cutout (such as COL001) , multiply the.
...ACTIVITY... value by the ...INPUT COST... value and enter the result on
Worksheet 14.
The iinal set of columns in Worksheet 14 ("Emissions of future EDZ
sources...") contains the sum of the entries in the "Activities x Input Cost"
columns for each grid square. For example, in Fig. Ill the emissions from
the Ml, M2, and M3 sources in grid square 00001 are added and the sum placed
in the final columns (S02 and particulate emissions a^e summed separately).
Some grid squares might contain only one EDZ source, as shown in Fig. Ill for
grid square 00003. The data in Worksheet 14 will be used in Worksheet 15
(Fig. 112)
Complete Worksheet 15
Total the Emissions of Exempt Sources. The second step in mapping
total potential emissions per square kilometer is to determine the total
emissions of exempt point and area sources.
The total emissions in each EDZ grid square from the exempt point
sources that you listed on Worksheet 10 must be calculated. This means that
the location of every point source is to be mapped on a copy of the gridded
general regional map (from Step 1) and its grid square location noted. After
all sources have been mapped, use the grid square locations and the emissions
data on Worksheet 10 to sum the projected emissions of the sources for each
grid square. Place the resultant values (in g/s) into the appropriate rows
in Column A of Worksheet 15 (list every EDZ grid square number on Worksheet
15, even if a square has no exempt point sources). See Fig. 112.
The total potential emissions in each EDZ grid square from the exempt
area sources listed on Worksheet 9 also must be calculated. The area source
portion of the emission inventory acquired in Step 6 includes a regional
------- An error occurred while trying to OCR this image.
-------
190
grid system that was used for dispersion modeling. The inventory lists the
area source emissions for each such square. These emissions ;jre to be re-
allocated from the emission inventory grid sYstem ;o the El1-7 ;>rid system.
This is done by assuming that area source emissions -ar~- uniform throughout
a given emission inventory square. Each EDZ grid sciuare r.;y-'?rs a certain
percentage of the land area of an emission inventoi"" square. The quantitv
of emissions to be allocated from an emission inventory square to an EDZ
grid square is equal to this percentage of the area scarce emissions in the
emission inventory square. The area source emissions oer EDZ grid square
(g/s) are to be placed in Column B of Worksheet 15,
Estimate Total Emissions per Square Kilometer. The third step in
this procedure is to complete Columns C - F of Worksheet Ifi. Column C is
completed by copying the entries from the final set of columns on Worksheet
14.
Column D is completed by summing the values in Columns A, B,, and C
across each row. The values for Column E should be copied from Worksheet 2.
Finally, Column F is filled in by dividing the Column D values by the appro-
priate Column E values.
The total potential emissions of each pollutant per square kilometer
(recorded in Column F) should now be mapped onto copies of the gridded gen-
eral regional map. Analysis of this map is discussed in Step 24D. A color-
coded mapping system is recommended. Make two maps — one for SO? and one
for particulates.
Map Emission Density Limits
Once the regional map of total potential emissions per square kilo-
meter has been made, the next step is to map the EDLs and see how they are
distributed. You will need six copies of the gridded regional map produced
in Step 2.
First, map the light manufacturing EDLs for S02 and particulates (the
...ACTIVITY... entries from the linear programming output, identified by
their COL XXX designations and the references in Worksheet 14) onto two of
the gridded general regional maps. The mapping may be simplified by divid-
ing the EDLs into five or seven classes and color-coding each class.
-------
191
Second, map all the medium manufacturing EDLs onto the two other
gridded general regional maps, and map all the heavy manufacturing EDLs onto
the last two maps.
STEP 24D: CHECK MAPS FOR REASONABLENESS
Maps of Total Potential Emissions
If the EDL-setting process has worked, you should find few unexplain-
able "large" differences or jumps in total per-square-kilometer emissions
between adjacent squares. There may be gentle undulations which occur from
differences in local meteorology, but no large changes unless:
• A particular EDZ grid square currently has no sources located
within it, and has no land zoned for manufacturing use. This
condition would cause a "valley" if surrounding squares have
several sources and/or land zoned for industrial classes (see
Fig. 113).
• A particular EDZ grid square contains many exempt sources
while adjacent squares contain few or none. Even if all
the squares had the same number of hectares zoned for the
Fig. 113. Three-Dimensional View of an Emission "Valley"
-------
same manufacturing classes, the square with more exempt
sources would show higher total emissions. ^his is due to
the upper bound placed on EDLs; only so much pollution may
come from a square if the square contains no exempt sources.,
(This "peaking" condition is shown in Fig. 114.)
• The region contains more than one PSD class. In this case,
the emissions on either side of the PSD boundary may bt-
expected to vary due to the difference in air quality stan-
dards being applied; see Fig. 115 for a view of this
situation.
If adjacent grid squares have similar land use zoning,, similar exempt
sources, and similar air quality standards — and have large: unexplainable
differences in total potential emissions — then actions may have to be
taken to smooth the total emissions. Some of the possible actions are out-
lined in the following discussions of EDL patterns.
Maps of Emission Density Limits
Several patterns that may be expected in the EDLs generated by the
linear program are discussed below. These patterns are not to be con-
sidered problems.
High Fringe, Low Central EDLs
A common EDL pattern is high EDLs in fringe areas and low EDLs in
the central city and close-in suburbs if existing sources are exempt from
EDZ. Since more development already exists in the central area, the expecta-
tion is that the current pollutant concentrations are higher. Thus, less
growth can be tolerated in central areas before air quality standards are
threatened. The anticipated effect of the high fringe EDLs is to promote
new source development in these areas and thus make best use of the
assimilative capacity of the air.
All EDLs at Upper Bound
All EDLs may be set at the upper bound in cases where the upper
bound has been set very low. This situation means that even with all new
sources using the maximum allowable emissions, standards will not be vio-
lated. If the region wishes to increase the emissions to possibly increase
the opportunities for economic growth, then upper bounds could be raised.
-------
193
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-------
194
Most EDLs Set at Upper or Lower Bounds
This is to be expected in regions with built-ap core areas. The pro-
gram will set the EDLs in areas near the center as low as possible to avoid
violations of air quality standards. Since the inputs to the program do not
allow solutions below the lower bounds, the lower bounds are where close-in
EDLs are set. Since the objective function of the basic EDZ model was to
maximize emissions, the fringe areas (which have much less impact on the
receptors in the central area — in other words, there are small transfer
coefficients for fringe sources to central area receptors) have their emis-
sions maximized by having EDLs at the upper limit.
This situation may cause political problems if individuals argue over
the location of the upper/lower bound boundary relative to their land. This
is particularly likely to be a problem when an EDL boundary line cuts across
a piece of land owned by one individual or company. There are at least six
acceptable adjustments that may be made if the upper/lower bound situation
causes friction.
First, since the EDLs have been set objectively and the grid system's
location is essentially a "line drawing problem," if legal counsel agrees
with the legality of this EDZ gridding methodology, the complaints may be
treated in a manner similar to the treatment of such complaints in tradi-
tional land use zoning. While this may not be politically expedient, it
would maintain the credibility of the solution given by the model and avoid
setting new precedents that might be harmful later. Be sure to consult legal
experts before taking any action.
Second, the EDLs of single-owner parcels which are divided by a grid
square boundary may have the EDL over the entire parcel averaged to make it
uniform throughout. For example, if Bilgewater Industries owns 10 hectares,
all of which is zoned for medium manufacturing, and three hectares have an
EDL of 6 (g/s)/ha and the other seven have an EDL of 4 (g/s)/ha, then the
EDL for the entire 10-hectare parcel could be set at [(3 x 6) + (7 x 4)]/iO =
4.6 (g/s)/ha at Bilgewater's option.
Third, the procedure shown in Fig. 116 could be followed. Here,
political expediency would temper the desire for maximizing emissions since
the EDLs would be graduated at the upper/lower bound boundary. Those squares
-------
195
BEFORE
AREA OF
SMOOTH
TRANSITION
AREA OF
LOW EDLS
AFTER
116. Three-Dimensional View of Gradually-Changing EDLs
-------
196
at the lower bound would remain at the lower bound, while those at the upper
bound along the boundary would have their EDLs changed to make a smooth
transition in EDLs from the lower to upper bound squares. In tiis way, no
parcels of land directly next to each other would have radically different
EDLs.
Fourth, if EDLs in one grid square are very high while those in nearby
squares are very low, it could be a function of the number of hectares zoned
in a particular land use class. If there is a desire to make EDLs more uni-
form, the number of hectares zoned for each land use class withm a grid
square can be changed and the EDLs reallocated to (or from) the newly zoned
land.*
Fifth, the linear program may be rerun using a "proximity constraint"
as discussed in Benesh et al. (1977). The proximity constraint would require
the program to produce an optimal solution while not allowing much variation
between adjacent grid squares.
Sixth, the upper and lower bounds may be altered for specific grid
squares and the linear program rerun to create a more uniform distribution of
EDLs. Altering the bounds will only work when most or all of the EDLs are
set at the upper or lower bounds and the direction of change desired is con-
sistent. For example, if EDLs within some squares are too high and the upper
bound constraint is binding, the EDL can be lowered by lowering the upper
bound being used in that grid square.
*This action will require the involvement of the local zoning commission or
board to effect a zoning change. If a zoning change can not be agreed upon,
then this approach is not practical. Note that rezoning would change the
objective function coefficients used in the linear programming and may
necessitate rerunning the linear programming package if extensive zoning
changes are made.
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197
STEP 25: FORMULATE THE EDL STRATEGY
SUMMARY
In Step 25 you will develop a land used based emission strategy
(LUBES) by extending the methodology presented in Steps 1-24. If you
select an EDZ strategy, this is quite simple. Three other applications of
emission density limits also are discussed briefly."
One of the following substeps should be completed depending on the
emission quota strategy chosen:
Step 25A: The Emission Density Zoning (EDZ) Approach
Step 25B: The Jurisdictional Emission Quotas (JEO)** Approach
Step 25C: The District Emission Quotas (DEQ) Approach
Step 25D: The Floating Zone Emission Quotas (FZEQ) Approach
Before trying any of these approaches, consult legal counsel to check on
equal protection and due process problems which may arise from these strate-
gies.
PRELIMINARY STEPS AND RESOURCE REQUIREMENTS
The decision on whether to use EDZ, JEQ, DEQ, FZEQ or another land
use based emission stragegy should have been made before the EDL-setting
procedure began. Research on the characteristics of each approach should
have been done at the time. As shown in Fig. 117, Steps 1-24 must be
completed before Step 25 is begun. Resource requirements are listed in
Table 29.
STEP 25A: THE EMISSION DENSITY ZONING (EDZ) APPROACH (emission rates
allocated per hectare of land)
Emission Density Zoning. This phrase, "emission den-
sity zoning," has been used to cover a widely varying group
of different land use-based air quality management strate-
gies. For example, Bosselman, et al., uses the term to en-
compass both emission allocation planning and district emis-
sion quotas. For our purposes here, emission density zoning
(EDZ) will be defined as an air quality maintenance strategy
-The discussion in Steps 25A - 25D is based on Brail, 1975a.
'"''Often called emission allocation planning (EAP) in the literature.
-------
198
23-
£4
I
\
25
Fig. 117. Prerequisites for Step 25
which requires that emissions of a pollutant be limited to
prescribed levels for a selected unit area. Thus, EDZ
might be more properly labeled unit area emission
quotas.
The pollutant limit would be developed in terms of
an amount per unit area per time period specific to a
particular land use category. For example, an EDZ regu-
lation might specify that heavy industrial land uses seek-
ing to construct in a municipality must emit no more than
two tons of particulates per acre of lot size per year.
Hence, a 100 acre establishment classified as heavy indus-
try would have to certify that it would emit less than 200
tons of particulates yearly before being allowed to con-
struct. (Brail, 1975a)
Table 29. Resource Requirements for Step 25
Necessary Skills'
Necessary Data
Data
Sources
Necessary
Hardware
Land Use Planning
Data Analysis
Administration and
Implement at ion
Regional Maps
EDLs
Step 2
Step 24
Cartographic Files
First on list directs this step.
-------
199
This approach is the conception of EDZ explained in the guidebook
Introduction. The idea is to set an EDL for each hectare of land in a region
based solely on the outcome of the dispersion modeling and linear programming
analysis. No political factors are considered beyond the number of hectares
of land zoned in a particular land use class within an arbitrary grid square.
In the case of EDZ, Step 25 is little more than a mirror of Step 24. The
only additional instruction is to administer the system in some legally de-
fensible, fair, and equitable manner. The level of government and the form
of the administering agency are beyond the scope of this guidebook, but con-
ceivably the system could be administered at the state, regional, or local
level.
STEP 25B: THE JURISDICTIONS EMISSION QUOTAS (JEQ) APPROACH (emissions
allocated to each political jurisdiction for distribution
within its borders, subject to reasonable guidelines)
Emission Allocation Planning (Jurisdictional Emis-
sion Quotas). The most broadly conceived of the emission
limitation techniques centers on placing lids on the
amount of pollutants emitted within defined political
boundaries, including municipalities, counties and states.
While Bosselman, et al., has referred to this Jurisdictional
emission quota concept as emission density zoning this
strategy has been usually referred to as emission alloca-
tion procedures or emission allocation planning (EAP).
California has been the most advanced in its thinking about
emission allocation planning, where a ceiling would be
established on the total amount of emissions in an air
basin and this total allocated to sub-areas within the basin.
Extrapolating from the California experience, it becomes ap-
parent that EAP is a flexible tool which allows local polit-
ical jurisdictions a great deal of latitude in dealing with
air quality problems.
Emission allocation planning does allow the local
decision-makers a great deal of flexibility. It is this
flexibility which is appealing and, at the same time, poten-
tially dangerous. The allocation of a quota in itself pro-
vides no guidance to the local area, and EAP may be such a
loosely-drawn concept that it would be ineffectual in main-
taining air quality. Regulations would have to be developed
to mandate compliance by jurisdictions with the quotas as-
signed, and might include mandatory review of land use and
transportation plans as well as monitoring of zoning and
subdivisions regulations, including variances. There is no
doubt that EAP has appeal as an air quality maintenance
-------
200
strategy. Some recent unpublished research work done for
EPA has advised its use in maintenance areas. (Brail,
1975a)
If you are using a JEQ strategy, sum the EDLs for all hectares within
a local jurisdiction to give the entire jurisdiction a single emission ceiling.
Each political unit could split up its allocation in any legal way it chooses,
provided it does not exceed its ceiling. For larger jurisdictions, emission
quotas would have to be set for subareas such as individual grid squares
within the jurisdiction to avoid violation of air quality standards. Unlike
EDZ, JEQ does not completely eliminate the possibility of local violations
of air quality standards since source clusters could occur within a juris-
diction. If review procedures were developed to oversee source siting, then
JEQ could become a very useful air management tool.
STEP 25C: THE DISTRICT EMISSION QUOTAS (DEQ) APPROACH (emissions allocated
to subareas of jurisdictions)
District Emission Quotas. A step down from EAP is a
strategy which limits the amount of pollutants to be emitted
during some time period from a planning district within a
jurisdiction. Thus, the amount of emissions generated by
an industrial zone of 100 acres might be limited to no more
than 200 tons of particulates per year. Once the emission
limitations had been established for a particular district,
new polluting sources would only be allowed if the quota
had not been exceeded.
Different kinds of districts, e.g., residential, com-
merical, and industrial, would be permitted varying amounts
of emissions. Through this strategy, "hot spots" could be
avoided and air quality possibly maintained. Since there has
been relatively little study into this technique, it is not
exactly clear how it would operate.
The land use planning implications of the district
emission quotas (DEQ) concept would center on the juxta-
position of different activities. The development of a
heavy industrial district, with accompanying relatively
high emission ceilings, would mean that residential dis-
tricts be located away at an appropriate distance and wind
direction. It is by no means clear that planners have the
freedom to simply locate different land uses in districts
so that there are few air quality problems. However, con-
sider the simple case where DEQ is used for indirect source
control. A district could be developed around the indirect
source, and an emission lid attached. Such a lid could be
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used to keep future emissions from the existing indirect
source, plus any additional sources, within the pre-estab-
lished ceiling. (Brail, 1975a)
As you can see, there are great similarities between DEQ and JEQ, and
so, the extension of the EDL methodology is basically the same. Sum the EDLs
for all hectares within the districts and allocate the total emissions to
each new source in an equitable manner. Delineation of the districts depends
on the local communities.
STEP 25D: THE FLOATING ZONE EMISSION QUOTAS (FZEQ) APPROACH (emissions with-
in a certain radius of a new point source cannot exceed a specific
amount)
Floating Zone Emission Quotas. As the name implies, the
"floating zone emission quota" (FZEQ) concept refers to limita-
tions on pollutants generated within an area of specified size
which can be drawn about any specific location within the
metropolitan area. For example, air quality maintenance ob-
jectives might include a limiting of emissions to 2,000 tons
of particulates within any one square mile of the air quality
maintenance area. The use of floating zone emission quotas
is best seen when evaluating the impact of a new industrial
facility. Given this 2,000 ton per year limitation on parti-
culates within a square mile, then a circle, containing an
area of a square mile, could be placed about the proposed
location of the new industry, the centroid of the circle.
Thus, if the new industry is estimated to emit 200 tons of
particulates per year, then the one square mile surrounding
the industry must have existing total emissions of no more
than 1,800 tons of particulates. The FZEQ concept was de-
veloped for use in Jefferson County, Kentucky. (Brail,
1975a)
As stated above, in FZEQ, a new source wishing to locate in an area
provides the FZEQ implementing authority with a statement of how much pollu-
tant the source will emit. After determining the emissions from all other
sources located in the area where the source wants to locate, the FZEQ autho-
rity permits or denies construction based on whether the total emissions near
the proposed site are above or below some objectively-set limit for the area
(see Fig. 118). The challenges in FZEQ are (1) determining the size of the
area around the source that should be considered in the analysis (e.g., the
radius of the circle placed around the new source), (2) determining the
quantity of emissions to be allowed in the area, and (3) keeping good records
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FZEQ: ONE SOURCE
• is a source. The circle has
a 1.2 km radius. The source is
allowed to use the entire quota
within the circle.
