MANUAL
FOR
ESTIMATING
SELECTED SOCIOECONOMIC IMPACTS
AND
SECONDARY ENVIRONMENTAL IMPACTS
OF
SEWAGE TREATMENT PLANT CONSTRUCTION AND OPERATION
Dr. Rae Zimmerman
Assistant Professor of Urban Planning
Graduate School of Public Administration
New York University
Prepared for
U.S. Environmental Protection Agency
Region II
New York, N.Y.
September, 1974
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TABLE OF CONTENTS
Introduction JL
Definition of Secondary Impacts 2
Range of Secondary Socioeconomic Impacts to be
Considered 4
Characterization of the Socioeconomic Profile 5
Socioeconomic Impast Assessment Methodologies 12
Models 12
Economic Spatial Allocation or Land Use Models
Nonspatial Economic Models
General Environmental Assessment Models
Public Perception Studies 19
Detailed Discussion of Selected Areas of Secondary
Socioeconomic Impacts 22
Impact on Water Resources 22
Impact on Land Resources 28
Impact on Land Values 29 -
Economic Impact 34
Isolated Industries
Fiscal Impact
Secondary Environmental Impact Assessment 40
Generalized Pollution Coefficient Matrices
Appendices
Appendix A: Population Projection Methodologies
Appendix B: Industrial and Ancillary Development Projection
Methodologies
Alternative Projection Methodologies
Limitations of the Projection Methodologies
Sources of Employment Figures and Projections
Application: Ocean County, New Jersey
Appendix C: Detailed Tables
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INTRODUCTION
The purpose of this manual is to provide an introduction
to the analysis of socioeconomic and related secondary environ-
mental impacts of wastewater treatment plant projects that is
part of the requirements of the National Environmental Policy
Act of 1969 and related legislation. An outline of directions
that can be taken and methods that can be used in assessing
selected secondary impacts are presented. As a basis for the
assessment of impacts, a set of socioeconomic: variables that
characterize the socioeconomic profile of a given area are also
presented against which and in terms of which impacts can be
measured.
While the manual is limited to the effects of sewage
treatment facilities, many of the methodologies discussed can
be applied to the assessment of socioeconomic impacts of other
kinds of facilities as well.
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Definition of Secondary Impacts
The Interim Regulations issued by EPA on January 17, 1973
require that beneficial and adverse secondary long term and
short term effects should be among the criteria an agency uses
in deciding whether an impact statement should be prepared
(38 FR 1696 Subpart B Article 6.20), and among those effects
that have to be evaluated in the body of an impact statement
(38 FR 1696, Subpart C, Article 6.32 (b) (1) ). A secondary
effect is defined in the interim regulations as follows:
"Secondary consequences result from activities
encouraged or induced by the Agency action"
(Subpart B, Article 6.20 (a) (2) ).
The CEQ Proposed Guidelines of May 2, 1973 define secondary
effects as being generally "in the form of associated invest-
ments and changed patterns of social and economic activities",
and exert their influence as "impacts on existing community
facilities and activities and through inducing new facilities
and activities ". The Proposed Guidelines note that secondary
effects can be more substantial than primary effects. A few
examples are highlighted, in particular the impacts on popula-
tion and growth, and the effect of these changes on "the
resource base, including the land use, water, and public
services, of the area in question " (38 FR 10856 (8) (ii) (B) )
While definitions of secondary impacts clearly emphasize
socioeconomic impacts in particular, which is the major subject
of this manual, they imply that secondary environmental effects
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exist as well. Such effects can be secondary to primary
environmental effects or secondary to socioeconomic impacts.
The secondary environmental effects related to socioeconomic
impacts will be emphasized, though in general the methodology in
assessing these is similar to that for assessing secondary environ-
mental impacts resulting from primary environmental effects.
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SECONDARY SOCIOECONOMIC IMPACT ASSESSMENT
The Range of Socioeconomic impacts to be Considered
The EPA guidelines only highlight a few examples of secondary
socioeconomic factors to be covered either as criteria for
writing an impact statement or as part of the impact statement
itself, such as:
....changes induced by the proposed action in population
distribution, population concentration, the human use of
land (including commercial and residential development),
and other aspects of the resource base such as water and
public services. (38 FR 1696, Subpart C, Article 6.32 (b)
(D)
Under longterm uses of the environment, the Proposed Guidelines
state that:
Those who may financially profit or suffer losses from
uses of natural resources that may result from the pro-
posed project (especially land) shall be identified
(38 FR 1696, Subpart C, (e) ).
A more extensive set of examples is listed in Subpart E of the
proposed guidelines as .part of criteria for deciding whether to
prepare an impact statement. Statements shall be prepared where:
(a) The treatment works will induce or encourage sig-
nificant changes in industrial, commercial or residen-
tial concentrations or distributions, the effects of
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which are not adequately reflected in an impact state-
ment on the water quality management or areawide plans
encompassing the works. Factors that must be considered
in determining if induced changes are significant include,
but are not limited to: the land area subject to in-
creased development as a result of the treatment works;
the relative increase in population which may be induced;
the potential for overloading sewage facilities; the ex-
tent to which landowners may benefit from the areas sub-
ject to increased development; and the nature of land
use regulations in the affected area and their potential
effects on the development.
(b) The works or plan will result in a significant dis-
placement of population.
(c) The works or plan will have significant adverse impacts
on public parks or other areas of recognized scenic or rec-
reational value. (38 FR 1696, Subpart E, Article 6.54
(a)-(c) ).
Among the numerous primary and secondary effects which
sewage treatment plant construction may produce, only a few of
these effects may be significant for the environmental impact
assessment process. The isolation of these few significant effects
is important in order that the assessment process be carried
through in an efficient and comprehensive manner. It is not
possible to produce a set of rules for determining which effects
are significant, and therefore must be included in an environ-
mental impact statement. Nevertheless, a screening process based
on the interrelationships between these effects and related socio-
economic conditions can aid substantially in identifying the
significant effects. It should be noted that the basic network
of interrelationships is different in each case and therefore
must be continually adjusted. For example, different socioeconomic
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conditions would exist in high population density and low
population density areas or in stable versus rapidly develop-
ing areas.
The proposed CEQ guidelines of May 2, 1973 caution about
discarding a small impact or effect too quickly:
In considering what constitutes major action significantly
affecting the environment, agencies should bear in mind
that the effect of many Federal decisions about a project
or complex of projects can be individually limited but
cumulatively considerable .... In all such cases, an
environmental statement should be prepared if it is reason-
able to anticipate a cumulatively significant impact on the
environment from Federal action. (38 FR 10856, 40 CFR Ch.
V No. 6).
Characterization of the Socioeconomic Profile
I. Population and Households *
Past, present and projected magnitude of population and
its distribution (See Appendix A for projections)
Population densities
Components of population change-migration, alterations in
the birth and death rate, etc.
Rural vs. Urban Orientation of the Population
Socioeconomic Profile
Income
Ethnicity
Age
School enrollment and education level
Occupation profiles
Household size and composition
Journey to work pa tterns
II. Housing (see U.S. Bureau of the Census, Census of Housing.
plus local records on building starts, etc.)
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Trends in and distribution of housing by type (single
vs. multifamily housing)
Trends in type of tenure (owner vs. renter)
Cost of housing
Space allocations (persons per room, rooms per unit)
Housing condition
Trends in land subdivision activity
Trends in residential construction (number of building
permits applied for and issued)
III.Economic Profiles *
Size and distribution of industry by employment, value
added and number of establishments
Industrial projections (see Appendix B)
Trends in firm size
Age of firms
Direction and magnitude of industrial migration
Labor intensiveness (employees per unit of value added, etc.)
Productivity measures (e.g., value added per employee or per
establishment, etc.)
Unemployment rates
Presence or absence of industries that have claimed or have
shown to exhibit economic hardship because of pollution
control identified from EPA sponsored national studies
and the EPA Department of Labor Economic Early Warning
System
Water use intensity of existing and projected industries
Trends in the development of industrial parks, size of
parks and tracts offered, amenity values
*Sources of data for population and economic profiles are
given in Appendix A and B. Other sources are listed in this section
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IV. Land Use Characterization - the distribution and
intensity of the following categories of land use
Commercial
Industrial
Residential
Open Space and Recreation
Transportation
In particular, the land use along shorelines and
accessibility of shorelines needs to be analyzed.
V. Recreation
Supply of recreational areas
Leisure time available to the population (hours worked
per week, occupational orientation of the popula-
tion - extent to which occupations are those that
allow greater leisure time)
Water orientation of recreation - extent of boat
registration, ownership and use, number of marinas,
extent of water availability (number, acreage and
depth of lakes, shore miles) etc.
VI. Local Government Finance
Sources and characteristics of revenue -
Bonding power, outstanding bond indebtedness,
requirements for bonding such as referenda
and percentage of votes required to pass issues,
flexibility in refinancing
Local credit ratings
Federal, state and local shares of local revenue
Property taxes - sources of property tax by type of
property; per capita property taxes; real estate
tax as a percent of value of taxable property
Taxes as a percent of local personal income
Sales Tax
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Capital vs. operating revenues
Sources and characteristics of expenditure -
Distribution of expenditures by source category -
water and sewerage, health (hospitals), fire,
police, education (schools, libraries), welfare,
recreation, judicial services, etc.
Per capita general expenditures
Sources of data for the analysis of local government finance
include the U.S. Bureau of the Census, Census of Government and
the annual publication, City Government Finances. Local assess-
ment offices, utility offices, and chambers of commerce can be
tapped for assessment rates, property taxes and so forth.
VII. The kinds of controls .available for controlling growth and
development directly or minimizing the environmental
effects of growth and development ("... the nature of
land use regulations* in the affected area and their
potential effects on the development")
An array of controls are listed below that cover a
whole range of financial, legal and administrative policies,
guidelines, regulations, plans and operating procedures that
have been brought into operation as a means of controlling growth
and development. The existence of these controls either at the
state or local level within whose area of jurisdiction the pro-
posed STP is to be considered needs to be established, along
with the potential of the area for establishing such controls.
The existence of and strength of application of these controls
can to a large extent determine the extent and direction of
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10
secondary socioeconomic and environmental impacts. For a detailed
discussion of these mechanisms see Kaiser, et a1.
Open space zoning
Flood plain zoning
Coastline or shoreline zoning
Steep slope zoning or hillside ordinances
Buffer zoning
Conservation and scenic easements
Density zoning and high density incentives
Large lot zoning
Subdivision regulations
Limitations on the installation of public facilities
(e.g. sewer moratoriums, water supply, and school
facility limits)
Building permits
Architectural appearance
Historic preservation
Tree preservation ordinance
Housing codes
Building codes
Sanitation (refuse ordinance)
Excavation or grading ordinance
Erosion control ordinance
Noise ordinance
VIII. Existence and comprehensiveness of a land use development plan
The secondary impact assessment process is made easier by the
existence of a comprehensive land use plan that integrates environmental
social and economic objectives, and has obtained a high degree of
public consensus. The environmental impact statement guidelines
put out by Region X, EPA in April, 1973 make this point:
We suggest that projects be considered in an overall
plan of development which allows for the secondary
impacts of the proposal in such a way as to insure rational
land use and to best protect the natural environment. . .
Since the proposed project will likely provide the impetus
in the area of substantial increases in commercial enterprises,
traffic, and the demand for public services, the sponsor
should indicate how the project fits into a master plan
for community development. (U.S. EPA, Region X, Environ-
mental Impact Statement Guidelines (April, 1973), p.101)
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While these comprehensive master plans have been required for many
years as a condition for receiving transportation, housing and other
kinds of federal and state money, their occurence is either too
sporadic, they are in a form that is too general for impact analysis,
are not current, or have not been subject to general public approval.
A survey done by the International City Management Association
found that of 962 reporting cities 30 percent did not have master
plans and of those with master plans over two thirds of the master
plans did not include an environmental section (International City
Management Association, p.258). Of a total of 1,038 responding
cities, only 30 percent had formal requirements for environmental
impact statements (International City Management Association, p.260).
Because of this, the secondary impact of a proposed project such as an
STP can at best only be compared with existing and projected land
use plans.
IX. Socioeconomic characteristics of alternative proposed sites for
STP construction
Existing use of the site and adjacent land uses
Existing zoning of sites
Existing property values of the proposed sites and adjacent
areas compared with property values of comparable areas
in the vicinity
Socioeconomic profile of residents, if any, including population
density
Socioeconomic profile of existing industrial and commercial
establishments, if any
Recreational use of land and water areas, if any
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SOCIOECONOMIC IMPACT ASSESSMENT METHODOLOGIES
Methodologies applicable to the delineation of socioeconomic
impacts range from complex mathematical models to an assessment of
public values and perceptions. Such a range of methodologies is
more or less implied in Section 102(2) (B) of the Act where "un-
quantified environmental values be given appropriate consideration
in decisionmaking along with economic and technical considerations"
All of these methods thus have a place in the environmental assess-
ment or impact process, and which method is used depends upon the
data available, the specific conditions of the area, the significant
impacts to be evaluated, and the extent to which the various methods
are adaptable to the size and location of the regions under study.
Models
The construction of wastewater treatment facilities in ex-
panding sewerage capacity acts like any expansion in utilities or
essential public services in altering land use, economic and social
patterns. STP facility construction acts on these systems through
two variables: population and the economic base, and to a lesser
extent on a third variable, the water resource system. Over the
years, many models have been developed that attempt to clarify
the.,interrelationships between the development of public services,
such as sewerage systems and the population and economic variables.
