EPA-450/3-74-020-a
 AIR  QUALITY FOR URBAN
AND  INDUSTRIAL  PLANNING
                     by

       John C. Goodrich, Scott T. McCandless,
 Michael J. Keefe, William P.  Walsh, and Alan H. Epstein

      Environmental Research & Technology, Inc.
                429 Marrett Road
             Lexington, Massachusetts
             Contract No. 68-02-0567


          EPA Project Officer: John Robson



                 Prepared for

       ENVIRONMENTAL PROTECTION AGENCY
         Office of Air and Waste Management
      Office of Air Quality Planning and Standards
        Research Triangle Park, N. C.  27711

                  March 1974

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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 - as supplies permit - from the Air Pollution
Technical Information Center, Environmental Protection Agency, Research
Triangle Park, North Carolina  27711;  or, for a fee, from the National
Technical Information Service,  5285 Peart Royal Road,  Springfield, Virginia
22161.
This report was furnished to the Environmental Protection Agency by
the Environmental Research & Technology,  Inc. , in fulfillment of Contract
No. 68-02-0567. The contents of this report are reproduced herein as
received from the Environmental Research  & Technology, Inc. The
opinions, findings, and conclusions expressed are those of the author
and not necessarily those of the Environmental Protection Agency.
Mention of company or product names is not to be  considered as an endorsement
by the Environmental Protection Agency.
                       Publication No.  EPA-450/3-74-020-a

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                                  PREFACE

     This Final Report for EPA Contract No. 68-02-0567, Air Quality for
Urban and Industrial Planning (AQUIP:  Extension) is divided into three
parts.  Part 1 presents a summary of work undertaken, including sections
describing the proposed scope of work for each of the three major tasks
for the study, together with a summary of the actual work undertaken and
an explanation of deviations, if any, from the intended scope of work.
The detailed findings of Tasks 1 and 2 are found in Parts 2 and 3 of
this Final Report, respectively] the detailed findings for Task 3 are
found in a separately bound report entitled, A Guide for Considering
Air Quality in Urban Planning.  The report on Task 3 is available free
of charge to Federal employees, current contractors and grantees, and
nonprofit organizations - as supplies permit - from the Air Pollution
Technical Information Center, 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.
                                    Ill

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                                CONTENTS
                                                                          Page
LIST OF TABLES	    v
LIST OF FIGURES	   vi
PART 1.  SUMMARY OF WORK UNDERTAKEN	    1
         TASK 1.   DEVELOPMENT OF IMPROVED EMISSIONS PROJECTION
                  ACTIVITY INDICES 	    2
         TASK 2.   DEVELOPMENT OF A METHODOLOGY FOR INCORPORATING
                  COST DATA INTO THE EVALUATION OF THE AIR
                  POLLUTION IMPACT OF LAND USE PLANS	    5
         TASK 3.   PERFORMANCE OF LAND USE - AIR POLLUTION IMPACT
                  SENSITIVITY STUDIES	    6
         REFERENCES FOR PART I	   12
PART 2.  TASK 1 REPORT.  DEVELOPMENT OF IMPROVED EMISSIONS
         PROJECTION ACTIVITY INDICES 	   13
         TERMINOLOGY	   14
         INTRODUCTION TO PART 2	   16
         DATA USED IN THE STUDY	   23
         ANALYSIS OF EMISSIONS DATA	   34
         PROJECTING FUTURE EMISSIONS 	   55
         REFERENCES FOR PART 2	   69
PART 3.  TASK 2 REPORT.  COST-EFFECTIVENESS PLANNING FOR
         ACCEPTABLE AIR QUALITY	   70
         SUMMARY	   71
         INTRODUCTION TO PART 3	   72
             BACKGROUND AND PROBLEM DEFINITION 	   72
             THE ECONOMICS OF AIR POLLUTION	   74
                  Trade-off:  Air Quality versus Economic Viability.  .  .   76
                  The Cost of Achieving Economic Viability in Terms
                  of Air Pollution Impact	   78
                  The Cost of Controlling Pollutant Emissions	   80
                  The Damage Function	   82
             LITERATURE SURVEY AND EVALUATION  	   84
         A METHODOLOGICAL APPROACH TO COST EFFECTIVENESS AIR QUALITY
         IMPACT LAND USE PLANNING	   86
             SCOPE AND OBJECTIVES	   86
             CONCEPTUAL DESIGN	'.	   88
             PROCEDURAL DESIGN 	   90
         APPLICATION GUIDELINES	   92
TECHNICAL REPORT DATA SHEET	   94
                                       IV

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                               LIST OF TABLES

Table                                                                       Page
 2-1   Two-digit Standard Industrial Classifications (SIC)  Codes
       for Manufacturing	26
 2-2   Parameters Examined	27
 2-3   Massachusetts 4-Digit SIC Fuel Data	35
 2-4   Massachusetts Total Sample for SIC 20	38
 2-5   New Jersey SIC 28 Emissions per Hour per Employee	43
 2-6   New Jersey SIC 28 Emissions per Hour per Square Foot	44
 2-7   Massachusetts 2-Digit SIC Fuel Data	48
 2-8   Propensities to Use Different Fuels	50
 2-9   Emissions per Hour per Employee	51
 2-10  Inventory of Air Quality Planning Data for Implementation
       Plan Questionnaires	63
 3-1   Reference Material 	   85
 3-2   Scope and Objectives	87

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                             LIST OF FIGURES
Figure                                                                         Page
 2-1    Five-step Procedure for Determining Emissions 	 17
 2-2    Example Input Data	32
 2-3    Data Coding Sheet	33
 2-4    SIC 20 Space Heating BTU's per Hour versus Employment	39
 2-5    SIC 20 Total Heating BTUs per Hour versus Employment	40
 2-6    Detail of SIC 20 Total Heating Bit's per Hour versus Employment	41
 2-7    SIC 28 Emissions versus Employment	47
 2-8    Two-phase Procedure for Projecting Emissions	56
 2-9    Permutations of Power Generation - a Sample 	 59
 2-10   Petroleum Refining Variation - a Sample 	 60
 3-1    The Dependence of Air Quality on Land Use	73
 3-2    The Economic Implications of Land Use Planning in Terms of Air
        Pollution Impact	75
 3-3    The General Relationship of Level of Urban Activity to Air Quality. .   . 77
 3-4    The Total Cost of Air Pollution	79
 3-5    The Cost of Controlling Pollutant Emissions	81
 3-6    Pro Forma Damage Functions	83
 3-7    Conceptual Methodological Design	89
 3-8    Procedural Methodological Design	91
                                        VI

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         PART 1
SUMMARY OF WORK UNDERTAKEN

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TASK 1   DEVELOPMENT OF IMPROVED EMISSIONS PROJECTION ACTIVITY INDICES

     The general approach to this task was to be based upon the most recent
point and area source inventories compiled for the development of state
implementation plans by the Environmental Protection Agency (EPA)  and its
Basic Ordering Agreement (BOA)  contractors with emphasis  on a large statis-
tical sample drawn from the industrial point source inventories.  Such data
were to be divided into specified land use categories and subcategories.
Activity indices were to be derived empirically from the  relationships
between activity levels and fuel use or process emissions.   Because of the
massive data-handling requirements, computer data processing was to be used.
Determination of activity indices provides factors for estimating current
emissions which can then be used as a basis for projecting  activity indices
for future time periods.  This  task emphasizes determination of current
activity indices, but also establishes the procedures for projecting activity
indices.  These projections may involve detailed assumptions concerning
changes in emission control regulations, industrial processes, fuel switching,
productivity, consumer preferences, and many other factors  affecting the
relationship between land use and resultant air quality
     Subtasks proposed included the following:

     1-a     Defining principal land use categories and specifications
             for statistical validity.
     1-b     Collecting 1970 implementation plan point and  area source
             inventories, particularly those available in machine readable
             format.  Conducting supplementary surveys of particular
             industrial, utility and commercial sources,  and surveys of
             planning and governmental agencies
     1-c     Compiling and processing the available data  using standard
             tabulation and statistical computer programs.
     1-d     Analyzing the data and deriving activity indices for current
             emissions estimation.
     1-e     Developing the methodology for projection of activity indices
             for future emissions estimation.

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     1-f     Documenting the data, methodology, and derived activity
             indices
     1-g     Incorporating the resulting activity indices into the AQUIP
             System (an acronym standing for Air Quality for Urban and
             Industrial Planning).

     Early in the study the following were ascertained relative to data for
Task 1:

     1)  Data from the National Emissions Data System (NEDS) inventories for
the 1970 Implementation Plan (IPP) information was not available in suffi-
cient quantity and appropriate form for our study.  The availability of this
data had been stressed as a necessity for successful completion of the
project.
     2)  The only data available in computer form was the point source
inventories for certain states from BOA contractors; there were significant
problems of confidentiality, making it difficult, if not impossible, for
EPA to release this data to us for our use in the study.  Moreover, most of
the states were not in a position to release the data at the time due to
litigation over the question of confidentiality.

     Accordingly, it was decided to obtain data for New Jersey and Massa-
chusetts directly from the states since ERT already had worked with agencies
in these states.   As documented in the monthly progress reports, this process
resulted in (1) significant delays, (2) unexpected efforts in data gathering,
verifying, and manipulation, and (3) certain redirection of subsequent sub-
tasks relying upon these data.
     By reducing the desired sample number and relying upon national indi-
cators of floor space per employee for industrial firms, the initial data-
gathering was concluded with the hope of maintaining sufficient detail of
information to carry out Task 1.  Efforts that would have otherwise been
devoted to suppler.eutal surveys and literature searches (Subtask 1-b) were
instead concentrated on coding, keypunching and data preparation.  Emphasis
was placed on industrial point source data to the exclusion of area source
data or other types of point source data.

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     The activity categories to be investigated were broadly defined as
"industrial" (Subtask 1-a); this was ta be narrowed according to the dis-
tribution of Standard Industrial Classification (SIC) contained in the final
sample obtained.
     Unfortunately, errors and missing information were found in the data.
Field trips to each of the major data sources  in Massachusetts and New Jersey
were then necessary to obtain supplementary information.   Time was spent in
the New Jersey Department of Environmental Protection, Bureau of Air Pollution
Control Office, reviewing printouts of point source emission rates, employ-
ment, fuel consumption and process rates.   A review of Bureau of Air Pollution
Control questionnaire files was needed to  supply information not currently
stored in computer data banks.   Likewise,  in Massachusetts a review of
Department of Public Health source questionnaires was made to supplement data
in existing printouts.
     It became clear as the technical work was concluded  that the data
analysis of Task 1 would yield less than the desired statistical sample and
analysis results because of the data-gathering problems of Subtask 1-b.
Therefore, the documentation stresses the  approach, problems, opportunities
and recommendations for further work, and  de-emphasizes concrete statistical
conclusions.  This re-orientation resulted from problems  in obtaining requested
data and has been consistently documented  in the progress reports.
     When all Massachusetts data was identified and received and the status
of all New Jersey data determined, definition  of the required tabulations
and statistical computer routines was begun.  As a part of the previous AQUIP
study  computer algorithms for comparing fuel  use, emissions, floor space
and employment by SIC had been partially developed and, since the changes in
Subtask-lb required that data be coded, a  form compatible with this software
was used.  The software requirements for Subtasks 1-c and 1-d were, thus,
simultaneously formulated.
     After initial computer analyses were  performed for both the New Jersey
and Massachusetts data, errors corrected,  and the final computer runs per-
formed, the interpretation of statistical  analyses (Subtask 1-d) was brought
to the disappointing and abbreviated conclusion that the accuracy and com-
pleteness of the data were not sufficient  to derive activity indices at the
level of detail anticipated.

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     The projection methodology (Subtask 1-e) was most affected by data-
gathering delays.  Preliminary work began with a literature survey and com-
parison of information with the initial Hackensack study  and concluded with
the postulation of the kinds of information and decisions necessary for
emission projection.  Given the late start for this subtask, the disappointing
results of Subtask 1-d (necessary as an input >to Subtask 1-e) , and the lack
of other readily usable information, no definitive results were forthcoming
from this subtask.
     Work related to documentation (Subtask 1-f) is contained in Part II of
this Final Report.  The small degree to which information was shown to be
statistically conclusive and, therefore, documentable, and the inability to
incorporate revised activity indices into AQUIP to any great degree (Sub-
task 1-g) has been a function of the disappointing results of Subtask 1-d
which came ultimately from the data gathering problems of Subtask 1-b.
Moreover, the necessity of concentrating on a specific source category-
industrial point sources— due to time and budget constraints precluded the
examination of activity indices which might have been more appropriate for
other source categories.

TASK 2   DEVELOPMENT OF A METHODOLOGY FOR INCORPORATING COST DATA INTO
         THE EVALUATION OF THE AIR POLLUTION IMPACT OF LAND USE PLANS

     The general approach to this task was to focus on collecting and
assembling the results of EPA-sponsored studies on source control costs and
on evaluation of economic impacts of various control strategies.  ERT was to
examine the data available and formulate a framework for presenting such
data relative to land use plans, developments, or facility designs.  Param-
eters to be examined were expected to include land use zone cost indices per
unit emission control
     Subtasks proposed included the following:

     2-a     Surveying of literature and federal contract research efforts
             to compile source air pollution control cost data and data on
             costs and economic impact of implementation plan control
             strategies.
     2-b     Developing a methodology for including cost data in the
             evaluation of land use plans.

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     2-c     Compiling representative  cost data to test and demonstrate
             the techniques for a hypothetical  application.
     2-d     Documenting results of the test  and demonstration and making
             recommendations for incorporation  of the cost  data base and
             methodologies into the AQUIP  system for planning use.

     The research showed that existing data and literature  were sparse but
that reasonable methodologies could be developed for including cost data in
the evaluation of land use plans.   Literature pertinent to  the economic
implications of air pollution may generally be  categorized  into three dis-
tinguishable areas of concern:

     1)  The tradeoff of economic activity with air quality.
     2)  The functional relationships;  of emission control  costs and
         damage costs to air quality,,
     3)  Cost/benefit analyses associated  with  the control  and damage
         costs of air pollution for a  given level of economic activity
         as a function of land use strategy.

     The findings of Subtasks 2-a and  2-b  determined the depth possible in
demonstrating the methodology.  Accordingly,  work on the hypothetical appli-
cation of the methodology (Subtask 2-c)  concentrated on a  review of the
literature on air pollution damage functions.  Documentation of the method-
ology  (Subtask 2-d) was done in the form of Part III of this Final Report,
entitled "Cost Effective Planning for  Acceptable Air Quality."

TASK 3   PERFORMANCE OF LAND USE - AIR POLLUTION IMPACT SENSITIVITY
         STUDIES

     The proposed approach to this task was to  utilize the AQUIP System to
carry out certain sensitivity analyses.  ERT  was to use the existing land
use data base for the Meadowlands, and,  in addition, incorporate the improved
activity indices resulting from Task 1.  Several planning  agencies were to
be contacted to identify a set of specific and  meaningful  small area and
facility design alternatives.  On the  basis of  such alternatives, ERT would
specify the number of scenarios or case studies and trade-off parameters to
be modeled.  Air quality would be projected for each of these scenarios and
the results correlated with changes in different design parameters.

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     Subtasks proposed included the following:

     3-a     Identifying case studies, land use configuration alterna-
             tives, and facility design choices to be included in the
             sensitivity analysis.
     3-b     Defining the parameters and scope  of specific sensitivity
             analyses.
     3-c     Preparing inputs for the AQUIP System appropriate to each
             sensitivity study.
     3-d     Running the AQUIP System and processing resulting air
             quality as a function of parameters described in Subtask 3-b.
     3-e     Documenting results in the form of guidelines of general
             applicability for planners.