FZEO: MULTIPLE SOURCES, TANCENT1ALLY
ARRANGED
• is a source. Each source is located
such that the quota used by each is
independent of the others.
A B
FZEO: MULTIPLE SOURCES WITH OVERLAPPING RADII Primary Ouestion: Can sources be
located in this manner and yet not violate standards? The main problem would be
in the shaded area in "B." Assume source 1 used all of the quota within its radius.
Can Source 2 use up all the quota within its radius while assuming the EDL within
the 1 and 2 intersection equals zero and yet still not violate the standards within
the intersection area? Does the problem intensify when source 3 comes in? Answers
to these questions should be determined before FZEO is attempted. In anv event, FZEQ
will require a sophisticated and up-to-date accounting system to assure that the
quotas are not assigned more than once.
Fig. 118. Floating-Zone-Emission-Quota Situations
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203
so that the implementing authority knows the emission quota remaining in each
area of the region.
The EDL process can do little to help in the political decision of
determining the size of the area to be studied, but it can help a great deal
in determining allowable emissions once the sizes have been determined.
To help set FZEQ quotas, all that need be done is to produce the EDL
map of Step 24, locate the future position of the new source on the map, and
sum the per hectare EDLs within a certain radius of the new source to provide
a figure showing the total amount of emissions that may be allowed in the
area. The total emissions for the area are compared to the planned emissions
of the new source. If planned emissions plus existing emissions are below
the quota for the area within the radius, then the source is permitted to
build. If not, the source will have to find another location.
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APPENDIX A: GLOSSARY
This glossary presents definitions of terms specific to EDZ and
selected aspects of air pollution control and land use planning. Many of
these terms have precise legal meanings that are beyond the scope of this
guidebook. The intent here is to give a summary of each term to help you
quickly interpret the word's general meaning and usage. The sources of
definitions not written by the guidebook authors are listed at the end of
Appendix A.
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Adjacent Points: Two corner-point feasible solutions [to a linear program-
ming problem] are said to be adjacent if the line segment connecting them
lies on the edge of the boundary of the feasible region. (14)
Air Pollution: The presence of unwanted material in the-, air in sufficient
amount and under such circumstances as to harm plants or annuals, or
interfere with comfort, health, or welfare of persons, or with full use
and enjoyment of property. (23)
Air Quality: Air quality involves the degree of concentration of undesirable
air pollutants. (21)
Air Quality Control Region; An area designated by the federal, state, or local
government where two or more communities - either in the same or dif-
ferent states - share a common air pollution problem. (8)
Air Quality Criteria: Compilation of knowledge of the relationships between
concentrations of air pollutants and their adverse effects. (2)
Air Quality Maintenance Area (AQMA); Those areas (counties, urbanized areas,
standard metropolitan statistical areas, etc.) which, due to current
air quality and/or projected growth rate, may have the potential for ex-
ceeding any national standard within the subsequent 10-year period. (11)
Air Quality Run: A type of dispersion model run used in setting EDLs. The
purpose is to generate source-to-receptor transfer coefficients used by
the linear programming model.
Air Quality Standards; The prescribed level of pollutants in the outside air
that cannot be exceeded legally during a specified time in a specified
geographical area. (8)
Ambient Air; The portion, of the atmosphere, outside buildings, accessible
to the general public.
Area Source; An aggregate of a large number of small individual pollution
sources, including transportation sources. This is a general definition;
area source is legally and precisely defined in federal regulations. (8)
Assimilative Capacity: The absolute limit of the ability of a body of water
or air to purify itself of pollution before the environment breaks
down. (8, 9)
Atmospheric Dispersion Model: See dispersion model.
Attainment: See nonattainment area.
Automatic Compliance Status: As used in the guidebook, an existing source
given automatic compliance status is assigned an emission density limit
equivalent to its pre-EDZ emission density, assuming the source is in
compliance with all other relevant air quality standards.
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Background Level: For air pollution, amount of pollutants in ambient air due
to natural or extraregional man-made sources.
Bas_cline_: A line in a land survey that serves as the origin for computations.
In EDZ, a meridian (line of longitude) that is used as the starting point
or line for a coordinate system. (24)
Base Map: A map having only essential outlines and used for the plotting or
presentation of specialized data of various kinds, (24)
Basic EDZ Model: A formulation of EDZ wherein total emissions within a
region are maximized on the theory that maximizing emissions will maxi-
mize opportunities for economic growth.
Calibration: Model calibration is a technique for improving the predictive
capability of a general air quality simulation model within some region
of interest, based on the comparison of model predictions with actual
air quality measurements. The term model calibration refers specifically
to the determination of the coefficients of a linear equation relating
observed with predicted values. In CDMQC the determination is done by
standard linear least-squares regression methods. (5)
Cartesian Coordinates: In a plane, a point can be located by its distances
from two intersecting straight lines, the distance from one line being
measured along a parallel to the other line. The two intersecting lines
are called axes (X axis and Y axis), (17)
Class I, II, III: These terms that classify land areas based on air quality
considerations have been given precise legal meanings by the Clean Air
Act Amendments of 1977. Information relating to these classes should be
received from the state air pollution control authority or a U.S. EPA
regional office.
Code: As used in the guidebook, to write data onto a "coding form" pre-
paratory to keypunching. The form shows the exact column where each
character is to be punched.
Coefficient: A number or factor put before and multiplying another. (20)
Column,^ Computer Card: A vertical line of punch positions on a card where
holes may be punched that will represent data. (20)
Column and Rows Sections (of linear programming matrix): An A-matrix in
linear programming. In an EDZ linear programming coefficient matrix,
the part of the matrix that describes the air quality contribution made
by each EDZ source to each EDZ receptor per unit emission density.
Compile: The act of using a compiler, or what a compiler does.
Compiler: A special program read into the computer that translates human-
oriented computer languages into machine language. Some compilers are
capable of detecting syntax errors while compiling, (A syntax error
consists of errors in spelling, logic, etc.) (10)
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Compiler Diagnostic Message: A note produced while a computer :LS compiling
a program which says that there is an error or nay be an error In the
program. (16)
our-
Composite Zoning Maps: As used in EDZ. maps that snow zoning i.nside and
side of incorporated areas, generally on a counts-wide or regional basis,
uomprehensive Plan: A comprehensive plan is an official public document
adopted by a local government as a policy guide to decisions about the
physical development of the community. (12)
Concentration; For air pollutants, the total mass (usually in micrograms) of
the suspended particles contained in a unit volume (usually one cubic
meter) at a given temperature and pressure; sometimes, the concentration
may be expressed in terms of total number of particles in a volume (e.g.,
parts per million); concentration may also be called the "loading" or
the "level" of a substance. (2)
Constraint: A restriction on available options. Something that does not
allow complete freedom. (14)
Controlled Emissions: Emissions that are lessened by means of pollution-
removing equipment, or conversion to cleaner fuels and processes. In
the guidebook, refers to emissions resulting from a source after com-
pliance with applicable air pollution control regulations.
Coordinate: Any one of a set of numbers used in specifying the location of
a point on a line, in space, or on a given plane or other surface. (20)
Corner Point (feasible solution): In linear programming, a feasible solu-
tion that does not lie on any line segment connecting two other feasible
solutions. See feasible solution. (14)
Criteria, Air Quality: See air quality criteria.
Disk Storage: A computer data storage device that uses magnetic recording on
flat rotating disks. (15)
Dispersion Model: An urban air quality simulation or dispersion model is a
numerical technique or methodology, based on physical principles, for
estimating pollutant concentrations in space and time as function of the
emissions distribution and the attendant meteorological and geophysical
conditions. (18)
District Emission Quotas (DEQ): A variation of emission allocation planning
where the strategy is to limit the amount of pollutants to be emitted
during some time period from a planning district within a jurisdiction.
(4)
Easting: The X coordinate in a Cartesian coordinate system such as the
Universal Transverse Mercator Coordinate System. Also, the north-south
line running through the coordinate. (13)
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EDZ Grid Origin: A point, southwest of a region setting emission density
limits, that serves as the southwest corner of the EDZ grid system.
All points on the giid system which are east and north of the EDZ grid
origin have positive values. The values of the coordinates at the EDZ
grid origin need not be 0,0.
EDZ Grid System: A grid system used for the air quality run of the disper-
sion model and for allocating emission density limits.
EDZ Receptor Point: A point used in the dispersion model as a location for
calculating air quality.
EDZ-Relevant Land Use Class: A land use class for which emission density
limits are set.
EDZ Source: One EDZ-relevant land use class within one EDZ grid square. An
EDZ source refers to the entire area of land in a land use class, not
to individual parcels.
Effective Stack Height: The height from the ground at which the centerline
of a plume becomes horizontal.
Emissions: All the substances discharged into the air from a stack, vent,
tail pipe, or other source. This term is generally used in regard to
discharges into the air. (23, 8)
Emission Allocation Planning (EAP): Also called "jurisdictional emission
quotas." An air pollution control strategy that places lids on the
amount of pollutants emitted within defined political boundaries, in-
cluding municipalities, counties, and states. Also see Step 25. (4)
Emission Density Limit (EDL): A ceiling set on the amount of air pollution
that may be legally emitted per unit of time per unit of land area.
Emission Density Zoning: A type of air pollution control regulation in which
the maximum legal rate of emission of an air pollution is based on
location, land area, land use zoning, and air quality constraints. (19)
Emission Inventory: A list of air pollutants emitted into a community's
atmosphere, in amounts (usually tons) per day, by type of source.
Related information such as stack characteristics are also included
in an emission inventory. The emission inventory is basic to the
establishment of emission standards. (8)
Enabling Legislation [or Enabling Statute]: Any statute enabling persons
or corporations to do what before they could not. It is applied to
statutes which confer new powers. (3)
Environment: The sum of all external conditions and influences affecting
the life, development and, ultimately, the survival of an organism. (8)
Exempt Source: As used in the guidebook, a pollution source that is not
controlled by EDZ and is not assigned an EDL.
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Exit Gas Velocity: The speed (generally m/s) at which stack gases leave a
stack.
Extraterritorial Jurisdiction: (a) The authority of local governments in
some states to regulate land use for a specified distance beyond their
corporate boundaries. (b) The area outside of the boundaries of a
local government over which the local government has authority to
regulate land use.
Feasible Region: In linear programming, if the problem is graphed, the
feasible region is the area wherein all the feasible solutions may
be found. (14)
Feasible Solution: In linear programming, a solution for which all the con-
straints are satisfied. (14)
Floating Zone Emission Quotas (FZEQ); An air pollution control strategy that
limits the pollutants generated within an area of specified size which
can be drawn about any specific location. Also see Step 25. (4)
Fugitive Dust: Solid airborne particulate matter emitted at or near ground
level from any source other than a stack, flue, or control system.
Gaussian Dispersion Models: Any of a set of dispersion models that assume
that pollutants disperse in a normal distribution. See dispersion
model. (22)
Grid Axes: The perpendicular lines running north and east from a grid sys-
tem origin.
Grid Cell (or square): A single square in a grid system.
Grid System: An array of grid squares.
Hot Spot; A place where there are high concentrations of pollutants. Often
hot spots are caused by the congestion of a few heavy polluters.
Index: A table of computer words or fields containing addresses of records
located in file storage. (20)
Infeasible Solution; In linear programming, a solution for which one of the
constraints is not satisfied.
Joint Frequency Function (Distribution); A statistical summary showing the
percentage of total time when two or more variables (here wind speed,
direction, and stability) coincide. (6)
Jurisdictional Emission Quotas (JEQ): See emission allocation planning.
Land Use: The activity on a parcel of land.
Land Use Class: Related land uses placed into a single group. (24)
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Land Use Plan: A document showing the intended uses of land. Generally
without force of law, the land use plan is supposed to be the basis for
the zoning ordinance.
Linear Programming: Sometimes called optimum programming or mathematical
programming. The analysis of problems in which the linear function of
a number of variables is to be maximized (or minimized) when those
variables are subject to a number of constraints in the form of linear
inequalities, or the solution of these problems. This technique is not
to be confused with computer programming, although problems using the
technique may be programmed on a computer. Linear programming is most
likely to be feasible when the quantity to be optimized, sometimes
called the objective function, can be stated as a mathematical expres-
sion in terms of the various activities within the system, and when
this expression is simply proportional to the measure of the activities,
i.e., is linear and when all the restrictions are also linear. (20)
Mass: The property of a body, commonly taken as a measure of the amount of
material it contains, that causes it to have weight in a gravitational
field. The mass of an object is constant, while weight varies with
gravity. (24)
Mass Per Unit Time Basis: A means of measuring air pollution by way of mea-
suring the mass emitted during a specified period of time, such as g/s.
Matrix: A rectangular array of numbers subject to mathematical operations,
such as addition, multiplication, and inversion, according to specified
rules. (20)
Meridian: A great circle on the surface of the earth passing through the
poles and any given place. (24)
Mixing Height: The height (generally in meters) from the ground at which
there is a somewhat stable layer of air. The mixing height is as high
as pollutants will rise.
Mobile Source; A moving source of air pollution, such as an automobile. (8)
Monitoring: Periodic or continuous determination of the amount of pollutants
present in the environment. (8)
Nonattainment Area; The term nonattainment area means, for any air pollu-
tant, an area that is shown by monitored data or which is calculated
by air quality modeling,... to exceed any national ambient air quality
standard for such pollutant. (7)
Nonconforming Use: At the time a land use zoning ordinance is originally
adopted, there are, in almost every district, some uses that existed
before the ordinance was adopted, which do not conform to the use regu-
lations or the dimensional regulations for the district. These are
known as nonconforming uses. Other such uses are created from time to
time by amendments to the zoning ordinance. (12)
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Normalizing (UTM Coordinates): Placing the entire EDZ grid system on a
common set of axes such that all lines within the grid are straight and
perpendicular and based on the UTM coordinate system. Normalizing is
needed only in regions containing a UTM grid junction.
Northing: The Y coordinate in a Cartesian coordinate system such as UTM;
also the east-west line running through the coordinate. (13)
Objective Function: The function of the independent variables whose maximum
or minimum is sought in an optimization problem. (20)
Offset Policy: A policy that permits in certain nonattainment areas the
construction and operation of new sources of air pollution if existing
sources reduce emissions to compensate for the new source's emissions.
(19)
Optimal Solution: An optimal solution is a feasible solution that has the
most favorable value for the objective function. (14)
Packaged Program: A standardized computer program supplied by a software
manufacturer to perform specific functions.
Parameter: (a) A quantity, in a mathematical calculation, that may be
assigned any value; (b) a constant or a variable in mathematics that
remains constant during some calculation; (c) a definable characteris-
tic of an item, device, or system. (20)
Particulates (or Particulate Matter): Finely divided solid or liquid (except
uncombined water) particles in the air or in an emission. Particulates
include dust, smoke, fumes, mist, spray and fog. (8 & 1)
Parts Per Million (ppm): The unit commonly used to represent the degree of
pollutant concentration where the concentrations are small. Larger
concentrations are given in percentages. In air, ppm is usually a
volume/volume ratio. (8)
Plume; The emissions from a flue or chimney. (8)
Plume Rise: The height a plume attains once it is emitted from a stack.
Plume rise is affected by velocity of the effluents at the top of the
stack, temperature of effluents, diameter of the stack opening, wind
speed, temperature of air, wind shear with height, and atmospheric
stability. (22)
Point Source; In air pollution, a stationary source of a large individual
emission. This is a general definition; point source is legally and
precisely defined in federal regulations. (8)
Pollution, Air; See air pollution.
Receptor: See EDZ receptor point.
Run: One performance of a program on a computer. (20)
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Simplex Method: The general method for solving linear programming problems.
By using the method, one can find the point at which the objective
function is maximized or minimized if there is one. (14)
Simulation: The representation of physical systems and phenomena by compu-
ters, models or other equipment; an imitative type of data processing
in which a computer program is used as a model of some entity, such as
the process of air pollutant dispersion. when information enters the
computer to represent the factors of process, the computer produces
information that represents the results of the process. Also see dis-
persion model. (20)
Solution: In linear programming, any specification of values for decision
variables, whether feasible or not. (14)
Source: In EDZ, an emitter of sulfur dioxide or particulates.
Source-by-Source Basis: Pollution controls operate on a source-by-source
basis if the controls are placed on each individual producer of pol-
lution.
Source Contribution Table: For each receptor, the CDMQC program prints out
the computed concentration (or, optionally, percentage) of each pollu-
tant that arrives at the receptor from each source. This list is a
source contribution table. (5). Also see Transfer Coefficient.
Source, Existing; A source operating at the time an EDZ regulation goes into
effect.
Source, Small; A source not listed in an emission inventory.
Stability Class; A classification system has been developed to determine
the relative "calmness" of the atmosphere at a given location. Wind
speed, solar radiation, and cloud cover are among the variables used
to produce the six classes, A-F. A stability class is any one of
the six. (22)
Stack Gas: The emissions coming from a stack, including all air contaminants,
water vapor, and air.
Stack Height: The distance, above the ground, at which a pollutant is
emitted from a source.
Standard Metropolitan Statistical Area (SMSA): One of many metropolitan
regions of the U.S. which fit designated criteria set up by the Depart-
ment of Commerce for the purpose of collecting statistical data. (1)
Stationary Source: A pollution emitter that is fixed, rather than moving.
(8)
State Implementation Plan (SIP): A plan written by a state under author-
ity of the Clean Air Act (and its Amendments) for the control of air
pollution.
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Sulfur Dioxide (SOa)'. A heavy, pungent, colorless gas formed primarily by
the combustion of fossil fuels. 862 damages the respiratory tract as
well as vegetation and materials and is considered a major air pollu-
tant. (8)
Total Potential Emissions: As used in the guidebook, total potential emis-
sions are the emissions (in g/s) that would occur if all the land
within a grid square had controlled emissions that were at the maximum
allowed by the EDL plus emissions (in g/s) from EDZ-exempf: sources.