Much of this work is highly applicable to assessing the impact of
an expansion in sewerage and sewage treatment capacity on the di-
rection of population and economic growth, though many of the models
were initially adapted to the requirements of developing specific
kinds of facilities such as transportation systems
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Basically, three kinds of models are relevant here: land
use or economic spatial allocation models, non-spatial economic
models, and general environmental/socio-economic assessment models.
The discussion below will highlight some of the models and how they
might be applied to the problem of assessing the impact of STP con-
struction. Since an extensive review of such models is beyond the
scope of this manual, the reader is referred to the references
sited. The reader is also cautioned that data limitations and the
absence of rigorous testing of these models may be a severe limitation
in their application to environmental assessment process.
Land Use or Economic Spatial Allocation Models:
Land use models were essentially developed to integrate trans-
portation considerations into the prediction of alternative land
use and growth patterns, and in some cases to generate facility
development plans that would satisfy existing and projected socio-
economic needs translated into spatial patterns. A good summary and
critique of many of these models can be found in the recent publi-
cation by the National Bureau of Economic Research by H. James Brown,
et al., Empirical Models of Urban Land Use: Suggestions on Research
Objectives and Organization (1972), the entire May 1965 issue of the
Journal of the American Institute of Planners (JAIP), and the review
article in JAIP by W. Goldner, "The Lowry Model Heritage" (March 1971)
The relevance of these models to the impact of a change in
public services, such as STPs, is essentially the way they relate
population, industrial and commercial employment, transportation,
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residential development and public services to one another, and
ultimately arrive at spatial distributions of each. Figure 1 gives
a generalized picture of the components of many of these models,
emphasizing where STP construction lies in the general framework.
In reality these models differ from one another in the level of detail
of specific components, and the degree and order of interaction of some
of the components. The models generally have in common the fact
that population and/or employment growth start the entire cycle of
impacts in motion, however, eventually feedbacks occur, and population
and employment are affected in a .second or third generation. Methods
of projecting or obtaining these projections of population and economic
growth are given in Appendices A and B. The various population and
public and private institutional components are assigned locations
based upon their unique locational requirements (often assumed from
the findings of location factor surveys) as well as the characteristics
of land areas, such as cost, zoning, availability, accessibility, etc.
Models vary in the degree to which locational requirements and land
area characteristics are considered fixed. The land area to which
uses are allocated are generally divided up into zones based upon
available data, usually grids superimposed on land use maps from local
planning boards or constructed from census data tracts. Land uses
are allocated to parcels by means of any one of a number of functions,
a common one being the gravity function. The formulation of such an
allocation function could be modified for STP impact assessment
by identifying and placing a heavier emphasis on those in-
dustries that would tend to gravitate to the service area because
of the availability of sewage treatment services. Industrial establish-
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FIGURE 1
GENERALIZED ECONOMIC GR01VTH INTERRELATIONSHIPS
Resources
I
Occupation
Profile
EMPLOYMENT
Resource
Oriented
Industry
(Basic)
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16
ments that are heavy water users and small firms, for instance,
might do so, since both are generally more likely to use a municipal
wastewater treatment facility rather than construct their own
facility.
In applying these models to discerning changes in land uses
attributable to an expansion in wastewater treatment capacity, it
is important to recognize that the migration of economic units and
households more directly attributable to the existence of sewerage
capacity will occur between the unsewered and proposed sewered regions.
Since the price of sewage treatment services is a function of the
type and volume of wastes rather than the distance the wastes have
to travel, it is unlikely that the existence of the sewage treatment
plant would alter land uses within the service area (except, of course,
around the immediate vicinity of the plant).
Non-spatial Economic Models:
Just as planning models exist to predict future land use
changes that result from current land use decisions, economic models
exist that attempt to predict the effect of present investment
decisions on future patterns of economic growth. The construction
of anSTP like any expansion in public services or utilities represents
an investment that leads to the development and growth of other
sectors of the economy. Input-Output Analysis is such a technique.
Input-output tables exist for the nation and for selected areas
throughout the U.S. published by the U.S. Department of Labor,
that give for an expansion in a given industrial sector within
the national or regional economy, the related expansions that will
occur in other industrial sectors (in dollars and employees.)
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The difficulty in applying the existing data bases in input-output
analysis to the assessment of the economic impacts STP construction
is that few regions have existing input-out tables, generating input-
output tables is extremely expensive, the few regions that have in-
put-output tables may not coincide in boundaries with STP service areas
and the construction of STPs is generally net included among the
inudstrial sectors. Methodologically, there are weaknesses in the
technique such as the staticness of the input output coefficients
(they represent relationships over one point in time only).
The national tables can be consulted, however, to get a rough
weighting of the interdependences among industries. Once an
initial set of industries that definitely would be affected in an
area by STP construction is isolated, the industries that would
be affected by their expansion and construction could be isolated.
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Socioeconomic/Environmental Models
A number of models are listed below that have been developed
in recent years in various stages of refinement that attempt to
synthesize socioeconomic and environmental impacts, and combine them
into one conceptual framework. The advantage of these environmental
models over the economic and land use models in assessing the impact
of STP construction is that the environmental models take into
account the water quality improvement and indirect environmental
effects of STP construction, while the economic models are limited
to the economic function of STP construction as a public service
only. The environmental models impose environmental as well as
economic constraints on.development.
1. Ecologic/Economic Input Output Analysis (Isard, et al., 1972)
2. U.S. EPA "State of the System" Model
3. Batelle Environmental Evaluation System
4. J. Forrester World Dynamics Model
5. Residuals Management Model (Ayres and Kneese)
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Public Perception Studies
There has been a considerable emphasis on public involvement
in water management decisions in both NEPA and FWPCAA 1972 and in
the regulations and guidelines that have been issued under these
laws. Public action comprises a significant socioeconomic impact
in its own right as well as influencing the magnitude and direction
of other socioeconomic impacts. Public hearings are generally used
as a device for assessing the public interest. However, they are
often not adequate for this purpose since they often occur late in
the decision-making process after there has been a considerable ex-
penditure of resources, are limited in time, do not draw the public
out and do not receive 'statements from the public for which they
can hold the public accountable, nor do they apply a means of
weighing all of the collective public interests that may be expressed,
Because many of these factors have contributed to the delay and
stoppage of many STP projects, especially during the impact assess-
ment process, it is important that public attitudes and opinions be
assessed in the analytical stages of the assessment process and not
only in the implementation stages.
The following steps are suggested as a means for incorporating
public perceptions of socioeconomic impacts of proposed STP projects.
It must be kept in mind that these procedures are a means of assess-
ing the public interest only, and not a means of measuring de-
ficiencies in the existing levels of public involvement and communi-
cation with agencies.per se, nor a means to design a strategy for
public participation. These latter considerations would fall under
the Section 208 plan specified in FWPCAA 1972 or in that section
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of the impact statement that would deal with methods of minimizing
socioeconomic impacts.
1. Identification of the groups that would be impacted
a. the unorganized public — the general users of land, water
and other resources that would be impacted by the proposed
facility; both transitory and permanent users need to be
identified.
b. local government officials — both elected officials and
departmental representatives and employees engaged in
water quality management functions.
c. Citizens groups: (1) special purpose groups such as conserva-
tionists, sportsmans associations, watershed associations, and
other environmental groups: taxpayers associations, property
owners associations, and
(2) general purpose groups, such as civic groups and general
community organizations.
d. Private organizations — business establishments and firms,
trade associations, political groups.
These groups can be identified by means of interviews with
agency officials who have a general familiarity with correspondence
files and other data, from lists maintained by the U.S. EPA, Public
Relations Division, from a scan of attendance lists for previous
public hearings, and from a general familiarity with the news media,
2. Identification of the stated goals, interests and area of
jurisdiction of these groups from the media and from various files
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and records kept by these groups and public agencies; in particular,
identification of the degree of coalition formation among the groups/
ana. how representative these groups are of the general public.
These group characteristics might give an idea of the potential
of various interest groups to influence the magnitude and direction
of socioeconomic impacts.
3. Interview of a stratified sample of these groups or the universe
of all groups if the number is small as to their interests and value
orientation toward the environment in general and toward the effect
of STP construction in particular. Formal organization spokesmen
should be interviewed where possible; ideally, responses should be
evaluated against the socioeconomic characteristics of the respondents.
The kinds of concerns that can be investigated should include
not only public values with respect to water resources, but with
respect to land, air, recreational, economic and commercial re-
sources as well. One example of such an assessment is the Battelle
study for the U.S. Atomic Energy Commission (February, 1974, pp.17-63).
4. Analysis of group perceptions of socioeconomic impact and sensi-
tive areas, including weighting of the perceived impacts of all groups.
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DETAILED DISCUSSION OF SECONDARY SOCIOECONOMIC IMPACTS
Impact on Water Resources
STP construction can induce the following impacts on water
resources, many of which are discussed in other sections of the
manual:
(1) Improve water quality by reducing pollutants from direct dis-
charges in its service area and areas hydrologically related to
it, and hence, increase land values, local and regional tax bases,
and recreational benefits (see p. 29 ).
(2) By allowing a certain level of development, STP construction
induces the deterioration in the quality of water resources via
overland runoff, erosion, and sedimentation.
Development inevitably increases the amount of impervious
surface in a given area, which reduces recharge areas for ground
water increases overland runoff of pollutant laden stormwater,
and construction sites and other exposed areas can result in
erosion and sediments entering surface water. Methods for
quantifying some of these effects are given in the section on
Secondary Environmental Effects.
(3) Impact of development on the magnitude and distribution of
water supplies
(38 FR 10856, (8) (ii) (B) refers to the following example
of secondary impacts to be considered: the effect of changes in
population and growth on "the resource base, including land use,
water/ ....).
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Figure 2 portrays in a simplified way the change in the demand
for water by type of water supply used as the level of land
development changes (whether development is for industrial, domestic,
commercial, or municipal purposes). An increasing trend in water
needs as a function of development actually has been projected to
occur in the counties surrounding the Mid-Hudson area, where
withdrawal of Hudson River water for water supply is being investi-
gated.
Several stages can be isolated in Figure 2 that relate to the
substitution of water supplies as urbanization, and hence, develop-
ment, progresses:
(1) Surface water (such as water diverted from the Hudson) would
begin to supplant ground water supplies significantly at about
point X-, , when any one of a number of conditions occur:
a. when it becomes cheaper to use surface water than to
develop new sources of ground water (assuming that the cost
advantage of surface water does not decrease as water quality de-
clines from increased runoff of polluted waters due to urbanization) j;
b. when ground water sources become depleted from increased
demand for water in general; from a decrease in the recharge areas
created by increasing impervious surface construction accompanying
urbanization; from an increased use of surface water, which upsets
the balance between the flow of water between ground and surface
water supplied causing the former to run into the latter at a
faster rate^
c. when surface water supplies become more plentiful relative
to ground water supplies due to increased runoff from clear cutting
and construction of impervious surfaces^
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Figure 2
t
WATER
CONSUMPTION
(GPD)
DEVELOPMENT
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(2) Surface waters may eventually reach a plateau at X , and no
longer be able to meet water demand. At this point a water deficit
occurs, such as is projected to occur in Nassau County after 1980.
(3) At X_ the growth in water needs begins to slow down as water
conservation measures take place to eliminate the deficit, assuming
that no other water supplies are available or that their cost is
too great, i.e., much greater than the cost of water conservation
measures.
(4) Finally, at point X^ water consumption is brought into line
with supply, and development presumably cannot advance beyond this
point unless:
a. New sources of water are found
b. A breakthrough in water conservation technology occurs
c. There is a tradeoff where high consuming water users are
substituted for low water users.
The existence of changes such as the ones hypothesized in
Figure 2 need to be identified, where these changes are a result
of the population growth and associated residential, commercial
and industrial development that are allowed to occur because of
an expansion in sewerage capacity, and as a result of the location
of the discharge of the proposed wastewater discharge. Where
discharges occur significantly downstream from the source of
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26
the original community water supply, they may essentially be
having the effect of removing ground and water supplies and
discharging them ultimately into the oceans at a faster rate than
would otherwise occur naturally through the hydrologic cycle.
Projected water demand calculations in larger urban and urbani-
zing areas can often be obtained from existing studies in this
area. These projected demand figures when compared with the safe
yields from existing water sources comprise the impacts on water
supply resulting from providing sewerage and sewage treatment,
since the population figures that are the basis of the water demand
calculations are essentially the same figures that are used to
design future STP capacities. In the New York area water demand
projections have been made by the Northeast Water Supply Study
under the direction of the U.S. Army Corps of Engineers (NEWS study),
and the reports of the Temporary State Commission on the Water
Supply Needs of Southeastern New York (SEWS study). The former
study covers the entire northeastern U.S., and the latter study
covers the five boroughs, Nassau, Suffolk, Rockland, Orange,
Ulster, Dutchess, Putnam and Westchester counties in New York State.
Where these calculations do not exist, a rough estimate can
be obtained from applying an average per capita water use figure
(e.g., 150 gallons per capita per day, which is the figure used
to include residential, light industrial and commercial use) to
projected population figures, which will give an estimate of the
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amount of water demand from residential development and population
related light industrial and commercial activity- For heavy
industry water usage coefficients (see Appendix) can be used and
applied to economic projections.
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Impact on Land Resources
(1) Magnitude of direct space utilization
While the utilization of space by wastewater treatment facilities
is a primary impact, wastewater treatment plants by utilizing space
compete with and may even deprive other uses of land. Wastewater
treatment facilities in addition to requiring a certain set amount
of space, need to be located in specific areas, i.e., near or ad-
jacent to waterways. This location factor is a function of the
technologic and economic limits on the length of outfalls.