     Visits to planning agencies in New Hampshire, New York,  New Jersey,  and
Massachusetts indicated the specific need for a document containing guide-
lines dealing with air pollution for planners.   Accordingly,  such a guide-
lines document became the main goal of Task 3,  with the sensitivity studies
a major contributor to this final product.  This guidelines document is a
separately bound report, entitled "A Guide for  Considering Air Quality in
Urban Planning."
     ERT conducted several interviews of planning agency personnel projected
to be potential users of the findings of any air quality planning guidelines.
ERT initiated a dialogue with these professionals concerning the goals,
methods and data to be used in the study procedure and attempted to determine
which tools and format the intended beneficiaries of the investigations would
prefer to see developed.  By acquainting potential users of the guidelines
document with the early stages of data collection, it was hoped that the
guidelines might be more usefully tailored to the needs of such users.
     In addition to the air quality agencies in Massachusetts and New Jersey,
as well as EPA, four regional planning agencies w.ere interviewed:  Southern
New Hampshire Regional Planning Commission (Manchester, New Hampshire),
Central Massachusetts Regional Planning Commission (Worcester, Massachusetts),
Tri-State Regional Planning Commission (New York City), and the Hackensack
Meadowlands Development Commission (Hackensack, New Jersey).

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     The information obtained from speaking to these agencies can be sum-
marized as follows.  Traditional planning agencies appear to be relatively
ignorant of the relationships between planning practices and air quality.
While each knew that transportation vehicles and industries  are the primary
sources of air pollution emissions, the concept of planning  for air quality
was a new one.  Each agency seemed eager to have some capabilities in the
area of planning for air quality.   Motives among the agencies for wanting
these capabilities were diverse, however.   Some saw AQUIP a.s a tool for use
in zoning, public hearings, or transportation planning.   Each agency
wished to have the study  provide a simplistic approach  to air quality
planning.  The planners seemed much mere interested in receiving a manual
that would provide a cookbook methodology for studying air quality than a
semi-theoretical discussion of air qua.lity parameters.  Specifying land uses
by SIC code (Standard Industrial Classification) was universally accepted
as desirable since both land use planners and air quality agencies were
familiar with them.  Other parameters received mixed reactions as being
acceptable indices.  Some preferred floor area, etc., as a means for
projecting emissions activity.  Each agency was anxious  to see the guide-
lines document to be produced.
     Subtask 3-a required the identification of "case studies, land use
configuration alternatives, and facility design choices  to be included in
the sensitivity analyses."  As described in greater detail in the guide-
               2
lines document,  these were chosen to be the following.

     Case Study  No. 1

     Relative effects on annual average air quality resulting from clustered
versus dispersed area sources.  Land use configuration alternatives: (1)
clustering, and (2) dispersal.  Facility design choices:  (1) single area
source, and (2) four dispersed area sources.

     Case Study  No. 2

     Relative effects or Annual average air quality of clustered versus
dispersed point sources.  Land use configuration alternatives:   (1) clustering,

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 and  (2) dispersal.  Facility design choices:   (1) single point  source,  and
 (2)  four dispersed point sources.

     Case Study No. 5

     Relative effects on air quality  of clustered versus dispersed  sources
 in a worst-case situation.  Land use configuration alternatives:  (1)
 clustering, and (2} dispersal.  Facility design choices:   (1) one point
 source and one area source, and (2) one point  source and four dispersed
 area sources.

     Highway Sources

     Facility design choices:  (1) elevated,  (2) depressed, and  (3)  at-
 grade.

     Subtask 3-b required the definition of "the parameters and  scope of
 specific sensitivity analyses."  As described  in greater detail  in the
 guidelines document, these were chosen to be the following.

     Case Study No. 1

     Comparison of annual average air quality  for a concentrated area
 source with annual average air quality for four smaller dispersed area
 sources having the same total source strength.  Parameters included:
     1)  Meteoro1ogica1 Cond itions - annual stability wind rose  for
         Newark, New Jersey.
     2)  Source Strengths - (a) area source of 4,000 grams/sec,  (b)
         four dispersed area sources,  each emitting 1,000 grams/sec.

     Case Study No. 2

     Compar-\,.->n of annual average air quality  for ,a concentrated point
s^'^ue with annual average air quality for four smaller, dispersed point
sources having the same total source strength.  Parameters included:

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        1)  Meteorological Conditions -  annual stability wind rose for
            Newark, New Jersey.
        2)  Source Strengths - (a)  point source of 4,000 grams/sec,  (b)
            four dispersed point sources, each emitting 1,000 grams/sec.

        Case Study No.  5

        Comparison of worst-case air quality for a concentrated point source
   and a concentrated area source with worst-case air  quality for  the same
   concentrated point source and four dispersed area sources.   Parameters
   included:
        1)  Meteorological Conditions (identical  for both source configurations)
            (a)  high stability,  (b)  low  wind speed, and (c)  southwest wind.
        2)  Source Strengths - (a)  point source of 5,000 grams/sec,  and  area
            source of 4,000 grams/sec;  (b)  point source of 5,000 grams/sec,
            and four dispersed area sources,  each emitting 1,000 grams/sec.

        Highway Sources
        Discussion of worst-case air quality for  different highway designs
   under different meteorological and traffic  conditions.   Parameters included:

        1)  Meteorological Conditions -  (a)  stability,  and (b)  wind  speed.
        2)  Highway Designs  -  (a) elevated,  (b) depressed,  and  (c) at-grade.
        3)  Source Strenth Dependence on  Traffic  Characteristics:   (a) volume,
            (b)  speed distribution,  and  (c)  vehicle year and  make  mix.

        Subtask 3-c required the preparation of "inputs for the AQUIP System
   appropriate  to  each  sensitivity  study" while Subtask 3-d required the
   running of the  AQUIP System and  the processing of "resulting air  quality
   as  a function of parameters described  in  3-b." The revised  scope of  the
   sensitivity  studies  led to  the decision  to  use them in illustrating an
   important factor in  planning  for  air  quality:   the  influence of source
   configuration for both  stationary and  highway  sources.   For  this  purpose,
   the most effective use  of the AQUIP System (Subtasks 3-c and 3-d) lay in
   using the diffusion  modeling  capability,  MARTIK  (based upon the  EPA  Martin-
   Tikvart Model), independently  of  the rest  of the system.   For describing
10

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the influence of source configuration on highway sources, results obtained
from the ERT numerical simulation model, EGAMA (developed ^y Qr- B_ ^m Egan
and Dr. J. R. Mahoney).were used.  EGAMA is not part of the AQUIP System,
but its results have been found to be most useful in determining the impact
of alternative highway configurations on air quality.
     The improved activity indices resulting from Task 1 were not available
to the sensitivity studies on account of data limitations.  This precluded
the incorporation of such improved indices into the existing land use data
base for the Meadowlands.  The processed air quality results for the
sensitivity studies are depicted graphically in the guidelines document.
As discussed above, the guidelines document itself represent the results  of
Subtask 3-e.
                                                                              11

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                             REFERENCES FOR PART 1
       1.   Willis,  B.  H.  et  al.  The Hackensack Meadowlands Air  Pollution
               Study,  Summary Report  and Task Reports, Tasks  1-5, prepared
               for  the State of  New Jersey, Department of Environmental
               Protection, Trenton, 1973.

       2.   Epstein, A.  H.  et al. A Guide  for Considering Air Quality in
               Urban Planning, prepared for USEPA, March 1974,
               Contract No.  68-02-0567.
12

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                 PART  2
               TASK 1 REPORT
DEVELOPMENT OF IMPROVED EMISSIONS PROJECTION
              ACTIVITY INDICES

                    by
              John C. Goodrich
              Scott T. McCandless
              Michael J.  Keefe
              William P.  Walsh

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                               TERMINOLOGY

     Because the terminologies of 5>everal different professions are used
in this task report, often in unfamiliar ways,  this brief discussion of
terminology is presented to show the context within which different terms
were used.
     The basic land use and transportation planning units of intensity of
use - such as square feet of industrial plant space -  are called the activities
or the activity level.   The parameters which translate the activity levels
into demand for fuel for heating purposes are called activity indices; for
instance, BTUs (British Thermal Units of heat demand)  per square foot for
industrial plant space.
     A distinction has  been made between fuel-related  and nonfuel-related
activities or sources of emissions.   The fuel-related  sources use fuel for:
     1)  Heating area,  for example,  heating a building in the winter.  The
         amount of heat required and the fuel consumed is a function of the
         temperature or the number of degree-days (the sum of negative
         departures of average daily' temperature from  65°F).  This fuel
         use is that required for heating, or space heating (or cooling).
     2)  Raising a product to a certain temperature during an industrial
         process.   The  amount of fu
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particular fuel or fuels (the fuel use propensity) determines the actual
fuel used to satisfy the heat requirement.
     Different types of activities may have varying activity indices or per-
cent space heat or fuel use propensities; for instance, each industrial
category in the U. S. Census 4-digit Standard Industrial Classification
(SIC) may have a unique value.  However, we may know information only by
broad industrial groups comprising aggregates of the 4-digit classification
(e.g., 1-digit or 2-digit SIC codes).  Using the value applied to the
larger or broader group for the smaller or more detailed group, when the
unique value is not known,  has been termed a default parameter in these
studies.
     Emissions from sources that do not result from the burning of fuel;
for example, evaporation from a refinery storage tank, are termed separate
process emissions or process emissions.  Note the distinction between
process heating related or combustion emissions and separate process or non-
combustion emissions.  Although the combustion of fuel is involved, trans-
portation emissions have been considered as process rather than fuel emissions
in this study for simplicity, since they do not vary with heating degree
days.
                                                                             15

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                            INTRODUCTION TO PART 2

      The  Environmental Protection Agency  (EPA) and the New Jersey Department
  of  Environmental Protection  (NJDEP) sponsored a study, commencing in  1970,
  addressed to  their mutual  concern for improving future air quality through
  the planning  of  land use and transportation activities.  The two fundamental
  objectives of the Air Quality for Urban and Industrial Planning  (AQUIP)  study
  were:   (1)  to develop a broadbased methodology for considering air pollution
  in  the  formulation and evaluation of alternative urban plans; and, (2)  to
  demonstrate this methodology in detail by applying it directly to the
  planning  alternatives developed for the New Jersey Hackensack Meadowlands
  District.
      One  of the  major goals of the AQUIP  study was to develop a methodology
  to  aid  planners  in determining air pollutant emissions directly from  land
  use and transportation activity data.  Procedures traditionally used  to
  estimate  emissions from  land use and transportation planning data often
  emphasize empirical derivation of emissions indices as a one-step function
  of  "activity  categories  (e.g., activity times and index yields emissions).
  In  the  AQUIP  study, however, a multistep  approach was developed so that:
  (1) all assumptions and  constraints involved in transforming the levels  of
  activities into  emissions  could be examined; and  (2) procedures for updating
  the information  which the  planner doe:; not directly input could be specified.
       In response to the  study objectives  a five-step procedure was formulated
  in  the  AQUIP  study as shown  in Figure  1.

       Step 1 - Activities.   For  each  land  use  or  transportation  planning
       category identified for analysis,  the  "level  of activity"  is  specified,
       such as  10  dwelling units  per acre  for  residential  density.

       Step 2 - Activity  Indices.   For  each category of activity,  "default
       parameters" for  determining  fuel  requirements  are  developed,  such as
       1C tiTUs  (British Thermal Units of heat  demand)  per hour per square
       foot of  residential floor  area.

       Step 3 - Fuel Use.   For each category of  activity  (and  geographical
       subregion of  the study  area) default parameters  for  the "propensity"
16

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in
                                                                 to
                                                                 a
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                                                                       17

-------
     to use different fuels are applied to the fuel  requirements,  such
     as the degree to which oil is used (65%)  rather than  natural  gas
     (25%) for home heating.

     Step 4 - Emission Factors.  For each category of activity,  engineering
     estimates of fuel and nonfuel (process) related "emission factors"
     are developed and applied directly to fuel use  and  process  rates to
     determine emissions,  such as 10 Ibs of participates per  1000  gallons
     of fuel oil burned.

     Step 5 - Emissions.   Emissions calculated from  fuel and  process sources
     are adjusted for season of the year, based on temperature variation
     (degree-days) and default parameters representing the percent of fuel
     used for "space heating" purposes.
     Of particular importance in the AQU1P study was the application of these
five-step procedures in two distinct and consecutive phases.   In the first
phase current planning data and current fuel use are correlated  to produce
projecting indices - the  default parameters.   In the second phase  these
projecting indices are modified to reflect future time periods and are
applied to planning data  so as to generate future fuel demand and  emission
levels.  Current data on  fuel use and emission factors are likewise used to
predict future information when better estimates are not known.  The first
phase analysis provides the majority of the default  parameters to  be used
in the second phase in conjunction with the planner's own  inputs.
     The application of the emissions projection methodology  to  the Meadow-
lands plans showed that the five-step procedures were workable and, in fact,
quite adaptable to the land use considerations that  were encountered.  In
particular, the development of a "conversion  factors catalog" and  sets of
"default parameters" demonstrated that the planner need  input only planning-
related data to use a tool such as the AQUIP  System.
     However, it was found that the planner must specify data he does not
normally deal with, such  as the sizes of developments in terms of  their
heating requirements, and the types of manufacturing operations  anticipated.
Furthermore, the level of detail available for empirically deriving the
default parameters was unsatisfactory for discerning between  related activi-
ties; this was particularly true for deciding fuel use and determining

-------
process-related emissions.  Consequently, the greatest need for further work
was shown to involve the empirical derivation of activity indices and default
parameters.
     Activity indices as shown in Figure 2-1 are the basic factors used  to
convert land use activities to either fuel combustion or process emissions.
This is precisely the area, however, for which it was shown in the previous
AQUIP study that there is the greatest lack of data, and for which there is
the greatest need for detailed and accurate data (fuel use data and emissions
factors are generally readily available from EPA).
     The objective of Task 1 of the current AQUIP Extension Study was, there-
fore, to define, collect and process activity data so as to derive improved
activity indices for the AQUIP System.
     In previous studies (including the AQUIP Study) it has been demonstrated
that one of the principal variables in determining existing and future air
quality levels is land use.  This is because land use dictates emission
source characteristics in terms of both the levels  of activity and the
activity indices.  In order to develop the desired activity indices for
land uses, it is necessary to define the land use categories to be studied
and to arrange these categories in a manner that facilitates statistical
analysis.
     Of the major categories of emission sources we determined that indus-
trial point sources most warranted development of activity indices that could
be readily used in the planning process.
     The two largest contributors to source emissions have been shown to be
transportation and industry.  Most other source categories are either rela-
tively insignificant or have been studied to some extent.  Since research
was already being supported by EPA in regards to transportation sources and
emission patterns, it was decided in conjunction with the project officer
to concentrate the efforts of Task 1 of the AQUIP Extension on industrial
sources.  For several years research with respect to air quality has focused
on mobile sources.  As a result, there are several  documents now in publi-
cation that enable one to calculate existing vehicle emissions and to fore-
cast emissions from given numbers and types of transportation vehicles for
                   2
future time periods .  It was also decided that the air quality impacts of
transportation systems are both very specialized and usually localized.
This being the case, major transportation studies should be accompanied by
                                                                             19

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  individual air quality assessments based on much more specific and detailed
  data than that which could be collected, analyzed, and organized in the
  AQUIP Extension Study.  In addition, transportation sources cannot reason-
  ably be grouped as a land use for air quality considerations.  Transportation
  systems are products of other land uses and activities, but the variables
  which affect pollutant emission fron mobile sources are riot at all similar
  to the variables that affect emissions from point sources.  Accordingly,
  land use and transportation need to be treated as separate, but interdependent
  emission sources.
       Quasi-public activities, such £.s incinerators and power plants, are
  point sources which warrant individual analysis.  Such activities do not
  lend themselves well to statistical analysis since designs, capacities
  and, therefore, emissions, may differ radically.  Generally, the relatively
  small number of such emission sources makes this individual assessment
  feasible.
       Other sources may generally be considered insignificant on a regional
  scale.  Although it is obvious that commercial and residential facilities
  have a space heating function, the levels of emissions due to the space
                                                                               3
  heating needs are both small and fairly predictable using current information  .
  These sources do, however, need to be considered as inducers of transportation
  sources.
       The previous AQUIP study  concluded that industrial sources are of
  special significance because of the uncertainty as to the amount of fuel
  required for process heating and the incidence of separate process emissions.
  Efforts to develop a statistical sample of the propensity to use fuel for
  process heating by industrial category, based upon the existing emission
  inventories, were not successful in the previous study.  It was possible
  to divide the industrial SIC codes into only two major categories of
  "relatively clean" and "relatively unclean" industries.  The clean indus-
  tries were assumed to operate fewer hours per year and use a greater
  percentage of their fuel for space heating.
       A separate study of process emissions corresponding to the industries
  proposed for the New Jersey Hackensack Meadowlands was made as a part of
  the previous study.  With the exception of possible sources in the chemical
  and petrochemical and prir.ury rretals area, the SICs proposed for the
  Me;u!o\ lanes were not found to be significant separate process emitters.
20

