Contract Order No. 68-01-2851
March, 197 6
METHODOLOGIES FOR THE ANALYSIS OF SECONDARY
AIR QUALITY IMPACTS OF WASTEWATER
TREATMENT PROJECTS LOCATED IN
AIR QUALITY MAINTENANCE AREAS
Environmental Impact Office
Environmental Protection Agency
26 Federal Plaza
New York, New York 10007
BOOZ.ALLEN & HAMILTON Inc.
Management Consultants
4733 Bethesda Avenue
Bethesda, Maryland 20014
656-2200
Area Code 301
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Contract Order No. 68-01-2851
March, 1976
METHODOLOGIES FOR THE ANALYSIS OF SECONDARY
AIR QUALITY IMPACTS OF WASTEWATER
TREATMENT PROJECTS LOCATED IN
AIR QUALITY MAINTENANCE AREAS
Environmental Impact Office
Environmental Protection Agency
26 Federal Plaza
New York, New York 10007
BOOZ.ALLEN & HAMILTON Inc.
Management Consultants
47 33 Bethesda Avenue
Bethesda, Maryland 20014
656-2200
Area Code 301
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TABLE OF CONTENTS
Page
Number
I. INTRODUCTION 1
1. Background 1
2. Objectives of the Study 2
3. Applicable Air Quality Standards 4
4. Characteristics of Air Quality
Maintenance Areas 8
5. Organization of the Report 9
II. OVERVIEW OF THE AIR QUALITY IMPACT
ANALYSIS PROCEDURE 12
1. The Decision Process 12
2. Considerations in Land Use and
Population Projections 14
III. ALTERNATIVE AIR QUALITY ANALYSIS METHODS 16
1. Factors Affecting Ambient Air
Quality 16
2. General Approach to Air Quality
Analysis 20
3. Alternative Methods for Preparing
Wastewater Service Area Emission
Inventory 22
4. Alternative Atmospheric Simulation
Models 24
IV. PROPOSED METHODOLOGY TO SCREEN WASTEWATER
PROJECTS FOR ADVERSE SECONDARY AIR QUALITY
IMPACT 41
1. Define the Impacted Area 43
2. Estimate Base Year Emissions 44
3. Project Emissions to Desired Year 52
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Page
Number
4. Determine Base Year Air Quality
5. Project Air Quality to Desired Year
6. Evaluate Air Quality Impact of
Proposed Project
7. Estimate Cumulative Air Quality Impacts
of Multiple Wastewater Projects in an
AQMA
56
56
58
59
V. STUDY OF TWO TEST PROJECTS
1. Town of Colonie
2. Rockland County Sewer District
Number 1
60
60
74
BIBLIOGRAPHY
90
APPENDIX A - Discussion of Measures to
Mitigate Adverse Air Quality
Impact
APPENDIX B - Discussion of Methods to
Estimate Vehicle Miles
Travelled (VMT)
APPENDIX C - Application of Proposed
Methodology to Estimating
VMT in Rockland County
Sewer District No. 1
APPENDIX D - State Air Quality Standards
in EPA Region II
APPENDIX E - Input Requirements for the
Modified Rollforward Model
for CO
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LIST OF FIGURES
Page
Number
II-l. Decision Flow Diagram 13
V-l. Capital District AQMA 61
V-2. Proposed Service Area in Town of Colonie 63
V-3. New York City Metropolitan AQMA 76
V-4. Rockland County Sewer District Number 1 77
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LIST OF TABLES
Page
Number
1-1.
1-2.
III-l.
III-2.
IV-1.
V-l.
V-2.
V-3.
V-4.
V-5.
V-6 .
V-7.
V-8.
V-9.
V-10.
National Ambient Air Quality Standards 5
Significant Deterioration Criteria 7
An Example of Developing Emission Factors
Based on Lane Use 25
Commonly Used Air Quality Models Applicable
to Specific Pollutants and Averaging Times 27
Air Quality Data Requirements for Base Year 57
Significant TSP Point Source Emission in
Albany County and Town of Colonie 67
Estimated TSP Emissions in Albany County,
1975 68
Allocation of Countywide TSP Area Source
Emissions to Service Area, 1975 (Town of
Colonie) 70
TSP Emission Projections for Service Area,
1990 (Town of Colonie) 71
Projected Total and Incremental TSP and SO-
Concentrations in the Town of Colonie, 1990 73
Existing and Projected Population Rockland
County 78
Estimated CO Emissions in Rockland County,
1975 81
Projected Impact on N02, SO-, and TSP Concen-
tration in Rockland County Sewer District No. 1 84
Estimated HC Emissions in Rockland County, 1975 86
Existing and Projected HC Eirissions in Rockland
County Sewer District No. 1 87
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I. INTRODUCTION
The Environmental Protection Agency has expressed
concern that the Federal action of awarding sewage system
construction grants, might contribute to community growth
which in turn could adversely affect air quality in Air
Quality Maintenance Areas (AQMA's). Thus in June of 1975,
EPA solicited a study of the effect of AQMA requirements
on the planning and design of sewage treatment projects.
The study took place between June and December and included
an assessment of the air quality implications of projects
in the Town of Colonie, New York, and in Rockland County
Sewer District No. 1.
The introduction to this report on that study is pre-
sented in the following sections:
Background
Objectives of the Study
Applicable Air Quality Standards
Characteristics on Air Quality Maintenance Areas
Organization of the Report.
1. BACKGROUND
Application of various air pollution control measures
has attained the national ambient air quality standards in
most parts of the U.S. Application of more controls will
result in the attainment of the standards in the remaining
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parts. However, continued urban growth in many metropolitan
areas presents a potential for violation of these standards
in the future. Such areas have been designated as Air Qual-
ity Maintenance areas (AQMA). In order to maintain the air
quality in these areas below the national ambient air quality
standards, careful planning of residential, commercial and
industrial development is needed.
Another important consideration in planning for urban
development is the provision of adequate wastewater collec-
tion and treatment facilities. The Federal Water Pollution
Control Act Amendments of 1972 authorized the EPA to provide
financial assistance to local municipalities and other respon-
sible agencies to design and construct wastewater management
systems within their jurisdictions. The vrastewater projects
are typically designed with a capacity to serve a 20- to 50-
year projected population (20 years for wastewater treatment
units and 50 years for interception). Construction of such
projects in an AQMA may contribute to urban growth which may
have adverse air quality impacts. EPA does not wish to fund
water pollution control projects which may at a later date,
contribute to violations of ambient air quality standards.
2. OBJECTIVES OF THE STUDY
The sizing of wastewater collection and treatment facil-
ities is based upon growth projections for an area. If these
growth projections would result in the future violation of
ambient air quality standards, it is EPA's desire to limit
Federal funding of such facilities to a capacity consistent
with these standards. The intent of this report is to pro-
vide a procedure for applicants to assess the air quality
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implications of proposed wastewater collection and treat-
ment facilities, and a procedure for EPA to assure that
all grant applicants give due consideration to air quality
impacts in project planning.
The specific task set forth in the work statement in-
cluded the following:
EPA methodology. Develop a methodology which EPA
can use to assure that all sewage treatment plant
applicants give due consideration to air quality
impacts in project planning.
Applicant procedures for air quality assessment.
Propose methodologies for use by wastewater pro-
ject grant applicants to assess the impact of
their plans on ambient air quality, and including
the following specifics:
Determine whether the design population might
result in standards violations for each pol-
lutant for which the areas have been designated
AQMA's
Recommend possible mitigative measures, where
standards violations are indicated likely
Provide for consideration of cumulative ef-
fects of several sewage treatment projects
on the same AQMA
Project evaluation. Evaluate and make recommen-
dations for two test projects in the Town of Colo-
nie and Rockland County.
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3. APPLICABLE AIR QUALITY STANDARDS
National ambient air quality standards have been estab-
lished for both total pollutant concentration and for incre-
mental changes in pollutant concentration. These standards
are summarized below. In addition, the states are permitted
to establish more stringent standards which must also be
considered in assessing the impact of a Federal action. Such
standards for the states in EPA Region II are included in
Appendix D.
(1) National Ambient Air Quality Standards (NAAQS)
The NAAQS have been established for six air pol-
lutants: sulfur dioxide (SC^), total suspended parti-
culate (TSP), nitrogen dioxide (NC^)/ carbon monoxide
(CO), hydrocarbons (HC), and photochemical oxidants (0 ).
X
Although a separate standards for hydrocarbons is given,
attainment of the oxidant standard is considered to
assure the attainment of the hydrocarbons standard. The
NAAQS consist of primary and secondary standards. The
primary standards are designed to protect the human wealth
whereas the secondary standards are intended to protect
the public welfare (property damage, aesthetics, etc.).
The NAAQS are given in Table 1-1.
(2) Incremental Ambient Air Quality Standards (Deteri-
oration Criteria)
These criteria are designed to prevent signifi-
cant degradation of air quality in areas having air
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Table 1-1
National Ambient Air Quality Standards
Pollutant
Primary Standards
Sulfur Dioxide
Total Suspended
Particulate
Carbon Monoxide
Photochemical
Oxidants
Non-methane
Hydrocarbons
Nitrogen Dioxide
80 ugm/m (aam)
0.03 ppm
365 ugm/m3
0.14 ppm (24 hr.)
75 ugm/m3 (agm)
260 ugm/m"* (24 hr.)
10 mgm/irt3 (8 hr.)*
9 ppm
10 mgm/
35 ppm
L60 ugn
0.08 ppm
40 mgm/m3 (1 hr.)^
160 ugm/m3 (1 hr.)"*"
, 3 1,2
160 ugm/m
0.24 ppm
3
100 ugm/m (aam)
0.05 ppm
1
—
not to exceed more than once a year
2
—
6 a.m. to 9 a.m.
aam
=
annual arithmetic mean
agm
=
annual geometric mean
ugm
=
microgram
mm
=
milligram
ppm
=
parts per million
m3
=
cubic meter
Secondary Standards
1300 ugm/m3
0.50 ppm (3 hr.)
60 ugm/m3 (agm)
150 ugm/m3 (24 hr.)^"
10 mgm/m3 (8 hr.)"1"
9 ppm
40 mgm/m3 (1 hr.)1
35 ppm
160 ugm/m3 (1 hr.)^"
0.08 ppm
160 ugm/m3 (3 hr.)^"'^
0.24 ppm
100 ugm/m3 (aam)
0.05 ppm
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quality better than national standards. These cri-
teria are established only for sulfur dioxide and
total suspended particulates. While the NAAQS ap-
ply to net pollutant concentration, the significant
deterioration criteria apply only to incremental con-
centration. There are three different sets of cri-
teria applicable to three different classes of areas
in the country:
Class I represents those areas in which
any commercial and industrial development
may result in significant degradation of
existing air quality
Class II represents those areas in which
development associated with normal growth
rate may be tolerated
Class III represents those areas in which
degradation of air quality up to national
standards may not be significant.
The allowable incremental concentrations for Classes
I and II are shown in Table 1-2. For Class III, the
ambient air quality may degrade up to the NAAQS.
Currently, all areas in the nation are desig-
nated as Class II. However, the states have the
option to reclassify any part of the state after
conducting a public hearing for each reclassifi-
cation action.
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Table 1-2
Significant Deterioration Criteria
Pollutant
Allowable Increments
Particulate matter
Annual geometric mean
24-hour maximum
Class I
3
ug/m
5
10
Class II
ug/m"*
10
30
Sulfur dioxide
Annual arithmetic mean
24-hour maximum
3-hour maximum
2
5
25
15
100
700
For Class III, the above concentrations could increase
until the air quality degrades up to the national ambient
standards.
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If comparison of expected pollutant concentration with
air quality standards shows potential for violation of the
standards, the next step in the analysis as indicated in the
decision flow diagram should be taken. When no violation is
indicated, the project should be approved from air quality
perspective.
4. CHARACTERISTICS OF AIR QUALITY MAINTENANCE AREAS
The Air Quality Maintenance Areas represent those areas
which, because of existing air quality and projected growth
rate, may have the potential for exceeding any National Am-
bient Air Quality Standards during the ten-year period be-
tween 1975 and 1985. AQMA's are designated by the states
and may be structured in accordance with one or more of the
following groupings:
Standard Metropolitan Statistical Areas (SMSA)
Air Quality Control Regions (AQCR) (designated
originally by the Department of Health, Education
and Welfare as regions having common air pollution
problems)
Urbanized areas
Counties
Groupings of: cities, townships, and boroughs
Planning regions used for land use, transporta-
tion, or other planning
Sub-state planning districts.
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The AQMA designation is pollutant specific. An area
may be an AQMA for one or more of the following pollutants
for which national ambient air quality standards exist:
Particulate matter
Sulfur dioxide (SC^)
Nitrogen dioxide (NC^)
Carbon monoxide (CO)
Photochemical oxidants {0 ).
x
However, regardless of the pollutants designated for a given
AQMA, the AQMA has a single boundary.
EPA currently is requiring that each state prepare an
AQMA plan, as a part of the state implementation plan, con-
sisting of modified or additional regulations necessary to
ensure future maintenance of ambient air quality standards.
When such plans are completed, they will serve as the basis
for the establishment of design populations and will provide
for land use controls required to maintain air quality. Thus,
they will likely preclude the necessity for most of the air
quality analyses described in this report.
5. ORGANIZATION OF THE REPORT
The remainder of the report is presented in four chap-
ters and appendices as described below:
II. Overview of the Air Quality Impact Analysis
Requirements
Describes the basic procedural steps proposed
for an applicant to assess air quality impacts.
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Ill. Alternative Mr Quality Analysis Methods
Summarizes available methods for air quality
analysis, references EPA and other reports on
each method, and presents the advantages and
disadvantages of each.
IV. Proposed Methodology to Screen Wastewater
Projects for Adverse Secondary Air Quality
Impacts
Describes in detail the proposed air quality
analysis procedure for screening sewage sys-
construction grant applications for potential
violations of ambient air quality standards.
V. Study of Two Test Projects
Presents results of application of the pro-
posed methodology to two test projects in
EPA Region II.
Appendices
A. Overview of Possible Air Pollution
Mitigating Measures
B. Discussion of Methods to Estimate
Vehicle Miles Traveled (VMT).
C. Application of the Proposed Methodology
to Estimating the VMT in Rockland County
Sewer District Number 1.
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State Air Quality Standards in EPA
Region II.
Input Requirements of the Modified
Rollforward lVodel for CO
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II. OVERVIEW OF THE AIR QUALITY IMPACT ANALYSIS PROCEDURE
This chapter describes the steps proposed to be followed
by a sewage project grant applicant in assessing the air qual-
ity impacts of his project. It is presented in the following
parts:
The Decision Process
Considerations in Land Use and Population Pro-
jections .
1. THE DECISION PROCESS
A procedure is proposed for use by sewage project grant
applicants to screen their projects for possible air quality
impacts. This procedure is depicted in Figure II-l and is
characterized by the following four sequential steps, with
each additional step required only if air quality problems
are still indicated:
A simple and conservative method is proposed to
assess air quality and screen projects
A review of land use and population projections
is proposed, since recent growth projections
frequently do not fully recognize declining
population growth rates.
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FIGURE 1-1
Decision Flow Diagram
KEY
ACTIONS
DECISION STEPS
END
IS THERE A POTENTIAL
FOR VIOLATING AQ
STANDARDS?
/ STOP 1
NO * I ANALYSIS
YES
NO
IS REVISION NECESSARY?
YES
NO
YES
NO
YES
/ STOP X
' ANALYSIS ^
AND REVISE
PROJECT SCOPE
' IS THERE STILL A \
POTENTIAL TO VIOLATE
V AQSTANDARDS /
THERE STILL
A POTENTIAL FOR
VIOLATING AQ
STANDARDS? ^
REVIEW LAND USE AND
POPULATION PROJECTIONS
ASSESS AIR QUALITY IMPACT OF
DESIGN POPULATION - WORST CASE
REVISE THE PROJECTIONS AND
ASSESS IMPACT OF THE REVISED
PROJECTIONS ON AQ
APPLY MORE SOPHISTICATED AQ
MODELS TO ASSESS AQ IN
CONSULTATION WITH LOCAL/STATE
PLANNING AGENCIES AND EPA
CONSULT WITH LOCAL/STATE PLANNING
AUTHORITIES AND EPA FOR DETAILED
AQ ANALYSIS COUPLED WITH
ANALYSIS OF MITIGATING MEASURES
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More sophisticated air quality assessment proce-
dures are suggested, to be applied in consultation
with local/state environmental staffs.
Consultation with local/state planning officials
is suggested to consider mitigative measures for
air quality, which will generally be outside the
scope of the applicant.
Considerations in reviewing population and land use projections
are discussed in the following section.