Fair, Geyer, and Okun give the following land utilization figures
for various types of wastewater treatment facilities:
Trickling Filters (80-90% suspended solids removal, 65-85% BOD
and COD removal)
mgd per acre
Trickling filters with stationary
nozzle fields 1.0 - 4.5
Trickling filters with traveling or
rotary distributors 3.0 - 45
Prototype, fill and draw contact beds 0.1 - 0.3
(Fair, Geyer and Okun, 1972, p. 35-6)
Intermittent Sand Filters (90-95% BOD removal, 85-95% Suspended
Solids removal, and 95-98% bacteria removal)
mgd per acre
Irrigation of cultivated soils .003
Irrigation of grasslands .025
Intermittent filtration of raw sewage .080
Intermittent filtration of settled sewage .200
Intermittent filtration of biological
effluent .500
(Fair, Geyer, and Okun, 1972, p. 35-4)
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29
Guidelines and formulas are also given for calculating the
acerage requirements of activated sludge systems and stabilization
ponds. (See Fair, Geyer, and Okun, 1972, pp. 35-26 and 35-33)
(2) Compatability of existing land uses adjacent to the proposed
site with the STP
(3) Identification of competing land uses, i.e., land uses that
could equally occupy the proposed sites, such as recreation areas,
industrial sites, residential sites, etc.
(4) Extent of visual impact of the proposed plant — An area of
visual impact around the proposed plant sites has to be identified.
A number of approaches can be taken here ranging in degree of
quantification. A detailed site analysis taking into account top-
ography, visibility, land cover, etc. is generally required. For
an example of such an approach as applied to power plants, see
Battelle Pacific Northwest Laboratories (February, 1974), pp. 64-147
Impact on Land Values
Changes in land values due to construction of a wastewater
treatment facility are a major socioeconomic impact that is re-
ferenced in the regulations ("the extent to which landowners may
benefit from the areas subject to increased development).
Land values can be altered in any number of several ways due
to the construction of an STP:
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30
(1) The land area adjacent to the STP can decline in value due to
actual or perceived nuisances, such as odors, and the generally
unattractive, unaesthetic character of an STP, and due to actual
or perceived public health hazards, such as the presence of coliform
aerosols that are known to exist around sewage treatment plants.
One approach to estimating the extent of changing land values
is by comparing land value differentials either cross-sectionally,
between areas adjacent to existing STPs and areas some fixed distance
away, or over time, comparing land values before and after STP
construction. Before extrapolating these findings to an area where
an STP is proposed, socioeconomic differences between the areas
where such data are available and the area where the data are to
be applied have to be taken into account.
(2) The land in the service area of the STP can increase in value
due to the existence and availability of the additional expansion
in public services, increasing its development potential.
One approach that might be taken here is similar to that in
(1) above: the extent of development in sewered and nonsewered
areas could be compared cross-sectionally or longitudinally, and
act as a basis for evaluating development potential of an area
proposed for STP expansion, as long as extraneous factors are
controlled for.
(3) Since STP construction ostensibly improves water quality, an
improvement in water quality can increase land values for private
property as well as for recreational use along the shore of the
improved water resources and some distance inland.
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31
"There are a number of studies that have related the
quantity and quality of water resources with land values for
both primary and secondary residential development and the de-
velopment of general recreational opportunities.
That water resources are related to the most popular re-
creational activities was brought out in the 1962 ORRRC
nationwide study of recreation. Water related activities
such-as swimming, fishing, boating and water skiing ranked
high in the nation among recreational activities engaged in
most frequently. In particular, water resources have attracted
extensive residential developments. Kusler notes the extensive-
ness of lake development in Wisconsin: "Since 1966 at least
11 lake development projects involving more than 12,000 sub-
division lots have been approved . . . "(Kusler, 1971 p.373).,
He notes further that it is the 100 acre or more lakes and
large rivers that "experience intense recreation pressure"
(Kusler, 1971 p. 377-8). In a study of vacation homes in
Michigan, Tombaugh found that 55% of the homes in his sample
purchased after 1952 were located on an inland lake, 24% on one
of the Great Lakes, and 10% on a river or stream (Tombaugh, 1970)
Burby's study of reservoir site development found that in a
sample of 268, 66.4% found that a view of the lake was important
(Burby, 1971, p. 98). Ragatz reports the American Land De-
velopment Association findings that in one third to one half
of all projects in 1972 and 1973 water oriented activities
were popular (Ragatz, 1974, p. 174). The Urban Land Institute
has reported that 55.1 percent of existing recreational land
development projects filed with OILS incorporates lakes
(Ragatz, 1974, p. 175).
Further studies show increased revenues and land values from
water resources. In 1965 alone, the TVA lakes had 50 million
visitors who brought in some 321 million dollars to facilities
serving the visitors (Seneca, et al. (1968), p. 530). In a
study of artificial lake developments in Wisconsin, David
(1969) found that the value of lake development property is
much greater where there is more surface water around.
Williams and Daniel (1969) found an increase in land prices
following reservoir construction, and a positive relationship
between land prices and access to the reservoir. Donald (1971)
notes that while land values increase rapidly after reservoir
development, the rate of increase soon returns to that of
areas without reservoir developments.
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32
Another set of studies primarily cross-sectional and con-
ceptual in nature relate a tendency for increase in water
quality to be related to increases in land values. The David
study of Wisconsin Lake developments shows that lake develop-
ment property was less valuable where lakes were more polluted.
A study of Lake Onondaga, an urban lake in Syracuse, New York
estimated that an increase in water quality would have a signi-
ficantly high dollar value in terms of increased revenues from
recreational opportunities (Faro and Nemerow, 1969) . In a
study of property developments on the Rockaway River in New
Jersey, Beyer (1969) found that higher property values are
generally associated with cleaner waters, though in the case
of primary homes other factors such as proximity to jobs may
be more important. A recreational demand model for Upper
Klamath Lake in Oregon concluded that an increase in water
quality would increase the recreational use of the Lake. In
particular, they estimated that if algae were removed from
the lake and the lake temperature lowered, the annual rise in
net economic value would be 2.65 million dollars plus 542,000
in household income (Reiling, Gibbs and Stoevener, 1973).
Dornbusch and Barrager (1973) studying sites on San Diego Bay,
the Kanawha River in Ohio and the Willamette River found that
pollution abatement could increase the value of waterfront sites
from 8 to 25 percent. Values were affected as far as 4,000
feet from the water's edge. They estimated that the total
capital value to waterfront residential and recreational
development would be from .6 to 3.1 billion dollars."
(Zimmerman, 1974).
The benefits of an improvement in water quality ultimately
vary according to who the users of the waterfront are. So far,
the relationship between water quality and only two uses, residential
and recreational uses have been discussed. An important first
step in assessing the impact of an improvement in water quality on
property values is identifying the entire array of existing users
(industrial, commercial, residential or recreational) and potential
or competing users of the land impacted by water quality improve-
ments. Knowing the extent to which various users can pay for
water oriented sites and the importance of water quality in their
ability and willingness to pay for sites, will give an indication
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of the extent to which redistributions in land use will occur due
to an improvement in water quality.
The methodological approach taken in the studies cited
above in analyzing the importance of water quality to land values
is multiple regression analysis. Water quality is one major
variable (measured in terms of D.O. levels, turbidity, nutrient
composition, etc.) and land values is the second major variable
expressed as assessed valuations, purchase price or per acreage
cost. A whole host of other variables are always included that
may contribute equally to variations in land value such as variations
in taxes, transportation or accessibility, etc. While regression
analysis does not establish causal relationships between land
value and water quality, it does give an indication of how strongly
the two have been related in the past, over and above the relation-
ship between land values and other factors. Knowing the degree
to which the proposed STP will improve water quality then, one can
approximate by inference from other studies what the probable impact
on land values might be from the construction of the STP.
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34
Economic Impact
(1) Impact on isolated industrial establishments
The cost of providing independent wastewater treatment
facilities or, alternatively, paying pretreatment costs and
user charges to a domestic wastewater treatment plant can result
in any one of a number of responses on the part of industrial establish-
ments, depending upon the economic conditions under which they
operate. These responses can occur along the following scale:
the firm shuts down and terminates operation, the firm moves to
another location opening a new plant that has pollution control
facilities, the firm stays in operation and raises prices to
recoup costs of facilities, stays in operation and engages in
product diversification or product substitution to raise additional
income, stays in operation and absorbs costs with excess capital,
stays in operation and actually benefits from pollution abatement
requirements by opening up a subsidiary dealing in the construction
of pollution control equipment, research or design.
The first step in deciding which firms would be more susceptible
to such economic impacts is to isolate those firms that are the
heaviest water users and in particular, the heaviest water dis-
chargers. Wastewater coefficients by four digit SIC codes can be
used for such an initial selection procedure, and a set of these
coefficients compiled from the literature is given in Appendix C.
A number of studies have evaluated the economic impact of
pollution control on selected industries across the nation.
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35
Table 1 summarizes the findings of an earlier study of eleven
industries at the four digit SIC level. This table gives annual
cost figures that can be applied to firms within the service area
of a proposed STP as a very rough indication of the economic impact
that such firms would encounter. The user of these figures, however,
is cautioned that these are probably for independent industrial
wastewater treatment facilities rather than the cost of treatment
via a municipal wastewater treatment system, and that more recent
and more detailed studies exist that should be consulted. Obviously,
The best data would be to consult the budget of the individual
companies in question, but this data is rarely available and too time
consuming to collect.
(2) Fiscal Impacts
The provision of any local service can have an effect upon
the tax base and the structure of the local service delivery system.
By allowing a certain level of population growth and hence, resi-
dential development to occur, the expansion in one local service,
sewerage, can lead to a strain on other local services, such as
health and safety, education, transportation and so forth, unless
these services are likewise expanded. Thus, the expansion in all
local services, both publicly and privately financed, has to be
projected along with population expansion. Such an expansion is
interrelated with a whole host of economic factors in addition to
the magnitude of population growth, many of which are related to
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TABLE 1 .—Economic impact of pollution control on selected industries in the United States,
summary of findings: 1972-1976
Industry
Baking
Cer.ent
Annual
Cost
$.03-. 10
per barrel
Percent Price Percent'
Change Cost is of
Sales
2 0.2
Plant
Closings
25
Employment
Loss
Electric Power
Fruit & Vegetable
Canning and-
Freezing
Iron Founcaries
Leather Tanning
Non-ferrous metals,
smelting and
refining:
Aluminum
Copper
Lead
Zinc
Petroleum
Pulp & Paper
Steel Making
n. a.
S.02-.032/lb.
Max. :$.05/lb.
$.012-.017/lb.
$.012-.027/lb.
$.06/barrel
$5.50-12.50/ton
$.43-.73/tonC
$6.60-9.60/,tond
7.0
1.4-2.3
1.7-5
2-3e
5-8
0-8
5
n . a.
$. OS/barrel
3.5-10
100
1.5-4 402S
1 neg .
2
12
60-65
7000
8000
600
1150h
1000
16000
0.7-1.5
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37
Notes:
aThis table is a summary of the findings and ongoing research of
several studies described in, The Kc.onom i c. Impart ol" J'oj luU on Control,
prepared for the Council on Environment a.I Quality, Drpt . ol" Commerce,
and Environmental Protection Agency, March 1972, U.S. Government
Printing Office, Washington, D.C.
These closings are. due to pollution control costs only, unless
specified otherwise.
°Annual cost in 1972.
Annual cost estimated for 1976.
eThis percentage change in price is a maximum figure; the
average given by the authors is 1%.
The gross plant closings from pollution control costs as v/ell
as other factors is estimated at 400 firms.
&The gross plant closings from pollution control costs as well
as other factors is estimated at 670 firms.
In addition to this absolute loss of employees, employment
growth is expected to slow down by some 3.6-14%.
Source: R.Zimmerman. The Effectiveness of Industrial Pollution
Control in the New York Region. Doctoral Dissertation,
Columbia University, New York. November, 1972. Pp. 255-6.
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38
the direction of growth. The direction of growth includes such
factors as the size and composition of households, and hence, the
demand on educational facilities; the income of households and
hence, the size, type and density of housing units, which can
influence the level of police and fire protection. These determinants
of the level public service demands accompanying population growth
must be an important component of the assessment of local service
levels required from allowing population growth to occur via an
expansion in sewerage capacity.
The direction and magnitude of the impact of an expansion
in sewerage capacity on local government finance depends to a large
extent upon the source of local revenues, i.e., whether services
are financed from property taxes, income tax, sales tax, user
charges, etc., the existing financial capabilities of the locality,
including its credit rating, the disparity in revenue and ex-
penditure levels, etc.
STP construction can have a direct effect upon local govern-
ment finance. The extent of this impact depends upon what portion
of the construction cost of the facility the locality bears compared
to the portion the state and federal governments provide, and the
mechanisms used by localities to recoup the cost of construction
and finance operating and maintenance costs.
One of the more important influences on the lev el of impact
on local government is the percentage of total revenues that the
local government share accounts for. Another is the bonding
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39
capability of the locality -- outstanding bond indebtedness and
credit rating, the kinds of bonds the locality is allowed to
issue, and the allowable cost recoupment devices. GAO, for instance,
has recently ruled that ad valorem taxes cannot be used to recoup the
costs of construction, operation and maintenance of STPs, since
they are not in keeping with FWPCAA 1972 amendment requirement that
the costs of providing treatment services be recouped in a manner
proportional to the use of the facility.
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40
SECONDARY ENVIRONMENTAL IMPACT ASSESSMENT METHODOLOGIES
Once the magnitude and direction of population growth,
ancillary development and industrial development induced from the
construction or expansion in wastewater facilities is estimated,
it remains to estimate what the secondary environmental effects
of this induced growth will be. These additional pollutants, and
particularly, the water related pollutants, are often not entirely
accounted for along with primary environmental impacts in the design
of the wastewater treatment facility. Such pollutants can be
roughly estimated by "pollution coefficients" that are widely
scattered throughout the engineering, scientific, and planning
literature, and applying these coefficients to the pattern and
magnitude of growth via any one of a number of allocation or dis-
tribution functions or ultimately via the socioeconomic/environmental
models listed on page 18 . Figure 3 illustrates the general pro-
cedure for such an analysis. Initially the sewage treatment plant
development allows a certain projected growth in population to occur.