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There were some potential emissions of participates and hydrocarbons from
selected industries.  These were accounted for by adding an arbitrary best-
estimate percentage of the fuel-burning emissions to the fuel emission
estimates, since no information was available on process rate.   The Meadow-
lands planners felt that there would be no petrochemical or primary metals
smelting operatins in the Meadowlands.
     As a result of missing data, the concept of "default parameters" was
developed in the previous study.  If information is desired according to
a detailed industrial classification for the propensity to use  different
fuels and the data is only available in aggregate form for all  industries
in the region, a default parameter is used to assign the industry-wide
factor to each individual industry.  If, at a later date, specific information
for an industry is available, it can be used in place of the default parameter,
     As a result of the previous AQUIP Study, it was clearly evident that
the activity indices required better specification.  It was hypothesized
that industrial point source emissions are a category particularly in need
for further study and that such sources would probably lend themselves to
some reasonable statistical analysis.  These hypotheses were based on the
fact that industries have several inherent similarities that should make
planning for air quality a reasonable undertaking.  The industrial activity
indices that were considered the statistical analysis were:

     1)  Employment
     2)  Floor area
     3)  Process weights (or production rates)
     4)  Fuel use
     5)  Hours of operation
     6)  Classification by product

     The fact that industries are conveniently organized by type of activity
is probably the factor that makes this system of emission projection seem
most feasible.  The U. S. Census Standard Industrial Classification (SIC)
codes aggregated land uses into numerical groupings by product.  It was
hypothesized, therefore, that industries with similar process emission
characteristics are grouped together.  These same SIC codes were chosen as
the common denominator since they are fai.iiliar to both planning agencies, as
                                                                              21

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   a tool to classify land uses, and to air pollution control agencies as a
   classifier of point source activities.
        The following chapters present the data used, analysis of emissions
   data, and discussion of projecting future emissions.
22

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                            DATA USED IN THE STUDY


     In order to derive activity indices for emissions estimations, a
substantial data base is required.  The original scope of work specified
that EPA would provide the desired data.  It was intended that the EPA
data collected for the National Emissions Data System (NEDS) would provide
sufficient data to perform statistical analyses.  Early in the study,
however, it was discovered that the EPA data could not be made available.
     Much of the information in the NEDS files is confidential material.
Although its nature is limited to pollutant-related statistics, industries
are very sensitive about releasing any information to their competitors.
Likewise, some industries are reluctant to have figures on emissions and
fuels available to groups and organizations that may lobby against the
interests of .the industries.  EPA was not able to provide the general
data files without ensuring that the confidence of the contributing
industries would not be violated.  Accordingly, the General Counsel of
EPA decided that the data files would not be released during the course
of the study.  Without these data the entire AQUIP Extension Study was
threatened.
     After consulting with the project officer, the consultant elected to
apply directly to several state air quality agencies for the data required
for the study.  The constraints of budget and time limited the number of
state air quality agencies that could be petitioned for information.  It
was decided that two reasonable sources of emissions data would be
Massachusetts and New Jersey, since both were familiar with the contractor.
Massachusetts data was filed in the Boston Office of the Massachusetts
Department of Public Health, only a few miles from the consultant's office
and through previous contracts in the state, a working relationship with
Massachusetts officials had been established.  New Jersey had been the
site of the previous AQUIP study, and was therefore considered an appropri-
ate state from which emissions data could be solicited.
     The consultant requested a fairly comprehensive set of data from both
states,  including point source information for each of the following cate-
gories:
     1)   SIC code for each pc'r.t source
     2)   Er.plC;.T..I;\" of the pcint source
                                                                             23

-------
         3)  Annual operating hours of the source
         4)  Fuel use by type and amount of fuel
         5)  Percent of fuel used for space heat
         6)  Product process rate
         7)  Floor area of the point source building.
         8)  Emmission rates for sulfur dioxide (SO ), total suspended
            particulates (TSP), carbon monoxide,  (CO),hydrocarbons (HC),
            and nithogen oxides (NO,,).

        As in previous air quality studies, the air quality agencies from
   each of the states were assured that all data used would be confidentially
   handled.  All point sources -were, accordingly, coded to a source number,
   and reference to the sources is by number and SIC codes only.  Since all
   data was supplied by the two states and since only EPA will review results
   there was no loss in confidentiality of the data.
        Although each of the states was quite cooperative, assembling the rele-
   vant data presented some unavoidable problems.  The commltant had hoped to
   expedite the data-gathering proces.s by securing the relevant materials in
   the form of computer tapes or computer card decks, but this was not possible.
   In each case compiling the data inventory involved obtaining clearances to
   examine state air quality data and extracting the appropriate information by
   hand from existing printouts and questionnaire files.  Tn many cases,
   accumulating the data required large amounts of time for cross-referencing
   and for normalizing the inputs into congruent sets of units.  In its final
   form the data inventory reflects the problems that were encountered.  Major
   shortcomings of the data set include problems in the following areas:
        Percent space heat or process heat data was not available for New
   Jersey.  Findings in this area are,  therefore, extrapolated from Massachu-
   setts data alone.  Employment figures for the point sources from both
   states were fragmented.  (In some cases the consultant does not feel
   confident that employment figures supplied are entirely accurate either,
   but the figures obtained from the state agencies were used without review.)
   Floor areas for the industries were not available.  Since floor areas were
   not available from the industries, they were derived using employment
   figures and a table uhich estinates unit floor areas per employee by SIC
   code.  This obviously ir
-------
     After screening and computer runs to organize the material, the final
form of the data consisted of some 5500 computer cards which outline informa-
tion (although fragmented and incomplete) for some 868 point sources of
industrial emissions.  Of these, 555 sources were from Massachusetts and
313 from New Jersey.  This included point sources with a wide range of SIC
code numbers, however.  The final working file of data was trimmed to
include only manufacturing industries - those whose SIC codes begin with
the digits "2" or "3".  Table 2-1 shows the two-digit classification  for  such
manufacturing industries.  The analysis concentrated on the general area of
industrial sources, whle institutional, commercial, and all other sources
which were presumed to be relatively minor were excluded from subsequent
analysis.  This decision left at least some statistical input for each of
a total of 644 manufacturing point sources.
     Management of the data collected presented the study with the prodigious
tasks of recording, keypunching, checking, sorting, aggregating and analyzing.
The consultant made use of its electronic data processing capabilities to
record, compile, aggregate, and tabulate the various point source-statistics.
The data management capabilities of the algorithms used are as follows:
     1)  For each point source the statistical analysis program produces
a summary, by source, of identifying information such as source identifica-
tion number and the following parameters of interest.
     SIC code
     Number of employees
     Operating hours/year
     Gross plant area (sq.ft.)
     Enclosed floor area (sq.ft.)
     Percent process heat (e.g., nonspace heating fuel use)
Missing data, if any , is so indicated.  Also, supplementary information
is generated as follows:  For each type of fuel, the amount of fuel, the
corresponding BTUs supplied and the percentage of the total BTUs supplied
by  this fuel, are so indicated.  Table 2-2 shows  the parameters  in  terms
of their computer algorithms.
     Also, for the sar.o fuel-u^e data,  given the total BTUs supplied (by
all  fuels), the totrl picccss K-it BTUs and the total space heat BTUs arc
co.vp'..: Led (if the percent proecv.s heat is kuov.n); the corresponding BTU/hcLii
                                                                           25

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                                  TABLF! 2-1
              TWO-DIGIT STANDARD INDUSTRIAL CLASSIFICATION  (SIC)
                           CODES FOR MANUFACTURING

                Industry Type                     SIC
                Food products                     20
                Textiles                          22
                Apparel                           23
                Lumber and wood                   24
                Furniture                         25
                Paper products                    26
                Printing and publishing.           27
                Chemical                          28
                Petroleum                         29
                Rubber and plastics               30
                Leather products                  31
                Stone, clay and glass             32
                Primary metals                    33
                Fabricated metals                 34
                Machinery                         35
                Electrical machinery              36
                Transportation equipment          37
                Professional, scientific
                precision-made instruments        38
                Miscellaneous manufacturing       39
26

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                        TABLE 2-2
                  PARAMETERS EXAMINED
general parameters
                                    PROC. RATE

                                    PERC. PROC. HEAT

                                    HOURS/YEAR
heat content
parameters
                                    FUEL BTU/HR
                                    pUEL BTU/HR_EMPL

                                    FUEL BTU/HR -ENC.*
(Broken down by Process, Space and Total heating,

 where TOTAL = PROCESS + SPACE)
fuel emission
parameters
                                    FUEL AMT/HR
                                    FUEL AMT/HR- ENC*
 (Broken down by pollutant: SO , Particulates, HC, CO, NO )
                              Z                          A
separate
process emission
parameters
                                    PROC AMT/HR

                                         AMT/HR-EMPL

                                    PROC AMT/HR-ENC*
 (Broken down by pollutant; S02, Particulates, HC, CO, NO )
                                                         -X
*BTU/hour per unit enclosed floor space

 AMT/hour per unit enclosed floor space
                                                                   27

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   (BTU  divided  by  operating hours/year), BUT/HR-EMPL  (BTU/HR per employee),
   BTU/HR-ENC.  (BTU/hour per unit enclosed floor area) are also computed for
   the process heat and space heat portions of total BTUs.  Note that  if
   such  variables as percent process heat, operating hours/year, number of
   employees, or enclosed  floor area are missing, the related categories will
   not be  created or shown.
        Emissions data are also analyzed; the amounts of fuel ('FUEL...) and/or
   separate  process ('PR0C...') emissions, by pollutant, are given.  Additional
   parameters such  as AMOUNT/ HR, AMT/HR-EMPL, AMT/HR-ENC may be computed and
   printed,  for  both fuel  and process emissions but only if certain variables,
   as mentioned  in  the previous paragraph, are not missing.
        For  compatibility  with the data sets from the previous AQUIP study,
   summary data  may also be given for FUEL BTU/HR-GR0., FUEL AMT/HR-GR0.,
   PROC  AMT/HR-GR0,  where  GR0 is the gross plant area.
        2)   The  statistical program will also aggregate or group, by 4-digit
   SIC codes, the total sample size, the number of non-missing data,
   the mean  and  the standard deviation of all of these variables.  The mean
   and standard  deviation  are determined as follows, where each of the 42
   parameters examined  (e.g., PROCESS FUEL BTU/HR) is symbolically represented
   as XL,  L  = 1,...,N:
                N
                Z   x
   and
                                        N - 1
   To conserve storage  of data  in  the  computer  files,  the  sums  Zx.  and Zx^
   are formed,  by point source  (N  is the number of  sources per  SIC).   It  is
   apparent  that  Ex.  and Zx,  may  be determined by  the following  iterative
   procedure:
28

-------
      For the k    source,
      I  x.  = x,      ,k=l
      L=l L    X
      k            k-1
      I  x.  = x,  + Z   x.     ,22lklN.

The means and standard deviations of all relevant variables (see Table 2-2)
are computed, using these intermediate sums, and a tabulation of all para-
meters, with nonzero means is then given; this includes the total sample
size, the number of non-missing data, the mean and the standard deviation
of all parameters, by activity code  (SIC).
     3)  The statistical program can also truncate the activity codes to a
specified number of characters (LENGTH-X) and reaggregate the total sample
size, the number of valid data, the mean and the standard deviation,
according to these truncated activity  codes.  In this way, statistics are
determined at the 1, 2, and 3-digit SIC levels.  The statistical program
performs this function in the following way:
     Given that Mm is the number of sources for the m   truncated activity
code, and xm and am are the corresponding mean and standard deviation,
respectively,
           J     _
           I  N  x
      x =
where
           J
      N =  £  N
            i
          m=l
intermediate sums (by activity code):
                                                                           29

-------
            I N
               m
            E N  x
               m  m
                   J
                   I ' {N  - 1) a   + N  x  }  NX 2
                    ,    m    J  m     mm
            a =\ |H£i	
                               N - 1

   intermediate sum:
                 - 1) a 2 + N  x 2}
               m       m     mm
  where there are J sets of parameters (activity codes) per truncated activity
  code.
       A tabulation of these variables, similar to that described above for
  4-digit SICs, will then be produced.

       4)  The statistical program also determines the percent of total BTU
  satisfied by each of the possible fuels.  In calculating the percentage of
  total BTU, by fuel, the following relationships are used:
       Given that btu. =  I  btu  (for all fuels)

  and  f.:   =   btu  (by fuel)
  for all N sources,
                   = f
                      J
F      'BTU
            =  L-1.J 'L-1,J +  J.'JJ >            L-2.3.....N
                     BTUL-1,J + btUJ
       PL,J= 10° '  FL,J,                              L=1,2,...,N
30

-------
 where
      L   =    source number
      j    -    activity code number
      FL-1 J and  B™L 1  J  are  the  fraction of tne previous total BTU,
            by fuel,  (up  to and including the L-lst.  source)  for activity
            code number J and  that  total  BTU, respectively;  f  and btu
                                                              *J        J
            are  the fraction  of the total BTU,  by fuel,  for  the present
            source  and that" total BTU,  respectively.

