EPA-600/2-77-0648
March 1977
Environmental Protection Technology Series
NATIONWIDE EVALUATION OF
COMBINED SEWER OVERFLOWS AND
URBAN STORMWATER DISCHARGES
Volume II:
Cost Assessment and Impacts
Municipal Environmental Research Laboratory
Office of Research and Development
U-S. Environmental Protection Agency
Cincinnati, Ohio 45268
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into five series. These five broad
categories were established to facilitate further development and application of
environmental technology. Elimination of traditional grouping was conscious?
planned to foster technology transfer and a maximum interface in related fields
I ne five series are:
1.
2.
3.
4.
5.
Environmental Health Effects Research
Environmental Protection Technology
Ecological Research
Environmental Monitoring
Socioeconomic Environmental Studies
TFPHMni nr-v beer> assigned to the ENVIRONMENTAL PROTECTION
i«S 7 ? • Y!eries" Thls senes describes research performed to develop and
demonstrate instrumentation, equipment, and methodology to repair or prevent
wnT^-H
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EPA-600/2-77-064
March 1977
NATIONWIDE EVALUATION OF
COMBINED SEWER OVERFLOWS AND URBAN STORMWATER DISCHARGES
Volume II: Cost Assessment and Impacts
by
James P. Heaney
Wayne C.* Huber
Miguel A. Medina, Jr.
Michael P. Murphy
Stephan J. Nix
Sheikh M. Hasan
Department of Environmental Engineering Sciences
University of Florida
Gainesville, Florida 32611
Contract No. 68-03-0283
Project Officer
Richard Field
Storm and Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research Laboratory (Cincinnati)
Edison, New Jersey 08817
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
Laboratorf n?%ber reviewfd ** the Municipal Environmental Research
catiS ^n^ ??YJr0nment:al Protection Agency,: and approved for puSi- ^
ii
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FOREWORD
The US Environmental Protection Agency was created because of increasing
public and government concern about the dangers of pollution to the health
and welfare of the American people.: Noxious air, foul water, and spoiled
land are tragic testimony to the deterioration of our natural environment.
The complexity of that environment and the interplay between its components
require a concentrated and integrated attack on the problem.
Research and development is that necessary first step in problem solution
and it involves defining the problem, measuring its impact, and searching
for solutions. The Municipal Environmental Research Laboratory develops
new and improved technology and systems for the prevention, treatment, and
management of wastewater and solid and hazardous waste pollutant discharges
from municipal and community sources, for the preservation and treatment of
public drinking water supplies and to minimize the adverse economic, social,
health, and aesthetic effects of pollution. This publication is one of the
products of that research; a most vital communications link between the
researcher and the user community.
A nationwide evaluation of combined sewer overflows and stormwater
discharges can be used by policy makers in allocating resources among
various environmental management programs. This report estimates, for
urban areas in the United States, the population and area served by type
of sewerage system; the quantity and quality of stormwater discharges from
these areas; the cost for various levels of control either as a single pur-
pose program or as a multiple purpose program wherein some of the costs are
assigned to dry-weather sewage treatment and/or urban storm drainage; and
evaluates receiving water impacts for a test city, Des Moines, Iowa.
Francis T. Mayo
Director
Municipal Environmental Research
Laboratory
iii
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ABSTRACT •
A nationwide assessment has been made of the quantity and quality of urban
storm flow emanating from combined sewers, storm sewers, and unsewered
portions of all 248 urbanized areas and other urban areas in the United
States. Available control alternatives and their associated costs were also
determined. Continuous simulation runs using one year of hourly data were
made to determine the attainable level of pollution control with a specified
availability of storage volume and treatment rate in five cities: Atlanta,
Denver, Minneapolis, San Francisco, and Washington, DC. This procedure was
used to derive generalized equations relating pollution control to storage
and treatment. . These results were combined into a simple optimization model
which determined the optimal Ttrfx of storage and treatment for any feasible
level of control for any city. Then the nationwide assessment is presented.
The results indicate annual costs ranging from $297 million for 25 percent
pollution control to $5,029 million for 85 percent pollution control. The
corresponding initial capital investment ranges from $2,47$. million for
25 percent control to $41,900 million for 85 percent control. These costs
can be reduced significantly if stormwater pollution control is integrated
with best management practices and integrated into a multi-purpose program.
The entire results from this project are contained in the three volumes
listed below:
1. American Public Works Association and University
of Florida,
Nationwide Evaluation of Combined Sewer Overflows
and Urban Stormwater Discharges: Volume I,
Executive Summary,
USEPA. 197?,
2. Heaney, J. P., W. C. Huber, M. A. Medina, Jr.,
M. P. Murphy, S. J. Nix, and S. M. Hasan,
Nationwide Evaluation of Combined Sewer Overflows
and Urban Stormwater Discharges: Volume II,
Cost Assessment and Impacts,
USEPA, 1977.
3. Sullivan, R. H., M. J. Manning, and T. M. Kipp,
Nationwide Evaluation of Combined Sewer Overflows
and Urban Stormwater Discharges: Volume III,
Characterization of Discharges, *
USEPA, 1977. .
This report is submitted in fulfillment of Contract No. 68-03-0283 by the
American Public Works Association and the University of Florida under
sponsorship of the US Environmental Protection Agency. Work was completed
in November,, 1976.
iv •
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CONTENTS
Disclaimer. *> . . . . . . .- . . • • • • • • • • • • •' • 13-
= " . ' •''-".• • - - -' f i " ' ' -'. v,
Foreword. , . . . . . . . • • • .*...» . ... . . . iii
Abstract. . ., iv
Figures • vii
Tables xi
Acknowledgments .,....*.......-......••• xv
I Conclusions ...........•••••»••• • • 1
Demographic Characteristics of the Urbanized Areas 1.
Runoff Analysis. ... 11
Prediction of Urban Runoff Quality 11
Nationwide Quality Assessment. . . . * . . ......... 16
Cost Assessment Methodology 16
Relative Impact of Wet- and Dry-Weather Flows on
Receiving Water 32
II Recommendations . . . . . . . . . • . . • • • • • • • • 35
Demographic Characteristics 35
Runoff and Quality Prediction 35
Cost Assessment Methodology. ..........." 36
Impact of Urban Water Pollution Control on
Receiving Water Quality 36
III Description of the Urbanized Areas. . . . . . • • 38
Urban Areas 38
Population, Land Area, and Location. 40
Population Density and Land Use Distribution 40
Population and Area Served by Type of Sewer System ..... 60
Abbreviations and Symbols. .... . . ^ . . 96
References • 97
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CONTENTS (concluded)
IV Quantity Analysis 98
Modeling of Urban Runoff 98
Precipitation Analysis. . 99
Runoff Analysis Using STORM 99
Runoff Analysis Using SWMM. . , 102
Runoff Prediction for Nationwide Assessment . . 103
Abbreviatipns and Symbols . 124
References. . 125
V Quality Analysis 127
Quality Parameters. . 127
Predictive Techniques ..... .. 132
Loading Prediction for Nationwide Assessment 139
Tabulation of Nationwide BOD Loads 154
Abbreviations and Symbols . -..••*. 172
References 175
VI Overall Cost Assessment. • • • • 177
Background. .. . . . . . .... . . . . . 177
Methodology . . 173
Control Technology and Associated Costs
Relationship Between Storage/Treatment and
Percent Pollution Control 137-
STORM Results . ^.92
Overall Cost Assessment ........ 235
Summary 254
Abbreviations and Symbols ............ 268
References 273
VII Impact of Urban Water Pollution Control on
Receiving Water Quality ..... 275
Problem Definition. 275
Methodology ....... 278
Effect on Streams . . . . . . . . . . . 3QQ
Application to Des Moines, Iowa ^07
Economic Evaluation of Treatment Alternatives . 332
Other Recent Receiving Water Impact Studies ........ 343
Conclusions ........................ 359
Abbreviations and Symbols 353
References 35,7
VIII Glossary ....... 350
vi
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FIGURES
Number
I-'l
1-2
III-l
III-2
III-3
III-4
III-5
IV-li
IV-2
IV-3
IV-4
V-l
V-2
V-3
V-4
V-5.
V-6
Single Purpose and Multiple Purpose Stormwater Pollution
Contrbl Costs for US. .'."'.'.' T . . ."."". ... 31
Overall Percent Precipitation Control vs Rainfall Intensity -
Atlanta, GA (1948-1972) .................. 33
1970 Urbanized Areas and .Five Regions.
39
Percent Undeveloped Land Use (US) and Open.Spaqe Land Use
(Ontario) vs Population Density . . ,. ... », . ..... .50
Relationship Between Gross and Developed .Population Density. .52
Population Density Distribution of Albany, New York, . .... 51
Characterization of Population Density in.Urban Areas. .... 64
Mean Annual Precipitation in the United States, in Inches,
and Regional Boundaries Used for Nationwide Assessment. .1QO
Month-to-Month Variation of Precipitation in the United
States. . . .... . . . lol
Imperviousness as a Function of Developed Population Density .105
Comparative Magnitude of Annual Wet- and Dry-Weather Flows . .123
Relationships Among Solids Parameters. .,.-.:;. . . .;: ., . . . .130
*
Relationships Among Nitrogen Parameters*,.-.., . . .... . . . .131
Relationships Among Phosphorus Parameters. ... ..«•,- • • • .131
Residential BOD Loadings vs Developed Population Density . . .141
Normalized BOD Loadings vs Developed Population Density. . . .146
BOD Concentration Variation Using Estimating Equation. * ... 148
vii
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FIGURES (continued)
Number
V-7 Effect of Street Sweeping Frequency on Annual BOD Concen-
tration in Urban Stormwater Runoff - Des Moines
Iowa .......... .'. . . .
VI-l Determination of Least-Cost Combination of Inputs 179
VI-2 Storage-Treatment Configuration Used in STORM Model. . . . . .184
VI-3 Average Twenty-Five Year Rainfall Duration . for Each Study 193
Area J
VI-4 Selected One-Year Rainfall Duration for Each Study Area . . .194
VI-5 Monthly Rainfall Distribution for Study Year for Each Study
Area. .
.195
VI-6 Storage-Treatment Isoquants for Percent BOD Removal with
First Flush - Region I - San Francisco. 199
VI-7 Storage-Treatment Isoquants for Percent BOD Removal with
First Flush - Region II - Denver 200
VI-8 Storage-Treatment Isoquants for Percent BOD Removal with ''
First Flush - Region III - Minneapolis 201
VI-9 Storage-Treatment Isoquants for Percent BOD Removal with
First Flush - Region IV - Atlanta 202
VI-10 Storage-Treatment Isoquants for Percent BOD Removal with
First Flush - Region V - Washington, DC 203
VI-11 Control Costs for Primary and Secondary Units in Storm
Sewered Areas, Atlanta 207
VI-12 Effect of Storage and Treatment Capacity on Number of
Overflow Events
* . * * * • •''CijJL
VI-13 Effect of Minimum Interevent Time on the Annual Number of
Storm Events 233
VT-14 Relationship Between Percent Runoff Control and Annual
Number of Overflow Events 234
VI-15 Cost Allocation Factors for Five Cities. . . . . . . . . , . ,252
Viil
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FIGURES (continued)
Number
VI-16 Effect of Design Storm and Number of Purposes on Cost
Allocation Factor for Various Levels of Control 253
VI-17 Storm Water Pollution Control Costs for the United
States 256
VI-18 Percent of Total Precipitation Volume vs Rainfall Intensity -
Atlanta, GA (1948-1972).... ..'.'.' 260
VI-19 Percent of Total Precipitation Hours vs Rainfall Intensity -
Atlanta, GA (1948-1972) V 261
VI-20 Overall Percent Precipitation Control vs Rainfall Intensity -
Atlanta, GA (1948-1972) 263
VI-21 Percent of Total Precipitation Volume vs Rainfall Intensity -
San Francisco, CA (1948-1972) .,265
VI-22 Percent of Total Precipitation Hours vs Rainfall Intensity -
San Francisco, CA (1948-1972) 266
VI-23 Overall Percent Precipitation Control vs Rainfall Intensity -
San Francisco, CA (1948-1972) 267
VII-1 Simplified Configuration of Mixed Waste Inputs to
Receiving Water 282
VII-2 Point Rainfall for Des Moines, Iowa 286
VII-3 Lag-k Autocorrelation Function of Des Moines, Iowa,
Hourly Rainfall, 1968 292
VII-4 Autocorrelation Function of Hourly Urban Runoff for Des
Moines, Iowa, 1968 294
VII-5 Definition of a Wet-Weather Event for Des Moines by Graphic
Procedure 297
VII-6 Hypothetical Results of Simulation. 308
VII-7 Map of Des Moines Area 309
VII-8 Location Map: River Sampling Points. . 314
VII-9 Application to Des Moines, Iowa 319
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Number
VII-10
VII-11
VII-12
VII-13
VII-14
VII-15
VII-16
VII-17
VII-18
VII-19
VII-20
VII-21
FIGURES (concluded)
Minimum DO Frequency Curves for Existing Conditions in the
Des Moines River 321
Minimiim DO Frequency Curves for Varied Percent of Combined
Sewer' Area. . . . :'l . . .•"/'.'.-. -.''".•'. . ."'.'' V" .''*' '1 . . . 322
'Minimum DO Frequency Curves' f or'Varied'Per cent of Actual
Measured Upstream River Flow. , . . . . . . ., . . .... 323
Minimum DO Frequency Curves for Varied DWF Treatment . . . . . 324
Minimum DO Frequency Curves for Varied WWF Treatment. . . . , . 325
Minimum DO Frequency Curves for Varied Treatment Alternatives. 326
Dry-Weather Minimum DO Frequency Curves for Varied DWF
Treatment Alternatives. ......... .... .... 329
Annual Minimum DO Frequency* Curves V .~ . . . . . 330
Existing DWF Process Profile . . . .' . . . ... .:. . . . . .'333
Trickling Filtration/Activated Sludge System . . . ... . . . 334
'Added Tertiary Treatment Uriit Processes, Incineration of
Chemical Sludge
335
Activated Sludge-Coagulation-Filtration Process Profile,. . . .. 340
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TABLES .
1-1 Demographic Characteristics of the Urban Areas. ...... 3
1-2 Land Use Distribution for the Urban Areas, in the US . . . . 4
1-3 Land Use by Type' of Sewerage System 6
1-4 Population by Type of Sewerage System . . 8
1-5 Population Density by Type of Sewerage System ....... IQ
1-6 Annual Wet-Weather Runoff for Combined, Storm, and
Unsewered Areas. . . . . . ... ........ . .. . . 12
1-7 Annual Dry-Weather Flow for Combined, Storm, and
Unsewered Areas. . ^'4
1-8 Dry-Weather BOD Loadings , . ...... ......... 17
1-9 Wet-Weather BOD Loadings. . . . . . . . • • ' ' * ' .*. • • • 19
1-10 Annual Control Costs - Combined Areas . . . .... ... , . . 21
1-11 Annual Control Costs - Storm Areas. ..... 22
1-12 Annual Control Costs - Unsewered Areas. 23
1-13 Optimal Percent Control for Specified Overall Percent
Control 25
1-14 Optimal Annual Cost per Acre for Specified Percent Control. 27
1-15 Optimal Annual and Capital Control Costs. . . 29
III-l Demographic Characteristics of the Urban Areas. ...... 41
1II-2 Land Use Distributions in Nine Ontario, Cities . 49
III-3 Distribution of Developed Land Uses in Ontario Test
Cities and US Cities ...... 51
III-4 Land Use Distribution for the Urban Areas in the
United States. 53
xi
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TABLES (continued)
III-5 Minimum Population Density for Sewered Portion of
Seven Urbanized Areas in Ontario. . . . 66
III-6 Land Use by Type of Sewerage System 67
III-7 Population by Type of Sewerage System 75
III-8 Developed Population Density by Type of Sewerage System. . . 82
III-9 Values of Coefficients ........ k .......... 89
IV-1 Precipitation Characteristics of Study Areas . 1Q2
IV-2 Effect of Urban Block Size on Ctkrb Length Density and
Imperviousness Due to Streets 107
IV-3 Annual Wet-Weather Runoff.......... ^ 108
IV-4 Annual Dry-Weather Flow
i
V-l Typical Quality Parameters of Urban Runoff Models 128
V-2 Quality Parameters Used in Nationwide Assessment . . . . . . 132
V-3 Parameters for Surface Pollutant Accumulation Used in
SWMM and/or STORM 135
V-4 Measured Curb Lengths for Various Land Uses. . . 137
V-5 Surface BOD Loadings for Residential Areas as Derived
From Effluent Measurements . . . 140
V-6 Normalized BOD Loading Data 145
V-7 Surface Loading and Pollutant Fraction Data. ... 152
V-8 Pollutant Loading Factors for Nationwide Assessment. .... 155
V-9 Comparison of BOD Loadings 156
V-10 Wet-Weather BOD Loadings 158
V-ll Dry-Weather BOD Loadings -."..... 165
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TABLES (continued)
VI-1 Wet-Weather Treatment Plant Performance Data 182
VI-2 Installed Costs for Wet-Weather Treatment Devices 185
VI-3 Cost Functions for Wet-Weather Control Devices. ...... 186
VI-4 Capital Cost of Storage Facilities. 188
VI-5 STORM Input Data for Study Areas 190
VI-6 Hydrologic Data for Study Areas . . 191
VI-7 Values of Parameters and Correlation Coefficients for
Isoquant Equations for Percent BOD Control
Without First Flush ...;... • 198
VI-8 Values of Parameters and Correlation Coefficients for
Isoquant Equations for Percent BOD Control
With First Flush 198
Vl-9 Annual Control Costs - Combined Areas 209
VI-10 Annual Control Costs - Storm Areas. . . . . . ....... 216
VI-11 Annual Control Costs - Unsewered Areas 223
VI-12 General Information . . . . . . . . . ... • • • • • • • • 236
VI-13 Land Use by Type of Use '. ' ' ' 236
VI-14 Land Use and Population by Type of Sewerage System. . . . . 236
VI-15 Quantity and Quality of Sewage and Stormwater Runoff. . . . 237
VI-16 Annual Control Costs per Unit of Developed Urban Area . . . 237
VI-17 Optimal Percent Control for Specified Overall Percent
Control 241
VI-18 Optimal Annual Cost per Acre for Specified Percent
Control -...:.... 243
VI-19 Optimal Annual Control Costs '• • 245
VI-20 Comparison of Annual Cost of Optimal Control Strategy for
Anytown, U.S.A. Using Storage-Treatment Alone and in
Combination with Best Management Practices
xiii
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TABLES (concluded)
VII-1 Land Use in the United States 277
VII-2 Pollution and Contamination Indices ............ 279
VII-3 Pollu*** Unit Loads for Drainage Area Above
Des Moines, Iowa. .
••/••••••••• 308
VII-4 Summary of Present Annual Metro Area Discharges. . . ... . 311
VII-5 Options Used for Des Moines Simulations. ..... . . . . . 315
VII-6 Volume of DO Deficit
* • • • • • • O JJ.
VII-7 Capital Costs for Tertiary and Intermediate-Stage
Treatment ...........
* * * * • • * • • • «J J /
VII-8 Operating Costs for Tertiary and Intermediate-Stage
Treatment 6
* * • • ', • . . . ,. , JJo
VII-9 Dry-Weather Flow Costs for Advanced Treatment . .342
VII-10 Wet-Weather Flow Control Costs 342
VII-11 DWF Tertiary Treatment vs WWF Control. .344
VII-12 Control Costs vs Violations of DO Standard 346
VII-13 Increase in Surface Runoff
***•••••• J.DJ.
VII-14 Increase in Annual BOD Mass Discharge. . . . 351
XXV
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ACKNOWLEDGMENTS
This volume is one part of a joiat effort between the American Public
Works Association of Chicago and the University of ?lorxda.. The; coop-
eration of Martin Manning, former project director for APWA and
William F. Henson of APWA was very helpful. Richard H Sullivan of APWA- ,,
provided overall project coordination and management. The advice and
guidance of our advisory committees on this US assessment and the Canadian.
assessment were very useful.
Richard Field of USEPA provided invaluable overall guidance and detailed
critical review of findings throughout the study.
Numerous persons at the University of Florida contributed..'to this effort.
Michael Fladmark and Dave Wolters coded and checked the input data for
the,248 urbanized areas. Henry Malec did the initial work of deriving
the isoquant equations. Gordon Quesenberry developed the storm event
definition. Typing of the numerous drafts and final report was done by
Ms. Mary Polinski who deserved special credit for her patience and per-
sistence in completing the manuscript.
xv
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SECTION I
CONCLUSIONS
During the past decade, much effort has been expended in identifying
and analyzing the wet^-weather pollution control problem. The initial
concern with combined sewer overflows expanded to consideration of
stormwater runoff in general. This study assesses the costs of control-
ling wet-weather pollution to varying degrees. A key question is what .
is the relative importance of various sources of wet-weather pollution
and how does wet-weather pollution control compare to dry-weather pollu-
tion control? Also, what is its impact on receiving water?
Control of wet-weather pollution is distinctly different than the
traditional dry-weather problem. In wet-weather pollution control, one
would normally use a mix of storage and treatment, not treatment alone.
Thus, new techniques are needed to determine optimal mixes of storage
and treatment. Numerous effectiveness criteria for wet-weather control
have been used, e.g., number of overflows, percent runoff control, per-
cent BOD control. For wet-weather control, the most critical impact on the
receiving water does not necessarily occur under low flow conditions.
How should the critical conditions be defined? Basic questions of this
nature arose throughout the study because it is such a relatively new
area of concern. Thus, the final estimate could vary widely if some of
these assumptions are changed. However, the approach is a fairly general
one and assumptions are stated explicitly. Thus, the interested reader
can refine the estimates as better information becomes available. The
remainder of this section presents conclusions. The following section
on recommendations will focus on data gaps and related matters.
DEMOGRAPHIC CHARACTERISTICS OF THE URBANIZED AREAS
Urban areas in this study have been taken as the 248 urbanized areas
defined by the Bureau of the Census of the US Department of Commerce in
the 1970 census and other urban areas. The 248 urbanized areas defined
in 1970 are generally characterized as having:
a central city or urban core of 50,000 or
* more inhabitantsj
-------
closely inhabited surroundings, consisting
of incorporated places of 100 housing units
9 or more; and small unincorporated parcels .
with population densities of 1,000 inhabitants
per square mile or more (386 per square km)J
and
other small unincorporated areas that may
eliminate enclaves, square up the geometry '
of the urbanized area or provide a linkage ,
• to other enumeration districts fulfilling
the overall criteria within 1 1/2 miles '
(2.5 km) of the main body of the urbanized
area.
All 248 urbanized areas in the United States were analyzed in varying
levels of detail. Population density distribution functions were
developed for 50 urbanized areas. These results were extrapolated
to the other 198 urbanized areas. Land use information was derived
based on a statistical analysis of 106 cities. The results for all
USEPA regions and the entire US are shown in Table 1-1, Demographic
Cfraragteri-stics of the Urban Areas. A total of almost 150 million people
live, in urbanized areas in the United States at an overall average popu-
lation density of 5.1 persons per acre (12.6 persons per ha). Urbanized
areas, as defined, are about 46.2 percent undeveloped as estimated in
Table 1-2, Land Use Distribution for the Urban Areas in the US. The
distribution of the develpped land uses is approximately as follows:
Residential
Industrial
Commercial
Other
Total
100.0
Information on population and area served by combined sewerage systems'
were provided by APWA. The population and area served by storm sewers
and in the unsewered area were estimated as residuals. All areas with
a developed population density of less than five persons per acre were
assumed to be unsewered. The results, shown in Table-1-3, Land Use by
Type of Sewerapp System, indicate that about 14.4 percent of ,the urban
area is served by combined sewers, 38.3 percent by storm sewers", and the
balance of the developed area is unsewered. Table 1-4, Population by
Type of Sewerage System indicates; that 25.2 percent of urban population
is served by combined sewer systems, 52.1 percent by storm sewer systems.
and the remaining 22.7 percent is unsewered. Table 1-5,.Population
Density by Type of Sewerage System indicates nationwide average developed
population densities of 16.73 (41.30), 13.00 (32.09), and 4.59 (11 33)
persons per acre (persons per ha) in combined, storm and unsewered'areas
"
-------
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I A i < r*r- r* i >.M ~ '__•»— i——•*— l""""^«»4—."*"• m . — . .
ii^?!L»i I!:2 5J'SL2*fl{-7-5' °*2 10()«°
f 2!.,NvI F46T5lIorH"4^~7*6! fTa tOo"'o
-- i --7-1 s:s! H::B [ -ss! 7?:;! is:; i TSS:;
[E?E.l I!!:!»i«• 111!;E j IE-! i in I' •»«r« i
JOO.fl
,31 .MD
T3l""pA'
I i
, 31 VA
22.ai41.8l 6*2110.6113.0
36l3 sfril'^rsi''^^?;!'^
46.9
31.0! fl. 6 I. 7.9.1. 9.7
-
30.71
7.8! P. 6
1,31.WV 147.5
fAJ/"RER""3l3777 36.4! 5.4f 9,211,1.3
""" J «<--- I -w-. | --,- ! ...,. 4 .... I .„«
41, . AL 161.3122.61 3.3J 5.7! 7.0
• i4l. FL 150.10129.2! 4»3J,7,4J,p 1
***•* I ^"*V^ I **mmm I ••*• — . h— ...
1 0 0 . 0 I
TppTo
100.0
•»•""••?*
100.0
— •• — — »
100.0
t , fll 6A
I...|.....
^ I , KY
.-—).—————
..41 , MS
.«••*•• i-*WM IP W
L . 4 I. . NC
a c i
aJ ,TN 159.4
-i-----!.--.
. RER a 53.6
i "sTp }"6Ta 1 "a!I
28.7
23.71 3.5J 6,01.7.4
"2"l" I «*•*!•» l-.^.j--«^
27.1! A.01 6.91 B.4
- 100.0
4176 I"6?"11o!5I 13^0 ITooZo
•n*>v*"ii
lOp.O
looZo
TonTo
100.0
1 00 . 0
i
47.1J30.91 4,61,7.8! 9.61100.0
-------
.TABLE *-? LAND USF DISTRIBUTION FOR THE URBAN AREAS IN THE U.P,
1 !