Transfer Coefficient: An index, or indication, of the amount of air pollu-
tion traveling from a source to a receptor. Transfer coefficients in
EDZ are generally based on unrealistically high quantities of emissions
so that the group performing EDZ will not overlook the effects of each
source on each receptor. Also see Source Contribution Table.
Universal Transverse Mercator (UTM) : The name given to a type of Cartesian
coordinate system using the Mercator map projection; the UTM system is
used extensively in military mapping. (13)
UTM Grid Junction; A meridian where two UTM grid zones meet. (13)
UTM Grid Zones: A 6° wide north-south strip of the earth with a unique base-
line from which UTM coordinates are measured. The baselines are the
equator and the meridian in the center of each zone which is given an
easting value of 500,000 meters; thus the easting value of UTM coor-
dinates within any one zone run from approximately 250,,000 meters to
750,000 meters. Northings in the northern hemisphere vary from 0
(equator) to somewhat less than 10 million meters. (13)
Variance; Sanction granted by a governing body for delay or exception in the
application of a given law, ordinance or regulation. (8)
Variable; A quantity that may assume any one of a specified set of values.
(24)
Zone: To partition (a city, borough, or township) by ordinance into zones
or sections reserved for different purposes (as residence, business,
or manufacturing or combinations of these) and governed by appropriate
building regulations (as the height and area of all structures). (24)
Zoning Class: A land use category in a zoning ordinance. The three largest
classes are residential, commercial, and industrial, which may be fur-
ther subdivided into the single family dwelling class, the two-family
class, retail business class, etc. (12)
Zoning District (Zone); A land area where specified land uses are authorized
by a zoning ordinance.
Zoning Map: A map included in a zoning ordinance that outlines the specified
land areas in which various land uses are authorized.
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Zoning Ordinance: A legal document showing the lawful uses of land and
various other land-use-related regulations within a jurisdiction,
enacted to protect the health, safety and welfare of the public.
Zoning^ Survey: In EDZ, a survey to find the areas of lands zoned in EDZ-
relevant land use classes within individual grid squares.
Sources of Definitions for Glossary
1. Abatement and Control Development Programs. A Compilation of Selected
Air Pollution Emission Control Regulations and Ordinances, U.S. Depart-
ment of Health, Education, and Welfare Publication Number 999-AP-43
(1968).
2. Air Quality Criteria for Sulfur Oxides, National Air Pollution Control
Administration Publication No. AP-75 (1969).
3. Black, H.C., Black's Law Dictionary., West Publishing Co., St. Paul,
Minn. (1951).
4. Brail, R.K., Land Use Planning for Air Quality Maintenance, Proc.
Specialty Conf. on Long Term Maintenance of Clean Air Standards, Lake
Michigan States Sec. of Air Pollution Control Assn., Chicago, J.J.
Roberts, ed. (Feb. 1975).
5. Brubaker, K.L., Polly Brown and R.R. Cirillo, Addendum to the User's
Guide for the Climatological Dispersion Model, U.S. EPA Report Number
EPA-450/3-77-015 (July 1977).
6. Busse, A.D., and J.R. Zimmerman, User's Guide for the Climatological
Dispersion Model, U.S. EPA Report Number EPA-R4-73-024 (NTIS PB-227-346)
(Dec. 1973).
7. Clean Air Act Amendments of 1977.
8. Common Environmental Terms, A Glossary, G.J. Studdard, ed., U.S. EPA,
Washington, D.C. (Nov. 1974).
9. Cosier, P.C. IV, Land Use Based Emission Strategies: Their Promise and
Problems, Planning Comment, 12 (2):31-48 (Fall 1976).
10. Cress, Paul, Paul Dirksen, and J.W. Graham, Fortran IV v^lth Watfcr and
Watfiv, Prentice Hall, Inc., Englewood Cliffs, N.J. (1970).
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216
11. Federal Register, 40 CFR 51.12(e), 35:15834 (June 18, 1973).
12. Goodman, William, and E.G. Freund, eds., Principles and Practices of
Urban Planning, 4th Ed., International City Manager's Assn., Washington,
D.C. (1968).
13. Grids and Grid References., U.S. Department of the Army, Technical
Manual TM 5-241-1, Washington, D.C. (June 1967).
14. Hillier, F.S., and G.J. Lieberman, Operations Research, 2nd Ed., Holden-
Day, Inc., San Francisco (1974).
15. Hughes, J.K., PL/1 Programming, John Wiley & Sons, Inc. (1973).
16. IBM System/260 Operating System, Fortran IV (G and H) Programmers
Guide., 5th Ed., IBM Corp., New York, N.Y. (Sept. 1973).
17. James, Glenn, and R.C. James, Mathematics Dictionary, 4th Ed., Van
Nostrand Reinhold Co., New York, N.Y. (1976).
18. Johnson, W.B., R.C. Skarlew, and D.B. Turner, Urban Air Quality Simula-
tion Modeling, Ch. 10 of Vol. 1 (Air Pollutants, Their Transportation
and Transport) of Air Pollution, 3rd Ed., A.C. Stern, ed., Academic
Press, New York, N.Y. (1976).
19. Robson, John, U.S. EPA, personal communication (1977).
20. Suppl, C.J., and C.P. Suppl, Computer Dictionary and Handbook, Howard
W. Sams & Co., Indianapolis (1972).
21. The Nett York Times Encyclopedia of the Environment, Paul Sarnoff, ed. ,
Quadrangle Books, New York, N.Y. (1973).
22. Turner, D.B., Workbook of Atmospheric Dispersion Estimates, U.S. EPA
Report Number AP-26 (1970).
23. Voorhees, A.M., & Associates, and Ryckman, Edgerly, Tomlinson &
Associates, A Guide for Reducing Air Pollution Through Urban Planning,
Background Reading Material, Appendix B, Glossary, U.S. EPA Office of
Air Programs, Washington, D.C. (Dec. 1971).
24. Webster's 3rd New International Dictionary of the English Language,
Unabridged, P.B. Grove, ed,, G & C Merriam Co., Springfield, Mass.
(1964).
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APPENDIX B: ABBREVIATIONS*
A Simplified zoning classification for area sources
AQCR Air Quality Control Region
AQDM Air Quality Display Model (a dispersion model)
AQMA Air Quality Maintenance Area
CDM Climatological Dispersion Model
CDMQC An improved version of the CDM
DEQ District Emission Quotas
EAP Emission Allocation Planning
EDL Emission Density Limit
EDZ Emission Density Zoning
EPA Environmental Protection Agency
FZEQ Floating Zone Emission Quotas
JFF Joint Frequency Function (meteorological)
K A computer term meaning 1024 bytes of computer memory space; also
means degrees Kelvin. Meaning must be taken from context.
LUBES Land Use Based Emission Strategy
Ml Light manufacturing zoning district
M2 Medium manufacturing zoning district
M3 Heavy manufacturing zoning district
MCD Minor Civil Division
MPSX Mathematical (linear) Programming System Extended
NAAQS National Ambient Air Quality Standard
NCC National Climatic Center
NEDS National Emissions Data System, an EPA data management system
PSD Prevention of Significant Deterioration
RHS Right Hand Side
SAROAD Storage and Retrieval of Aerometric Data, an EPA data system
SMSA Standard Metropolitan Statistical Area
SO Sulfur oxide
S02 Sulfur dioxide
SO Any oxide of sulfur
x
SSU Stratified Systematic Unaligned (dot pattern)
UTM Universal Transverse Mercator
^Abbreviations for metric and selected English units of measure are given
in Appendix C.
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218
x (1) any variable. (2) a horizontal axis in a coordinate system.
(3) the downwind direction from a stack.
y (1) any variable. (2) a vertical axis in a coordinate system.
(3) the ground level axis perpendicular to the downwind
direction from a stack.
z (1) any variable. (2) a third axis, beginning at the origin,
at right angles to the plane formed by x and y axes. The z axis
creates a three-dimensional space from a two-dimensional one.
(3) the axis beginning at the center of a stack rising upward
to infinity.
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219
APPENDIX C: METRIC UNITS AND CONVERSIONS
This appendix offers a brief explanation of the metric system of
measurement and selected conversion factors. The conversion factors listed
here are those you are most likely to need in order to convert data in
English units into the metric units needed for the dispersion and linear pro-
gramming models. Additional conversion factors may be found in standard
technical reference books.
If you are totally unfamiliar with the metric system, you should con-
sult a reference book; such a book should be available from most libraries.
Briefly, basic metric units of measurement are: the meter for length, the
kilogram for mass, and degrees Celsius for temperature. The metric system is
based on multiples of ten, with prefixes used to indicate the "size" of the
unit, as shown by the following examples:
• micro- (]a) means one-millionth
1 micrometer (ym) - 0.000001 meter (m)
1,000,000 ym =• 1 m
• milli- (m)* means one-thousandth
1 millimeter (mm) = 0.001 m
1,000 mm - 1m
• centi- (c) means one-hundredth
1 centimeter (cm) = 0.01 m
100 cm = 1 m
• kilo- (k) means one thousand
1 kilometer (km) = 1000 m
0.001 km = 1 m
There are, of course, many other metric prefixes; the four listed above are
some of the most common.
Following are selected metric/English conversion factors for units of:
• length • concentration
• area • ten.perature
• velocity and flow rate • emission rates and
• weight and mass emission density limits
*An "m" standing alone means "meter." An "m" in front of another unit means
"milli," as in mg (milligram) or mm (millimeter).
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220
LENGTH
1 foot (ft) = 0.3048 meter (m)
1 m = 3.281 ft
1 mile (mi) = 1,609 kilometers (km)
1 km = 0.6215 mi
AREA
1 square mile (mi ) = 2.590 square kilometers (km ) or 259.0
hectares (ha)*
1 km = 0.3861 mi2
1 hectare = 0,003861 mi2
1 acre = 0.004046 km2 or 0.4046 ha
1 km2 = 247.1 acres
1 ha = 2.471 acres
VELOCITY AND FLOW RATES
1 foot per minute (fpm or ft/min) = 0.00508 meters per second (m/s)
1 m/s = 196.8 ft/min
Emission inventories sometimes provide a "flow rate" for stack gas in
ft3/min. To find the gas' exit velocity in m/s:
' divide ft3/ min by 60, giving ft3/s
• divide ft3/s by the stack area [which is 3.142 times the stack
radius (in feet) squared], giving ft/s
• divide ft/s by 3.281 (the number of feet in a meter), giving m/s.
WEIGHT AND MASS
1 pound (Ib) = 453.6 grams (g)
1 g = 0.002205 Ib
CONCENTRATION
1 part per million 862 by volume (ppm) = 2860 micrograms S02 per
cubic meter (yg/m )**
1 yg/m3 S02 = 0.00034965 ppm S02
*1 ha = 0.01 km2 = 10,000 m2.
**At 0° Celsius and atmospheric pressure of 760 mm mercury.
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221
TEMPERATURE
Fahrenheit temperature in °F to Celsius temperature in °C:
subtract 32 from °F, then multiply by 5/9
°C to °F: multiply °C by 9/5, then add 32
Celsius temperature in °C to Kelvin temperature in K: add 273 to °C
K to °C: subtract 273 from K
EMISSION RATES AND EMISSION DENSITY LIMITS
1 pound/hour (Ib/hr) = 0.1260 grams/second (g/s)
1 g/s = 7.9365 Ibs/hr
1 pound/day (Ib/day) = 0.00525 g/s
1 g/s = 190.5 Ibs/day
1 (lb/hr)/acre = 0.3114 (g/s)/hectare
1 (g/s)/hectare = 3.211 (Ibs/hr)/acre
1 (lb/day)/acre = 0.01298 (g/s)/hectare
1 (g/s)/hectare = 77.04 (Ibs/day)/acre
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223
APPENDIX D: MIXING HEIGHT MAPS*
-Maps reproduced from Holzworth, G.C., Mixing Heights., Wind Speeds, and
Potential for Urban i.ir Pollution Throughout the Contiguous United Stales_,
U.S. Environmental Protection Agency, Office of Air Programs Publication No.
AP-101 (Jan. 1972).
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APPENDIX E: UTM GRID JUNCTIONS
The data on the location of sources in the NEDS emission inventory
are given in UTM coordinates. Monitoring station data usually give the UTM
location of the station. Thus, using the UTM system can simplify gridding
and locating these points.
As noted in Steps 1 and 2, UTM grid junctions occur every 6° of longi-
tude. In the United States and its territories, the junctions are located
at these longitudes: in the contiguous 48 states, at West 60°, 66°, 72°, 78°,
84°, 90°, 96°, 102°, 108°, 114°, 120°, and 126°; in Alaska and Hawaii at West
132°, 138°, 144°, 150°, 156°, 162°, 168°, 174°, 180°, and East 174°; and
in the U.S. territories, East 126°, 132°, 138°, 144°, 150°, 156°, 162°, 168°,
and other coordinates mentioned previously. See Fig. 121.
The junctions are places where two UTM grid zones meet. A UTM grid
zone is a 6° wide strip of the earth running from the equator to the poles.
The northing values are numbered from south to north starting at zero at the
equator. The central meridian of each zone is given an easting value of
500,000 meters. From the zone's central meridian westward the easting values
decrease, and eastward they increase. The purpose of the 6° strips is sim-
ple; since UTM is a Cartesian coordinate system, it consists of perfectly
straight parallel and perpendicular lines. As one tries to transfer the
features of the round earth onto flat paper, some deformation inevitably
takes place. By taking 6° segments of the earth's surface, this deforma-
tion can be lessened.
But, the increase in accuracy created by the 6° wide zones creates the
grid junction problem: on either side of the junction line a different base-
line is used — thus UTM lines on one side of the junction are not parallel
to those on the other side.
The dispersion model requires the UTM coordinates to be assigned rela-
tive to a single axis. This guidebook has coined the term "normalization"
to describe the act of placing UTM coordinates on either side of a grid
junction into terms of a single UTM zone Normalization must be performed
for any region containing a grid junction. Regions without junctions may
totally ignore all references to normalization.
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228
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229
For regions with grid junctions, normalization must be done for all
UTM coordinates used in the EDZ methodology. This means the EDZ grid sys-
tem, and the locations of EDZ receptors, monitoring stations, and pollution
sources must be in normalized UTM coordinates. Both hand and machine (com-
puter) normalization methods are described here. If computer resources are
available, the machine methods will be easier to use. Cartographic methods
should be used to plot the EDZ grid system. All of the mapping procedures
are assumed to be performed on the general regional map produced in Step 1.
All references to point normalization assume the point data, such as point
source and monitoring station locations, have been collected.
Hand Normalization Methods
Hand methods involve no computer assistance and, therefore, require
no special equipment beyond cartographic supplies and a calculator. Hand
methods are somewhat less precise than machine calculations, but the dif-
ference is small enough to not create major differences in EDLs. Hand
methods will tend to take more labor time than machine methods.
Cartographic Method. The cartographic method is probably the simplest
method available for normalizing UTM coordinates, and should definitely be
used for normalizing the EDZ grid system. First, determine which of the two
UTM zones in your region will be the "major zone," or the zone where co-
ordinates will not change. The decision should be based on items such as
the location of point sources and monitoring stations. The zone with the
fewest points to be changed should be the one to have its coordinates
change. For simplicity, we will call this zone the "minor zone."
For this discussion, it is assumed that the zone covering the western
points of the region is the major zone, while the eastern zone is the minor
zone. Those with the opposite situation should have no trouble making the
appropriate adjustments.
To cartographically produce the ED2 £rid system, simply extend the
horizontal UTM lines from the major zone into the minor zone. Place lines
perpendicular to the extended lines at least once every multiple of 8 km
from the EDZ grid origin. The northing values in the minor zone are now the
same as those of the major zone. The easting values are determined by
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230
extending the coordinates of the major zone into the minor zone. (see Fig.
122). Aggregating or subdividing squares proceeds as stated in Step 2.
Point sources and monitoring sations will have their UTM coordinates
given in terras of the UTM zone in which they are located. Therefore, the
coordinates of points in the minor zone must be normalized. One problem with
points is that the change from one zone to another represents a change in both
easting and northing. For cartographically normalizing points, first perform
GRID JUNCTION
GRID JUNCTION
ZONE
GRIDS BEFORE NORMALIZING
ZONE:
2. EXTEND HORIZONTAL (NORTHING) LINES
INTO MINOR ZONE
GRID JUNCTION
3. PLACE PERPENDICULAR (EASTING) LINES
INTO MINOR ZONE
i NATE:
4. ELIMINATE MINOR UTM SYSTEM AND
GRID JUNCTION
Fig. 122. Producing a Normalized Grid System
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231
the grid normalizing in the minor zone. Then, subdivide the 8 x 8 km grid
squares in the minor zone as shown in Fig. 123. From here, estimation is done
by reading the coordinates of each point off the map. A ruler should be
used to improve the accuracy of the estimate.
Mathematical Methods. The U.S. Army, which uses the UTM system to pre-
cisely locate targets, has developed a number of mathematical methods for
normalizing coordinates. Since these methods are fairly complex and involve
the use of a lengthy set of "transformation tables," if you wish to use them
request the following publications from Headquarters, Department of the Army,
Washington, D.C.: U.S. Army Technical Manual TM5-241-2, Universal Transverse
Meroator Grid Zone to ?,one Transformation Tables (1957); and U.S. Army Tech-
nical Manual TM5-241-8, Universal Transverse Mercator Grid (April 1973).
Machine Normalization Method
The machine normalization method uses a
the calculations. The UTM coordinates for all
required in order to use the computer program.
computer program to perform
points in the minor zone are
The program, U.S. Geological
8KM
MAJOR
ZONE
1
£
l=°
I*
'a
1°
li
1
1
1
i
MINOR
A
ZONE
ONCE THE NORMALIZED GRID HAS BEEN
COMPLETED, POINT NORMALIZATION MAY
BE PERFORMED BY PLOTTING THE POINTS
AND INTERPOLATING WITHIN THE SQUARES
NORM UTM=
/ 7700
/ 51000
NORM
8500
51000
\
50 -
49 -
48 -
47 -
46 -
45 -
44 -
y
T
i
C A
i
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7fl 79 ftfi ftl fiP B7 HA
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NORM UTM:
v 7700
43000
NORM UTM=
8500 _^
43000
IS AT NORM 7,845,46,300
IS AT NORM 8,100 , 44,750
IS AT NORM 8,300, 48,800
Fig. 123. Cartographic Method for Normalizing Points
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232
Survey No. J-380, is available on cards from: Chief, Branch of Fhotogram-
metry, National Center, Stop #510, 12201 Sunrise Valley Drive, Seston, VA
22092, or phone: 703-860-6271. An experienced programmer should be available
to compile and execute the program. The program is designed for a Fortran G
compiler and an equivalent computer region of 130K.