 BTU for activity code J,  for  each fuel, is  then calculated as:
                       BTU..  T  = btuT
                          •i j «J      «J
      BTU. T = btuT  +  BTU.  . _     L  = 2,3, ... ,N
        L f *J      J      Li— 1 j«J 9

     5)  The statistical program  took 320,000 bytes of capacity on  an  IBM
360/75.  In the course of the study the program  was used  in some seven
individual operational runs.  Typical run time was between 10  and  15 minutes.
The following pages give illustrative examples of example  input data (Figure  2-
2) and a data coding sheet  (Figure  2-3).
                                                                             31

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  PARAMETERS          HACKENSACK FOLLOW-ON
   &INPUT
          S C21,1  PARTIC'r'  C 0',' HYDRUC',I  N OX1. OUNIT=S*'
          FNAhsiCOAL't 'Rfc'S.  OIL'.'DIS. OIL1, 'NAT, GAS',
   &END
  RESET
  SRCE                1970 MASSACHUSETTS & NEw JERSEY  POINT  SOURCES
  POINT     2001
      1     3111
      2 1970      10               4460,        6!i>83   50,
      3            ,      158,         ,          ,
   .   4    1     12,         ,         ,          ,
  99999
  POINT     2002
      1     3111
      2 19700000014Q            0062440.      0087600095,
      3            t  0000630,         ,          ,
      4    10000049.     '    ,         ,          t          ,
  99999
  POINT     2003
      1     3999
      2 197000000020            0011220,      0030000100,            2,
      4    2       ,         ,         ,0000002.          ,
  99999
  POINT     2004
      1     3271
      * 197000000025            0023850,      0045500100,
      3            ,  0000164.         ,          ,
      4    10000012,         •         »          ,          t
  99999
  POINT     2005
      1     3443
      2 197000000053            0044732,      002D000100,            3.
      •4    £       ,         .         ,0000003,          .
  99999
  STATISTICS          3 & 4 DIGIT SIC'S
  PARAMETERS          TRUNCATE TO 2 DIGITS
   &INPUT LbNGTn=2»  SEND
  STATISTICS          2 DIG IT SIC'S
  PARAMETERS          TRUMCATE TO 1 DIGIT
   &INPUTLENGTH=1,  S.END
  STATISTICS          1 DIGIT SIC'S
  ENDJOB


                 Figure  2-2.  Example of Input Data.
32

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                    11  DATA
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                                                                     . PAOC- £..M.l±L;2
                                                                     I   !
                                                                                33

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                       ANALYSIS OF THE EMISSIONS DATA

     Using data and procedures described in the preceding chapter, the
relationship of industrial categories; (SIC code) to fuel and process emis-
sions for SO-, TSP, CO, HC, and NO,, was to be analyzed,  In terms of fuel
emissions the relationship of employment and floor area by SIC code to the
demand for space heating and total Bill demand was to be examined.  Likewise,
any propensity to use different fuels; was of interest as this might vary by
SIC code.  As previously mentioned, several constraints existed in examining
the data.  First, the distinguishing of emissions between fuel and process
was possible only for the New Jersey sample.  On the other hand, the variable
"percent process heat" was available for the Massachusetts sample only.
This parameter is necessary to distinguish between space heating and process
heating uses of fuels.  Finally, the sample sizes were not very good at the 3-
4-digit SIC level.  In fact, the sample sizes were not very good at the 3-
digit or 2-digit levels as well.  This made it quite difficult to analyze
very many subsamples or to perform meaningful statistical tests.
     Table 2-3 shows the results of the analysis at the 4-digit level  for
certain of the Massachusetts data.  The first column show:; the SIC code and
the second column the total sample size for each of the SIC codes.  The
next two columns show the mean for the parameter "percent process heat"
and the variance for that parameter as defined in the preceding chapter.
The next three columns show the sample size, mean and variance for the parameter
space heating in terms of BTUs per hour per employee.  The following three
columns show the same variables for the parameter total heating BTUs per hour
per employee.  The next six columns show the same variables, respectively,
for the parameters space heating BTU:; per hour per square foot, and total
heating BTUs per hour per square foot.   The final four columns show the
propensity to use different fuels by SIC code.  It can be clearly seen that
for the Massachusetts industries contained in the sample, residual oil was
by far the most commonly used fuel.
     For industry group SIC 20, the data is shown at the 4-digit SIC level
with the first code being SIC 2010.  A sample of only one existed here and,
therefore, no variance or analysis can be shown.  For SIC 2013 a sample size
of five existed.  The percent process heat is shown to be 88% with a variance
of seven.  The space heating BTUs per hour per employee is shown to be 3617
with a variance of 1972.  The total 3TUs per hour per employee is 30540

-------
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                                                     Z   2  00
                                                                                                                  
-------
with  a variance of  9285.  For many of the industries  shown  in this  sample,
information on floor area in terms of square feet was obtained  from a U.  S.
                                        4
Department of Transportation publication  which relates  employment  to square
footage by 4-digit  SIC.  Accordingly, the means and variances shown in  the
columns for BTUs per hour per square foot are directly proportional to  the
means and variances shown in the columns for BTUs per hour  per  employee;
the only variation  is that introduced by the varying  square feet per
employee by 4-digit SIC as contained in the DOT publication.
      In Table 2-3, only three 4-digit SIC categories were shown  to have  a
sample size greater than 10.  These are SIC 2821 with a  sample  of 10, SIC
3069  with a sample of 13, and SIC 3111 with a sample of  14.  In each case
some  variance is shown for the parameter percent process heat,  varying
between 19% and 24%.  For SIC 3069 and SIC 3111 a sample size of eight
exists for the BTU per hour variables.  For the space heating BTUs  per
hour  per employee and per square foot variables, significantly  high vari-
ance  is seen relative to the mean.  In the case of SIC 3111 the variance
is actually higher than the mean.  On the other hand, for the variables
total BTUs per hour per employee, and per square foot., the  variance is
reasonably good, particularly in the case of SIC 3069.  For these SIC
codes and for others which were examined, this type of pattern  often
existed, leading to the tentative conclusion that the information on total
fuel  use and, therefore, BTU demand, is probably more reliable  and
accurate than the parameter percent process heat.
     One  would expect  to find fairly good  correlation  between employment and
the heat  demand for that number  of employees,  or between square  footage  of
plant area and the heat demand for that  square footage.  In other words heat
demand in BTU/hr/employee or BTU/hr/square foot  should not  vary  significantly
from plant to plant within the same industry and climate.  However,  better
correlations were  found with total fuel  demand than  with the demand for fuel
heating.   This may be expla^r-cj  by the inaccuracies  for the variable percent
process heat:  when th<* percent process heat shows very little variance,  then
the relations!.-i-p between space heating BTUs per  hour and total  heat BTUs per
hour is shown to be quite good.   This is true,  for example, for  SIC 2071 with
a sample  size of four.
36

-------
     On the 3-digit level SIC 201 shows the same information as SIC 2013
because the only valid information at the 3-digit level comes from that
particular 4-digit SIC.  On the other hand, SIC group 208 includes the
information from SICs 2084, 2085 and 2086.  Here it is found that for a
sample size of four the variance for space heating BTUs per hour is greater
than the mean, whereas for total BTUs per hour it is approximately one-half
the mean.  This may be due in large part to the very large variance seen in
the percent process heat variable.
     At the two digit level there is a sample size of 30, a fairly large
variance for percent process heat for SIC 20, and, again, variances greater
than the mean for both space heating BTUs and total heat BTUs.
     Finally, if one looks at the variable space heating BTUs per hour per
square foot for all the 4-digit SICs shown in Table 2-3, one sees a variation
in the mean from 6 to 102 BTUs per hour per square foot, although this
should be a fairly uniform number if the data were accurate and the reporting
of the percent process heat, in particular, were accurate.  In the full
sample there is even greater variation as exhibited by the variance to the
mean.  This was to have been the base variable in the sense that if anything
could be thought to be uniform in terms of these parameters, it would be
the amount of heat required to heat "X" number of square feet of floor space.
The fact that one sees so much variation at the 4-digit level makes both
the use of this sample size and the reporting of the parameter "percent
process heat" in particular, suspect.  The relatively good results shown
for the variable_total heat BTUs per hour underlines the weakness in this data.
     Table 2-4 shows the  entire sample  for SIC  20  for Massachusetts.  It shows
the variables percent process heat, space heating BTUs per hour, total heating
BTUs per hour, and employment.   The data shown in Table 2-3 was derived from
this information.   Figures 2-4, 2-5 and 2-6 show the plots  of  the variables  space
heating BTUs per hour, and total heating ETUs per hour vs.  employment.
This information was plotted to examine graphically the variance so as to
provide better insight into the analysis of the information shown in Table 2-3.
     Figure 2-4 shows, on semi log paper,  space  heating BTUs per hour vs.
employment; whereas Figure 2-5  shows total heating BTUs per hour vs. employ-
ment.  Finally,  for the center area of the scale, Figure 2-6 shows on normal
graph pciper total  BTUs per hour vs. employment.  Again,  the variable total
BTUs per hour exhibits better correlation than docs the variable space
heating BTUs pc-r hour, although intuitively the reverse v.oulu be expected.
                                                                               37

-------
                      TABLE 2-4  MASSACHUSETTS  TOTAL SAMPLE  FOR  SIC  20
SIC Code
2010
2013
2013
2013
2013
2013
2026
2026
2033
2042
2042
2052
2062
2062
2062
2071
2071
2071
2071
2084
2085
2085
2086
2087
2094
2094
2095
2095
2098
2099
Percent Process
Heat
100
97
90
81
80
90
60
88
68
N/A
N/A
N/A
95
N/A
N/A
62
50
55
70
0
50
90
55
N/A
90
98
100
N/A
N/A
N/A
Space Heat
105 BTUs/hr

2.2
5.2
28.0
7.6
9.0
91.0
23.0
24.0
N/A
N/A
N/A
100.0
N/A
N/A
32.0
1150.0
:>7.o
152.0
200.0
46.0
4.3
37.0
N/A
7.3
1.9
N/A
N/A
N/A
N/A
Total Heat
105 BTUs/hr
90.0
75.0
52.0
140.0
38.0
91.0
230. Q
200.0
7S.O
N/A
N/A
N/A
2100.0
N/A
N:/A
84.0
360.0
59.0
174.0
200.0
92.0
43.0
82.0
N/A
73.0
93.0
N/A
N/A
N/A
N/A
Employment
150
250
200
700
125
200
200
500
250
N/A
N/A
N/A
500
N/A
N/A
100
500
200
1109
262
310
52
250
N/A
40
58
50
N/A
N/A
N/A
38

-------
      1400
      1200
      1000
      800
CD
Q.



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      400
      200
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                                               62071
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                Figure 2-4.   SIC 20 Space Heating  BTUs  Per Hour vs. Employment
                                                                            39

-------
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      1200
      1000
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                           Figure 2-5.  SIC Total Heating BTUs

                                   Per Hour vs.  Employment
   40

-------
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-------
Interestingly, in Figure 2-6,  based only on  a  sample size of four, SIC 2013
shows a decent fit between the four data points,  giving a nearly one-to-one
correlation between total heat demand and employment.   The fit for SIC 2085
and SIC 2071 is not quite as good but is also  promising, as is the case for
SIC 2094.
     In summary, based on the information in Tables 2-3 and 2-4,  as  well as
Figures 2-4, 2-5 and 2-6, there seems to be  some  promise  for finding good fit
between the variables under investigation.  However,  the sample sizes
and the lack of detailed information about how the data were originally
determined makes it impossible to prove or disprove any  usefulness in
these correlations.
     The next set of information exa-nined was  the 4-digit SIC data for
New Jersey for fuel and process emissions per  hour per employee and per
square foot.  The data for SIC 28 at the 4-digit and  3-digit levels are
shown in Table 2-5,  for emissions per hour per employee, and Table 2-6  for
emissions per hour per square foot.  In each case, the first column  shows
the sample size, mean and variance for total heating  BTUs per hour per
employee.  The remaining 10 columns show the mean and variance for S0_,
TSP, CO, HC'and NOY, respectively.   For each SIC code the first line shows
                  A
the fuel emissions and the second line shows process  emissions.  Immedi-
ately preceding the SO,, mean column in parentheses are found the sample
sizes for the fuel and process emissions.  At  the 4-digit level SIC 2818
has a total sample size of 10 with valid data  for nine sources for fuel
emissions and seven for process emissions.  The mean  for total heating
BTUs per hour per employee is shown to be 222,000 with a variance of 134,000.
Referring to Table 2-3 for SIC 2818 in  Massachusetts, there was a sample size
of two and a mean of 74,000 total heating BTUs per hour per employee with
a variance of 53,000.  This does not agree favorably  with the 222,000
mean shown for the New Jersey sample, but is quite characteristic of the
results that were found for all SIC categories.
     Looking at the emissions per hour per employee for fuel emissions,
the variance is greater than the mean for S0_  and'is  less than the mean
for the other four pollutants.  For TSP and CO in particular,  the variance
is one-third to one-quarter of the mean.  For  the process emissions,
based on a sample size of seven, only NO, shows a variance greater than "the
mean.  At the 3-digit level for SIC ,281 for a  sample  size of 19 the total

-------





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     BTUs per hour per employee  shows a mean of 329,000 with a variance of
     291,000.  Again, for  fuel emissions only S02 shows a variance  greater than
     the mean.  However, for process emissions a larger variability is shown
    with SO , HC, and NO  all showing variances greater than the mean.  One
           Z            A
    would expect that the process emissions would show greater variability
    than the fuel emissions.
         Table 2-6 shows the comparable data for emissions  per hour per square
    foot.  Again, at the 4-digit level the numbers are directly proportional
    to those shown in Table 2-5, since the square  footage data is based upon
    the U. S. DOT relationship of employment to square feet at the  4-digit
    level.
         For those SICs that end in 9, such as 2899 or 289, the data would be
    expected to show additional variation because the industrial categories
    themselves are aggregations of disparate industries.   If one examines SIC
    289 it is seen that for a sample of 27, the mean for total heating BTUs
    per hour per employee is 582 with a variance of 1018.   For fuel emissions
    both S0_, HC, and NOY show variances greater than the mean,  whereas for
           £            A
    process emissions one also sees that S09, HC and NO  show variances greater
                                           
    -------
            Figure 2-7 shows the plotting of S02  fuel and process emissions per hour
       vs. employment for SICs 2810, 2813, 2815,  2818,  2819 and 28121.  For fuel
       emissions all but three of the sampled industries shown used 100% residual
       oil.  The percentages  of residual  oil  vs. other  fuels  are shown  on  the graph
       for the  three  other  sources  sampled.   With  the exception of these three
       sources, a  good  correlation  between  fuel  emissions  and  employment would be
       expected, since  fuel emissions should  correlate  well with total  BTUs  per hour
       and vary with  space  heating  Bills  per hour only insofar  as percent process
       heat varies.   A  fairly good  correlation is  shov.n for SIC 2818  and SIC  2819,
       the two  A-digit  SICs with a  reasonable sample size.  Insufficient data
       exists for  an  analysis of the process emissions  in  Figure  2-7.  There  are two
       data points for  SICs 2818 and 2319.
            In  summary, as  in the case of the Massachusetts data, no  definitive
       conclusions can  be reached because of  the sample sizes.  However, where the
       samples  are reasonably good  the variance  relative to the means for  the
       fuel emissions seems reasonable.   This is borne  out by  the information shown
       in Figure 2-7.   The expected variability and lack of information  for process
       emissions relative to  fuel emissions was  also found,
            The variance between the SIC  categories seems  high enough to make the
       analysis by SICs worthwhile.  In  other words, the variation shown in  the
       means at the 4-digit SIC  level is  quite high relative to the variance  from
       the mean for any particular  SIC.   As a result further research in this
       direction should prove valuable in determining useful  indices.   In  particular,
       it is seen  that  the  variances relative to the mean  at the 3-digit level for  SIC
       281 are  quite  high relative  to the variances at  the 4-digit level for  the SIC
       281 series.
            Table  2-7 summarizes the BTU  per hour parameters analyzed at the  2-digit
       and 1-digit SIC  levels for the Massachusetts sample.  The first  column shows
       the SIC  code;  the second, the sum  of the  New Jersey and Massachusetts  samples
       for each SIC category; the third  column,  the Massachusetts sample;  the fourth
       column,  the percent  process  heat;  the  next  three columns, the  sample  size,
       mean and variance for  space  heating  BTUs  per hour per  employee;  the next
       three columns, the sample size, mean and  variance for  space heating BTUs
       per hour per square  foot; and, the last three columns,  the sample size, mean
       and variance for total BTUs  per hour per  square  foot.   It shows  the sair.e
       type of  information  as Table 2-3  except at  the 2-digit  rather  than the 3-digit
       and 4-digit levels.
    46
    

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         No systematic findings can be made; the BTUs per hour and per employee
    vary quite a bit with some fairly high variances.  In fact, it is not clear
    whether one introduces more accuracy by using 2-digit indices than would
    be found with a single index for manufacturing as a whole.  The variability
    between 2-digit SICs is high for both the total heating BTUs per hour and
    the space heating BTUs per hour.  Thus, it is not just a matter of inaccurate
    reporting of the percent process heating variable.
         Table 8 shows the propensities to use different fuels: residual oil,
    distillate oil, natural gas and coal for both the Massachusetts sample and
    the New Jersey sample for 1-digit and 2-digit SIC categories.  From an
    examination of Table 2-8, it is clear that variation in geography  and supply
    of fuels rather than SIC code predominate.  There is a much  higher propensity
    across the board for SIC categories to use residual oil in Massachusetts than
    in New Jersey.  However, it should be noted that the lowest percent residual
    oil occurs for SIC 34 in both New Jersey and Massachusetts, and that in all
    cases in New Jersey where the percent of residual oil is over 90,  the percent
    of residual oil in Massachusetts is over 99%.  It is difficult to examine the
    fuel propensities in much greater detail because of the high dependence upon
    residual oil for all industries sampled in both states.
         Table 2-9 shows fuel and process emissions per hour per employee on a
    2-digit SIC level for both New Jersey and Massachusetts data.  The New Jersey
    data is an extension of Table 2-5 but at the 2-digit rather than the 3-digit
    and 4-digit levels.   The Massachusetts data shows aggregated fuel  and process
    emissions since no breakdown could be determined from the sample.   The
    numbers in this table were used to derive the industrial emissions in the
    Task 3 Report.5  Both the fuel and process emissions from the New Jersey
    data were used as a guide as to whether an industry was an A, a B, or a C
    classification and,  further, whether it were a B-, a B, or B+ subclassifi-
    cation.  Where insufficient data for a particular pollutant was available
    from the New Jersey data, the Massachusetts information was used but less
    reliability was attached to it.  Finally, for certain SIC categories (parti-
    cularly SIC 23 and SIC 25) default information was used, based upon other
    parameters in the table, with a general caveat that such industries were of
    the cleaner variety.
         In general, it was found that at the 2-digit level the variances are
    quite high relative to the means although in the case of TSP the variances
                                                                                49
    