That amount of growth consists of that part of the projected popula-
tion for an area that achieves a density requiring sewage, and is
the initial input into the design of STP capacity, unless growth
controls are applied. Pollutants generated from spinoff develop-
ments associated with population growth, however, are not necessar-
ily planned for especially those discharges that are nonpoint sources
rather than point sources or direct discharges.
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41
The difference between pollution generation from existing
development and pollution generation from the growth component
represents the net impact of the expansion into wastewater treatment
capacity. A more conservative estimate would subtract that part of
the industrial component that is believed to be independent of pop-
ulation and whose wastewater treatment facilities are independent
of those whose impact is being assessed.
Generalized Pollution Coefficient Matrices
An outline of the pollution coefficient approach is given
below for selected estimates primarily for total "potential"
pollution generated by the different induced growth sectors,
which assumes that no pollution reduction via waste treatment or
recycling occurs. Methods for estimating "actual" pollution
generated by the different growth sectors assuming various levels
of waste treatment are briefly discussed. Estimates of pollution
on a regional scale using this approach have been carried out by
the Regional Plan Association (1968), Greenberg and Zimmerman (1973),
and Smith and Braster (1972). The advantage of developing coefficients,
expressed as wastes generated per unit of economic activity (usually
production or employment) is that total pollution for areas of any
size can be estimated as well as the effect of alternative spatial
arrangements on waste generation once the aggregate or point source
location of economic activity is known. Because the tremendous
variation in conditions for economic activity in terms of both
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FIGURE 3
I
Ap
Projected
Population
Change
for time t
Projected
v Ancillary
Development
Change for
time t
\'
Projected
Industrial
Development
(resource-based)
for time t
p T A
f, i ,/\
Existing
Population, Industry,
and Ancillary
Development
Generalized Pollution
Coefficient Matrices .
Distribution or
-Allocation
Function
Total Pollution
Change Matrices for
Projected Development
Total Pollution
Matrices for
Existing Development
Checklist of Existing
Standards, Criteria,
Guidelines, Value con-
sensus, etc.
Any
Excess
Pollution
?
no
yes
_Reexraluate
Project
Project adequate, or proceed
to non-impact decision
processes
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43
magnitude and intensity generates variations in waste generated
per unit of activity, a range for each coefficient should be used
rather than a precise number.
Coefficients given below are also categorized as direct
pollution discharges or emissions and indirect pollution (such
as runoff, soil loss), the latter only being given for residential
and commercial development.
Pollutants from agricultural and mining activities are not
considered here. The reader is referred to the EPA-430 series
publications of 1973 for this information.
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Industrial Pollution Coefficients
An approach for estimating direct industrial pollution
for air, water, and solid wastes is outlined in the summary
matrix below.
Direct industrial wastewater coefficients for estimating
potential pollution from process water are given for a selected
set of forty industries in Tables c-4 through C-7 in Appendix C
from Zimmerman (1973). These industries are generally considered
the largest generators of wastewater and water pollutants of
all industries.Table C-4 gives coefficients for wastewater
volumes by industry.Table C-5 compares wastewater volumes from
literature sources to actual reported wastewater discharge
figures in permits required under the Federal Water Pollution
Control Act Amendments of 1972 (the National Pollutant Discharge
Elimination System). A fairly good correspondence is noted.
Table C-6 gives water pollutants in terms of concentrations and
Table C-7 gives them in terms of pounds per pound of industrial
output. The code for data quality in these tables is:
Data Reliability Index
1 = Data represent the convergence of many independent
inventories, each consisting of a large sample taken
over relatively long periods of time and at frequent
intervals.
2 = One or two inventories with a large number of samples
3 = plant operating experience for several plants consisting
of 24-hour composite samples taken at frequent intervals,
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Water
Non-process
30 gal./capita
per day
Direct Industrial Waste Generation
Summary Matrix
Potential Waste Generation
Air
Process
(See accompanying
tables for waste-
water coefficients
for about forty
industries at the
four digit SIC
level in Appen-
dix C.)
30 gal./capita Consult U.S.EPA
per day
eft ..uent guide-
lines or reduce
potential pollu-
tion by alternative
percentages, rang-
ing from 80-99%.
See: U.S.EPA. Compila-
ation of Air Pollutant
Emission Factors.
Research Triangle Park,
N.C.: U.S.EPA, April,
1973.
Actual Waste Generation
New Source Performance
Standards; Stationary
source standards; plus
Potential emissions
for materials not cover-
ed by standards.
Solid Wastes
Non-process* Process
Unknown Unknown
Unknown
For specific
data see Steiker
(1974)
*Non-process solid wastes from industry include packaging material from supplies, waste
office paper and cardboard, cafeteria wastes (bottles, paper, organic waste, cans),
construction and demolition wastes, waste from sanitary facilities, flyash and cinder
from onsite power facilities, if used.
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46
4 = Plant operating experience for only a few plants,
consisting of 24-hour composite samples over a
limited range of time, or an estimate based on
national averages.
5 = Data based upon grab samples only.
Potential water pollutants can be reduced to actual
pollutants by (1) substituting effluent guidelines where they
exist (2) reducing them by the percentage of known removal
efficiencies of various wastewater treatment systems.
Indirect water coefficients can be calculated in a
manner similar to that suggested for residential and commercial
development in the next section.
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47
Residential/Commercial Pollution Coefficients
Direct water pollution from residential and commercial
facilities is generally averaged for combined residential and
commercial development at approximately 150 gallons per capita
per day of wastewater and at about .08 to .14 pounds per
capita per day for biological oxygen demand.
Direct air pollution is estimated almost exclusively as
fuel usage and coefficients for various kinds of fuel are given
in the Compilation of Air Pollutant Emission Factors.
Direct air pollution from residential and commercial
activity in addition to resulting from fuel usage consists of air
pollutants generated during solid waste disposal. Particulate
emissions for alternative methods of disposal are:
Open burning — 50-100 Ib/ton
Poor apartment house incineration— about 50 Ib/ton
Good apartment house incineration — 10-20 Ib/ton
Average municipal incineration 20-30 Ib/ton
Good municipal incineration 10-20 Ib/ton
(Regional Plan Association, 1968, p. 93.)
Preliminary solid waste coefficients, based upon the New
York Metropolitan area figures are .51 to .68 tons per capita
per year for residential solid wastes and .75 to 1.75 tons per
employee per year for business solid wastes, which includes light
industry and commercial wastes (Regional Plan Association, 1968,
P. 91).
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The increase in indirect potential water pollution genera-
tion takes the form of increased runoff from impervious surface
created by new development. Runoff is a function of the intensity
of rainfall, the slope of the land, land cover, and the size
of the drainage area. The general formula for computing runoff
is:
R = CIA
where C is the coefficient of runoff, I the intensity of rainfall
and A the size of the drainage area in acres. Some of the values
of the terms for specific types of development are given in
Table 1 below.
The runoff equation only estimates the volume of water
that will run over the land instead of being absorbed by it
given a certain level of development. The impact of the develop-
ment really has to be expressed as the difference between the
amount of runoff that would occur were the development not to
take place and if it does take place. Furthermore, the runoff
equation does not give the amount of water pollutants carried
s
by the water runoff. A number of studies have been done to
measure the pollutant content of runoff, and coefficients can be
developed from those studies done in areas analogous to the area
to be studied.
These potential pollutant figures could be transformed
into actual figures using the degree of treatment generally accomp-
lished for storm water runoff.
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49
Table 1
Estimated Values for Terms in the Runoff Equation9
C (Coefficient of Runoff)
Residential Development C
10 families per acre 0.3-0.5
40 families per acre 0.5-0.7
More than 40 families 0.7-0.9
per acre
Commercial Development0 0.9
Open areas
"Macadam, compacted earth
and gravel, without plant
growth. 0.7
Impervious soil, with plant
cover 0.5
Lawns and- planted areas, with
normal soil 0.2
Woods " 0.1
I (Intensity of rainfall)
I = K/t+b, where
I = rainfall in inches per hour
t = average duration of storrrs in minutes
K and b are coefficients assuming the following values:
aSource: K. Lynch, 1962, p. 173-175.
Includes impervious areas, lawns, etc.
°Assumes that commercial development is entirely impervious
and has a zero slope. Includes parking lots, access roads, and
roofs.
^Assumes a zero slope. For slopes, coefficient should be
increased.
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50
Residential development in New Jersey (based on 5 and
10 year storms which Lynch recommends for these areas):
K = 131-170
b = 17-19
Commercial development (based on 25- and 50 year storms
which Lynch recommends for these areas):
K = 230-250
b = 24-27
A (Drainage Area)
Drainage areas can be identified and measured from
a U.S.G.S. topographic map, or from engineering studies if these
are available.
The amount of acreage and impervious surface generated
by residential development that results from population growth has
to be determined before the runoff equation can be applied to
estimate indirect water pollution. Acreage car. be estimated
from zoning maps or from coefficients of space use per housing
unit by housing type. Exten-1- of impervious surface for the area
has to be estimated by population density and housing type.
In order to apply the runoff equation to commercial
development, the extent of commercial development in acreage
that will accompany population growth has to be estimated. This
can be estimated in the following ways:
-------
(1) From zoning maps: commercially zoned land can be
measured.
(2) From modifications of coefficients such as the following
for shopping centers:
Population Served Selling Area
Neighborhood Center (10,000) 40,000 sq ft.
Community Center (20,000 - 100,000) 100,000-300,000 sq. ft.
Regional Center 50, 75 or 125 acres
(depend on amount of
expansion planned)
Source: Lynch, 1962, p. 327-8.
Once acreage is determined, the amount of impervious
surface has to be estimated in order to obtain a value of C
for the runoff equation. It must be remembered that the degree
of impervious surface for commercial developments depends upon
the layout of the development.
Another indirect form of pollution from residential,
commercial or any other kind j, f development is the loss of soil
or erosion caused by the action of water on land exposed during
construction, or whose capacity to resist water movement has
been reduced because of changes in the slope or cover of the land,
The amount of soil lost per acre of exposed land during a given
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52
storm period can be estimated from an equation, called the
Universal Soil Loss Equation, originally developed by the
Agricultural Research Service for erosion from agricultural
activity. The general form of the equation is as follows:
Soil loss =RxKxSLxC
where,
R = rainfall intensity or the average annual rainfall index for
the area.
K = a scale factor for soil erodability
SL = slope length and the angle of the slope
C = soil cover
Details on the precise application of the equation can
be obtained from the U.S. Department of Agric .Iture's Soil
Conservation Service.
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53
References and Selected Material
for Further Reading
1. Robert U. Ayres and Allen V Kneese.. "Pollution and Environ-
mental Quality," in Harvey S. Perloff, ed. The Quality
of the Urban Environment (Baltimore: Johns-Hopkins,
1969), pp. 35-71.
2. Battelle Pacific Northwest Laboratories. A Technique for Environ-
mental Decision Making Using Quantified Social and Aes-
thetic Values. Richland, Washington: Battelle, Feb. 1974.
3. Besselievre, Edmund B. Industrial Waste Treatment. New York:
McGraw Hill Book Company, Inc., 1952.
4. Beyer, Jacqueline. Water Quality and Value of Homesites on the
Rockaway River. New Brunswick, New Jersey: New Jersey
Water Resources Institute, Rutgers University, 1969
5. Brown, H. James. Empirical Models of Urban Land Use. New York:
National Bureau of Economic Research, 1972.
6. Burby, Raymond. Household Decision Processes in the Purchase
and Use of Reservoir Recreation Land. Chapel Hill, N. C.:
Water Resources Research Institute, Univ. of N. Carolina.1971,
7. David, Elizabeth and William B. Lord. Determinants of Property
Value on Artificial Lakes. Madison, Wisconsin: Depart-
ment of Agricultural Economics, Univ. of Wisconsin, May,1969.
8. Dornbusch, David M. and Stephen M. Barrager. Benefit of Water
Pollution Control on Property Values, Washington, D.C.
GPA, October, 1973.
9. -Eckenfelder, W. Wesley, Jr. Industrial Waste Pollution Control.
New York: McGraw Hill Book Co. 1966.
10. Fair, Gordon M.; Geyer, John C.; and Okun, Daniel A. Water and
Wastewater Engineering, Vol. 2 Water Purification and
Wastewater Treatment and Disposal. New York: John Wiley,
1968.
11. Faro, R. and N.L. Nemerow. Measurement of Benefits of Water
Pollution Control. Syracuse, New York: Syracuse Univer-
sity Department of Civil Engineering.
12. Federal Water Pollution Control Administration. The Cost of
Clean Water. Vol. Ill: Industrial Waste Profiles.
Washington, D.C.: U.S. Gov't. Printing Office, 1967.
-------
13. Greenberg, Michael R. and Zimmerman, Rae. "Estimating Industrial
Water Pollution in Small Regions." Journal WPCF, 45
(March, 1973), 462-469.
14. Gurnham, C. Fred. Industrial Wastewater Control. New York and
London: Academic Press, 1965.
15. International City Management Association. Municipal Yearbook 1974
Washington, D.C: The Association, 1974.
16. Isard, Walter, et al. Ecologic-Economic Analysis for Regional
for Regional Development. New York: The Free Press, 1972.