The data card for each point to be normalized has the following
format:
Columns
1-33
34 - 44
45
46 - 55
56
57 - 58
59 - 62
63 - 64
65 - 80
blank
F11.3
blank
F10.3
blank
12
blank
12
blank
___
Northing
Easting i
Number o:
Number o:
Contents
Northing of a point in the minor zone
Easting of the same point in the minor zone
The map in Fig. 121 should be used to identify the zone numbers required in
Columns 57 - 58 and 63 - 64.
Again, the EDZ grid system is assumed to have been produced by car-
tographic methods. The machine method is only to be used for point normaliza-
tion.
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233
APPENDIX F: GRID SYSTEMS OTHER THAN UTM
Appendix F explains what to do if the locations of monitoring stations
or point sources are in state plane coordinates, latitude and longitude, or
other coordinate systems. All location data must be in UTM coordinates for
use in the dispersion modeling described in Steps 14 - 16. Cartographic,
mathematical, and machine methods are available for transforming non-UTM to
UTM coordinates; each method is described below. The location data, in
non-UTM coordinates, must of course be secured before the required transfor-
mation to UTM coordinates can begin.
Cartographic Method
The cartographic method is identical to the one described in Appendix
E and will not be repeated here (plot the UTM system over the other system
and change the coordinates to UTM). Without a computer, this is unquestion-
ably the easiest way of performing the transformation.
Mathematical Method
This method is fairly complex and is best described by U.S. Army
Publication TM 5-241-8, Universal Transverse Meroator Grid (April 1973). It
is available from Headquarters, Department of the Army, Washington, D.C.
Machine Method
The machine method involves a packaged computer program that converts
geographic coordinates to UTM coordinates. This means that state plane or
other coordinates must be converted to geographic coordinates before they can
be transformed to UTM. A conversion for state coordinates to geographic (or
even to UTM) may be available from the state agency in charge of mapping.
If it cannot provide conversions, then the points to be converted must be
mapped and geographic coordinates estimated. The geographic coordinates,
expressed in degrees, minutes, and seconds, are input into U.S. Geological
Survey Program No. J-380, which is available from: Chief, Branch of
Photogrammetry, National Center, Stop #510, 12201 Sunrise Valley Drive,
Reston, VA 22092, or phone 703-860-6271. An experienced programmer should
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234
be available to compile and execute the program. The program is designed
for a Fortran IV G compiler and an equivalent computer region of 130K.
The data card for each location for which coordinate conversion is
required has the following format:
Columns Type Contents
1-4
5
6-7
8
9-10
11
12 - 17
18
19 - 21
22
23 - 24
25
26 - 31
32 - 55
56 - 58
A4
Al
12
blank
12
blank
F 6.3
Al
13
blank
12
blank
F 6.3
blank
13
four digit identifying number
blank or minus sign
degrees latitude
minutes latitude
seconds latitude
blank or minus sign
degrees longitude
minutes longitude
seconds longitude
UTM zone # (for those areas w:
59 - 80 blank
grid junctions use only the zone to
zone transformation option if desired.
Otherwise leave blank. See Appendix E
on UTM grid junctions.)
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235
APPENDIX G: DIFFERENCES BETWEEN THE CDMQC AND AODM
DISPERSION MODELS
There are a number of differences in the input requirements and grid
structure of CDMQC and AQDM which you should be aware of if you plan to use
AQDM. These differences are summarized below.
1. Source Contribution Output
CDMQC can provide source contribution output (culpability lists) at
all receptors. You may suppress the source contribution listing at any
receptor if desired.
AQDM can provide source contribution output at only five receptors per
run. You may choose the five receptors; if you do not, the program chooses
the five receptors with the largest total concentrations at ground level for
each pollutant. When using AQDM for EDZ, then, multiple air quality runs
must be made so that source contribution lists will be provided for all
receptors. For example, for 50 receptors, 10 runs must be made. However, a
programmer can modify AQDM to produce a single, large source contribution list.
2. Superimposed Area Sources
CDMQC does not allow superimposed area sources; that is, estimates of
the air quality impact from light, medium, and heavy manufacturing districts
of each area source must be made in three separate air quality runs.
AQDM, however, does allow superimposed area sources, so only one air
quality run is needed.
3. Size of Area Source Grid Squares
CDMQC requires that the area source grid squares have sides whose
lengths are integral multiples of a basic grid square.
AQDM has no restriction on the size of area source grid squares.
4. Receptor Point Locations
CDMQC allows up to 200 receptors to be used; they may be placed any-
where in the region.
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236
AQDM allows up to 225 receptors to be specified; however, their
locations must correspond to intersections in a grid pattern having equi-
distant horizontal and vertical spacing. In addition 12 non-grid receptors
may be used in the region, for a total of 237 receptors.
5. Monitoring Stations
CDMQC requires that there be at least three but no more than 50 moni-
toring stations. Monitoring stations are considered to be receptors; the
limit of 200 receptors includes monitoring stations. Pollutant concentra-
tion data and source contribution lists are provided for monitoring stations
as for any other receptor.
AQDM uses monitoring stations for calibration only; they are not
considered to be receptors. There must be at least three but no more than
100 monitoring stations per pollutant. If you wish to obtain concentration
data or a source contribution at a monitoring station, a receptor must be
placed there (a non-grid receptor unless the monitoring station location
happens to coincide with a grid receptor).
6. Maximum Number of Point and Area Sources
CDMQC allows a maximum of 200 point sources and 2500 area sources.
AQDM allows a maximum of 1000 point and area sources combined.
7. Meteorological Data Requirements
CDMQC requires the following data:
• joint frequency function of wind direction, wind speed,
and stability class, based on six stability classes (Class
4 is separated into classes for day and night)
• vertical dispersion functions (SZA) for the six stability
classes in meters
• average afternoon mixing height in meters
• average nocturnal mixing height in meters
• average annual atmospheric temperature in degrees Centi-
grade.
AQDM requires the following data:
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237
• joint frequency function of wind direction, wind speed, and
stability class (same as that used for CDMQC with the excep-
tion that only five stability classes are used. There is no
separation of Class 4 into day and night. To get the corre-
sponding five-class function from the six-class function, add
together the frequencies of Classes 4 and 5 - that becomes
Class 4, and Class 6 becomes Class 5).
. average afternoon mixing height in meters
• average annual atmospheric temperature in degrees Kelvin
• average annual ambient pressure in millibars
8. Emission Inventory Data Requirements
Both CDMQC and AQDM require the following data for point and area
sources:
• coordinates
• stack height in meters (actual stack height for point sources,
estimated average effective height for area sources)
• stack gas exit velocity in meters/second (point sources only)
• stack diameter in meters (point sources only)
In addition, CDMQC requires:
• annual average emission rate in grams/second
• width of grid square (area sources only) in meters
• stack exhaust temperature in degrees Centigrade (point
sources only)
In addition, AQDM requires:
• annual average emission rate in tons/day
• source area (area sources only) in square kilometers
• stack exhaust temperature in degrees Kelvin (point sources only)
9. Coordinate System
Both CDMQC and AQDM can use any coordinate system with non-negative
coordinates; however, the UTM system is recommended for both.
10. Monitoring Station Data
Both CDMQC and AQDM require the measured annual arithmetic mean con-
centration of each pollutant at each monitoring station, in micrograms/cubic
meter.
-------
239
APPENDIX H: THE USE OF LINEAR PROGRAMMING PACKAGES
OTHER THAN MPSX IN EDZ
INTRODUCTION
Linear programming packages are programs marketed by computer hardware
and software companies that are designed to solve linear programming problems.
They are often sold as part of a mathematical programming package which may
also include mixed integer and separable programming capabilities.
Most packages allow the user to write a control program in a Fortran-
like language. The control program calls for procedures that perform linear
programming and related functions. Less common are packages which have a
single control card on which options are specified. The former gives the
sophisticated user more flexibility in solving a problem; the latter is
simpler for those unfamiliar with linear programming.
Basic procedures available in all packages include those to set up
the problem work matrix, solve for the optimal feasible solution, modify an
existing problem, output the solution and related information, and do sen-
sitivity analyses. The control languages consist of arithmetic, logical,
program flow control, and data movement statements.
Following are reviews of the systems requirements and suitability for
EDZ of linear programming packages available from some of the major computer
manufacturers. Table 30 summarizes the capabilities of these packages.
Refer to package documentation and company contacts for more details.
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241
TEMPO/BASIC (Burroughs Corp.)
Machine B 7700/B 6700 Systems
Address Burroughs Corp.
Eastern Center
2450 Swedesford Rd.
Box CB7
Malvern, PA 19355
Business Management & Scientific Systems
Burroughs Corp.
Burroughs Place
Detroit, MI 48232
Reference Publication Burroughs B 7700/B 6700 Systems TEMPO Mathematical
Programming System User's Manual, Publication #1073665.
System Requirements
The problem size TEMPO/BASIC can handle depends on the amount of
memory and the disk storage space available. Theoretically, TEMPO can han-
dle a problem up to 11,000 rows, but machine time would make this a practi-
cal impossibility. The designed maximum is in the range of 4000-5000 rows.
The following table illustrates the relationship of problem size, memory,
and storage space.
rows x columns and
No. of non-zero matrix elements
Memory and Storage
Q
Memory Required (words)
Disk Storage Space (bytes)
100 x 200
100
16,000
0.2 x 106
1000 x 2000
8000
32,000
1.5 x 106
2000 x 5000
20,000
40,000
3 x 10s
4000 x 10,000
40,000
50,000
6 x 106
In Burroughs systems, a word is 48 bits.
TEMPO files, the devices they require, and their functions are listed
below. Required for solving a simple LP problem are CARD, DISKIN, and an
output device (such as the printer). Work files are generated by the proce-
dures. For large problems you can assign specific disk space to the files.
File Name
BRANCH
CARD
CARDIN
CARDOUT
DISKIN
Device
disk
card, disk, tape
card, disk, tape
punch
card, disk, tape
Function
save and restart data
control language statements
revision data, selection lists, etc.
problem and basis data
problem data
-------
File Name Device
DISKOUT disk
MACROLIB disk
OLDZPROF disk
TAPEIN tape
TAPEOUT tape
ZPROF disk
ZSOLF disk
OLDSOLF disk
Suitability for EDZ
242
Function
problem and basis data
control language statements
problem storage
problem and basis data
problem and basis data
problem storage
storage of solution data
storage of solution data
The designed normal maximum problem size that TEMPO can solve should
make it suitable for any EDZ problem (see Step 12 for a discussion of EDZ
problem size). A bounded variable feature is available to set limits on
the values that structural variables may take (necessary for Step 21).
APEX-III (Control Data Corp.)
Machine CDC CYBER 70 Systems - Models 72, 73, 74, 76
CDC CYBER 170 Systems
CDC 6000 Series Systems
CDC 7600 Systems
Address Control Data Corp.
CYBERNET Services
P.O. Box 0 HQW05G
Minneapolis, MN 55440
Reference Publication APEX III Reference Manual, Publication #76070000.
Comments
APEX III has two basic systems, one of which is an all-in-core system
(APEX-III Base System 1) and one which has the capability of using disk or
extended core storage as additional storage (APEX-III Out of Core System 1).
The APEX-III Parametrics Option and Matrix Reduction Option are not part of
the basic system and must be purchased separately.
Apex III allows you to write a control program to control the solu-
tion strategy of an LP problem, as do many other packages, but the user may
-------
243
alternatively solve a problem by submitting, instead of a control program,
a single card on which desired program options are specified. Although you
lose flexibility with this alternative, it is easier and quicker. The options
that can be specified using the single card include type of output, type of
starting basis, and whether the solution basis should be saved.
System Requirements
CDC uses a unit called field length as a measure of central memory
requirement. Field length may be expressed in octal or decimal numbers. The
minimum field length for APEX-HI processing is 600008. Equations for esti-
mating the amount of central memory required for a given problem are found
in Appendix A of the reference manual. The maximum problem size that can be
solved regardless of the amount of memory available is 8190 rows, 32,760
columns, 32,760 unique, non-zero elements, and 2045 non-zero elements in a
column.
You may wish to compare memory requirements given in field length to
its equivalent in IBM bytes. A method for estimating the number of bytes is
to multiply the field length in decimal numbers by 10. For example,
600008 = 2457610
24576 x 10 = 245,760 bytes
245,760 T 1024 = 240 K bytes
McDonnell Douglas ran models for comparative purposes using APEX III
on a CDC 7600 and MPSX on an IBM 370. Results comparing CPU times were:
CPU Times
CDC 7600
13.5
67.1
50.1
(seconds)
IBM 370
67.1
158
115
Model
easy
medium
difficult
depending on
density of
inverse
Files used by APEX-III are listed below with their default file names.
You may change the file name if you want the data assigned elsewhere. The
files may reside on tape or direct access devices.
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244
File Name Purpose
TAPE 1 contains problem data for input
TAPE 3 contains user-specified starting basis
TAPE 4 contains solution basis
TAPE 5 contains information saved for a later restart:
TAPE 12 contains solution data
TAPE 13 an output file containing problem in input format
TAPE 14 contains revision data
TAPE 15 used by REDUCE option
TAPE 16 used by REDUCE option
TAPE 17 used by REDUCE option
Suitability for EDZ
The theoretical maximum problem size that APEX-III can handle should
make it suitable for any EDZ application (see Step 12 for a discussion of
EDZ problem size). A bounded variable feature is available to set limits on
the values that structural variables may take (necessary for Step 21).
LP 360/370 LINEAR PROGRAMMING SYSTEM (Haverly Systems, Inc.)
Machine IBM System/360 or System/370
Address Haverly Systems Inc.
78 Broadway
Denville, NJ 07384
Reference Publication LP 260/370 Linear1 Programming System User and
Operating Manual
Comments
Haverly Systems, Inc., is an independent software company that provides
five versions of the LP 360/370 Linear Programming System corresponding to
various sizes and operating systems for the 360/370 machines. The version
discussed here will be OS LP/360, which is designed for medium to large com-
puters of the 360/370 series in an operating system environment.
A difference between OS LP/360 and MPSX is that OS LP/360's optimizer
is less expensive to run than MPSX's; however OS LP/360 can handle problems
-------
245
of up to only 4096 rows. Another difference is that OS LP/360 does not have
provisions in its input data format for bounded variables and ranged con-
straints, as does MPSX. Thus, bounds on variables must be expressed as
separate constraints, and constraints which have both upper and lower bounds
must be entered as two separate constraints.
System Requirements
OS LP/360 requires a minimum core of 64K. The problem size it can
handle is dependent on the amount of core available as shown in the following
table.
Core Size Maximum # of Rows
64 K
128 K
256 K
920
2515
4095
The LP system is distributed on tape but should be transferred to a direct
access device such as a 3330 disk pack. See Appendix M of the reference for
details.
System files required by OS LP/360 are:
HSILP 360 - the file containing the agendum programs
PRINTFILE - printer or other output device
CARDFILE - card reader or other input device
If the problem is small enough, it can be run entirely in core. If
not, the agenda (procedures) INPUT and OPTIMIZE will require the following
files, which may reside on disk or tape:
INPUT 1. LPMATRIX - contains binary matrix records
2. SCRATCH - contains negative slack variables
OPTIMIZE 1. LPMATRIX - contains binary matrix records
2. SCRATCH - contains eta records
3. SOLUTION - scratch tape
Other agenda (procedures) require other files that may be assigned by the
user. For more details, see page B-3 of the reference. For information on
calculating storage requirements for these files, see page P-18. A general
rule is to allocate, for any given file, a minimum of 50 cylinders on a
2311 disk, or 30 cylinders on a 2314 disk.
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246
Suitability for EDZ
OS LP/360 does not have bounded variable or constraint range features.
Therefore the bounds calculated in Step 21 must be incorporated into the LP
model as two constraints for each variable - one constraint for the upper
limit and one for the lower limit. In this case the number of rows (con-
straints) in an EDZ problem will be the number of receptors £l_us two times
the number of structural variables. OS LP/360 can handle a maximum problem
size of 4096 rows, however, so it should be able to handle any EDZ problems
(see Step 12 for a discussion of EDZ problem size).
SERIES 200 LINEAR PROGRAMMING SYSTEM H (LP H) (Honeywell)
Machine Series 200 Processors
Address Honeywell Information Systems
Management Sciences
600 Walnut Street
Wellesley Hills, MA 02181
Reference Publication Honeywell Series 200 Linear Programming System H.,
Publication #BB72, Rev. 0.
System Requirements
The minimum equipment configuration required to run LP H is:
• A series 200 processor with minimum memory size of
32,768 characters (32K);
• Five magnetic tape units; and
• A card reader, printer, and card punch
Optional equipment for problem sizes larger than the minimum are (1) addi-
tional memory up to a maximum of 131,072 characters, and (2) two magnetic
tape units.
Problem size is dependent on the amount of memory available, as
shown below:
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247
Core Size Max. Problem Size
(memory) (// rows)
32K 70
40K 125
48K 250
65K 500
Input data may reside on cards or as card images on a magnetic tape.
Input data use two types of cards: announce, or indicator cards, which tell
which type of data is to follow, and problem data cards, which contain the
actual values.
Suitability for EDZ
LP H does not have bounded variable or constraint range features.
Therefore the bounds calculated in Step 21 must be incorporated into the LP
model as two constraints - one constraint for the upper limit and one for the
lower limit. In this case the number of rows (constraints) in an EDZ pro-
blem will be the number of receptors plus two times the number of structural
variables. Because the maximum problem size for LP H is in the area of only
500 rows, this system is only useful for small EDZ problems (see Step 12 for
a discussion of EDZ problem size).