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    are in general less than the mean.  Both the incidence and the accuracy of
    process emission data are quite variable and no definitive conclusions can
    be reached.  Again, the more traditional point source pollutants, SO , TSP
    and secondarily NO ,  show a more complete set of information.   Examples of
                      A
    the overall variability and unreliability of the data at an aggregated level
    can be seen by examining the 1-digit figures at the bottom of the table.
    For SIC 2 under SO ,  a value of 255 x 10   Ibs/employee-hour for fuel emis-
                      *- s
    sions and 1570 x 10~   Ibs/ employee-hour for process emissions is found;
    however, the sum of those, as exhibited in the Massachuestts total data, is
    only 40 x 10~" Ibs/employee-hour.   For SIC 3 the fuel emissions are 42 x 10"
    Ibs/employee-hour and the process  10 x 10   Ibs/employee-hour whereas for
    Massachusetts the aggregated number is 32.x 10   Ibs/employee-hour and for
    those sources where separate proces.s emissions were available the mean was
    142 x 10~  ibs/employee-hours.   For SIC 2 the same kind of findings are ex-
    hibited for TSP, whereas for SIC 3 Massachusetts values are of an order of
    magnitude greater than the New Jersey ones.
         Although reasonable results were not expected at such an aggregated
    level, the variability that was found was quite disappointing.  Moreover,
    if one examines SIC 20 for S00  one sees that fuel emissions are 165 x 10
                                                                         f\
    Ibs/employee-hour from New Jersey  data and total emissions 21.6 x 10   Ibs/
    employee-hour Massachusetts data.   In both cases the variances are greater
    than the means.  In general, there were too few 2-, 3- or 4-digit categories
    that had enough information or showed similar findings to be able to make
    either positive or negative conclusions about the accuracy or usefulness of
    the different comparisons.
         Accordingly, it  was not fruitful to use this information to project
    future emissions or to update the  activity indices of the AQUIP System.
    

    -------
                              PROJECTING FUTURE EMISSIONS
    
         The previous AQUIP study  pointed out that current data should be used
    as much as possible to develop the future inventory as shown in Figure 2-8.
    For the purpose of consistency, sources in the current inventory should be
    carried forward to the future time period and only the most significant
    new sources added as point sources; other sources should be added as area
    sources.  Regional and national protective data and "control totals" as
    to fuel use, population, and employment should be used in conjunction v.'ith
    the most reasonable activity indices.  Many of these indices, such as the
    heating demand per square foot, need not vary greatly from region to region,
    except with variation in temperature.  Others, such as propensity to use
    different fuels, are highly a function of current uses in the particular
    region.  Fairly reasonable estimates can be made of the number of hours
    of operation for each type of facility and for process heat for all land use
    categories except industrial.  Lack of information and tremendous variation
    in this variable, as experienced in the point source inventory, affected
    the results of the previous study.  Finally, with uncertainty in inter-
    national fuel supplies, even one to two years in the future, it was vir-
    tually impossible to make reasonable estimates by land use category for
    1990 as to fuel usage.  In using the activity indices determined in the
    previous AQUIP study, the planner is constrained by the national and
    regional availability of fuel-use related data.
         The projection of fuel consumption for 1990 made in the previous AQUIP
    study was based largely on national trends.  Little information is avail-
    able on the different regional areas such as the New York metropolitan
    area.   Furthermore, it was beyond the scope of the previous AQUIP study to
    undertake a detailed regional fuel projection analysis.  Several nationwide
    projections are available, the results of which are inconsistent with each
    other.   The majority of these projections were made before 1965 and all
    projections make assumptions that are suspect.
         An elaborate system was set up in the previous AQUIP  study to project
    percent process heating, schedule, fuel use propensity and process emissions
    for existing New Jersey industrial sources to 1990.  Indices derived from
    current activity data for the individual source as well as data on current
    erployces,  enclosed spcice and gross pl;mt circa \;crc requested for each
                                                                                 55
    

    -------
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    industrial source in the inventory.  The data obtainable for a large number
    of sources were the number of employees; therefore, this parameter was used
    as the major projection variable.  The availability of this parameter (and
    the unavailability of other parameters) was confirmed in the AQUIP Exten-
    sion Study.
         For each point source the number of BTUs for space heat per hour per
    employee was derived in the previous AQUIP study.  It was assumed that this
    parameter v/ould not vary significantly by industrial category; however,
    when summaries were made by industrial category, wide variation was found
    and no statistical conclusions could be drawn.  This is, no doubt, due in
    part to the inaccuracy in the percent process heat variable from which
    the amount of space heating vs. process heating is derived.  Again, similar
    findings have occurred for the AQUIP Extension study, although the data
    base has been larger.
         Information was determined on the ratio of 1980 to 1969 employment
    by county and SIC code from the New Jersey Bureau of Labor and Industry
    in the previous AQUIP study.  Many assumptions had to be made because of the
    categories of SIC codes for which the data are available and the labor market
    areas (cutting across county boundaries) for which information was assembled.
    It was intended in the previous AQUIP study to project 1990 space heating
    directly in BTUs per hour using the employment ratios and any assumed change
    in the BTUs per hour and employee index.  This would then be combined with a
    new projection of percent process heat to yield total BTU heat demand for a
    source for 1990.  Accordingly, information on current percent process
    heat was used to develop an index of percent process heat by SIC.  This
    parameter yielded two broad categories of industrial use.  It was therefore
    concluded in the previous AQUIP study that present information was not
    sufficient to carry through the analysis as intended.
         Initially, our intentions in the AQUIP Extension study were to improve
    upon the data and to relate indices contained within the basic categories of
    economic, geographic and demographic factors to emissions associated with
    industrial activities.   Intuitively, this approach is sound for residential
    and commercial activity where heating of homes and transportation are the
    principal activities which generate emissions.  Here one could expect a
    high decree of correlation bctueon such indices as "floor space," "number of
    dialling units," "nu~Ver of occi'p''nt:s," "dc';ree-days," "passenger miles," etc.,
    
                                                                                 57
    

    -------
        and  emission  levels.   In  fact, suitable and accurate relationships have been
        derived  by applying regression techniques to actual observations for  such
        variables.  In  such cases the fuels burned, the technology of heating and
        the  technology  of the  combustion engine were fairly uniform throughout the
        area and range  of observations made.  Furthermore, the technology  of emis-
        sion control  associated with the technology of heating and transportation
        were also fairly uniform.
             Relative to industrial activity, one would expect that for any industry
        (described by an SIC code) similar relationships between such indices as
        "floor space" or "employment" and emissions might also pertain, provided
        there was uniformity in:
             1)   Technology  of supplying power and heat
             2)   Technology of the processes  (manufacturing and production)
             3)   Technology of emission controls applied
        Unfortunately,  such a  constraining condition is rare within any industry.
        or group of industries.   For example, if today one were to select any
        industry that has the  facility to supply its own power and heat, one  will
        find a vast' spectrum of fuels burned  and applied technology.  Figure  2-9
        depicts  schematically  the range of possibility involved in this one instance.
        Figure 2-10 illustrates in a similar way a limited  range of possible process
        variations in petroleum refining with just a small number of applied  tech-
        nologies.   As a consequence, one would expect to find for any respectable
        sample of facilities within any one industry a large variance between
        activity indices which are purely demographic, geographic and economic in
        character and emission levels.  This  is especially true when one takes into
        consideration the vast array of emission-generating processes involved.
        Indeed,  observations associated with  the analyses of variance recently
        undertaken by others   have vindicated this expectation.
             As  the aforementioned realizations become more apparent, the plan for
        simply relating activity  indices to emissions by regression analyses  was
        augmented to  include constraints of power generation technology, process
        technology (particular to a given industry) and applied emission control
        technology.  In accomplishing these investigations we would expect to:
             1)   Establish relations between  economic productivity, demographic
                 and  geographic variables and emissions for each industrial element
    58
    

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              (as codified by SIC) employing similar energy-generating and
             process technology.
         2)  Establish relations between applied technology  and emissions
             for the industrial elements considered.
    These observations would assist in establishing trends in the effects
    certain technologies might have in either augmenting or alleviating
    emissions and would form the quantitative basis upon which the forecasts of
    future emissions might be derived by planners.  Furthermore, such studies
    would also entail predictions relative to the qualitative changes in emis-
    sions that would be likely as a consequence of application of more advanced
    technology.
         Our intentions were to establish models that would employ available
    economic and technologic information as well as an appropriate analytical
    framework for such models.  The analytical framework depends mainly on
    a multiple regression approach wherever the data permit.   For the purposes
    of formulating structural models, the number of data observations were to
    be developed on a geographical basis for use in the regression models.  Since
    consistent and comparable data on emissions and other related variables would
    probably not be available for any time periods of significant duration, the
    analysis to be carried out in this regard would be chiefly a cross-section
    one on a spatial basis, rather than a time series against the two objectives
    previously mentioned: (1) structural relations to explain the correlation
    between emissions and related indices and technologies, and (2) prediction
    of future emissions for use in planning formulations.
         Development of forecasts would be carried out in two stages with
    respect to quantity and to type.  The latter would have to be developed
    separately from the regression models since the models cannot be used in
    predicting changes in composition or technology on a qualitative basis.
         As previously mentioned, the analytical approach which was envisioned
    involves fitting regression equations by the least-squares method of
    estimation for different types of emissions.  The success of this approach
    depends upon the number of independent observations (at least ten) that
    can be collected on a cross section basis for each type of emission within
    the individual industries to be considered.
                                                                                    61
    

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           A search for available informatian within various industry classifi-
      cations that contained the distinction required relative to activity
      indices, process technology,  power technology and emission control tech-
      nology, proved disappointing.   All indications pointed toward a rather
      extensive program of data-gathering by such means as interviews and question-
      naire mailings since the existing data bases were so incomplete and incon-
      clusive.  Since neither time nor resources had been budgeted for this purpose,
      investigations in this regard were curtailed.  This direction appears to
      hold considerable promise and such research should be supported.
           Having worked with the 1970 Implementation Plan emissions data pro-
      vided by the states has given the consultant some insight into the kinds
      of data required to plan for urban and industrial air quality.  While there
      is sufficient information available to hypothesize planning procedures,
      the quantity and quality of existing data are useful for only tentative
      conclusions.  It is suggested that future studies work within the framework
      of this AQUIP Extension study,  but that future results are now limited by
      the availability of pertinent data.  With this in mind, it is suggested
      that the type of data inventory shown in Table 2-10 is necessary for further
      studies of this nature.
           Item 1
           A point source code number is necessary for data handling since con-
           fidentiality is important in dealing with emissions factors.   Such a
           code number would be used as a mechanism for storage and retrieval
           by computer systems, of relevant  air quality data.   A numerical code
           could be used at either the state or national level.  A national cod-
           ing of alphabetical and numerical figures would prove to be most
           useful.  For those states that already have their own coding systems
           conversion to a national system would be relatively simple.  For
           mapping purposes x and y coordinates in some standardized coordinate
           system would nlso be asked for.
           Item 2
           SIC codes are currently used in Implementation Plan data.  Any
           reasonable data file would incorporate SIC codes as a means of
           grouping point sources by both land use and point source charac-
           teristics.  In future work, however, the use of SIC codes could
    62
    

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                  TABLE 2-10
    INVENTORY OF AIR QUALITY PLANNING DATA
    FOR IMPLEMENTATION PLAN QUESTIONNAIRES
    1.
    2.
    3.
    4.
    5.
    6.
    7.
    8.
    9.
    10.
    11.
    12.
    13.
    so2
    TSP
    NOY
    A
    HC
    CO
    Point Source Code Number (assigned by air quality agency) MA- 1234
    Standard Industrial Classification Code Number (s)
    Year to Which Data Applies
    Employment of the Point Source
    Annual Hours of Operation
    Floor Area of the Point Source
    Annual Fuel Use
    Percent of Fuel Used for Space
    Degree Days at Point Source
    Process Weight Rate (pounds per
    
    
    
    (square ft.)
    Coal (tons!
    R. Oil (10, gal.)
    D. Oil (10-5 gal.),
    Nat. Gas (10. ft )
    Heating
    
    hour)
    Solid Waste Rate (pounds per hour)
    Percent Fuel for Incineration of Solid Waste
    Space Heat
    Emissions
    Tons/Yr
    a. 43.0
    b. 12.0
    c. 6.4
    d. 8.2
    e. 2.4
    14. Separate
    Process
    Emissions
    Tons/Yr
    a. 0
    b. 0
    c. 2.1
    d. 12.2
    e. 3.1
    2431
    1970
    215
    6000
    40,000
    a. 0
    b. 155
    c. 0
    d. 20
    60
    4000
    100
    0
    0
    15. Solid Waste
    Emissions
    Tons/Yr
    a. 0
    b. 0
    c. 0
    d. 0
    e. 0
                                                                63
    

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    be greatly expanded.  In this particular study the inventory
    of manufacturing point sources was relatively small.   This made
    it necessary to use 2-digit SIC codes to group industries with
    simlar point sources.   These 2-digit groupings are in large part
    responsible for large variances because of the tremendous diversity
    within 2-digit SIC groupings.  Future work should be done with a
    sufficient base of data to fully use the SIC coding system.
    The SIC codes for industries could be used on a 2-, 3-, or 4-
    digit basis depending on the size of the data base.  The 2-digit
    codes which have been used in this study (e.g., SIC 23) identify major
    industry groups such as "manufacturers of food and kindred products."
    At this stage there was only enough information to compile a marginal
    statistical sample for most industrial groups.  With an expanded
    set of point sources and with increased data completeness, 3-digit
    codes (e.g., SIC 231)  which identify subgroups within an industry,
    or even the 4-digit codes which specify industries by specific
    products (e.g., SIC 2311 meat packing plant) could be used.  As the
    code became more detailed so should it be expected that air quality
    planning parameters would become more accurate..
    
    Item 3
    
    The year to which data applies is merely a "bookkeeping" measure.
    Since data will be solicited and checked periodically some means of
    insuring consistency in time periods is required.
    
    Item 4.
    
    Employment is one of the two major units for quantifying activity
    indices in the planning proces.s.  Production rates, population,
    and economic grov;th are all dependent upon employment to seme
    measure, yet Implementation P.'.an data fails, to give employment by
    point source.  In the course of this study the emplo}inent figures
    provided relatively major data management problems.  It was neces-
    sary to seek employment estimates from multiple sources.  In some
    cases questionnaires from the sources outlined the employment
    figures.  In other cases it v.as necessary to petition state agencies,
    

    -------
    such as the New Jersey Department of Commerce and Labor for employ-
    ment figures.. These figures, when available, were of questionable
    reliability for several reasons.  First of all, the employment
    may or may not correspond to the time period for which the Imple-
    mentation Plan data is valid.  Secondly, the employment may not
    correspond directly to the point source (i.e., a manufacturer
    may produce two or more products at different locations within the
    state but the state agency will list employment by corporation,
    rather than by point source).  All the variables dependent upon
    employment were affected by the quality of the employment figures
    that had to be used in the study.  Statistical analysis categories
    that were affected by employment figures include the following:
    1)  "FUEL BTU/HR-EMPL" - the activity index which indicates
        a propensity to consume energy.  (This might also be phrased
        as a unit demand for fuel in BTUs per manhour.)
    2)  "FUEL AMT/HR-EMPL" - is the activity index which describes
        the unit propensity to emit pollutants in pounds of pollutant
        per manhour for space heating.
    3)  "PROC AMT/HR-EMPL" - is the activity index which describes
        the unit propensity to emit pollutants from industrial processes,
        in pounds of pollutant per manhour.
    Briefly, then, three major projection indices are directly affected
    by the quality of employment data.
    