2. CONSIDERATIONS IN LAND USE AND POPULATION PROJECTIONS
If the worst case analysis based on design population in-
dicates a potential for violation of ambient air quality stan-
dards, the design population projections should be carefully
reviewed. As mentioned above, recent population projections
do not frequently recognize declining growth rates. Reevalua-
tion of a population estimate that underlies a wastewater system
design will be required in the following cases:
Changes in population subsequent to the date of
the estimate indicate a high probability that
the estimate is overstated
The estimate is based on an extrapolation of the
trend for a small area
The estimate is based on an extrapolation of a
trend for a larger area (SMSA or state) but for
too short a time period or for a nonrepresenta-
tive time period.
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The share of a larger area's projected develop-
ment assigned to the service area of the proposed
system has not considered:
The amount of land available for develop-
ment or redevelopment within the service
area of the proposed facility
Transportation access and travel times to
work centers
Attractiveness of the area with respect to
recreational facilities and other community
services.
Population projections meeting these criteria will be revised
accordingly, so that valid projections are available for the
subsequent analysis.
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III. ALTERNATIVE AIR QUALITY ANALYSIS METHODS
Depending upon the availability of data and analytical
resources, there are different ways to determine air pollu-
tant emissions in an urban area, and relate them to the am-
bient air quality. This chapter discusses the various fac-
tors affecting air quality and presents a general approach
to air quality analysis. Alternative methods available for
obtaining emissions data are discussed, together with alter
native atmospheric simulation models available for transla-
ting emissions into air quality. The organization of the
chapter is as follows:
Factors Affecting Ambient Air Quality
General Approach to Air Quality Analysis
Alternative Methods for Preparing Wastewater
Service Area Emission Inventory
Alternative Atmospheric Simulation Models.
1. FACTORS AFFECTING AMBIENT AIR QUALITY
Ambient air quality is generally measured at ground
level, where people and property are most often exposed
to the air pollutants. The ground level concentration
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of air pollutants at a typical urban monitoring site depends
upon many factors including:
Rate of pollutant emission in the area
Geographic distribution of the emission
sources
Source operating conditions including
Elevation of emission source
Temperature and velocity at which the pollu-
tants are emitted
Meteorological conditions including
Wind direction and speed
Atmospheric stability
Topography
Pollutant decay and the rate of reaction of an
air pollutant with other air pollutants and at-
mospheric substances.
Each of these factors is discussed below.
(1) Emission Rates
If all the other factors are held constant, the
contribution from an air pollutant emission source to
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the ambient pollutant concentration at a downwind re-
ceptor point is directly proportional to the rate at
which it is discharged into the atmosphere. However,
the total ambient concentration at the receptor point
is generally made up of contributions from a large num-
ber of emission sources and the natural background
levels. Since the other factors, especially the geo-
graphic distribution of the emission sources, usually
do not remain constant, the ambient concentration at
an urban receptor point does not vary in direct propor-
tion to the overall emission rate.
(2) Geographic Distribution
The ambient concentration of an air pollutant varies
with distance from the source because of mixing and di-
lution with the air. In addition, changes in wind di-
rection alter the path of the pollutant. Thus the pol-
lutant concentration at a receptor (monitoring site) is
greatly influenced by the relative location of the emis-
sion sources.
(3) Source Operating Conditions
Most air pollutants are formed as products of com-
bustion and are typically emitted through a stack or an
exhaust vent. The exhaust gases are generally warmer
and hence lighter than the surrounding air. Because of
their initial momentum and buoyancy, these gases tend
to rise through the air until an equilibrium with the
surrounding air is reached. Their ultimate rise depends
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on the physical stack height and diameter, exhaust
temperature and velocity and local meteorological
conditions. The ground level concentration of the
pollutants is inversely proportional to the total
rise of the pollutants in the atmosphere.
(4) Meteorological Conditions
Speed and direction of wind and atmospheric stabi-
lity play an important role in dispersing the air pol-
lutants. Higher wind speeds generally tend to rapidly
disperse the pollutants and reduce their concentration.
Similarly, changing wind direction distributes the pol-
lutants around the emission source. Greater atmospheric
stability tends to reduce the plume rise with resulting
higher ground level concentrations. However, the actual
pollutant concentration depends upon the combination of
all meteorological conditions.
(5) Topography
Local topography influences the air flow patterns
in the region, which in turn affect the pollutant dis-
persion. The air flow in a valley, for example, is
quite different than that over relatively flat, un-
obstructed terrain. Because of the flow restrictions,
the pollutant concentrations in a valley may be signi-
ficantly higher than those in an area with flat terrain.
Differences in elevations between emission sources and
receptor sites may also affect the ground level pollutant.
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(6) Pollutant Decay and Reactions
Some of the air pollutants remove themselves from
the atmosphere by settling down or they react with other
substances in the atmosphere to form new substances.
The particulates, for example, coagulate to form
larger and heavier particles which eventually settle
down. However, at the same time, pollutants such as
SC>2, react with atmospheric substances to form other
sulfur compounds, some of which remain suspended in the
atmosphere as particulates.
Similarly, the photochemical oxidants are formed
in the amosphere as products of chemical reactions
involving the nitrogen oxides, reactive hydrocarbons,
and sunlight.
The mechanisms of pollutant decay and the chemical
reactions mentioned above are not yet fully understood.
2. GENERAL APPROACH TO AIR QUALITY ANALYSIS
The principal objective of an ambient air quality im-
pact analysis of wastewater projects is to predict expected
air pollutant concentrations in the study area as a result
of changes in the urban environment. Such concentrations
can then be compared with the applicable air quality stand-
ards .
The process of relating changes in the urban environ-
ment to ambient air quality involves several steps:
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Identify and quantify existing air pollutant
emission activities in the study area
Determine emission factors for converting the
emission activities into emissions
Determine existing emissions
Obtain existing air quality and meteorological
data and estimate background concentration
Relate existing emissions to existing ambient
air quality by using atmospheric simulation
models
Determine growth factors for the emission acti-
vities during the desired time period
Determine the future emission factors for the
desired year
Project emissions to the desired year
Project ambient air quality to the desired year.
The existing emissions and air quality data are re-
quired when using certain air quality models such as the
proportional rollforward model. This information is also
useful for calibrating other air quality models. The indi-
vidual steps in the above process are discussed in detail
in Chapter IV. The next two sections in this chapter dis-
cuss the alternative methods for preparing an emissions
inventory and alternative atmospheric simulation models
for analyzing the air quality.
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3. ALTERNATIVE METHODS FOR PREPARING WASTEWATER SERVICE
AREA EMISSION INVENTORY
There are two approaches to preparing an emission in-
ventory for a wastewater project service area.
Estimate countywide emissions and allocate
them to the service area
Directly estimate the service area emissions
The first method is recommended when local data are not
readily available. It requires less effort than the second
method, but the second method is more accurate. These
methods are discussed below.
(1) Determining Wastewater Service Area Emissions
from Countywide Emissions
This method consists of the following steps:
Estimate existing countywide emissions
Allocate county emissions to the wastewater
service area
Project future service area emissions.
The first step relies on available county emissions
data. Most states have developed estimates of county-
wide emissions for 1975 as part of their State Imple-
mentation Plans. The countywide emissions inventory
is also maintained in the National Emission Data Systems
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(NEDS) operated by the U.S. EPA. The states are required
to update the NEDS inventory every six months. The NEDS
data are published annually. However/ latest data are
available on request through the EPA regional office.
If countywide data are not available, the alternative
methods discussed in the next section should be used.
The county emissions can be allocated to the waste-
water service area by using different allocation para-
meters. For example, emissions from countywide resi-
dential fuel combustion may be allocated to the waste-
water service area using the ratio of the service area
population to the county population. Volume 13, of the
EPA guidelines mentioned above presents several methods
for allocating county emissions to subcounty areas.^
These methods are discussed in Chapter IV.
Once the existing emissions for the service area
are estimated, future emissions can be projected using
projection methods described in Chapter IV.
(2) DIRECTLY ESTIMATING SERVICE AREA EMISSIONS
This method relies on local emissions data obtained
primarily through interviews with state and local plan-
ning agencies and operators of major emissions sources
in the service area. The EPA has published guidelines
for estimating existing as well as future emissions for
(2)
a county or smaller area. The procedures given in
these guidelines can be applied to estimating emissions
in the wastewater service areas.
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As an aid in determining the effects of future land
use patterns on the service area's emissions, emission
factors based on different types of land use may be de-
veloped as shown in Table III-l. The emission factors
shown in Table III-l are highly specific to the parti-
cular study area. An attempt was made by Argonne Na-
tional Laboratory to develop generalized land use emis-
sion factors, but the results were inconclusive.^
Thus, it would be necessary to develop separate land
use emission factors for each service area, which nay
be impractical for this analysis.
4. ALTERNATIVE ATMOSPHERIC SIMULATION MODELS
Atmospheric simulation models are designed to predict
ambient pollutant concentration by using the emissions data.
A number of different atmospheric simulation models have
been developed because:
The six criteria pollutants exhibit different
source-receptor relationships, requiring dif-
ferent analytical treatment.
The ambient standards are specified in terms of
pollutant concentration averaged over different
time periods, which also require different analy-
tical treatment.
The atmospheric processes affecting the ambient
concentrations are complex in nature and, there-
fore, cannot be uniquely simulated.
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Table III-l
An Example of Developing Emission Factors Based on Land Use
Pollutant Emissions
(lb/year/acre)
Land Use Category
TSP
S02
CO
HC
NO
X
Residential
10 Dwelling units/acre
25
1
35
12
7
20 Dwelling units/acre
180
120
4
54
85
30 Dwelling units/acre
180
120
4
54
85
50 Dwelling units/acre
250
160
5
75
120
80 Dwelling units/acre
200
140
4
63
100
Commercial & Industrial
Commercial
60
45
1
12
95
Manufacturing - Light
1100
1100
10
140
850
Manufacturing - Heavy
5400
5400
60
900
5400
Research
2
15
1
5
35
Distribution
60
45
1
12
95
Special Use
60
45
1
12
95
Airport*
100
1000
3000
350
100
Transport Center
180
130
2
36
300
Cultural Center
45
35
1
9
70
Open Space
0
0
0
0
0
Other**
Highway (lb/10^ VMT)
Emission Factors
700
400
11000
1000
1500
Parking lots (lb/10^
4
4
12
3
1
hrs idling)
* Assumes 400,000 flights/year from Teterboro Airport,
and 700 acre area.
** Activities are not specified on basis of omissions/
unit area.
Source: The Hackensack Meadowlands Air Pollution Study
Summary Report, Environmental Research and
Technology, October 1973.
-25-
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Detailed emissions data are not always available,
and different assumptions must therefore be made.
Commonly used atmospheric simulation models applicable
to specific pollutants and averaging times are shown in
Table III-2. Other models, such as the Sampled Chronologi-
cal Input Model and the SAI Photochemical Model are still
being tested and are not available for general use. These
models are discussed below.
(1) Simple Rollforward Model^
The simple rollforward model is based on an expres-
sion relating pollutant concentrations (X) to pollutant
emission rates (Q) and a background concentration (b):
The rollforward model assumes that the dispersion
parameter k does not vary with time or with the source-
receptor relationship, and that changes in emission
rates are uniform across the area. Thus the relation-
ship of emissions (^f^ure^ an^ a^"r c3ua^ity a
future year (xfuture) t*ie emissions (Qbase) an<^
air quality (Xbase) a base year can be expressed
by the following proportionality:
X = kQ + b.
(Eq. III-l)
X
future
-b
(Eq. III-2)
X
base
-b
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Table III-2
Commonly Used Air Quality Models*
Applicable to Specific Pollutants and Averaging Times
S02 and TSP
Annual Average
AQDM, CDM, FAQM
Hanna-Gifford
Miller-Holzworth
Rollfoward
SC>2 and TSP
24-Hour Average
Hanna-Gifford***
with point source
model
AQDM,** CDM,** FAQM**
Rollforward
S02 and TSP
3-Hour Average"
Hanna-Gifford***
with point source
model
AQDM,** CDM,**
FAQM**
Miller-Holzworth***
Rollforward
CO
1- & 8-Hour Average
APRAC-1A***
Hanna-Gifford***
with HIWAY
Modified Rollforward
X
1-Hour Average
Appendix J
NO^
Annual Average
Rollforward
Source: Based on reference 4,
is ic
~ ~ ~
Listed in descending order of level of detail and
applicability.
Statistical conversion of averaging times required.
Repetitious application of model to each hour under
consideration is required for averaging times longer
than 1-hour.
-27-
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The basic assumption in the model is that a given per-
cent reduction or increase in pollutant emissions will
result in a similar reduction or increase in pollutant
concentrations. It is simply a tool for scaling con-
centrations up or down to reflect similar changes in
the gross emission rates.
The rollforward model is applicable to most pol-
lutants and averaging times as shown in Table III-2.
Input to the rollforward model requires total area-wide
emissions for the base year and for 1985 or other years
of interest. A pollutant concentration representative
of air quality for the area and the averaging time of
interest is also necessary. It should be noted that
since there is no allowance for specifying the disper-
sion parameter k or other meteorological parameters,
this model cannot be used to estimate concentrations
at sites where representative air quality data do not
exist.
The rollforward model is applicable anywhere for
which there are basic data on area-wide emissions and
representative air quality for a particular base year.
The simple rollforward model can be applied with hand
calculations and is widely used.
The rollforward model in general is valid for the
simplified case of only one type of source uniformly
distributed across an area affecting a receptor. Ac-
curacy is lost as the variability of source types and
emission rates increase and the impact of atmospheric
processes on pollutant concentration increase. Thus,
due to the importance of point sources for TSP and
-28-
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SO,, and the reactive nature of N0_ and 0 , this model
2 2 x'
can provide only very crude estimates of concentrations
for these pollutants. A modification of the simple roll-
forward model to provide more accurate estimate of car-
bon monoxide concentration is discussed next.
(2) Modified Rollforward Model for CO^
High CO concentrations are observed primarily along-
side heavily travelled streets where the major CO contri-
bution is from local traffic. However, the simple roll-
forward model assumes that the CO levels are proportional
to the total CO emissions in the entire service area, thus
giving undue weight to stationary source CO emissions and
to vehicle emissions growth in the suburbs as compared to
vehicle emissions growth on streets in the fully developed
parts of urban areas where most existing air sampling sites
are located. The following model mitigates these problems
by giving the most weight (80 percent) to local traffic
near the air sampling station and relatively less weight
(20 percent) to total regional emissions.
The model divides the observed CO concentration
into two parts: that attributable to local traffic,
and that attributable to the entire urbanized area.
Changes in emissions from each of these components are
projected, and the future concentration is predicted
using modified rollforward techniques. The model
equations are:
F = F. + F + b (Eq. III-3)
t 1 u ^
-29-
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F1 ¦ PL1GL1EL1 + PH1GH1EH1 <»>• iii-4)
O.S(B-b) PL1 + PH1
F = PT CT E_ + PtI G„ E„ + P„ G„ E_
u Lu Lu Lu Hu Hu Hu Su Su Su
0.2(B-b) 100%
(Eq. III-5)
where: F = Total future CO concentra-
tion
F^ = Future concentration at-
tributable to local traffic
F = Future concentration attri-
u
butable to urban emission
b = Background concentration
B = Baseline concentration
(measured or estimated)
PT = Percent emission from
Li
light-duty vehicles (gross
vehicle weight <6000 lb)
P„ = Percent emission from
H
other mobile sources (gross
vehicle weight > 6000 lb)
P = Percent emission from
s
stationary sources
-30-
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G
Growth factor over the pro-
jection period
E
Expected ratio of future emis-
sion to baseline emission for
a composite source
Note: Subscript 1 is for traffic on local
streets near critical air sampling
stations.
Subscript u is for traffic in the
general urban area.
The information needed to apply the equations is dis-
cussed in Appendix E.
(3) Miller-Holzworth Model
The Miller-Holzworth model is more sophisticated
than the rollforward models because it considers meteor-
ological conditions in the study area. The model relates
pollutant concentrations to emissions and meteorological
conditions through an integration of a Gaussian type dis-
persion model; the integration is performed across an
(4)
urban area. The Miller-Holzworth model is expressed
as:
X/Q = 3.613H
0.130
+ S
0.088UH
1.26
2HU
S
for S/U < 0.471H
1.130
(Eq. III-6)
-31-
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where: X = Average city-wide concentration,
mg/m
Average emission density,
mg/sec-m2
H = Mixing depth, m
Along-wind distance of the
city, m. When this is not
known, assume S = Varea.
The "area" is the urbanized
portion of the city.
U = Average wind speed, m/sec
In cities in which S/U < 0.471 mixing depth
is unimportant, and X is given by
X/Q = 3.994 (S/U)0,115 (Eq. 111-7*
The Miller-Holzworth model is applicable to esti-
mating annual as well as one-hour average concentra-
tions of sulfur dioxide and particulates. A discus-
sion of the dispersion model and appropriate seasonal
average mixing heights and wind speeds is given in EPA
publication AP-101.^ This publication also provides
median, upper quartile and upper decile (X/Q) values
for various city sizes. Thus a range of pollutant
concentrations can be estimated for the more restrictive
meteorological dispersion conditions. However, neither
the emissions inventory used as input nor the output of
-32-
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the dispersion model makes it possible to estimate spa-
tial variations in pollutant concentrations across the
area.