17. Kaiser, Edward J., et al. Promoting Environmental Quality Through
Urban Planning and Controls. Washington, D.C.: U.S.
GPO, February, 1974.
18. Kusler, Jon A. "Artificial Lakes and Land Subdivisions."
Wisconsin Law Review: 369-448. 1971
19. Lynch, Kevin. Site Planning. Cambridge, Mass.: MIT Press, 1962.
20. Nemerow, Nelson L. Liquid Waste of Industry. Theories, Practices,
and Treatment. Reading, Mass.: Addison-Wesley Pub., 1971.
21. Powers, Thomas J. Ill: Sacks, Bernard R.; and Holdaway, James L.
National Industrial Wastewater Assessment Manufacturing
Year 1963. Cincinnati, Ohio: Federal Water Pollution
Control Administration, 1967.
22. Ragatz, R.L. Associates, Inc. Recreational Properties.
Eugene, Oregon, May, 1974.
23. Regional Plan Association. Waste Management. New York: Regional
Plan Association, March, 1968.
24. Reiling, S.D.; K.C. Gibbs and H.H. Stoevener. Economic Benefits
from an Improvement in Water Quality. Washington, D.C.:
U.S. GPO. January, 1973.
25. Rudolfs, Willem. Industrial Wastes. Library of Engineering
Classics. New York: Rheingold Pub. Co. 1953.
26. Seneca, J.J.; P. Davidson and F.G. Adams. "An Analysis of
Recreational Use of the TVA Lakes," Land Economics
44:529-534. Nov. 1968.
-------
27. Smith, E.T. and Braster, R.E., "Mathematical Models for .Regional
Economic and Waste _,oad Projections." Proceedings 18th
Annual Technical Meeting of the Institute of Environmental
Sciences, New York: May 1-4, 1972.
28. Steiker, Gene. Solid Waste Generation Coefficients.: Manufacturing
Sectors. Philadelphia, Pa.: Regional Science Research
Institute, December, 1973.
29. Tombaugh, Larry W. "Factors Influencing Vacation Home Locations,"
J.Leisure Research, 2:54-63. Winter, 1970.
30. U.S. EPA. Compilation of Air Pollutant Emission Factors. Research
Triangle Park, N.C.: U.S. EPA, April, 1973.
31. Williams, Edward R. and House, Peter W. The State of the System
(SOS) Model: Measuring .Growth Limitations Using Ecological
Concepts. Washington, B.C.: U.S. GPO, February, 1974.
32. Williams, B.C., Jr. and Bonnie L. Baniel. The Impact of
Reservoirs on Land Values: A Case Study. Water Reseources
Research Institute, Missisippi State University, 1969.
33. Zimmerman, R. "Hydrologic Modifications Associated with the Vaca-
tion Home Market." Presented at the Tenth American Water
Resources Association Conference, Puerto Rico, Nov. 20-22,
1974.
34. Zimmerman, R. "Industrial Wastewater Coefficients (SIC)
and Water Management." Proceedings of the 28th Purdue
Industrial Waste Conference. May, 1973.
-------
APPENDICES
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A-l
Appendix A
POPULATION PROJECTION METHODOLOGIES
The purpose of this section is to present background
information for developing criteria for obtaining and critically
evaluating population estimates and projections, which are one
of the bases for designing sewage treatment plants, estimating
their social impact, and describing service area population in
accordance with EPA's interim regulations for the preparation
of environmental impact statements. (38 FR 1696, Subpart C,
Article 6, 32 (a)). Population projections for a given area
from several different sources often do not agree, since they
are based on different assumptions so that such estimates often
have to be altered or developed anew, after they have been
evaluated. Literature citations for more extensive work in this
area are also given.
Estimates of the magnitude of future population are vital
to the investment in facilities whose capacities are designed to
meet the future needs of the population. In its most general
form, population in a given time period is a function of a
number of demographic features: the number of births in that time
period plus the number of migrants into that area minus the
number of deaths and the number of emigrants out of the area.
The projection of future population has to be made from a pro-
jection of each of these factors simultaneously. This can be
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A-2
done either directly from trends in these primary parameters
or from other parameters than can be shown to be determinants
of these primary parameters. Before going into a description
of alternative projection methodologies a discussion of the basic
components of population change will be given.
Births
Births are primarily a function of the fertility rate
of the frmale population of child bearing age. Data for develop-
ing this rate for selected local jurisdictions are found in
the U.S. Bureau of the Census' Census of Population or from
state and local health department statistics. The Census
of Population is issued every ten years, and generally gives
a distribution of births per 1000 women by age. It is this
fertility rate parameter that is generally the largest source
of error in population projections, since it fluctuates widely
over time. Errors in this parameter accumulate over time.
In the 1940's, 50's and 60's population projections
consistently underestimated population, while current pro-
jections tend to overestimate population. These discrepancies
are largely attributable to the unpredictability of the
fertility factor, which factor, which after a large increasing
trend over the last few decades has begun to decline.
Accompanying an analysis of fertility rates are parallel
analyses of marriage rates and trends in household size and
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A-3
composition. Again these factors are distributed by age.
Deaths:
Like births, death rates are given by age group, and
often as a survival rate for a certain number of births. Survival
rates apparently do not vary as much as fertility rates over time,
however, they do vary regionally, as does the variation in age
groups (Masser, 1972, p. 23).
Migration:
Partial data on migration exist in the Census of Popula-
tion in terms of defining for individuals and households the
place of last residence down to the state and county levels.
Migration to and from a state or among regions or localities
within a state depends upon many socioeconomic factors such as
economic or job opportunities, personal factors such as cultural
tradition, place of birth, location of relatives and friends,
physical features of the area with respect to aesthetics,
accessibility to urban and, more recently, recreational amenities,
cost of living, and the amount of space tha a household can
purchase for a given level of income. In addition, for a given
household or individual, the type of factor influencing migration
will vary depending upon the stage the individual or household
is at in the life cycle.
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A-4
The migration factor in a given area has to be put
in the context of the larger region of which it is a part.
Migration flows between regions can be expressed in a matrix
form, i.e., as an accounting system for all population move-
ments. Migration data, of all the data inputs into population
projections is the least available. Like births and deaths,
migration can be expressed in terms of rates for some unit
of population.
One source of migration data found in the Census is
school enrollment. Alternatively, migration can be estimated
via probabilistic and simulation models which treat migration
as a random process. Linear programming models have been used
to estimate migration by optimizing accessibility and other
amenity factors subject to constraints tending to inhibit
migration such as ethnic composition, density limits set by
zoning ordinances, land capacities, etc.
Alternative Population Projection Methods
Models for projecting population vary in complexity by
the extent to which they disaggregate the basic demographic
factors outlined above into causative factors and by the extent
to which they disaggregate projections spatially. Only basic
model formats will be presented here but the user should be
aware that variations on each of the basic models exist. Models
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A-5
based on Census data while having the advantage of having a uni-
formly collected data base suffer from the problems and limi-
tations of census data outlined in the introductions to the census
materials, such as the extent to which the sampled population
reflects the whole population, comparability of collection methods
from year to year, etc. Many of the models discussed below are
often not applied directly and are usually altered via the
subjective decisions of the modeler about the specific characteris-
tics of the area Like zoning and land capacity constraints.
1. Trend Analysis
Past population trends used to project future trends
either by interpolation or extrapolation are expressed in a
linear, geometric or logarithmic form. The linear form is:
Pt = a (t - t0) + PtQ
and the geometric form is:
P =Pt e^V
t ro
where P+. is population at time t and P is popula tion at a
r o
future time period t .
The technique is at best only good for very short
periods of time and for very large areas. The assumptions and
limitations of this method are:
a) It is assumed that the mathematical equation reflects
the actual behavior of the population over time. There
is no empirical basis for this belief.
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A-6
b) It glosses over all of the components of population
change such as fertility rates, survival rates and
migration discussed above.
2. "Step down" or ratio techniques.
This approach is generally used to allocate population
projections made for'a larger area down to subareas within that
larger area. Methods are analogous to shift-share techniques
developed for projecting employment. The major assumption of this
technique is that in allocating growth proportionally to the
existing population size of the subareas one is assuming tha1"
local and regional growth patterns are the same, however, this
assumption becomes weaker the smaller the subarea is in size.
3. Regression techniques_
This technique is one of the most widely used in combina-
tion with other techniques by government agencies. In this method
relationships between population change and changes in a whole
range of factors is assumed to exist and each factor is expressed
as a term in the regression equation. Such parameters include
voter registration, tax returns, telephones registered, water
meters, births, deaths, bank deposits, and school enrollment
In the multivariate regression technique, those factors are
chosen for predicting future population that give the best fit
for past population change. The technique is highly adaptable
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A-7
to differences in the kind of data available from place to
place. The general form of the equation is:
dP = aQ + aLdX, -f- + nndxn
where P is population projected and the X's are the variables
discussed above. The coefficients, a^ ,have been established via
multivariate analyses using existing data. This general equation
can be broken down by age group or any other socioeconomic
characteristic of the population into a series of equations.
The assumptions of the regression model are:
a. The coefficients, a., in the regression equation remain
constant from the present to the projected time period.
b. The form of the regression equation is stable over time.
c. The linearity of the equation reflects the behavior of
the population.
4. Cohort Survival
This method estimates the population that will appear
in subsequent age groups given the survival rates and fertility
rates of the population broken down by age group. A general
formulation for this model is:
AP. + M = P
T:Q r
where A is the matrix of fertility and survival rates at tQ distribu-
ted by age, Pt is the distribution of population by age at to and
M is the distribution of net migration by age group.
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A-8
Extensions- of this model include projecting population
for several regions simultaneously that have the same birth and
survival rates. The model can be further complicated by dis-
aggregating figures by other population characteristics such
as sex and marital status. Assumptions and limitations of the
model are:
a. The fertility and survival rates are assumed constant
in the base period and the projected time period.
b. Even though disaggregations have been made for some
parameters disaggregation of the model for other parameters
such as race education, income, birth control and abortion
practices, etc. which studies have shown relate to fertility are
generally not taken into account. This reduces the accuracy
of the model when applied to populations that are heterogenous with
respect to these variables.
5. Zoned capacity estimates
These are estimates of future population based upon
known zoning and development plans. Development plans are
often quantified by means of a tabulation of building permits.
Population is estimated by making assumptions about the mix
of housing types planned and density within housing units.
The major assumption or limitation of this technique
is that development plans tend to overestimate the actual amount-
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A-9
developed, since they simply estimate the maximum number of
housing units a given site can accommodate of a given housing
type assuming current zoning densities. Zoning laws can change,
economic conditions can change including housing demand and
cost, tending to change estimates.
In conclusion, each of these population projection
techniques should be applied to a given area, and all estimates
should be averaged with some estimates weighted more heavily if
there is reason to believe they have greater accuracy for a
given situation. In support of this approach, Morrison points
out that after selectively testing a number of models "no single
method of estimating local population shows consistently
greater accuracy," that "evidence consistently shows that
lower average error can be attained by averaging together
estimates made by different methods," "average error tends to be
lower for counties whose populations are large or metropolitan,"
and "average error varies with rates of population growth."
(Morrison, p. 26, 27).
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A-10
Sources of Population Projections
New Jersey and New York
1. State of New Jersey
Department of Community Affairs
Trenton, N.J.
Population estimates and projections are prepared for
selected areas: statewide, county and municipal. The technique
generally employed for population estimates is the zoned capacity
method. This can be thought of as a miximum population estimate,
since it assumes that all areas planned and zoned for residential
development will proceed as planned.
2. Universities and private organizations
Several universities in and around New Jersey have and
are currently developing computerized packages for population
projections. In universities these are generally being done
in departments of urban planning, demography, geography, and
economics.
Technical assistance and software are available from
the following sources:
Professor Willard Hansen (Cohort-Survival)
Graduate School of Public Administration
New York University
738. Tisch Hall
New York, N.Y. 10003
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A-ll
Greenberg, Michael R.; Krueckeberg, Donald A.. Mautner, Richard.
Long Range Population Projections for Minor Civil
Divisions: Computer Programs and User'.s Manual.
New Brunswick, NJ: Rutgers University, Center for
Urban Policy Research, May 1973.
3. U.S. Water Resources Council, 1972 Obers Projections.
The scope of projections are by economic area devised
by the Bureau of Economic Analysis, by water resources region
and subregion delineated by the Water Resources Council in 1970,
and by State. Thus, the finest breakdown for New Jersey
would be the water resources subareas that approximately follow
county boundaries. Projections are made at 10 year intervals
to 2020.
4. U.S. Bureau of the Census (Cohort-survival)
U.S. Bureau of the Census. Preliminary Projections of the
Population of the States: 1975-1990. Washington, B.C.:
Government Printing Office, March 1972 (Current
Population Reports, Series P-25. no. 493.)
5. Tri-State Regional Planning Commission, New York.
Data on population and projections for counties in
the. tri-state region. New Jersey Counties include all of those
in Northeastern NJ.
6. Regional Plan Association, New York
County level projections.
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A-12
7. Utilities
Telephone and electric companies often have projections
broken down.by service area.
8. County Planning Boards.
If population figures are not available for boundaries
of alternative service areas under consideration, several
approaches can be taken to remedy this deficiency:
1. Take the projections of the next smallest area, and
break down the remaining areas by census tract and use tract
data or request unpublished data from the census bureau to
make a separate projection for these areas. These projections
can then be added to the major projection. Projections can be
done using trend data accompanied by judgements about projected
land use and zoning.
2. Compare projected population rates in the smaller and
the larger area and approximate the desired area's population
as an intermediate.
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A-13
References and Selected Material
for Further Reading
1. Greenberg, Michael R. ; Krueckeberg, Donald A; Mautne'r,
Richard. Long Range Population Projections fo'_- Minor
Civil Divisions: Computer Programs and User's Manual.