EPA (STATE!
RFC! . 10 I
6
. 6
, 6
, 6
. 6
! . AP I
I.LAI
I . NM I
I . OK I
AW SET, 6!
7
, 7
. 7
7
AV
. 8
. 8
. 8
- 8
8
. 8
AV
. 9
. . 9
1 9
... , 9
9
LAV
,10
I"1* i
1 , KS !
I . MO !
J . WE 1
REG. 7«
I CO 1
J. . . MT I
I . NO 1
1 .SO !
!. . UT I
!. ' . WY !
ISEP 8!
! AK I
1 . A7 I
1 . CA.
! , HI 1
1. NV
RER 9
I , 10
Ti»l • OR.
.1 0 I . . WA
LAV.
IAV
5EG 10
| mmmfm
U.S.
LAND
1
UNDV |
58.81
39.1 I
50.1 I
61.11
56.01
55.3!
46.41
46.71
43.01
50. a
41.9
46.5
39.2
47.8
53.7
50.0
46.5
60.1
53.7
35.4
36", 0
57.1
38.6
45.9
45. P
46. *
46.1
146,2
USE AS %, OF THTAL AREA
RES ICnMMINnLinTH I TOTAL
35.6!
s"-,2!
22.71
25.7!
23.1 !
31.1 1
33.31
28.9!
33?9!
31.31
3^5!
30.5!
2731!
31,?j
23^3 !
27.0!
37.71
37.41
25.0!
35, 9j
31.61
31.61
131.41
31.5!
mmmmt
31.4!
3.51
5.?!
/i . 3 1.
3.31
3.PJ
3.FI
""3;^! l'
fl.6l
fl.91
tf.3!
5.01
5.?!
«.5I
. 4.01
4,'?!
3,4!
4.01
5.6!
5.a5l
3.7!
5.13!
«.7I
4.71
4.61
-»«,-!
fl.6l
6.1 1
9.0!
7.4!
5.8!
6:si
6.6!.
TSj
7.9!
7.9!
8,4!
7.31
. 8.6!
7.9!
. 9.01
7.7,
. 6.91
7.4!
. 7,91
5.9!
.6.9!
. 9.61
9.5!
6.'3I
9.11
8.0J
8.01
7.91
«•»•»• |
.8.01
mmmm \
7.51100.0
1.1 .11 100.0
P.I
7,1
«.0
100.0
300*0
1 0 0 . 0
,8,1 1 100.0
"7^1100.0
9,81100.0
9.71 1.00.0
10.4 If 00.0
9 . 0 i ! 0 0 . 0
1 n . 6
9.7
100.0
100.0
1 1.1 I 1 0 0 . 0
P. 5116 0.0
p.4i.ion.o
9.1
-9,7
•7-i.
P. 4
1.1.8
11 .6
.7.8
77.2
9.B
9.9
;p.e
*p*8
fmmm
9.8
ioo.o
10 0.0
s o o . o
100.0
100.0
100.0
100.0
1 00.0
100,0
100.0
100.0
looTo
100.0
1- — ...
-------
TABLE 1-3 LA.NO USE .BY TYPE OF SEWFBABE
L ' ,. ' Af?EA SFRVED BY TYPF hp SYSTEM
! EPA I STATE I , 1000 ACPF.e
l«2i..I2.| "NnV ' CfWp. I. STPPMf.' UNSFWJ TOTALI
[II! i IlEiI i IE!!-! i II!F*'"""•?' r"^?*i i "58*78 l
* L.i ! _.HL I .I." • * I " "^ \ """'-<> I """2675 ''"23573 I
ill! I IIE-I-! If It H \ Ii£i ' 2 j "po" I "27678 I "96*"7o I
I Li! , _ i _ _ i 51-71 0 „ 0 I , «-u.ll.l2^l.9j
TI REG
j2 I
31
.. I.
• NV. I 197..11 245.61 2(11.11,1^5791 829.7
"""" I "9147"" ""*72"*T 1 "5fl5"'Z I ""*7"*"3 ' •"•"-"-
i j '-''-'••'. -'•»r.ni,3f7.e:
.2E.J. ^°>61 6«8!' 1'7.2'lr
•31 MD I
I...I.....I
1 .31. PA I
I...).....I,
12-7'
""
'*••*» 1'
0.01
n 3395
[ .31 • VA j 266."8| 28jo'j. 15«r3 i 7l2o7o I ~56*"7""
i•»—i.....t......|......i....—_i„... _i_ "
'- .31 . WV I 76 71 *^76I "?'^f'*^'7i7ZTZ
iTP'sr"""" • •"-"""
I...I.
i . a i:
31 979.6! 19«%5J B2P.61, 601,4 I 2598.2
AL
"FL" i "6377*4!
-i
. 0.01 . 105.J I, 159, J. J 68?!. 0
•"•"»'»' .«*«•••»» l .•
O.fll. 3H.6I
i.
I- 41. KY j I19..flj
!•«••• I ..... I ...... | i
I- • «J MS I 551.21
I ...|.....|......|,
L.!!..i!E.! '?^9-11
j.""**!"'""""""!"""*"?!'
I--., i..... i...... i.
I .41, TM I «27.0!
!.TL"?E""4i265!78i"
""51 ""IL" I "34-7*"!'
I.....|......I.
*607"" I "lO?77 I "18178 ! "6967""
9.6 I " 88.7)~"8774 t~30"""7""
'.£;2!II!I;^j "I**-*' "2767"?
"o7oi"rr377i"r787r!"65o79
•"•^w 1 ....»|. I ...... I ......
0.01 62..1 I. 94.81 343.6
.
.....
25, 7J 9fi.P|, 167.51 718,9
..^,. i ...... | ...... i ...... i
95.91. 933. 61.1267. 4(4948, fej
•51 IN j 366.3,
...I .....i......|
I.' 5! ' MI ' fl06.'8l
[II! i """5" '"^'-*i
f"""5l*""*nH"j"77577|
I ""5 f """wi"| "341 79 I
167,91. . 57.3llfl6l'
....q,. j ....^. [ ...... i ......
233.8! 156.pl. 295.«li09?»0
"T^^r '""rr"1" ' --••-•s* i««...^.
fl7.9. 9fl.il, 172.9!
!
I-TI. 5FR. 5 12609 .'2 I
I ... ..... ......i
•"••""*•* P»«""W»|. '..OT... | ......
211.21. 237.61. fle9..0U7is76
""S7"*" ....^. | ...... j ......
31,81 Iflfl.fll, 170.01 688, 2
'Z!T!!''l"> ' »~<"»^» I w"«-»«ii» «*.-.«.
999,51 9tP. 01.1635. 816156. 6
-------
.TABLE 1*3
.EPAISTATE
REG!. , ID.
I- , 61 AR
I . 61- , LA
I ,61 - NM
1,61- OK
1,61 TX
| wnm I mmmrn**
ITL , REP 6
-71 I A
,7.1- . KS
,71 , MO
, , 7'l . NE
TL REG- 7
,8! -CO
, 8! - . MT
. . 8J.. , ND
1 . 81- . UT.
I , 81- WY
IT!. £E(3, 8
1 91 , . AK
1.. . 91. . AZ
I Q 1 f* fl
. , 91. . HI
, . 91, , NV.
ITL: REG, , 9
I. 101. • ID
1 ,101. - OR
LAND USE BY -TVPE-t
AREA SERVED BY TYF
1000 ACRE
. UNDV. 1 COMB. J . .STORM
!77.0
128,2
87.4
365.8
1426.8
„„„_— —
2185.2
•»•>•* w •( •*
323,8
.151.9
. 331.6
79,1
866,4
140.5
3P.2
1-9.5
3?. 7
124.1
24.6
379.5
29.6
206.2
1002.0
38.3
68.3
1344.4
36.8
139.8
i 101 , WA j 252.6
431.2
I.TL U.S.. . 13409.
11,6.1
0.0!
. , 0,0!
, 5,1 1
16.71
.8.91
.30.1
. 130.0
, .38.?
. 87.6
«89.3
,775.3
. 76.5
21.31, 66.8
163.6 J - 29. ft
•29.61
223,31
1 • • 1.8J
1 0.0
1,1.0
| 0.7
1 .0.0
1 0.0
1. .3.4
1 0.7
1 0.0
1 67,1
1 0.0
1 2,8
70.6
1 0.0
1 32.7
1 79.9
1 112.6
1 2248.
. . 19.4
192.6
)F SEWERAGE SYS
>F OF SYSTEM •
"A ' . . -
..UNSEWI TOTAL
r"82?4l"'30?"o
, 69.5
, 48.9
, 145.3
, 624.8
, 970.9
. 126,7
.. 87.6
. 185.4
f 55.8
. 43575
...9B.r. 94.5
. 24. ?i. 19, 8
. 18.5 , 10.6
, . 15.PI.. 20.1
46.31, 60.8
1. 11.3 , 13.4
1, 213.3 . 219.3
1, 6,7i, 12.1
1. 80.71, 97.1
11050.4 , 708.7
1. 36.9 < 31.1
1 . 1 7 . 3 1 , 31.1
IM9J..9 , 880.1
1, , 2.1.9 , 23.8
1 45.7 , 86.7
I. 68.8 . 14/1.3
1, 136.5 , 254.6
J.59873 739^3
1 327.7
1 598,8
1,2546,0
13948.21
1 535,9
J 327*5
1 710.4
I 183.9
11757.7
1 334,9
I .8?. 2
1 49.7
1 68.4
1 231,2
I 4P,3
I 815.6
} 384.0
J 2828.1
1 106,?
I H9.6
13487,0
1 305.0
1 545.6
1 935.1
J29Q37.
TEM
•
-------
TABLE ]>4
I'EPAI STATE
IRFGI . ID
!..:!. .El.
1. . 1 1 MP
j. . 11 MA
1.. . 1! , NH
1. . 11. . R!
1. 11. VT
IT| , ?EG 1
1 . , 21- , NJ
1 21.. NY
ITI REG 2
I..JI..2E.
1 31, . DC
1 31 . MD
t. .31. PA
1 31 VA.
1,31 , WV
ITI.. REG. 3
l..f!..;b.
!:..f J.^t.
1., , 41 GA
1 . fll. . KY
I. , 41 .MS
I-. .{.....
I . til, . SC
I .41 . TN
LT|.. PER 4
I.I.SL T-
'POP
COMB
692
372
1155
. 286
3«6.
69.
. 2919,
405.
- 9603.
10007.
83.
400.
r 0,
"3*54,
298.
515.
?651.
0.
6.
590.
. U6.
0.
0.
0.
3123
. toisr
6109.
J .51 IN 18457
1 , 5J , . MI 3293.
1. .51..MN 593.
1 .51 .OH. 26473
L . 51 WI 679.
j-TL. REfi 5 15166.
ULATIHN RY TYD
POPULATIOW SER
ciooo PERSONS
STORM), UNSE
.979.1 673
0?i . 135
2404. !, 12e54
•0,1 131.
178.1, 202?
. 0.1. 74.
366*. J, 247oT
4473.1. 14953
5369.1. . 639.
9842. 1. 2*34,
210.1, 101.
357.1,, o3
2545.1 460,.
58023! 187?:
2143. I. . 4923
4?.!. . .l?23
1H00.1. 2451.
123fl!l, 7733
4075?! 13fl53
Tl563l 10223
l787. ! 3843
. 596. J. 39l3
TiTi.f . 97?3
697.1- 5363
12493 1, . 7463
11512.1 6209.
1582. I, 1530.
715.1. . 8,1 1,
1885. 1. 13B2.
1218. 1 7163
3429. J. 19443
1272.] 960*3
"0101.1 73433
E OF SEW
VFD
3
. TOTAL.
.. 23443
5073
48?33
• 417.
8263
1433
. 905o3
, • 63723
, 156113
. 219833
""35i3
"^7i73
. 30053
, 84333
, 29333
6803
162033
2011.
546^3
2768.1
1637.
9873
2287*3
?2333
23073 1
......^ |
18745.1
922l3l
337l3l
""65593!
""25273!
80?l3l
2911 3 1
"126103!
EPARE SYSTEM
1
1
1
1
1
1
-------
.TABl
1
I.EPA.
IREG
1 6
1 6
! . : 6
«. ,6
I . 6
I.-PI-
IT! f
1- 7
! . . 7
1.7
I. , 7
.E 1-4
STATE
. . IP.
. . AR
LA
. ' NM
. . OK
, . TX
?E(5 6
1 IA
. KS
. . MO
, . NE
I.TL, REG . 7
1 , 8
I..B
I, 8
1- , 8
1.8
I 8
tTl"f
I, .9
! . 9
! 9
1, . 9
1 9
•
. , CO
. MT
. ND
. , SD
, . UT
, WY
?EG . 8
. . AK
. , AZ.
CA
, . HI,
. NV
POPl
f
. COMB
. 80.
. o.
0,
0,
101.
181,
252.
254?
1635.
419.
?5S9?
36,
0.
10.
. 8.
0.
. 0?
55.
10.
0.
1663,
0.
41.
IT|., Bli. 9 . 1713?
1 10
!-..
1 , .10
1. . ID , 0.
. . OR 427?
L . WA. 903.
JLATION
SOPULAT'
(1000 f
STORM
345.
2012?
. 485.
«••»»«•»«!
1150.
6031.
10023.
754.
. 768.
254.
""260?
""2036?
1260?
256.
197.
183,
589?
126.
2610.
93.
989.
13493.
499.
217.
15291?
23?.
574.
871.
ITI.REP. 10 1330. 1677?
ITL"!
37606. 77853?
BY TYPE
[ON SER\
'ERSnNS'
, UN SEW
537?
. . 394.
, 226?
. 590,
2801?
. 45^9?
609?
. 463,
mmmmmm
. -1389,
. 234.
. 2695.
, 341,
. 1.16*
67?
. 106?
. 265,
. 75?
1070?
"""44?
. 419.
"2986?
139,
- • 139,
. 3727?
. 155,
- 402.
""701?
. 1258?
p> . m
:. OF SEWE
/PD
. TOTAL
962.
, 2406?
, 8934.
. 14753,
. 1615?
. 1485.
mmwmmm^
. 3278.
. 913.
, 7291?
, 1737.
, 372?
. 274,
- 297?
. 854?
, . 201.
. 3735?
147*
- 1408?
. 638?
396?
. 20731?
, 387.
. 1403?
""2475?
. 4265?
"TTfQ/ii I 4 f\ Q ft f- t-
t5 j <* *.' O M J1 »i ** ' «P O O ^
•RAGE SYSTEM
.
-------
TABLE 1-5 POPULATION DENSITY BY TYPE OF SEWERAGE SYSTEM
i
.II |
Hi I
... i.
21
...I.
21
'
rnMBISTPPMIWRFW! i AVER
JH I 9.021 0.0 I "7<5? l~7775 I
* [ «.75l"o7n~l~7773l"p779l
7177. ?717177 71""? 7! "p"7tp I
-_ i.....i_____i_____i_____i
J_[lq.?7ll«.fl3i T./ISI R.*7I
"2! sft"7«'' "•"•-' --^--' --:.-1
-! i.:!!!'23?si"a79Pi"PTfl71
. . - - ••-- >"ivii."-ri*i-i-o'"'AVER I
'.» I __L-_ i .2:2-! "s-'"' "**« 1i?7o6'i
..2!—2. I"""' 7377s I "7o6! "77i«7 \
A V"R i?"~r} 7n"p"' "^"5"' --^--' --:!-1
II! I II7r! ^*" ™'"'•"' "* •"~' "7"i'
""7!"Tin"!"""'"'••"•"'--••-'--^--I
...!_.^2.|i2:no1 P
-------
RUNOFF ANALYSIS •
An examination of precipitation patterns led to the division of the
country into five zones for purposes of analysis with the Corps of
Engineers' STORM model: Pacific Coast, Rocky Mountain, Midwest and
Texas, South and Southeast, and Northeast. STORM was run on a repre-
sentative city for each of these regions: San Francisco, Denver,
Minneapolis, Atlanta, and Washington, DC. Results from these runs
were used in developing the nationwide assessment methodology and also
used to calibrate the elementary technique used for r^unoff prediction
for the 248 urbanized areas.
Annual wet-weather runoff was generated using a runoff coefficient
that is a function of imperviousness which in turn is a function of
population density. Table 1-6, Annual Wet-Weather Runoff for Combined,
Storm, and Unsewered Areas indicates average runoff of 16.5 inches (41.9
cm) per year, 14.8 inches (37.6 cm) per year, and 10.8 inches (27.4 cm)
per year from an average precipitation of 33.4 inches (84.8 cm) per year
in combined, storm and unsewered urban areas, respectively. Dry-weather
flow is a function of population density on the basis of 100 gallons per
person-day (379 liters per person-day). Table 1-7, Annual Dry-Weather
Flow for Combined, Storm, and Unsewered Areas illustrates runoff magnitudes
by USEPA region and for the US as a whole.
Average annual dry-weather flow (DWF) is significantly greater than average
Jwet-weather flow (WWF) only in the arid areas of USEPA regions 8 and 9. The
'heavily urbanized regions 2 and 3 produce the highest dry-weather flows
(although above average precipitation partially offsets these values). How-
ever, in most parts of the country, dry-weather flows represent 30-50 percent
of the total (wet plus dry) runoff from urban areas.
PREDICTION. OF URBAN RUNOFF QUALITY .
Analysis of available urban runoff quality data indicates a great number
of disaggregated urban runoff studies from which it is highly difficult
to draw meaningful conclusions'as to pollutant loading rates. For
instance, there are no known studies in which both.: surface, and effluent
data have been gathered simultaneously. In;addition, there is a wide
variation in the manner in which data are reported (e.g.; "average" con-
centrations) and in the amount of related information provided about
the catchment areas (e.g., population density)..
On the basis of the available data, pollutant loading estimates were
developed for wet weather for BOD5, suspended solids, volatile solids,
total phosphate (PO.) and total nitrogen (N), and derived as functions
of precipitation, land use and population density, the latter, only for
residential land use. Other land uses are commercial, industrial and
open. These estimates indicate that, for the same population density,
loads from combined sewered areas are approximately four times higher
than those from separate sewered areas. Furthermore, higher population
11
-------
TABLE 1-6 ANJNUAI • WET-WEATHE«? RUNOFF
IRFG!
I — I
_ I
ID"IPPEHP
I
1 I
1! i
1 . 1 1
ME I 03.5
I
1.1'
I.-., i
MA ! (U3.6
|
MH I ai.O
COMdJSTnPMIUNisfW! W/ER j
i«!
I
-I
16.01 O.Ol ?3:6.l"Ti:2l
17.6J ,19.6! l?:?!*?^!
.-I
15.41. O.Oj"I!:5f"7oT7.i
' I' RT j 40,0 18.51 .16.SI 12.0!
Immm|.....j.....
i ii VT i 35:0
IAV BEG
I.-. I
-..,-|„...,.|..**.Imrnm+mI
13.11 0.01 J?V.6I J2?-
17.21
l .21. NJ j 42.8 19.11 19.41 52:2!
1---I-----!---*.!...-. i. ._..i_; _i
38.11
f™?™-*!1™-'51 25'2J ?0-31 ll-9! 18T2
j""31""DE"i"45:0!"I?:?1""9:0!"73rii "T?"?
I mmm I ..... |
-!
| -3| DC j 41.01 24.81,18.2! .0.0! 20;7*
... 1 ..... J .»..„. j mmmm.mLmmmm.m \ -„.„., | m „ . _ .
i • *' wn j aa.o!. o.o! i8,5i nai 16:9
i i i ' i **-••»*-' i j. _i v» * ' j. <;* % v
, 3j""PA"i"3I?oj"I7!61"7e:2!"17:4!"7i:5
42:91 16j9i
1.31 WV I 41.01 15J4!
i A-V/'RFR""! i "42:7! "77^4!
I...J..... [,
L.fl!' Ah ' 55-81 °-01 23-'"22:71"7?:sI"75:6
i-1
,-i
t-41 . NC
!,*»•» I
I. . 4 I SC
•l-.-l
!
46
0.01. J 8. 61 14%6 J6..4I
. .!
!:-.fi..^.|.!S;! .12;Jj.!2;2iIi?S
f«.?EG 4! it9'6-1 18-5J '?i-7'
IL I 35.0
\mm5\'m IJj I ^7.2
I""!I'""MI" t"=r''"
I--.-I
t .51 MN
(...« |.....
L . 51. . OH
l*"5[""wl"
L—I
O, .3
W._^
18:61
13.4
••* , M - - 1 i - - •»W««^|— I
l«,7 .15.1 .10..9 13.31
...^. J mmm,m,m \ ..._w | ..... I
13.6! 12.91 , 9^1 lt:9l
!AV REG
I —(-..
26.01 i0.5l"7o":6l"~7:4
•"•-»•• I mmmw^m I .....
14.6J J5.6! 10^2
"74:5j"7o:8!""9:2!
51 32.7
• I.....
-I
14.8J 12.9! 9:81
12
-------
-TABLE 1-6 ANNUAL WET-WEATHER RUNOFF
L 1 • IN/YR WFT-WEATHER,. FLOW I
EPA1STATE ANNL. (INCHES PER .-YEARV- . 1
LREGI, . ID PRECP . COHBlSTORMIUNSrW 'AVER.- 1
t. , 61. . AP «8.6
, , 61 , LA
1
15, OJ P0.6I 1.6-.4
56.0 0.01 27.5.1 J.7.4
...61- • NM . 1 9^0 oToi . 3.6J . 2U5
L 61.. DK
L -61 TX.
LAV REG 6
L ,71-. I*
L , 7 1 . . K 8
. , 71. MO
.32.7 0.01 ,14.2! . 9.7
31.0 24.71 ,14.21 .10.1
35^3
31,3
IITo
36?8
.: : 71 , NE. 26.5
,AW. RES- 7 . 31.9
. . B! , CO- 14.5
L. , 81, . MT.
L . 81. . NO
. . si. , sr>
14.0
2l!o
Is!o
.. r 81, . UT 15;0
1,. ,8j,. WY 15.0
17.91. .16,21 JO, 7
. 18.01. .12.1! 9,7
14.1 1 13. ?l JO. 4
1 1'
14.21. 13.31 12W7
11.41 M.5I .7*7
14,01 12.61 .10te8
6.11. 5.81 . a.O
0,0 J. , i.'fll' - 4tal
8.3! 8.31 - 6.9
10,41, 10.4! . 7,91
0.01 , 6.3f 4W4
0.01.. -5.?| .flfc7
IAV. ais. 8 IT*?!! 7?si, 6^4 i «r?
, , 91 . AK, , 30,0 13.1 ). 13.1 ! ,.8.6
. . 91. • AZ. 9.0
L 9! CA
L, . 91. . HI
L , 91. , NV
IAV REG. 9
O.OJ 3.1! : 2.2
17.2 11. 3J . 5.91 . ll»6
23.0 o!oi - 9.9! . 6.9
in
2.91. , 1.6! . U2
16.9 10.91 , 5.8 J . «T3
L 101 .ID. 11.0
1. 10 J. , HR 39.3
I 101. WA. . 30.3
• <••> | ••«.»«« m atv mm
,AV RES, iO ^6.9
.«<*• | «••«••» ««•«!«•
• AV.y.S. 33.4
0.0 I . 4.21 . 3fc5
I7.2i 16.8! 12,2
12.0! 15.51 .10,6
o9«*«p«n>|PM>lirtE*iq|W 1 W n» w «t **
13,51 lfl.ll 10.5
mm**m* | •»•»'«»•*•< ! <*w WSt1"
16.51., ia.81 .10,8
17,4!
2fl.ll
XI 1
«p.»i
M..7I
ts.sl
.13.41
-mmar^m I
11. 1 1
12.11
13 v5!
9,7,
12,2!
*•••<••*•• 1
.5.1!
5,01
Mow^w |
7.8!
9,1!
5.31
5;3!
5.V.
•ra—.^— 1
10,6!
2?7i
5?7!
. 8,7!
T!5l
5?5i
3.91
14.7!
12.3!
mm mm." \
12.4!
«*•*•>• t
••is. aj
13
-------
TABLE 1*7
I
EPA 1 STATE
RFC! ID
1 1 CT
. . J ! ME
.11 MA
11, NH
.1! RI
.11 . VT
AV PER 1
21, ,. NJ
21 K'Y
AV PEG 2
• 31. DE
, - 31 DC
31 MD
•»••* | *mm •
31 .PA
3! . VA
31 . WV
AV PER 3
.4! . AL
HI FL
41. GA
41 KY
• ••i 1 «• ™
.01 , MS
4! NIC
, fl! SC
«»•» 1 ^ •
41 TW
AV PER 4
51. II
5! IN
51 MT
5J . MM
.51. OH
""SI""HT"
AV PEG 5
AMNt
IIM/YR
ANNL.
PRECP
43?7
43.5
^3.6
41?0
40.0
35.'0
air?
Jirsi
38?1!
40.5!
45?0!
4t?oi
42,01
4i;oi
42.A9j
41.01
42.11
SSJ8J
56.5)
42.3!
54.5!
. fl6.'OI
46.71
48.31
49.61
35.01
. 37^2!
31.0!
?6.0I
37,21
...... |
R9.7I
32.*7I
JAL DRY-WFATHER FLOW
DRY-WFATHER. FLQVJ 1
(INCHES PER YEA.R.3 ... 1
COMB 1 STORM I UMSFWI £VER*
18.11 15.91 6,2! 1i;3!
1 Jl. 21 0.01 .6,91 9.6!
15.2! 20,1 1 .. 6,1 1 t2.0l
12.1! -0.0! , 8,0! 10.4!
21.31 15.61 5.61 11.81
11.8! 0.0! 10^4! H.OI
15.1! 18.41 6J3! )1.5!
20.5! J9.9! . 4,6! H ?2 1
S2.5J- 29.91 5.9! 33?2 I
49.4] 24.4' . 5.01 21.21
16.4! 16.41 6.71 S2.0!
42.21 19.1 1 .0.01 26.9!
0.0! 16,0! ,6,31 14,0!
20.41 18. 0! 5,1 1 13.2!