    Item 5
    Annual hours of operation are presently included in Implementation
    Plan data.  Operation hours are necessary to calculate unit time
    figures for projection.   The primary projection units (grams per
    employee hour and grams  per enclosed floor area hour) require some
    means of time normalization.  The annual hours of operation provide
    this means.
                                                                              65
    

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             Item  6
    
             Floor area  is the only index, other than employment, that can
             currently be used to quantify emissions into a systematic unit
             grouping for planning calculations and projections.  Implementation
             Plan  data are void of floor areas, however.  In fact, preliminary
             investigations show that there are no figures on floor area avail-
             able  from any source.  This directly affects the remaining set of
             activity indices.
    
             1)  "FUEL BTU/HR ENC" - is the fuel demand per unit  time and
                floor area.
             2)  "FUEL AiMT/HR ENC" - is the unit propensity to pollute per
                unit time and floor space for space heating.
             3)  "PROC AMT/HR ENC" - is the unit propensity to emit industrial
                process emissions per unit time and floor space.
             Since no floor area data were available to input directly into
             statistical analysis, rather than abandon consideration of floor
             area  as a useful tool in calculating and projecting  emissions,
             floor areas were synthesized from employment numbers.  While this
             provided some unit activity indices incorporating floor area, it
             must  be realized that all floor area data is limited by the quality
             and availability of employment figures (since it is derived from
             employment).  Where employment is missing, floor area is missing;
             where employment is not accurate, neither is floor area; but even
             with  reasonable employment date., floor area validity cannot be
             assumed.  Average floor height could also be obtained so that heat-
             ing demand  reflects building volume as well as floor space.
    
             Item  7
            Annual  fuel use  is currently included in Implementation Plan data.
            Since fuel use is a major variable in pollutant emissions its
            inclusion in data files is imperative.  Current, detail in providing
            fuel data is adequate, however,
    66
    

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    Items 8 and 12
    
    The allocation of fuel use is particularly relevant to planning
    for air quality.  The knowledge of how a fuel is used helps project
    the level of emissions by specifying the combustion characteristics
    and also by providing a means for estimating related plant activi-
    ties.  Traditionally, air quality inventory data has included per-
    cent process heat and percent space heat as the two means of fuel
    use.  It seems that probably three categories of fuel use may be
    warranted.
    1)  Percent Space Heat Fuel has generally been estimated in the
        past.  The quantities of fuel required for space heating
        should be fairly predictable and generally unrelated to
        SIC code.  Space heat is dependent upon enclosed floor
        area and heated volume for the most part.
    2)  Percent Process Heat Fuel has generally been taken to be the
        total annual fuel consumption less the space heating fuel.
        This process fuel use has been the index for estimating
        process heating emission levels.  The process heating
        emissions are those that may be expected to differ from
        industry by SIC codes.  The greater the fuel use the
        greater the process emissions that are anticipated.
    3)  Percent Incineration Fuel is not currently a data file vari-
        able.  In many cases solid waste may play a large part in
        total point source emissions, yet the use of fuel for incin-
        eration is unquantified.  While this may be unimportant on
        the large scale it may be quite important for local considera-
        tions where solid waste disposal is a significant element.
    
    item 9
    Degree days are not available from Implementation Plan data, yet
    are a necessary element in projecting space heating requirements.
    Given the number of degree days and the floor area of a plant, a
    reasonable space heating fuel estimate can be made.  The accumu-
    lation of degree-day data would be relatively easy.  By knowing
                                                                               67
    

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           the location of each point source, degree-days for each point source
           could be determined using data from the weather service and from
           local distributors.
    
           Item 10
           Process Weight Rate is currently included in Implementation Plan
           data inventories.  This information is useful in forecasting process
           emissions.  Its current use and availability is adequate.
    
           Item 11
           Solid Waste Rate is necessary for projecting solid waste incinera-
           tion emissions.  Assuming that certain similar products have
           certain similar wastes, it can be seen that as process emissions
           are characteristic of industries (by SIC code), so are solid waste
           emissions by product.  It must be realized, however,  that not all
           facilities with similar production activities can be  expected to
           have similar solid waste characteristics; some may have all wastes
           trucked away, some may burn all wastes, and some will be somewhere
           between the two extremes.  Solid waste, then, becomes; an independent
           variable for which data is required.
    
           Items 15, 14 and 15
    
           Space heating emissions and separate process emissions are currently
           catalogued in Implementation Plan data.  There is, however, no
           current classification for solid waste emissions, or  they are
           recorded as process emissions.  Where there are process emissions
           there is no means for distinguishing which of the emissions are
           due to separate process and which are due to solid waste burning
           or incineration.  Stack parameters - height, exit velocity, tem-
           perature, etc.  - would also be. useful information for diffusion
           studies.
    68
    

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                               REFERENCES FOR PART 2
    
    1)    Willis,  B.  H.,  et  al,  The Hackcnsack Meadowlands Air Pollution Study,
             Summary Report and Task  Reports,  Tasks 1-5,  prepared for the
             State of New Jersey,  Department  of Environmental Protection,
             Trenton, 1973.
    2)    See the following:  Kircher, D.  and  D. Armstrong.  An Interim Report
             on Motor Vehicle Emissions Estimation, USEPA, Office of Air Pro-
             grams,  October 1972;  and Compilation of Air Pollutant Emission
             Factors, USEPA, Office of Air Programs,  Publication No.  AP-42,
             February 1972  and April  1973.
    3)    See the reports produced  for the Land Use Planning Branch, Office of
             Air Programs,  by Argonne National Laboratories,  1970-1973.
    4)  See Estimating Land and Floor Area Implicit in Employment Pro-'
            jections, Vols. 1 and 2,  U. S. Department of Transportation,
            Federal Highway Administration, 1970.
    5)   Epstein,  A.  H.,  et  al, A Guide for Considering Air Quality in
            Urban Planning, prepared  for USEPA, March 1974,
            Contract No. 68-02-0567.
    6)    See the  following  reports by the Hudson Institute:   "Study on the
             Corporation Environment, 1975-1985," and "Energy and Energy Fuels,"
                                                                                     69
    

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                PART 3
            TASK 2 REPORT
    COST-EFFECTIVE PLANNING FOR
      ACCEPTABLE AIR QUALITY
    
                by
          Alan H. Epstein
                    70
    

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                                     SUMMARY
    
         This document presents a methodological approach to planning for and
    evaluating the impact of land use on air quality.  The planning process is
    viewed as a series of sequential steps in which the economic implications
    of planning decisions are evaluated in terms of their dollar value impact
    on air quality.  In this way postulated plans may be designed to be compat-
    ible with both air quality criteria and various development preferences.
    In addition, because running tallies of both benefits fostered by a given
    plan and the resultant costs of air pollution control and damage are kept
    for each planning option considered, alternative plans may be conveniently
    compared and ranked according to how effectively, from a cost standpoint,
    each utilizes the air resource.
         The material presented is divided into three distinct but closely
    interrelated chapters.  The first chapter discusses the essential concepts with
    which the methodology attempts to deal.  The second chapter presents the methodology
    itself, and the third chapter provides some application guidelines.
         It is not intended that this document, of itself, be sufficiently com-
    prehensive to enable the planner to implement the concepts presented.  Rather,
    it is expected that considerable effort will be required to fully research
    individual areas of concern so that information sufficent to apply the
    methodology is compiled.  Furthermore, because specific applications of
    the methodology are heavily dependent upon available site specific data,
    the general technical and operational capabilities of the individual planner
    or planning group, and the ever present constraints of time and money to do
    detailed planning studies, it is anticipated that both the level of detail
    and degree of sophistication of individual applications will be widely varied.
         It is intended that this document, with its suggested analyses, be
    used as a guide by the planning community in making and evaluating planning
    decisions.  It is hoped that sufficient ingenuity will be brought to bear in
    the application of the concepts presented so that each use of this methodology
    represents an accurate appraisal of actual physical and economic phenomena.
                                                                               71
    

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                                   INTRODUCTION TO PART 3
    
       BACKGROUND AND PROBLEM DEFINITION
    
            Because the urban planning process  offers  a fundamental means of
       controlling long term air quality,  it  is necessary that the planner be able
       to evaluate alternative land use  plans in terms  of their relative air
       pollution impact.
    
    
            The air quality of given  regions  or sub-regions depends  upon two  sets
       of phenomena:
    
            1.   The assimilative capacity  of  the air environment for pollutant
                material
    
            2.   Pollutant  emission characteristics.
    
            The capacity of a given air  environment for atmospheric pollutants is
       determined by ambient  meteorological,  topographical,  climatological, chemical,
       and biological conditions.  In general,  very little  functional control can
       be exercised over these phenomena on a regional  scale.  Consequently, it
       does not appear practical to postulate a regulatory  program of air quality
       based upon the specification of desired  conditions of naturally occurring
       physical phenomena.
    
            Pollutant emission characteristics  include  the  quantity of pollutants
       released to the atmosphere, the physical  location  of  emission sites, and
       source types (e.g.,  mobile, stationary,  elevated,  arid depressed sources).
       These characteristics  are determined from the specification of land use
       type, level of activity or process  rate,  types  and amounts of fuels used,
       source controls,  and activity schedules.
    
            Because pollutant emission characteristics  are  directly derivative
       from the specification of the mix,  locations and intensities of land use,
       it is evident that  the urban and  transportation  planning process offers an
       effective means of  controlling long term  air quality.  Furthermore, both
       analytical techniques  and empirical data  for estimating the relationships
       between  land use and air quality  have  reached the point of making air
       quality  impact-land use planning  a  practical tool  in helping to manage
       urban growth and development
    
            For the planning  process to  effectively accommodate the requirements
       of an expanding population within a limited air  resource,, it is necessary
       that the planner be  able to differentiate  between and evaluate alternative
       plans in terms of their relative  air pollution impact.  The problem then
       is how to apply existing analytical techniques and data for quantifying
       the impact of land  use on air quality  to  an evaluative methodology for
       estimating how effectively the air resource is used.
    72
    

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                                       THE PLANNER
                                         AND THE
                                    PLANNING PROCESS
                                           1
                                       LAND USES
    
                                        •  Mix
                                        •  Intensities
                                        •  Locations
        ASSIMILATIVE
        CAPACITY OF
          THE AIR
        ENVIRONMENT
    POLLUTANT EMISSION
    CHARACTERISTICS
     •  Quantity
     t  Locations
     •  Source Types
    EMISSION
    CONTROLS
                                       AIR QUALITY
    Figure 3-1:   The Dependence of Air Quality on Land Use - Shows how
                 the planning process can contribute to the determination
                 of air quality and suggests that the planner needs a way
                 of evaluating the consequences of planning decisions in
                 terms of air pollution impact.
                                                                                73
    

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    THE ECONOMICS OF AIR POLLUTION
    
         Cost implications provide the most tangible and immediate means of
    evaluating the air pollution impact of alternative land use plans.
         Air pollution may be viewed as the result of using the atmosphere as
    a waste disposal medium for the urban processes supporting the economic
    structure of a region.  Because the air resource is not limitless,  it is
    apparent that a means of evaluating the effectiveness of 'spending1  this
    resource is important to the decision making processes which prescribe
    urban growth and development.   The ideal measure of effectiveness would
    be a single quantitative indicator which is generally applicable  to  the
    spectrum of planning variables as ar optimization parameter.   On  this
    basis, cost effectiveness is the obvious comparison of proposed alternative
    land use strategies relative to their air pollution impact,  as well  as to
    the other constraints within which the planner must operate.
    
         There are three broad areas of economic concern identifiable within the
    scope of a cost effective approach to evaluating regional air pollution
    impact.   They are:
    
         1.   The trade-off between air quality and those urban activities
             which determine regional economic viability
    
         2.   The costs incurred in controlling pollutant emissions
         3.   The costs  of damage resulting from expected air pollution  levels.
    
    Their interrelationship with the planner and the planning process may be
    represented as shown in Figure 3-2.
    represented as shown in Figure 3-2.
         The first of these is important to the planning process because it defines
    attainable limits for proposed land uses in terms  of both air quality and what-
    ever measure of economic viability the planner chooses.   Federal and state air
    quality standards and regulations have, in effect,  placed constraints on the mix,
    intensities, and locations of the various land use categories,  particularly those
    involving heavy industry and motor vehicular transportation.  These constraints re-
    quire the planner to reconcile the pressures for economic development, with their
    anticipated impact on air quality, to a level of detail  encompassing the spatial
    allocation of both the sources and receptors of air pollutants.
    
         Collectively, the cost of controlling pollutant emissions and the cost
     of air pollution damage define the total cost of air pollution for a given
     land use plan.  Evaluation of total cost relative to the flow of benefits
     inherent in the land use configuration which gives rise to these benefits
     is the basis for determining the relative air quality impact cost effective-
     ness of alternative plans.
    
         Quantification of allowable Umits to preferred land uses and cost ef-
     fective evaluations of alternative proposed land use plans generated within
     these limits may be taken as the initial and final steps in a methodological
     approach to cost effective air quality impact land use  planning.  Subsequent
     sections of this chapter discuss individual aspects of air pollution economics
     as they peitain to land use planning, and subsequent chapters outline the meth-
     odology indicated above and present a set of guidelines for its application.
    

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                  1
               TRADE OFF:  AIR QUALITY VS. URBAN ACTIVITY
                                               1
                                      Planning Constraints
                                       • Air Quality
                                       • Economic Viability
                                               I
                              The Planner and the Planning Process
                                           Land Uses
                 Assimilative
               Capacity of the
               Air Environment
                               Pollutant
                               Emission
                            Characteristics
              Emission
              Controls
                                           Air Quality
                                   2
                             AIR POLLUTION
                                DAMAGES
                                               I
    
    3
                                         THE TOTAL COST
                                        OF AIR POLLUTION
    THE COST OF
    CONTROLLING
    POLLUTANT
    EMISSIONS
    
    
    Figure 3-2.
    The Economic Implications of Land Use Planning in Terms of Air
    Pollution Impact - Shows how three basic economic concerns may
    be combined to provide both constraints to and a means of
    evaluating land uses in terms of air pollution impact.
                                                                                       75
    

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         Trade-Off:   Air Quality  vs  Economic  Viability
    
             Because land use activities which promote a healthy regional economy
         tend to degrade regional air quality, there is a trade-off between these
         two basic planning goals.
             It has  long been recognized that activities which are conducive to a
        healthy economy can, and often do, result in unwanted side effects.  Known
        in economic  linguistics as external diseconomies, these effects generally
        demonstrate  a positive correlation with the level of their causal activity.
        Air pollution is an outstanding example of an external diseconomy because
        it is the unwanted result of urban activities which ultimately exist to
        enhance the  quality of urban life.  Further, it generally increases as
        the levels or intensities of these activities increase.  The result of this
        cause and effect relationship on a regional scale is an overall decrease
        in air quality as a function of increasing urban activity,
    
             Given that some general inverse relationship exists between air quality
        and economic potency, the planner must at the outset reconcile these opposing
        planning goals.  On the one hand, severely curtailing those activities which
        are major contributors to air pollution may provide for good air quality
        but may, at  the same time, so severely limit both productivity and mobility
        that the regional economic base may be eliminated.  On the other hand,
        vigorous pursuit of an effective and vital economic base may cause air
        quality to be unacceptable.  It is essential that the planner be able
        to quantitatively establish these extremes at the outset so that both
        good air quality and an effective economic base are 'built-in' to proposed
        land use schemes.
    