New Brunswick, NJ: Rutgers University, Center for Urban
Policy Research, May, 1973.
2. Isard, Walter.' Methods of Regional Analysis: an Introduction
to Regional Science. New York: John Wiley, 1960. Chap. 2,
3. Masser, Ian. Analytical Models for Urban and Regional Plan-
ning. New York, John Wiley, 1972. Chapter 2.
4. Morrison, Peter A. Demographic Information for Cities: A
Manual for Estimating and Projecting Local Population
Characteristics. Santa Monica, Calif..: Rand Corp.,
June, 1971.
5. Newling, Bruce. Population Projections for New Jersey to
2000. New York, N.Y.: City College, 1968.
6. U.S. Bureau of the Census. Handbook of Statistical Methods
for Demographers,, by A.J. Jaffe. Washington, D.C.:
Government Printing Office, 1951.
7. U.S. Bureau of the Census. Preliminary Projections of the
Population of the States: 1975-1990. Washington, D.C.:
Government Printing Office, March 1972. (Current
Population Reports, Series P-25, no. 493).
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B-l
Appendix B
INDUSTRIAL AND ANCILLARY DEVELOPMENT
PROJECTION METHODOLOGIES
Economic activity can be expressed in terms of employ-
ment, number of establishments, value added and other parameters.
Employment tends to be the most useful parameter for the purposes
of estimating economic impacts, and the most readily available
parameter. it is also useful for projecting secondary environ-
mental impacts from industry, since pollution coefficients
generally expressed in terms of weight of pollutants per unit
of production can be translated into weight of pollutants
per employee by making assumptions about labor intensity by
industry. Production would be more suitable for all purposes,
but this information is rarely available.
Industrial development is primarily manufacturing
activity. Ancillary development is distinct from industrial
development in that it consists generally of population growth
dependent activity primarily represented by the commercial and
service sectors. Different methods for determining economic
projections for these two types of activity have not been
consistently demonstrated. No one method of projection is best
for all areas and for all types of industry, and it is best to
run projections by each of the different methods and test each
one. This will not pose any difficulties if the procedures are
computerized, since the different methods rely on roughly the same
data base.
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B-2
Alternative Employment Projection Methodologies *
1. Constant share method: population constant share
_ rto
Eirt ~ : • Eint
Pnt0
This method implies that industrial employment in the region
is a constant share of industrial employment in a benchmark
economy, usually the nation. The constant share is the ratio
of population in the region to population in the benchmark
economy.
2. Constant share method: employment constant share
Eirt0
Eirt = — ' Eint
Eint0
This formulation is similar to the one above except that
instead of the population ratio, the employment ratio is used.
The formulation can be refined by introducing population
relationships and trends into it, generating the population
weighted employment constant share method (Greenberg, 1970):
P / P
irtr, _ rt / rt0
0
TT - U E
Eirt ~ ' int
Eint0 Pnt / Pnt0
While this is an apparent improvement in that it takes into
amount a number of trends which may be relevant to industrial
growth, the error in these projections can be larger.
*See pg".B-6at end of section for explanation of terms in equations
-------
B-3
3. Fixed ratio of employment to population (Hellman and Marcus,
1970; p. 92)
Eirt = • Prt - Eirt
Here the ratio of employment to population in the region
during the base period is assumed constant. This is the only
method that can be used at the present time to project employment
beyond time t, since it is the only formulation that would not
have to make use of a benchmark economy employment figure at
time periods beyond t which may not be available.
4. Shift-share method - This is one of the better methods for
projecting non-labor oriented industry.
The formulation of the shift-share technique is:
E.
— f~*i i
Eirt = — • Eint +
E.
int
o
E*
'irt,.
E
int
Jint
The shift-share expression defines the growth of the region
as a function of two factors: the proportion of region growth
accounted for by the benchmark economy's growth, and the shift
in the relationship between region and benchmark economy
employment over time. This version of the shift-share method
-------
B-4
is called the implicit shift share method, because the factors
causing employment to change are not made explicit, that is,
future employment is a function of past changes in employment
not the explicit factors causing employment to change. The
formulation of shift-share given above represents an important
deviation, and, the author feels, a refinement of the usual
formulation. The ratio E£rt can be obtained by projecting
by means of polynomial projection techniques the values of
E- /E-j_n for roughly annual increments available from County
Business Patterns. Usually just two points in time are taken
for this part of the expression, since this is all the data usually
available in the Census of Manufactures. For a detailed discussion
of this refinement see Zimmerman (1974a).
Note that it may appear tautological at first that
E- . appears on both sides of the equation. However, its
appearance on the right side has been marked by an asterisk (*)
to denote that different methods have been used to obtain this
value and the final value on the left hand side.
The shift-share technique was originally developed
historically to discern historical trends and differences in
employment among regions (Ashby, 1965; Perloff, et al. , 1959).
Recently the shift-share technique has been used as a projection
technique with varying degrees of success, and its use is a
-------
B-5
subject of much debate in the literature. in a projection of
employment statewide in New Jersey, Hellman and Marcus (1970)
found that for local market oriented industry* a version of
shift-share requiring information about location factors called
explicit shift-share worked well, while implicit shift-share
did not work as well. In a projection of employment in the
New York Metropolitan Region, Greenberg (1970) found that
implicit shift-share did not work as well as other models. James
and Hughes (1973), however, after testing several different
models, use implicit shift-share in an exponential form to
project employment in New Jersey at the state and county levels.
The Obers projections by the U.S. Water Resources Council use
implicit shift-share for the projection of supply oriented
industry** exclusively.
*Local market oriented industry is industry producing for
local consumption, and is generally characterized by values of
the ratio: being around 1.0.
**Supply oriented industry is'industry producing either too
much for the population, i.e., for export, or too little for the
population so that importation of goods in that industry is needed.
The values of the above ratio for this type of industry are generally
much less than or much greater than 1.0.
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B-6
Explanation of Terms and Sources of Data
Symbol
rt,
nt,
rt
nt
Jirt
Jint
E.
irt
o
Eint,
Explanation
Base time period.
Time period for projection.
Population of the region r.
at t
Source of Data*
o •
Population of benchmark
economy n, at t .
Population of the region r,
at t.
Population of the benchmark
economy n, at t.
Employment in industry i,
in region r, at t.
Employment in industry, i,
in benchmark economy, n at t.
Employment in industry i,
in region r, at t .
Employment in industry i/
in benchmark economy n,
at t^.
U.S. Bureau of the Census
Census of Population (1970)
See section on Population
Projection.
U.S. Water Resources
Council, 1972. Obers
Projections. Vol. 5, p. 128
To be projected.
State of N.J. Dept. of
Labor and Industry-
U.S. Bureau of the Census/
County Business Patterns
or Census of Manufactures
U.S. Bureau of the Census
County Business Patterns
or- Census of Manufactures
*See bibliography for detailed citations.
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B-7
Choice of Employment Projection Methojfl
The alternative employment projection model formulations
and some discussion of their use has been given above. As
mentioned above, the various methods have been used in the
literature with varying degrees of success. Each of these
methods can be tested by means of a test statistic, U, developed
by Theil (1965). This statistic has the following formulation:
U =
where Pj_ is the projected employment value for industry i and
A.; is the actual employment value for industry i at some
known time period. The lower the value of U, the lower the
error is. A value of 0 indicates no error in projection,
and a value of 1 indicates that the magnitude of the error
equals the magnitude of each employment figure.
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B-8
Classification of Industry Relative to Markets
and implications for Choice of Employment
Projection Techniques
Industries can be classified according to the kinds
of market they serve: (1) industry producing for local consump-
tion and (2) industry producing either too much for the
population, i.e., for export, or too little for the population
so that importation of goods in that industry is needed. Indus-
try producing for either export or import is called supply, orien-
ted while industry producing for local consumption is called local
market oriented. The method used for classifying industry into
these two categories is described in detail below.
The classification of industry into local and supply
categories is often used as a criteria for using various projection
techniques, since different categories of industries distinguished
on the basis of the markets they serve may behave differently,
and thus imply different methods of projecting their future
employment in a given region. However, not enough consistency
has been found after testing these estimates to warrant these
generalizations.
There are several ways of classifying industry accord-
ing to its marketing characteristics, i.e., into local market
oriented and supply (export, import) oriented industry. These
methods include the population quotient, the location quotient,
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B-9
and several other more complicated techniques involving inter-
viewing.
(1) The population quotient
PQi =
Eir / Pr
E- / P
in / n
For each industry under consideration, the population quotient
simply compares the ratio of employment in that industry in
the county and the state to the ratio of population in the
county and in the state (Hoyt, 1944).
(2) The location quotient
E.
LQi = X
Ein / En
This expression simply classifies industry according to the
comparison of the share of county employment the industry
accounts for with the share of state employment it accounts
for.
The location quotient is more deterministic than the
population quotient, since it implies that a certain number of
industries must be supply oriented and a certain number must.
be local oriented. Also, by not including population into
the location quotient, the market is never really accounted for.
Because of these limitations of the location quotient, the
population quotient should be used to classify industry. For
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B-10
illustration the quotients and industrial classification for
industry in Ocean County, N.J. calculated for the Ocean
County Environmental Impact statement are given in Appendix
Table c~l-
Given the choice of the population ratio as the means
for classifying industry, criteria have to be developed in the
form of cutoff points or va.lues of the ratio that would define
each type of industry. Ideally, the population ratio of a local
market oriented industry should equal 1, i.e., its share of the
benchmark economy's employment should equal its share of the
benchmark economy's population. However, because of market
imperfections, variations in the intensity of labor and its
relationship to industrial output, a range around 1 is often
chosen as the criteria. Ideally, the range should be established
empirically, however, for expediency values used in the litera-
ture are applied instead. Hellman and Marcus in projections
of employment at the state level in New Jersey used 0.85 to
1.64 as the range for the population quotient defining local
market oriented industry, while Greenberg (1970) used 0.8
to 1.64 for the New York Metropolitan Region, later refining il;
to 0.8 to 1.426. The population quotients of industries in Ocean
County and their classification as supply or local market oriented
using a criteria of 0.8 to 1.64 for local oriented industry
is given in Appendix Table C-l.
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B-ll
Both methods of classification before being used to
choose projection methods should meet certain conditions that
were pointed out by Mattila and Thompson (1955), including
the stability of the coefficients over time, and the extent
to which the benchmark economy approximates a closed economy and
has consumption patterns similar to those of the region.
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B-12
Limitations of the Projection Methodology
1. Industrial location is actually a function of a large
number of factors which empirical studies are gradually compil-
ing. The projection techniques used above take a "black box"
approach - given historical changes and not knowing the causes
of change - future changes are predicted on the basis of histori-
cal trends and relationships between the region and the larger
economy within which it is located. When more factors become
isolated and tested, regression techniques can be employed as
an alternative method of employment projection. Harris and
McGuire (1969) and Burrows and Metcalf (1971) have already
developed regression formats for county level employment pro-
jections .
As an improvement of the projection techniques described here,
regression techniques combined with input-output techniques could be
employed to estimate the extent to which new industry would be
drawn into an area on the basis of wanting to be near industry
already in the area as a source of inputs or as a market for
outputs.
2. All of the methods assume constancy of employment ratios
over time, or that historical ratios will hold in the future.
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B-13
Sources of Employment Figures and
Employment Projections
Employment Data:
There are several major data sources for county and state
employment figures in New Jersey:
1. Original surveys done by the New Jersey State Department
of Labor and Insutry published in the annual NJ Covered
H
Employment Trends.
i
2. Bureau of Labor Statistics (coordinated with the NJ State
Department of Labor and Industry).
3. U.S. Department of Commerce economic censuses (Census of
Manufactures, etc.).
4. U.S. Department of Commerce County Business Patterns
Reasons for the choice among the various data bases by
researchers is never made explicit.
The major advantage of the County Business Patterns
data (which is based on figures collected by the Social Security
Administration) is its annual coverage of employment figures al-
lowing for finer employment projections. The Department of
Commerce's economic censuses have in the past only been published
in 1963 and 1967, while between 1962 and 1972, County Business
Patterns gives annual figures except for 1963. The County
Business Patterns will in some cases be more susceptible to
-------
B-14
disclosure problems than the economic censuses, however, cor-
rections for these categories can be (and have been) made by
making use of the distribution of establishment by employment
size categories or using supplementary data from the NJ Covered
Employment Trends. The economic census, in particular the
Census of Manufactures does not give as fine a breakdown for
County employment as the County Business Patterns
does, giving only number of establishments by several employment
categories for all two digit industries, and in all cases one
would have to make some assumption about where in a particular
category the firms lie, while using GBP this only has to be
done in a few cases.
A comparison of County Business Patterns data for Ocean
County with data available from the economic censuses for the
same categories is given in Appendix Tables C-2 and C-3 for illus-
tration. In practically every instance except in some of the
service categories, figures given by the economic censuses are
greater than those from the CBP.
The major disadvantage of the County Business Patterns
coverage of employment is that it excludes self-employed workers
which accounts fro about 10% of the work force in the U.S.
-------
B-15
Included among workers not covered by social security
and hence not covered by CBP, affecting selected categories
are domestic workers, railroad workers on interstate railroads,
and farm workers probably affecting the services category
(particularly (SIC 72), the transportation category, and
agriculture respectively. Government workers are not included
by these form a separate category outside of CBP coverage.
The comparability between the economic censuses and CBP
with respect to the classification of industry is discussed
in the introduction to each CBP volume. The basic difference,
mainly affecting the comparability of the retailing sector, is
that where the economic census classifies establishments
located within larger establishments as employment in the
larger establishment only, CBP will tabulate the smaller
establishment and its employment separately. Another minor
variation is that CBP excludes government owned liquor stores
which would affect SIC 59.