HONEYWELL MATHEMATICAL PROGRAMMING SYSTEM (MPS) (Honeywell)
Machine Series 6000 Processors
Address Honeywell Information Systems
Management Sciences
60 Walnut Street
Wellesley Hills, MA 02181
Reference Publications Series 6000 Mathematical Programming System
Implementation Guide, Publication #DC55, Rev. 0.
Series 6000 LP6000 System Agenda Control Language, Publica-
tion #BQ19.
LP6000 System Input File Preparation, Publication #BQ01.
-------
248
Comments
MPS evolved from and is fully compatible with Honeywell's Series 6000
Linear Programming System. It differs in that it has a remote processing
capability, can call user-written programs, and has facilities for converting
IBM 360/370 LP models and Honeywell Series 200 models into a format ac-
ceptable to MPS.
System Requirements
MPS requires the following minimum equipment configuration:
• Series 6000 central system and consol
• one magnetic tape unit (unless MPS resides on disk or drum)
• disk or drum storage
• a card reader, printer, and card punch
The following optimal core requirements are given for various problem
sizes.
Elements in Problem*
10-7500
7500-35,000
35,000-200,000
>200,000
Core Requirement
36K-48K
48K-56K
56K-66K
66K+
*Elements include rows, columns, matrix
coefficients, and RHS elements.
Three logical devices are required by MPS for temporary storage of the
eta file, alpha file, work file, and possibly the problem file. If the pro-
blem file is large, you may wish to assign bulk storage (disk or drum) space
for it. For details on the amount of storage space required, see Appendix C
of the first reference.
Suitability for EDZ
Honeywell's MPS can handle maximum problem sizes of over 200,000 ele-
ments (roughly corresponding to a problem size of 200 rows by 1000 columns.
It should be suitable for most EDZ problems (see Step 12 for a discussion of
EDZ problem size).
-------
249
A bounded variable feature is available to set limits on the values
structural variables may take (necessary for Step 21).
MATHEMATICAL PROGRAMMING SYSTEM/360 (MPS/360) (IBM Corp.)
Machine System/360
Address Local IBM data processing sales office or
International Business Machines Corp.
Data Processing Division
1133 Westchester Avenue
White Plains, NY 10604
Reference Publications MPS/360 Version 2,, Linear and Separable Programming
User's Manual, Publication //GH20-0476-2.
MPS/360 Version 2} Control Language User's Manual, Publication
//GH20-0290-3
Comments
MPS/360 is obsolete and has been replaced by IBM's MPSX, which is
more efficient and has greater capabilities. It is included here, however,
because as some installations may still use it.
System Requirements
MPS/360 requires an IBM System/360 with a minimum memory region of 64K
bytes (65,536 bytes). The problem size it can handle is dependent on the
amount of region available as shown in the following table.
data bytes
rows 82,500 475,000 992,000
designed norm 200-700 1600-3500 >3500
maximum 2120 4095 4095
The maximum problem size assumes a minimum number of buffers and work regions
and no ranges or bounds in the problem.
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250
System files and their associated devices required by MPS are:
• SYSMLCP: Contains machine language representation of
the control program. Must be a direct access
device.
• SYSPRINT: Contains output of program. May be a line
printer or a magnetic tape unit.
• SYSIN: Contains input stream. Can be a card reader
or magnetic tape.
Internal files used for temporary and permanent storage of data are:
• PROBFILE: Contains problem in input format
• MATRIX: Contains work matrix
• ETA: Contains the product form of the inverse
• SCRATCH 1: Used for utility functions
• SCRATCH 2: Used for utility functions
• MPSCRAT: Work region for post-optimal procedures
These files may be on direct access devices or tape. Space allocation
for these files is dependent on the problem size, configuration, and density.
See Figure 81 of the first reference for a method of calculating this.
MPS/360 supports the 2311, 2314, 2301, 2302, and 2303 direct access
devices, and the 2400 series magnetic tape units.
Suitability for EDZ
MPS can handle a maximum problem size of 4095 rows, but that is for a
problem with no bounds or ranges. The designed normal maximum is about 3500
rows, which makes it large enough for any EDZ problem (see Step 12 for a dis-
cussion of EDZ problem size).
A bounded variable feature is available to set limits on the values
structural variables may take (necessary for Step 21).
-------
251
MATHEMATICAL PROGRAMMING SYSTEM EXTENDED (MPSX) (IBM Corp.)
Machine System/360 or System/370
Address Local IBM data processing sales office or
International Business Machines Corp.
Data Processing Division
1133 Westchester Avenue
White Plains, NY 10604
Reference Publications Mathematical Programming System - Extended (MPSX),
and Generalised Upper Bounding (GUIS) Program Description., Publication
SH20-0968-1.
MPSX Control Language User's Manual, Publication SH20-9932-0-
Comments
MPSX differs from MPS/360 in performance improvement and added fea-
tures. The coding of the PRIMAL and INVERT procedures has been changed, the
routines for obtaining a good starting basis have been improved, and new
procedures have been introduced which check for possible size reduction and
provide different optimization mechanics. New features of MPSX include in-
creased processing capacity (to 6,000 rows), simplified output filing, use
of labeled tapes, new communication region cells, and new procedures.
System Requirements
MPSX requires an IBM System/360 or System/370 with a minimum region
of 128K. The problem size that MPSX can handle is dependent on the amount of
region available as shown in the following table.
data bytes
rows 100,000 500,000 800,000
designed norm 200-800 2500-5000 4000-6000
maximum 2617 13,729 16,383
The maximum problem size assumes a minimum number of buffers and work regions
and a problem with no ranges or bounds.
-------
252
A 2400 or 3400 series magnetic tape unit is required for installation
because the program is released on tape. Direct access storage requires
approximately eight cylinders on a 2314 device. MPSX also supports the 2311,
2319, 2301, 2302, 2303, 2305, and 3330 direct access devices.
Files required by MPSX are:
• SYSIN: System input file.
• SYSPRINT: System output file.
• SYSMLCP: Contains machine language representation of
the control program. Must be a direct access
device.
• MATRIX 1: Contains the work matrix
• ETA 1: Contains the product form of the inverse.
• SCRATCH 1: Used for temporary data storage
• SCRATCH 2: Used for temporary data storage
• PROBFILE: Contains problem in input format
Optional MPSX files include:
• OLDPFILE: Contain problems in input format.
• MATRIX 2-4: Up to four devices can be used to store the
work matrix of the model.
* ETA 2-4: Up to four devices can be used to store the
product form of the inverse.
• MPSCRAT: Work region for post-optimal procedures.
Must be a direct access device.
Suitability for EDZ
MPSX can handle a maximum problem size of 16,383 rows, but that is for
a problem with no bounds or ranges. The designed normal maximum is about
6000 rows, making it large enough for any EDZ problem (see Step 12 for a
discussion of EDZ problem size).
A bounded variable feature is available to set limits on the values
structural variables may take (necessary for Step 21).
-------
253
NCR LINEAR PROGRAMMING SYSTEM (LPS) (National Cash Register Co.)
Machine NCR Century
Address Local NCR Branch or
National Cash Register Co.
Management Sciences
741 Marketing Building
5225 Springboro Pike
West Carrollton, OH 45449
Reference Publication Linear Programming
Comment s
NCR LPS can run with a memory size of 16K or 32K. The difference is
that with 32K memory a restart facility is available and larger problems can
be handled. This system is easy to run because there is no control language
with many procedures and statements; control over the program is provided by
a single card on which the user specifies the options desired. Although this
is good for the novice and those running simple programs, the more sophisti-
cated user may note the lack of flexibility.
System Requirements
NCR LPS can run with a memory requirement of 16K bytes or 32K bytes
(K = 1024). Storage requirements for each case are given by the following
table:
Memory Requirement Pack 1 Pack 2
16K 2300 sectors*
32K 2400 2400
32K w/output filed on disk 3650 2400
*A sector is a division of a disk pack: for ex-
ample, a 655 pack contains 8900 sectors and 512
characters/sector.
The largest problem size that can be solved is:
-------
254
bytes //row //columns
16K 49 180
32K 120 1000
Input data can be on cards, paper tape, or disk; output Is to the
printer or to disk.
Suitability for EDZ
NCR LPS used with an available memory size of 16 K would, be suitable
for only small EDZ problems. Run with an available memory size of 32K, LPS
is suitable for larger EDZ problems (see Step 12 for a discussion of EDZ
problem size).
NCR LPS has a provision for specifying bounds on the values structural
variables may take (necessary for Step 21).
NCR includes as part of its optimal solution report output the product
of the optimal variable activity and the objective coefficient. This eli-
minates a time-consuming set of calculations - that of multiplying ACTIVITIES
x INPUT COST in Step 24B.
FUNCTIONAL MATHEMATICAL PROGRAMMING SYSTEM (FMPS) (Sperry Univac)
Machine 1100 Series
Address Chicago Commercial Branch
Sperry Univac
Marina City
300 North State Street
Chicago, IL 60610
Reference Publication Sperry Univaa 1100 Series FMPS Programmer Reference,
Publication //UP-8198.
System Requirements
The problem size FMPS can handle is not limited by the program, but by
hardware capability. Some examples of the memory size required for a given
problem size are:
-------
255
memory size in words 30K 43K 63K
number of rows <500 <1200 <3400
A word = 36 bits = 6 bytes.
Internal files required by FMPS are:
• MATRIX: Contains the internal representation of the matrix.
• INVERSE: Contains the product form of the inverse.
• UTIL 1: Scratch file.
• UTIL 2: Scratch file.
• PREPDEVI: Contains compiler output of the control language
program.
The first four files may be assigned by the program or the user; the fifth
may not be assigned by the user. These files are all assigned to mass
storage, which can be either disk or drum. Optional files are:
• RESTART: Used to save problem files and status for a later
resumption of the run.
• 'ANYNAME': User-defined files for communication between FMPS
and user programs.
These files are assigned by the user to any sequentially accessed device.
The mass storage space requirements for a given problem may range from
2 to 10 positions, where one position = 64 tracks and one track ** 1792 words.
For example, a problem of 4000 rows by 4000 columns would need approximately
6 positions. For more detail on estimating mass storage requirements, see
Section 2.5.3 in the reference.
Suitability for EDZ
Because problem size limitations are imposed only by hardware require-
ments and not by the linear programming system itself, FMPS should be suitable
for any EDZ problem provided there is enough memory available (see Step 12
for a discussion of EDZ problem size).
A bounded variable feature is available to set limits on the values
that structural variables may take (necessary for Step 21).
-------
257
APPENDIX I: SPECIAL REGIONAL PROBLEMS
[or, what to do if the region has • less than 3 monitoring sta-
tions • more than 50 monitoring stations • more than 200 point
sources * more than 200 receptor points • more than 200 point
sources and more than 200 receptors • an area less than 350
square kilometers • more than 350 area sources (grid squares)]
Less Than 3 Monitoring Stations
CDMQC's calibration run will not work with fewer than three monitor-
ing stations. In this situation, the background concentration must be esti-
mated through methods which are beyond the scope of the guidebook. The
slope of the regression line should be taken as 45°, i.e. b = 1, unless the
available monitoring data show that it should be otherwise.
More Than 50 Monitoring Stations
The CDMQC calibration run allows for a maximum of 50 monitoring sites
to be used in calculating calibration coefficients. If there are more than
50 sites in your region, select a sample of 50 sites to be used in the
calibration run. The rest of the monitoring stations may be ignored. Cri-
teria for selection of one station over another may be random or based on
quality of the data or the location relative to other monitoring sites.
More Than 200 Point Sources
For large regions, more than 200 point sources will be found in the
emission inventory. This can be a problem because under no circumstances
may more than 200 point sources be input into any one run of CDMQC.*
The solution to this problem in the right-hand-side run is to run
CDMQC more than once and combine the results of each run by hand or by
computer if there is a programmer available. This could be done through
combining culpability lists for each receptor. In this way, the contribu-
tion from each exempt source will be noted and the pollutant concentrations
at each receptor kept separate.
^Unless an experienced programmer on your staff increases the dimensions of
the CDMQC program to take a greater number of sources.
-------
258
More than 200 point sources in the calibration run also poses prob-
lems. The calibration run provides a regression line showing the correla-
tion between pollutant concentrations calculated by the model and concen-
trations observed at monitoring stations (Fig. 124 illustrates a regression
line for a region with six monitoring stations.*) Since this is a simple
straight-line regression, in those areas with more than 200 point sources,
the regression may be done by using a statistical computer package or a
pocket calculator with regression analysis capability.
The steps to be followed for the more-than-200-point-sources cali-
bration run are:
1. Collect monitoring station and emission inventory data.
2. Keypunch these data and place the cards into the deck
according to the directions in Step 9.
3, Change the following card:
The Type 2 Card for the calibration run should be removed
from the deck and the Type 2 Card for the air quality and
right-hand-side runs substituted. This change is needed
to avoid excess run time. Since calibration is not
desired, this change tells the computer not to bother.
4. Run CDMQC as many times as is required to include all
sources. (So, for 201 to 400 sources, the model is run
twice, for 401 to 600 sources, the model is run three
times, etc.)
5. Find the total pollutant concentration calculated at each
monitoring site by summing the contributions made to each
monitoring site from the sources included in each run.
6. Use the monitoring station data and the calculated concen-
trations to perform the regression analysis. The slope and
intercept values you obtain are used in Step 20.
More Than 200 Receptors
Since you create receptor points, the decision on how many are suf-
ficient to cover the region is totally your decision. If more than 200
are selected, CDMQC will have to be run more than once for the light, medium,
and heavy manufacturing air quality runs, and the right-hand-side runs, with
the receptors changing from run to run. An alternative solution is to have
an experienced programmer change the dimensions of the CDMQC program.
This graph is not CDMQC output; it is provided to illustrate the concept
of a regression line. CDMQC provides values for the slope and intercept
of a regression line.
-------
259
70.0 r
60.0
to
E
,3- 50.0
o
h-
2 40.0
o
o
o
CM
O
CO
oc.
LU
CO
CD
O
300
20.0
10.0
'MONITORING
STATION
0.0
I I I I I
10.0 20.0 30.0 40.0 50.0
CALCULATED S02 CONCENTRATION
60.0 70.0
0.0
Fig, 124. Sample Sulfur Dioxide Regression Line
More Than 200 Point Sources and More Than 200 Receptors
This can be a problem only in the right-hand-side run. Regions
exempting many point sources may exceed the 200 limit. In the > 200
sources, > 200 receptors case, CDMQC must be run twice for every multiple
of 200 plus twice for any fraction over 200. Results from all the runs
are combined to give the overall picture as shown below. An alternative
solution is to have an experienced programmer change the dimensions of the
CDMQC program.
-------
260
No. of
Point
Sources
up to 200
201 to 400
up to 200
201 - 400
401 - 600
No. of
Receptors
up to 200
up to 200
201 to 400
201 to 400
401 to 600
No. of Runs
Required
1
2
2
4
6
No . Runs with
Point Source
Card Changes
0
1
0
1
2
No . Runs with
Receptor Point
Card Changes
0
0
1
1
2
Regional Area Less Than 350 Square Kilometers
The rule of thumb stated in Step 2 suggests that 350 grid squares be
used. In regions smaller than 350 square kilometers, this is neither neces-
sary nor desirable. In such regions, the entire area could be covered with
one kilometer grid squares to provide increased accuracy.
More Than 350 Area Sources (Grid Squares)
The number of grid squares in a region is optional, but grids larger
than 16 x 16 kilometers are not recommended since their use tends to de-
crease the model's accuracy. CDMQC can take up to 2500 grid-square-defined
area sources; therefore, the authors would like to make it clear that
350 is an arbitrary number believed to be a manageable number of squares.
A few more than 350 will not make any major difference. Many more than 350
will make the problem very, very large and cause a problem with the time
required to run the linear programming package. Use your own judgment in
deciding on the appropriate number of squares. Be sure to re-read Step 17
(on construction of the linear program matrix) before choosing more than
350 squares.
-------
261
APPENDIX J: REPRODUCIBLE WORKSHEET FORMS
-------
26:
WORKSHEET i (FOR STEP 2 )
LIST OF SOUTHWEST CORNERS
EDZ GRID SQUARE NUMBER
ni | FP OUT PY:
OF EDZ GRID SQUARES
NORMALIZED
NORTHING IN km
DATE PG
OF
NORMALIZED
EASTING IN km
i
!
-------
263
WORKSHEET ? (FOR STFP ? ) Fll .1 FH OUT BY:
PERCENTAGE AND AREA OF EDZ SOURCES
DATE
PG
OF
EDZ
GRID
SQUARE
NUMBER
AREA OF
GRID
SQUARE
(km2)
SIMPLIFIED LAND USE CLASS
AREA;
% « GRID
SQUARE
AREA (km?)
Ml
AK£A:
% * GRID
SQUARE
AREA (km2)
M2
AREA:
% x GRID
SQUARE
AREA (km?)
M3
AREA:
% > GRID
SQUARED
AREA (km2)
-------
264
WORKSHEET 3 (FOR STEP 3 ) FILLED OUT BY:.
LIST OF RECEPTOR LOCATIONS
_DATE..._
PG.
OF.
RECEPTOR
NUMBER
EASTING
IN km
NORTHING
IN km
NOTES ON THE REASON FOR SELECTING THIS LOCATION
FOR A RECEPTOR (CENTER, SENSITIVE, POLITICAL
JURISDICTION, MONITORING SITE, ETC )
-------
265
WORKSHEET 4 (FOR STEP 4)
MFTFDROI DKIfAl DATA
FILLED OUT BY
DATF
Pfi 1
OF 1
THIS WORKSHEET SHOWS THE EXACT FORMAT THAT THE
METEOROLOGICAL DATA MUST BE IN FOR INPUT TO CDM-QC.
MEAN ANNUAL ATMOSPHERIC TEMPERATURE
FOR EXAMPLE, I0i°i'|3|8|3| WOULD BE READ BY CDM-QC
AS 13.83° CELSIUS.