14. 3{ 16.71 . 5.5! 13.01
12.01 16,21 . 6.9! 10.81
18,31 J8.1 ! , 5.5! J3.5I
0.01 35.8! 6,51 10,2!
19,11 17.61 . 5.71 11.5!
J3.2! J5.ll .7,6! 10.8!
16,21 1B..1 ! .5.9! 12.3!
O.OJ 15.6! .7,1! 10.61
O.Oi. -15,51 . 7..3I 10.51
O.OJ 15.1 1 .7.61 10.6!
16.31. J 7.01. . 6V0'. !0.6I
14.4J 16.61 ,6.6! 11.01
26.81 . q.7! 6,4! 14. 6J
14. 8j .16.81 . 5.8! 11.0!
18.9,|. .16.2! . 6B3l 12.91
16.61 16.7! 5^6! To.6l
16.81 JP.4! 5.3! U.5I
»•"»««• 1 mfnim I «r-W^« I mm*~.*i |
28,7' .11.8! .7,61 H.3!
20T«i J4. 91 6.0! 12:»4I
14
-------
TABLE 1-7
EPA STATE
RFG . ID
, 6 AR
6 LA
••;•£ m
. , 6 .1 OK
6! TX
AM REB 6
,7 -IA
. 7 . KS
. 7 HH
/in. . 'ME"'
AV »E6 7
,8 . . CO
. . 8 MT
81 WD
. , 8 an
.. , e , UT
,8 WY
AV Rf-G 8
. t-9 • AK
.•,;? '. AZ
91. C A
5 I.' HI
, 9! ' . NV
AM. SER 9
. Jy ID
...10 -OR
10 . WA
«M« mmmmm
AV REG. ,10
* mm ! i»f> <••»-!•
• •IKK •»»•»"•«
.AV U.S.
ANNUAL DRV-WEATHEP FLOW .
IN/YRI DPY-WEATHER. FLOW 1
AMNL.I flWCWES PKR YF.Apy I
PRECPI. COMB J STORM IUN3PWI AVER , 1
08. or 9^3! 15.AI . 8J8I 10,4!
56.01 O.OJ ?0.8! . 7W6« 16.21
9,01 0.0! .17.01 . 6*21 11,01
32.71 O.OJ .17.61 5*51 10.01
31.01 iJ6,6l J6.6I . 6WQ! 10,7!
^5^3! l«:6i"17.a» . 6.31 11.21
31.3! 38;i) 13. ?!. 6,5! -10.21
33, Oi 16.01. 'IS* 51 . 7.11 11.41
36.8! 13.41. 11.41 10.11 1J.6I
26.51 19.01 18,01 5^6! 11.7!
31.9! 15.41 14.21 8,0! 11.21
14.51 26.81 17.3! .6.3! 12.01
.14;OI 0.01 .14.2,' 7W9! 11.4!
21.0! 14;3i ,14.31 8J3! 1-2.21
25.01 16.4J 16.41 ,:'7.1I 11.21
15.01 0?Q! 17.1! ,5.9! 10.71
15.0! 0;0i 15.0! 7>6I 11,0!
J7.4"! 21.4! ,16.4! ,6.61 11.51
. 30VO 18.51 .1.8.5!' 4.91 10.11
9;o O.OJ .1,6.51 , 5,8 1 10,6!
17.2! 33.3! 17.31 , 5.71 13.41
P3.0! 0.0! 18.2F . 6,0! 12.6!
SJS! 19.31 16.8! 6.01 10.41
16.9! 32.61 17.2! . 5.7! 13.0!
H.QI o.oi .1.4.?! . 8wei ii;4i
39.3 17.5J 16,91 . 6J2 ' 11.4!
30.3 15, 2 i 17,d 6.5! 11.4!
»«•••••. | •»•»«••!•• \ «•»••«»• | •»•««.<• I !"«••»•«« f
26.91 15.91 16.51 6.61 11.41
.mmmmm \ mmm,mp> \ mfmmm \ ftmtfmf I mm.mm1* I
•••«••«•» 1 «•••••• 1 «ifnr» mm ( mmmm.'* \ mmmm<" 1
33.4! 22,5.1: J7.5! ,6.2! 12.81
15
-------
densities in combined sewered areas will increase the ratio even more
density. SS "* ***""** t0 be ™ increasinS function of population
NATIONWIDE QUALITY ASSESSMENT
Annual BOD5 loads were calculated for the 248 urbanized areas for
both wet and dry-weather conditions, the latter under the assumption
of 0.17 pounds per person-day CO. 08 kg per person-day). Annual loads
*JL°S^r Pafameters mfy Be easily calculated for any urbanized area
from information provided in Section y.
The national summary as shown in Table 1-8, Dry-Weather BOD Loadings
and Table 1-9, Wet-Weather BOD Loadings. Loading rates for untreated
dry-weather flow are higher than for wet-weather flow. However if 85
percent secondary treatment is assumed for dry-weather BOD generation
wet-weather loads are seen to he'one. third of the total residual
loadings i* urban areas. .Moreover, BOD. loadings from combined sewered
areas are comparable ' to loads ' due to secondary effluent,
COST ASSESSMENT METHODOLOGY
A generalized method for evaluating the optimal mix of storage and
Thif Sn, ^l deS±r^ leWl °f P°llutant control was presented.
This method can be used for any city in the United States to obtain a
first approximation of control costs. Five cities CAtlanta, Denver,
Minneapolis, San Francisco, and Washington, DC) were used in the more
flush ^ r J"; ^ effSCtS °f treatment Pla<* efficiency and first
rj.usn are included.
An evaluation was made of the relative desirability of using a mix of
storage with either primary treatment or secondary treatment. The
basic tradeoff to be evaluated is whether primary treatment is suffi-
ciently less expensive than secondary treatment to offset its lower
removal efficiency which necessitates treating a much larger amount of
flow to effect an equivalent BOD removal. The results indicate that a
primary type of facility is preferable up to BOD removals of about ten
percent. A secondary facility is preferable for higher levels of
control.
The annual average percent runoff control and the annual number of over-
flow events were correlated to permit the reader to use either criterion
as an effectiveness metric. A precipitation event was assumed to termi-
nate following 12 hours of no precipitation.
The final assessment: results (annual costs per acre) are shown in Table 1-10,
Annual Control Costs - Combined Areas, Table 1-11, Annual Control Costs -
Storm Areas, and Table 1-12, Annual Control Costs - Unsewered Areas.
16
-------
TABLE 1-8
1
EPA I STATF.
RFGI in
, 1 ! CT
1! ME
1 ! HA
1
. 1 I NH
1
1 ! RT
.11 VT
AV PEC 1
5 i . . Nil
.21. NY
AV PER 2
31 DE
3i PC
. 3! M.D
31. PA
31 VA
. 31 WV
AV PEG 3
. ai At,
. 4 i FL
a! GA
41 KV
4i MS
4 ! . NC
. 4 ! SC
ai TN
AV PEG 4
5! It
DRY-WEAK"
IM/YRI OP
AN'N!..| (I
PPEf.PI COMB
43.71 836.
03.51 51«.
.0 3 . 6 ! 7 0 a .
41.01 560.
ao.Ol 984.
35.01 544.
a 1.11 700.
42.8! 948.
38.1 12428,
40.512284.
45.01
41.0
fll.O
41.0
42.91
760.
1950.
0.
94?..
660.
41.0 555.
42.1
55.8
846.
0.
56.5 88?.
46.5
42.3
! 54.5
46.0
1 46.7
48.3
49.6
1 35.0
. 51. . IN. 1 37,2
,51 Ml
5! MM
1 31,0
609.
747.
0.
0.
0.
755.
663.
1236.
683.
875.
26.0 768,
51 PH 37.2 778.
.51 WT 29.7
AV PEG 51 32,7
1324.
94?.
ER POD
Y— WFAT
BP/ACR
STDPM
736.
. 0.
931.
0.
721.
0.
«51.
9?1.
13P3.
1 126.
760.
884.
830.
834.
86?.
748.
838.
73?.
PI?.
699.
837.
719.
718.
697.
785,
766.
450.
. 775.
750.
771."
896.
, 547?
688.
I n«Dl
HFR BC
E-YFAP
JMSFW
?8P.
318.
?81.
368.
P6J .
460.
?9().
?lfl«
27?.
229.
309,
. n.
29?.
?34.
?Sfl.
320.
?53.
302,
264.
349.
273.
330,
339.
351,
277.
304.,
295.
?70,
290.
257,
247.
35-1..
279,,
SIRS
0
)
AVER
521.
444.
554.
flfll.
546.
509.
533,
520.
1533.
979.
553.
T?43.
647.
60P.
60?.
49P.
62?.
473.
533.
490.
566.
490.
487.
488.
49.1 .
507.
676.
50P.
594.
49?.
531 .
522.
. 571.
17
-------
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
I
I
1
1
!
1
TABLE 1-8
1 1
EPA (STATE!
RFGI I'D 1
6!
61
61
. 6j
61
LA j
TX I
AV REP 61
7!
7!
7J
. 71.
IA !
. KS |
NE 1
IAV REG 7!
L
1
• 8!
. 81
8!
81,
81
I...I-.
. 91. .
. 91 .
! 9!
I.-.I..
IAV RES
1
IA
10!
JO!
v sip
IAV. u.q
i »..i..
CO !
MT- 1
SO |
LIT 1
WY C
; 8i
AK I
AZ !
CA f
HI 1
NV I
Wain
91
10 1
OR I
. !
— I.
HRY-WEATHER Ptin IOADINGS-
JN/YRi DRY-W.FATHFR- SOD
£nckA (I. BS/ACRE-YPAR)
P°ECPI COMB! STORM IUMSFW 'AVER
48.0 1
56.0!
9.0!
32.71
31.01
35.'3|
33.0
36.8
26.51
31?9I
14.51
21?OI
?5.0I
15?0
15?0
17.4
30.0
9.'0
17.2
23.0
5.'5
mmmmm
16?9
rr?o
39?3
430. I
0. 1
O.I
0. 1
1228?!
675.1
1763. 1
741."]
621.1
880?i
712. J
1238?!
O.I.
662?J
759?|
O.I
0?l
988. 1.
854?!
0.1
1539? r
O.I
893?!
ISO??]"
30?3
--*-!.
33.411
0. I
810.1
703?)
039?!"
712. 1
961. I
786. !
815. 1
766. 1
803. I
612.1!
714.'!
529j!
833?!
657. 1
798.1
657.1
662. 1
759.1
790. !
405. 48?
352. 749
?87. 506
25?. 464
278. 496
29J. 520
299. 473
328. 525,
465,
?60.
367.
290.
537.
541.
520.
555.
364? 52R.
384. 563.
328? 516.
271. 495.
693.! 349. 506.
760. 1
854.. !
761. 1
798. 1
841. I
303. 53?.
?27.
268.
262,
277?
777.1 277.
797?! "263?
657?!
780.1
785. 1
55"!-
405.
288.
302.
mmmm
285.
466.
49?.
617.
583.
->mmm*l
480.
"601?
526.
528.
525.
mmmmm
526.
mmmmm
594.
18
-------
I
1 I. CT !
mmm\mmmmm\m
, H ,ME !
mmm\mmmmm\m
:,ij- MAJ
""I (""NH"
M
i
>«••>••> 1
68.5!
1A9.6I 3B.7J 31.3 fe?.2
w™»«« | «,»«.«!«. f mvm^f *"*•«"
160;U S9.2I 30.1 36.2
!
OE !
187^8J*«oTi!"2912 11 64.4'
mmmmm^mmmmm
I
, , 31, DC. !
"3!""HD"|
mmmlmmmmm
. , 3! PA
mmm\mmmmm
31 VA
l""3l""wv'"
\mm»lmmmmm
1 AV. RES 3
|mmm\mmmmm
\ , 41. . At
iTSir'Ft"
l""2]r"5r
!*"4!.""KY"
i.""4! ""MS"
I!""«I""NC*
lmmm\mmmmm
! ,41. SC
|'"""4l"*TN"
1 I —,
LITRES ^4
JTii'Tit"
!'.**5!"™IN*
U—[— ---
I. . 51- • Ml
Immm|mmmmm
. 51. • MM
!'"75l"*OH"
tn^o|mmmmm
I 5 I - WI
I t M tt)
|.Ay*PEG.' 5
,OjOl 87.81
"IeToi"3Trol 35^6
,Jw,W»>S»|-«.?«»!fjW» = ^W
»!. W««3 |
41.01
"42!o
• ni. ^j~
41,0
"42?9
"srro
"flSTT
"sirs
"56?5
wir>n|^
HO,3> *«/'.»'. r- ».<•»•• —"-w~ ~ i
««»•»! I re'*"•*&'* \ mmmm^m I mmmmm \ '""5'^lT I
42?3I153.21. 37.2! 30.0 39.8
„„«,» I ,»»«B«» I mmm^m \ mmmm.m J
0.0! 46.2! 40.5 "2.9
""orOl'sSTol 34!o
mmmmm j MWW^" I »»«»«*«j*»
0.01,40.2! 35.3
».«^«< I mm mmm J -
*• ^"* ^ T* "TT'IT-r~~^^»1 • ^ .»
147.01 37.4! ?8»0
•i»w««pl»»»«^«l"«»«^l<"tl»""^"
147.8! 38.31 31.8 45.8
mmmmm | mmmm,m \ w^».^w j w«w-^-
137,61 38.61 S2.3 lOfl.l
».«»- J W«W«?- I IP.-^W """S^S
147^5! 37.81 ?9.6 47.9
mmmmm Immm^m ) mmmm,m ] mmm m,m 1
O.OJ 48.71 41.6! 44.41
„„.-» ......
„„-
190?01 51.0! 40.8 45.9
mmmm.m I mmmm,m \ mmmm.m \ mmmm.m \
159.71.40.4! 35W3 58.6
54.5
mmm,m
46.0
V«»ffP OB«t
46.7
48.3
36.0
166:21742:3! 34J7
mvw^m J „».».«;<» | w-w^" I "•"
.
37.21
"
"49^6 16l!oi,"'44r9!1"l7!2)^45.5!
.35.0
mmmm.m
37.2
31.0
mtnmam
. 26.0
13381.
,
?4W6 64.6
J, ^ -uf *p ** r • ft.^v*^**' •-• w i I
""'*" f "IHS I "?6!I I "68!8 I
I mmmmm I — «^- ! --*•*•
;27-1!-?-*-!"-UJ!
W9o!s!"22?!!"Ti^Ij ^52;!!
"37:2 i25?3!"3T!9!"?4?8r49?3j
"29!?iTi6!2 i"2537!"?i3i!"li?ij
mmmmm I mmfrnm \ mmmmm \ mmmrn^m \ *1™'""*™ J
32?7 124!0! 27.41 23.5 52.8
19
-------
TABLE 1-9
^**
STA-IE
REG ID
WET-WEATHER Bor< LOADING?
R WFT-WFATHfTR BOD'
- (LBS/ACBE.YPAP)- -
PRECP COMB I STORM fLIMSrWI AVER
t, , 6!
6!
61
. 61
. . 61
AR
LA
NM
OK
TX
AV REG 6
. . 71
7!
7!
IA
KS
MO
ME
AV REG 7
. 81
81. .
...j .
. 81
81 .
cn
MD
SD
UT.
Av"RiG~"a
, 91
. 91
91
9J. .
.AV REG
. to J
. .101
.»••<* J mm
»•['
AV. REG
AV U.S
AK,
AZ
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9
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20
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T*6(_E I-1C
EPA 1 STATE
IRFC. 1 10
"l CT
TI ME
, "l MA
II NH
1 1 RI
1 1 VT
AV' PEG 1
21 NJ
?l NY
AV REP ?
31 DE
31 OC
31 MD
31 PA
.31 VA
31 wv
A*' RFC 3
Hi AL
ill FL
. ai s*
fll K,Y
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til NC
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TABLE T-ll
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r 4 w . i ,•* f "4 . 11 0 v K £ rj '
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TABLli I-12
1
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-------
In order to obtain an overall wet-weather pollutant control of, say 50
percent in a given urbanized area,' the optimal strategy is to use a blend
of control in the combined, storm, and unsewered portions of the urbanized
areas such that the marginal cost of control in each of these three areas
is equal. The results are shown in Table 1-13, Optimal Percent Control for
Specified Overall Percent Control. Knowing this result and the control cost
equations for each type of sewerage system in each urbanized area the
optimal cost per acre can be determined as shown in Table 1-14, Optimal
Annual Cost per Acre for Specified Percent Control. Lastly, the costs per
CA?P™? multiplied by the acreage in the combined, storm, and unsewered
I 15 ESJS A ? ^ final assessment results which are shown in Table
for thePeur!re^rathan^ ??™ ^^ ^^ The results indicate that,
wet-weather control increase significantly. This is due to the
^ C°ntr01 Un±tS ^^^ t the less
An analysis was made of the possibility of cost allocation among wet-weather
wet weLh°f ^ dr^Weathf 4«*Ut7 control (with flow equalilation) and
STS? SUan y C°?tr01 (W±th St°rage "quired to reduce runoff rates -
« ,hn f ^ Te?UltS SUggSSt that sig*"icant savings might be realized
as shown in Figure 1-1, Single Purpose and Multiple Purpose Stormwatsr Pm^I
tion Control Costs for TTS, which indicates reductions ranging from 70 percent
at low control levels to 30 percent at high levels.' Percent
Son^other %
-------
fABLE 1-13 OPT
!Ee!STIOE COMB
"I "~CT" ""17"
IMAL PERCENT
25X 1
3TORMIUNSEWI
™! I "?!•?!
1 ME 27.9 O.oi 3.01
,mm\ mmmmm mmmmm ..... I ..... 1
1 MA 34.61 0.01 26. Ij
I NH 28.3
...1..... .....
1 RI 26.5
... 1 ..... .....
1 VT 32.0
0.0! 3.71
mmmmm ) mmmmm \
O.OI 0.81
2 NJ 29.0
""I ""NY" "277"
4.01 42.7!
"~276 1 "5475 !
CONTROL FDR SPECIFIED OVERALL PERCFNT CON
OPTIMAL PERCENT CONTROL
COMBISTOHMIUNSEw! COMB 1 STORM 1 UN3EW COMB 1
mmmmm \mmmmm mmmmm j ..... | ..... ..... .....
54.61 29.1 56.11 78.11 54,2 83.5 85.0
mmmmm \ mmmmm
60.51 18,4
53.2! 0.0
mmmmm \ mmmmm
50.51 32.1
28.1
*"i7" "*77"
i ! !
31 DE 36.7
3.51 27.51
31 PC 25.8 22.'5i O.oi
3 MO 0.0
~16.ll 46.2]
31 PA 32.21 8.5! 42.4
3 "A 44.6
... ..... .....
3 WV 27.2
3.81 36.51
mmmmm | mmmmm \
O.OI 6.51
1 II
4 AL 0.0
41 PL 40.9
4 GA 37.8
41 KY 44.0
41 MS 0.0
...I..... .....
4! NC 0.0
0 1 SC 0.0
... ! ..... .....
41 TN 39.0
1
10.41 36.3
9.91 43.0
0.91 II. 4
6.61 37.3
12.11 35.3
"II77! ~3477
1 13.1 | 33.9
1
61 in 27.9!
48. 9j 52.8
O.OI 40.4
...i. \mm-m- \
54.41 84.91
29.51 78.11
...... 1 ..... I
59.41 75.11
mmmmm \ mmmm_m \
?6.0! 81.81
1 1
70.21 77.9!
"""7"I""~75J
. !...,.!
54. "0! 85.01
O.OI 72.01
73.01 0.0!
55.71 33.51 70. 3i 83.31
69.21 27.8
..... 1 mmmmm
52.31 9.6
1
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63.51 B5.0
mmmmm \mmmmm
33.11 77.2
1 1
62.1! 0.0
64B7I 33.9! 69.21 85.0
62. 3j 25.4
47.41 85.0
68.41 30.6! 64. 0'l 85.0
O.OI 36.2
mmmmm \ mmmmm
O.OI 35.8
O.OI 37.3
mmmmm 1 mmmmm
63.31 25.2
1
5! IL 22.01 24. 7l 40.8 45. 7J 52.6
.i! I*1 28.4
51 MI 28.2
... 1 ..... .....
.51 MN 34.4
... ! ..... .....
51 OH 30.6
... | ..... .....
5i WI 18.6
0.0! 19.31 53. 7j 1/1.6
0.81 27.7
..... 1 .....
O.OI ?9.5
!..... 1 .....
O.OI 28.3
I ..... \mm-mm
1 20.41 35.8
52,61 25.9
"5875 1*2376
i "**7" 1*1 "7*
! "39771*467"
61.01 0.0
..... 1 mmmm^m
60.4! 0.0
1 *9."l 0.0
..... 1 ...«f .
58.11 85.0
I 70.41 71.9
46.81 78.3
1 54.81 77.0
"""71 1*8*71
mmmmm | mmmmm
56.9| 80.7
mmmmm | mmmmm
63.51 63.7
mmmmm mmmmm
42.2 81.2
0.0
mmmmm
57,8
~""7o
63.0
mmmmm
54.3
52.7
82.8
70.8
55.3
mmmmm
85.0
mmmmm
51.21
1
85.0
"*"7"
TR01.
S?§RM.IUNSEW
"8*7"i"e"7"
..... ..... .....I
85.01 85.0 85.01
85.0 0.0 85.0
mmmmm mmmmm | mmmmm \
85.0 85.01 85.01
..... ..... ..... 1
85.0 0.0 85,01
85.0
"""7"
1
85.0.
mmmm^m
85.0
81.0
0.0
85.0
62.41 85.0
61.5
"337"
62.1
"""7"
85.0
66,61 85.0
51.6
75.1
62.5 85.0
62.5
*6l73
85.0
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61. 6J 85.0
83. 5j 85.0
85.0
85.01
mmmmm
85.0
85.0 85.0 0.0
0.0
85.0 85. Oi 85.01
85.0
mmmmm
85.0
.....
85.0
. 1
85.0J
85.0 85.0! 85.01
85.0
85.0 85,01
85.0 85.6 85.6J
0.0
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0.0
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85.0
mmmm,m
85.0
.....
85.0
85.0
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85,0
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|
85. Ol 85.0 85. OJ
39.11 73.61 85.0
51.01 82.0
I "~77"l """7"
! mmmmm \ mmm~m
40.91 84.0
1 85.0
1 ......
1 85.0
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mmmmm
85.0
85.0 85.0
85.0
mmmmm
85.0
mmmmm
85.0
1 mmmm.m
85.0
1 85.0
\mmmmm
1 85.0
mmmmm
65.0
"""7"
25
-------
TABLE 1-13
E^AlSTATE
RECI ID
"*6
""S
WWW
6
6
"*6
AR
""LA"
wwwww
NM
.....
OK
"~TX"
WWWWW
71 I A
WWW I WWwWw
71 K3
•ww 1 wwwWw
71. MO
WWW
-.2
1 8
WWW
1...
NE
mmmm
""MT"
WW«*QW
ND
mmmmm
9D
mmmmm
8 UT
13
WY j
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91 AK j
9
""5
""5
""5
WWW
"T5
"T5
1".
* W
AZ
~"CA~
""HI"
"f3v"
WW W W W |
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..... t
OR 1
.....1
MA 1
COMB
"mllll
""b"7o
i""o7o
1.....
I o.o
— - - — ~ __• . - — 1_ ' *T V *" *•••»»' * I- "• <-/*(. I>riUb, r t,'\Wl 11 1 HJ
25X , §(J| PERffEKIT CnN|§9L
STORMIUNSEWI COMB I 3TOHM 1 LJNSEW 1 COMB ISTORM 1 UNSEW ! COMB
WWWWW, | WWwWW \ WWWWW f WWWWW
1.41 19.31 80.11 25.5
mmmmm I mmmmm 1 mm mm mm mm mm t mm mm. mm mm
1 wwwww
! ««•*
"85?0
~»™^w~» | mjmimi-m'mM | wwWWW I Www^W | mm mm 9* mi m* wwwww
_13.6J 54.31 .0.01 37.71 81.6 0.0
""772 1 "3176 1 "~o7o 1 "5175 1 "677s
""575 1*3975
1 wWwWw | wwwww i wwwww
25.71 10.21 39.1
I...*.
1 12.7
| wWwWw
15.7
| . O.Oj 29*4
1 ..... | ... — .
1 44.91 3/1.3
! I
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o7o
1 65.5 0.0
I wwwww
1 65.3
i •
1 36. 5 i 30.91 4l74l 63.9
mm.mm - 1'Z\ ^ '' i
27.7
""o7o
WWwWW
mlm'mm
.22:2
0.0
WWwWW
0.0
"3671
0.0
"1577 i
WWwWW
0.0
I!°P
mmmmm
0.0
wWwWw
31.9
WWwWW
J2:2'
O.OJ 4.2
0.0
[ 62.61 28.7
! wwwww 1 wwwww
[ 56.31 2/1.1
j 28.71 52.5 20.3
II.I!
1 36.7
!_____
-
""o7o
"I575r32^4i~7671
..... 1 ..... I — - ...
9.51 35.51 68.9
wwwww | WWwWw t wwwWw
3/1.0
ss7s
.....
69.2
| WWWWW
1 56.8
1"
WWWWW
1 71.1
1 WWwWW
i 64,4
..... 1 .....
77.51 85.0
.•-.. I mmmmm
85.01 0.0
..... 1 . — — ..
85.0 0.0
1 "6*177 i~8_J7o
o7o
!"6479l"857o "es"o
! !
53, 7j 73.5 85.0 85.0
.....
85.0
!~54.4
wwwww wwwww | mmmmm
?9.8| 81.21 49.2
56.3
70 9
76.8
1 44.8
I
73.8
wwwww wwwww wwwww
mm.'ll ---'-- — ?«?
"407I
"3J.76
w w-» w^
SB. 3
"6l7/»
1 67.3
1 .....
! 67.9
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85.01 67.6
WWWWW | WWWWW
85.01 61,8
6.91 41.91 . o.O 30. 9[ 67.8 o.'O
~Is7oi "36751
!__„_!