The time period at which CBP collects employment data
is in mid-March. There are seasonal variations in employment, in
some industries, which would make the March figure low for some
industries and high for others. For instance, many retailing
establishments open up just before Christmas and close just
after Christmas; eating and drinking places, resorts and amuse-
-------
B-16
ment parks probably have a higher employment in the summer
months. This seasonality factor would affect all existing sources
of employment data equally, since the raw data does not account
for seasonality.
Employment Projections:
Several projections of employment exist for the State
of New Jersey and its counties. They vary from one another in
geographic or temporal scope or in the number and level of
industrial categories projected.
A-t the state level, four projections currently are
known to exist, listed below along with a description of their
scope:
1. U.S. Water Resources Council Obers Projections for NJ (Vol. 5,
1973)
Projects dollar output at ten year intervals from 1970
through the year 2020 for selected two-digit categories. Coverage
at the two digit level is not as extensive as other projections,
and projection by dollar output is difficult to transform into
employment. While Obers does give indices of production for
1980 from which employment figures could be computed, these
are given for an even less extensive group of two digit categories
than the projections by dollar output.
-------
B-17
2. Hellman and Marcus (1970)
Projections are given at the three digit industrial
SIC level, but for manufacturing only, and only through 1975.
Their work is important more for methodology than for projection
results.
3. James and Hughes (1973)
Projections are given at the two digit SIC level,
through the year 1980, but only for manufacturing.
4. NJ State Department of Labor and Industry
The most extensive set of projections given for two-
and three-digit categories through 1980.
Two sources of county level projections are:
1. James and Hughes (1973)
Projections are made at the two digit SIC level through
1980 for manufacturing only, though projections are also made
for the broad categoxies of wholesaling, retailing, services,
etc.
2. NJ State Department of Labor and Industry
Projections made through 1980 not at the two digit level,
but rather for selected number of gross categories only. These
figures were arrived at as residuals after the State computed
employment for major labor markets in the State.
-------
B-18
References and Selected Material
For Further Reading
Ashby, Lowell D. Growth Patterns in Employment by County 1940-
1950 and 1950-1960. Washington,,D.C.: uTs . Dept. "of
Commerce, Office of Business Economics, 1965.
Greenberg, Michael R. "A Test of Alternative Models for Projecting
County Industrial Production at the 2,3, and 4-digit
Standard Industrial Code Levels." Regional and Urban
Economics, 1 (Feb., 1972), 397-417.
Hellman, Daryl and Marcus, Matityahu. A Critical Analysis of
Employment Projection Methods: A Test Case of New Jersey.
New Brunswick, NJ: NJ Water Resources Research Institute.
Rutgers, May, 1970.
Hoyt, J. In: The Economic Status of the New York Metropolitan
Region. New York: Regional Plan Association, 1944.
James, Franklin J. and Hughes, James W. Modeling State Growth:
New Jersey 1980. New Brunswick, NJ: Center for Urban
Policy Research, Rutgers U., 1973.
. "A Test of Shift and Share Analysis as a Predictive
Device." Journal of Regional Science, 13 (August, 1973)
223-231.
Mattila, John M. and Thompson, Wilbur R. "The Measurement of the
Economic Base of the Metropolitan Area." Land Economics,
31 (August, 1955), 215-228.
Milliman, J.W. "Large-Scale Models for Forecasting Regional
Economic Activity: A Survey." In Essays in Regional
Economics. Edited by John F. Kain and John R. Meyer.
Cambridge, Mass: Harvard University Press, 1971.
Mincer, J. and Zarnowitz. "The Evaluation of Economic Forecasts."
In Economic Forecasts and Expectations: Analysis of
Forecasting Behavior and Performance. Edited by J
Mincer. New York: National Bureau of Economic Research,
1969.
Perloff, Harvey S.; Dunn, Edgar S. Jr.; Larnpard, Eric E. ; and
Muth, Richard F. Regions Resources and Economic Growth.
Lincoln/ Nebraska: U. of Nebraska Press, 1960.
-------
B-19
State of New Jersey. Department of Community Affairs. Division
of Local Government Services. Statements of Financial
Condition of Counties and Municipalities.
Report. Trenton, NJ, 1972.
1971 Annual
State of New Jersey. Department of Labor and Industry. Division
of Planning and Research. Bureau of Operational Statis-
tics and Reports. NJ Covered Employment Trends. Trenton,
NJ. Published annually.
Theil, H. Applied Economic Forecasting.
Publishing Co., 1966.
Chicago: North Holland
U.S. Bureau of the Census. Census of Population: 1960. Vol. I:
Characteristics of the Population, Part 32, New Jersey.
Washington, D.C.: U.S. Government Printing Office, 1963.
U.S. Bureau of the Census. Census of Population: 1970. "General
Social and Economic Characteristics." Final Report PC
(1) - C32 New Jersey., Washington, D.C.: U.S. Government
Printing Office, 1972'.
U.S. Bureau of the Census. County Business Patterns, 1962-1972.
New Jersey. CBP -62-32 through
CBP-72-32. Washington, D.C.: U.S. Government Printing
Office, 1963-1973.
U.S. Bureau of Labor Statistics. Patterns of U.S. Economic
Growth. Bulletin 1672. Washington, D.C.: U.S. Govern-
ment Printing Office, 1970.
U.S. Water Resources Council, 1972 Obers Projections. Regional
Economic Activity in the U.S. Vol. 1: Concepts, Methodo-
j.ogy and Summary Data.^ Washington, D.C.: U.S. GPO, 1972,
U.S. Water Resources Council, 1972 Obers Projections. Regional
Economic Activity in the U.S. Vol. 5: States. Washington,
D.C.: U.S. GPO, 1972.
-------
APPENDIX C
Detailed Tables
-------
TABLE C-l: STABILITY OF POPULATION QUOTIENTS OV=R TIME, 1960-62 and 1970-723
Ocean County, New Jersey
rpn^ p 1 — — —
.. Agriculture, for . ,fish.
07 Agric. Serv. & Hunting
09 Fisheries
.. Mining
. „ Contract Construction
15 General Building
l6 Heavy Construction
17 Special Trade
.. Manufacturing
20 Food & Kindred Prod.
23 Apparel & Other Textiles
24 Lumber & Wood Products
25 Furniture & Fixtures
27 Publishing & Printing
28 Chemicals & Allied Prod.
32 Stone, Clay & Glass Prod.
3^1 Fabricated Metal Prod.
35 Machinery, exc . electrical
36 Electrical Equip .&Supplies
37 Transportation Equip.
.. Transportation & Other P.U.
^2 Trucking and Warehousing
M Water Transportation
Ii8 Communication
I|9 Electric, Gas & Sanitary
.. Wholesale Trade
.. Retail Trade
52 Building Mater. & Farm Equip.
53 General Merchandise
5^ Food Stores
55 Autom. Dealers & Service Sta .
56 Apparel & Accessory
57 Furniture & Home Furnishings
58 Eating & Drinking
59 Misc. Retail Stores
.. Finance, Insurance, Real Estate
60 Banking
6l Credit, other than banking
63 Insurance Carriers
6ft Ins . Agents, Brokers, Service
65 Real Estate
66 Combined Real Estate, etc.
o *
-. Services
70 Hotels, other lodging
7? Personal Services
73 Misc. Business Services
75 Auto Repair Serv., Garages
To Misc. Repair Services
78 Motion pictures
tj-. i.
(9 Amusement & Recreation, n. e .c .
W'Medical & Other Health Serv.
(U
^ Legal Services
°2 Educational Services
* Non-profit
89 Misc. Services
Unclassified Establishments
PQ (1960-62)
3.65
2.42
9.97
1.84
1.37
1.53
0.16
1.42
0.23
0.17
0.35
1.45
0.72
0.27
. .
0.26
0.01b
m 9
• •
0.59
0.53
0.75
0.43
0.79d
1.15
0.42
1.05
1.97
0.56
1.26
1.52
0.62
U.86
1.13
1.26
0.70
0.96
0.66
. ,
0.82
1.42
1.40
fl Q4
U • -' *
5.82
0.44
0.11
0.61
0.40
• •
1 1 7
L . /
1 1A
1 . JH
1 Aft
A . HU
OAQ
. oy
n 41
U . " *•
0.84
i \~>
l . ->/
^
Class
S(E)
S(E)
S(E)
S(E)*
L
i •
5(1)
L
S(I)
5(1)
5(1)
L*
S(D
S(I)
S(D
S(D
S(DC
S(I)C
s(D
S(D
s(D
S(D
S(D
L
S(D
L
S(E)
s(D
L
L
S(D
L
L
L
S(D
L
S(D
L*
L
L
L,*
S(E)
5(1)
S(D
5(1)
S(I)
• •
L
L
L
SfD
*J x J
S(I)
*-* V J
L*
L*
PQ (19~>Q-72]
2.23
1.80
6.13
1.25
1.06
1.19
0.68
1.13
0.18
0.10
0.03
1.98b
1.67
0.29
0.41
0.22
0.04
0.08
0.10
0.19
0.41
0.19
0 48
0.65
0.94
0.20
0.93
1.70
0.79
1.18
1.09
0.59
1.04
1.03
0.88
0.66
0.82
0.52
0.19
0.71
1/4 1
. 4 1
, _ _ 0
1.32e
0.64
1.69
0.51
0.12
0.53
0.45 •
1 .74
0.83
0.90
0.96
0.54
0.53
0.58
0.58
IClass
S.E)
x *-* /
S(E)
\ J
S(E)
^—, ^
L*
L
L
5 CD
L
5(1)
S(T)
s(t)
S(E)
S(E)-
5(1)
5(1)
S(D
S(D
5(1)
5(1)
s(D
s(D
S(D
S(D
5(1)
L
SU)
L
S(E)
S(D
L
L
S(D
L
L
L
S(I)
L
S(D
S(D
S(D*
L
SfTl*
V. .'
5(E)
S(D
S(D
S(I)
5(1)
SfEl
U \L. J
L
L
L
Sfl)
V * /
S(D
V J
S(D*
sm*
\ j
-------
Abbreviations:
PQ=popuIation quotient; L=local market oriented industry; S(I)=supply
oriented industry that is import oriented;S(E)=supply oriented industry that
is export oriented.
Notes: The classifications marked with asterisks (*) indicate that
for that particular industry, the change in the population quotient
was such as to cause the classification to change from local market
oriented industry to supply oriented industry or vice versa. The
classification system uses the criteria of PQ=0.80 to 1.64 for local
market oriented industry.
The ratio of county population to state population uses data for
1960 (this equals 0.0178), while the ratio of county industry employment
to state industry employment is for 1962 for PQ (1960-62);
The ratio of county population to state population uses data for
1970 (this equals 0.0291), while the ratio of county industry employment
to state industry employment is for 1972 for PQ (1970-72).
The data base for these classifications is from the State of New
Jersey, Department of Labor and Industry, NJ Covered Employment Trends,
rather than from the U.S. Bureau of the Census, County Business Patterns
tnat is used in calculating the other figures.
c
Even though PQ was not known for 1960-62 it can be assumed from
trends in other years that these two industries were probably supply
oriented and import oriented in the 1960-62 period.
dThe data base for this calculation is also the NJ Covered Employ-
ment Trends, however, the figure given for Communications in Ocean County
in 1972, also includes other utilities as well. Thus, the figure of 0.79
is somewhat larger than it should be. This fact does not alter the classi-
fication of the industry.
Employment in this industry in Ocean County in 1972 was estimated
from the distribution of establishments by employment size to be 85.
This figure was obtained from taking the mid-point of each size category
and multiplying that number by the number of establishments in the category.
In 1971 the actual employment was 78, so that an estimate of 85 for 1972
is probably not far off from the true total.