PLEASE PLACE YOUR FIGURE HERE; USE NO DECIMAL
POINT. ENTER THIS NUMBER IN COLUMNS 61-66 OF
CARD 4, FOR ALL FIVE CDM-QC RUNS. SEE STEP 9.
|0|Q1 I l I I
2. MEAN ANNUAL MIXING HEIGHTS
FOR EXAMPLE, loi-0l'Hio|3| WOULD BE READ BY CDM-QC
AS 14,030 METERS.
PLEASE PLACE YOUR FIGURE HERE; USE NO DECIMAL
POINT. ENTER THIS NUMBER IN COLUMNS I9~24
AND 25-30 OF CARD 4, FOR ALL FIVE CDM-QC
RUNS. SEE STEP 4.
IQI I I 1 I I ioi I i I i i
AFTERNOON NOCTURNAL
(MORNING)
-------
266
WORKSHEET 5 (FOR STEP 5) FILLED OUT BY'
MONITORING STATION DATA
DATE ._
PG.
OF.
NAME
OF
STATION
STATION
I.D. NUMBER
(FOUR
DIGITS)*
J
UTM LOCATION
(NORMALIZED) IN
km TO ONE DECIMAL
PLACE XXXX.X
EASTING
NORTHING
MEASURED
CONCENTRATIONS,
ANNUAL ARITHMETIC
MEAN (pg/m3)
so2
PART
STANDARD 24-HR
GEOMETRIC
DEVIATIONS
(>ig/m3 )
so2
PART
^MAXIMUM OF 50 STATIONS.
-------
267
WORKSHFFT fi (FOR STFP 6 ) Fll 1 FD OUT BY:
FMISSION iNVFNTORY POINT SOURCES
DATF
PG
OF
NAME OF
POINT SOURCE
. .
_ .
- - -
POINT SOURCE
ID NUMBER
(FIVE DIGITS)
_
UTM LOCATION
(NORMALIZED)
IN km
EASTING
-
NurtTHING
— -
EMISSION RATE
-------
268
WflRKSHFFT 7 (FOR STFP fi) Fll | Ffl OUT RY: O^TF:
EMISSION INVENTORY AREA SOURCES
PR
np
AREA SOURCE
1.0. NUMBER
FROM
EMISSION
INVENTORY
(5 DIGITS)
UTM LOCATION
(NORMALIZED)
OF S.W. CORNER
OF THE GRID SQUARE
(km)
EASTING
NORTHING
WIDTH
OF THE
GRID SQUARE
(m)
EMISSION
RATE
(g/s)
S02
PART
ESTIMATED
AVERAGE
STACK HEIGHT
(m)
-------
269
WORKSHEET 8 (FOR STEP 6) FILLED OUT BY=
EMISSION INVENTORY DATA FOR STEP 24
DATE
PG.
OF.
POINT SOURCE
NAME
_
. _ _ .
..
POINT SOURCE
I.D NUMBER
(FROM
WORKSHEET 6)
~:
- --- -
— - - --
SIMPLIFIED
LAND USE
ZONING
CLASS*
.
- - -
EMISSION RATE
OF POINT
SOURCE (A)
IN g/s
S02
- ---
PART
- - -
..
LAND AREA
OF POINT
SOURCE(B)
IN ha
_ ..
EMISSION DENSITY
U-rB)
IN (g/s)/ha
S02
—
PART
-
*LIST ALL POINT SOURCES IN A GIVEN ZONING CLASS CONSECUTIVELY.
-------
270
WORKSHEET 9 (FOR STEP 7 )
AREA SOURCE EXEMPTIONS
Fll 1 FD OUT RY:
DATF:
Pfi
OF
AREA SOURCE
ID NUMBER4
(5 DIGITS)
.
—
_
UTM LOCATION
(NORMALIZED)
OF S.W. CORNER
OF THE GRID SQUARE
(kn)
EASTING
- -
NORTHING
---
-
WIDTH
OF THE
GRID SQUARE
(m)
-
...
FUTURE
EMISSION
RATE
(g/s)
S02
- -.
—
PART
AVERAGE
STACK HEIGHT
(m)
* PROBABLY AN AREA SOURCE GRID SQUARE NUMBER FROM THE EMISSION INVENTORY.
-------
271
WORKSHEET 10 (FOR STEP 7) FIIIFDOIITRY
EXISTING POINT SOURCE EXEMPTIONS
DATE
Pfi
OF
NAME OF
POINT SOURCE
— -
-
---
—
POINT SOURCE
10 NUMBER
(FIVE DIGITS)
UTM LOCATION
(NORMALIZED)
IN km
EASTING
NORTHING
FUTURE
EMISSION RATE
(«/*!
SOg
... ._ _
PART
STACK
HEIGHT
(m)
STACK
DIAMETER
(IB)
EXIT
VELOCITY
OF STACK
GASES
(m/i)
TEMP
OF
STACK
GASES
(°C)
-------
2?:
WORKSHEET II (FOR STEP 8 ) FILLED OUT BY:
STACK HEIGHTS FOR AIR QUALITY RUNS
DAT E
PG
OF
POINT SOURCE NAME
POINT SOURCE NUMBER
(FROM WORKSHEET 6)
SIMPLIFIED LAND USE
ZONING CLASS
STACK HEIGHT
FROM WORKSHEET 6 (m)
-------
273
WORKSHEET )2 (FOR STEP20) FILLED OUT BY=.
DATE
CONVERTING GEOMETRIC MEANS TO ARITHMETIC MEANS FOR PARTICIPATES
PG.
OF.
RECEPTOR
NUMBER
|_A_
AIR QUAL/TY STD
APPLICABLE AT
RECEPTOR,
ANNUAL GEOMETRIC
MEAN, IN /jg/m3
(Mg)
j_B_
STD GEOMETRIC
DEVIATION (24HR)
AT RECEPTOR,
IN /ig/m3
(sg)
SQUAREROOT
OF sg
(Vsg)
-----
„ __ ^
NATURAL
LOGARITHMS OF
SQUAREROOT
OF sg
(InVigJ
LO
COLUMN B
RAISED TO THE
COLUMN C
POWER
(sg"1^)
ANNUAL
ARITHMETIC
MEAN,
IN /jg/m3
(COL. A X COL.D)
X= Mgsg'"^
-
-------
274
WORKSHEET 13
RIGHT-HAND-
(FOR
SIDE
STEP 20)
VALUES
FILLED
OUT
RY: HATF:
PG
OF
RECEPTOR
NAME
_ .
RECEPTOR
NUMBER
(ROW)
r -
—
AIR POLLUTANT CONCENTRATIONS (^Q/m3 )
CALIBRATED
AIR QUALITY
STANDARD
(A)
SOg
-
PART
-- -
- --
AIR QUALITY
IMPACT OF
EXEMPT SOURCES
(B)
S02
_
- --
PART
._ __ .
.. .._...
RIGHT-HAND-SIDE
VALUE
(A-B)
S02
_
PART
_
-
-------
275
WORKSHFFT 14 (FOR STFP 74) Fll 1 FD OUT BY:
FINDING THE POTENTIAL EMISSIONS OF EDZ SOURCES PER km2
DATF:
PG
OF
COLUMN
NUMBERS
FROM LP
OUTPUT OR
MATRIX
-- --
--
EXPLANATION
OF COLUMNS0
ACTIVITIES X INPUT
COST = EMISSIONS
PER EDZ GRID SQUARE
PER LAND USE CLASS
(g/s)b
S02
PART
EMISSIONS OF FUTURE EDZ
SOURCES LOCATED IN EACH EDZ
GRID SQUARE IF FUTURE
CONSTRUCTION USES ALL OF
THE EDL ALLOCATED
(g/s)c
S02
PART
From LP matrix. The "explanation" for each column number consists of an EDZ
grid square number and a land use class.
From LP output; see text.
'The number of entries in this column will equal the number of EDZ grid
squares included in the LP matrix.
-------
27t
CD
Q.
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S-
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QJ 00 >,
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cn u x:
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S- i — ro
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ro QJ £
S- S- O
QJ ro S-
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QJ 3 QJ O
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S- 13 00
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Cu
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QJ . QJ * — -
Q_ QJ X UD
CL 5_ QJ
ro ro CL «3-
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CT-C +->
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S_ - — • . 00 QJ
r-~ $_ •!- s_ .^- ^
Cn+J OS- -^
CL ro •*-> cn S—
QJ r~J o C O
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OO UJ i — > E
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S- ro C +J C
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QJ O oo oo Q. o
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S- ro E S-
3 -O <1J CL i —
O QJ O CL ro
oo E •>-> QJ ro C
-t-J 3 cvj -l-> QJ H-
CL oo ^1
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QJ QJ QJ O O
X S- +-> S- O S-
UJ rO OO U_ +J U_
ro -Q U
-------
277
APPENDIX K: THE CLEANAIR CITY TEST CASE
The Cleanair City Test Case was created to test the methodology and
provide a sample problem showing how each guidebook step would be completed
in a somewhat real situation. Each step has been followed as if we, the
authors, were setting emission density limits for this community. The test
case presents each step of the guidebook methodology, in sequence, showing
the maps, completed worksheets, and calculations that would be required to
set EDLs for Cleanair City. We have attempted to make the problem as small
as possible to allow you to take our data and quickly run through the
methodology. It should not take very long to complete, provided the disper-
sion model and linear programming package are available and ready for use.
While the EDZ guidebook is intended for use on the regional level,
the test case example sets EDLs only for the City of Cleanair. Beyond the
mapping and gridding procedures in Steps 1 and 2,* the region is ignored, in
order to decrease the size and complexity of the problem. We would like to
stress that this purely local analysis is not the normal use of the guidebook,
but is only done as a matter of convenience.
The test case was developed from a diverse data base.** Cleanair City,
which has a population of about 40,000, lies in the center of a small three-
county region and is surrounded by a few satellite communities, a large lake,
and a number of farms. Five major polluting industries are located in
Cleanair; their characteristics are shown in Table 31.
Table 31. Cleanair City Industry
Industry Products Employment
Vespucci Industries Plastic packaging 50
Columbus, Inc. Vinyl sheeting, film, plastic wraps 750
Drake-Magellan & Co. Laundry and diycleaning equipment,
sheet metal fixtures 700
Prince Henry's Small boats, roofing and building
Navigation, Amal. materials 900
Cook's Corp. Farm Equipment, tractors 4,400
--These could only be adequately illustrated on the regional scale.
-'-Although the UTM coordinates place Cleanair in Moline, Illinois, Cleanair
City's characteristics are not those of Moline.
-------
278
MERRY COUNTY
Eowin Twp.
Boromlr Twp.
"PEREGRIN COUNTY.
I Gondolf Twp.
I
LEGEND
COUNTY BOUNDARY
MUNICIPAL BOUNDARY
TOWNSHIP BOUNDARY
FLOOD BASIN BOUNDARY
BOUNDARY OE AREA WITH
HIGH DEVELOPMENT POTETIAL
RAILROAD
i ] PREVENTION OF SIGNIFICANT
1 ' DETERIORATION, CLASS I AREA
Fig. 125. Step 1: General Regional Map for Cleanair [The only item produced
in Step 1 is this map. The map features shown here are the mini-
mum required.]
KILOMETERS
5
i i i I i i
10
\ I
-------
279
EDZ GRID ORIGIN
EASTING^ 706km
NORTHING =4590km
Fig. 126. Step 2: EDZ Grid Origin and 8 km Grids for Cleanair [Note the
4 km spacing to the south and west between the regional boundary
and the grid origin.]
-------
280
EDZ GRID ORIGIN
- |EASTING=706km
- j NORTHING=4590km
Fig. 127. Step 2: Subdivided EDZ Grid System for Cleanair [This i-~, the
final "EDZ Grid System" for Cleanair. To streamline the problem,
far fewer than 350 squares were used.]
-------
281
1
2
3
5
13
4
6
8
7
9
II
.— Rkm
- EDZ GRID ORIGIN
_ /EASTING -706km
Xi|4i i i i
81
m
10
12
m
(5
16
17
24
26
34
44
46
54
25
27
45
47
56
18
28
35
48
55
57
59
19
29 30
31 32
36 37
38 39
49 50
51 52
20
22
33
40
42
53
21
23
41
43
58
60
61
62
63
64
66
69
71
74
70
72
65
67
73
75
68
76
77
78
79
80
81
82
83
Fig. 128. Step 2: Numbered EDZ Grid System for Cleanair [Note: These grid
square numbers are read as four digit codes, such as 0001 and 0026.1
-------
282
ZONING BOUNDARY
RAILROAD
ZONING CLASSES
OS, PUBLIC OPEN SPACE
R-I TO R-6, RESIDENTIAL USES
C-ITO c-3, CBD,COMMERCIAL USES
M-I LIGHT MANUFACTURING
M-2 MEDIUM MANUFACTURING
M-3 HEAVY MANUFACTURING
Fig. 129.
ZONING CLASSES
r PUBLIC OPEN SPACE,
A RESIDENTIAL USES, AND
(COMMERCIAL USES
M-l LIGHT MANUFACTURING AND CBD
M-2 MEDIUM MANUFACTURING
M-3 HEAVY MANUFACTURING
Step 2: Cleanair City Zoning Maps [The top map shows the city's
land use zoning; the lower map is in the simplified form recom-
mended for use in EDZ. From this point on in the test case, the
analysis will be at the local level.]
-------
283
GRIDDED SIMPLIFIED ZONING MAP
CRX> SQUARI NUMBERS fROM
RSCIOHAL CfODDDK PROCJUUJRf
7H
716
718
720
722
721
CM
^H
CO
o
—H
CO
CO
O
CO
CD
o
co
•. , ...
• v . j . . ", - .
^ _ -. -. -", . •» — «. *.
•' '
..«.« . . .
•;/ '. . SBU'DOT*PATTERN- '..*"'
i"" . .SUPERIMPOSED ON * . "
714
716
718
720
722
721
Fig. 130. Step 2: Gridded Cleanair City Zoning Map with SSTJ Dot Pattern
[Grid pattern from Figs. 127 and 128; SSU dot pattern less dense
than recommended in Step 2 for simplicity.]
-------
284
WORKSHEET 1 (FOR
LIST OF SOUTHWEST
STEP 2 )
CORNERS
FILLED OUT BY
OF EDZ GRID
SQUARES
DATE
PG
OF
EOZ GRID SQUARE NUMBER
NORMALIZED
NORTHING IN km
NORMALIZED
EASTING IN km
MIL
7/5
38
30
3Z
33
7V5L
7/4
HA.
7/7.
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^25
37
31
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43
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4407
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7/5
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L
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7/i_
7/f
720_
722
722_
723
J2T__
L
721
1ZZ
Fig. 131. Step 2: Completed Worksheet 1 for Cleanair City
-------
285
WORKSHEET 2 (FOR STEP 2) FILLED OUT BY:
PERCENTAGE AND AREA OF EDZ SOURCES
DATE
PG
OF
EDZ
GRID
SQUARE
NUMBER
ooosu\
ooozs
ji.
«•
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sr
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71
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(km2)
/
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Fig. 132. Step 2: Completed Worksheet 2 for Cleanair City
-------
286
WORKSHEET 3 (FOR
LISTOE RECEPTOR
STEP 3 )
LOCATIONS
FILLED
OUT
RY
DATE
PR
OF
RECEPTOR
NUMBER
EASTING
IN km
NORTHING
IN km
NOTES ON THE REASON FOR SELECTING THIS LOCATION
FOR A RECEPTOR (CENTER, SENSITIVE, POLITICAL
JURISDICTION, MONITORING SITE, ETC )
OQOL
7/5. JT
_ 0.001
ooos
117.0
7/8.S
-OOC&-
L_
720.9
723.7
V6/0-8
460?.f
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j 7"8 HOSP , 5Og_ MOAI t-raft.
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¥608.2. 1 Popousnoio CTH> Ciry op Ko«-u/g
OJ
^H
to
o
*«l
CO
CD
O
UP
CO
O
CO
crnr ZONING
RECEPTORS + MONITORING STATIONS
08
A RXCfPTOR
Koaronan saaov
711
716
718
720
722
724
Fig. 133. Step 3: Completed Worksheet 3 and Receptor Point Map for Cleanair
City [Note: Monitoring stations are used as receptor points.]
-------
287
WORKSHEET 4 (FOR STEP 4)
METEOROLOGICAL DATA
FILLED OUT RY p/lTF
PG I
OF 1
THIS WORKSHEET SHOWS THE EXACT FORMAT THAT THE
METEOROLOGICAL DATA MUST BE IN FOR INPUT TO CDM-QC.
MEAN ANNUAL ATMOSPHERIC TEMPERATURE
FOR EXAMPLE, |Q|Q|'|3|8|3| WOULD BE READ BY CDM-QC
AS 13.83° CELSIUS.
PLEASE PLACE YOUR FIGURE HERE; USE NO DECIMAL
POINT. ENTER THIS NUMBER IN COLUMNS 61'66 OF
CARD 4, FOR ALL FIVE CDM-QC RUNS. SEE STEP 9.
2. MEAN ANNUAL MIXING HEIGHTS
FOR EXAMPLE, lol'l*|o|3|o| WQULD BE READ BY CDM-QC
AS 14,030 METERS.
PLEASE PLACE YOUR FIGURE HERE; USE NO DECIMAL
POINT. ENTER THIS NUMBER IN COLUMNS 19-24
AND 25-30 OF CARD 4, FOR ALL FIVE CDM-QC
RUNS. SEE STEP 4.
AFTERNOON
NOCTURNAL
(MORNING)
Fig. ISA. Step 4: Completed Worksheet 4 for Cleanair City
-------
288
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-------
291
WORKSHEET
MONITORING
5
(FOR STEP 5}
STATION DATA
FILLED
OUT
BY: n^TF
PG
OF
NAME
OF
STATION
SrAT£ C/tvnf
T£^Mo$£
2M°+CH«TNUT
^Afr^ftouj 1
LofljeM HOSP.
STATION
I.D. NUMBER
(FOUR
DIGITS)*
0100
0300
0*00
OSOO
OfeOO
UTM LOCATION
(NORMALIZED) IN
km TO ONE DECIMAL
PLACE XXXX.X
EASTING
Q41S-S
07/7.0
07/g.S
J17/M
072L.I
NORTHING
VblO.S
til Xt£ Ff
* ftCr T"__f
41,07.3
<&io-l_
MEASURED
CONCENTRATIONS,
ANNUAL ARITHMETIC
MEAN (;jg/m3)
so2
39.0
48.0
L_ 35.0
^C-0
PART
£Z>0
no
4/-0
6/-C?