Although this is a simple model to use its relia-
bility is questionable. A calibrated version of this
model is available. However, when applied to the
two test areas discussed in Chapter V, the predicted
TSP and SO2 levels using both the original and the
calibrated models did not correlate well with the
observed data.
(4) Hanna-Gifford Model
The Hanna-Gifford model is used to estimate an
average concentration for any defined area. In its
(7)
simple form it is expressed as:
X = CQ/U (Eq. III-8)
3
where: X = Concentration, mg/m
Average emission rate per
2
unit area, mg/sec - m
U = Mean wind speed, m/SEC
C = A constant whose value depends
upon the pollutant
This model applies to stable pollutants such as SC>2
and TSP and can be used to estimate their annual
average concentration. The values of C were deter-
mined by correlating the observed data in a large
-33-
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number of areas with the values predicated by the model,
and were found to be 225 for TSP and 50 for SC^.
The model is basically applicable to areas where
there is no point source information available so that
all emissions are grouped into area source emissions.
However, the reliability of the model under such cir-
cumstances is questionable. The accuracy of the model
can be increased by applying it to area sources only.
The point sources can be separately modeled by using
appropriate point source models such as ADQM or CDM.
The total pollutant concentration is then determined
by adding the point and area source contributions.
A more sophisticated form of the Hanna-Gifford
model is available to estimate one-hour as well as 24-
hour average concentrations from area sources. This
model requires detailed meteorological data input, in-
cluding hourly wind speed, wind direction, and atmos-
(4)
pheric stability.
(5) Air Quality Display Model (AQDM)
The AQDM is a computerized urban dispersion model,
primarily used to determine annual or seasonal concen-
i o \
tration of SC^ and TSP. It is capable of calculating
concentrations at multiple receptor points by consider-
ing the contribution of a large number of point and area
sources.
The contribution from each point and area source
to a receptor point is calculated separately. For point
-34-
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sources, the standard Gaussian diffusion model with Hol-
(9)
land's plume rise formula is used. An option to use
Brigg's plume rise formula is also available. The ADQM
uses a modification of the virtual point source method
for area sources.
The AQDM input requirements include detailed point
and area source emissions as well as meteorological data.
The point source data include location, emission rate,
stack height and diameter, and temperature and velocity
of gases leaving the stack for each point source. The
area source data include emission rate, area, and average
stack height for each area source. The meteorological
data include a joint frequency distribution of six clas-
ses of wind speed, sixteen sectors of wind direction,
and five classes of atmospheric stability. In addition,
an average annual or seasonal mixing height is also
required.
Since the AQDM calculates the contribution for
point and area sources separately, it can also be used
as a point or area source model only. A feature of the
AQDM is that it retains a separate data file containing
individual source contributions to concentrations at
each receptor point. Such information is useful in
developing a control strategy.
The AQDM is more accurate than the simple models
discussed earlier. However, there are certain limita-
tions to its use. It can only be sued for stable, non-
reactive pollutants such as SC^ and TSP. It can provide
reliable results only for long-term average concentra-
tions. A method developed by E. I. Larsen based on
-35-
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statistical analysis of observed data, is available to
obtain short-term average concentrations from long-term
average. However, the accuracy of this method is
questionable. The AQDM is developed for relatively flat
terrain and modifications for complex terrain are not
available. However, the model can be calibrated with
observed data to account for effects of complex terrain.
(6) Climatological Dispersion Model (CDM)
The CDM is similar to the AQDM in many respects.
The principal difference between the two is the method
used for calculation of the area source contribution.
The CDM uses the narrow plume hypothesis to calculate
the impact of area sources. This method is regard-
ed to be more accurate than the virtual point source
method used in the AQDM. The CDM also assumes that the
wind speed varies with altitude according to a power
law. Plume rise in the CDM is calculated by using
(12)
Brigg's formula. The input requirements for the
CDM are the same as those for the AQDM. However, the
individual source contributions are not readily avail-
able in the CDM. The CDM has the same limitations as
those for the AQDM, but it takes approximately seventy-
three percent of the computer running time for the AQDM.
(7) Fast Air Quality Model (FAQM)
The FAQM was developed by the Texas Air Control
(12)
Board and is conceptually similar to AQDM and CDM.
The input requirements for the FAQM are identical to
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those for the CDM, and its accuracy is comparable to
that of the CDM. However, the FAQM requires about
eighty times less computer running time than the CDM.
The major time-saving feature of the FAQM is the method
used for calculating point-source contribution. A se-
parate program was run, which solved the Gaussian plume
equation for many combinations of effective source
height and downwind distance in each stability class.
The results are incorporated into the FAQM as a table
of coefficients for each source-receptor configuration,
the FAQM interpolates in the table instead of solving
the Gaussian plume equation explicitly.
Another major time-saving difference between the
CDM and FAQM involves the calculation of concentrations
due to area sources. Area source concentrations are
determined using the simple technique of Hanna and Gif-
(7)
ford, whereby concentration is proportional to emis-
sion rate per unit area at the receptor divided by the
surface wind speed. This is the only major conceptual
departure from the CDM. As the behavior of diffuse pol-
lution sources is not well understood, it is possible
that the Hanna-Gifford model is a better simulation for
low diffuse sources than the more complex Gaussian plume
approach of the CDM.
A third time-saver is the FAQM's treatment of the
meteorological joint frequency function. An average
wind speed independent of wind direction is calculated
for each stability class. Within a stability class,
the spread in wind speed is typically small, and wind
speed is a weak function of wind direction, so the
simplification seems justified. Several other
-37-
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concentrations of three pollutants at once, compared to
two for the CDM. Five different sets of meteorological
conditions and emissions data for a given area may be
modeled in one run to the FAQM. This capability was
included to allow four seasons and annual weather and
emissions to be run simultaneously for climatological
studies, though other applications are possible. The
FAQM is "self-calibrating." It is capable of perform-
ing a first-order least squares regression analysis of
observed vs calculated concentrations. It then applies
the resulting coefficients to the calculated concentra-
tions, thus "calibrating" the model. Concentrations
are calculated for a uniform grid of receptors of no
more than 50 rows and 50 columns (thus a maximum of
2500 receptors) of any dimensions and spacing.
The FAQM is applicable only to estimating long-
term average concentrations of SC^ and TSP in an area
with relatively flat terrain. The program documenta-
(14)
tion is available from the Texas Air Control Board.
(8) Other Air Quality Models
This section presents a brief summary of other
atmospheric simulation models available for urban air
quality analysis.
For a detailed analysis of urban carbon monoxide
concentration, two computerized models are available.
The HIWAY model is a line source model applicable to
(15)
motor vehicle emissions along highways and streets.
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The APRAC-1A model, on the other hand, considers both
the line and area sources of automotive pollutants.'
The reduction in the hydrocarbons emissions neces-
sary to attain the national ambient photochemical oxi-
dants standard in an urban area can be approximately
(17)
estimated by using the Appendix-J method. A photo-
chemical dispersion model called the SAI model has been
developed to estimate the regional concentration of the
/ IP \
photochemical oxidants. This program is not yet
available for general use.
For estimating the hourly average concentration of
SO. and TSP, a computerized model called the sampled
(19)
chronological Input Model (SCIM) has been developed.
Its reliability has not been widely determined, and it
is not yet available for general use.
* * * *
It is clear from the above discussion that a simple but
accurate method for urban air quality analysis is not avail-
able. The accuracy of the air quality analysis depends upon
adequate consideration of the various factors affecting the
air quality. The simpler air quality models ignore some of
these factors with subsequent loss of accuracy. The more
complex atmospheric simulation models provide more accurate
results than the simpler models because they consider indivi-
dual point and area source emissions and the local meteoro-
logical conditions. The simple Hanna-Gifford model, when used
with small areas, can estimate the contribution from area
sources with reasonable accuracy. However, there is no simple
method for estimating the contribution from point sources to
ambient air quality. The sophisticated computer models must
-39-
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be used for obtaining accurate and reliable results from an
air quality analysis.
A simple screening procedure using the rollforward
models can be developed to identify proposed wastewater
projects in an AQMA with potential for causing violation
of the ambient air quality standards as discussed in the
next chapter.
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IV. PROPOSED METHODOLOGY TO SCREEN WASTEWATER
PROJECTS FOR ADVERSE SECONDARY AIR QUALITY IMPACT
This chapter presents a simple methodology for the use
of both the EPA and the construction grant applicant to screen
proposed wastewater projects in an AQMA, and identify those
projects with potential for causing violation of ambient air
quality standards. The projects with potential air quality
problems should then be further analyzed using appropriate
computer models as described in the previous chapter.
The proposed methodology is based on the use of the
proportional rollforward models discussed in the previous
chapter. Although the accuracy of these models is ques-
tionable, they can provide conservative estimates of future
air quality, provided existing air quality data are avail-
able at the receptor point, where the worst air quality in
the study area is expected.
The simple rollforward model implicitly assumes that
the future emissions increases in the study area would be
distributed such that each existing emission source would
experience the same percentage increase as the total emis-
sions in the study area. For example, if the total emis-
sions in the study area are expected to double in the next
ten years, each existing emission source in the study area
would be assumed to double its emission rate. Therefore,
the average ambient air pollutant concentration less the
natural background at any receptor point in the study area
-41-
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would increase by the same percentage. Since the worst air
quality is most likely to be observed in the most developed
parts of the study area with little scope for additional de-
velopment, the rollforward technique based on the worst air
quality data is likely to overestimate the future air pollu-
tant concentration. It, therefore, can serve as a screening
tool for most pollutants, including TSP, NC^/ and HC.
In the case of CO, the modified rollforward model pro-
vides more accurate results. However, the simple rollfor-
ward model is recommended in the proposed methodology as a
preliminary screening procedure. If the simple model indi-
cates a violation of the ambient CO standards, then the mo-
dified rollforward model should be used as described in
Appendix E.
Simple methods to evaluate the impact of urban growth
in a small area, such as a typical wastewater project ser-
vice area, on the ambient concentration of photochemical
oxidants are not available. Therefore, the EPA does not
expect the grant applicants for wastewater projects^located
in an AQMA for photochemical oxidants^to analyze their pro-
jects' impact on the ambient photochemical oxidants levels.
However, such grant applicants are expected to evaluate the
impact of their projects on the regional hydrocarbons emis-
sions, which take part in the formation of photochemical
oxidants. The proposed methodology, therefore, includes
methods to estimate the impact of urban growth on the hydro-
carbon emissions.
The proposed methodology involves the following steps:
Define the impacted area
Estimate the base year emissions for this area
-42-
-------
Project the emissions to the desired year
Obtain the base year air quality data
Project the air quality to the desired year
using the Equation (III-2)
Evaluate the Air Quality Impact of the proposed
project.
Estimate cumulative Air Quality Impact of Multiple
Wastewater Projects in the same AQMA.
1. DEFINE THE IMPACTED AREA
When only area sources and small point sources are involved
and photochemical oxidants are not a problem, the air quality im-
pact of urban growth in the service area of a wastewater project
is generally localized. The effect of such urban growth in ad-
jacent areas on the air quality in the service area and vice versa
should be negligible. Therefore, in such cases, the project ser-
vice area is defined as the impacted area.
When large point sources such as power plants are pre-
sent, the impact of their emissions may be felt over an
area larger than the wastewater service area. In such cases,
point sources outside the service area may have to be considered
in estimating the air quality in the service area.
Photochemical oxidants are formed by a complex set of
reactions involving hydrocarbons, nitrogen oxides, and
-43-
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sunlight. They are formed away from the source of emis-
sions and present a regional problem. As mentioned earlier,
the grant applicants are not expected to evaluate the im-
pact of their projects on the ambient photochemical oxidants.
Only the impact of urban growth in the project service area
on the HC emissions should be estimated.
2. ESTIMATE BASE YEAR EMISSIONS
The most recent year for which the best local ambient air
quality data are available should be selected as the base year.
The procedure for preparing an emissions inventory con-
sists of first identifying the air pollutant emission acti-
vities and then quantifying the emissions. The emission
activities can be divided into five broad classes:
Stationary fuel combustion
Industrial processes
Solid waste disposal
Transportation
Miscellaneous (e. g., forest fires, agricultural
burning, etc.)
Each of the above classes can be further subdivided
according to the type of sources. For example, the sta-
tionary fuel combustion category can be divided into resi-
dential, commercial/institutional, industrial, and utility
fuel. These sub-categories can be further divided accord-
ing to the type of fuel used (e. g., coal, oil, and gas).
Finally, each source can be classified as a point or area
source. A point source represents a large emission source,
-44-
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typically emitting over one hundred tons of an air pollutant
per year (e. g., a large fossil fuel fired power plant). An
area source, on the other hand, represents a combination of
small and diffuse emission sources such as houses with indi-
vidual oil or gas fired heating furnaces. Motor vehicles
travelling on a roadway represent a line emission source, but
can be included in the area source category. For the screen-
ing procedure, the emissions activities are grouped into the
following categories:
Fuel combustion
Residential (area)
Commercial/institutional (point and area)
Industrial (point and area)
Utility (point)
Industrial processes (point and area)
Solid waste (point and area)
Transportation (area)
4
Light-duty and heavy-duty motor vehicles
Aircrafts
Railroads
Off-highway vehicles
Miscellaneous
The emissions for the service area are estimated by
considering the point and area source emissions separately.
As discussed in Chapter III, the service area emis-
sions from the various categories can be estimated in two
-45-
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ways. The most accurate way is to estimate the emissions
directly/ and it should be followed, if sufficient data can
be obtained. The allocation method is less accurate, but
if recently updated county emissions data are available, it
can provide reasonable accuracy in allocating the county-
wide emissions from some source categories to the service
area. The method discussion below is a combination of the
two methods, providing greater accuracy than the allocation
method but requiring less effort than the direct estimation
method.
The procedure for estimating the base year is described
below in two parts: point source emissions; and area source
emissions.
(1) Estimate Point Source Emissions
The point source emission data for the service
area may be obtained directly from the point source
emission file maintained by the State Air Pollution
Control Agency.
If the state emission file is not complete, large
emissions sources in the service area should be contacted
directly to obtain the emissions data. Volume 7 of the
Guidelines for Air Quality Maintenance Planning and Analy-
(2)
sis should be consulted for this purpose.
If a large point source is located outside the
service area but there is a reason to believe that it
has significant impact on the air quality in the ser-
vice area, the emissions from the source should be
included in the inventory.
-46-
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(2) Estimate Area Source Emissions
The area source emission, in the service area, in
general, can be estimated with reasonable accuracy by
allocating the countywide emissions to the service area.
However, if sufficient data are available, the service
area emissions from certain source categories should be
directly estimated. The methods for estimating emis-
sions from the various source categories are discussed
below.
1. Residential and Commercial/Institution Fuel
and Solid Waste
The fuel use for residential and commercial/
institutional purposes and solid waste generation
are approximately proportional to the population.
Therefore, residential and commercial/institutional
fuel and solid waste emissions in the service area
are estimated by allocating county emissions ac-
cording to the fraction of the county population
residing in the service area. More sophisticated
allocation methods are described in the EPA guide-
lines, ^ and may be used if sufficient data are
available.
2. Industrial Fuel
Industrial fuel use may be assumed to be
proportional to the industrial employment or land
use. The countywide industrial fuel emissions,
-47-
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therefore, are allocated to the service area ac-
cording to the ratio of industrial employment in
the service area to that in the county. If data
on industrial employment are not available, in-
dustrial land use may be used for the allocation
purpose. For more sophisticated methods, the EPA
guidelines^ should be consulted.
3. Industrial Processes
Industrial process emissions depend upon the
type of process and size of the facility, and
therefore, should be allocated by locating indi-
vidual industrial sources in the service area.
If sufficient data are not available, allocation
based on industrial employment or land use, as
explained above, should be made.
4. Motor Vehicle Emissions
Motor vehicles emit significant quantities
of CO, HC, and N0x and relatively small quanti-
ties of S(>2 and particulates. Motor vehicles
form the largest source of CO, HC, and NO emis-
X
sions. The motor vehicle emissions depend upon
the vehicle type, age, speed, and operating con-
ditions and number of vehicle miles travelled
(VMT).
Motor vehicles are generally divided into
five classes:
-48-
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Light duty vehicles (LDV): automobiles
Light duty trucks (LDT): gross weight
up to 8500 lbs
Heavy duty gasoline vehicles (HDG):
gross weight over 8500 lbs
Heavy duty diesel vehicles (HDD): gross
weight over 8500 lbs
Motorcycles.
The procedure for estimating the emissions
from each of the above classes involves the fol
lowing steps:
Determine the VMT for each class
Determine the emission factor per VMT
for each class averaged over different
vehicle age, speed, and operating con-
/
ditions
Multiply the VMT for each class by the
average emission factor.