Source: Zimmerman (1973)
-------
Source of Data and
Employment Size
Class
Census of
Manufactures
Total
1 to 19 employees
2u to yy "
100 to 249 "
250 and over "
County Business
Patterns (1964)
Total
1 to 3 employees
4' to 7 "
8 to 19 "
20 to- 49 "
50 to 99 "
100 to 249 "
250 to 499 "
500 or more "
Total
180
141
33
5
1
154
57
30
37
18
9
2
_
1
20
20
14
5
1
-
17
6
3
3
2
2
1
_
1
Type of Industry CSIC Code)
21 22 23 24 25 26 27 28 29 30 31
(Number of Establishments)
1963
32
33
34
35
18
5
5
4
20
7
6
4
3
6
2
1
1
1
18
4
1
12
1
36
3
1
1
1
37
180 20
141 14
33 5
5 1
1
1
1
-
-
-
21
11
9
1
-
10
9
1
-
_
10
10
_
-
-
24
20
4
-
-
9
4
3
1
1
2
2
_
-
_
2
1
1
-
_
1
1
-
-
_
2.0
17
3
-
-
1
1
-
-
-
6
6
-
-
_
9
9
-
-
-
5
2
1
2
-
30
24
6
-
-
29
15
6
3
4
1
38
1967
Census of
Manufactures
Total
1 to 19 employees
20 -to 99 "
100 to 249 "
250 and over "
184 12
145 10
34 2
3
2
3
3
-
-
-
25
15
10
-
-
6
4
1
1
_
8
5
2
1
-
1
1
-
-
-
23 10
17 6
6 2
-
2
1
1
-
-
_
2
1
1
-
_
1
1
-
-
_
19
14
5
-
_
3
2
1
-
_
13
13
-
-
_
11
10
1
-
_
5
4
-
1
_
28
25
3
-
_ _
12
12
-
_
_
County Business
Patterns
Total
1 to 3 employees
4 to 7
8 to 19 "
20 to 49 "
50 to 99 "
100 to 249 "
2oU to 499 J
DUU or mort;
157
53
35
35
21
8
4
15
5
2
5
1
1
I
19
3
3
5
4
J
4
2
23
8
4
4
6
1
5
2
1
1
20
2
9
7
10
5
5
-
11
5
3
2
4
1
_
-
23
11
5
3
3 ~
-------
TABLE c- 3
COMPARISON OF ECONOMIC CENSUS AND COUNTY BUSINESS
PATTERN DATA FOR WHOLESALING, RETAILING AND
SERVICES, Ocean County, NJ: 1963 and 1967
Economic
Censuses3
County
Business,
Patterns
Economic
Censuses0
1967
County
Bus ine.-^s
Pat te rns
.. Wholesale Trade
.. Retail Trade
52 Building Mater. & Farm Equip.
53 General Merchandise
54 Food Stores
55 Autom. Dealers & Service,
56 Apparel & Accessory
57 Furniture & Home Furnishings
58 Eating & Drinking
59 Misc. Retail Stores
.. Finance, Insurance,Real Estate
60 Banking
6l Credit, other than banking
63 Insurance Carriers
6h Ins. Agents,Brokers,Service
65 Real Estate
66 Combined Real Estate, etc.
.. Services
70 Hotels,other lodging
72 Personal Services
73 Misc. Business Services
75 Auto Repair,Serv., Garages
76 Misc. Repair Services
78 Motion pictures
79 Amusement & Recreation,n.e.c.
949
8621
P- 530
1085
1486
a. 1203
457
S 252
2582
1026
777
5904
422
908
1278
860
294
187
1214
741
9926
V497
1334
1827
1593
520
454
2343
1298
7320
„ 456
" 908
1544
1092
344
305
1765
906
2613d
661
744
269
199
196
91
453
4161
1014
388
129
104
73
218
2953C
968
759
339
224
198
81
384
5760
1161
512
310
133
85
252
Footnotes:
Employment is through November of 1963 for paid employees; the figures given are
for paid employees plus proprietors of unincorporated businesses.
Employment is for 1964, since no County Business Pattern Data Exists for 1963
Employment is through March of 1967 for paid employees; the figures given are
for paid employees plus proprietors of unincorporated businesses.
figure is for selected services only.
-------
TABLE C-4
WASTEWAKR DISCHARGED PE° in IT OF PRODUCTION
Code
2011
2013
2021
2022
2023
2024
2026
2031
2033
2036
2037
2040
2050
2071
2082
2083
2084
2086
2094
2231
2261
2262
2621
2631
2640
2815
2818-
Industry
k-1eat packing plants
Sausages and other prepared
meats
Creamery butter
Cheese, natural and processed
"Condensed and evaporated milk
Ice cream and frozen desserts
Fluid milk and cottage cheese
Canned and cured seafoods
Canned fruits and vegetables
Fresh or frozen packaged fish
Frozen fruits and vegetables
Grain mill products
Bakery products
Confectionery products
Malt liquors
Malt
Wines, brandy and brandy
spirits
Bottled and canned soft drinks
Animal and marine fats and oils
Weaving and finishing mills,
wool
Finishing plants, cotton
Finishing plants, synthetics
Papermi 1 Is
Paperboard mi 1 Is
Misc. Converted Paper Products
.Cyclic intermediates and crudes
Industrial organic chemicals
Waste Vlater Discharged
( cia 1 1 ons per un, i t cf
product! on )
0.5-2.5
4.1-13.5
1.6-2.7
3.1-4.2
6.2-12.0
0.1-0.7
3.0-10.0
20.0-90.0
2.0
102.0-150.0
190
0.1-0.9
1.1
180.0-541.0
2.1
10.0-20.0
3.4-10.7
0.6
61 .5-73.7
5.0-38.0
3.0-29.0
25,000-84,000
10,000-80,000
0-72
640.0-4,230.0
420.0-3,470
Unit of Production
Pound of live weight
'killed
Pound of butter
Pound of cheese
Pound of mil k
Gallon of ice cream
Pound of mil k
Pound of fish
Equivalent case of
#303 cans
Pound of fish
Equivalent case of
#303 cans
Ton
Pound
Pound
Bbl of beer
Pound
Wine gallon
Case
Pound
Pound of finished cloth
pound of finished cloth
Pound of finished cloth
Ton of paper
Ton of board
Pound of product
Ton
Ton
n 3 •*- a
i
2
7
9
2
2
3
£
r
d
6
7
8
9
9
9
9
6
10
10
10
11
12
6
13
13
Data
Qua! i ty
Code
1
2
2
2
2
1
3
2
4
2
4
4
4
4
-4
4
4
4
1
1
1
1
1
d
2
3
-------
SIC
Code
2819
2821
2822
2823
2824
2934
2841
2851
2861
2891
2892
2899
2911
3111
3312
Industry
Industrial inorganic cnernicals
Plastic materials an:! resins
Synthetic rubber
Cellulosic iT.a-n~.acie fibers
Organic fibers, noncel lul osic
Pharmaceutical preparations
Soap and other detergents
Paints and allied products
Gum and wood chemicals
Adhesives and gelatin
Explosives
Chemical preparations
Petroleum refining
Leather tanning and finishing
Blast furnaces and steel mills
Waste Wafpr Discharged
(qal 1 ons per uni t of
producti on )
3,930
0.5-20
12.0-400.0
140
61.8
153.0
0.3-2.8
10.3
11,540
5.5
142.0
4,940
a) 18.0-1,400.0
b) 50.0-250.0
5.0-10.5
9,860.0-13,000.0
Unit of Production
Ton
Pound
Pound
Pound
Pound
Pound
Pound
Gal Ion of paint
Ton
Pound
Pound
Ton
Bbl
Pound of hide
Ingot-ton
Data
Source
6
"* 1
15
6
6
6
16
5
6
6
6
6
17
18
19
• a *• 2
Quality
Code
£
2
-
A
d
L
<
^
4
4
L
4
i
3
Data Sources to this table are given in
Appendix
Source: Zimmerman (1973)
-------
TABLE c-5
Flow
(gallons per unit of production)'1
Industrial
Wastewater RAPP
Code Industry Coefficient
2011 Meat packing plants 1.1-2.1
2022 Cheese, natural and processed 1.6-2.7
2023 Condensed and evaporated milk 3.1-4.2
2024 Ice cream and frozen desserts 6.2-12.0
2026 Fluid milk 0.1-0.7
•-'036 Fresh or frozen packaged fish 2.0
2042 Prepared feeds0 126
2094 Animal and marine fats and oils 0.6
-621 Paper-mills 25,000-84,000
'631 Paper-board mills 10,000-80,000
Appl icar
0.11
0.34
0.40
1 .50
0.35
1 .78
1 .80
836.40
0.08
0.56
0.76
0.80
1 .89
3.00
3.85
3.95
4.0
5.0
9.3
1 .1
1.6
2.0
4.3
1,562
5,562
8,571
15,315
16,964
17,916
21 J14
27,500
32,225
41 ,000
58,000
82,285
10,000
10,530
12,227
15,613
-------
2641 Paper coating and glazing .72 0.3
1 .4
2818 Industrial organic chemicals 0.2-1.7 9.1
production units as in Table 2.
Applications received by the Region II (New York) office of the United States
Environmental Protection Agency only.
GThis 4-digit category is not listed in Table 2; the wastewater estimate given
here is in gallons per ton of animal feed, and is a National Estimate.
Zimmerman (1973)
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C-6
SIC
2011, -3
2021
2022
2023
2024
2025
2031
2033
2036
2037
20^0
2050
2C71
2082
2083
2084
2086
2094
2231
2261
2262
2621
2631
2640
2815
2818
2819
2321
2822
•Biochemical
CteT.an.d
600-1 SCO
2300-7000
l5GG-2
-------
~ "- ^
~" ~
•-•'--
2822
232-
2*2-
2*1-
2?r~
2££^
2391
2892
2899
?9 V.
-^
, , \ \ / 1 rs »
^:f:.'anc
3i!iJ
100
400-3000
5CC-1200
'100
400
1100
100
300
100-2400
--'50^'"'^=:
.30 ! 1 jS
100-100?
400-21 00
100
200
20-120
Grease
300
100 "
20-60
Ch«mi-.jl "---'a1
^xyqen Sj^'ssol /91 i"ot^-
'Demand Scli'Js Solids Misct' lanec. v3
700-3000 900-3000
400- 1 800
300
100
300-600 Alkalinity:5-285
Data
Quality
. Code .
5
5
3
4
5
5
5
5
5
2
311V 1000-2400
3312
1200-4800 2000-4500
1300-1700
Phosphorous:0.5-13.4
Ammonia:5.7-40
3775-12846 Sulfur:60-200
Chromium:20-120
Tin:0.2
Iron Chloride:19.3
Hydrochloric Acid:5.2
Lube Oi:s:22-37
Ammonia-N:0.72-0.96
Iron Sulfate:97-160
Sulfuric Acid:26-43
Chromiun:0.48-0.72
Cyanide:0.24-0.36
Flouride:0.24-0.36
a,-.
Figures are rounded to the nearest hundreds.
^Approximately the 20-80 per cent range.
Source: Zimmerman (1975)
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TARIT C-7
INDUSTRIAL WASTLVIATUR COEFFICIENTS
2021C
2022
2023
2024
/03
2046
(all figures in weight per unit of production)
Raw Wastes
Bi.ologi cal
Oxygen Suspended
Demand . Solids Miscellaneous
^631
291 1
8.3-16.2
9.8-20.2
3.4-17.3
0034-.0168
.0045-.03
.0037-.0062
.0037-.0062
.0005-.0026
7-71
8.3-100.0
0.26-0.92
2.7
10.6
8-50
17-40
.05- 40
3.871 .
7.2-9.6']
1 .6-16.6^
6.1-18.1
7.0-17.6
2.3-13.5
3-29
8.3-83.3
1.0
10-60
40-200
4.90
7.1-13.1
1.6-92.8
COD: 0.73
Total
solids:
0.6-3.1
Phenol:
.005-.03
Su1 fur:
.003-.01
Units
Pounds per 1000 pounds
of 1 ivew'eightkil led
Pounds per pound of
product
Pounds per ton of
product
Pounds per standard
bushel
Dounds per bbl beer
Pounds per ton of
grapes processed
Pounds per ton
Pounds per ton
Pounds per 100 pounos
of hide
aD-il"ey J P.; Erickson, E.E.; and Halvorson, H.O. Industrial Waste Study
QC fhe Moalf^p^tsJ^duVtry.. Minneapolis, Minnesota: North Star Research and
D?-"velo~pnferTf Institute, 1971. Draft. lin./10 i \ ^ A
The first range is for a set of 11 plants (18 samples); the second
IC,P is from earlier surveys; and the third range is the influent from meat packing
l'process inn plants into a city plant-
b|ruM-al Water Pollution Control Administration, Tho_Cpst o_f. C1j^an_ W_a_to_r
? Iri'lij'Jridl W«j'stc Profile No. 8, "Meat Products". Washington, D.C.: U.S.
11 Printing 01 f K.e, 1967.
-------
'-Liter Pollution Control Administration. The_Cp_st _o_f_nean_l'later.
Vol. Ill: I ndus tri jjj_jjas te Prof i 1 es . No. 6, "Canned and Frozen TnTillfltnci '
Veortabl^s . " Vfa^h'i nnton , D.C.: U.S. Government Printinq Office, 1967.
l!io I'iduro ni von excludes wastes for beets and potatoes, which havo a
•v-dnm «. Parcel and Associates, Inc. I_nojisJ:ria1 Mas to Study ftepprt
',_r_jin *i 11 inn industry. St. Louis, Mo.: overdrup R Parcel and Associates ,"7^71 . Qra
An averaqe for the wastes of thirteen plants.
Associated Hater and Air Resources Engineers, Inc. Industrial Waste
Survey of the Malt Liquor Industry. Nashville,' Tenn: Associated Water and Ai-r
Resources Ena., Inc., Ia71. Draft.
fHaynes, Edwin; Stevens, Georqe; and Russel , Paul Jr. "Winery Wastewater
I re a tnen t . " Proceedings 3rd National Symposium on Food Processing Pastes .
•'ashinnton, H.C.: U.S. Government Printinq Office, 1972.
°Wapora, Inc. Industrial Waste Study of the Paper and Allied Products Indus
trj_es. Kishinq.ton, n.f..: Wapora, Inc., 1971. Draft.
Waste averanes over a two week neriod, for bath Sh: 2621 and 2631.
^"nd'-'ral Mater Pollution Control Administration. The Cost of Clean WAtor.
Vol. (II: Industrial 'Jastq Profjle , No. 5, "Petroleum Refininn". WasTiinqton^
D.r, .: ''.'>. Government Pri ntino Office, 1967.
1Efie>"r-ori, n\viqht R. and Nemerow, Nelson L. "Miqh Solids, Riolonical
Aeration of I'nneutral i zed , Unsettled Tannery Wastes." Purdue ilni versity
! ndus trial vlaste Conference Proceedings . #1 35 (May , 1 969) .
^Soroul , 0.,].; Keshawan, K.; and Hunter, R.E. "Extreme Removals of
cus?ended Solids and BOD in Tannery Hastes by Coanulation with Chrome Tan
;[>ump Liquors." Purdue University Industrial Waste Conference Proceedings,
#21'"
\,
Stanley Consultants, Inc. Effluent Requirements for the Leather Tanning
"
ln'LJilL1\bir:riJ Lnji!lstiy_- Muscatine, Iowa: Stanley Consultants, Inc.: 197"
Dr'aff.
Source: Zimmerman (1973)
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