77.0
STANDARD 24-HR
GEOMETRIC
DEVIATIONS
(jjg/m3)
so2
y^
j,^y
/•5^
33Q
PART
/££
.
.JAL
±-5O
Fig. 136. Step 5: Completed Worksheet 5 for Cleanair City (Data were taken
from U.S. EPA data files. Monitoring station locations are mapped
in Fig. 133.]
-------
292
WORKSHEETS (FOR STEP 6 } FILLED OUT BY;
EMISSION INVENTORY POINT SOURCES
.DATE.
PG.
OF.
NAME OF
POINT SOURCE
POINT SOURCE
ID NUMBER
(FIVE DIGITS)
UTM LOCATION
(NORMALIZED)
IN km
EASTING NOTHING
EMISSION RATE
ig/s)
S02
PART
STACK
HEIGHT
(m)
STACK
DIAMETER
(m)
EXIT
VELOCITY
OF STACK
GASES
(m/s)
TEMP.
OF
STACK
GASES
WQ8.7
tWT.I
ja*^
CootOs
ooeo'f
ooooS
TV&£
7/4.*
7/8.8
/.73/0
pr»yj«x^
7.0900
Q.QO&
0.3600
(0.2.
J50
2.M
/.83
t.55
20^8
VIZ*
SIkPUFIED CLEANAIB aTY ZONING
POINT SOURCES
714
716
718
720
721
Fig. 137. Step 6: Completed Worksheet 6 and Point Source Map for Cleanair
City [Note: Data from NEDS file.]
-------
293
WORKSHEET 7 (FOR STEP 6) FILLED OUT BY-
EMISSION INVENTORY AREA SOURCES
OATF
Pfi
OF
AREA SOURCE
ID NUMBER
FROM
EMISSION
INVENTORY
(5 DIGITS)
UTM LOCATION
( NORMALIZED)
OF S.W. CORNER
OF THE 6RID SQUARE
(kml
EASTIN6
NORTHIN6
WIDTH
OF THE
GRID SQUARE
(m)
EMISSION
RATE
S02
PART
ESTIMATED
AVERAGE
STACK HEIGHT
(m)
40/4$
QO/66
003&L
003&S
06307
7/0.
7fO.S5
7/+5S
7/BJ6
7/6.SS
7M,S5
OWS5
21
4000
4000
4000
Vooo
nooo
S.OOC
35-07
ao-oi
63-O2
o.o
S9.ZO
&S.OQ
7*.VJ
ty-2.7
/Q.Zt
7.S
/O-O
/O.Q
s.o
S.Q
s.o
OOHS
OOIS7
00)09
CITY ZONING
INVENTORY
tot CJUD
oosaw amr
oossioir omorroxr
CMD sqouu tnnom
CD
711
724
Fig. 138. Step 6: Completed Worksheet 7 and Emission Inventory Grid System
Map for Cleanair City [Note: Data from emission inventory; emis-
sion inventory grid squares mapped on top of EDZ grid system.]
-------
294
WORKSHEET 8 (FOR STEP 6) FILLED OUT BY=
EMISSION INVENTORY DATA FOR STEP 24
DATE
PG
OF
POINT SOURCE
NAME
POINT SOURCE
I.D. NUMBER
{FROM
WORKSHEET 6)
SIMPLIFIED
LAND USE
ZONING
CLASS*
EMISSION RATE
OF POINT
SOURCE (A)
IN g/s
S02
PART
LAND AREA
OF POINT
SOURCE(B)
IN ha
EMISSION DENSITY
(A-rB)
IN (g/s)/ ha
S02
PART
fi.He.MM
COOKS
QOOO l
ooooZ.
000 03_.
ooooS
0.467$.
1.131
O-00(aS
0.340
0.0683
JL-OO_
2.67
3.33
7,0*0
0.
^.2835
Fig. 139. Step 6: Completed Worksheet 8 for Cleariair City
WORKSHEET 9 (FOR STEP 7 ) FILLED
AREA SOURCE EXEMPTIONS
f)HT RY: DATF: PG
OF
AREA SOURCE
ID NUMBER*
(5 DIGITS)
00/43
00/4+
00/6$
OO'bd
0^187
00/8B
$03Q(
003D3
06305
eowf
UTM LOCATION
(NORMALIZED)
OF S.W. CORNER
OF THE GRID SQUARE
(km)
EASTING
7/*55
7/0-55
7/^. *5
7/V-SS
7/8-S5
7/8. 5*5
7Z2.55
722.S5
7^2.55
722-55
NORTHING
V60V.22
^08.22
^Of. 22
*/6^^. 22
VfeO^.22
V408.22
V40V-2^
Wb-ZZ
^608.22
V(^^. 2.2
WIDTH
OF THE
GRID SQUARE
(m)
V00P
Ljf\/\f\
i VW
¥^
-------
295
wnRKSHFFTio (FOR STPP 7) FMI PROMT BY.
EXISTING POINT SOURCE EXEMPTIONS
n/JTF
PR
OF
NAME OF
POINT SOURCE
POINT" SOURCE
10 NUMBER
(FIVE DIGITS)
UTM LOCATION
(NORMALIZED)
IN km
EASTING NORTHING
FUTURE
EMISSION RATE
(g/s)
SOg PART
STACK
HEIGHT
(m)
STACK
DIAMETER
(m)
EXIT
VELOCITY
OF STACK
GASES
(m/s)
TEMP
OF
STACK
GASES
QA-f
+j- ire
IO&+
VESPUCCI
- M
/?
oooio
OOQO[
cocoa.
OOOCl
0000+
oooos
7/5.9
7/5:5
7/5.5
7/fc.V
4608-7
'./
V4/0.2
6-000
i,73
J.33
VW
Q.OO&
0,360:
0.234
38JO
S3.3+
W50
iS.72
LQ3
j,i e
L.X3
4S.OO
Fig. 141. Step 7: Completed Worksheet 10 for Cleanair City [In the test case,
existing sources are treated as nonconforming uses and future power
plants as exempt sources; Worksheet "IDA" is completed accordingly.
(If all existing sources were exempted, Worksheet "10B" would
have been used.) Worksheet 10A is used in this test case.]
WORKSHEET II (FOR STEP 8 ) FILLED OUT BYi
SUrK HEIGHTS FOR AIR QUALITY RUNS
DATE
PG
OF.
POINT SOURCE NAME
POINT SOURCE NUMBER
(FROM WORKSHEET 6)
SIMPLIFIED LAND USE
ZONING CLASS
STACK HEIGHT
FROM WORKSHEETS (m)
VESPUCCI
Fig. 142.
OOOO)
OO002.
ooooS
ooooH-
Ml
Ma.
AI+ Po,ur SouAciu : 22.2.81 -J-5 * 4ftf.
SO, m AVC4*£'
Step 8: Completed Worksheet 11 for Cleanair City [Note: Data from
emission inventory; average stack heights are arithmetic means.]
-------
"296
IIGHT MANUFACTURING AIR QUALITY EON DATA
LIGHT MANUFACTURING AIR QUALITY RUN OF CDM-QC FOR THE CLEANAIR CITY EXAMPLE
A1 A2 F1 P200001 000000005000060000700000000.00000000.0000001.00000001.
000000 0000 OOOOOOOC.000000000.0 302 PART
00075.0001.01000.011900.00395.00714.04606. 010.091030.0
0004.001.00001.000011.90011.90011.90011.90011.90011.9099999.99999.
(JOINT FREQUENCY FUNCTION)
(BLANK CARD)
716.004608.0002000.00010000.0010000.038. 10000035
718.004609.0001000.00010000.0010000.038.10000036
716.004606.0002000.00010000.0010000.038.10000048
718.004607.0001000.00010000.0010000.038.
719.004607.0001000.00010000.0010000.038.
718.004606.0001000.00010000.0010000.038.
719.004606.0001000.00010000.0010000.038.
000714.
000715.
000714.
000717.
000718.
OOJ718.
000720.
000721.
000723.
000722.
5004610.8
5C04610.5
0004606.0
0004609.7
0004609.4
4004607
9004611
1004607
7004610
10000049
10000050
10000051
10000052
(BLANK CARD)
8004608.2
00000300
00000300
00000300
00000300
00000300
00000300
00000300
00000300
00000300
00000300
0001
0002
0003
0004
0005
0006
0007
0008
0009
0010
MEDIUM MANUFACTURING AIR QUALITY RON DATA
MEDIUM MANUFACTURING RUN FOR CLEANAIR CITY—CDMQC APEIL 1,1978
A1 A2 E>1 P200001 OOCOD00050C0060000700000000.00000000.0000001.00000051.0
000000 0000 00000000.000000000.0 302 PART
00075.0001.01000.U11900.00395.00714.04606. 010.091000.0
0004.001.00001.0CO000.COOOO.OCOOO.OOCOO.00000.00000.0099999.99999.
(JOINT FREQUENCY FUNCTION)
(BLANK CARD)
714.004608.0002000.00010000.0010000.053.34000034
715.004607.0001000.00010000.0010000.053.34000045
715.004606.OCO1000.00010000.0010000.053.34000047
(BLANK CARD)
(RECEPTOR CARDS FROM LIGHT MFG.RUN)
HEAVY MANUFACTURING AIR QUALITY RUN DATA
HEAVY MANUFACTURING AIR QUALITY HUN OF CDM-QC FOR THE CLEANAI& CITY EXAMPLE 4178
A1 A2 E1 P200001 000000005000060000700000000.00000000.0000001.00000001.0
000000 0000 00000000.000000000.0 302 PART
00075.0001.01000.011900.00395.00714.04606. 010.0910JO.O
3004.001.00001.000006.21006.21006.21006.21006.21006.2199999.99999.
(JOINT FREQUENCY FUNCTION)
(BLANK CURD)
718.004610.0001000.00010000.0010000.043.79000031
714.004608.0002000.00010000.C010000.043.79000034
716.004608.0002000.00010000.0010000.043.79000035
721.004609.0001000.00010000.0010000.043.79000041
721.004608.0001000.00010000.0010000.043.79000043
(BLANK CARD)
(RECEPTOR CARDS FROM LIGHT MFG.RUN)
Fig. 143. Step 9: Listing of Five CDMQC Data Decks for Cleanair City
(continued on page 301)
-------
297
CALIBRATION RUN DATA
CALIBRATION RUN FOR CLEANAIR CITY, APRIL 1,1978
A1 A2 PI P200001 0000000050000600007
000006 0003 00000000.OCOOOOOOO.O S02 PART
00350.0002.01000.011900.00395.710.554604.2 010.092030.0
0004.001.00001.000005.44005.44005.44005.44005.44005.4499999.99999.
(JOINT FREQUENCY FUNCTION)
715.904608.70 000.0679000.0065038.1001.83 CO.36900204.61
10 001.7310000.3600053.3402.4400.40800276.85
002.3320000.0683044.5002.1803.88100204.11
007.0900001.3240045.7201.83010.240082.25
715.504607.
718.5046C7.20
716.404608.40
718.804610.20 002.7620000.2362041.2501.55026.9600166.85
(BLANK CARD)
710.554604.2204000.00018.06013.34000007.50000143
710.554608.2204000.00018.06013.34000007.50000144
714.554604.2204000.00035.07034.920Ou010.00000165
714.554608.2204000.00060. 18039.00030010.00300166
718.554604.2204000.00030.01021.04000010.00000187
718.554608.2204000.00063.02043.46000010.00000188
722.554604.2202000.00009. 13010.96000005.00000301
722.554606.2202000.00000.0003.630300005.00000303
7 22. 5546C8.2202000.00004.5708.220000005.00000305
722.554610.2202000.00009.13010.93000005.00000307
(BLANK CARD)
0715.5004610.500 0039.054.000
0717.OOC46C9.700 0048.000.000
0718.5004609.400 0043.553.000
0718.4004607.300 0035.052.000
,000 0030.049.000
0721.1004607.
0723.7004610.900
0042.056.000
00000 00
00000300
00000300
00000300
00000300
00000300
03001
03002
00003
03004
03005
0001
0002
0003
0004
0005
0006
RHS RUN DATA
RIGHT HAND SIDE RUN FOR CLEANAIR CITY APRIL 1, 1978 EXAMPLE
A1 A2 P1 P200001 00000000500006C0007000000CO.00000000.0000001.03000001.0
000000 0000 00000000.CCOOOOOOC.0 S02 PART
00350.0001.01000.011900.00395.710.554604.2 010.091000.0
0004.001.00001.000004.01004.01004.01004.01004.01004.0199999.99999.
(JOINT FREQUENCY FUNCTION)
006.0000000.11600045.003.00025.000000175.0 03010
(BLANK CARD)
16.0000000007.500143
15.0000000007.500144
37.0000000010.000165
36.0000000010.000166
32.0000000010.000187
40.0003000010.000186
12.0000000005.000301
05.0000000005.000303
05.0000000005.000305
12.0000000005.000307
(BLANK CARD)
(RECEPTOR CARDS FROM LIGHT MFG.RUN)
721.84608.8
710.554604.22004000.22.00
710.554608.22004000.20.00
714.554604.22004000.38.50
714.554608.22004000.55.00
718.554604.22004000.40.00
718.554608.22004000.60.00
722.554604.22002COC.12.00
722.554606.22002000.5.00
722.554608.22002000.5.00
722.554610.22002000.10.00
Fig. 143. Step 9: Listing of Five CDMQC Data Decks for Cleanair City
(continued)
-------
298
STEPS 10, 11, 12, and 13
In these four steps, the CDMQC and MPSX computer programs were acquired,
installed on the computer, and tested.
The services of a programmer experienced in the use of dispersion
models and linear programming are essential to the completion of these steps.
Because no printable results come from these steps, no results are
shown here.
-------
299
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------- An error occurred while trying to OCR this image.
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-------
303
STEPS 17, 18, 19, 20, and 21
The coefficient matrix drawn in Step 17 and completed in Steps 18-21
is shown in Fig. 148 on page 309. The methods described in the text were
used to complete the matrix.
Worksheet 12 in Step 20 was not needed in this test case because the
geometric mean particulate concentrations were assumed equal to arithmetic
mean concentrations. Worksheet 13 is shown in Fig. 147 on page 308.
Shown below are the calculations needed to fill in Column A on Work-
sheet 13 (see pp. 158-160 in Step 20); these calculations use the current
secondary air quality standards for S02 and particulates:
Calibrated SO2 Standard
x - ¥ - ^oTsP - 123'712
Calibrated Particulate Standard
60^_4^68 = 6
U. 2o
-------
304
WORKSHEET 13
RIGHT -HAND-
(FOR
SIDE
STEP 20)
VALUES
FILLED
nilT RY: HATF:
PG
OF
RECEPTOR
NAME
RECEPTOR
NUMBER
(ROW)
AIR POLLUTANT CONCENTRATIONS ()JO|/m3 )
CALIBRATED
AIR QUALITY
STANDARD
(A)*
S02
PART
AIR QUALITY
IMPACT OF
EXEMPT SOURCES
»
S02
PART
RIGHT-HAND-SIDE
VALUE
(A-B)
S02
PART
ltt.1I
•oo
aoo a
T&I-/Q3&
^PMOO*^
00/0
60.75
6V-
UAt.
mn
vtente,
Fig. 147. Step 20: Completed Worksheet 13 for Cleanair City
------- An error occurred while trying to OCR this image.
-------
306
NAM
RCW
N
I
I
1
I
L
L
I
L
I
L
CCL
E
s
OEJF
BCHG01
RCW002
ECWQ03
ECH004
ECW005
KCWCQ6
EOWC07
BCW008
BCWC09
ECH010
UMNS
COL0001
CCL0001
CCL0001
CCL0001
CCL0001
CCL0001
CCL0002
CCL0002
CCL0002
CCL0002
CCL0002
CCL0002
COLOC03
CCLOC03
CCLOC03
CCL0003
CCL0003
CCL0003
CCL0004
CCLC004
CCL0004
CCL0004
CCL0004
COLOOG4
CCL0005
CCLC005
CCI0005
CCL0005
CCL0005
CCLC005
COL0006
CCL0006
CCL0006
CCL0006
COL0006
CCL0006
CCLC001
EDZ2
OBJF
BOW002
B0h004
ROK006
BOWCQ8
EOW010
CEJF
ROK002
BOWOC4
EOK006
EOW008
EOW010
CBJF
EOW002
BOW004
BOKCC6
EOW008
BOW010
OBJF
EOW002
EOW004
EOS006
ECW008
ROK010
CBJF
EOK002
EOW004
ROW006
BOR008
BOW010
CEJF
ECK002
BOW004
BOW006
EOK008
R0h010
OBJF
31.25000
2.45100
2.48000
6.09400
.98300
1.17200
87.52000
10.94900
8. 17800
4. 00900
1.99200
1.78500
18. 72000
3.22700
2. 15800
.95400
.49300
. 42300
12. 48000
2. 17400
5.88400
1.33600
. 56700
.40800
37. 44000
5.85000
11.76700
3.66600
1. 43900
1. 16800
18.75000
1.98500
4. 57900
6.51200
.82700
. 86600
6.25000
EOW001
ROW003
ROW005
EOW007
ROW009
EGW001
ROH003
ROW005
ROW007
ROW009
BOW001
ROH003
ROW005
BOW007
EOW009
ROW001
BO WO 03
ROW005
ROW007
ROW009
&OW001
ROW003
ROW005
ROW007
ROW009
BOW001
EOW003
EOW005
EOW007
ROW009
EOH001
78400
32200
29500
96100
1.27800
12.28300
8.46700
5.67300
1.62300
1.02900
1
1
12
.37900
.09700
.39300
.34400
.23400
31300
.28200
..27100
.30600
.22300
i. 67600
.80200
i.67200
.87600
.64100
16400
27800
43200
.87200
.57600
.09800
Fig. 149. Step 22: Listing of MPSX Data Deck for Cleanair City (continued
on pages 311 and 312)
-------
307
Fig.