The methods for estimating the service area
VMT are given in Appendix B. The Level 3 method
is the most accurate, followed by Level 2 and
Level 1. The choice of the method depends upon
the availability of data. If sufficient data are
available, Level 3 should be used, otherwise Level 2
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or Level 1 may be used in decreasing order of
preference. If local data for VMT for each vehi-
cle class cannot be obtained, national statistics
may be used to divide the total service area VMT
among the various classes as follows:
LDV: 80.4%
LDT: 11.8%
HDG: 4.6%
HDD: 3.2%
The motorcycle emissions are usually very small
and, therefore, may be ignored.
The methods to estimate the emission factors
for each vehicle class are given in AP-42.If
local data on vehicle age, speed, and operating
conditions are not available, the national statis-
tics given in AP-42 may be used.
The total motor vehicle emissions are obtained
by multiplying the VMT for each vehicle class by
the corresponding emission factors and summing the
products for each class. If local data on VMT as
well as emission factors for each class cannot be
obtained, the total emissions may be obtained by
multiplying the total VMT by the national average
emission factors given in Table 7-1 of AP-42.
The above procedure applies to all air pol-
lutants. However, the TSP and SC^ emissions do
not vary significantly with the vehicle type,
age, speed, and operating conditions. Also, the
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total TSP and SC^ emissions in the service area
do not depend as strongly on the motor vehicle
emissions as the other pollutants. Therefore,
simpler estimation methods may be used, if only
TSP and SC^ emissions are to be estimated. Such
methods include estimating the total service area
VMT and using average emission factors to calcu-
late the emissions, or allocating the countywide
emissions to the service area based on VMT or
population ratios.
5. Aircraft, Railroad, and Off-highway Vehicle
Emissions
Aircraft and railroad emissions primarily oc-
cur near airports and railroad yards respectively.
Therefore, they are allocated according to the lo-
cation of the airports and railroad yards.
Off-highway vehicle emissions are usually
small compared to the other categories and may be
allocated based on judgment.
6. Miscellaneous Emissions
Miscellaneous sources include those not in-
cluded in the above categories (e. g., fugitive
dust, forest fires, agricultural burnings, gaso-
line marketing). Miscellaneous emissions are
allocated by reviewing the type of activity and
using an appropriate parameter such as population,
land use, or employment.
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Once the point and area source emissions are estimated,
they are added to obtain total emissions for each category as
well as the total for all categories.
3. PROJECT EMISSIONS
The purpose of this step is to determine emissions in
the year corresponding to the "worst case" conditions in the
service area. The emissions from some categories are pro-
jected by multiplying the base year emissions by appropriate
growth factors such as population growth. The emissions from
other categories are projected by detailed examination of the
existing and future emissions activities. The emission pro-
jection procedure for each category is explained below.
(1) Residential and Commercial/Institutional Fuel
Residential and commercial/institutional fuel
emissions can be projected by using the equation:
Qp = Qb x G (Eq. IV-1)
where: QD = Projected emissions
'P
Growth factor
Qb = 1975 emissions
The growth factor is assumed to be the same as the
population growth ratio for the service area for the
given period. Industrial fuel emissions can be pro-
jected by using Equation IV-1, with the growth factor
equal to the ratio of industrial employment in the
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projection year to that in 1975 for the Standard Metro-
politan Statistical Area (SMSA) in which the service
area is located. If industrial employment data are not
available, manufacturing earnings data may be used.
The projections for the SMSA's are given in OBERS pro-
(21)
jections. If the service area is not located in
an SMSA, similar projections are given for larger eco-
(22)
nomic areas.
(2) Utility Fuel Combustion
Information on expansion of existing power plants
or addition of new power plants may be obtained by con-
tacting the utility companies. Another source of in-
formation is the Federal Power Commission, which requires
the utilities to document their expansion plans on the
FPC Form 67. To project emissions, determine the amount
of electricity to be generated by powerplants located in
the service area,* and which fuels will be burned and
their quantities used in a year. Estimate the emissions
from the known sulfur and ash content of each fuel, and
using the emission factors given in AP-42.^*^
(3) Industrial Processes
To project the industrial process emissions, the
industrial sources in the service area should be con-
tacted individually to determine their expansion po-
tential. Local planning boards should also be contacted
Or in adjacent areas if it is determined that the power plant emis-
sions would have significant impact on the air quality m the ser-
vice area.
-53-
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to estimate the location and type of potential new
sources. Once the existing source expansion data are
obtained, the emission factors given in AP-42 are used
to project future emissions. To project the emissions
from new sources, the procedure given in "Accounting
for New Source Performance Standards in Projecting and
(23)
Allocating Emissions' should be used.
If sufficient data are not available, the emissions
from industrial processes may be projected by using the
following equation:
Qp = QBGE (Eg. IV-2)
where: Qp = Emissions for the projection year
Q_ = Base year emissions
D
G = Growth factor
E ¦= Emission adjustment factor
The growth factor is assumed to be equal to the ratio
of the service area industrial employment in the pro-
jection year to that in the base year. If the indus-
trial employment data are not available, the growth in
the manufacturing earnings may be used to estimate the
growth factor. The manufacturing earnings for the SMSA
(21)
are given in the OBERS projections.
The emission adjustment factor represents the
reduction in emissions expected from emission control
regulations on new sources. Unless more specific data
are available, E = 0.4 is recommended.^ If suffi-
cient data are available, the procedure given in Re-
ference (2) is recommended.
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(4) Motor Vehicle Emissions
The motor vehicle emissions can be projected using
the same methods used for estimating the base year emis-
sions. The methods to project the VMT are given in Ap-
pendix B,. whereas the methods to estimate future emis-
sion factors are given in AP-42.
(5) Aircraft^ Railroad, and Off-highway Vehicle
Emissions
The growth in these transportation activities is
generally proportional to the increase in the economic
activity in the area. The economic activity can be re-
lated to the total earnings in the area. Therefore,
the emissions from these transportation sources can be
projected by multiplying the base year emissions by the
ratio of total earnings for the SMSA in the projection
year to those in the base year. Total earnings are
(21)
given in the OBERS projection.
(6) Solid Waste
The amount of solid waste generated in a community
is usually proportional to the population. Therefore,
solid waste emissions can be projected by multiplying
the base year emissions by the population growth factor.
(7) Miscellaneous
The miscellaneous source emissions should be pro-
jected by reviewing the type of emissions and applying
-55-
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an appropriate growth factor to the base year emis-
sions .
The emissions for each category should be added
to obtain the total emissions for the projection year.
4. DETERMINE BASE YEAR AIR QUALITY
Depending on the pollutants for which the area has
been designated as an AQMA, the required air quality data
for comparison with the NAAQS vary as described in Table IV-1.
The air quality in the service area should be determined from
air quality monitoring conducted in the service area by state
or local air pollution control agencies. If there are more
than one monitoring stations in the service area, the highest
concentrations among the various monitoring sites should be
used. If there are no monitoring stations in the service area,
data from monitoring stations located in nearby areas may be
used, provided that the emission sources and mix, topography,
and meteorological conditions in those areas are representa-
tive of those in the service area. If representative monitor-
ing data are not available, the grant applicant should con-
tact the Air Branch of the EPA's regional office.
5. PROJECT AIR QUALITY
In the simple methodology, it is assumed that the am-
bient concentration less the background concentration of an
air pollutant in an area is proportional to the amount of
that pollutant emitted in that area. The projected pollutant
concentration is given by the following equation:
XPN = bN + (XBN ~ V (Eq- IV~3)
qbn
-56-
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Table IV-1
Air Quality Data Requirements
for the Base Year
Pollutant
Sulfur dioxide
Particulate matter
Nitrogen dioxide
Carbon monoxide
Averaging period
3-Hour
24-Hour
1-Year
24-Hour
1-Year
1-Year
1-Hour
3-Hour
second highest)
second highest)
arithmetic mean)
second highest)
geometric mean)
geometric mean)
second highest)
second highest)
-57-
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where: X_„
PN
Projected concentration of pollu-
tant N
XfiN = Base year concentration of pollu-
tant N
fc>N = Background concentration of pollu-
tant N
QpN = Projected emissions of pollutant N
Qbn = Base year emission of pollutant N
The background concentration of SC>2 and N02 is assumed
to be zero, while that of particulate matter is determined
from available data from the state or local air pollution
control agencies. For carbon monoxide, background concen-
tration of 1 ppm of 8 hours ^ and 5 ppm for 1 hour^^ is
assumed. As mentioned before, it is not necessary to pro-
ject the oxidants concentration for the screening purposes.
6. EVALUATE THE AIR QUALITY IMPACT OF THE PROPOSED PROJECT
After the future air quality levels are projected, they
must be compared with the applicable ambient air quality stan-
dards. If the standards are met, no further air quality analy-
sis is necessary. If a violation of the standards is indi-
cated, further analysis as shown in the Decision Flow Diagram
in Chapter II would be required. However, if a violation of
the CO standards is indicated, the modified rollforward model
for CO described in Chapter III and Appendix E should be ap-
plied first. If the modified rollforward model also indicates
a violation, the next step in the decision flow diagram should
be taken.
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7. ESTIMATE CUMULATIVE AIR QUALITY IMPACTS OF MULTIPLE
WASTEWATER PROJECTS IN AN AQMA
When only area and point sources are present and photo-
chemical oxidants are not a problem, the cumulative air quality
impacts of several wastewater projects in the same AQMA would
be negligible, because such impacts are generally localized.
When large point sources are present, they should be in-
cluded in the air quality analysis as discussed in Section 1,
Subsection (1) of this Chapter.
The photochemical oxidants present a regional problem
and must be analyzed on a regionwide basis. Such an analysis
would be beyond the scope of a wastewater project grant ap-
plicant. However, the grant applicant should estimate the
contribution of his service area's growth to the regional
HC emissions by using the methods discussed in Sections 2 and
3 of this Chapter.
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V. STUDY OF TWO TEST PROJECTS
In order to aid in the development of the proposed
methodology and to test it, the EPA selected two test pro-
jects in the State of New York, involving expansion of
wastewater collection and treatment facilities in:
Town of Colonie
Rockland County Sewer District Number 1
The two projects were selected because they are located in
AQMAs for different pollutants and both indicated high ur-
ban growth potential. The application of the proposed me-
thodology to assess the secondary air quality impact of the
two projects is described below. This methodology is equal-
ly applicable to any wastewater project in the U.S.
1. TOWN OF COLONIE
The Town of Colonie is located in Albany County, New
York. It is included in the Capital District AQMA, which
is an AQMA for total suspended particulates and sulfur dio-
xide. Figure V-l shows the boundary of the Capital Dis-
trict AQMA.
The topography of the region is generally rolling.
The prevailing wind direction is from the south and the
(25)
average wind speed is 8.2 mph.
-60-
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FIGURE V-l
Capital District AQMA
ts^°^ c0 ^
1 j
iV
P^o ~ .
^ s®rihl(ilP j
** >> JC
J Soutfc /
\ C«r»Dtb J
\ MORIAU
AQMA
LOINBJNG CO«'"T
5r«AJPO*0
jWI^ON fc4l(
riCkD
Seh«>.l»L15ii
5 priBf
^ i K 1 TOWN 1 / 1 \ I _
> ^ I x /r1 \ ^cn»oO
>¦>>:. i j^k.....-: j I """.rrj,
T^n vSTILLWAft*!
» 'MALTA) J
i Hound Sull*
BalUton
^ J ^\Schenectady^
SPkSgto:
• CI
« caqlolv •••«?\
ycoDyc^*" f ~G*>Arro* / BL..a
it I I "Lmo
*-iC.r«n UUr^
»<««o»evikbi
9..»»»«3*""V"",i*hurf
-v _ . * litv I ""'^oSmmuW 11111 "
\CMb2«-^ f£?N* 1 J V v '*
/ » 1 *_cT77i»«<"> » /
'UL-os I ' I I- •)«.».»„. *'*
> «
villi — -nT"^
V "» , — i
to I ¦¦»"-Ti~o«r I ir>v .!•>•• \ c,s
>. 1 ""•* I 1
V"— ! OV"2
o*£tSt f
cilooa
Cb*^*^
DU«H4k|
^OBr, l
T\^ | OmINT
c,mo yrr^h-p^ L
land WIN9han.
„ A*h*n# n I
i phiiotfet
&d?c* ;
^Onr Ccavchac^
' I
TAGmkAHIC COFAKC
GAUATiN
-61-
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Major air pollutant emission sources in the AQMA in-
clude large manufacturing and chemical industrial plants,
an electric generating station, heating plants for hospi-
tals, colleges, and schools, and a paper mill.
Because of the state air pollution control regulations
ambient air quality in the AQMA has improved over the past
five years and the ambient air quality standards are being
met in most parts of the AQMA, except in the City of Albany.
The annual TSP levels in the Town of Colonie decreased from
55 micrograms per cubic meter in 1973 to 51 micrograms per
(26)
cubic meter in 1974. Although the TSP levels in Albany
have decreased, the AAQS for TSP were violated in 1974.
Similarly, the sulfur dioxide standards were exceeded in
Albany while they were being met in the other areas. Am-
bient concentrations of the other pollutants in the AQMA
were below the applicable AAQS.
Although the existing TSP and SC^ levels in the Town of
Colonie are below the AAQS, a rapid urban expansion in the
area may create a potential for violation of the AAQS in the
future. The strategic location of the Town in relation to
the industrialized City of Schenectady and the State Capital
Albany makes it an attractive area for residential and as-
sociated commercial development. The proposed wastewater
project in the Town of Colonie was designed in 1969 based on
this growth potential.
The proposed project (EPA Grant Application Numbers
C-36-742 and C-36-781) includes construction of lateral
sewers, trunk and intercepting sewers, pumping stations and
treatment facilities within the Town of Colonie as shown in
Figure V-2. The service area of the proposed wastewater
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project encompasses the Town of Colonie excluding the Villages
of Colonie and Menands and certain other areas as shown in
Figure V-2. The service area is divided into two parts: Mo-
hawk River watershed area and Hudson River watershed area.
The total service area is approximately 12,750 hectares
(31,500 acres). The wastewater from the Mohawk River water-
shed will be conveyed to the proposed 18,925 cu m/day (5 mgd)
capacity treatment plant to be located along the Mohawk Ri-
(28)
ver. The wastewater from the Hudson River watershed will
be conveyed to the 132,475 cu m/day (35 mgd) treatment plant
(29)
in Albany. Approximately 18,000 cu m/day (7.4 mgd) capa-
city of the Albany plant is assigned to the Town of Colonie.
Total population of the service area in 1970 based on
the 1970 census was 57,863 with 20,425 in the Mohawk River
watershed area and 37,438 in the Hudson River watershed area.
The design of the treatment plant in the Mohawk River water-
shed area is based on a population of 35,500 in 1990 as pro-
{28}
jected in 1969. The sewers in that area are designed for
a 50-year projected population of 61,000. The sewers in the
Hudson River watershed area are designed for a 50-year pro-
jected population of 79,000.^^ while the portion of the
Albany treatment plant assigned to the Town of Colonie is
designed for a projected population of 52,540 in 1990. Thus,
the total design population to be served by the proposed
wastewater treatment facilities in 1990 in the Town of Colo-
(31)
nie is 86,900. The estimated population in 1975 is 71,000.
The population projections for New York State as well as
the Capital District Metropolitan area have been considerably
(32)
reduced. The latest projection for the service calls for
a population of 80,000 by the year 2000. Thus, the estimate
of design population of 86,900 to be reached in 1990 is highly
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conservative. The secondary air quality impact assessment
of the proposed project discussed below is based on this
estimate and, therefore, represents the "worst case" analy-
sis .
The study area for the air quality analysis consists of
the proposed project service area. The year 1975 was se-
lected as the base year and 1990, when the design population
of the treatment facilities was expected to be reached, was
selected as the projection year.
The various steps in the proposed methodology are applied
to the proposed project as described below:
Estimate base year emissions
Project future emissions
Determine base year air quality
Project air quality
Compare projected air quality with AAQS.
(1) Estimate 1975 Emissions
The procedure for estimating TSP emissions is ex-
plained below. Similar procedure applies to estimating
SC>2 emissions.
The first step in estimating the 1975 emissions is
to obtain individual point source emissions from the New
York State DEC. The DEC maintains a data file for signi-
ficant point sources which have a potential to emit in
excess of one hundred tons per year of any air pollutant.
However, because of emission controls, these sources
-65-
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generally emit less quantities. The point source TSP
emission for Albany County as well as for the Town of
Colonie are shown in Table V-l. The DEC data file con-
tained total emissions for each point source. The in-
dustrial point source emissions were, therefore, sepa-
rated into fuel and process emissions by using the same
proportion as in the County point source emissions dis-
cussed below.
The next step is to estimate the area source emis-
sions. These were estimated by allocating the county
emissions to the service area. The emissions data for
Albany County were obtained from the New York State
Implementation Plan prepared by the Department of Envi-
ronmental Conservation (DEC) . Table V-2 shows the
estimated point and area source TSP emissions in 1975
for the various source categories. These emissions are
estimates only, and should not normally be used, if ac-
tual data are available.