WWWWW | wtvwww |
0.3J 39.0]
1 WWWWW |
1 *_ 1
mmmmm
II!:2! !.!-£!
"To76l""67ol
"~27s I "35*761
— _____ 1 '_ _ 1
.....
WWWWW |
"1673 rso i
"""7o
27.71
..... t
23.8,
. 0.0
"6071
WWWWW
37.41
1
...... 1
2/1.41
WWWWW |
32.61
mmmm
""177 "38*76 j
""o7o
wwwww
54.1
mm mm mmmm mw i
35.01
WWWWW |
27.01
wwwww 1 vwwww |
1
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..(>:<>
mlmmm
.....i
__ * _ 1
2276 !
"157?!
62.*1 0.0
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64.9 85.0
wwwww | wwnww
..I:!1 °'°
~747«
"7779
..... i .... .
_80.8 85.0
"5574l"857o
1
85.0
WWWMW
85.0
wwwww
85.0
...••.t
85.0
! www-ww | wwwWw
| 64.3 85.0
85.0
.....
0.0
.....
85.0
.....
85.0
.....
0.0
1 ... --I----- --— --
wwwww
56.4
1..—..
65.1
WWWWW | WWWWW
_69.9I 71.1
""o7o
~6i77l"8o7r
.....
"597!
"547T
"077
WWWWW
""o7o
WWWWW
81.6
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82.5
WWWWW
68.2
WWWWW
53.5
wwwmw
85.0
85.0
.....
85.0
o7o
..— — . 1 .....
85.0 85.0
WWwWW
85.0
""o"7o
••••w t wwwww
85.01 85.0!
Ii
wwwWw
65.6
wwwww
47.2
WWWWW
44.3
"8475
WWWWW
80.1
WWWWW
76.6
""o7oj
WWWHW I
'" ! "UU
85X
1 STORM IUN8EW
1 mmmmm \ mmmmm
1 85.01 85.0
1 aw — — «t t — mmmm
I 85.01 85.0
1 WWWWW | WWwVw
1 85.0! 85.0
I WWWWW 1 WWwWW
1 85.01 85.0
1 mmmmm \ mmmmm
i i I
1 wwwww | wwwww
1 85.01 85.0
I wwwww | wwwww
1 85.01 85.0
1 WWWWW 1 WWWWW
j 85. Oj 85.0
85.0
mmmmm
85.0
85.0
WWWWW
85.0
WWWWW | WWWWW |
85.01 85.01
----- \mmmmm I
85.01 85.0
WWWWW | WWwWW
_|J5.0 JJJ5.0
mmmmm
85.0
"857o
"857o
"857o
WWwWW
85.0
WWwWW |
85.01
WWwWW
85.0
WWWWW
85.0
wwwww | wwwww
85,01 85tO
wwwww | wwwww
wwww.w
85.0
wwwww
85.0
WWWWW
WWwWW
85.0
.....
85,0
ww'www'
26
-------
EMT
fABLE 1-14 nPTIMAL ANNUAL (
:*»A STATE
?CSI ID
25X 1
CQMBI STORM IUNSEWI
... ----- "j;57j""ll7 "11".
».. ...... .».»«« I ...«•«• ....
1 ME 18.1 ^0. ^4.
"T "MA" ""4i7i "7 "57
swot mmmmm «aOw»«t |
1 NH 19.!
11 RI ^46.
*"I "VT" ""Is"
i
amm | ••••• ••»•«
2 NJ 55.
"I "NY" "Ii67
i...-.
31 DC
"31 ""DC"
""31 ""MO"
48.
.....
i25.-
11. ^10.
..... .....
"°i67 "°i^7
:tJST PE« *CRt rni«
0 Tlos ' V
COMB 1 STORM IUNSEWJ
*i!IlIIIi:l
"5o7i""o7
30.J
"117 1
ll«7j"°3l7j^287j
....^.....^..-.-^
133.
mm (• Wo
.49.
"1577
..... —j|-j
.....i
46.
"4507 1 "557
12. IT.
""517 """o7
.....j..... — |--
"31 "PA" ""627 i" H7 ""137
"I "~v*j""55:
..... — -|-
.. .....
.125:
"4607
I"".:
Ijji:
._.!
..22:
30.1
"iT«7 "~~o7|
""57
56.1
»_« 1
"l837j""«97 ""41, i
"T5&7
4 AL 1 0.1 15. 21.
""l"""
"ToT7 \""W. ""47
"4 ! "5 A"! ""477 l"!^ ""IT7
... I .....
4 , KY
""41 "MS"
"H7
— S7
"*4l""c~ """S7
""4 "55"
~I~2*
1 1
...I.....I— ...
"Ii7 ^"137
"117 ""IT7
..... — ---
"157 "^T|7
"• li" ""57
"51 "IN" ""3T:i"""o" """7"
""51 ""Hi"
.....
...I..... i.— i
51. MN 30.
...I.....
51. OH
...1 ...e.
51 WI
«*«90*CP
! ->"-- — --
"*"o" ""T
113 112
""117 ""15
"397
",2!sl
..12s I
0. 59. 54.
"2667 ""57
"IIi7!"327
3!ls
""o7
.....
mmt» 9Btm
' 2:
""17
""557
"lit
.....
W*aw«»
..21: '
"i^
"497 "Si!
"°io7
"*627 ""/ill
46.
"367
"1557 l"~467l ""467
"I?: I "517 1 "157
"Toil !"Ii7! "237
•»B»a*»w
80.
"TII7
CV0W W
.if!:
"Io7
"157
t 4>a»w«*
18.
""Ii7
"*367i"~34j
si*uuir
A u U run^u'1!! vL.'tip(\v*^
PER ACRE
COMB 1 STORM IUNSEWI COMB 1 STORM UN8EW
°37l7|
I!!!:!
"3557l
"l477l
^396. j
"l377i
Il2I:l
i9i57j
|
374.1
16967
m • -<•» i
...2:
"6i27
°3ol7
"l497
..-.2:
.61E.
"Ilo7
"350*7
111°:
"3877
"6677
"III7
•IVM0W
285.
"5117
"0&7
l"4597
92» -8I° .51°:L2":
°""o7i"I17 ]|T*' •!""*£:
.*.•
"To7j
....ii....: ^«__= i .».»i !.»»»:
196.1 0.
....:i«...: ....:
O.j 33.
lllll
"4257
—22:
~4367
»BW«^ |
"?I7
157.
10687
113.
297 *!...:
"""7
"lii:
130.
131:
"?47 2797.J2271. 74.
"^877
o7
.....
. I.....
35467 "48l7
..... f .....
: ' ' »»
101,
III!:
233.1 90. j O.j 430. -<(90.
"l7l7|""737 "7 Ii7j "4747 "*7S7
"ill:
"Hi"
„,
178.
..82:
.....
127.
305. 473.
"Io67j*36o7
0.
"l7T7rTiI7|"6ro7
""9o7l"l37
"164°
i"l647
111!.
1.121:
1 115.
j;ig:
(....«
!"~697
310.
84. 350.
"i'2:
0.
3^*U»2:
"Ii7j""o7
""997
340.
88.
»o..o;
101.
1277
"ITS;
3S:
"iiilu.lS:
"4oI7l"Tl3.
"3487
311:
387.J 458.
"?87 T?2?7
""527 i "5o57
"fe37! "§967
j"527j^~«77
229.
"isn:
flVW «•«•<•
364.
114.
3in
99e
""i:
""7?:
270. j _70.
"in:
ri!!:!ll*!l I!t!sil.iii
81.
13?
i"Ti77i"777 TIo57j"ni7 -WJJ«
27
-------
, TABLE I«14 OPTIMAL ANNUAL CCST^ER^ACRE FOR SPECIFIED PERCENT
IEPAISTATE
IREGI ID
I www | mm mmm
1 61 AR
25X
COMBISTORMIUNSEW
45^1 ""7^ ""75"
j»ww|..ww. Iww.w. j...~ mmmm
6 LA 0.
1 www 1 wwwww 1 mmmmm
I 6| NH I 0.
1 www 1 wwwww 1 mmmmm
1. .1 °. ' °*
|""6l""TX"|""827
1 ww. { www.w | mmmmm
\mrnm
j •
j 71 IA
""{""KS"
""(""MO"
WW. 1 WWW..
49
WWWWW
""28*7
! W.w.w
m.l\m,mm.\ mmmmm
mmm
""a
i.w...
I...2.
I'mJml.
53 . 44.
mmmm mmmmm
7 6.
""TJu ~"l2T
""lil ""I"
""Til ""TH
"To" ""To7
wwww — .wwww
0. 7.
mmmmm mmmmm
I
WWWWW 1— -- _
— .*;
{""a; ND i 37.
1 WWW | WWWWW | .....
8
mmm
mmm
1 8
l.ww
9
WWW
9
9
""£
1 9
10
ww.
wi2
!.i!l
j SO 1 44.
'...I.!....;
! 1111"! "I
| AK j" 627
1 WWW.W |w..W.
| AZ 1 0.
I WW... | W.W..
..C- | w63*
Hi"
""NV"
ID
mmmmm
nD t
0.
WWw.W,
w.il:
— .•
; OR 1 6i.
.....I W.WW.
.....!....:
mm i_S ..U'
""T57 ""TT7
.HE: """'"
_COMB 1 STORM 1 UNSEW 1 COMB 1 S?§^M 1 UNSEW
1 . . 1 w.www ..... J ww.ww
www.W wwwwwl . 1— — --I 12*»j 110.
..„.' _l/|8.j""2T7
III.:
""o7
IEiI:
,.18'i 16«
""377j"~327
mm 6°9*' 13*"
"""o7
mmmmm
0.
mmmtf m | •*•»•*.»•» nw •§•»•»
41.1 36. 743,
""637j""3o7
..... | mmmmm
-...:' 66
"1397
.""II ~~: ! -•— —
J347
-ii2:
.1!!:
"1157
W..W.
• o.
•i •»•».••»
36.
"°277
1 WW.WW
18.
132
"in
«*•»•*•»«
30.
"337 1*4727
HI!.
•»•»•>•?•»
126.
"2877 1 "~757
mmmmm ] mmmmm
202. 47.
mm •»•»•»
230.
mmmmm
mmmmm mmmmm
29. 412.
mmmmm \ mmmmm
..!!.•!.••
mmmmm
mmllm
mmmmm
mlmmm
1 «"•»«"•»
75.
wwwww
CPNTROL
COMB STfi^MI UNSEW
mmmmm. mmmmm mmmmm \
141. 372. 146.1
mmmmm mmmmm mmmmm \
0. 1096. 138.1
mmmmm mmmmm mmmmm \
0« 139. *0.
mmmmm
0.
mmmmm
1665.
WW.WW WWWWW
73 2688.
mmmmm
-mlmm
""477
mmmmm
50.
mmmmm
mmmmm
~..i
"~627
""TT7 ""To7 "116^
mmmmm mmmmm mmmmm
12. 11. 0.
mmmmm mmmmm mmmmm
12. 11. 0.
mmmmm mmmmm mmmmm
15. 13. 160.1
mmmmm mmmm-r mmmmm !
6. 5.- . 0.
mmmmm mmmmm mmmmm
mm i°. 10*
— ... —-JJ-
w.2fl'! 26«
""177 ""277
mmmmm mmmmm
31. 28.
mmmmm
m.llm
••••w. wwBk
mmmmm
T*X
mmmmm
22l7j
mmmmm \
mm
mmmmm
«26.
wwwww 1
.Immm m *"* '* 1 2?&r
•»«•»»•»
75.
WWWW.
""737
"827
mmmmm
147.
"I.-.
mmmmm
mmmm.
140.1 62.1
— -— — — . .WWW | WWWWW
mmtm'm .mm* ml *** '
""si)7 """o7'"2437
— — — — — W — WWW 1 WWWWW
mmmmm °'1 185'
"~697
WW.WW
25.
•ww.w I wwwww
426.1 426.1
mmmmm
62.
•»•«*•«
50.
WWW..
ww6w*
mmmmm
69.
•wwww | wwww. | www..
0.! 112.1 25.
WWWWW ..WWW | WWWW. I.W.WW
tO. 201.! 170.1 40.
t*mmnm
61.
""TS7
54.
...!: ...hi.ff.:,'^^- "5^
33. 28.1 2847l""887 ""737
w.w.w mmmmm\mmmmm\ mmmmm \mmmmm
""o7
7427
mmmm
mmmm
5097
wwwww
M4.
"6o7l
""6l7
""T37
wwwww 1 mmmmm
|
""157
469.
WWW. | ..WWW
314.1 432.
wwww 1 wwwww |
mmmmm
55.
112.
" * •" •
-S
28
-------
TAB'. I: 1-15
IEPAISTATE
IREGI ID
""71 "MA"
"7l" NH
---I .....
It PI
...I .....
1 I VT
._-I----•
n. "»t:B j.
""31""oc"
""'""n"
"31 ""PA"
.....I.
6.7i
...,-l.
. 2-1!
"7771'
.....i.
H.2I
OF
li ' "$".5 1
'5.4! 73.SI
..,.1, 1-
1.91 5.ai
*57i) i ~""37£ i
.-,_l-~----l.
Q <$! 1 . 'I I
5t
8T6 1 "o7(i39 1
o" 1 3 1
8*771 0.0061
?472l"o7(i75j'
0.2121
_c.af»- I
0.0161
o7o«2 i
0.330J 0.5731
"o7o931
ma»mmom~7« i ~""7«' "I?761 ""i",? i "o7"Ic i
"""67" I ~~~77« I """" I "~9o77 I "7(157 j
""777«i""577si~75"7i"2937?i""7746
ouol
S7299 1
77209 !
T7l 75J
"961 1
"47517 1
"5797I i
"
"17273 1
"77918 1
7o7392 1
7175701
0.1461
"o7\ O.(l2?l
[9l"("7o43 I
..l_^....| ,
.11 0.0241
0.0711
o7o58l
,.._.-I
0.0631
o.631l
0.2101
"o77sir
"o7764l'
0.4091
r..... I
0.2551
tf» »««<• I
0.5001
'o7268l
>l
41 TM
___! .„---
TI
s./n
"4772 1
31
[3 I""7394l'
0.1191
"77o43j
57838
n.599l
i7o7oi
3.6471
"o77?5l
5
?P.5i 85.Si '63.M 437.01 O.?38j £.714 I_2.200j
"676l'""7:r>74|""^77l""«679l "()7"55I 0.1621 0.4351
51
5^
'7279i""3i72i""96r7ri557«i""o7T6«i~0.294r 0.802J 1.2971
• I.
51
35.
"«".
"a?"
0.2941
"o7o74l"
"o7357|'
* T'
• i-
51 MM
..!_._--
51 nH
...!:SI
12.61
»l
:. I •
81
.31 4?
"«r224
,i'll 0.0221
!7l"o7"o5l
23
77s
575l""7i76i""4u7?i~*73iri~0~o46i 0~130j 0.371J n.652j
0.195]
"967
•l«
0.3541
"778751
51 to I
15.61 44.51 75.II 0.0461 0.1301 0.3711
T""RE"" "687? i "20771' "S9s7^ I 7o247^l I ""?73 I "77730 I "Z7970 I 8.549 i
29
-------
TABLE J-15 0PTIKAI. AWMUAL 4NO C*PIT4|
—'-- -lnPTf
PAJSTATE
RFGj 10
""l""AR
...I....
61 LA
...I....
61 nK
...t....
61 TX
TL"725.415029.0
rM>Ol«a«J f
0.0521
>*»»«VWM |
0.0111
O7o06j
O7o08l
b"7o26l
0*7o06l
"77o*9l
'o"7o()6l
'o7o21l
'«VWW I
0.4941
mmmm** I
mmmmmmI
^0.1421
"o"7o27
"07018
mmm»mm
0.023
mmm»tam
0.068
"o7oiii'
""i^ir
12*2^!'
I2:£§!'
""7439
•••"••• i
0.0031
-
. . J.587
"o*7oo8i"o7oar
"07(139!
- .... . 0,230
"o7l24ro7Il7
0.0411
......f
0.0281
•m.m.m|
0.0391
......|
.2:llf
"o7o?Si
"S7494I
......i
^0.0341
"o7o96l
"57400!
••><••!<•• |
0.1131
B>..... |
0.0!6|
*.....I
2.6981
•....«|
0.0331
»•»••»•••• |
0.3991
>.T...i
0.5771
......(a.m...
7.391122.744
......(....„.
30
-------
540O
EQUATIONS- '
89.9aO.046R,
SINGLE PURPOSE : STORAGE - TREATMENT
ONLY
MULTIPLE PURPOSE : PORTION OF STORAGE
TREATMENT COSTS ASSIGNED TO OTHER
PURPOSES
SINGLE PURPOSE : STORAGE-TREATMENT
AND BEST MANAGEMENT PRACTICES
SINGLE PURPOSE : STORAGE -TREATMENt
ONLY - RESULTS FOR COMBINED
SEWERED AREAS
CO
o
O 2400
U.S. URBAN POPULATION = 149 XIO
U.S. DEVELOPED URBAN AREA = 15.6 X IO6 OC
% BOD REMOVAL , R,
Figure 1-1. Single Purpose and Multiple Purpose Stormwater Pollution
Control Costs for US
31
-------
~ The NCWQ estimate includes '
for constructing storm sewers. This study
does not view storm sewers as chargeable to pollution
control.
2. Choice of a Design Storm - The NCWQ studies used control
°f,^,tW°/ear' °f hour storm as the basis for their mean
estxmate of control costs. The concept of a design storm
was not used in this study because it was felt that a
continuous characterization in terms of percent of the
runoff which could be treated was more appropriate
since no accepted design event condition exists which
also specifies a design antecedent dry-weather period.
Figure 1-2, Overall Percent Precipitation Control vs. • '
Rainfall Intensity. Atlanta, GA (1948-1972^ gh™.Tg «.vn-
using a frequency of one month would permit, capture of
90 percent of the precipitation volume. Sizing for the
two year, one hour storm yields relatively little in-
cremented control and requires a much higher control
volume .
Improved estimates can be obtained using local data, particular
"* knowledSe °f the numbers of oStfalls permits
of
RELATIVE IMPACT OF WET- AND DRY-WEATHER FLOWS ON RECEIVING WATER
on J f^< S °f Wet weathe^ the urban runoff
contribution of BOD is the most significant
among all of the urban BOD sources;
32
-------
q
to"
to
OJ
o
rf1
cc
O
h-
co
H
CO
UJ
CvJ
+J
•r)
O
o
. S ..
0 - . - -
UJ
UJ
UJ
O CO O h- U_
O TJ
-------
2* IoLSXifin§ treatment facilities in Des Moines,
XUWel. US. T)Ot"r»^-r» •*- f\f f-T-i— - — *. f '
ot the wet-weather events were
cted e
a 4 0 S/! 7- ^ mathematical models to violate
a 4.0 m/1 minimum DO standard. During these
3. During periods of dry weather, effluent from the
secondary treatment facilities violated the same
stream DO standard two percent of all the dry^
weather days in 1968. y
4. Combining the effects of wet weather and dry
weather, the models predicted that DO standard
violations would occur 33.total days out of the
5. An evaluation of costs incurred indicates that
25 percent BOD control of wet-weather flow,
while providing secondary treatment of dry-weather
sanitary flow, is an effective treatment strategy
Violations are reduced to 26 days out of the year
at an incremental cost of approximately $800,000/yr.
6; The benefits received from a reduction of shock
loads from urban runoff are not readily quanti-
fiable but should be considered when compared to
strategies that involve, high levels of municipal
wastewater control.
-------
SECTION II
RECOMMENDATIONS
DEMOGRAPHIC CHARACTERISTICS
There does not exist, at present, reliable information regarding land
use in urban areas in the United States which is developed from a
consistent set of assumptions and definitions. This information is
vital to assessments such as this one which are based on contro costs
per acre of "urban" land. A comprehensive and meticulous data gathering
effort is needed to provide the improved information base on land use.
Significant gaps exist in available information on population and area
served by" type of sewerage system. More refined estimates are needed
Xr combLed'sewer systems . First estimates need to be made of star*
sewered and unsewered areas. Also, the number of overflow points needs
to be inventoried. This effort would need to include an unambiguous
way to distinguish "drainage channels" from "receiving waters." Given
some standardized definition of terms, much valuable information could
be obtained from current USEPA 208 planning studies.
RUNOFF AND QUALITY PREDICTION
Both runoff and quality prediction techniques may be improved through
use of additional data! Since a large amount of data prese ntly exists
it needs to be aggregated to enable comparative analyses to be performed.
In tnis manner, useful statistical and regression relationships can be
developed incorporating several demographic and hydrologic paramet ers,
and regional variations may be more easily discerned. J^^Stlon
part of current USEPA 208 studies should be incorporated. In addition,
studies are needed in which, both, surface (e.g., dust and dirt ) and
effluent Ce.g., concentration), data are gathered simultaneously so that
their relationship may be determined.
•Future reports containing data should be careful to define precisely
m.ZXS?£^x£^^
should be reported for each area on which sampling is being conducted.
35
-------
COST ASSESSMENT METHODOLOGY
The general methodology appears to work well. It needs to be extended to
cover a wider range of situations. More sensitivity analysis is needed?
treatment
need
The methodology should be extended to account for the interrelationship
detentf" r trf tment especially at higher levels of control ?
^ K timeS/n St°raSe are significant. Associated with this
A a Udy °f thS lmpact °f diffe^nt reservoir operating
A constant release rate is assumed at present.
The isoquant equations should be refined to account for snowmelt. An
annual ove?: flow event* and
analys±S of the cost Allocation formulation needs to
IMPACT OF URBAN WATER POLLUTION CONTROL ON RECEIVING WATER QUALITY
In order to have basic information on the behavior of receiving waters
cfacit cont *" P°11Utant StreSSSS beyond their natural alsililative
•rnrif-fT^r.' C°* nuous "ydrologic models coupled with pollutant transport
routines must be applied. It was found throughout this study that large
available; however, these data were somewhat less
leling purposes. In the area of data requirements
specific recommendations are;
1. The water quality indicators that will be used
for planning purposes should be clearly identi-
fied before actual data collection.
The data collection system should be designed
to be representative of the receiving water
being investigated. Flow velocities, diffusion
and dispersion coefficients, tidal cycles, etc.,
affect the frequency of sampling.
Sampling of receiving waters should be conducted
before> during, and after periods of urban runoff.
2.
3.
36
-------
4 The laboratory procedures utilized should be clearly
described, for example, whether natural or artificial
(deionized) dilution water was used in performing the
standard BOD, test and the particle size and settling
velocity definition of suspended solids.
5. Kinetic reactions of biochemical tests (i.e.,
deoxygenation rates of BOD) should be reported. ,
and compared with other locally obtained values.
6. Additional data on photosynthesis, algal respir-
ation, and benthic demand of water bodies are
needed.
7. Measurements of the nitrogenous oxygen demand of
waste inputs and the receiving water 'are needed,
as they are becoming more significant since greater
numbers of secondary treatment plants are operational.
8. Both mass loadings and concentrations of pollutants
should be estimated and reported.
In the realm of modeling efforts, further work is required:
1. The response of receiving waters to urban runoff
and dry-weather flow inputs should be characterized
when storage of waste streams is considered in combi-
nation with treatment.
2. Simplified techniques to approximate the complex
mechanisms of pollutant transport in lakes, bays, and
estuaries should be developed.
37
-------
SECTION III
DESCRIPTION OF THE URBANIZED,AREAS
This section presents a summary and -analysis of data on the followine
characteristics of urban areas:
• population, land area and location; and
» population density and land use distribution.
These categories are discussed below.
URBAN AREAS
Urban areas in this study have been taken as the 248 urbanized areas
defined by the Bureau of the Census of the US Department of Commerce
in the 1970 census and other urban areas. The 248 urbanized areas
defined in 1970 are generally characterized as having:1
9 a central city or urban core of 50,000 or
more inhabitants;
closely inhabited surroundings, consisting
of incorporated places of 100 housing units
e or more; and small unincorporated parcels
with population densities of 1,000 inhabi-
tants per square mile or more (386 per square
km); and
other small unincorporated areas that may
eliminate enclaves, square up' the geometry
of the urbanized area or provide.a linkage
to other enumeration districts fulfilling
the overall criteria within 1 1/2 miles
2.4 km) of the main body of the urbanized
area.
The distribution of urbanized areas across the United States is shown
in Figure III-l, 1970 Urbanized Areas and Five Regions.
The choice of a sample of 50 urbanized areas was based upon a
representative distribution reflecting variations in climate and geo-
graphy. This sample of 20 percent of all urbanized areas was selected
38
-------
co
I
&0
U
0)
o
!=!
o
39
-------
for further analysis. The 50 selected urbanized areas are listed in
Table III-l, Demographic Characteristics of the Urban Areag.^ along '
with the remaining urbanized areas and the residual urban areas not
located within urbanized areas. These 50 urbanized areas are denoted
under column 7, "CODE" with a C. All other cities are coded "E" The
population distribution of the sample of urbanized areas was" as follows:
Population
Range
50,000 <_ Population <_ 100,000
100,000 < Population <_ 250,000
250,000 < Population <_ 500,000
500,000 < Population <_ 1,000,000
1,000,000 < Population
Number of
Urbanized Areas
12
11
7
13
A sub-sample of the five cities selected for more intensive analysis
(San Francisco, Denver, Minneapolis, Atlanta, and Washington, DC) is
shown in Figure III-l along with the five regions into which the
country was partitioned.
POPULATION, LAND AREA, AND LOCATION
The 1970 census population and land area for each of the 248 urbanized
areas were obtained from the County and City Data Book, 1972.1 In
addition, the total urban population for each state was obtained from
the same source. If an urbanized area encompassed more than one state,
its area and population were apportioned to the states based on the
population of the major cities constituting the urbanized area. The
results, presented by state and EPA region, are shown in Table III-l.
POPULATION DENSITY AND LAND USE DISTRIBUTION
The overall population density for an urbanized area may be obtained
using the data in Table III-l. In general, population densities have
decreased during the past generation reflecting the availability of
improved transportation systems, the desire of individual home owner-
ship, etc. No detailed data on urban land use for all of the urbanized
areas in the US could be found.