CCL0007
COL0007
CCL0007
CCL0007
CCLOC07
CCL0008
COL0008
CCL0008
CCL0008
CCL0008
CCL0008
CCL0009
CCL0009
COL0009
CCL0009
CCL0009
CCIOC09
CCL0010
COL0010
CC10010
CCL0010
CCL0010
COL0010
COLC011
CCI0011
CCL001 1
CCL0011
CCL0011
CCL001 1
COL0012
CGL0012
CCI0012
CCL0012
CCLOC12
CCL0012
CCLC013
CCL0013
CCL0013
CCL0013
CCLOC13
CCL0013
COL0014
CCL001 4
CCL0014
CCL0014
CCL0014
CCLOC14
CCL0015
CCL0015
CCL0015
CCI0015
CCL0015
CCLOC15
149. Step
ROH002
ROW004
EOW006
ROWOC8
BCW010
OEJF
BOKOC2
ROK004
ROW006
BOK008
RCW010
OEJF
ROW002
ROW 004
BOW006
EOW008
BOKO 10
OBJF
ROW002
BOW004
BOKQC6
ROWOC8
ROK010
OBJF
ROW002
ROW004
BOB006
BOW008
BOKO 1C
OBJF
&OW002
ROW004
ROW006
ROB008
ROK010
OEJF
ROK002
BOW004
ROKOC6
ROW008
RCW010
OBJf
ROW002
ROK004
ROW006
ROW008
BOW010
OEJF
ROK002
ROK004
ROK006
BOWC08
BOH010
22: Listir
1
1
12
3
3
43
4
1
3
12
1
12
1
2
75
5
9
84
9
4
6,
1
1.
37,
1,
2,
18.
3.
1.
25.
1.
1.
7.
4.
1.
.23600
.25600
. 15100
,36300
, 51700
50000
39300
63500
34000
23500
03300
75000
23800
81300
58700
99300
57700
50000
.05400
.33700
.66500
.28400
.21200
. 50000
.76800
. 41800
. 78800
. 72300
.31900
. 00000
. 84100
.86600
.24100
.90300
.20500
.25000
,28900
,53300
,60600
28600
40000
50000
59200
41000
76500
54100
32400
00000
05700
44100
92600
35000
06900
ROW003
ROW005
ROW007
EOW009
ROW001
ROW003
ROW005
ROH007
ROW009
ROM001
ROW003
ROW005
ROW007
HOW009
ROW001
ROW003
ROM005
ROW007
ROW009
ROW001
ROW003
ROW005
ROW007
ROW009
ROn/001
ROW003
ROW005
ROW007
ROW009
ROW001
ROW003
ROtfOOS
ROW007
ROW009
EOW003
ROW005
ROW007
ROW009
ROW001
ROH003
ROW005
ROW007
ROW009
2,
2.
1.
4,
1,
17.
1.
.04400
.39200
.79400
.39500
.35700
.09100
.85300
.97000
.52100
50200
07000
85500
.40500
.35000
.47900
.24000
.34700
.11100
.13000
,59100
,69800
,01800
19900
17300
48900
63100
32900
83500
1.72000
.20000
.11600
.80300
.14600
.14200
1
2,
4,
.38000
.09800
.03100
.45100
.5C200
,72700
,10300
,85000
,65700
42400
of MPSX Data Deck for Cleanair City (continued)
-------
308
RHS
S02X
SC2X
S02X
SC2X
S02X
PABT
PART
PART
PART
PAE1
BCUNDS
LO SC2X1.50
OP S02X1.50
OP S02X1.50
1C SC2X1.5C
OP SC2X1.5C
UP S02X1.50
LO SC2X1.50
UP SC2X1.5C
OP SC2X1.50
10 S02X1.5C
UP SC2X1.5G
10 SC2X1.5C
UP SC2X1.50
UP SC2X1.50
UP SC2X1.50
UP SC2X1.5C
OP SC2X1.50
UP SC2X1.5C
UP SC2X1.50
UP SC2X1.5C
LC PAB11.50
UP PABT1.5C
UP PAB11.50
LO PAB11.5C
UP PAB11.50
UP PAR11.50
LO PAB11.50
UP PAR11.50
UP PAB11.50
LC PAB11.5C
UP EAB11.5C
LC PAR11.5C
UP PABT1.5C
UP PAB11.5C
UP PAB11.5C
UP PAB11.50
UP PAB11.50
UP PAE11.50
UP EAB11.50
UP PAE11.50
ENDATA
BOK001
EOH003
BOK005
BOW007
BOK009
BOW001
BOW003
BCW005
BOW007
BOW009
COL0001
COL0001
COL0002
COL0003
COL0003
COL0004
COL0005
COL0005
COLOC06
C010007
COL0007
COL0008
CCL0008
COL0009
COL0010
COLOC11
COL0012
COL0013
COLOC11
COLC015
COL0001
COL0001
COL0002
COL0003
COLOC03
COIOOOM
COL0005
COL0005
COL0006
COL0007
COL0007
coLOCoe
COLOC08
COL0009
COL0010
COL0011
COL0012
COLOC13
COL0014
COLG015
79.45000
89.09000
62. 96000
75.40000
69.53000
24. 74000
34.39000
8.25000
20.69000
14. 82000
.69960
4. 14300
.64935
.69960
4. 14300
.03484
.69960
4. 14300
.03484
.69960
4. 14300
.69960
4. 14300
.64935
.64935
.03484
. 03484
.03484
.03484
.03484
.02055
.35430
. 13500
.02055
.35430
.00330
.02055
.35430
.00330
.02055
.35430
.02055
.35430
.13500
. 13500
.00330
.00330
.00330
.00330
.00330
RCW002
BO HO 04
ROW006
ROW008
BOW010
BOW002
ROH004
ROW006
ROW008
ROtfOlO
70.
64.
72.
72.
75.
15.
9.
17.
17.
08000
59000
16000
4600C
07000
37000
88COO
45000
75000
20.36COO
Fig. 149. Step 22: Listing of MPSX Data Deck for Cleanair City (continued)
-------
309
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-------
311
WnRKSHFFT 14 (FOR STFP ?4) F/l / FD OUT BY:
FINDING THE POTENTIAL EMISSIONS OF EDZ SOURCES PER km2
DATF:
PG
OF
COLUMN
NUMBERS
FROM LP
OUTPUT OR
MATRIX
EXPLANATION
OF COLUMNS"
ACTIVITIES X INPUT
COST = EMISSIONS
PER EDZ GRID SQUARE
PER LAND USE CLASS
(g/s)b
S02
PART
EMISSIONS OF FUTURE EDZ
SOURCES LOCATED IN EACH EDZ
GRID SQUARE IF FUTURE
CONSTRUCTION USES ALL OF
THE EDL ALLOCATED
(g/sc
S0
PART
4QOf
^Q3S
QOtL
QQf*-
00/3
oo't
001*
OQSJ
£L2i&_
Q.11L.
JL
From LP matrix. The "explanation" for each column number consists of an EDZ
grid square number and a land use class.
From LP output; see text.
cThe number of entries in this column will equal the number of EDZ grid
squares included in the LP matrix.
Fig. 152. Step 24: Completed Worksheet 14 for Cleanair City
-------
312
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WORKSHEET 15 IFOR STEP 24) FILLED OUT BY:
COMPARISON OF TOTAL EMISSIONS WITHIN EACH SQUARE KILOMETER
DATE
PG
OF
LA]
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SOUARE
NUMBERS
EMISSIONS
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GRID SQUARE
S02
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GRID SOUARE
(g/s)b
S02
EMISSIONS OF FUTURE
EDZ SOURCES LOCATED
IN EACH EDZ SRIO SOUARE
IF FUTURE CONSTRUCTION
USES ALL OF THE EDI
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lg/s)'
S0
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TOTAL POTENTIAL
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(g/si
D= A+ 6 + C
S02
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TOTAL
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Exempt sources from Step 7 are mapped onto the general regional map and the emissions of the exempt sources
are summed for each EDZ grid square The results are placed in Column B. Use the gridcled zoninc maps from
Step 2 to assist in locating the exempt sources.
From the emission inventory (Step 6)
to the appropriate knT grids.
From final column of Worksheet 14
Emissions from each emission inventory grid square are reallocated
tfu^n D tyo-h *4«f /* C.^«n 8.
Fig. 154. Step 24: Completed Worksheet 15 for Cleanair City
-------
315
STEP 24: EMISSIONS MAPS
The total potential emissions (TPE) map for S02* (Fig. 155) shows a
relatively low TPE area with a number of taller islands. With the exception
of the squares with Ml zones, these islands occur in squares containing manu-
facturing lands. For most of the squares, the high TPE are probably caused
by the much higher stack heights expected in the manufacturing areas. In a
region larger than Cleanair, the effects of the taller stacks should be
lessened due to increased distances from sources to receptors.
The particulate TPE map*" (Fig. 156) shows much less variation than
that for SOa. This is primarily due to the difference in the upper bounds.
Again, manufacturing zoning correlated with higher TPE, again suggesting the
stack height factor.
The emission density limits for each zoning district, shown in Figs.
157-162, are per hectare emission limits in terms of the grams-per-second
emission rates of SOz and particulates.
Sulfur Dioxide EDLs. The light manufacturing EDLs were set at either
their upper bound or at zero, their lower bound. The center of Cleanair,
which had the highest reported concentration of S02 in the monitoring station
data, is protected from further pollution increases by the zero EDL. All
medium manufacturing EDLs were set at their upper limit. For heavy manu-
facturing, the EDLs were set at the upper bound and between the upper and
lower bound. The M3 area just north of downtown has the lowest EDL due to
its nearness to the sensitive downtown receptor. The striking feature about
this map is the change in the EDL in the M3 area spanning the boundary of
grid squares 34 and 35. The upper bound was too high to allow it on both
sides of the line without violating air quality standards. In Cleanair City,
no borderline adjustments will be made (such as those suggested in Step 24)
because the small size of the region would probably cause any changes in EDLs
to cause large changes in air quality.
"Note that grid square 34 was split into four parts (using Worksheet 16 data)
to better illustrate the high total potential emissions in the M3 area.
:"Here grid square 34 was not divided; if it had been, the M3 corner would be
shaded like square 33, and the rest like 35.
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316
Particulate EDLs. The light and medium manufacturing EDLs for particu-
lates are basically the same as those for S02. No heavy manufacturing par-
ticulate EDLs were set at the lower bound; again, the downtown receptor is
protected by lower nearby EDLs. The EDL split between grid squares 34 and
35 that occurred in the SOz map is absent from the particulate map. The
particulate upper bound was low enough that it was appropriate for both grid
squares.
STEP 25
The EDLs on the maps in Figs. 157-162 would be transferred to the EDZ
implementing authority for passage and administration.
-------
317
(g/»)/kzH»
0 660 - 5
cm
1 5 . 001 - 15
25 .001 - 35
1
TOT4L POTOTTlAL EMISSIONS , 30_
711
35.001-45
716
Fig. 155. Step 24:
720 722 721
Map of Total Potential S02 Emissions for Cleanair City
716
718
720
722
724
Flg> 156> ^tep ?4: -ap of Total Potential Particulate Emissions
for Cleanair City
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318
CO
CO
•*•
CO
o
CO
CO
o
CO
7H 716 718 720 722 724
Fig. 157. Step 24: Map of S02 EDLs for Light Manufacturing in Cleanair City
MEDIUM MIX! . EDLa FOR 303
714 716 718 720 722 724
Fig. 158. Step 24: Map of S02 EDLs for Medium Manufacturing in Cleanair City
-------
319
oi
^4
CO
o
^H
co
00
o
CO
CO
o
CO
7H 716 718 720 722 724
Fig. 159. Step 24: Map of S02 EDLs for Heavy Manufacturing in Cleanair City
CM
*-H
CO
o
•-<
CO
00
a
CO
CO
o
CO
LIGHT MFC . EDLs FOR PART .
7H
716
718
720
722
724
Fig. 160. Step 24: Map of Particulate EDLs for Light Manufacturing in
Cleanair City
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320
MEDIUM MFC . EDLs FOR PAST .
714 716 718 720 722 724
Fig. 161. Step 24: Map of Particulate EDLs for Medium Manufacturing in
Cleanair City
711
716
718
720
722
724
Fig. 162. Step 24: !lap of Particulate EDLs for Heavy Manufacturing in
Cleanair City
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321
REFERENCES
The following publications, referenced in the text of the guidebook,
are recommended reading.
Benesh, F.H., et al., Emission Density Zoning, U.S. EPA Report EPA-450/3-77-
006 (March 1977).
Berry, B.J.L., Sampling., Coding, and Storing Flood Plain Data, Agriculture
Handbook No. 237, Economic Research Service, USDA (Aug. 1962).
Brail, R.K., Land Use Planning Strategies for Air Quality Maintenance, Proc.
Specialty Conf. on Long-Term Maintenance of Clean Air Standards;, Lake
Michigan States Sec. of Air Pollution Control Assn., Chicago, J.J. Roberts,
ed., pp. 275-289 (Feb. 1975a).
Brail, R.K., et al., Emission Density and Allocation Procedures for Main-
taining Air Quality, U.S. EPA Report EPA-A50/3-75-075 (June 1975b).
Chapin, Stewart, Urban Land Use Planning, 2nd Ed., p. 296, University of
Illinois Press, Urbana (1972).
Cohen, A.S., et al., Chicago Air Pollution Systems Analysis Program: Long-
Range Planning in Air Resource Management, Argonne National Laboratory Report
ANL/ES-CC-008 (Jan. 1971). See Sec. 3; Zoning Survey — Procedure and
Application.
Goodman, William, and E.G. Freund, eds., Principles and Practices of Urban
Planning, 4th Ed., International City Manager's Assn., Washington, B.C.
(1968).
Hadfield, S.M., Land Use Updating Survey, Chicago Area Transportation Study
(Dec. 1966).
Harvard University, Users Reference Manual for Synagraphic Computer Mapping,
"SYMAP," Version V, Laboratory for Computer Graphics and Spatial Analysis,
Cambridge, Mass. (1968).
Jaffe, M.S., et al., Legal Aspects of Emission Density Zoning, report pre-
pared by American Society of Planning Officials for Argonne National Labora-
tory (1978).
Larsen, R.K., A Mathematical Model for Relating Air Quality Measurements to
Air Quality Standards, U.S. EPA Report Number AP-89 (NTIS PB 205277) (Nov.
1971).
Robinson, A.H., and R.D. Sale, Elements of Cartography, 3rd Ed., John Wiley &
Sons, Inc. New York, N.Y. (1969).
-------
322
Additional Recommended Reading
The following publications also are recommended reading:
Benesh, F.H., et al., A Review of Considerations and Issues in Emission
Density Zoning., GCA Corp., Bedford, Mass. 01730 (June 1977).
Cosier, Land Use Based Emissions Strategies: Their Promise and Problems,
Planning Comments, :Z2(2):31-47 (Fall 1976).
Roberts, J.J., E.J. Croke, and S.W. Booras, A Critical Review of the Effect
of Air Pollution Control Regulations on Land Use Planning, APCA Critical
Reviews #3 - Air Quality Management and Regional Land Use, 1975. Originally
appeared in J. Air Pollution Control Assn., 25(5):500-520 (1975).
Stiftel, Bruce, Estimating the Impact of Emission Density Zoning on an Air
Quality Maintenance Area: Proposal for Decision Tools, U.S. EPA Office of
Air Quality Planning and Standards working paper, Research Triangle Park,
N.C. 27711 (revised, Sept. 1977).
-------
-T
!
TECHNICAL REPORT DATA
;cad Instruction'; on the reverse before coi'iplclingl
3 RECIPIENT'S ACCESSIOteNO
EMISSION DENSITY ZONING GUIDEBOOK
A Technical Guide to Maintaining Air Quality Standards
through Land-Use-Based Emission Limits
jfa REPORT DATF
__S epternb_er_)J_78
6 PERFORMING ORGANIZATION CODE
ron, Jr., Alan S. Cohen, and
Linda M. Mele (Technical Editor: Kathryn S. Macal)
8. PERFORMING ORGANIZATION REPORT NO
9 PERFORMING ORGANIZATION NAME AND ADDRESS
Energy & Environmental Systems Division
Argonne National Laboratory
9700 South Cass Avenue
Argonne, Illinois 60439
12 SPONSORING AGENCY NAME AND ADDRESS
Environmental Protection Agency; Office of Air Quality
Planning and Standards; Strategies and Air Standards
Division; Land Use Planning Office
Research Triangle Park, NC 27711
Argonne #P-7711A
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO
EPA-IAG-D7-01157
13. TYPE OF RE PORT AND PERIOD COVERED
Final
14 SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
An emission density zoning (EDZ) regulation is an air pollution control strategy
that is similar to traditional land use zoning (which controls development density in
particular areas) in that EDZ controls air pollution by setting emission density
limits (EDLs) for certain areas of land. EDLs restrict the rate of pollutant emission
per unit area (such as grams per second per hectare).
This document is step-by-step procedural guide designed to help regional planning
and air pollution control agencies set EDLs for sulfur dioxide and particulate matter
within a metropolitan area. The EDLs, set through the use of a computer-assisted
dispersion model and linear programming package, ensure the attainment and maintenance
of the National Ambient Air Quality Standards and compliance with Prevention of
Significant Deterioration regulations. Difficult concepts are explained in appendices
to the guidebook.
17
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
Area Emission Allocations
Atmospheric Diffusion
Flux Density
Land Development
Land Use
Linear Programming
Particulates
-Plann'
Regional Planning
Sulfur Oxides
Urban Planning
Zoning
13 DISTRIBUTION STATEMENT
Unlimited
b.IDENTIFIERS/OPEN ENDEDTERMS
Air Pollution Control
Air Quality Maintenance
Emission Density Limits
Emission Density Zoning
19 SECURITY CLASS (This Report/
Unclassified
20 SECURITY CLASS (Thiipage)
Unclassified
COSATl Held/Group
21
NO. OF PAGES
322
22 PRICE
EPA Form 2220-1 (9-73)
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