There are some differences between the point source
emissions data shown in Table V-l and Table V-2. Since
Table V-l contains more recent data, some adjustments
were made to the county emissions data. The significant
countywide Commercial/Institutional fuel point source
emissions were subtracted from the total countywide Com-
mercial/Institutional fuel emissions to obtain the county
area source emissions. The significant industrial point
source emissions were separated into fuel and process
emissions in the same proportion as that for the total
industrial point source emissions given in Table V-2.
The countywide industrial fuel and process area source
emissions were then obtained using the same procedure
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Table V-l
Significant TSP Point Source Emissions
In Albany County and Town of Colonie, 1975
Emissions (tons/year)
Source Category
Commercial/Inst. Fuel
Industrial Fuel*
Utility Fuel
Industrial Processes*
Solid Waste
County
149.83
140.4
1932.00
1263.6
Service Area
11.34
8.4
74.6
Total
3485.83
94.34
*
Assumed to be in the same proportion of the total industrial point
source emissions as in Table V-2
Source: New York State Department of Environmental Conservation,
Division of Air Resources, Albany, New York
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Table V-2
Estimated TSP Emissions in Albany County, 1975
Source Category
Residential Fuel
Commercial/Inst. Fuel
Industrial Fuel
Utility Fuel
Industrial Processes
Solid Waste
Transportation
Motor vehicles
Aircraft
Railroad
Off-highway
Miscellaneous
Total
Source: Reference (33)
Emissions (tons/yr)
Point Area Total
599 599
17 516 533
334 209 543
1,932 - 1,932
3,341 44 3,386
0 395 395
535 535
169 169
275 275
13 13
5,624 2,755 8,379
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described above for Commercial/Institutional Fuel
category.
The revised countywide area source emissions are
shown in Table V-3. These county area source emissions
were then allocated to the service area by using the
allocation parameters as shown in Table V-3.
(2) Project 1990 Emissions
Using the total base year emissions for each cate-
gory and the growth factors* as indicated in Table V-4,
the emissions were projected to 1990 as shown in Table V-4.
To obtain conservative results, the growth factors were
applied to both point and area source emissions instead
of area sources alone. Although the industrial process
emissions were projected based on the control factor of
0.4, they were assumed to remain constant in the subse-
quent analysis for obtaining conservative estimates. The
(21)
growth factors were obtained from OBERS projection.
(3) Determine Base Year Air Quality
The most recent data for the Town of Colonie were
available for 1974. Normally, it would not be accept-
able to use 1974 air quality data with the 1975 emissions.
However, the 1974 air quality data were used in this case,
because the past air quality data indicated a trend to-
wards lower pollutant concentrations. Therefore, the
projected air quality levels would be conservative.
Growth factors were estimated as discussed in Chapter IV, Section 3.
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Table V-3
Allocation of Countywide TSP Area Source
Emissions to Service Area, 1975
(Town of Colonie)
Source Category
County
Emissions
(t/yr)
Allocation Parameter
Name
Ratio*
%
Service
Area
Emissions
(t/yr)
Residential Fuel
Comm/Inst. Fuel
lnd. Fuel
lnd. Processes
Solid Waste
Transportation
Motor Vehicles
Aircraft
Railroad
Off-highway
599
383
403
2122
395
535
169
275
13
pop.
land use
or emp.
land use
or emp.
land use
or emp.
pop
pop
location
location
pop.
7.41
7.41+
7.41
7.41+
7.41
7.41
100
0
0
44.4
28.4
29.9
157.2
29.2
39.6
169.0
0
0
Total
2755
497.74
Service area to county
+ Because of lack of data, assumed to be proportional to population.
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Table V-4
TSP Emission Projections for Service Area, 1990
(Town of Colonie)
Source Category
Base Year Emission
Emissions, QB Growth Growth Control
(ton/yr) Parameter Factor Factor
Residential Fuel
Comm/Inst. Fuel
Industrial Fuel
Solid Waste
Transportation
Motor Vehicles
Aircraft
Railroad
Off-highway
Miscellaneous
44.4
39.7
38.2
Utility Fuel
Industrial Processes 231.8
29.2
39.6
169.0
pop.
pop.
manufact.
earnings
forecast
manufact.
earnings
pop.
pop.
total
earnings
1.21
1.21
1.51
1.51
1.21
1.21
1.69
1.0
1.0
1.0
0.4
1.0
1.0
1.0
Total 591.9
Assuming industrial process emissions to remain unchanged.
Projected
Emissions, QP
(ton/yr)
53.7
48.0
57.7
140.0
(232)*
35.3
47.9
285.6
668.2
(760.2)*
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The second highest 24-hour and 1-year average TSP
concentrations in 1974 in the Town of Colonie were 119
(2 6)
and 51 micrograms per cu. m. respectively. The
natural background levels of TSP in the region are ap-
proximately 35 micrograms per cu. m.
(4) Project Air Quality
Using the estimated 1975 and 1990 service area TSP
emissions (Q and Q ), the base year TSP concentrations
a P
(B), the background concentration (b), and the Equation (IV-3),
the 1990 TSP concentrations were projected as shown in Table V-5.
The table shows the expected total TSP concentration (Xp) as
well as the incremental TSP concentrations (Xj). The results
for SO2 are also shown in Table V-5. The procedure followed
for estimating the SC^ concentrations was similar to that for
TSP.
(5) Compare Projected Air Quality With AAQS
The projected air quality is compared with two sets of
air quality standards:
National Ambient Air Quality Standards (NAAQS) —
Comparing the projected TSP and SC>2 concentrations
with the applicable primary and secondary NAAQS
indicates that these standards are not likely to
be violated.
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Table V-5
Projected Total and Incremental TSP and
SC>2 Concentrations in the Town of Colonie, 1990
Pollutant XB b Xg-b Qp/Qg* XT xp
TSP
24-Hour 119 35 84 1.28 23.5 142.5
(second highest)
1-Year 51 35 16 1.28 4.5 55.5
(geometric mean)
so2
24-Hour 129 0 129 1.15 19.3 148.3
(second highest)
1-Year 37 0 37 1.15 5.5 42.5
(arithmetic mean)
X_, = base year concentration
a
b = background
Op = projected emissions
0_ = base year emissions
O
Xp = projected concentration
Xj = projected incremental concentration
*
Assuming industrial process emissions to remain unchanged.
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Nondegradation Criteria — All areas in New
York State carry the initial EPA designation
of Class II. Comparing the projected incre-
mental TSP and SOj concentrations with the
Class II requirements indicates that the non-
degradation criteria would also be met.
The preceding analysis indicates that the projected ur-
ban growth in the Town of Colonie corresponding to the design
population of the proposed treatment facilities is not likely
to cause violation of the ambient air quality standards. The
results are based on conservative estimates of population
growth as well as the air pollutant emissions. As mentioned
in the previous section, the population forecasts for the
Albany area have been recently revised indicating much lower
population growth than predicted earlier. The projected pol-
lutant concentrations based on the revised population pro-
jections would be lower than those given in Table V-5. Thus,
the proposed wastewater treatment facilities for the Town of
Colonie should be approved from *-^e air quality perspective.
Since the interceptor sewers are sized for 50-year population
growth, the uncertainties involved in predicting air quality
impact of such long term growth would be great. The use of
the excess sewers capacity depends upon the availability of
additional treatment capacity. Thus, the air quality impact
analysis of the urban growth corresponding to the sewer capa-
city should be done at the time of expansion of the treatment
facilities in the future. The next section describes the air
quality analysis of the Rockland County Sewer District No. 1.
2. ROCKLAND COUNTY SEWER DISTRICT NUMBER 1
Rockland County Sewer District No. 1 is included in
the New York City Metropolitan AQMA, which is an AQMA for
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SO2/ TSP, N02» CO, and oxidants. Figure V-3 shows a map of
the AQMA.
The Sewer District has a rough terrain. It is bounded
by a mountain range to the east, north, and northwest. The
prevailing wind is from the north and the average wind speed
(34)
is about 6.8 mph. Ambient SC>2 and TSP levels in Rockland
County are monitored by the State DEC. These levels showed
a slight improvement in 1976 over the 1973 levels. The am-
bient levels of SO~ and TSP in the Sewer District were well
(26)
below the ambient standards.
The ambient CO, N02/ and oxidants levels are not moni-
tored in the Rockland County. Air quality monitors in the
surrounding region indicated that the annual average NO2 and
1-hour average CO levels in the region in 1974-75 were below
the AAQS. However, the 8-hour CO and the 1-hour oxidants
standards were frequently violated at most of the monitoring
sites.<26>
Because of its proximity to New York City and the general
trend towards moving out to the suburbs, Rockland County Se-
wer District No. 1 is expected to grow rapidly. Such growth
would adversely affect the ambient air quality. This section
analyzes the air quality impacts of the projected urban growth
in the Sewer District using the screening procedure.
A map of Rockland County Sewer District Number 1 is
shown in Figure V-4. The Sewer District includes most parts
of the Town of Ramapo and the Town of Clarkstown as shown.
The Sewer District has an area of approximately 18,103 hec-
(35)
tares (44,670 acres). The service area is predominantly
residential with some light industrial and commercial
-75-
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FIGURE V-3
New York City
Metropolitan AQMA
r.
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development. The New York Throughway passes through the service
area in the east-west direction, while the Garden State Parkway
and the Palisades Interstate Parkway are the major highways con-
necting Rockland County to the business districts of New York
City and adjacent New Jersey.
The proposed wastewater project (EPA grant application
(35)
Number C-36-744) consists of three stages. The first two
stages include construction of a 37,850 cu m/day (10 mgd) capa-
city treatment facility and sewerage system as shown in
Figure V-4. Stage I has been completed while Stage II is
partially completed. The proposed Stage III consists of
expansion of the existing treatment plant from 37,850 cu m/day
to 75,7 00 cu m/day (20 mgd) capacity and construction of addi-
tional sewerage as shown in Figure V-4. The additional sewer-
age would serve the outskirts of the Town of Ramapo and Clarks-
town.
The existing and projected population for Rockland County
and the project service area are shown in Table V-6.
Table V-6
Existing and Projected Population Rockland County
Year
Sewer
Rockland County District No. 1
1970
240,000
121,000
1975
270,000
144,000
1980
310,000
166,300
Maximum Land
Capacity
408 ,500
210,700
Source: References (36) and (37).
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The existing wastewater treatment facility in Sewer
District No. 1 serves a population of approximately 80,000.
The rest of the population in the service area is served
by package treatment plants and individual septic tank sys-
tems. The proposed treatment facility is designed to serve
a population of 161,680, which was the previously projected
saturation population to be reached in 1985. The population
projection in Table V-6 indicates that the proposed treatment
plant would serve a large portion of the existing population
and the design population of the plant would be reached be-
fore 1980.
The air quality analysis described below is based on
the projected 1980 population of 166,300 which is slightly
greater than the treatment plant design population and is
considered to be the "worst case." The year 1975 was selected
as the base year.
Since the New York City metropolitan area is an AQMA
for TSP, S09, CO, NO , and oxidants, the impact of urban
growth on the ambient concentration of each of these pol-
lutants must be assessed. The procedure for assessing the
impact is basically similar to that described in the case
of Town of Colonie. However, because of the significant
contribution of motor vehicles to the emissions of CO, NO2,
and hydrocarbons, it is important to obtain an accurate
estimate of the motor vehicle emissions. In the following
analysis, the procedure for estimating CO emissions from
motor vehicles is explained in detail. Similar procedure
applies to the other pollutants. The discussion of the
impact analysis is organized as follows:
Estimate 1975 emissions
Project 1980 emissions
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Determine baseline air quality
Project 1980 air quality
Each of the above steps is described below.
(1) Estimate 1975 Emissions
As mentioned earlier, the first step in estimating
emissions is to estimate point source emissions. The
major point source emissions data file for Rockland
County was obtained from the New York DEC. The data
for CO indicated negligible CO emissions from the major
point sources within the Sewer District. The emissions
from such sources within the county were also very small.
The area source emissions are determined next. The
estimates for total countywide CO emissions were obtained
(38)
from the SIP as shown in Table V-7. The estimates
indicate that over 99.5 percent of the total CO emissions
in Rockland County came from motor vehicles. Because
of such a significant contribution from motor vehicles,
the CO emission from motor vehicles in the Sewer Dis-
trict are estimated directly, rather than by allocating
the county emissions to the Sewer District. Since the
CO emissions from the remaining sources are relatively
insignificant, those can be ignored in this particular
case. However, such emissions may have to be considered
in other areas, using the methods given in Chapter IV.
Since the simple rollforward model will be used
for an initial screening, only the total CO emissions
in the Sewer District need to be determined at this
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Table V-7
Estimated CO Emissions in Rockland County, 1975
Source Category
Residential Fuel
Commercial/Inst. Fuel
Industrial Fuel
Utility Fuel
Industrial Processes
Solid Waste
Transportation
Motor Vehicles
Aircraft
Railroad
Off-Highway
Miscellaneous
Point
16
30
Emissions (tons/yr)
Area
395
39
91,590
Total
395
16
69
91,590
Total
46
92,024
92,070
Source: Reference (38)
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time. These emissions can be determined by multiplying
the VMT in the Sewer District by an appropriate emission
factor.
The Sewer District VMT were estimated using the
methods described in Appendix B. The results are sum-
marized in Appendix C. For illustration purpose, the
VMT were estimated using each of the three levels of
analyses given in Appendix B.
However, since the Level 3 estimate would be the
most accurate, those are used in this analysis.
Because of the lack of more specific data, the CO
emission factor for the Sewer District was assumed to
be equal to the average emission factor based on national
statistics for highway vehicles given in Table 7-1 of
AP-42.'20'
The estimates 1975 CO emissions in the Sewer Dis-
trict are summarized below:
VMT
747.1 x 10 per year
Emission
Factor
61.1 gm/mile
O
Emissions : 456.4 x 10 gm/year
(2) Project 1980 Emissions
The procedure for estimating the 1980 CO emissions
is similar to that discussed above. The Level 3 VMT
estimate from Appendix C, the national average emission
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factor from AP-42, and the estimated CO emissions are
as follows:
VMT : 870.9 x 10^ per year
Emission
Factors : 31.0 gm/mile
O
Emissions : 270.0 x 10 gm/year
(3) Determine Baseline Air Quality
There has been no reported monitoring of ambient
CO concentration in Rockland County. Normally, it would
not be acceptable to use the rollforward models without
such monitoring data. However, the data for the period
between January 1 to December 31, 1974 from the monitoring
station at Mamaroneck in neighboring Westchester County
are available and are used here for illustration purpose.
The observed second-highest 1- and 8-hour average CO
(39)
concentration at Mamaroneck in 1974 are as follows:
1-hour : 23.70 ppm
8-hour : 13.2 ppm
The background CO levels are assumed to 5 ppm for 1-hour
average and 1 ppm for 8-hour average as discussed in
Chapter IV.
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(4) Project 1980 Air Quality
The 1980 CO concentration is projected using the
simple rollforward model. The estimated 1-hour and
8-hour average concentrations are as follows:
1-hour : 16.1 ppm
8-hour : 8.2 ppm
Since these estimates are highly conservative, and
below the NAAQS, it is not necessary to use the Modified
Rollforward Model.
The projection methods for NC>2, TSP, and SC>2 concen-
trations are similar to those described in the case of
Town of Colonie. However, the emissions from motor vehi-
cles are calculated using the VMT estimated in Appendix C
and the national average emission factors given in AP-42.
Using the TSP and S02 data from the monitoring station in
Clarkstown and the NC>2 data from Mamaroneck, the
1980 concentrations were projected. The results are sum-
marized in Table V-8.
Table V-8
Projected Impact on NO2, SO2, and TSP
Concentrations in Rockland County Sewer District No. 1
Emissions Ambient Concentration
(t/yr) (ug/m3)
Pollutant
1975
1980
1975
1980
S0o
492
571
24-hour*
97
113
A
1-year**
14
16
TSP
433
491
24-hour*
115
125
l-year+
50
52
no2
5,107
4 ,696
1-year**
70
64
Second highest.
Arithmetic mean.
Geometric Mean.
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Comparison of the projected concentration of CO, TSP,
SC^/ and NC>2 with the corresponding National ambient air
quality standards given in Table 1-1, indicates that the
NAAQS would not be violated. Further comparison of the pro-
jected incremental TSP and S02 concentrations with the non-
degradation criteria given in Table II-2 indicates that these
criteria would also be met. Although the 3-hour S02 concen-
tration was not estimated, it may be inferred from the 24-hour
and 1-hour concentrations that the 3-hour standards would also
be met.
According to the discussion in Chapter IV, the impact on
ambient oxidants concentration was not estimated. However,
the hydrocarbon emissions were estimated as discussed below.
The estimated 1975 hydrocarbon emissions for Rockland
County were obtained from the SIP and are given in Table V-9.
The New York State DEC file of significant point source emis-
sions indicated no significant point emission sources of HC
in the Rockland Count? Sewer District No. 1. The nonmotor
vehicle emission sources in the sewer district would thus
contribute less than two percent to the HC emissions. The
nonmotor vehicle emission sources are, therefore, ignored in
this analysis.