For nine urbanized areas in Ontario, the area occupied by each of the
following five types of land uses was determined: residential,
institutional, industrial, commercial, and open space.2 Land use maps,
if available, were used. Aerial photographs were employed if land use
40
-------
TABLE 2II-1 DFMOGRAPHTC. CHABACT
LEPAISTATE) URBANI7ED AREA
LRPGI ID 1
11 CT 1 BRIDGEPORT
11 CT (BRISTOL
ii CT IOANBURY
11 CT (HARTFORD
11 CT IMERIDEN
11 CT (NEW BRITAIN
1 1 CT INFW HAVEN
11 CT INORWALK
11 CT 1 STAMFORD
11 CT (WATFRRURY
11 CT (OTHER URBAN ARFAS
i i
i i CT ITDTAL FOR STATF.
11 ME ILFWISTON
1! MF IPHRTLAND
11 ME IOTHFR URBAN APFAS
1 1
11 ME ITOTAL FOR STATF
11 MA (BOSTON
11 MA (BROCKTON
1 1 MA IFALl. RIVER
11 MA IFTTCHBURG
11 MA (LAWRENCE
i i MA ILHWFLL
11 MA (NEW BEDFORD
1! MA IPITTSFIELD
1 1 MA ISPRTNGFIEI D
11 MA (WORCESTER
11 MA IOTHFR URBAN APFAS
1 " 1
11' MA ITHTAL FOR STATF
11 WH (MANCHESTER
11 NH (NASHUA
11 NH IOTHFR URBAN APFAS
1 1
11 MM I THTAL FOR STATF
11 RI IPROVIOENCF
11 PI IOTHFR URBAN AREAS
1 1
1 1 PT (TOTAL FOR STATF
11 VT IIJPBAN AREAS
1 1
1 1 VT (TOTAL FOR STATE
11 ITHTAL FOR REGION l
21 NJ (ATLANTIC CITY
§1 NJ INFW YORK CITY METRO
1 NJ (PHILADELPHIA METRn
21 NJ (TRENTON
21 NJ IVTNFLAND
21 NJ ITOTAL FOR STATE
21 NY IA| BANY
21 NY (BfNRHAMPTON
21 NY (BUFFALO
21 NY INF.H YORK CITY
21 NY (ROCHESTER
21 NY (SYRACUSE
?l NY (UTICA
21 NY IOTHFR URBAN AREAS
21 NY ITHTAL FOR STATE
. 21 . .IT"TAL FOR RFGION 2
rRisnr
1000
ACRFS
05:
»:
41:
25.
6P.
27.
45.
3P.
7?.
. 550 .'
44,
36.
156.
235.'
•H:
55:
It:
2?,
28.
15?:
54.
91.
966.'
•™:
?S:
. 1?5.
156.
6.
16?.'
35.
. 35.'
208?.
47,.
130«,
31.
4?.
54.
1479.'
97,
33:
137.
243.
93.
61,
48,
117.
830."
. 2309?
S OF THE
197" POP
in On
— -sis:
7i-
67.
465:
9s:
m:
346.
107.
185:
157.
301.
?344.
"""65:
m:
507.
. ?t»:
^ :
200:
!«:
134.
63.
514.
247.
454.
/'813.
05.
61.
261.
417.
7«5.
31.
8?6.
143.
143.'
P050.
134.
«?688.
202.
274:
74:
6372.
486.
167.
1086.
1^519:
601:
376:
180.
?196.
15611.
21983.
URBAN AREAS
1
POP 1
AVE PDICODE
4.331 E
3.041 E
1.901 E
5.551 C
2.161 E
5.251 E
5.081 E
3.981 E
4.131 E
4.191 E
4.191
1
4.191
1.491 E
2.961 C
2.151
...:1!L—
6.241 C
4.391 E
5.051 E
2.001 E
3.72" E
4.611 E
6.161 E
?..?4I F
3.371 F
4.591 E
4.981
4.981
3.811 C
2.801 E
3.341
1
3.341
5.091 C
5.091
5.091
1 4.1«l E
, 1
4.141
4.^51
3.131 E
4.351 E
( 6.44" E
1 6.591 E
1 1.361 E
1 1
!...:..!.—
! IJi: i
1 7.931 E
1 43.251 E
1 6.431 E
1 6.121 E
I 3.751 E
1 18.821
! 18.82J
1 9.521
41
-------
TABLE III-l DFMDGRAPHTC CHARACTrRlSTlrS OF THF
1070 nfipj
r,,.,.,STA,IEI "RBANI7En ARFA
RFGi i" i
"31 ""OF" i WILMINGTON! ""
3 nr IOTRFR URBAN APEAS
3[ OF ITOTAL'FOR STATF
3[ nc [WASHINGTON"^"""
FOR
DC MFTPfl
OTHPR URBAN ARFAS
3 I MD
31 nn
31 MD
I
..!! M2 ITnTAL F|""* STATE
31""PA"|ALLFNTOWN " """
31 p* !^LT!?OMA
PA
31
31
1
II
31
31
31
31
3
IHARRTSBURR
PA IRFADIMG
PA ISCRANTPN
IWTLK-FS-BABRF
IYHRK-
IHTHFR URRANi APFAS
PA
PA
PA
31 PA iTHTAL FOR STATF
3
VA MFWPOBT NFWS
VA 1 NORFOLK
VA IPETPRSBURr:
VA IRTCWMOND
VA IROAMOKE
DTHFP URBAN A»FAfi
VA [TDTAL FOR STATF
"wv"iCHARLESTON"""""""
,*iX !Hi|!^Tif(JGTnM
| 1
31
3j WV lOTHFR URBAN APFAS
3| WV ITnTAL FOR STATF
.. .....|....................
3 ITHTAL FOR RFGlOM 3
•-!-----|--..-..__....._.__..
1000
ACRF
"""70
5
75
3"
ill
13.
II'-
^Io:
381 .
63l
53.
15ft.*
1350.
•3f
100.
0 .
56°;
1 n 0 0
'IPBAN ARFAS
POP I I
AVf: PC
3«5 ,"|
"757"!
757.!
"""96l""E
6.161 F
30r,5.'| 7.461
.
1P3.I
101.1
416.1
1251.1
ill
6.411 E
ft.21! E
4.P3I F
5.361 E
4.691 E
A.491 E
4. A4 I F
!:§?! 1
5:f«! i
ft.241
6.241
...... I....
3.001 E
2.931
3.491
3.761
4.4AI
3.721
12.451
5.15'
E
E
E
C
E
E
5.151
I
40.1
93:i
2ft8. |
6flO.'|
'Tft203"i'
3.341 E
5.191 E
4.21 1
>l<
42
-------
TABLE III-l DEMOGRAPHIC CHARACT
1 1
EPAISTATEI URBANIZED AREA
IRFGI ID 1
l
19999999 9
1
1
01
01
01
01
01
01
01
01
01
01
01
1
ti 1
til
til
til
til
til.
01
1
til
til
til
01
01
01
1
til
ti 1
01
01
til
01
til
til
til
til
til
til
01
01
01
1
01
01
01
til
til
1
01
01
01
til
01
01
41
fll-
AL IBYRMINGHAM
AL IGADSDEN
AL IHHNTSVILLF
AL IMOBTLE
AL (MONTGOMERY
AL ITUSCALOOSA
AL IOTHFR URBAN AREAS
t
AL 1 TOTAL FOR STATE
FL IFT.l AUDERDAI.F
FL (GAINESVILLE
FL (JACKSONVILLE
FL (MIAMI
FL 1 ORLANDO
FL IPF.NSACOLA
FL IS7. PETERSBURG
FL ITALI AHASSEE
FL ITAMPA
FL IWFST PALM BEACH
FL IOTHFR URBAN AREAS
FL (TOTAL FOR STATE
GA IAIBANY
GA (ATLANTA
GA 1 AUGUSTA
GA (COLUMBUS
GA IMACON
GA (SAVANNAH
GA IOTHFR URBAN APFAS
1
GA (TOTAL FOR STATE
KY IHHNTINGTON MFTRO
KY ILFXTNGTON
KY ILOUTSVILLE
KY (OWENSBORO
KY IPTHER URBAN AREAS
1
KY (TOTAL FOR STATF
MS I8TLHXI
MS IJACKSON
MS (OTHER URBAN AREAS
f
MS (TOTAL FOB STATE
NC IASHEVILLE
Nf (CHARLOTTE
NC (DURHAM
NC IFAYFTTEVILLF
NC 1 GREENSBORO
NC IHTGHPOTNT
NC IRALFIGH
NC lnTNSTON.SALFM
NC (OTHER URBAN AREAS
1
NC (TOTAL FOR STATE
Sr (CHARLESTON
SC (COLUMBIA
SC IGREFNVILLE
SC .IOTHFR URBAN AREAS
1
SC (TOTAL FOR STATE
TN ICHATTANQOGA
TN IKWOXVILLE
TN (MEMPHIS
TN 1 NASHVILLE
TN IOTHFR URBAN AREAS
, T*J !TOTAL FOR STATE
1 TOTAL FOR REGION . 0.
ERISTIC
1000
ACRFS
10'J.
J:
Ji
683."
166 1
80.
0?.
103.
8ol
87.
310.
33*.
01 .
697.
13.
26.
130.
ft.
120.
30 u.'
01 .
189!
?76.
20.
6ft.
07 1
39.
fs:
4? I
306.
65!.'
63.
66.
169!
. 30U.
IS:
iSS:
203.
719.
09«9.
S OF THE URBAN AREAS
1 1
1970 POP! POP 1
IfiOO IAVE PHICODE
558.1
66.1
106al
P6*.l
POlJ.'j
610.1
69.1
530*1
jlfjj
1330*1
1
•5065. 1
1173,1
209*1
l?8.l
160. 1
86911
1
17.1
160.1
739*)
6«8* 1
121.1
190.1
676.1
9ft7.'l
280*1
10ll{
107Sri
157^1
606.1
.1233.1
220^1
660^1
ooa.i
?307.'|
l«705.l
1*931
1.851
2.001
0.261 ,
3.051
I
0.531
3.721
2.361
7.361
3*951
O.BOI
0.061
«.00 1
3.311
0.291
1
3.601
3I08I
3.921
a. oo(
3.971
3.971
3.731
6.25!
5.501
6.901
5.5til
5.50!
2.951
0.121
3*571
3.57i
2.961
31671
3.05!
3.P9I
2.B2I
3 'ill
3*361
3.511
3.511
3.601
3.671
3.ti6l
3.591
3.591
2.9
-------
TABLE III-l DEMOGRAPHIC CHARACTERISTICS OF THE
|EG'*TIOE! URB*NIZEO AREA
51
51
I 1000 11970 POP
•ACRES! 1»00
i 1 AURORA
L IBLOOMINGTON
L (CHAMPAIGN
TL
IL
(CHICAGO
JDAVFNPORT MFTRH
IL IDECATUR
T
1 it ip^RJA
5 IL IROCKFORD
5 IL ISPRTNGFIEID
5} IL (OTHER UR
.»(...!: ,ITnTAL FOR STATF
~! *M !ANDERSON"""""""""""
IN CHICAGO MFTRO
URBAN AREAS
POP I I
AVE PD CODE
..to...(....
4.731
5.391
9.681
9.131
4.971
4.221
4.431
RBAN AREAS
IN IEVAMSVILI.F
IN IFORT WAYNF
51
51
51 IN isoUTfTBEND
5 IN (TERRA HAUTE
51 TN inTHFR URBAN APFAS
^51 IN (TOTAL FOP,STATE
*"si""MI"iINN"ARBOR"""""""""""
51 MI I BAY CITY
51 MI iorr°oiT
51 MI IFIIMT
51 "I IG"AND RAPTOS
51 MT IJACICSHN
51 MI IRALAMAZOn
51 MI (LANSING
51 MT (MMSKEGON
51 MI (SAGTNAW
5 , MI JOTHFR URBAN APEAS
..El-.^I jTnTAl- Pri" STATE
MN ID'JLHTH
MN (FARBO METPO
MN (MINNEAPOLIS
MM I
MN I
I
MN [TOTAL FOR STATE
ROCHESTER
OTHFR URBAN AREAS
DH (CANTON
OH (CINCINNATI
OH CLEVELAND
OH (COLUMBUS
OH (DAYTON
OH (HAMILTON
OH ILTMA
OH ILORAIN
M !fSiNVIlLF
OH IYOUMGSTOWW
OH OTHER URBAN AREAS
OH
[TOTAL FOR STATE
iS?«s5c;™"""
WT IDIJLUTH
WI
WI
WI
WI
WI
WI
(LA CROSSE
(MADISON
(MILWAUKEE
IOSHKOSH
(RACINE.
(OTHER URBAN AREAS
WI ITOTAL FOR. STATE
1 TOTAL. FOR REGION 5
-------
TABLE III-l DFMOGRAPHTC CHAPACTFRlSTirS OF THF URBAM AREAS
II
LEPAI STATE 1 URBANIZED AREA
IRFGI IR 1
61 " AO (FORT SMITH
61 AR ILTTTLE ROCK
61 A° IPTNF BLUFF
61 AP IOTHFR URBAN ARFAS
II
61. , AR ITOTAL FOR STATE
61 I.A (BATON ROUGE
61 LA ILAFAYETTE
6i LA ILAKF CHARLES
6l LA (MONROE
61 1 A INFW ORLEANS
61 LA ISHRFVEPORT
61 LA IOTHFR URBAN AREAS
61 LA ITOTAL FOR STATF
61 NM 1 ALBUQUERQUE
61 MM IOTHFR URBAN APFAS
61 MM ITOTAL FOR STATF
61 OK ILAWTON
61 OK (OKLAHOMA CITY
6 1 OK | T'lL'^A
61 OK IOTHFR URBAN ARFA.S
i i
6i. OK ITOTAL FOR STATE .
61 TX IARILENE
61 TX IAMARILLO
61 TX (AUSTIN
61 TX IBEAilMONT
61 TX IBPQWNSVILI.E
61 TX IBRYAN
61 TX (CORPUS CHRIST!
61 TX IOALI AS
61 TX IEL PASO
61 TX IFnRT WORTH
61 TX IGALVESTON
61 TX IHARlINGEN
61 TX (HOUSTON
6i TX ILARFOO
61 TX ILUBROCK
61 TV IMCA1.LEN
61 TX IMTOLAND
61 TX (ODESSA
61 TX IPORT ARTHUR :.
61 TX ISAN ANGELO
61 TX ISAN ANTONT.P
61 TX 1 SHERMAN
61 TX ITEXARKANA
6i TX ITF.XAS CITY
61 TX (TYLER
61 TX (WACO
61 TX IWICWITA FALL . '
61 TX IOTHF.R URBAN AREAS
61 TX (TOTAL FOR STATE
"~6l""~""lTofAL~FOR~R!?GlOKl 6
1000
ACRES
61*
1 8P .
301.'
!ft*
2*1
26,
6«I
32P.'
73.
102.
175,
2171
238!
590.'
|ft.
51"
4P,
i°*
:!
,11:
is:
21.
20,
lo »
ill:
ii:
53.
16.
5*,
5?o:
2546.
"!!5!:
1
197" POP I POP 1
1000. lAVT PDICOOE
76.1 1.981 E
2?3ll 3.671 C
61.1 2-5,2! E
602.1 3.?OI
. 1 , 1
9*2.1 3.?0l
249.'l 4.581 C
78*1 4.M8I E
B8.I 4.041 E
90.1 3.521 C
962.1 17.891 E,
234.1 3.B9I E
705.1 7.341
?406.| 7.341
297!! 4.071 C
41.4.1 4.071
.711.'! 4.07!
580*1 2.671 E
371*1 3.?2I C
693.1 2.«»ll
1740. 'I P.91I
90. 1.801 E
4—ry* T*4CI IT
1?7» 3«?5 1 E
?64. a.floi E
116. ?.42 1 E
53. 1 5.521 E
f4l *1/I4I IT
i:l 1:5!! I
1338.1 3.101 C
lg:j l:»! .i
16T8*| 4*86! E
70. I 4.971 E
150.1 3.041 t
91 . 1 4 .31 J t
60,1 C*«TI P
J 2 . 1 5. 1-3 J E
64*1 2*941 E
772ll 5. UK E
5b*l 2.461 E
58*1 ?.92I E
60*1 31751 E
142- 1-2?! I
9o . 5 *O5 I t
1999T 3.511
P934.' l£l\mmmm
"14753T ""3174 j""""
45
-------
TABLE HI-i DEMOGRAPHIC CHARACTFRISTICS OF THF KRRAN AREAS
RFGI8TTRE! "RBANI7ED ARFA
••»l---«- 1 ..--..-..._
71 IA IDAVFNp'**^™""""""""
71 TA IRF^ MnVkJFc
Z' I* IDUBIIQUE
71 IA ISTOUX CITY
71 IA (OTHER URBAN AREAS
7j IA (TOTAL FOR STATE
71* 1
-------
.TABLE TII-1 DEMOGRAPHIC CHAPACT
LEPAISTATEI HRBANI7ED AREA
RFiGI ID 1 . .
91 AK IIJPBAN AREAS
I 1
9.1 AK ITOTAL FOR STATE
91 AZ IPHOFNIX.
91 A7 ITIIC80N
91 -A7 IOTHFR URBAN A&FAS
91 AZ ITOTAL FOR STATE .
91 CA IBAKFRSFIELD
91 CA IFPESNO
91 CA ILOS ANGELFS
91 CA IMOOPSTO
9! CA IOYNARD
91 CA ISACRAMENTO
91 CA ISALTNAS
91 CA ISAN BERNAND.INn
9! CA ISAN DIEGO
• 91 CA ISAN .FRANCTSCO
91 CA ISAM JOSE
9i CA ISANTA BARBARA
91 CA ISANTA ROSA
91 CA ISEASIOF
91 CA ISTMT VALLEY
91 CA ISTOrKTON
91 CA IOTHFR URBAN ARFAS
i i
.91 CA ITOTAL FOR STATF
91 HI (HONOLULU
91 HI IOTHER URBAN APFAS
II
91 HI ITOTAL FOR STATF
91 NV ILAS VEGAS
91 NV • iRENn
91 MV IOTHFR URBAN APE*S
1 t
9i NV ITOTAL FOR STATE
9i ITOTAL FOR RERTON 9
"91 TD IBHISE
inl ID IOTHFR URBAN A=>FAS
1 I
101 ID (TOTAL FOR STATE
101 OR IEUGFNE
101 OP IP"RTLAND
191 OR ISALFM
inl HR IOTHFR URBAN APEAS
'. \ \
,101 OP ITHTAL FOR STATE
Tol WA I8FATTLE
101 WA (SPOKANE
101 WA ITACHHA
in| WA .IOTHFR URBAN AREAS
- 1 1
101 WA ITOTAL FOR STATE
"ol . 1 TOTAL FOR RFGION "o
___l____-l-_________-_--------
1 . (TOTAL FOR THE U.S.
rRisTir
'i 0 o n
ACRrs
.49.
**:
"&*1
67.
6*?.'
38U.
it:
1006.
7?r
156.
10.
19««
?4U.
436.
-177.
2U.
2fl.
!•=;.
16,
30.
311.
2«2P.
74.
3-5.
106."
77.
24,
i*r
.
. 120.
3487.
19.
66.
8«5.'
i
w —
o -»irj-4w
in jitz^A
• , t * > • j
264.
50.
83.
149.
. 546,
. 935.
P9037T
S OF THE
1970 POP
inoo
.. I«7;
ia.7.
863.
294.
. 25lT
1408.
176.
263.
*3<51.
Ml:
' 6ll:
'584.
110B.
?hl:
l^i:
3:
160.
• 1995.
1M«2."
442.
196.
638."
237.
100.
59.
3P6.
2C731.
3?!:
3P7.
Hii
346.
1403.
1236.
230t
332^
67b.
?475.
— — — •__ — _
149366.
DRBAM AREAS
1
POP 1
AVE PDICODEI
2.991 E
2.991
3.481 C
4.381 C
3.671
1
3.671
J.82I E
5.?OI E
«.30! E
•a. ATI E
3.421 E
4. "61 C
6.461 E
2.941 E
4.011 E
6>6I C
5.781 E.
5.491 E
3.081 E
6.051 F-
3.561 E
5.321 E
6.411
6.411
6.011 E
6.01 1
6.011
3.061 E
4.111 C
3.311
3.M I
5.951
4.581 C
4.581
4.581
3.951 E
4.R3I C
3.931 E
4.601
4.601
a. 6ii E
4.021 E
4.541
1 .
4.541
5.141
47
-------
maps were unavailable. These photos presented a problem in that
' Land Use Distributions -in Nine Ontario r-ft-f^ w * ^.i .
Thnc JM A ^ ", W as °ne Person Per acre (2.5 persons per ha)
mdevel±d fr S inCl?deS S±^ificant acreages of laL whJch^re * '
undeveloped and would not be served by sewerage systems.
is ^SveLped! i?e.!° ^^^ "^ Perceat °f tKe ^-iz.d area which.
where
Z = 1.0 e- _
(r - -0.57)
Z - proportion undeveloped land,
PD - average gross (developed and undeveloped)
population density, persons per acre, and
r = correlation coefficient (-1.0 £ r £ 1.0).
PD.
PD e
0.17PD
(III-l)
(.IIIr-2)
48
-------
n)
co
H
H
H
O
O
I
1
•a
01
4J
co
u
o
M
O
H
s
•O
•H
W
CO fH net
-•3 § §8
o. oi ran
£ Q S S
•o
01
N
8
o
in
o
m
o
CO
o
co
0
co
00 •
Burlington
o
o
sr
o
vo
O
oo
o
CO
o
CM
N "-I
a- o
*m
a
3
0
o
sr
0
CM
ft
o
*"*
o
>n
o
Jo
i-» o\
CO CO
CM O
iH i-l
Kingston
8
o
r-
m
o
CM
0
O
o
.3.
0
CM
voO
in vo
CM
o
o
H
H
Kitchener-Wate
Cl
o
CO
o
VO
o
VO
o
-------
PD, persons/hectare
40
50
60
70
-:?& UNDEVELOPED • 100 x e-°''7PD
r --0.57
XXX
©-U.S. % UNDEVELOPED LAND
X- ONTARIO % OPEN SPACE
' 0
10 15 20
POPULATION DENSITY, PD, persons/acre
Figure III-2. Percent Undeveloped Land Use (US) and Open Space
Land Use (Ontario) vs Population Density. Note
that best fit line is forced through point 100
percent at PD = 0.
50
-------
r'
Equation III-2 is shown in Figure III-3, Relationship Between Gross
and Developed Population Density. Note that the developed population
density is about six persons per acre at the lowest level of urbani-
zation (one person per acre). The developed population density
approaches the gross population density as PD increases. Indeed, they
are quite close at PD >_ 25 persons per acre (62 persons per ha).
After correcting for the percent undeveloped, the proportion of the
land in the developed uses was determined as a percent of developed
urba.n land only. After this transformation, was made, the. percent of
land in the developed uses was statistically independent of population
density. The resultant distribution of developed land by use and
undeveloped land is shown in Table III-3, Distribution of Developed
Land Uses in Ontario Test Cities and US Cities. Note the similarity of
the Ontario and US land use distributions.
The land distributions for all US cities are determined using equation
III-l and the US land use distributions shown in Table III-3. The
results are presented in Table III-4, Land Use Distribution for the Urban
Areas in the United States. In determining the control costs, only the
developed portion of the urbanized area is considered. Thus, it is
important to check the validity of this assumption in future assessments.
Actual field data need to be gathered and analyzed using a consistent
set of assumptions regarding land use categories.
Table III-3. DISTRIBUTION OF DEVELOPED LAND USES IN ONTARIO TEST CITIES
AND US CITIES
of Total
Land Use
Residential
Commercial
Industrial
Othera
Ontario
52.5
9.0
14.1
24.4
US
58.4
_8.6
14.8
18.2
aRecreational, schools and colleges, and cemeteries.
^American Public Works Association and University of Florida,
"Evaluation of"the Magnitude and Significance of Pollution Loading
from Urban Stormwater Runoff - Ontario," Environmental Protection^Ser-
vice and Ontario Ministry of Environment, Toronto, 1976.
51
-------
persons/hectare
20 30
0 5 ,10 15 20
GROSS POPULATION DENSITY, PD, persons/acre
60
60
Figure III-3.
Gr°SS and Developed Population
52
-------
TABLF III-4 LAND USF DISTRIBUTION FOR THE
T i i LAND USF
E«»A!STATEI . "RBANIZEH APFA 1 1
FGI . ID 1 IIINDVIPFS
11
II
1 I
•11
1 1
11
1"
11
ij
11
11
11
: 1 1
11
jj
11
1 1
n
?i
21
. 21
l\
CT
H
CT
H
CT
CT
CT
ME
ME
ME
ME
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
NH
NH
NH
NH
Rl
Rl
PI
VT
VT
NJ
NJ
NJ
NJ
NJ
NJ
NY
NY
NY
MY
NY
NY
NY
NY
NY
•BRIDGEPORT
(BRISTOL
IDANBURY
(HARTFORD •
IMFRTDEN
(NEW BRITAIN
INFW HAVEN
INfiRWALK
ISTAMFjDRO
IWATFRBURY
IOTHFR URBAN APFAS
i
IAVE. FOR STATF
ILFWTSTON
(PORTLAND
IOTHFR URBAN AREAS
1
IAVE. FOR STATE
1 BOSTON
IBROfTKTON
IFALL RIVEP
(LAWRENCE
ILOWFLL
INF> BEDFORD
IPITTSFIELO
(SPRINGFIELD
(WORCESTER
IOTHFR URBAN APEAS
IAVE." FOR STATF
(MANCHESTER
(NASHUA
(OTHER URBAN AREAS
IAVE.' FOR PTATF
IPRQVIDENCF
IOTHFR URBAN AREAS
IAVE.' FOR 8T»TF
IUP8AN AREAS
1 AVEC FOR 'STA'TF ,
1 AvIT FOR REGION 1
(ATLANTIC CITY
(NEW YORK CITY METPO
(PHILADELPHIA METRO
(TRENTON
IVTNFLANO
i
IAVE. FOR STATF
(ALBANY
IBINGHAMPTON
(BUFFALO
(NEW YORK CITY
(ROCHESTER
(SYRACUSE
IUTICA
IOTHFR URBAN APEAS
IAVE.* FOR STATF
"l AvI?~FOR""pERinN .2
147. 9130. a
|72l4ll6ll
I69l3ll7l9
I02l2l33l8
ISO.BI28.7
(49.6129.5
149.9129.3
1*0.0120.21
1 1 1
i*o.0'29.?