             Considering the reliance upon the combustion of fossil fuels as the
        major source of energy, there will s.lways be some level of urban activity
        beyond which acceptable air quality is simply unobtainable.  It should be
        recognized however, that emission controls offer considerable latitude in
        specifying activity types and levels which meet air quality criteria.  As
        indicated in the accompanying figure, control strategies have the effect of
        either increasing the capacity of a given environment for urban activity at
        a given level of air quality (control strategy C^), or increasing the level
        of air quality for a given level of urban activity (control strategy GS),
        or some combination of the two (control strategy C ).
    
             Unfortunately, there are some very real economic constraints associated
        with the degree of emission controls; that a given land use strategy can
        tolerate.   For example, if a plan is; proposed which attempts a very high
        level of emission control (in order to allow, for instance, for high industrial
        employment),  it may become economically impossible for certain of the
        activities which are being relied upon as potential employers to survive
        on a competitive basis.   The  next section discusses in further detail the
        effects  of emission controls  and air pollution damage  in terms of achieving
        regional  economic viability.
    76
    

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                                      JO / .9/I.97
                    rfwouooj /Duo/Bdy ijoddng 04
                                                                 >%
                                                                 >
                                                                 tj
    
                                                                 c
                                                                 o
                                                                 .a
                                                                 i_
                                                                 ~D
                                                                 »4—
                                                                 O
                                                                 "a)
    Figure 3-3.
    The General Relationship of Level of Urban Activity to  Air  Quality
    Shows the options available to the planner in terms of  both air
    quality and urban activity through the specifications of emission
    control strategies.
                                                                   77
    

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        The  Cost of Achieving Ecomomic Viability  in Terms of Air Pollution  Impact
    
             For a  given  land  use configuration, the cost optimum level of attainable
        air  quality may be determined  from the combination of costs resulting from
        the  implementation of  various  control strategies and the corresponding
        costs resulting from air pollution damage.
            The  cost of achieving regional economic viability in terms of air
       pollution impact may be defined as the return received (e.g., in regional
       employment)  for each dollar spent on air pollution.  Quantification
       of this parameter for alternative proposed land use plans may be used as
       the basis for performing cost effective evaluations and consequent ranking
       of the alternatives in terms of their relative air pollution impact.  It is
       therefore essential to be able to determine the total cost of air pollution
       for each  of  the proposed alternatives which have been postulated to provide
       for both  regional economic viability and acceptable air quality.
    
            As indicated in the accompanying figure, the total cost of achieving a
       given level  of air quality for a gi^en land use plan is the sum of the costs
       resulting from controlling pollutant emissions and the corresponding costs
       resulting from air pollution damage.  Ideally, the planner should attempt
       to attain, for each of the proposed alternatives, that level of air quality
       which results in the minimum total cost of air pollution.  This would re-
       quire specification of an emission control strategy which provides for the
       cost optimum level of air quality.  However, as a result of satisfying either
       basic planning constraints or the requirements of legislated air quality reg-
       ulations,  it may well be that the planner has had to specify emission controls
       as an integral part of one or more of the alternative plans.  If this is the
       case, and if the control strategy is such that it becomes impossible to
       operate at the cost optimum air quality level for that land use plan, addi-
       tional emission controls should not be specified.  The point is that the
       planner should always attempt to operate as close to the minimum total cost
       as possible  for each plan considered because the utility received is inherent
       in the specification of the plan.
    
            By the  same token, a plan which does not operate at the minimum total
       air pollution cost should not necessarily be abandoned.  If the utility
       received  from the plan is high, the cost effectiveness evalution may
       indicate  that it is the most desirable alternative.  Again, it is not the
       total cost of air pollution which is the indicator of relative worth, but
       rather the air quality cost effectiveness.  This is the most critical
       concept involved in the economic evaluation of air pollution impact and its
       importance cannot be overstressed.  Assuming that it is necessary to spend
       air quality  to buy economic viability, it is essential that the maximum
       return be realized for the damage fiat is done.
    78
    

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                                                                  ^Unacceptable..
                                                                         Quality.]
                                               Total Cost
                                                 Curve
    
                            Cost of
                           Control
                                                                                     o
                                                                                    o
                                          Level of Air Quality
               Figure 3-4.   The  Total Cost of Air Pollution  -  Shows how the curves of
                            control and damage costs for  a given plan combine to form
                            a  total cost curve the minimum of  which determines the
                            cost optimum level of air quality.
    o
    CD
                                                                                       79
    

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         The  Cost  of Controlling  Pollutant  Emissions
    
             The  cost of air pollution control is the economic indicator defining
        the  aggregate ability of the public, corporate, and ultimately the individual
        sector to supply clean air.
             Legislated air pollution control strategies and standards effectively
        shift some of the direct cost burden of air pollution from the individual
        to the corporate and public sectors.  That is, rather than the individual
        having to bear the direct burden of air pollution damage, public and cor-
        porate facilities are required to assume the responsibility for air pol-
        lution control.  Other things being equal, internalization of air pollution
        costs results in a decrease in net productivity.  In turn, the ability to
        shift this burden back to the individual will generally determine the via-
        bility of public and corporate facilities within the structure of the re-
        gional and national economy.
    
             For the public sector, the cost of controlling air pollution results
        from the combined costs of directly controlling pollutant emissions from
        municipally owned sources (i.e. power plants, incinerators, public trans-
        portation facilities, etc.) and from the costs of operating and maintaining
        air pollution control and regulatory services and facilities (e.g.  enforce-
        ment, research and development, monitoring, information services, litigation,
        etc.)  In as much as these facilities are publicly owned, the cost of
        pollution control is generally parsed on to the individual in the form of
        increased taxes or usage rates.
    
             Air pollution control strategies and regulations will have a signifi-
        cant effect on the corporate sector as well.   The final effect of pollution
        control on quantity of output, facility location, profits, and consequently
        prices will generally depend upon   a) pricing policy of the industry in
        which the individual firm is located,  b) the direct cost of abatement
        in terms of equipment required to meet emission standards and the operating
        and maintenance costs of that equipment,  c)  the structure of the industry
        (other than pricing policy),  d) the demand elasticity for the firm's product
        and,  3) the structure of the market.  In order to counterbalance the effects
        of internalizing pollution costs, individual firms will try to shift as much
        of the burden resulting from the decrease in productivity as is possible to
        the individual in the form of higher prices.
    
             Ultimately of course, the largest burden of air pollution control costs
        will be borne by the individual.  While direct cost increases to the public
        and corporate sectors do not, in general, translate dollar for dollar to
        price and tax increases to the individual, for purposes of establishing a
        relative system of accounts to perform cost effective analyses of alternative
        land use plans, these cost increases are sufficient.  The justifying assumption
        for this statement is that the factors defining multiplier effects in the
        regional economy will not be significantly altered by differences in growth
        options. The cost of controlling pollutant emissions msiy then be defined in
        terms of direct costs to the corporate and public sectors.
    80
    

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                                                    LEGISLATED
                                              Air Pollution Control
                                            Regulations and Strategies
                                   1
                         Cost to Public Sector
                          t  Maintenance and
                             operation of
                             regulatory agencies
                          •  Emission controls
                             for public
                             facilities
                                   1
                        Cost to Corporate Sector
                        Depends on
    
                         •  Pricing Policy
    
                         •  Abatement Costs
    
                         •  Industry Structure
    
                         •  Demand Elasticity
    
                         •  Factor Market Make Up
                   Indirect Costs
                    Higher Taxes
                    § Usage Rates
                                                      .Direct
                                                      Costs
                                   I
    1
    I
                                                   Direct Cost
                                                        of
                                              Air Pollution Control
              f
    Indirect Costs
    
     Higher Prices
                I
                                           Total Cost of Air Pollution
                                               Control Borne by the
                                                Individual Sector
          Figure 3-5.  The Cost of Controlling Pollutant Emissions - Shows how the
                       individual costs of pollution control may be considered for
                       purposes of defining the total cost.  The ability of the
                       individual sector to support this burden may then be viewed
                       as the aggregate ability of society to supply clean air.
    o
    •*
    o
                                                                                               81
    

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           The Damage Function
    
                Air pollution damage functions define the extent  of aggregate dollar
           value damages attributable to polluted air.
    
    
                The  total nation-wide cost of  air pollution damage  for 1968 has been
           estimated at approximately 16 billion  dollars.  This figure, while admittedly
           a rough approximation,  nevertheless, demonstrates  that air pollution damages
           are both quantifiable and significant.   More recent  studies have  estimated
           national damage  costs ranging upwards  of 300 billion dollars,  indicating a
           more complete understanding of the  effects of air  pollution as  well as  a
           broader range of analytical capabilities with which  to define  its  economic
           consequences. As the body of knowledge concerning the effects  of polluted
           air continues to grow,  it is expected  that damage  estimates will  increase
           even further.
    
                Existing studies into the construction  of air pollution damage functions
           have postulated  the following indicators as  those  best suited  to  establish
           the deleterious  effects of polluted air:
    
                •   Health  Costs
    
                •   Materials Damage
    
                •   Property Devaluations
    
                •   Vegetation Damage
    
                •   Soiling Costs
    
                •   Animal  Losses
    
                •   Asthetic Effects
    
                •   Litigation Expenses
    
                Of these, sufficient data exists  only for attempted quantification of
           the first four.   Even for these categories,  data limitations have  prevented
           construction of  reliable empirically based damage  functions.   Those postu-
           lated to date are pro forma representations  of indicated trends generated
           through multivariate statistical  analyses.  In addition, it has not as  yet
           been possible to postulate an aggregate damage function of all  pollutants.
           Rather, damage functions are currently expressed by  pollutant.
    
                Data limitations notwithstanding,  information currently available  to
           estimate  per capita damage costs  a:; a  function of  pollutant concentrations
           is  sufficient to indicate the comparative  air pollution  impact  of  alternative
           land use  schemes,  particularly for  the  industrial  pollutants,  i.e., SCL
           and particulates.
    82
    

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          O
          CJ
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          OJ
          Q_
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          V)
          O
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          £
    
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    rS02=-$5.90+0.66 X
                               Particulates
    
    
                         = - $4. 7O + O.47X
                                          100
                                     200
                X=Weighted Annual Average  Pollutant Concentrations (ug/m  )
           Figure  3-6.  Pro Forma Damage  Functions for S02 and  Particulates - Show
                       the effect of pollutant concentrations  on annual  per capita
                       damage costs.
    VC
    K5
    O
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                                                  83
    

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    LITERATURE SURVEY AND EVALUATION
    
         Although a considerable amount of material is available concerning each
    of the various aspects of air pollution economics, a comprehensive methodolog-
    ical approach to cost effect air quality impact planning is not available on
    a level commensurate with both the requirements and constraints of existing
    planning practices.
         Available literature relative to the economic implications of air pollu-
    tion may be categorized into the three individual areas of concern indicated
    previously:
    
         1)  The trade off of air quality with urban activity
         2)  The damage functions
    
         3)  The cost of controlling pollutant emissions.
    
         Information relevant to the first area of concern is fairly abundant
    and relatively easy to obtain.  Unfortunately, much of the available litera-
    ture is descriptive in nature and, for the most part, reflects efforts to
    implement various control strategies for existing land use configurations.
    The outstanding examples of implemented studies in this area, and those for
    which the preceding generalization does not apply, are referenced in the
    accompanying table.
    
         In terms of documented studies attempting to establish empirically based
    damage functions, very little has been accomplished to date.  Conceptually,
    this area has been relatively well defined; however, difficulties in compiling
    data have as yet precluded the availability of anything but pro forma repre-
    sentations.  It is anticipated that extensive research efforts currently under
    way will provide more suitable information.
    
         Since the adoption of the Clean Air Act, considerable effort has been
    spent on defining the costs to be incurred in controlling pollutant emissions.
    Much data is already available and this is being supplemented on an almost
    daily basis.
    
         As discussed in the Summary, it is not expected that the application
    of the concepts presented here be accomplished through consideration of
    this document alone.  Because of a multiplicity of factors involving site
    specific concerns of individual areas, the capabilities and limitations of
    various planning groups, the availability of representative data, and the
    relative position that air quality occupies within the priority structure
    of a given state, region, or municipality, it is anticipated that extensive
    literature and agency surveys will be required of potential users.  It is
    therefore suggested that references cited in the accompanying table be
    obtained and examined as a first step in performing such surveys.
    

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                           TABLE 3-1.   REFERENCE MATERIAL
      1.  "The Arizona Environmental and Economic Trade-Off Model."  Columbus,
               Ohio:  Battelle Laboratories, September 1972.
    
      2.  Barrett, L.B. and T. E. Waddell.  Cost of Air Pollution Damage: A Status
               Report.  E.P.A. Publication No. AP-85, February 1973.
    
      3.  Benedict, H.M. and R.E. Olson.  Economic Impact of Air Pollutants in
               Plants, Annual Report, Vol. I.  Irvine, California:  Stanford
               Research Institute, August 1970.
    
      4.  Crocker, Thomas D.  Urban Air Pollution Damage Functions:  Theory and
               Measurement.  Riverside, California:  University of California,
               June 1971.
    
      5.  "Demonstration of a Regional Air Pollution Cost/Benefit Model."  TRW
               Systems Group, EPA Contract No. PH 22-68-60, McLean, Va., July 1971.
    
      6.  "The Economics of Clean Air."  Annual Report of the Administration of
               EPA, U.S. Government Printing Office, Washington, D.C., 1972.
    
      7.  "Environmental Quality."  The Third Annual Report of the CEQ, Washington;
               Government Printing Office, 1972.
    
      8.  "Environmental Quality."  The Fourth Annual Report of the CEQ, Washington:
               Government Printing Office, 1973.
    
      9.  Fogel.   "Comprehensive Economic Study of Air Pollution Control Costs for
               Selected Industries and Regions."
    
     10.  Peckham, Brian W.  "Bibliography of Literature Relating to the Economic
               and Legal Aspects of Air Pollution."  Chapel Hill, North Carolina:
               University of North Carolina, September 1971.
    
    11.   Purdom,  P. Walton.  Environmental Health.  New York:  Academic Press, 1971.
    
    12.   Ridker,  Ronald G.   Economic Costs of Air Pollution.  New York:  Frederick A.
               Praeger Publishers, 1967.
    
    13.   Williamson, Samuel J.  Fundamentals of Air Pollution.  Reading, Massachusetts:
               Addison-Wesley, 1973.
    
    14.   Wolozin,  Harold.   The Economics of Air Pollution.   New York:   W. W. Norton
               and Co.,  1966.
                                                                                    85
    

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                  A METHODOLOGICAL APPROACH TO COST EFFECTIVE AIR QUALITY IMPACT
    
                                        LAND USE PLANNING
         i
         SCOPE AND OBJECTIVES
    
              The methodology presented here attempts to provide an analytical frame-
         work for considering the economic implications of air quality impact land
         use planning.
    
    
              Current concern about the environment has fostered attempts to improve
         urban air quality.  The major focus of these attempts has been on emissions
         control through fuel utilization and waste gas cleansing devices.  Unfor-
         tunately, this type of approach recognizes neither the problems of planning
         for long term air quality, nor basic questions related to the economics of
         air pollution.  The methodology presented here combines some of the most
         significant aspects of air pollution economics with an existing air quality
         impact planning process to form a "bare-bones" procedural outline for:
    
              1.  Making preliminary planning decisions involving acceptable
                  amounts of heavily polluting land uses in terms of both
                  air quality and one or more planning goals considered rep-
                  resentative of economic viability
    
              2.  Evaluating alternative proposed land use plans in terms of
                  how effectively they use the air resource to obtain stated
                  planning goals.
    