Instead of using the county emission data, the HC emis-
sions for the sewer district were computed using the VMT esti-
mated earlier and the national average emission factors given
in AP-4 2. The estimated VMT and HC emissions in 1975 and 1980
are shown in Table V-10.
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Table V-9
Estimated HC Emissions in Rockland County, 1975
Emissions (tons/yr)
Source Category Point
Residential Fuel
Commercial/Inst. Fuel 12
Industrial Fuel
Utility Fuel 895
Industrial Processes 140
Solid Waste 45
Transportation
Motor Vehicles
Aircraft
Railroad
Off-Highway
Miscellaneous
Total 1/092
Area
100
100
8
11,648
Total
100
12
895
240
53
11,648
11,856
12,948
Source: Reference (39).
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Table V-10
Existing and Projected HC Emissions
in the Sewer District No. 1
1975
1980
870.9
VMT (106/year)
HC Emission Factor*
(gm/mile)
747.1
8.8
5.4
HC Emissions
(10® gm/year)
65.7
47.0
* Source: Reference (20), Table 7-1
The estimates in Table V-10 indicate that the HC emis-
sions in the Sewer District would decrease in 198 0 by about
29 percent of the 1975 value. The impact of such HC reduction
in the Sewer District, on the region's oxidant levels cannot
be easily determined. However, the impact of the proposed
wastewater project on the Sewer District's urban growth, hence
on the KC emissions, can be qualitatively estimated as discussed
below.
A review of the population growth and land-use patterns
in the service area shows that the projected urban growth in
the proposed project service area to 1980 is not likely to be
dependent upon the availability of sewerage for the following
reasons:
While the proposed treatment plant together with
the existing plant has a capacity to serve a total
of approximately 161,000 persons, about 144,000 of
them have already settled in the service area.
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There is some vacant land in the service area which
if not sewered, could still be suitable for low den-
sity residential development with the use of indivi-
dual septic tank systems. Such development could
accommodate 17,000 more persons in the next four to
five years.
Thus, construction of the proposed treatment plant is not
likely to induce excessive growth in the service area. There-
fore, it is not likely to have significant impact on the area's
HC emissions.
The results of the preceding analysis indicate that the
urban growth to be served by the proposed treatment plant is
not likely to cause violation of national air quality stand-
ards for TSP, S02; NC>2, and CO. In addition, the proposed
project is not likely to affect the area's KC emissions. There-
fore, the proposed wastewater treatment facility should be ap-
proved from the air quality standpoint.
In order to evaluate the impact on the oxidants levels,
a regional air quality modeling study needs to be undertaken.
Such a study would identify the problem areas and would also
allow testing of various control strategies to attain ambient
oxidant standards in the region.
*****
The above analyses of the two test projects demonstrated
the application of the proposed screening procedure in Chapter IV.
Although the proposed screening procedure is quite general, pos-
sible approximations in specific situations were illustrated in
these applications. The application of the screening procedure
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conservatively estimated the secondary air quality impact of
the two wastewater projects and indicated that construction
of those projects would not create a potential for violation
of ambient air quality standards in the respective AQMA's.
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BIBLIOGRAPHY
1. Guidelines for Air Quality Maintenance Planning and
Analysis: Allocating Projected Emissions to Subcounty
Areas, Volume 13, U.S. Environmental Protection Agency,
November 1974.
2. Guidelines for Air Quality Maintenance Planning and
Analysis, Projecting County Emissions, Volume 7, U.S.
Environmental Protection Agency, January 1975.
3. Air Pollution/Land Use Planning Project, Phase II,
Final Report, Volume II, Argonne National Laboratory,
Center for Environmental Studies, May 1973.
4. Guidelines for Air Quality Maintenance Planning and
Analysis, Applying Atmospheric Simulation Models to
Air Quality Maintenance Areas, Volume 12, U.S. Envi-
ronmental Protection Agency, Research Triangle Park,
North Carolina, September 1974.
5. Guidelines for Air Quality Maintenance Planning and
Analysis, Designation of Air Quality Maintenance
Areas, Volume 1, U.S. Environmental Protection Agency,
September 1974, pp 39.
6. Holzworth, G.C.; Mixing Heights, Wind Speeds, and
Potential for Urban Air Pollution Throughout the
Contiguous United States; Office of Air Programs
Publication No. AP-101 (NTIS PB 207103); Office of
Technical Information and Publications; U.S. EPA;
Research Triangle Park, N.C. 27711; January 1972.
7. Hanna, S.R., Simple Methods of Calculating Dispersion
from Urban Area Sources, presented at the Conference
on Air Pollution Meteorology, sponsored by American
Meteorological Society in cooperation with the Air
Pollution Control Association, Raleigh, N.C., April
6, 1971.
8. TRW Systems Group; Air Quality Display Model; Pre-
pared for the National Air Pollution Control Admin-
istration under Contract No. PH-22-68-60 (NTIS PB
189194), DHEW, U.S. Public Health Service, Washing-
ton, D.C., November, 1949.
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-------
9. Turner, D.B.; Workbook of Atmospheric Dispersion
Estimates; PHS Publication No. 999-AP-26 (NTIS PB
191482); Office of Technical Information and Publi-
cations, U.S. EPA; Research Triangle Park, N.C.
27711; 1969.
10. Larsen, R.I.; A Mathematical Model for Relating Air
Quality Measurements to Air Quality Standards; Office
of Air Programs Publication No. AP-89 (NTIS PB
205277); Office of Technical Information and Publi-
cations; U.S. EPA, Research Triangle Park, N.C.
27711; November 1971.
11. Busse, A.D., and Zimmerman, J.R.; User's Guide for
the Climatological Dispersion Model"; Environmental
Monitoring Series EPA-R4-73-024 (NTIS PB 227346AS)
NERC, EPA, Research Triangle Park, N.C. 27711,
December 1973.
12. Briggs, G.A.; Plume Rise, U.S. Atomic Energy Com-
mission, Office of Information Services, 1969.
13. Christiansen, J.H., and Porter, R.A.; Ambient Air
Quality Predictions with the Fast Air Quality Model,
Texas Air Control Board, Austin, Texas.
14. Christiansen, John H., Data Processing Section,
Texas Air Control Board, 8520 Shoal Creek Boulevard,
Austin, Texas 78758, Phone (512) 451-5711.
15. Zimmerman, J.R., and Thompson, R.S.; User's Guide
for HIWAY: A Highway Air Pollution Model; Environ-
mental Monitoring Series EPA-650/4-008, NERC, EPA,
Research Triangle Park, N.C. 27711 (in preparation).
16. Ludwig, F.L., and Mancuso, R.L.; User's Manual for
the APRAC-1A Urban Diffusion Model Computer Program,
Prepared for U.S. EPA Division of Meteorology Under
Contract CAPA-3-68 (1-69) (NTIS PB 213091); U.S. EPA,
Research Triangle Park, N.C. 27711; September 1972.
17. U.S. EPA; Requirements for Preparation, Adoption,
and Submittal of Implementation Plans — Appendix J;
Federal Register 36; No. 158; p. 15502; August 14,
1971.
18. Systems Applications Inc.; Urban Air Shed Photochemi-
cal Simulation Model Study — Volumes I - III; Prepared
for U.S. EPA, ORD Under Contract Number 68-02-0339;
EPA-R4-73-030q: Washington, D.C. 20460; July 1973.
(Available from APTIC).
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-------
19. Koch, R.C., and Stadsklev, G.H.; A User's Manual for
the Sampled Chronological Input Model (SCIM); GEOMET
Report No. E-261 prepared for U.S. EPA Under Contract
Number 68-02-0281; U.S. EPA, OAQPS, Research Triangle
Park, N.C. 27711; August 1973. (Available in draft
form only).
20. Compilation of Air Pollution Factors, AP-42, U.S.
Environmental Protection Agency, April 1973. This
document is continuously updated. The latest supple-
ments to AP-42 should be used. The latest supplement
for motor vehicle emissions is Number 5, April 1975.
21. 1972 OBER's Projections, Standard Metropolitan Statis-
tical Areas, Volume 5, U.S. Water Resources Council,
Washington, D.C., April, 1974.
22. 1972 OBER's Projections, Economic Activity in the U.S.
BEA Economic Areas, Volume 2, U.S. Water Resources
Council, Washington, D.C., April, 1974.
23. Accounting for New Source Performance Standards in
Projecting and Allocating Emissions, published as an
Appendix to reference 1. above.
24. Guidelines for Air Quality Maintenance Planning and
Analysis, Evaluating Indirect Sources, Volume 9, U.S.
Environmental Protection Agency, January 1975.
25. Seasonal and Annual Wind Distribution of Pasquill
Stability Classes, Star Program, Albany, New York,
(1/74 - 12/74), National Climatic Center, Ashville,
N.C.
26. New York State Department of Environmental Conserva-
tion, Division of Air Resources, 50 Wolf Road, Albany,
New York 12233, Private communications with Mr. William
F. Eberle and Mr. William J. Bernaski.
27. Pure Waters Program, Town of Colonie, Albany County,
New York, Charles T. Male Associates, January, 1969.
28. Environmental Assessment Summary for the Proposed
Mohawk View Water Pollution Control Plant and Trunk
Sewers, Mohawk River Watershed Area, Town of Colonie,
Albany County, New York, January 1974.
29. Environmental Assessment Statement for Proposed Trunk
Sewers, Hudson River Watershed Area, Town of Colonie,
Albany County, New York, January, 197 3.
-92-
-------
30. Proposed Trunk Sewers, Hudson River Watershed Area,
Wastewater Facilities Report, Town of Colonie, Albany
County, New York, March 1971.
31. Assuming linear growth rate between 1973 and year
2000 and using the projections from reference 32. below.
32. Preliminary Regional Development Plan, Capital Dis-
trict Regional Planning Commission, Spring, 1975,
Appendix VII, Chart 2.
33. Implementation Plan to Achieve Air Quality Standards,
Upstate New York, Division of Air Resources, Depart-
ment of Environmental Conservation, State of New York,
1972.
34. Seasonal and Annual Wind Distribution of Pasquill
Stability Classes, Star Program, White Plains, New
York (1/68 - 12/72), National Climatic Center, Ash-
ville, N,C.
35. Engineering Report, Stage 3, Rockland County Sewer
District Number 1, Rockland County, New York, 1969.
36. Information obtained from the Rockland County Plan-
ning Commission, July, 1975.
37. Information obtained from the Tri-State Regional
Planning Commission, July, 1975.
38. New York City Metropolitan Area Air Quality Implemen-
tation Plan, Department of Environmental Conservation,
State of New York, Revised May, 1972, Table 8-11.
39. New York State Air Quality Report, Continuous Moni-
toring System, DAR-75-1, New York State Department of
Environmental Conservation, 1974.
40. Federal Regulations. The Environmental Reporter,
Bureau of National Affairs, Inc., Washington, D.C.,
1973, pp. 121-0155.
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APPENDIX A
DISCUSSION OF MEASURES TO MITIGATE ADVERSE
AIR QUALITY IMPACTS
Measures to mitigate the adverse air quality impact of
urban growth may be grouped into three basic categories:
Control of Pollutant generating activities
Control of source emissions
Control of pollutant dispersion.
The applicant for a wastewater treatment project can only
affect pollutant generating activities, to the extent that
limitations in collection and treatment capacity limit
growth. However, to effectively deal with air quality
issues, he should understand the reange of potential miti-
gating measures. The appendix discusses briefly each of
the above categories, and is intended to provide general
background information.
1. CONTROL OF POLLUTANT GENERATING ACTIVITIES
Pollutant generating activities are generally cate-
gorized into point sources (fixed and mobile) and non-
point or area sources. Potential restraints on point and
nonpoint source categories include basic restraints on
urban growth and/or the location of specific pollutant
generating activities, and transportation controls which
limit the use of vehicles. Specific mitigating measures
in each of these areas are discussed below.
-------
APPENDIX A(2)
If the relationship between urban growth and air quality
can be determined from the air quality impact assessment,
then one of the mitigating measures considered could be to
restrain the population growth in proportion to the required
reduction in air quality impact. This implies the develop-
ment of zoning restrictions which would indirectly limit
the population in a given area. In major urban centers,
such controls would have limited usefulness because of
existing developments and existing zoning requirements.
In addition to zoning restrictions, growth could conceiv-
ably be restrained by limiting wastewater service to a
given area. The wastewater service could be limited in
the following ways:
Cut back the capacity of the treatment facility
or interceptors and restrict growth to that
capacity
Build a treatment plant in stages and review the
air quality at each stage to determine if further
expansion is acceptable from an air quality stand-
point .
Application of the above land use control techniques
can be expected to be highly controversial, since the in-
troduction of air quality planning into the land use issue
is relatively new. Furthermore, the relationship between
population growth and air quality may be complex, requiring
the repetition of the air quality impact analysis with
different growth scenarios.
The control of mobile sources through the application
of transportation control strategies has also been a
-------
APPENDIX A(3)
highly controversial issue for the past two years. Trans-
portation control strategies include the following:
Restriction on auto usage through exclusive bus
and carpool lanes
Auto commuting taxes and/or parking surcharges
Restrictions on onstreet parking during commuting
hours
Limitations of offstreet parking during commuting
hours
Banning of automobiles from critical downtown areas.
The authority of EPA to impose such transportation controls
has been removed by the courts, leaving the issue a local
one. Since the automobile plays such an important part in
American life today, this problem will not be easily solved.
However, without a doubt, it is one of the key mitigating
measures which will be required in the future to maintain
air quality in critical Air Quality Maintenance Areas.
Finally, in new communities, land use planning can be
used to reduce the number of automobile trips generated and
the amount of home heating fuel consumed. A planned com-
munity minimizing the distance between residential and shop-
ping areas, and providing mass transit alternatives, can
significantly reduce the use of the private passenger car.
Also, multifamily development generally results in more
efficient use of heating fuel, thus reducing the amount of
fuel burned and the resultant emissions. Again however,
-------
APPENDIX A(4)
such planning is restricted primarily to new communities
and would have limited impact on the major urban areas which
presently comprise the bulk of the designated AQMA's.
2. CONTROL OF SOURCE EMISSIONS
One new concept for controlling point source emissions
is the concept of "emission density zoning." Emission den-
sity zoning regulations would place a legal limit on the
amount of an air pollutant that may be emitted in any given
time period from a unit area. If this area represents an
industrial plant, it puts the burden upon the owner to con-
trol whatever multiple sources may exist on the site to
conform with the overall emission requirements, regardless
of specific point source requirements represented by EPA
emission guidelines. Of course, these latter emission
control requirements can also be tightened for specific
point sources, where areawide requirements so indicate.
In the case of vehicle emissions, new car emission
controls have already significantly reduced emissions since
1969, and many states are now adopting a vehicle inspection
and maintenance program to assure that these emission
reduction gains are maintained. Emissions inspection and
the resulting vehicle maintenance places a significant
economic burden upon the consumer, and can also be expected
to be a controversial local issue. In weighing vehicle
inspection and maintenance requirements, it will be necessary
to conduct tradeoff studies of the cost to the consumer of
the inspection program versus transportation controls which
would limit the use of automobiles altogether in critical
air quality areas.
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APPENDIX A(5)
3. CONTROL OF POLLUTANT DISPERSION
Major point emission sources may be dispersed over a
larger area by increasing the height of an exhaust stack.
The resulting effect would be lower pollution concentration
at the ground level, where the most impact is felt. EPA
has generally frowned upon this alternative for air quality
control, but it is mentioned here for completeness.
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APPENDIX B
DISCUSSION OF METHODS TO ESTIMATE
VEHICLE MILES TRAVELLED (VMT)
Three different levels of analysis are outlined for
estimating both baseline and future year vehicle miles of
travel (VMT). The three levels correspond to variable levels
of effort and not necessarily to differing degrees of accuracy.
Level 1. Several methods are described for esti-
mating subcounty VMT using "broad-brush" statis-
tics pertaining to roadway mileage, automobile
ownership, and average VMT per vehicle.
Level 2. The approach outlined under this level
of effort involves the use of county traffic volume
and roadway inventory data.
Level 3. This level makes use of special data
and studies done for the region, county, and sub-
county areas. In that it incorporates land-use
and travel parameters unique to the subcounty
study area, it is more sensitive to the individual
factors which contribute to internally generated
VMT than Level 1 and Level 2 methods. The pro-
fessional effort required with this approach, how-
ever, is considerably greater than with the other
methods.
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APPENDIX B (2)
1. INVENTORY UPDATING PROCEDURES
Total vehicle miles of travel (VMT) within a given
area is the resultant of three components of travel
trips which both begin and end within the study area (de-
noted internal-internal); trips with one end of the trip
within and the other outside the study area (denoted in-
ternal-external) ; and trips which only pass through the
study area enroute elsewhere (denoted external-external).
Past studies have shown that internal-internal, and inter-
nal-external trips are integrally related to population,
land-use, and demographic characteristics of the study area.