177.6113.1 1
160.5123. 1
149.9(17.6
1 . 1
169.9117.6
.134.6138.21
107.4130.7
It'2.41.33.7
171.21 16.81
I35lll37l9
168.41 !«.5
Ifl5lsi3ll7
144. 213?. 6
1 1
144. 213?. 6
152.4127.8
162*112?. 1
1^6. 912*. 2
1 . t
I*l2lll33l8
109.5129.5
..!,.. ..L..... ...
149.3120.6
1*8.8120.1
I33l5(3al9
131.6139*3
179.411?.!
1 a8. 5130.1
(02.5(33.6
(42.6133.5
I26.0I43.?
I33l5(38l8
135.3137.8
152.9127.5
03.8(4a.5
!?3.8I44.5
.|.---t— —
.139. 613*. 3
URBAN AREAS IN THE II.
AS X OF TOTAL AREA 1
1 1 1
tOMMJlNDL OTH (TOTAL 1
4.5! 7.7
3.51 6.0
2.41 a.ll
5.31 9.0
Sill 8l7
5.01 P. 6
«.2I 7.3
4.31 7.5
4.31 7.4
4.31 7. a
4.31 7.4
1.91 3.31
2.'6l a. 5
5.61 9.7
5lO( 8^5
a*5! 4"?
4l7l Rio
5.61 9.6
§:I! fe?
4.71 8.01
" 1
4.81 8.3
4.11 7.1
" 1
3.7( 6. a
5.01 8.6
5.01 «. 6
5."OI 8.6
4.31 7.5
1 i
1 a. 31 7.5
1 3.5'j 6.T
5l7l 9l8
1 a .'41 7.6
4^91 8.5
4l9l 8.5
1 6.4UUO
1 8l6lia.8
1 5.71 9.8
5l6l 9l6
( 4.K 7.0
1 6.6111.3
|. .!.)..»
9.5'ino.oi
7.31100.01
5.01100.01
11.11100.01
5. 6HOO. 01
10.7UOO.OI
10. 51100. OJ
8.91100.01
9.21100.01
9.1UOQ.OI
9.1 1100.0)
.1 .1
9.1 1100.01
4.11100.01
" 1 1
5.51100.01
11.91100.01
9. 6UOO.OI
10.51100.01
5.21 100.01
8l§ll06loi
9l9HOoIol
11.81100.01
5.81100.01
7.91100.01
9.91100.01
10.21100.01
10.2MOO.OI
8.71100.01
6.91100.01
7l8[100.0j
7.81 100.01
10.51100.01
10.5MOO. 01
. 1 1
10.51 100.01
9.21100.01
7.51100.01
9»5'lOO»Ot
12*1 ' i^0«0 1
9.41100.01
10,41 lOoIol
13151100.01
18.21100.01
112.11100.01
1 11.81100.01
8.61100.01
13.91100.01
13.91100,01
TI'oiTooToi
53
-------
(TABL
LANJO USF r>TSTRTt*UT
ARFA
...I
31
31
31
-..|
31
I
31
...I.
3 I
31
31
I
x i
.- I .
3'
31
31
31
31
31
31
31
31
3 I
31
31
31
31
.. I.
31
3 I
3 I
31 -
31
31
31
31
31
..I..
31
I!
31
* I
!
31
E III--4
STATFI
OF If.iTHPR liRBifJ APFAS
..°^ !AV^." FpR STATF
ION FOR THE
I i I '
IMMBVIPFS I. ~
|.—-|..._)_.
MO. 8 I 34. 6 I 5
M0.8,34.ft! 5
Ifi0.8l3fl.fel 5
JAVF." FOR STATF
I RALTIMQRt-"••--"-
, nf.
AREAS IN THF n <;
. nr TOTAL AREA i
.lNOL ,OTH (TnT4| j
«- 1 .... i .... i ..... i
.) I «.*! 10. 81100. oi
.1 ^.flltO.flllOOroi
.11 «.*Mio.ftil00.ni
. i .... | ._.._ | mmmmm i
.3}ia.2M7. 51100. 01
MH
"PA"
PA
PA
PA
PA
PA
PA
PA
PI
PA
PA
PA
PA
PA
'VA
VA
VA
VA
J,nTHFR URBAN APFAS
i A»E::.; FOR
I AI.'""""""""
I Al
IEPIF
I LANCASTER
IPWII AI?EI P
I PTTTSRUI7RM
I A VET Fflf? ST4TF
.1..;.
i?5.e
135.1
IPP./.1
I
137.7
133.7
134.8
l"5:t
l?3:6
I « 3 . 9
143731*6]
137.91 5.
M1.PJ 6<
141 .PI 6.
I3"7"l""]
I3S.7I 5.
il5:J!l-
!!?:?! «:
MU.6I 6.
|3^.7| a*
. .
2 10.61 13.01100^01
?UO.A|13.0I100.0I
71
fel
p i
..
?•! 120.01
.
00.0!
OQ.oi
IMF-.PORT
l K
.
M9.0
I /Jl .a
136.3
I.
136.3
. | --•,-
1^6.t
.71
'3a.?l ,..„
I37.?| S.<5
I37.?| 5.S
.
7.
r .. 100.0!
1 9.3UOO.OI
I 10.71 100.01
I
I
4 I
PI
VA
VA
MA^ISI :i i
nc
URBAN
.0 I
"pjT?J!^i
7:0! 8R:i!}88:o°!
1-2 2-Z!J2o«0'
VA IAVE. FHR STATF
•---I---- — -.---.-.-...
i«V I STEliBFWv/Ti.LF "FTPn
^ V I 1THFR IJRBAN .ARFAS
s«V IAVE. FDR STATF
1 T"^'!~FnR"pE"TnN 3
I 46.9131.0
II .
I U6.9-I 3 1.0| 4
. I..-.I.._.)_.
1^6:3137:?! 5
i'j7:5i3o:7i 4
I I I
M7.5130.71 a,
I3777I3"~!~5!
I 4:&i T'.QI
:Ij W,MW!J88:fl°!
,Qi 8.7110.71100:01
51 7.P,! 9.61100.01
51 7.fl| 9.61100.01
l--.:._. . ERTnN 3 I^7.7I3A.UI 5.41 9.2111.31100.01
54
-------
TABLE III-4 LAND USF DISTRIBUTlnN FOR THE URBAN AREAS IN THE U.S.
.1 1 1 L*NPi USF AS X OF TOTAL AREA 1
EPAI9TATEI HRBANI7En AREA II 1 1 |
PF.GI in 1 . . IUNDVIRFS irtlMM | IMDL IOTH ITOTALI
9444444 41
44999999999 9.
.
41
41
41
41
41
41
41
41
1
199949 9
I
1
41
41
.• 41
1
41
41
. 41
41
41
41
41
41
41
41
41
41
41
41
41
41
1
41
i
144444 4
1
• ':
1
AL
AL
At
AL
&
AL
1
Ft
FL
FL
Ft
'FL
GA
GA
GA
GA
GA
GA
GA
KY
KY
KY
KY
KY
KY
MS
MS
MS
MS
N.C
NC
NC
NC
NC
NC
8*
NC
NC
NC
SC
£
SC
. SC
TN
TN
TN
TN
TN
(BIRMINGHAM
IGADSDEN
IHUNTSVILLF
(MOBILE
IMONTGOMERV
(TUSCALOOSA
10THF.R URBAN AREAS
IAVE.* FOR STATE
IFT.I AUDERPIAI.E
IGAINESVIL1 E
(JACKSONVILLE
IMTAMI
(ORLANDO
IPFNSACOLA
1ST. PETERSBURG
(TALLAHASSEE
(TAMPA
IWFST PALM BFACH
IOTHER URBAN AREAS
IAVE.' FOR STATE
I Al BANY
i ATL»NTA
IAUGUSTA
1 COLUMBUS
IMACHN
rS»VANNAH
IOTHFR URBAN AREAS
i
IAVE. FOR STATE
IHUNTINGTON METRH
ILEXTNGTON
ILOUTSVILLE
(OWENSBORO
IOTHFR URBAN APFAS
i
IAVE. FOR STATF
iBTLnxi
(JACKSON
IOTHFR URBAN APFAS
IAVE." FOR STATE
1ASHFVILLE
(CHARLOTTE
(DURHAM
IFAYFTTEVILLE
IGREFNSBORO
IHTGHPOINT
IRALFIGH
IWTLMTNGTON
(WINSTON-SALEM
IOTHFR URBAN AREAS
1
IAVE. FOR STATE
(CHARLESTON
(COLUMBIA
GREFNVILLF
OTHFR URBAN AREAS
AVE.' FOR STATE
(CHATTANOOGA
IKNOVVILLE
IMFMPHIS
(NASHVILLE
(OTHER URBAN AREAS
1
IAVE. FOR STATE
IAVE. FOR REGION
15171 2" ?
172.0116.3
I66l5llo",6
148.513(1.1
2.41 4.1
2.31 4.0
l'l\ 5-2
159.5123.7 3l5i 6^0
161. 3(2?. 6 3.31 5.7
I.I 1
161. 31??. 6 3.3| 5.7
146.3131.3
I67".OI 19l3
. 154.1126.8
I501ll29ll
147. 3130.8
157.0125.1
150.0129.?
150.0129.2
143.2133.2
la8. 9129. 9
ISO. 5(28. 9
(50l6l2'O
ISO. 5128. 9
1 I
1*3.1 127.4
I34.6I3«.2
I3ol9(4ol3
i«;lls^
160.5123.1
(49.6129.4
1 _. 1
160.5123.1
149.6129.4
(S3. 6127. I
IS5.7I2S.9
l6lZ9l2?l3
IS6l6l2d!l'
1*6.5125.4
155. 2126. 2
(55.2(26.2
IS4.2I26.7
153.6127.1
I55.6I2S.9
154. 3126. 7
IS4.3I26.7
160.1123.3
I55.6I2S.9
140. 7134. 7
170.8117.1
159. 4123. 7
159.4123.7
4 (S3. 6127.1
4.61 7.9
4.01 6.9
2.81 4.9
"»?' 7'-Z
4l6l flT3
4.31 7.4-
4.51 7.8
3l7l 6.4
* | *
4.31 7.4
4.91 «.4
4.4| 7.6
4.31 7.3
3.51 6.0
4.21 7.2
4.2" 7.3
4.3IC7.3
4.-3J 7.3
5. 1H
4.9i;
6.11:
00.01
00.01
•00.01
ooloi
7. 01166. oi
7.'0j 100.0 j
9.8H
8.51:
6.0J
sl9i :
10:21:
?:li
?;!|1
9,1 jl
10.31]
9.31
9.01
7.41 1
8.9 H
9.01
9.01
1
9.0H
00.01, -...
00.01
00.01'
ooZoi
00.01
00.01
00.01
ooZoi
00.01
00.01
00.01
00.01
00.01
00.01
00.01
00.01
00.01
4.01 6.9 . s.siioo.oi
5.61 9. 7111. 91100. 01
5.21 9.0 11.1HOO.OI
5.9110. 2M2.6HOO.OI
5.21 9.0111.11100.01
5.21 9.0 11.1(100. 01
I:§! ':§ l:\\
3l9l 6l7 fl!?l
3."9I 6.7
B;?I
3^41 S.9J 7.2*
4*101 6*191 8l4l
3.81 6.61 8. 11
4l2i 7l2 elei
3.31 5.6 6.91
|I7I 6.41 7.91
3l7l Ili! 7lll
3.91 6.6 8.21
3 ."91 6.61 B.2J
!:?! 8:5
3:«j 8:8
8.31
i*4i
! 3.41 5.9J 7.3J
1 3.81 6.61 6. 11
1 5.11 «. 8110.81
1 2l5l 4^31 5*^31
1 3T5I 6.01 7.41
. 3.'5I 6.01 7.4
j.2:2
00.0 1
.00.01
LOO. 01
,00.01 .
A:\\
§§:§!
^§:§!
00.01
100.01
100.01
100.01
100.01
LOO. 01
m:i\
§§:§!
100.01
100.01
55
-------
TABLE III-4 LAND USE DISTRIBUTION FOR THE URBAN AREAS IN THE L
IS* STiJE "RBANIZED AREA ! UN? "SE,48 *,PF W*CptF.
£-£ ~£2. .„ } "NO V [RES I COMM j INDL HTM T0TAL !
5 IL (AURR " " - " -
51
SI
5
5
5
mm
5
ICHlrAG
IL
L (JOLIET
IPEORIA
IROCKFORD
ISPRTNGFIEID
(OTHER URBAN ARFAS
ia0U8l3?.3|
J?2:9I45:0
?1.2146.0
42.9133.3
148.8129.9
147.1130.91
I54.2IP6.8
140.8134.6
138.9135.7
s:?.i
".'
8, '
$.$iji:«if2:oj
*.S!il.?M«rs
•«*.jiHE:."5.2IiIf —.--»..o, o.i
IN rAWDERSON""""""""""""':=":'~~~~'"~~"
IN CHICAGO MFTPO
.18 IJiA-rt-
0.01
«:9i*A:ii!o:«i o§:o!
*"*' f:!' !J!!i§§$
138.9(35.7( 5;3i 9*0 llinioo'jii
?8.7141.6 I 6. II 10.5113.0 1100.0 I
I
I
MUNCIE
.
TN
IN
IN
IN
IN
IN JAVE.* FOR STATF
— ... .....;... a i > '
" ~~ """" ""''
IM
I SOUTH BEND
(TERRA HALltE
[OTHER URBAN ARMS
i!
51
SI
SI
51
SI
5!
5
.
(ANN ARBOR
(BAY C
IDFTPQ
(BAY CITY
QfT
(FLINT
(GRA
ND RAPIDS
IJACKSON
IKALAMAZDO
(LANSING
(MUSKEGON
ISAGTNAW
IOTHFR URBAN APFAS
IAVE; FOR RTATF
MN (MINNEAPOLIS
HN I ROCHESTER
IOTHER URBAN AREAS
HN
51
'Si'
51
MN
"OH"
OH
OH
FOR STATF
(AKRON
ICANTON
-.. CLEVELAND
OH (COLUMBUS
OH (DAYTON
OH (HAMILTON
5 OH (LIMA
s OH ILORAIN
? Rg !8*8?SiftiLD
SB l?J!E!«5MmLfr
OH IYOUNGSTOWN
OH (CITHER URBAN AREAS
OH IAVE.' FOR STATF
«•«"• I ••••MMWW«MMMM«»MW««
WI IAPPLETON
w IDMLUTH METRO
WI (GREEN BAY
wl IKENOSHA
WI (LA CROSSE
w: IMADISON
WI (MILWAUKEE
5 WI OSHKOSH
51 WI (RACINE
5j WI JOTHFR URBAN AREAS
51 WI IAVE. FOR J
-...;. .-:;.^:.::::..... i :'r"*a
°:9!lo6:6i
.
151.1128.61
147.1130.91
147.1130.91
136.41
IS4.1I
•I I
1
813631
8I3U7
13?.51
13?.01
4120.8
0I41.S
8129.
3130.21
5139.4
139.2
,29.4,
56
-------
TABLE LH-4 LAND USE DISTRIBUTION FOP
ill I LAND
IEPAISTATEI URBANIZED ARFA I '
FGI m i IIN[H,- - - ,
..I...»-I.-•...-•----------"--1—«•-1--"i "
61 AP IFHRT SMITH I74*£US*7
£l AP II TTTLE ROCK 1*3.6127.1
t\ AR IPINF BLUFF Sg^Sill-?,
61 AR IOTHFR URBAN AREAS *8.8J2fl.l
6i AP IAVE: FOR STATE ___ J^SiS}E2:l!
61
61
61
61
61
I
I. A
if A
t'i
iBATnN ROURE
ILAFAYETTE
ILAKF CHARLES
IMDNPOE
INF*I ORLEANS
"URBAN AREAS
IU5.9I31.
I '4.815*.6!
l^9ll I3**6|
I I I
179.113*.61
IN THE U.S,
,- T~-tREA !
v,., uNDurtTw J32I1L!
ilii'uTii'iT^iiooToi
4l6l Ol 9l8HOoloi
3T5I 6lll 7.*jlOO.O| .
3.'5I 6.11 7.5J100.0J
4r7l"8"oi°9l6l"oo.OI
'• L- 813110131100.01
7.41 9.0;I100.0I
*.7! g.i'100.0!
all
8.21;
5!§]-
I 8. 81100. P
ill. 1U
OO.OI
i.' FOR STATE
, .•_I•••....•••••••••«•
OK JLAniTHN
PK IDKLAHOMA CITY
IFO.1120.?
6) LA, IAVE.* FOR RTATF . - --.- ^m.
*6l""NM IAIBIIQUROUF _ j15?*!!??*^'
fe!
6<
..I.
61
61
61
61
.
IQTHFR URBAN AREAS
61
6!
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
6<
61
61
61
61
61
61
61
6!
• mm \
*!
mmm I
IAVE.' FOR STATF
i ABILENE"""" ""
IAMARILLO
I AUSTIN
I BEAUMONT
IB»OWNSVILI E
I B R Y A N
I CORPUS CHRIST!
I DALLAS
163.5121 ."51
1*7.8120.61
1*1*1
161.112?,Tl
IFriRT WORTH
IGALVESTOKI
IHARLINGEN
ILARFDO
OK
"TV"
TX
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TV
TX
TX
TX
TX
TX
TX lAVE.' FOR STATE
—-1 wirFsr si jjinr ^r j g:;. is-i
5.21 9.OM.1.1 MOO.Pj
""J7II """»I "9"! I 100. OJ
al3l 7l4! 9.11100.OJ
4.'3 I 7.4J 9.1II00.0|
"i"e i ~".s i "elo i Too .01
111! Wl fifliS?!?1.
llSI *I«I 7.1H00.01
I I.I I
I *.8I 7.11100.01
IMCAI.LEN
IMTDL AND
infiESSA
(PORT ARTHUR
ISAN ANGELP
ISAN ANTONIO
ITFXARKANA
ITFXAS CITY
1 TYLER
j 3."8I 6.61 8.1J100.0I
j....|.»-•|.--•I•»•••I
57
-------
TABLE
IJI
Ij4 LAND USE DISTRIBUTION FOR THE URBAN AREAS IN THF U S
i ,.„„. . _. I LAND IISF AS X TIP TrtTAi *DC» ~ . •'
71
71
71
71
71
71
71
"7l"
7!
71
.-I.
71
71
71
7!
..!!,
71
71
7!
71
"71"
"si"
81
81
81
I
fll
.-I.
81
81
81
I
81
..).
81
I
IA
IA
IA
IA
KS'
KS
Kfl
KS
"MO"
MH
MO
MO
MO
MO
AREA
CEDAR RAPTDS
IDAVFNPORT
|DPS MOINES
ID'IBUOUE
•SIOUX CITY
I WATERLOO
IOTHPR URBAN AREAS
156.8125.?
. • -•-»--• ^_ _•»• | .j • i t
169.4117.91 2^61
I53.7l27:n S:oi
140.3134:9 -*y!
165.4120.?!
AS XfOF TOTAL AREA
iiNDLinTH ITOTAU
i----1 ....1.....i
I'll 6*2' 7.91100.ni
I
5.61 100. ni
5*li fl'ei e:£1 2-5! 7'ai '.iiioo.oi
!A!!• .F^R REGio"""ria67siir7i!~47&!"77?!"577!Too7o!
.................|....,....,mm_m,mmmm,mmm_,_____,
58
-------
TABLE
IH-4 LAND USE DISTRIBUTION ^.^^UPB A^AREA^I^THE- U .S
PAISTATE! URBANIZED AREA
F.SI
91
1
91
91
91
91
1
91
91
91
91
91
91
91
91
91
91
91
91
91
91
9,
9,
1
91
9,
91
91
91
9 1
91
91
91
101
101
101
101
10!
101
101
101
10!
101
. 1
101
"Toi
... i
...,
. ID 1
AK ,U»3AN AREAS
1
AK IAVE.* FOR STATE
AZ IPHOPNIX
AZ ITUCSON
AZ IOTHER URBAN APF.AS
i
AZ IAVE.' FOR STATF . .
CA IBAKF.RSFIEI. D
CA IFRESNO
CA ILOS ANGELFS
CA IMHOFSTO
CA IOVNARD
CA (SACRAMENTO
CA ISALTNAS
CA ISAN BERNANDTNP
CA ISAN DIEGO
CA ISAN FRANCTSTO
CA ISAN JOSE
CA ISANTA PARRAPA
CA ISANTA ROSA
CA ISFASIDE
CA ISTMT VALLFY
CA ISTOrKTON
CA IOTHFR URBAN APPAS
i
CA IAVE. FOR STATF
Hi IHnNflLULU
HI IOTHFR URBAN AREAS
i
HI IAVE. FOR STATE
NV ILAS VEGAS
NV IRENH
MV IOTHFR URBAN AREAS
i
NV IAVE.' FOR STATF
lAvIT FOR REPIOM 5
ID IB"ISE
TD IOTHFR URBAN AREAS
ID IAVE. FOR STATE
OR IEUGFNE
OR IPORTLAND
OR 1 SALEM . . •
OR IOTHFR URBAN APFAS
OR IAVE.* FOR STATF
WA ISF.ATTLE
WA 1 SPOKANE
WA ITACOMA
WA IOTHFR URBAN AREAS
INA IAVE.' FOR STATF
._...,----.-----«..-----*-
1 AVERAGE FDR. THE U.S
I 1 1 1 1 II
|H\r>ViRFS ITO^HIIMDL OTH ITOTALI
IAO. 1123. 31- 3.41 5.9 7. 3UOO. 0!
1 1 I.I I.I I
I60.'ll?3.3l 3.41 5.9 7.3U00.01
155.4126.11 3.81 6.6 8.11100,01
147.5130.6! 4.51 7.8 9.5 100.01
IS3.7I27.0J 4.01 6.9 8. 41100. PI
1 III I 1
IF3.7I27.0I 4.01 6.9 8.41100.01
144. 013?. 71 4.81 8.3 10.2HOO.OI
IU1.3I34.3I 5.0I 8.7I10.7HOO.OI
IP4.4I44.2I 6.5I11.2 13.8HOO.OI
1/43:713?.°! 4l«l 8.3 10.2100.0!
I55.9I2S.7I 3*81 6.51 8.0HOO.OI
S0»ll?9*ll Ol 7.41 9.1HOO.OI
I33.4I3».<»I 5.7, q.Q 12.11100. 01
160.6123. 01 3.41 5.8 7. SHOO. 01
!5J:S!2M!-§:?!iS:Sili:Iil88:8!
!x7*4n».5l 5^41 9^311. 1.41100.0!
|T9r3C?«;.4l 5.2, 9.0111 .OHOO.OI
59l2l'23l8l 3*51 6.0 7.4HOO.O'
m5*7l37.«5l 5^51 9.5 11.71100.01
|4ol5l34^P| 5^11 8lfl| 10*61100*0!
I35!«I37.7I 5.61 9.6M 1 .8 HOO.O 1
1*5. 4l37.7i 5.6! 9.6I11.8HOO.OI
136.0137.41 5.51 9. 5111.61100. 0'
n6"oi37'4l 5^5, 9:5111.61100.0!
1 1 I.I 1 I.I
136.0137.41 5.51 •?. 51U. 61100. Oj
1^9. 4123. 7| 3.51 6.01 7.4,100.0!
149.7129.4, 4.31 7.41 9.2HOO.OJ
1*57.112^.0! 3.7, 6.3! 7.8] 100. 01
1 1 1 ' 1 ''
IS7.1I2S.OI 3.71 6.31 7.8HOO.OI
138.6135.9! 5.3-1 9.1111.21100.01
05:9131:21 4T7! Ol 9:81100.01
1 I'll 1 1
145.9,31.61 4.71 ».0, 9. 81100.01
IS1. 1128.61 4.21 7.21 8.9HOO.O|
144. 013?. 71 4,ei 8.3 10.2 100.0
!^:I!!?:a6! S:5! l:l\ *:?U88:°o!
i t ii i i i
145.8131.6, 4.71 8.0J 9.91100.0!
lilrfii?:*! &\ SiJPWIISilrSi
IS:!!!?:!-! S:i|:?:3j JrijlWj
146. 313.1. fll «. 61 7.91 9.8HOO.OJ
"luSITisTTii'flTS, sToi 9T8|ioo.oi
. | .... 1 .... | .--" 1 ..-- 1 .... I m-mmmm I
T ! 46TH ! 3*7^ ! "HIS i "sTo i "9*8 n oo . o i
59
-------
. POPULATION AND AREA SERVED BY TYPE OF SEWER SYSTEM .....
Combined Sewers
for S£?nJT:CVf dfa.was a 1967 inventory conducted by the APWA ,
for USEPA of local authorities with combined sewer systems:5 For that •
SSL'
would underestimate the total area If
was available, but is compeSaSd for
If the calculated
were av^ilable. This
P°pulation estimate
°Ve°Pulati°n estimate.
t proportion °f
a.-=riE
by population density and gouped
lowest density to highest densitv
Albany, New York, SsJoL S Sure
bution of Albany! New YoS. §
An equation of the form
five C!nSUS.tracts were ranked
Cateories Banging from
ity °f
Density Distri-
PD
ax
(III-3)
60
-------
o
tn
o
12
CO
UJ
0
.§•
2
.
15-
.
-
H
10-
•
t
5-
0-
(
.-
D 20 40
.
,. '- .-
60
-40
-35
-30
-25
-20
-15
-10
-5
-O
80 100
o>
w.
"o
0>
-x
CO
c
o
0)
Q.
% OF URBANIZED AREA
Figure III-4. Population Density Distribution of
Albany, New York
61
-------
where PD = gross population density, persons per
acre
x - percent of urbanized area (0 < x £ 100) , and
a,b = parameters, •
K2,
"'Potion density in any interval,
/x?