              In order that a cost effective planning and evaluation scheme for air
         quality be of practical value to the planner, it must be both easily applicable
         to a variety of planning situations and sensitive to time and budgetary con-
         straints imposed on all planning exercises.  Most especially it must be
         flexible in scope, tolerant of a variety of operating variables, simple
         to implement, and compatible with existing data and techniques for
         relating land use to air quality.
    
              The ultimate objective of this methodology is the improvement of the
         decision making process concerned with the growth and development of urban
         configurations in terms of air quality.  The intention here is not to put
         forth a dogmatic planning procedure but rather to provide a quasi-analytical
         framework within which the planner can accomodate and evaluate the effects
         of special characteristics of individual study areas.  This is neither a
         mechanism for producing ideal land use schemes nor a means of attaining
         pollution free air.  It is instead a planning aid and management tool for
         conserving the air resource in the face of severe.pressures for economic
         development and given the restrictions of a fossil fuel energy supply and
         an imperfect emissions control technology.
    
              Because this document is very conceptual and does not provide for the actual
         application of the methodology presented, Table 3-2 may prove useful to those who
         might wish to attempt an application.  It presents the general objectives of the
         methodology and indicates what these objectives translate to in terms of scope of
         application.  It represents, therefore, a concise statement of the intended util-
         ity of the methodology.  Additional comments on application may be found in
         the third chapter.
    86
    

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                        TABLE 3-2.  SCOPE AND OBJECTIVES
     Objectives of the Methodology
         Implications to Scope
           of the Methodology
    1)  To anticipate and evaluate the
    air quality and economic implica-
    tions of planning decisions.
    1)  Must recognize and include
    quantification of air quality and
    economic criteria.
    2)  To be compatible with existing
    data and air quality and economic
    analytical techniques.
    2)  Must allow for a range of
    sophistication and detail.
    3)  To recognize and accommodate
    the specific concerns of indi-
    vidual planning areas.
    3)  Must be tolerant of a variety
    of operating variables over a
    range of physical scales .
    4)  To accommodate both the
    operational capabilities and
    limitations of individual planning
    groups.
    4)  Must be sensitive to time and
    budgetary constraints.
                                                                               87
    

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          CONCEPTUAL DESIGN
    
               Conceptually, the methodology a;.ms at establishing the most effective
          use of the air resource from among alternative proposed land use plans which
          accomodate both air quality and economic development criteria.
    
    
               In addition to the general problem of defining appropriate state and
          regional planning control strategies in compliance with the requirements
          of legislated air quality criteria, a very practical problem facing most
          communities is planning for the future to accommodate anticipated or desired
          growth.  Government environmental regulations and citizen group activities
          have caused many communities to re-evaluate the process by which growth is
          achieved,  so that environmental quality is now as much a. concern as the more
          traditional problems of housing,  employment,  crime,  and tax base.   Consequently,
          planning decisions involving the magiitude and direction of community and
          regional growth must be cognizant of both economic and air quality constraints
          and should be based upon the effectiveness of air resource utilization.
    
               Generally,  economic constraints to planning options will appear in the
          form of minimum increases of industrial,  commercial,  and residential land
          uses necessary to accommodate existing and anticipated or desired  changes in
          the economic,  social,  and political structure of a given study  area.   Factors
          to be considered in establishing this 'lower  bound to growth' should there-
          fore include the existing land use configuration,  indicated development trends,
          physical characteristics of the area which either tend to promote  or inhibit
          certain types  of development,  specific political or legislative requirements,
          and the development preferences of the existing population.
    
               Air quality constraints  to planning  options  are  somewhat less  difficult
          to  define  than economic  constraints.   In  most  cases  these air quality
          constraints will  be  legislated  federal  or state standards.   In  addition to
          absolute limits  of individual pollutant concentrations,  these may also
          involve incremental  limits,  to  either  emission densities  or  pollutant
          concentrations,  over fixed  values.   In  any event,  these  constraints will
          translate  to maximum amounts  of industrial, commercial,  and  residential
          land  uses  which  can be tolerated  within a given study area.
    
               Together, the upper and lower bounds to  growth,  as established through
          a consideration of air  quality and economic constraints,  provide a range  of
          land use types and intensities from which preliminary development  options may
          be specified.   Designing within these limits  will enable proposed  plans to
          effectively accommodate  both economic and air quality requirements.  However,
          there are  any  number of  combinations, of land  use mixes and intensities which
          may be permitted in a  given area so that  most often a series of alternative
          configurations will be postulated.  An evaluation of the alternatives, defining
          how effectively each makes use of the air resource, provides a  basis for  indi-
          cating the preferred direction and magnitude  of area growth. This evaluation
          is accomplished by performing a simplified relative cost effectiveness analysis.
    
               The following section discusse:; the implementation of these concepts with-
          in the planning process.
    88
    

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              Economic Constraints
                       on
                   Development
         Air Quality
       Constraints on
         Development
              Lower Bound to Growth
    Upper Bound to Growth
                                    Land Use Options
                                     •  Mixes
    
                                     •  Intensities
                                     •  Locations
                               Cost Effectiveness Analyses
                                           of
                                     Various Options
                                    Preferred Growth
                                         Option
    Figure 3-7.  Conceptual Methodological Design - Shows how air quality
                 and economic development constraints are used to establish
                 preferred growth options.
                                                                             89
    

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         PROCEDURAL DESIGN
    
             The procedural design of the methodology is effectively a step-wise
         sifting process in which economic and air quality constraints are sequen-
         tially applied to possible growth configurations and the residuals are
         evaluated  in terms of their relative air pollution impact cost effectiveness.
    
    
             Translating the conceptual design of the methodology, as defined in the
         previous section, into a practical, operational planning tool requires delin-
         eation of  a procedural set of working steps.  Ideally, these steps are indi-
         vidual operations which apply the current body of relevant information to
         specific planning concerns and do so in a chronological sequence which eliminates
         less promising plans early on and examines the residuals in further detail.
         As indicated in the previous section, an Air Quality Impact-Land Use Plan-
         ning Process has already been defined and documented.  The process is outlined,
         by its component steps, in the accompanying figure.  It is the foundation upon
         which the  procedural design of the methodology is laid.
    
             Establishing the upper and lower bounds to growth, as required by the
         conceptual design, is accomplished by performing Steps 1 through 3 of the
         Air Quality Impact-Land Use Planning Process (denoted by the dashed line).
         These steps represent a 'quick and dirty1  means of defining the trade-off
         between air quality and the pollution generating activities which generally
         support the economic structure (i.e., industry and transportation).  The
         output of  Step 3 is in the form of a series of preliminary alternative plans,
         specified  in terms of types and amounts of industry and transportation.   Each
         of these preliminary plans has been generated within the limits set by stated
         air quality and economic criteria.
    
             Steps 4 and 5 of the Air Quality Impact-Land Use Planning Process de-
         velop and  refine preliminary designs into 'final' land use plans and provide
         a statement of expected pollutant concentrations and their relative spatial
         distributions within the planning area.  This information is applied to cen-
         sus tract  data (or its equivalent) to obtain per capita air pollution damages
         from pollutant specific damage functions.   The total cost of air pollution
         damage for the planning area is then the sum, over all census tracts, of
         the total damage in each tract.
    
             The cost of controlling pollutant emissions is determined from the com-
         bination of costs fostered by controls imposed to limit pollutant emissions
         in excess  of federal emission requirements and the cost of operating state,
         regional, and/or municipal control and regulatory agencies.  Together, damage
         costs and  control costs define the total cost of air pollution for a given
         land use plan.   By iterating on control strategies and working back through
         the Air Quality Impact-Land Use Planning Process, it is possible to define,
         for each land use configuration proposed,  the cost optimum level of air quality.
         Emission controls should be specified to obtain this level as far as pos-
         sible.
    
             The total cost of air pollution is then used as the denominator in a
         ratio of the dollar value of benefits received from a given plan to its air
         pollution costs.   This ratio represents the relative cos;t effectiveness of
         that plan.  Comparison of this value with those derived for other plans
         establishes a preferential ranking of all alternatives considered.
    90
    

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           The Air Quality Impact -
           Land Use Planning Process
           STEP 1
           Establish the Air
           Quality Baseline
           STEP 2
           Define the Tolerance of
           the Planning Area to
           Additional Pollutant
           Emissions
           STEP 3
           Set Constraints on
           Industry and
           Transportation
           STEP 4
    
           Generate Comprehensive
           Land Use Plan
           STEP 5
    
           Evaluate Air Quality
           Impact
                                                          Basic Planning Goals
                                                           •  Acceptable Air Quality
                                                           •  Economic Viability
    Upper Bound
    to Growth
    Lower Bound
    to Growth
                                                                 Utility of Plan
                                                        Total Cost of
                                                        Air Pollution
    I	I
                                                          Cost Effectiveness =
    
                                                                Utility
                                                                 Cost
      Figure 3-8.   Procedural Methodological Design - Shows how the
                   implementation of the methodology interfaces with
                   the Air Quality Impact-Land Use Planning Process.
                                                                                91
    

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                                   APPLICATION GUIDELINES
              The methodology presented is applicable to a variety of planning
         situations over a range of physical .scales each subject in its turn to a
         variety of operating variables.
              As  indicated previously, the methodology presented is a loosely
          structured  framework within which planning decisions are formulated and
          evaluated in  terms of how effectively the air resource is used to
          obtain stated planning  goals.  Because planning exercises are invariably
          site specific,  and because the special concerns of individual planning
          areas are extremely difficult to address in a generalized context,
          application of  the methodology to a specific problem requires that
          preliminary decisions relative to physical scale, operating variables
          defining utility, and data requirements be made.  Generally, these
          three concerns  will dictate both the scope and levels of detail
          of specific analyses required within the generalized procedural
          framework.
    
              The physical scale over which the methodology may be: applied is
          considerable, ranging from macroscale studies involving several hun-
          dreds of square miles to microscale studies of perhaps a square mile
          or less.  In  the macroscale case the primary focal point of analytical
          studies  is  the  definition and evaluation of the aggregate effects of
          all land uses within a  given study area, in terms of both their economic
          and air  pollution impacts.  By definition these are regional scale
          planning exercises and  are particularly effective for establishing
          general  development preferences in terms of over all land use types,
          intensities,  and relative locations.  Microscale studies will usually
          involve  the design and  placement of individual facilities within a
          given study area.  In many cases, it may be desirable to use such
          studies  as  a  tuning mechanism for regional scale exercises.  For
          example, after  defining an acceptable regional configuration, alterna-
          tive subregional and local development options may be examined and
          evaluated within that regional context.
    
              In  choosing a physical scale which is appropriate for a given
          study, it is  important  that the scope of impact of various planning
          options  be  recognized.  The environmental consequences of a regional
          scale planning  decision may be limited to the microscale.  By the
          same token, the economic implications of microscale development
          options  will  often be regional in nature and might therefore require
          an iteration  on the macroscale scheme.
    
              Once a physical scale has been chosen, it is necessary to structure
          the work study  programs defining utility (benefits) and non-utility  (costs)
          The first consideration in doing this should involve the specification of
          operating variables.
    92
    

    -------
         The number and types of operating variables available to represent
    the utility of a given plan are as varied as the connotative meaning of
    the word  'utility.'  Any single planning goal or objective, or any
    combination of goals which can be quantified, and to which a dollar
    value assignation may be made, is fair game.  These variables may be
    either positive or negative, although any plan which demonstrates a
    net negative utility in terms of a combination of variables defining
    stated goals is, by definition, less than useless and should be summarily
    rejected.  Optimally, operating variables should be specified so as to
    be mutually independent, and therefore directly additive on a dollar
    value basis.  It must be noted here that the evaluation of alternative
    plans in terms of their relative air quality cost effectiveness is
    meaningless unless the same operating variables are used to define
    utility for each plan.
    
         The variables which may be used to define non-utility, or costs,
    appear in two basic sets:
    
         1)  Expressing the costs of air pollution damage
    
         2)  Expressing the costs of air pollution control.
    
         The operational definition of the damage function is limited by
    lack of extensive available data.  As indicated in the section entitled The
    Damage Function, the present definition would involve the summation of health,
    vegetation, property devaluation, and soiling costs.  The costs of
    air pollution control are more varied and will be more site specific
    in nature.  For this reason, a bit more creativity can be brought
    to bear in their operational definitions for a given study.   In
    either case, it will usually be the availability of empirical
    data which will dictate the variables to be used.
    
         Because the validity of any quantitative evaluation schene is
    critically dependent on the validity of the data used, it is important
    that comprehensive regional or locally specific data be used in defining
    the operating variables.  If this type of information is unavailable,
    serious consideration should be given to a redefinition of utility
    for that series of planning options.  Where a redefinition cannot
    retain the intended meaning of utility, pro forma data representations
    may be used.  However, even where relative evaluations will suffice,
    results derived from these data should be closely scrutinized.
    
         It should be painfully obvious at this point that an actual application
    of the entire methodology will require a considerable effort.  Furthermore,
    there appears to be a general reluctance on the parts of both the planning
    and economic communities to accept the validity of a cost effective approach
    to air quality planning.   However, it would be unreasonable to assume that a
    cost effective approach to allocating perhaps our most precious resource can
    be dismissed without further scrutiny.
                                                                                93
    

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                                       TECHNICAL REPORT DATA
                                (Please read Instructions on the reverse before completing]
     1. REPORT NO.
      EPA-450/3-74-020-a
                                  2.
                                                               3. RECIPIENT'S ACCESSIOI*NO.
    4. TITLE AND SUBTITLE
       Air Quality  For Urban and Industrial  Planning
                                                               5. REPORT DATE
                                                                 March  1974
                                                               6. PERFORMING ORGANIZATION CODE
                  C.  Goodrich, Scott T. McCandless,
    Michael J.  Keefe,  William P. Walsh, & Alan  H.  Epstein
                                                               8. PERFORMING ORGANIZATION REPORT NO.
                                                                ERT Document No. P-434
    9. PERFORMING ORGANIZATION NAME AND ADDRESS
       Environmental  Research and Technology,  Inc.
       429 Marrett  Road
       Lexington, Massachusetts  02173
                                                               10. PROGRAM ELEMENT NO.
                                                             11. CONTRACT/GRANT NO.
                                                                  68-02-0567
     12. SPONSORING AGENCY NAME AND ADDRESS
                                                               13. TYF'E OF REPORT AND PERIOD COVERED
       Environmental  Protection Agency
       Office of Air  Quality Planning & Standards
       Research Triangle Park, N.C. 27711
                                                             14. SPONSORING AGENCY CODE
     15. SUPPLEMENTARY NOTES Prepared in cooperation  ^ £(-, the New jersey  Department of
       Environmental  Protection, Office of  the  Commissioner, Labor and  Industry Building,
       Trpntnn. N..1.
    16. ABSTRACT
                This  Final  Report presents a  summary of work undertaken,  including
            the proposed scope of work for each  of the three major  tasks,  a sum-
            mary of the  actual  work undertaken,  and an explanation  of  any deviations
            from the  intended scope of work.  The  report also contains  the findings
            of Task 1, the  development of improved emissions projection activity indices;
            and Task  2,  the development of a  methodology for incorporating cost data
            into the  evaluation of the air pollution impact of land use plans.  The
            results of Task 3 are presented in a separate report entitled "A Guide
            for Considering Air Quality in Urban Planning", EPA-450/3-74-020.
    17.
                                    KEY WORDS AND DOCUMENT ANALYSIS
                      DESCRIPTORS
                                                  b.IDENTIFIERS/OPEN ENDED TERMS
                                                                           c. COSATI Field/Group
       Land Use
       Planning & Zoning
       Local  Governments
       County Governments
       Regional Governments
       State Governments
       Air Pollution Control
    18. DISTRIBUTION STATEMENT
                                                19. SECURITY CLASS (ThisReport)
                                                 Unclassified
                                                                             21. NO. OF PAGES
                                                                                  87
       Unlimited
    
    EPA Form 2220-1 (9-73)
    94
                                                20 SECURITY CLASS (Thispage)
                                                 Unclassified	
                                                                             22. PRICE
    

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