Through trips (external-external), however, occur indepen-
dent of population and land-use characteristics of the study
area. In this connection, most of the VMT analysis/projection
techniques described herein, deal with internally generated
travel and "through" travel as separate components.
(1) Level 1
It is likely for most major urban areas that base
year VMT data is available at least on a countywide
basis. Procedures are presented, however, both for alio
eating VMT to a subcounty area when countywide VMT is
known, as well as when countywide VMT data is not avail-
able .
Method 1. Assuming countywide VMT estimates
are available, subcounty VMT can be approxi-
mated by using subcounty to county propor-
tions of route or lane miles. This approach
is predicted on roadway supply (i.e., route
-------
APPENDIX B(3)
lane mileage) being proportionately related
to demand (i.e., VMT). To account for the
differing traffic volumes typically carried
per lane mile on freeways vs. other highways
vs. local streets, the following formula which
incorporates weighting factors is recommended:
VMTs = VMTc (3Fs + 2Hs + As)/(3Fc + 2Hc + Ac)
where: VMTs = Subcounty VMT
VMTc = County VMT
F = Freeway lane miles
H = Highway lane miles
A = Arterial street lane
miles
s refers to subcounty
c refers to county
The above weighting factors are based upon
approximate per lane capacity differences for
the various categories of roadways as esta-
blished in the Highway Capacity Manual, 196 5. ^
It should be noted that local streets other
than arterials need not be included in com-
puting roadway mileage in that typically only
15 to 20 percent of total VMT occurs on these
(1)
Highway Capacity Manual, 1965, Highway Research Board Special
Report 87, National Academy of Sciences, National Research
Council Publication 1328.
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APPENDIX B(4)
streets. Route and lane miles for county and
subcounty areas can be obtained from county
highway or planning departments or scaled
from county base maps.
Method 2. To establish internally generated
VMT, this estimation technique involves the
straight forward multiplication of the number
of automobiles owned in the subcounty study
area by the annual VMT per vehicle. Automobile
ownership data at county, minor civil division
(MCD), and census tract detail is available
from U.S. Census reports. Average annual
mileage per vehicle is usually available at
county, region, or state detail. This method
can be expressed as:
VMT = (population) x (vehicles/
person) x (VMT/vehicle)
In order to obtain the VMT by the various vehicle
classes, the toal or automobile VMT obtained for the
above methods should be multiplied by appropriate
factors. Such factors may be obtained from local
transportation studies, otherwise national statistics
may be used.
(2) Level 2
For most major urban areas, base year VMT data
is available at least on a countywide basis. A
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APPENDIX B(5)
procedure for allocating countywide VMT to a subcounty
area is described utilizing county or state highway
department traffic flow maps and roadway inventory
data (see Table 1). By eliminating the final two
steps, however, which involve the calibration of
Level 2 procedure findings with county VMT estimates,
the below outlined approach is viewed as a suitable
method for establishing subcounty VMT in the absence
of prior countywide estimates.
Step 1. Color code roadway segments on
county traffic flow maps to correspond with
functional classifications shown in column
(I) .
Step 2. Fill in columns (2) through (8)
for Study Area freeways on a roadway segment-
by-segment basis.
Step 3. Multiply the volumes in columns (6),
(7), and (8) by the route miles in column
(5), and enter the resulting products into
columns (9), (10), and (11), respectively.
Step 4. Compute the weighted average speed
for Study Area freeways by multiplying for
each Study Area entry column (4) by column
(II) and dividing the sum by total Study
Area VMT.
Step 5. Repeat Steps 2 through 4 for the
other roadway functional classifications.
-------
VMT Summary
(1)
Functional
Classification
A. Freeway
1
2
Subtotal Study
Area
n
Total Freeway
B. Other Highway
1
2
Subtotal Study
Area
n
Total Other
Highway
C. Arterials
1
2
Subtotal Study
Area
n
Total Arterials
D. Local Streets
1 Total Study
Area
2 Total County
TOTAL STUDY AREA
TOTAL COUNTY
Segment Annual Average Daily Traffic VMT
Identification
(4)
Average (5) (8) (11)
(2) (3) Speed Route (6) (7) Total (9) (10) Total
Begin End (mph) Miles Auto Trucks Vehicle Auto Trucks Vehicles
>
~d
M
25
O
H
X
tu
en
-------
APPENDIX B(7)
Step 6. Sum columns (5) through (11) sub-
totals for the Study Area and enter in the
appropriate row totals at the bottom of the
table.
Step 7. Perform Steps 2 through 6 for the
balance of roadways within the county.
Step 8. Compute an adjustment factor by
dividing the county's own estimate of VMT
by the county VMT established through the
above calculations and multiply the sub-
county total by this adjustment factor.
It is unlikely that all of the input data speci-
fied with the above approach is actually available.
Traffic volumes on local arterial streets, for example,
are rarely recorded other than on a spot basis. Traffic
counts are usually available, however, for all freeways
and most major highways. Vehicle classification data
(i.e., trucks vs. autos is also usually only available
for a few selected locations. In this connection, the
above approach requires certain judgments on the part
of the user. To the extent that average daily traffic
data at known locations establish a pattern which can
be used to approximate volumes on "like" roadway seg-
ments, the above method can still be considered reason-
ably accurate. One end product of this approach —
average speed is a useful parameter for air quality
calculations.
-------
APPENDIX B(8)
(3) Level 3
The approach outlined under this level embodies
a simplification of the modelling techniques used in
the comprehensive transportation planning process. It
is responsive to the unique land-use and demographic
characteristics of a Study Area, and builds upon these
characteristics to establish vehicle trip generation
and attraction rates; volumes of travel between sub-
county areas or zones, between these zones and external
locations; and the average trip length (vehicle miles
of travel) which are accumulated with the subcounty
Study Area in making these trips.
In that a significant portion of VMT which occurs
within a subcounty area comprises trips to and from
other county subareas (including those which only
pass through the study area), the methodology present-
ed comprises estimation of total countywide VMT, and
the extraction of subcounty area VMT from the overall
county total. Through traffic (i.e., external-external)
is estimated using a similar approach to that outlined
under Level 2. Specific steps are as follows:
Step 1. Define analysis zones consistent
with some subcounty areal unit at which
population and land-use is forecast. Usu-
ally these zones would correspond to muni-
cipality boundaries or groups of municipalities.
External zones (i.e., those outside the
county) could correspond to county boundaries.
-------
APPENDIX B(9)
Step 2. Determine from past 3-C transporta-
tion planning studies home-based person trip
generation rates by type of dwelling unit or
density (e.g., single family and multifamily
housing) applicable for the county.
Step 3. Establish the number of dwelling units
by type within each Analysis Zone during the
base year from prior land use inventories.
Step 4. Determine total daily person trip
productions for each Analysis Zone.
Step 5. Determine from past 3-C transpor-
tation planning studies person trip attrac-
tion rates for nonresidential land-uses
applicable for the county.
Step 6. Establish base year nonresidential
acreage within each county Analysis Zone
from prior land-use inventories.
Step 7. Determine total daily person trip
attractions for each Analysis Zone.
Step 8. Using the above person trip produc-
tion/attraction estimates, develop a trip
distribution matrix which links zonal pro-
ductions and zonal attractions.
Several methods have been developed in com-
prehensive transportation studies for link-
ing or distributing productions and attractions.
-------
APPENDIX B{10)
The three most commonly used are the Growth
Factoring (or Fratar Method); Gravity Model;
and the Intervening Opportunities Model.
Descriptions of these models are given in
PB 237-867, Air Quality, Land Use and Trans-
portation Models published by the California
(21
State Air Resources Board. These models
are developed using data established through
in-depth home interview surveys and are
based upon trip interchange between any two
analysis zones being related to the relative
spatial separation between these zones as
compared to the spatial separation of the
production zone and all other attraction
zones. Travel impedence factors, if
available from prior 3-C comprehensive
planning studies, can be used as a guide in
developing the trip distribution matrix.
In the absence of impedence data, judgment
on the part of the user utilizing some sub-
stitute for impedence factors, such as
travel time, travel distance, etc., between
zones will be necessary to develop the trip
distribution matrix. At a minimum, however,
the proportion of travel which occurs internal
to the county and to and from the county
should be available from prior 3-C planning
studies.
(2)
Air Quality, Land Use and Transportation Models. Evaluation
and Utilization in the Planning Process, California State
Air Resources Board, PB 237-867, July, 1974-
-------
APPENDIX B(11)
Step 9. The next step involves the conver-
sion of person trips to vehicle trips. Again,
prior 3-C planning study findings, in this
case with respect to mode usage and auto-
mobile occupancy factors, should be relied
upon. If appropriate and available, separate
conversion factors should be developed for
internal-internal and internal-external trips.
The process involves the multiplication of
each entry in the trip distribution matrix
by the corresponding person trip to vehicle
trip conversion factor, and entering the
resultant vehicle trips into a vehicle trip
distribution table.
Step 10. This step comprises development of
a trip length matrix for the roadway mileage
which would be traversed within the bound-
aries of the Study Area in travelling be-
tween each Analysis Zone pair. These mile-
age data can be approximated using county
roadway maps, and certain judgments as to
the likely travel routes which would be
used when travelling between any two zones.
In general, these over-the-road distances
are approximately one-third greater than
straight airline distances.
Step 11. By multiplying the volume of
vehicular travel between each zone pair
established in Step 9 by the corresponding
trip length determined in Step 10, a
matrix table of the vehicular miles of
-------
APPENDIX B(12)
travel accumulated daily between each zone
pair is produced. Summation of the indi-
vidual zone-to-zone estimates yields total
private auto VMT which occurs within the
Study Area.
Step 12. To account for other vehicular
traffic/ vehicle classification counts
taken on Study Area roadways or the other
vehicles factors developed from prior 3-C
transportation planning studies should be
used to factor private auto VMT established
in Step 10 to total vehicle VMT.
Step 13. The final step in the analysis
is to add VMT produced by through traffic
to that which has been calculated in the
preceding steps. The approach recommended
is to estimate total VMT which occurs on
"through" roadways using recorded traffic
counts and mileage data for these facilities,
and to subtract out that portion of the
recorded traffic which based upon the fore-
going estimates is being produced by inter-
nally generated trips. The through traffic
VMT may be divided according to the various
vehicle classification as discussed in the
previous step.
-------
APPENDIX B(13)
2. FORECAST PROCEDURES
Presented herein are various methods for forecasting
future year vehicle miles of travel. Three different levels
of analysis, consistent with precedures presented in the in-
ventory and updating section are outlined.
(1) Level 1
It is likely for most major urban areas that future
year VMT has been forecast at least at countywide de-
tail. Procedures are presented, however, both for
allocating projection year VMT to a subcounty area
based upon available countywide projections, as well
as for projecting forecast year VMT in the absence of
prior projections.
Method 1. Assuming countywide VMT forecasts
are available, subcounty VMT can be approxi-
mated by using subcounty to county propor-
tions of route or lane miles. Future year
route and/or lane miles can be extracted from
county roadway master plans. The formula to
be used for projection year forecasts is
identical to that presented in the inventory
upgrading and updating section, with the sub-
stitution of future year for base year input
data.
Method 2. To establish future year VMT, this
estimation technique involves the use of pro-
-------
APPENDIX B(14)
jected subcounty population, auto ownership,
and annual average VMT per vehicle. To
project the VMT, use the formula given for
base year estimate with the above quantities.
(2) Level 2
Projecting future year VMT with this technique
involves the factoring of base year traffic volumes
by annual growth factors used by the county highway
department, or the direct use of traffic volume pro-
jections evolved through 3-C comprehensive transpor-
tation planning traffic assignments. The first ap-
proach is suitable for short-term (5 to 10 year) fore-
casts, the latter for longer term projection periods.
The latter approach is also more suitable when signi-
ficant changes to the roadway system are expected with
in the forecast period. The method comprises the up-
dating of annual average daily traffic volumes (shown
in columns (6), (7), and (8) in Table 1 of the in-
ventory upgrading and updating section) for each road-
way segment inventoried in the base year. New road-
way segments if programmed for opening within the
forecast period should also be added to the inventory
tabulations. Other than the above updating, the
general procedures with this method are the same as
presented previously for Level 2.
-------
APPENDIX B(15)
(3) Level 3
The methodology used for forecasting future year
VMT for this level is identical to that presented pre-
viously. Input parameters with respect to forecast
year land-use, person trip production and attraction
rates, mode usage, and vehicle occupancy factors must
be updated, however.
-------
APPENDIX C
APPLICATION OF PROPOSED METHODOLOGY TO ESTIMATING VMT IN
ROCKLAND COUNTY SEWER DISTRICT No. 1
Presented in this section are illustrative examples
of the various base year VMT estimation and forecasting
methodologies as applied to Rockland County, New York, Sewer
District No. 1. In developing these estimates, various
Tri-State Regional Planning Commission and Rockland County
Planning Board Reports and data were used. Sources relied
upon most heavily are:
Interim Technical Report 4471-1302, Projecting
Vehicle Miles of Travel in a Metropolitan Region,
Tri-State Regional Planning Commission, September
1967.
Interim Technical Report 4471-1302, 1970 County-
to-County Travel By Purpose, Tri-State Regional
Planning Commission, September 1974.
Interim Technical Report 4456-1508, Vehicle Trip
End Forecasts 1985, 1990, and 2000, Tri-State
Regional Planning Commission, June 1974.
Various Rockland County Planning Board data per-
taining to population forecasts, automobiles
available, road mileage, traffic volumes, land-
use inventory, work location, and dwelling unit
inventory.