«.
* x Xl
,
QX •
(IH-4)
^-100 PDcalc-
5.03
for Albany,
PD « 108 x~°'933
density is 5.1
108
(III-5)
100-x,
(III-6)
LOO
PD - — 1 r b
2-100 - 100-2 / - ax dx»
(III-7)
or
a =
98 PDC1+M
100(1+b) - _
Thus, the final equation for gross population density is
b
PD = ax with x.. <_ x <_ 100.
(III-8)
(III-9)
62
-------
Given.the equation in the form PD = axb, one can find the average population
density, the proportion of the population within certain densities and so
forth.
The method for determining which of the 50 cities would ^selected for
the city in question was as follows. All 50 cities were divided into their
respective USEPA regions. The mean and standard deviation of the population,
land area, and population density were determined. Then a range of 1:
acceptable values was found for these parameters withto each. TJSEPA region.
For each city in question, two approaches were used in selecting the
were
"those'test cities outside the respective TJSEPA region
10 percent of the mean values for the parameters of the
listed. The order of priority in selecting tha reference
test city was as follows:
1. proximity of population density and land area
in same USEPA region; and
2. proximity of population density and land area
outside USEPA region.
In selecting the "best" reference test city, location and land area
were the dominant factors because they have a definite influence on
population density. Similar location implies that the city in ques-
tion would probably have developed at the same time and have been
influenced by similar national population shifts, manufacturing tech- ,
nologies, urban growth patterns, etc. Land area was chosen simply
because population density is a function of the size of the urbanized
area. For the above reasons, the majority of the cities in question
were referenced to a test city outside their respective USEPA region.
The population density function, PD = axb, is given in terms of the
total urbanized area. Thus, it needs to be modified to integrate
over only the developed portion of the urban area as shown in Figure
IH-5, Characterization of Population Density in Urban Areas. In
order'for the area under the two curves to be equal, one must
that
have
100
ax dx
100(1-Z)
a'x dx
(111-10)
or
a' = a{100
C1+b>i/Kiooa-z))C1+b) -
(III-ll)
63
-------
9aoD/suosjad'ad'A1,SN3a
SSOd9
64
-------
Then,
(IU-12)
PDj = a'x
d -*•
where PD = population density in developed portion of
the urban area,
a" = adjusted coefficient from equation III-ll, and
x = calibrated lower limit on percent urbanized area.
The percent of the urban area which is sewered is known for the nine Ontario
cities. Computing the corresponding PD for seven of the cities resulted
'in the values shown in Table III-5, Minimum Population Density for Sewered
Portion of Seven Urbanized Areas in Ontario. Guelph and West Toronto
were considered extreme values and not entered into Table III-5. Based on
these data, a cutoff minimum developed population density of five persons
per acre (12.4 persons per ha) was used to delineate the sewered part of
the urban area. Solving equation 111-12 for x2 yields
x,.
min [(5/a)1/b, lOQ(l-Z)]
(111-13)
where
x = percent of the urban area which is sewered.
Knowing the percent of the urbanized area which is undeveloped, i.e.,
100Z, the combined sewered area from the survey data and the
percent of the urban area which is severed * then the other sewered
and unsewered developed areas can Be calculated as residuals. The cal-
culation procedure is summarized below:
Sewered Areas as a Percentage of Total Urbanized Area
1. Undeveloped Area = 100(Z) = xu
2. Sewered Area = x^
3. Combined Sewer Area = XG, from APWA data
4. Storm Sewer Area = X2 ~ xc
5. Unsewered Developed Area = 100 ~ xu ~ X2
65
-------
Table
City
Miaimum Sewered Population Density, PD
(persons/acre) (persons/ha) d
Burlington
Kingston
Kitchener
St. Catharines
Sault Ste. Marie
Thunder Bay
Windsor
Average of 7 cities
3.93
7.28
4.49
5.79
4.67
6.12
3.87
5.16
.
9.7
18.0
11.1
14.3
11.5
15.1
9.6
12.8
are sho™ ta Table IIt-6,
Population Served by Type of Sewerag
1. Combined Sewers CP ) ;
c
Pp = APWA estimate
66
-------
.TABLE-IH-6
II I
EPA I STATE I
61 10 I
LAND USE BY TYPE
URBANIZED AREA
OF SYSTEM
II
i!
II
II
i
11
CT IBRinGEPORT
CT IBPISTOL
CT IDANBURY
CT (HARTFORD
CT IMFRTDFN
CT INFW BRITAIN
CT INFW HAVEN
CT INORWALK
CT (STAMFORD
CT IWATFRBURY
CT lOTHgR URBAN AREAS
CT ITOTAL FOR STATE
.«•. i..-.-.».««--—---'
ME ILP.WfSTON
tj SI PMBAN AREAS
II ME (TOTAL FOR STATE
7 i """MA*" i BOSTON"*""""""
MA (BROCKTON
MA (FALL RIVER
MA IFTTCH8URG
MA (LAURENCE
MA (LOWELL
MA IN'W BEDFORD
MA IPTTTSFIEL15
MA (SPRINGFIELD
MA (WORCESTER
MA (OTHER URBAN
MA ITOTAL FOR STATED
"NH"iMANCHESTER
NH (NASHUA
NH IOTHFR URBAN AREAS
i
NH ITOTAL
STORM rUNSEWI TOTAL I
• •»••• | ••»«•••• I »«»•»—^" I
"" 26.61 95.41
5>l 23.71
S;5I 6.21 15.21
fclll 2«'5! 81.8!
11
II
.11.
li
— I
21
It
.11.
2t
I!
i!
NJ
NJ
KJ
NJ
NJ
.=>•<
NY
NY
NY
NY
NY
NY
NY
NY
UNDV. ( COMB I
.(..«=.«»
§.»,
.11 0.01
25.51 0-01
36.91
aa.si
38.ai
O.'OI 26.51 155.31
48.21 162.2
««..•*•> I «----- I «-a»-
o.oi
VT uAN AREAS
VT (TOTAL FOP STATE
11
... .
- - '
I Mr " I 'J"r\ '.• 4. • i "£I_^
(PHILADELPHIA METRO
I TRENTON
IVTNFLAND
ITOTAL FOR STATE
'I ALBANY*"""
IBfNSHAMPTON
(BUFFALO
INFW YOBK CITY
(ROCHESTER
I8VRACI.IS
loTHFR URBAN AREAS
67
-------
TAPI
I
RFC I
""!
31
31
31
.*!.
3!
31
31
31
31
31
31
31
31
..I.
E III-6
STATE I
ID I
I AND USE RY TYPE
"RBANI7EO ARFA
DC
DC
"MD'
MD
MD
"PA"
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
'OTHER URBAN AREAS
JTOTAL FDR STATE
'iWASHINGTri"^??"""
•TOTAL FOR
BALTIMORE
WASHINGTON nc METRO
HTHFR URBAN AREAS
•i LLFNTOWN
LANCASTER
WTLKES-BAPRF
X5I OP SYSTEM
JO.6 I
"us!1
.-1*5'
"|p2l"
36T3I1
23. <
I
THTALI
mmm^m{
70.41
4.61
-i-i:i--:!;>!...2:2L *><*{
-
22.7
31.0
0.01
n.oi
o.oj 66:41
.2:2! i90*51 9?.9
"«r4.r"?o^5i""r8r6
4T6i 1:2
8.41 ^!6
63.41
IglSI
28.21
II
31
I
31
-.1.
31
31
I
31 wv
"31""""
..I.....
VA
VA
VA
VA
VA
VA
VA
VA
"v"
WV
WV
WV
WV
[OTHFR URBAN AREAS
[THTAL FOR STATE
ILYNCHBERG""""""""
I K
JPFTFRSBURR
IRTCWMOND
JQTHFP. URBAN AREAS
FOR STATE
JOTHFR URBAN AREAS
[TnTAL FOR STATE
I TnTAL"FOR~RFGlnNi""'
I— ——..._.«...„_.
31.51
0.01 12.51
17.11 fl.OI
•5.9 6»6I
.".0 a;6l
10.3 49^91
^(..Sf^Lf'S-OI
8.81
36.1 I
26%ot
9.SI
56.61
.Wl
53
H:Z.
'?!:?!
43T3I
22.51
12.11
0.1 I
10.41
0.01
0.0!
».OI
1".9I
n.3l
1.51
n.O"
Q.
OI
339.5
•-"•t»
0.0
'
ft.QI
86^91
O.M
is«;3-l
!J:JI
0.01
120,'OJ
iSI-.fii
0.21
n-i{ »-oj o.ii 0:01 * on
66.81 2«.0 154;3J 120;o! 569.'2
^I""P "HJi™:?!"l!-i
H'll J:f| 8:|! S-S fl-f
68
-------
TABLE III-6
I I
PA I STATE I
F.GI 10 I
I AND USE BY TYPE
URBANIZED AREA
rl,
. I.
OF SEWERAGE SYSTEM
AREA SERVED BY TYPE
1000 ACRE?
UNDV I COMB I STQRMI
.!<
.1.
OF SYSTEM i
UNSEWI TOTAL I
•-35:51-*""
iMI
JWI
SWI
41 AL IBTRMINGHAM
41 AL IGAOPOEN
41 AL IHIINTSVILLF
41 AL IMflBTLE
41 AL IHONTGOMERY
41 AL IT'ISCALOOSA
41 Al. IHTHFR URBAN AREAS
I I
41 AL (TOTAL FOR STATE
..(...•.I,
74,51
lI'Sl
» J %~ *
16,81
0.0
o.O
O.OJ
o.O.I
0.01
o.o!
41
41
41
41
41
41
41
41
41
4 I
41
1
41
Ft
Ft
Ft
FL
Pb
FL
FL
FL
FL
IFT I AUDERHAlF
(GAINESVILLE
I JACKSONVIILF
418:81
— ,-|.
(ORLANDO
IPFNSACOLA
I ST.PETERSBURG
ITAIJ AHASSFE
(TAMPA
IWFST PALM BF.ACH
IOTHFR URBAN AREAS
i
(TOTAL FOP STATE
9.91
150.41
"7.41
45.71
45:51
9.61
39.71
49^1
I
0.0 I
"oTc
o.O
0.0
0.0
0.3
0.0
0.0
0:0
o.o
o.o
".1
1
35.21
78.71
OTlSI
52:6!
637.41 0.4! 311.61
31'
41 GA (ALBANY
41 GA (ATLANTA
41 GA U'lGHSTA
41 GA (COLUMBUS
Ul GA IMACON
4t GA ISAVANNAH
fll ,r,A IOTHFR URBAN AREAS
II
iii GA ITOTAL FDR STATE
.
40.2!
110.51
352.il
' I«
'J:«!
•*:*!
i*.9'
6fi.?|
102."7I 181.8i 696J9I
,...^. | „«•-•>- | mmmmqm \
1.81 3.51 12.61
li:ai 5*4| ?5*6I
41
41
41
41
41
I
41
KY.
KY
KY
KY
kY
IHUNTINGTON
ILFXTNGTON
ILOUTSVILLE
lOI-'ENSBORO
IOTHFR URBAN APF.AS
i
6,71
5§*l!
|:«i
U8.7I
!!:t!
KY i TOTAL FOR STATF
-"i ; i
9.61 S8.ll
7*71
124:2!
87.41 SOfli'ill
41 MS IRILOXI
41 MS (JACKSON
tt) MR IHTHFR URBAN APFAS
II
U) Hfi IT«T4L FOR STATE
24.Rl
i?5:2!
4 I
41
41
41
a!
4l
41
41
41
41
• *tm 1 a
41
41
4 I
41
41
... I.
41
41
41
41
41
I
41
...I.
41
MC (A9HFVILLE
MC IC^ARLOTTE
MC (DURHAM
NC IF6YFTTEVILLE
Mr IRPEFNSBORO
NC IHtGHPOINT
NC (RALEIGH
NC IWTNSTON«SALFM
NC IOTHFR URBAN ARFAS
I "
NC I TOTAL FOR STATE
"sc" i CHARLESTON"""
sr ICOL'.'MRIA
sr. IGREFNVILLF
sc IOTHFR URBAN AREAS
SC. ITOTAL FOR STATE.
"TN"iCHATTANOOGA"*
TN IKNOXVILLE
TN (MEMPHIS
TN INASHVTLLE
TN IOTHFR URBAN AREAS
TN I TOTAL FOR STATE..
....I..................
359.11
11:1!
91.fit
. 186.'7I
WHdMWtM | 1
45.Ol
30.61
5i:«
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6 \
P.O
2.91
not
53.51
It 3." 7 I
""11*61
b.ol
0:01
0,0' /10.5I
0.01 62.lt
""iril*"'!!!!
01
,427.01
.„..,.!.
2651.81
i!S:iLiiii.i
JJ:|! .{|;]|
46:61 16819)
94.81 3a3.6|
»»«•>««• | mmtsmcttf |
18.81 74,91
J|:?! "-
25.71 98,'8I 167..5
"95T9.r5sI?6 U 267^4
7i8.'9l
69
-------
TAILC III-6
..I.
51
51
3!
51
I!
51
.2!
51
il
I! *
LAND USE BY TYPE
URBANIZED AREA
* *•* I
ft
it
1
IL
— •••
IN
JDAVFNPORT MF.TRO
\w$"*
ISPRTVCFTFIb
jOThf.rt urtciAN AREAS
I TOTAL FOR STATE .
51
ii
55j
51
..I.
51
51
51
'!
.=!,
51
51
51
51
51
SI
51
51
51
51
51
51
5!
51
..I.
||
51
51
II.
*!.
TN
IM
TM
IN
IN
IN
IN
1*
mmtn
MT
Mt
MT
MT
MT
MT
MT
MT
VI
MT
Ml
ifVlNSViu.-r.
FORT WAYNF
IMMNCIL
ISnUTH BEND
ITFRRA HAUTE
JOTHFR URBAN ARFAS
jTf.iAL FOR STATE.
IAMN'ARBOR""""""""
'BAY CITY
IOFTSOIT
!?LIMT
RAPIDS
IKALAMAZOO
iLANSING
MUSIfESON
ISAGTMAW
[OTHFR URBAN ARFAS
-^ I TOTAL FOR STATE
M" i O'"LIITH"""""""""""""
IFARRO METRO
IMTN^EAPOLTS
(ROCHESTER
IOTHFR URBAN APE/SS
I
.- JTOTAL FOR STATE
oH-iZ?RnN •
ICANTPN
ICTNCINNATT
MI
MN
MN
MN
MM
MN
OH
nw
OH
n"
OH
nn
ow
OH
nw
OK
OH
nw
OH
OH
ICI.EVFLANO
i COLUMBUS
ILORAIN
'MANSFIELD
ISPRTNGFIFLD
'OTHER URBAN ARFAS
OH ITDTAL FOR STATE
••"•«• I »«*V«V»W«M«MWWMWo.B.
•'• IAPPLETON
:TRn
mmmm
wi
wf
WT
IKF.NOSHA
"-Sffl"
IMADTSON
IHTLWAUKEE
WI
WT
wi
*• I -..»_,, „ ^
wi IOSHKOSH
wi IRACTNE
wt IOTHFR URBAN AREAS
WI [TOTAL FOR STATE
•»•" I Bif^mmmmmmmmmmfimmmmt
[TOTAL FOR REGION . 5.
UNSEWI
.......
n.fl
0.0
0.0
204?9
166.61
••-»»•
: 11 : «*!:!
172.9 695.'5I
"Ifr?!"!^!!!
1260.9.21
Immmmmmj
70
-------
TABLE
I1I-6 1 AND USE BY TYPE OF SEWERAGE SYST
EPAISTATE
FT, 1 ID
61
61
61
61
. 61
6 1
6 1
6 1
61
61
61
1
61
61
l
1
61
61
6 I
61
61
6>
61
61
6 1
6 1
61
61
61
61
6 1
d '
6 1
61
61
6 1
*> 1
r5 '
61
61
61
61
61
61
61
61
61
61
'J
-mm 1
AR
A*
AR
AP
LA
LA
LA
L A
ts
LA
1 A
NH
MM
OK
OK
PK
nx
TX
TX
TX
TX
TX
TX
TV
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TV
TV
TX
TX
TX
TX
TX
TX
TX
TX
1 URBANIZED AREA
1
IFORT SMITH
(LITTLE ROCK
IPTNE BLUFF
IOTHFR URBAN AREAS
(TOTAL FOR STATF
i BATON ROUGE
II iFAYETTE
ILAKF CHARI. ES
IMONROF
INFW ORLEANS
ISWRFVEPORT
inTHFR URBAN AREAS
1
(TOTAL FOR STATF.
(ALBUQUERQUE „ ie
IOTHPR URBAN AREAS
I
(TOTAL FOR STATF
IIAWTON
(OKLAHOMA CITY
1 TUL^A
iritHFR URBAN AREAS
1
(TOTAL FOR STATF
1 ARII ENE
1 AMARILLO
1 AUSTIN
1 BPAIIMONT
IBROWNRVILI E
IBOY4N
(CORPUS CHRISTT
(DALLAS
IFI PASO
IFORT WORTH
IRALVESTON
1 HARI INGEN
1 HOUSTON
ii & R F" nn
ILUBROCK
IMCAI LFN
1 MTDLAND
1 ODESSA
IPORT ARTHUR
IRAN ANGELO
IRAN ANTONTO
ISHERMAN
ITFXARKANA
(TEXAS CITY
ITYLER
1 WACO
IWTCMITA FALl
IOTHFR URBAN AREAS
ITOTAL FOR STATE
~ITnTAL""FOR~REGlON 6
UMDV 1
27la!
32.61
6.2)
110.8)
1
177 ."01
25.01
7.01
10.91
14.1 1
2.61
31.11
37.61
128.21
36.51
50.91
1
87.41
137:71
66.6)
145.71
1
365.81
36.71
22.51
24.41
31.81
. 3. PI
14.0 1
53:81
254.61
35.91
16S-?1
7.2 1
14.71
150.91
6.01
.''9.41
1 0 .2 1
12.41
6.71
30.61
13.2 1
56.91
12:il
40.61
8,51
40.51
14.51
319.21
.14 26. "81
?185?2I
EM
BY TYPE
1000 ACRES
COMB 1 STORM!
*".;!"
0.01
o.Ol
"•5!
0.0
» . r, 1
O.Ol
O.Ol
0,01
o.Ol
n.Oj
O.Ol
O.fll
O.Oj
O.Ol
O.Ol
O.Ol
o . r, l
n.Oj
O.Ol
0.01
n.fll
? . 8 1
0.0 1
0 : 0 1
o.Ol
?•?!
1 . ! 1
o.o
tl.fi i
o.Oj
O.Ol
Ofk 1
.0
0.0
O.Ol
0.01
0,01
0 : 0 1
0.0
o.oi
0.01
0.01
...!:i!
"16171
.•?:*',
* i
30.11
ig.sj
5.1
,4.9 1
3.8!
51.21
12.61
38.11
130.01
?2" 1
38.31
29151
1
87.61
1^:9'
3.1'
4.4 1,
*\ f I
% »o '
i o :9 1
74.21
18.21
*§•§
I!
106161
« * I
^•7 '
C * 3 1
:
«:7i
•541
11
4.4 1
6l2t
4.41
109, 5j
489. '31
mmmmm^m I
775.31
OF SYSTEM 1
UNSEWI
20I7I
1
82.41
e *e
5 *"S "I
. ' • ~ *
0.01
1*>.5
20.4
69.51
48.9
49 1 7
I?:?
145.-3
8.41
10.0
J0.2
•!
iff if
58.0
8?:S
3*3
i?»2
l!-8
1
TOTAL 1
60.81
lil:!!!
301.01
m 1 A I
16.01
** 4 O 1
14*2
II •§
53.81
60.21
96.01
^ I
101.71
174.'7I
28,21
217.01
T * c o i
1 15»H 1
238.51
. 1
598.81
49.91
39.01
55.01
9*^1
.6 1
41i:4!
76.21
253.4 1
2i:«'
3U5lOt
I •• . i '
>?*1 1
1 16101
1 46.71
i «< • i
44:2! iSi:?!
6.2! 32.41
5^1 19:8!
8:ij 53.11
amU\ 16.01
10.91 57.61
8.01 ?6.9I
139.81 569..7I
624.8
mmmitnf*
970.9
I?546.'OI
!3948?2I
71
-------
I
TABLE III-6 I AND USE RY TYPE
[ "RBANl7En AREA
I ...........„.„.„.
EPA I
PFGI
— .1
71
71
71
71
71
71
71
I
71
...|
71
71
71
71
I
71
71
71
71
71
7 I
I
71
71
71
I
71
.. I,
71
"pi"
Rl
Rl
Rl
Rl
I
PI
.. I.
Rl
Rl
I
Rl
.. I.
81
Rl
I
Rl
"fit"
01
81
-.1.
81
Rl
fll
81
I
81
— I.
81
I
Rl
-I-.
STATE
I"
ir
TA
TA
TA
IA
TA
IA
m m»•
KS
KS
KS
KS
KS
IDA'VENPORT
IDFS MOINES
IDUBUOUF
ISTOUX CITY
'WATERLOO
IOTHFR URBAN AREAS
[TOTAL FOR STATF .
I WTCHITA
IOTHFR URBAN AREAS
F0» STATF
MO (KANSAS CITY
M" JST.TOSEPH1^
"2 ST. LOUIS
MO IflTHFR URBAN
wn [TOTAL FOR STATF
WE: iLTNroLN" '
NJF IOMAWA
MF IHTHFP URBAN APFAS
."L !TnTAL FnR STATF •
CO ICnCnRADO SPPTUGS
rn I DENVER
en IPIIERI n
rn IQTHFR URBAN AREAS
Cn [TnTAL FHR STATE
'MT"!B?L"NGS '
MT IGREAT FALLS
MT IfiTHFR URBAN APFAS
MT ITnTAL FOR STATF
N'D
«• «•
SP
sn
sn
m m m
|lT
UT
UT
NT
UT
"WY"
WY
IOTMFR URBAN AREAS
[TnTAL FOR STATE
I STn'ix'pALL S~""" "
IOTHFR URBAN APFAS
I TOTAL FDR STATF
IPPOVO
[SALT LAKE CtTY
IOTHFR URBAN AREAS
[TnTAL FOR STATE
i UPB AN'AREAS"""""""
{TOTAL FOR STATE
FOR'REGIP""
OF SEWERARE SYSTEM
APEA SEPVFD RY
OF SYSTEM I
L^J/LI
i§:;
323.81
"23?6I
>ll3l
laiii
278171
".'
.
•5.71
79:5' i":?!
1*51."I 2.1.3!'
• ---,-I...... I,
18.5 I o.OI
151.7} 21.01
?4.?| o.OI
6.11 1/1 /i i
?9.7I
10.41
3.41
1R.1 I
35161
66.81 87.6
3.11
17.81
I—.-:
331."6I 163. b\
-»...|-..__„ |,
15.? I o.OI
10.71 20.91
23.2, 8.7,
79.11 2".61
29.81
.....|
8."4 I
19.
o.OI
O.OI
0.31
0^31
I
• -T-I
4.61
10.51
S9.RI
5.51
17.71
. I
695:li
'«[
•JWI
_185.4|
9.6|
29.'8
16.41
55.81
.-...,.
455.51
""""791
15>
54.91
33.91
67T2I
171.41
327.51
•«-•»• I
?6.9|
260.51
28 :i!
2?f:i!
-
'^3.61
38.21
irii .
15.61
19. 51
.
o.OI 15 T
o.OI ?
orai""!!'
o.fil laT
l.fl
i?:il
94.^1
"""«"TI
2191
?1.2[ 19.81
"ilrll'TJ!
18.51 10.81
32.7) 0,71
20 ."3 I "oi"
?9Toi ol01
17.61 " - '
?4.1 I
?S /I f. i
15.01
"alii'
,1:5 i
20.l!
......I
V •-•
.1.0
o.O
0.0
>i ""7o
i
o.o
26.3
6T6
i • i i
£f.6|___0.0l__11.3l
I
I
46.31
...,-(.
. r
11.31
1 I •
;i
60.81
"137JI1
13.4|*
*mm + m* I
?19.3
—.—..•|
33 ."31
96.61
54.01
_l«3.9l
175777!
-...^.|
9.01
57l6l
l'»7.5l
334.'9 I
..«.*.|
17.31
1411 I
soTSi
82.21
......I
9.81
39T9I
49.7!
--•-,.|
17.31
51.11
. I
68.4 I
"mmm^m\
39.01
41:61
117.81
32181
231 ."2 I
•...^.|
49.31
49.'3 I
i...^«|
815.61
...... I
72
-------
TABI E
III-6 1 AND USE BY TYPE OF SEWERAGE SYS
1 ' ARF* SERVED
1EPAISTATE
RFGI ID
91
1
91
91
91
91
91
91
9 1
91
91
9.1
91
91
91
91
91
91
91
91
91
91
91
91
1
91
91
91
j
91
91
91
91
1
. 91
91
101
101
. 1
1->l
101
1 0 1
101
- 1
101
101
101
101
101
1
1'}l
*""!"
AK,
AK
A7
A7
A 7
A7
CA
CA
CA
CA
P. A
C A
CA
CA
C*
CA
CA
CA
CA
CA
CA
CA
CA
CA
HI
H!
HI
K'V
MV
NV
MV
ID
ID
TO
0»
nR
OR
OR
OR
MA
WA
MA
WA
«•*!
! URBANl7Er> ARFA
1
IURBAN AREAS
I
ITOTAL FOR STATE
1 PHOENIX
1 TUCSON
IOTHFR URBAN APFAS
1
ITOTAL FOR STATF
IBAKFRSFIELD
IFRESNO
ILOS ANGELFS
IMODESTO
IOXNARD
(SACRAMENTO
ISALTNAS
ISAN BERNARDINO
ISAN DIEGO
ISAN FRANCISCO
ISAN JOSE
(SANTA BARBARA
1 SANTA ROSA
I8FASIDE .