-------
LEVEL 1 BASE YEAR VMT ESTIMATES
ROCKLAND COUNTY SEWER DISTRICT NO. 1
AVERAGE DAILY 1975 VMT ESTIMATES
METHOD 1
Sub-County VMT
^(Sub-County Freeway (Sub-County Highway +Sub-County Arterial
Route Miles) Route Miles) Route Miles
(County Freeway
Route Miles)
+2
(County Highway
Route Miles)
X County-Wide VMT
^County Arterial
Route Miles
Sewer-District VMT =
3 (24) + 2 (50) + 80
3 (47) + 2 (78) + 117
X 3.50 X 10
,609 X 3.50 X 106 = 2.13 X 106
METHOD 2
^ ^. ,, .Autos Per. .. .Annual VMT . . 2
1975 Sub-County VMT = Base Year Population X ( ) X ( , . , ) t 365 2
Person Per Vehicle g
z
o
M
13 250 6 *
144,000 X .388 X ' = 2.028 X 10 n
365 ~
-------
LEVEL 1 FORECAST YEAR VMT ESTIMATES
ROCKLAND COUNTY SEWER DISTRICT NO. 1
AVERAGE DAILY 1980 VMT ESTIMATES
METHOD 1
^(Sub-County Freeway (Sub-County Highway +Sub-County Arterial
Route Miles) Route Miles) Route Miles
Sub-County VMT
(County Freeway +2(County Highway
Route Miles) Route Miles)
X County-Wide VMT
^County Arterial
Route Miles
Sewer District VMT
3 (24) + 2 (50) + 85
X 4.4 X 10
3 (47) + 2 (78) + 130
.602 X 4.4 X 106 = 2.65 X 106
METHOD 2
Projected 1980 Projected Autos Projected Annual >
1980 Sub-County VMT = X X * 365 ^
Population Per Person VMT Per Vehicle W
a
H
12 725 6
166,300 X .407 X ~^rjrz = 2.36 X 10 ^
365 O
u>
-------
APPENDIX C(4)
LEVEL 2 VMT ESTIMATES
¦ROCKLAND COUNTY SEWER DISTRICT NO. 1
Average Daily 1975 Summary Estimate (Sewer District No. 1)
Functional
Classification
Freeways
Other Highways
Arterials
Local Streets
Average Speed
{mph)
47
37
25
15
Route Miles
24
50
80
365
Annual
Average
Daily Traffic
28,000
12,000
6,000
1,000
Estimated VMT
(000s)
672.0
600.0
480.0
365.0
TOTAL
35
519
2,117.0
Estimated Average Daily 1980 Summary Estimate (Sewer District No. 1)
Functional
Classification
Freeways
Other Highways
Arterials
Local Streets
Average Speed
(mph)
47
37
25
15
Route Miles
24
50
85
420
Annual
Average
Daily Traffic
33,500
14,000
6,600
1,000
Estimated VMT
(000s)
804.0
700.0
561.0
420.0
TOTAL
35
579
2,485.0
-------
APPENDIX C(5)
SUMMARY OF LEVEL 3 AVERAGE DAILY VMT ESTIMATES (000s)
ROCKLAND COUNTY SEWER DISTRICT NO. 1
Base Year-1975
Within County
Within Sewer District
Internally
Generated VMT
Autos
2,127.9
1,425.3
Other
106.4
71.3
Through Traffic
900.0
550.0
Total VMT
3,134.3
2,046.6
Projection Year-1980
Within County 4,247.6 121.4 1,080.0 3,629.0
Within Sewer District 1,643.7 82.2 660.0 2,385.9
-------
APPENDIX C(6)
LEVEL 3 ANALYSIS
ESTIMATED 1975 POPULATION AND
LAND USE CHARACTERISTICS - ROCKLAND COUNTY
(1)
Dwelling Units Commercial-
Zone Population Single-Family Multi-Family Industrial Acres
A 68,535 15,110 3,110 1,130
B 77,410 14,555 6,830 435
C 59,765 10,995 4,480 960
D 32,655 5,535 3,200 515
E 16,350 3,540 700 415
F 15,235 2,760 2,135 315
Total 269,950 52,495 20,455 3,770
ESTIMATED 1975 PERSON TRIPS
Productions
Zone Single-Family Multi-Family Total Attractions
A
105,920
15,270
121,190
149,400
B
102,030
33,535
135,565
54,050
C
77,075
21,995
99,070
119,275
D
38,800
15,710
54,510
63,990
E
24,815
3,435
28,250
51,560
F
19,350
10,485
29,835
39,145
Total
367,990
100,430
468,420
468,420
^See Appendix C(13) for Zone Delineation
-------
Origin Zone
A
B
C
D
E
F
Outside
County
Total
Origin Zone
A
B
C
D
E
F
Outside
County
Total
APPENDIX C(7)
ESTIMATED 1975 PERSON TRIP DISTRIBUTION (000s)
Destination Zone
A
B
C
D
E
F
Outside County
Total
47.2
14.0
30.0
3.9
3.8
3.0
19.3
121.2
33.9
17.8
24.7
13.4
14.1
10.1
21.6
135.6
19.8
5.0
33.3
11.1
8.2
5.9
15.8
99.1
8.5
3.5
5.8
21.5
5.7
0.8
8.7
54.5
3.7
2.2
3.2
2.6
11.1
1.0
4.5
28.3
4.9
3.0
3.3
1.3
0.5
12.0
4.8
29.8
22.4
8.6 19.0
10.2
8.2
6.3
74.7
140.4
54.1 119.3
64.0 51.6 39.1
74.7
543.2
ESTIMATED 1975 VEHICLE TRIP DISTRIBUTION (000s)
Destination Zone
A
B
C
D
E
F
Outside County
Tot a]
22.0
6.5
14.0
1.8
1.8
1.4
10.1
57.6
15.8
8.3
11.5
6.2
6.6
4.7
11.4
64.5
9.2
2.3
15.5
5.2
3.8
2.8
8.3
47.1
4.0
1.6
2.7
10.0
2.7
0.4
4.6
26.0
1.7
1.0
1.5
1.2
5.2
0.5
2.4
13.5
2.3
1.4
1.5
0.6
0.2
5.6
2.5
14.1
11.8
4.5
10.0
5.4
4.3
3.3
-
39.3
66.8
25.6
56.7
30.4
24.6
18.7
39.3
262.1
-------
APPENDIX C(8)
TRIP
LENGTH
WITHIN
COUNTY
(MILES)
To Zone
From Zone:
A
B
C
D
E
F
Outside County
A
3.0
5.0
9.5
4.5
7.0
15.0
12.0
B
5.0
3.0
8.5
10.5
13.0
6.5
9.0
C
9.5
8.5
4.5
14.0
16.5
13.5
6.5
D
4.5
10.5
14.0
2.0
2.0
13.5
15.0
E
7.0
13.0
16.5
2.0
1.5
16.0
17.0
F
15.0
6.5
13.5
13.5
16.0
1.5
12.0
Outside
12.0
9.0
6.5
15.0
17.0
12.0
-
County
TRIP LENGTH WITHIN SEWER DISTRICT (MILES)
To Zone:
From Zone:
A
B
C
D
E
F
Outside County
A
3.0
5.0
7.5
3.0
3.5
14.5
9.0
B
5.0
3.0
6.5
7.5
7.5
6.0
6.5
C
7.5
6.5
-
9.0
9.0
11.0
2.0
D
3.0
7.5
9.0
-
-
10.5
9.0
E
3.5
7.5
9.0
-
-
10.5
9.0
F
14.5
6.0
11.0
10.5
10.5
-
9.0
Outside
9.0
6.5
2.0
9.0
9.0
9.0
-
County
-------
APPENDIX C(9)
ESTIMATED 1975 INTERNALLY GENERATED
VMT WITHIN COUNTY (000s)
Destination Zone
Origin Zone
A
B
C
D
E
F
Outside County
Total
A
66.0
32.5
133.0
8.1
12.6
21.0
121.2
394.4
B
79.0
24.9
97.8
65.1
85.8
30.6
102.6
485.8
C
87.4
19.6
69.8
72.8
62. 7
37.8
54.0
404.1
D
18.0
16.8
37.8
20.0
5.4
5.4
69.0
172.4
E
11.9
13.0
24.8
2.4
7.8
8.0
40.8
108.7
F
34.5
9.1
20.3
8.1
3.2
8.4
30.0
121.7
Outside
County
141.6
40.5
65.0
00
o
73.1
39.6
440.8
Total
2,127.9
ESTIMATED 1975 INTERNALLY GENERATED
VMT WITHIN SEWER DISTRICT (000s)
Destination Zone
Origin Zone
A
B
C
D
E
F
Outside County
Total
A
66.0
32.5
105.0
5.4
6.3
20.3
90.9
326.4
B
79.0
24.9
74.8
46.5
49.5
28.2
74.1
377.0
C
69.0
15.0
-
46.8
34.2
30.8
16.6
212.4
D
12.0
12.0
24.3
-
-
4.2
41.4
93.9
E
6.0
7.5
13.5
-
-
5.3
21.6
53.9
F
33.4
8.4
16.5
6.3
2.1
-
22.5
89.2
Outside
County
106.2
29.3
20.0
48.6
38.7
29.7
-
272.5
Total 1,425.3
-------
APPENDIX C(10)
ESTIMATED 1980 POPULATION AND
LAND USE CHARACTERISTICS - ROCKLAND COUNTY
Zone
(1)
Dwelling Units
Population Single-Family Multi-Family
Commercial -
Industrial Acres
A
80,000
16,850
4,850
1,370
B
88,800
16,285
8,560
500
C
63,200
11,515
5,000
1,010
D
40,000
6,650
4,315
630
E
20,000
4,095
1,255
500
F
18,000
3,180
2,555
375
Total
310,000
58,575
26,535
4,385
ESTIMATED 1980 PERSON TRIPS (000s)
Productions
Zone
A
B
C
D
E
F
Single-Family Multi-Family
118,120
114,160
80,720
46,615
28,705
22,290
23,815
42,030
24,550
21,185
6,160
12,545
Total
141,935
156,190
105,270
67.800
34,865
34,835
Attractions
171,070
62,435
126,120
78,670
62,435
46,825
Total
410,610
130,285
540,895
547,555
(1)
See Figure 1 for Zone Delineation.
(1)
See Appendix B(13) for Zone Delineation.
-------
APPENDIX C(ll)
1980 PERSON TRIP DISTRIBUTION (000s)
Destination Zone
Origin Zone
A
B
C
D
E
F
Outside County
Total
A
58.6
16.4
32.8
4.8
4.6
3.6
21.1
141.9
B
41.2
20.3
26.4
16.3
16.9
12.1
23.0
156.2
C
22.5
5.4
33.0
12.6
9.2
6.8
15.8
105.3
D
11.1
4.3
6.6
27.6
7.3
1.0
9.9
67.8
E
4.8
2.7
3.6
3.4
14.1
1.3
5.0
34.9
F
5.9
3.5
3.6
1.6
0.6
14.5
5.1
34.8
Outside
County
27.0
9.8
20.1
12.4
9.7
7.5
86.5
Total
171.1
62.4
126.1
78.7
62.4
46.8
79.9
627.4
ESTIMATED 1980
VEHICLE TRIP DISTRIBUTION (000s)
Destination
Zone
Origin Zone
A
B
C
D
E
F
Outside County
Total
A
27.3
7.6
15.3
2.2
2.1
1.7
11.1
67. 3
B
19.2
9.5
12.3
7.6
7.9
5.6
12.1
74.2
C
10.5
2.5
15.4
5.9
4.3
3.2
8.3
50.1
D
5.2
2.0
3.1
12.9
3.4
0.5
5.2
32.3
E
2.2
1.3
1.7
1.6
6.6
0.6
2.6
16.6
F
2.8
1.6
1.7
0.7
0.3
6.7
2.7
16.5
Outside
County
14.2
5.1
10.6
6.5
5.1
3.9
"
45.5
Total
81.4
29.6
60.1
37.4
29.7
22.2
42.0
302.4
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APPENDIX C(12)
ESTIMATED 1980 INTERNALLY GENERATED
VMT WITHIN COUNTY (000s)
Destination Zone
Origin Zone
A
B
C
D
E
F
Outside County
Total
A
81.9
38.0
145.4
9.9
14.7
25.5
133.2
448.6
B
96.0
28.5
104.6
79.8
102.7
36.4
108.9
556.9
C
99.8
21.3
69.3
82.6
71.0
43.2
54.0
441.2
D
23.4
21.0
43.4
25.8
6.8
6.8
78.0
205.2
E
15.4
16.9
28.1
3.2
9.9
9.6
44.2
127. 3
F
42.0
10.4
23.0
9.5
4.8
10.1
32.4
132.2
Outside
County
170.4
45.9
68.9
97.5
86.7
46.8
-
516.2
Total 528.9 182.0 482.7 308.3 296.6 178.4 450.7 2,427.6
ESTIMATED 1980 INTERNALLY GENERATED
VMT WITHIN SEWER DISTRICT (000s)
Destination Zone
Origin Zone
A
B
C
D
E
F
Outside County
Total
A
81.9
38.0
114.8
6.6
7.4
24.7
99.9
373.3
B
96.0
28.5
80.0
57.0
59. 3
33.6
78.7
433.1
C
78.8
16.3
-
53.1
38.7
35.2
16.6
238.7
D
15.6
15.0
27.9
-
-
5.3
46.8
110.6
G
7.7
9.8
15.3
-
-
6.3
23.4
62.5
F
40.6
9.6
18.7
7.4
3.2
-
24.3
103.8
Outside
County
127.8
33.2
21.2
58.5
45.9
35.1
321.7
Total
448.4
150.4
277.9
182.6
154.5
140.2
289.7
1,643.7
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APPENDIX C(13)
Analysis Zones
County of Rockland
-------
APPENDIX D
STATE AMBIENT AIR QUALITY STANDARDS IN EPA REGION II
The EPA Region II includes the States of New York and
New Jersey, Puerto Rico, and Virgin Islands. The ambient
air quality standards (AAQS) for Puerto Rico and Virgin
Islands are the same as the national ambient air quality
standards (NAAQS) given in Table 1-1. The AAQS for the
state of New Jersey are also the same as the NAAQS, except
for the secondary standards for TSP. The New Jersey AAQS
include, in addition to the NAAQS, the following secondary
3
standards for TSP: 60 microgram/m , annual arithmetic
3
average and 260 microgram/m , 24-hour average not to be
exceeded more than once per year. The New York AAQS for
carbon monoxide, hydrocarbons, and photochemical oxidants
are identical to the corresponding NAAQS. However, the
ambient sulfur dioxide and particulate matter standards
for the State of New York differ from the NAAQS. The New
York SO2 and particulates standards are discussed below.*
1. STANDARDS FOR S02
During any 12 consecutive months, 99 percent of the one-
hour average concentrations shall not exceed 0.25 ppm (650
3
ug/m ) and no one-hour average concentration shall exceed
3
0.50 ppm (1300 ug/m ). During any 12 consecutive months,
99 percent of the 24-hour average concentrations shall not
3
exceed 0.10 ppm (260 ug/m ); and no 24-hour average concen-
3
tration shall exceed 0.14 ppm (365 ug/m ). During any 12
The Environment Reporter, State Air Laws, Bureau of National
Affairs, Inc., Washington, D.C.
-------
APPENDIX D(2)
consecutive months, the annual average of the 24-hour average
concentrations shall not exceed 0.03 ppm (80 ug/m3).
2. STANDARDS FOR PARTICULATES
The State of New York has established standards for sus-
pended as well as settleable particulates. The standards in
different parts of the state vary according to the air quali-
ty classification. The classification is based on land uses
and includes four classes, Level I through Level IV. The
Level I areas represent the cleanest areas, whereas Level IV
the most developed. The standards for the four classes are
given below:
(1) Suspended Particulates
For any 24-hour period, the average concentration
3
shall not exceed 250 ug/m for all levels. During any
12 consecutive months, 50 percent of the values of the
24-hour average concentrations shall not exceed:
3
Level I 45 ug/m
Level II - 55 ug/m3
Level III - 65 ug/m3
3
Level IV - 75 ug/m
During any 12 consecutive months, 84 percent of
the values of the 24-hour average concentrations shall
not exceed:
3
Level I - 70 ug/m
Level II - 85 ug/m3
3
Level III - 100 ug/m
3
Level IV - 110 ug/m
-------
APPENDIX D(3)
(2) Settleable Particulates(Dustfall)
During any 12 consecutive months, 50 percent of
the values of the 30-day average concentrations shall
not exceed:
2
Level I - 0.30 mg/cm /mo
2
Level II - 0.30 mg/cm /mo
2
Level III - 0.40 mg/cm /mo
2
Level IV - 0.60 mg/cm /mo
During any 12 consecutive months, 84 percent of
the values of the 30-day average concentrations shall
not exceed:
Level I
Level II
Level III
Level IV
- 0.45
- 0.45
- 0.60
- 0.90
mg/cm2/mo
mg/cm2/mo
mg/cm2/mo
mg/cm2/mo
-------
APPENDIX E
INPUT REQUIREMENTS OF THE MODIFIED ROLLFORWARD MODEL FOR CO
The modified rollforward model for CO is described in
Chapter III. This appendix presents the procedure to obtain
the input data for the model. The input requirements include:
Baseline ambient CO concentration (B)
Background CO concentration (b)
General urban emissions data (P, G, and E)
Local emissions data (P, G, and E)
1. BASELINE CO CONCENTRATION
Use the second highest 1-hour and 8-hour average CO
concentrations during the base year at sites where the pub-
lic has access for at least 1- and 8- hours respectively.
If there are multiple monitoring sites, the data from the
site with the worst concentrations should be used.
2. BACKGROUND CONCENTRATION
For estimating 8-hour average concentration, use 1 ppm
if data to the contrary are unavailable. Similarly, for
1-hour concentration, 5 ppm may be used.
3. GENERAL URBAN EMISSIONS
General urban emissions data required include percent
emissions by different source categories and corresponding
-------
APPENDIX E{2)
growth and emission reduction factors in the service
area.
The methods for estimating base year CO emissions from
motor vehicles and other sources in the service area are
described in Chapter IV. Divide the motor vehicle emis-
sions into two groups:
Light-duty vehicle emissions (QT) comprising
Li
emissions from automobiles and light-duty trucks
Heavy-duty vehicle emissions (Q„) comprising
n
emissions from heavy-duty gasoline and diesel
vehicles.
The motor vehicle emissions QT and Q„ together with the
1j n
CO emissions from the other sources (Qg) form the total CO
emissions, QT« Obtain the percent emissions PL, PH, and Pg
by dividing Q^, Q^, and Qg respectively by QT>
The emission activity growth factors (GL, Gfl, and Gg)
and the emission reduction factors (E^, E^, and Eg) for each
category can be obtained separately. However, if the future
emissions are already projected, the product (G X E) for each
category can be obtained by taking the ratio of future emis-
sions from each category to the corresponding base year
emissions.
4. LOCAL EMISSIONS
The local emissions represent the emissions by motor
vehicles travelling on the streets next to the monitoring
site of interest. The emissions from stationary sources
are also included. The percent emissions by light- and
heavy-duty vehicles in the vicinity of the monitoring site
-------
APPENDIX E(3)
may not, in general, equal those in the general urban area.
The percentage of light-duty vehicles may be higher there
than in the general urban area. Also, the local traffic
growth rate is likely to be lower than that in the general
urban area because of saturation with the existing traffic.
To obtain the local emissions data, determine the
fraction (NL and NH) of light and heavy-duty vehicles tra-
velling on the local streets during the base year from
local traffic data. Determine the corresponding emission
factors (eT and e„) from AP-42. Obtain the fraction (K_
Li ri Li
and KH) of light and heavy-duty vehicle emissions using the
equations:
The percentage of stationary source emissions (Pg) in
the vicinity of the monitoring sites may be assumed to be
the same as that in the general urban area. Therefore, the
local percentage (PL and PH) of light and heavy-duty vehicle
emissions can be obtained using the equations:
K.
L
and
K.
'H
P
L
Kl x (100 - Pg)
and
P
H
Kh x (100 - Ps)
-------
-APPENDIX E(4)
The local traffic growth factors (GT and G„) can be
Li H
determined from local transportation planning studies or
from data obtained from local or state planning agencies.
The local emission reduction factors are assumed to
be the same as those for the general urban area.
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