ISTMT VALLEY
1 STOCKTON
IOTHFR URBAN AREAS
i
(TOTAL FOR STATE
1 HONOLULU
lOTHFR URBAN ARFAS
I
(TOTAL FOR STATE
ILAS VEGAS
IRFNO
lOTHFR URBAN ARFAS
t
ITOTAL FOR STATE
ITOTAL FOR REGION 9
IBOIfiE
IOTHER URBAN AREAS
ITOTAL FOR STATE .
IEUGFNE
IPORTLAND
ISALFM
IOTHER URBAN AREAS
1
ITOTAL FOR STATE
1 SEATTLE
(SpOKANE
ITACOMA
lOTHFR URBAN AREAS
ITOTAL FOR STATE
" i THT AL~FOR"RFGIO"I o~
"'" 1 •"""* | TOTAL"FOR"THE*ursr""
i
29.61
137.51
36:«l
, |
16.1 1
§.91
.4 1
9.51
40.11
78 .3 1
3.21
1 ?0 .3 1
105.81
135.91
66.31
9.31
i a. 4 i
5.51
8.71
12.21
110.21
1
.1002.01
26%5I
-11.81
1
. 38.31
46.01
12.1 1
tO. 21
1
68.31
30.31
, 1
18.01
75:21
12.11
34.5 1
139.81
119.21
22. PI
41.71
68.91
1
. 252.61
"ZsiTi!
..-.., i
13409.1
TEH
BY TYPE OF SYSTEM 1
1000 ACRES 1
CO^B 1 STORM 1 UNSEWI TOTAL I
0.71
0.7J
o.oi
O.OI
o.OI
1
n.Oj
o.oi
O.OI
o.oi
O.OI
O.OI
•5.61
o.OI
O.OI
0.0
54.1
0.0
O.OI
O.fl
o.o
0.0
0.0
7.4
67.1
o.O
0.0
o.O
O.OI
/5 _ ft
0.4
•
70.6
0.0
0,0
0.0
24*?
n,o
*'. i
3?. 7
37.9
19.4
".7
21.*
79t,9
~11?I6
"?248?
6.71
6.'7
49.6
16.7
14.4
80.7
..9.8
16.4
5*8.3
6 .7
1 2 .7
?9 .7
' 4.6
*9 .5
67 .2
80 .5
55.8
8.8
2 .7
8.7
2 »9
1 0 .6
115.5
1050. '4
25.5
11.3
3 6. '9
12.2
? »y
2 • o
17.3
1191.9
4^8
1 7 » 1
.2:3
5 *i
1 1 «3
45.7
i«:8
i 68.8
"IsST-S
5987.
12.11 49.21
1 . 1
12.11 49.21
61.2
18.5
17.3
97.1
To. 7
1.3 . 2
172.5
5.5
248.31
67.21
68.41
. 1
3*4.01
36.51
50.61
1006.11
?1.6I
1 8 .9 | 71 .7 I
4.2.6 156.21
1.71 9 .6 1
48.71 198.4J
7S •
1 65.3
2«3.B
415.8 1
1 55.1 177 mi I
1 5.6 23.7 1
7,3
1.1
?4 .3 1
1 5 .4 1
4.4 16.01
7 .3 30.11
1 77.9 311.01
1 708.7
28?8.*l!
1 21.5 73^6 i
1 9.6 32.61
. 1
31.11 106.21
1 T » 3
1 £l .ft
1 " '
1 31.1
1 880.1
1 1 ™ .6
I 23.8
' 2-S
i a6:l
1 21.4
1 86.7
( 2 C, m ™
1 ^ Q ^1
I
1 144.3
I "54T5
1 7393.
40 »j 1
17.81
J
119.** 1
3487.01
18.61
65 .9 1
84. 5 !
55-|j
23 «7 1
75.21
305. '01
49.91
82:61
148.81
. 1
545.61
"its?! 1
29037.1
73
-------
2. Storm Sewers (P ):
s
P =
dx - P
(111-14)
3. Unsewered (P ):
P = P - P - P
u c s
(111-15)
where A = total acreage of urbanized area, and
P = total population of urbanized area.
The resulting population by type of sewerage system is shown in Table
II:t~7» Population by Type of Sewerage System. The population densities
by type of sewer system are shown in Table III-8, Developed Population
Density by Type of Sewerage System. Lastly, the values of the coeffi-
cients used in the .above calculations are shown in Table III-9,- Values
of Coefficients. '
74
-------
TABLE IH-7
EPA 1 STATE 1
EGI ID 1
POPULATION BY TYPE
URBANIZED AREA
11 CT (BRIDGEPORT
11 CT (BRISTOL
it CT IDANBURY
11 CT (HARTFORD
11 CT- IMERTDEN
11 CT INEW BRITAIN
11 CT INFk HAVEN
11 CT NORWALK
II CT 1 STAMFORD
1 1 CT 1 WATERBURY
11 CT (OTHER URBAN AREAS
t 1
1 1 CT ITOTAL FOR STATE
11 ME 1
11 ME 1
1, MF ,
11 ME 1
LEWISTON
PHRTLAND
OTHER URBAN A&FAS
TOTAL FOR STATE
Jl MA (BOSTON
11 MA 1 BROCKTON
11 MA (FALL RIVER
1 1 MA IFITCHBURG
H MA ILiWRENCE
ii MA ILHWFLL
1 1 MA (NEW BEDFORD
1 1 MA IPITTSFI-ELH
11 MA ISPRTNGFIEIO
11 MA 1 WORCESTER
1 1 MA IOTHFR URBAN APFAS
11 MA ITOTAL FOR STATF
11 NH 1
11 NH 1
1 1 NH
11 NH- 1
MANCHESTER
NASHUA
OTHFR URBAN AREAS
TOTAL FOR STATF
11 RT (PROVIDENCE
11 RI IOTHFR URBAN AREAS
1 1 RI TdTAL FOR STATE
11 VT,
11 VT
"l!
URBAN AREAS
TOTAL FOR STATE
TOTAL FOR REGION 1
21 NJ ATLANTIC CITY
11 NJ INFW YORK CITY METRO
21 NJ IPHU AOELPHIA MFTRH
gl NJ TRENTON
21 NJ VTNFLAND
2! NJ ITOTAL FOR STATE
21 NY
•21 NY
21 NY
I' NY
21 NY
21 NY
21 NY
21 NY
... 1 .»...
(ALBANY
(BINGHAMPTHN
BUFFALO
NFW YORK CITY
(ROCHESTER
(SYRACUSE
IOTHFR URBAN AREAS
ITOTAL FOR STATE
OF SEW
P
•COMB
0*
0*.
0.
170.
so:
69?;
Ii
335.
0.
92.
8?:
101.
0.
109^
71.
36.
170.
333.
13.
^
69;
1910.
0.
?04.
20V
o!
27?,
145*
642.
676U.
240
1351
9603
--•--•»
ERASE SYSTEM
OPUI.ATIOM 8ERV
(1000 PERSONS)
STORMI UNSEW
42ll
M.I
i«.4.l
97Tl
76.1
19.1
l?6:i
979.'!
8:1
"•.!
O.I
1798.1
"i:l
li-J
Q.I
138.1
2?7.l
0.
8:
0.
267.
10. I
278.'
0.
0.
94.
4102.
22§:
4473.
94.
o.
3755:
243.
141.
5369.
ill:
18:
1?:
50.
86.
673.
i?:
7 U o
135.
510.
45.
36.
24:
22.
'«:
118.
1254.
25:
82.
131.
195.
?02.
74.
74.
2470.
1382.
40:
32.
1495.
120
146
0
1 118
65
90
639
EP
TOTAL
1
Hi:
IK-
ill:
301.
2344.
507.
2652.
S?:
.-2J«:
163.
.-Jjf:
454:
4813.
95.
61.
261.
417.
795.
31.
926.
143.
143.
9050.
74;
6372.
illg:
10519.
601 .
376:
180.
2196.
15611.
"219831
75
-------
POPULATION BY TYPE
URBANIZED AREA. j
TABLE IXI-7
•!*»*' STATE I
WEB I . - ID I .
""! ! "If" ! WTLMINCf ON ---------- I
5j OE OTHFR URBAN APEAS I
,.2L'PS |TOTAL FOR STATE I
""5 1 ""DC" W A5HIN6TONT57C?" |
MD
MD
MD
MD
PA
PA
PA
PA
OF SEWERAGE SYSTEM
POPULATION SERVED
(1000 PERSONS)
COMB I STORM I UNSEWI TOTAL
—---„!-....
198.
13.
83.'I 210.'
FOR STATE
i BALTIMORE""""""" ""
WASHINGTON DC METRO
OTHER URBAN AREAS
TOTAL FOR STATE
SCRANTON
JWILKE5-BARRE
PA jOfHER URBAN AREAS
PA TOTAL FOR STATE
-•--.-........_._.
NEWPORT NFWS
NORFOLK
VA
VA
"wv"
WV
WV
WV
WV
STATE
j .11.1 j 121* "FOR'RESION""!"
ROANQK
otSfR URBAV'ARE'AS
TOTAL. FOR.STATE
JTON
-JTON
JNVILLE METRO
BAN AREAS
CHARLEST
IHUNTlRST
ta
3577
«00.'l 3^7.
mmmm*m\..___^
S:! 'IT!:
O.I 806.
O.I 25
-------
TABI E III-7 POPULATION; RY TYPP OF SFuEOAfJE SYSTE* . ,
II
EPAISTATEI URBANIZED AREA
RFGI 10 1
Ul AL IGADSDEN
Ul AL IHUNTSVILLF
ai AL 1 MOBILE
ai AL (MONTGOMERY
ai AI ITUSCALOOSA
ai AL IOTHFR URBAN A»FAS
ai AL 1 TOTAL FOR STATE
Ul FL (FT.l.AUDEPnAI F ,
Ul FL 1 GAINESVILLE
U 1 FL 1 JACKSONVII LF
Ul FL IMTAMf
Ul FL IOPLANDO
Ul FL IPFNSACDLA
ai FL 1 ST. PETERSBURG
ai FL ITALI.AHASSFE
Ul FL ItAMPA
U 1 Fl 1 WFST PAL" BFACW
Ul Fl IHTHFR URBAN AREAS
II
Ul Fl (TOTAL FOR STATE
ui GA IAI.BANY
Ul GA (ATLANTA
a( • GA (AUGUSTA
al GA (COLUMBUS
ui GA IM*CON
Ul GA ISAVANNAH
al C* ItlTHFR URBAN APF.AP
1 1
al GA ITOTAL FOR PTATF
Ul KY 1 Hl'INTIMGTP*1 MFTRn
Ul KY (LEXINGTON
ai KY ILOUTSVILLF
at KY lOWENtSBORO
U| KY IQTHFR URBAN ARE'S
II
ai KY (TOTAL FOR STATF
ai MR IBTL.OXT
Ul MS IJACKSON,
Ul MS IHTHFR URBAN APFA.S
1 1 .
Ul MS ITOTAL FOP STATF
ai NC IASHPVILLE
Ul NC 1 CHARLOTTE .
ui Kir. (DURHAM :
al NC IFAYFTTEVIl LF
• Ul K'C IGPEFNSBORO
al N'C IHTGHPOINT
Ul NC IRALFIGH
Ul NC 1 WTLMINGTOK'
Ul NC IwTNITON-SALFM
ui NC IOTHFR URBAN AREAS
a' NC (TOTAL FOR STATF
U| SC 1 CHARLESTON
a! <*C (COLUMBIA
Ul SC IGREFNVILLE
al RC IOTHFR URBAN ARMS
ai $c ITOTAL FOR STATF
a 1 TN (CHATTANOOGA
al TN IKNOyVlLLE
Ul TN (MEMPHIS
U 1 TN (NASHVILLE
ui TN IOTHFR URBAN AREAS
UI TN ITOTAL FOR STATE
""("""" ITOTAL"FOR~RFGTON «
POPUI ATIHN S^RVtn
CHMR
ft
n.
ft .
o!
P .
0 .
0.
n.
ft*
ol
a.
ol
ol
0 .
1 .
6.'
76?
1 0 ^ .
73.
U7 .
0.
100.
1 8^ .
59ft.
7.
0.
tl? »
3°.
U7 .
116.
ft .
ft .
0.
8
0
ft
ft
p
ft
n
ft
p
0
0
0
ft
uo
p
180
106
. 31?
"Toiu
(flOOO PERSONS')
STORM L'NSEW
3*1."! ?07.
'J3.I 25.
06.1 50.
1 ** 9 ^ ^^ •
°5.i au.
30.1 56.
1238. 773.
uai. 173.
UO.I ?9.
3^6.1 17«,
10T8». 1fi?«
215. 86.
113. 5U.
368. .127.
2751 9Ul
1»1. 107.
002.1 "?37.
'J175. . 1385.
O.I 0.
6^6.1 U20.
O.I 76.
A 1 . 1 101.
»>a. I aa.
1 ? . I 52.
3A3. I.- 321 .
1" s
1l«;6j( 102?.
30.1' 20.
131.1 ? 9 .
5 U 5 . 1 ' 1 7 2 *
/ » 1 * " " •
a8 a i l 1 57.
, i
Al . 1 . 60.
UosT 1 ?68l
506.1 391.
i75li ib'1;,
U7. j 5'4
oi.l 7,0
t n3 . U15
?8. 66
B ii . o °
U 1 , 17
618T «57
1315. 07?
PSl 69
607.' 536
102. 96
107. 83
hi: ii|
U?3. ?53
' 12U9. 7a6
ITsTI? ~6209
TOTAL
68^
1 U6.
25 ° •
139.
756*.
2011.
,53oi
1 220 i
'. , .i *
167;
'195.
_ Q T
78.
369.
288.
1330.
5^65,
76.
i 1 7 3 ,
i 4 9- »
2f)9 ,
1 2^ •
a A. ft *
860.
?768*
• U7;
• L f\
160.
TTO
73q .
'• ton *
> 68^ .
1687.
121 .
190.
67°:
067.
' 101.:
161
« e 3
I be «
2 3 "
CO *
la?!
1075.
2287.
?u>"
C *» 1 a
157.
606.
1233.
190:
661.
"si:
2307.
77
-------
( TABLE III-7
IEPAISTATEI
JUPGI ID I
l"|!""lL"lAURnRA
IL '-•
IL
ft
i J- • W W Vrf M | \J
IL IjnLTET
IPF-OPIA
IROCKFORD
ISPRINGFIEID
OTHFR URBAN AREAS
FOR STATF
51
51
I!
51
51
POPULATIHM pv TYPE
URBANIZED ARFA
MFTPn
OF RE»ESA,R!,5YSTEM
ITt
II
IL
51
51
1!
51
51
51
51
51
51
I!
51
51
-_l _
5!
51
|!
I
.2!.
51
1!
51
51
51
51
51
51
._ I..
51
51
51
I!
51
SI
51
51
5I~
-I-
IN IANOFRSON
TN [CHICAGO MFTPfl
IKV I"*L'S.VILLF
IN ISnUTH'BEND
IN ITFRRA HAUTE
TN IPTHFR URBAN
IN (TOTAL FDR STATF
MI IANN'ARBOR" ""
MI BAY CITY
MI (FLINT
MT IG"AMD RAPTP>S
MI (JACKSON
MI iKALAMAZnO
.jjj .'"'jI^EGON
Ml
URBAN iPEAS
- jTOTAL FOR STATF
I
MN IFARfiO METRO
MN (MINNEAPOLIS
MN IRDCHESTER
"N InTHFR URBAN
MN ITHTAL FOR STATF
HH
OH ICTNCINNATT
njj ICI.EVELANO
OM ICOL'IMRUS
nH lOAYTHN
nH (HAMILTON
nH ILTMA
nn IL^RAIN
OH IMANRFIELD
HH ISPRTNGFIEI R
HH ISTEIIBENVII LF
nH IVnUNGSTOWM
rt» IOTHFR URBAN
nH iTnTAi FOR STATF
"WT"iAPP'ETON""""""
Wi DHLIITH METRO
wi IGREFN BAY
Kl IKFNHSHA
WT ILA CROSSE
WT (MADISON
WI (MILWAUKEE
wi IHSHKOSH
WI IRACTNE
WI (OTHER URBAN ARFAS '
_WI_jTnTAL FOR STATE
ITnTlL"FOR"REGlnN""i"
46. I
197.1
»8«S.| 138?.!
jllOlnl.l 7343.1 32610.1
....|......|......|......i|
78
-------
TABLE
III-7 POPULATE PY TYPF Of ^^PAn^SYSTEM^^
EPAISTATEI IJRBANI7ED ARFA
FT, 1 ID 1
61
61
6<
*!
1
61
61
I!
t\
61
61
1
*!
61
61
1
61
61
61
61
1
61
61
61
61
61
61
6 1
61
61
61
6 1
61
61
61
61
61
1!
!!
6|
*:
61
•61
6 1
61
61
1
6!
...1
AR (FORT SMITH
AR ILTTTLE ROCK
AR IPTNF BLUFF
AR IOTHFR URBAN APE»S
-AR (TOTAL FOR STATF
LA 1 BATON ROUGE
I A ILAFAYETTE
I A ILAKF1 CHARLES
LA 1 MONROE
LA INFW ORLEANS
LA ISHRFVF.PORT
1.. A IOTHFR URBAN APE*R
1 A ITOTAL FOR STiTF
MM 1 ALBUQUERQUE
NM IOTHFR URBAN APFAS
MM I.TOTAL FOR STATF
HK ILAWTON
OK (OKLAHOMA CITY
OK ITULSA
HK IOTHFR URBAN ABFAP
1
OK ITOTAL FOR STATE
TX 1 ABILENE
TX IAMAPILLO
TX (AUSTIN
TX IBFAUMONT
TX (BROWNSVILLE
TX ICnRPU!? CHPISTT
TX IDALI.AS
TX IEI PASO
TX IFORT WORTH
TX IGALVESTON
TX IHARI INGFN
TX I HOUSTON.
TX ILARFDO
TX ILtlBROCK
TX IMCALL.EN
TX (MIDLAND
TX lOHESSA
TX IPHRT ARTHUR
TX ISAN ANGELP
TX ISAN ANTONTO
TX (SHERMAN
TX ITFXARKANA
TX ITFXAS CITY
TX (TYLER
T X \ W A C ^
TX IWICHITA FALL „,._
TX IOTHFR URBAN ARpAS
TX ITOTAL FOR STATF
•"•" i JOT AL"FOR~REGION~~6
COMP
30.
.8:
50.
80. '
oC
o:
8:
n.'
0.
o!
0.
0.
0 .
0.
oZ
n.
0.
0 .
o.
0.
40.
^ *
0*
0
0
0
0
0
ft
0
ft
0
ft
A
W
ft
23
, 101
mmm~m
. 181
STTRMI
oil
»9.l
2u:j
345.1
172.1
962ll
I'lO.I
58911
2012.
2fl!*
fl«5.'
249^
59.'
i»?r
U2.
43r
)t\ 4 '4
011.
2^2.
471.
Q *
V e
1lff:
AOl
5j|:
50 .
77*
~ •
13^0 r
6031.
10023^
1
rp^pMSV 1
UMSCWI TOTAL I"
134.1
i2!"!
" 1
537.1
ll'\
23 . 1
34.1
51.1
C.I
94.1
116.1
394. j
94.1 ,
?26.l
176.1
122.1
?35 . 1
1
590.1
31.J
76ll
37.1
^'l
1 -^*f 1
'§?:!
! 206.1
22.1
/< « I
U 1 • 1
/I Q 11 I
t4*T U e 1
20.1
73.1
39.1
1 /111
1 t* 1 • 1
1 22.1
1 43^1
1 182.1
1 43.1
1 40.1
1 f . /I 1
I 3*» • '
1 =>C 1 '
1 CT> • I
l c *z * I
j 627-..
1 2801.1
| "454971"
76.1
223.1
61.1
602.1
^78'!
L *
90*1
962.1
234.1
705.1-
1
297.1
414.1
7H.|
580.1
371.1
693.1
1
1740.1
90.1
264^1
llfell
53*!
21|. j
677^1
62.1
1 fcTn * I
isoli
1S6*I
a/i* |
o ** • *
60 1
O '/ • '
119.1
98 1
7 rj o i
1999.1
8934.1
147537 j
79
-------
TABLE III-7
EPA I STATE I
RF.GI ID I
—-I
71
71
71
71
71
°OPULATIr)N BY TYDE
"RRANI7ED AREA
"*"•""*•* I "•••»•••»• WMMVMMHW
CEDAR RAPTDS
IDAVFNPORT
IDES MOINFS
I A'
STOUX CITY
WATERLOO
'OTHER URBAN ARFAS
71 IA
7l~"s
71 KS
KS
KS
71 KS
-- I.......
71 MO
71 MO
71 MO
71 MD
71 MO
71 MO
I
71 MO
.. I.....
71 ME
'TOTAL FOR STATE
[WICHITA
OTHER URBAN AREAS
({TOTAL FOR STATF
'(COLUMBIA""""""""""
KANSAS CITY
iST.inuis
'OTHER URHAN
THTAL FOR STATF
'LINCOLN! '•
IOMAHA
OTHPR URBAN ARFAS
I ,.. -- <> | ^ L,' M 11 M IT r »* «j
71 NF 'TOTAL FOR STATF
71
..I.
81
81
81
81
81
81 CO
mm \ mm~m
ft I HT
81
CO
CO
CO
CO
CO
I . - --. FOR REGlOM 7
iBOU'DER"""""
ICHLnRADO SPPTK'r;"*! 8cl*! '3A-' loaf;!
29:J «!•'! 22-! i93.!
I
12AO.I «flt. I 1737. i
256.1 116.1 372.1
46
791
106.1 297.1
O.I 3*5:i
0. I P3. I
,1 I
O.I 5«9.|
144.1 479.1
3«.l 12lTl
265.1 --" '
o?i r?6ri""*7?ri""
.-2*!..i?^*i 75v' 201-|!
55.1 26*0? I "107"' ""373s7 I
80
-------
TABLE IH-7 POPULATION BY TYPE
lEPAISTATEl
10
IRF6I
AREA
|-»|-ar
91 AK
im \ «•••§••
91 AZ
91 AZ
91 AZ
I
91 AZ
._!..—-
91 CA
91 CA
91 CA
91 CA
91 CA
CC*A
cd
cd
CA
Sj
T I
?!
I ORBA""AREAS"""""""""
'TOTAL FOR STATE.
! PHOENIX""""""""
iTUCSON
(OTHER URBAN AREAS
ITOTAL FOR STATE
IBAKFRSFliLo"
I FRESNO
(LOS ANGELES
(MODESTO
IOXNARD
91
I!
91
91
91
91 ...
91 CA
91 CA
91
I
. 91
UACRAMENTQ
AL
CA
CA
Si
91
",
91 Hi
...I.....
91 NV
91 NV
91 NV
91 NV
... (..—
91.
...I.—
101 ID
101 ID
TNAJ.
I SAN BERNANDTNP
iltK FRISCO
I SAN JOSE
(SANTA BARBARA
(SANTA ROSA
(SEASIDE
ISTMT VALLEY
(STOCKTON
(OTHER URBAN AREAS
'ITOTAL FOR STAT.EMM_
SoTHFR URBAN AREAS
I
ITOTAL FOR ST*TF_--I..
"!LAS'VEGAS
I pirun
IOTHER URBAN AREAS
! TOTAL F(3R STA3E._.-.
" 5 TOT AL'FQR'REGJO""?"
'\lrnl
iia.i
TOTAL
"u"
1,47.
i
COMB I STORM
___«^ | --••-«<
10.1 . «3.
10." I 93.'
'"""o:i""6?0~.
S:l i?!:
,..2;L.!:!;L.^:L-i^:
o'l l?9.l 47.! 17§.
§:! 747!:. ..i;.; _iM.
121:
863.
294^
251.
O.'l 099.
.. -—,i— «-*
- 0.! ISI-
139.1 638.1
...........I
«2.! ??."•
41.'!
!.l
2! 7.
1713.115291.
URBAN AREAS
101 10 (TOTAL FOR STATE
EUGENE"""""""""""
101
\l\
-l?
101
OR
OR
OR
OR
OR
(PORTLAND
!OTHER URBAN AREAS
. _. _ |-°-,*L_™!L2ItI!:---"
1 "To! ""A" i SEATTLE"""""""
191 WA ISPQKANE
i"! "« iTirnMi
IOTHER URBAN AREAS
I TOTAL FOR STATE
.I.........-.-——-
ITOTAL FOR RFGION 10
ii i ziiziizinm--'
(TOTAL FOR THE. U^.^
101 'WA
101 WA
...I....
. 1« I.
o.'l
232.
6.1
*5.
38.1
21.
396.1
3727.1 20731.1
, ...
3ol:;
155.1 387.1
*^-*«' |
._. .. «265;|
......|.......I
57606:!77853:!33906:11093661j
81
-------
TABLE HI-
(EPA!,STATE
?D
^• 2 ! *?*?** I
(SPRINGFIELD
CESTER
ER URBAN APEAS
I
JAVE. FOR STATE .
{MANCHESTER
,3fl| |4.'9«
. |..^..
JOTHER URBAN APEAS |
I
I AVE. FOR STATE
.PROVIDENCE""""""""
OTHER URBAN AREAS
AVEJ^FOR STATE.
URBAN'AREAS"""""""
IAVE: FOR STATE.
n
o.o
To
o
0.0.
^-»i»-
S-'l I.!?i
?.:iii i-fti
r
m
I11.27IJ3
-r
. 60
mm
.0
.'o.
mm
71
.i..i«j.;;;;, 2*9?' 7'75'
jtsras'jiirto 4:lo{ Srr?!
115.85111
j"5775!"o
I. 8.751. 0
!""•»!= I »"
4.'20
mm-mn
7:73
7 .'73
«.67I
8 .'79
mmmmm
8.1?
8.'19
8.58
.2"
FOR STATF
ALB 1 157
JAVE.'^FOR STATE .
82
-------
ABLE IH-8 DEVELOPED POPULATION I^nYJ* TYPE,*
PA..STATE. nRBAN,I7F.n ARFA cnMp j ,TnRM ,
SYSTEM
AVER I
31
31
OF
DF
1 WILMINGTON
IDTHFR URBAN &PFAS
31 DF. IAVE." FOR STATF
2.21112.231 u.98l 8.91
2.23112.231 tt.9«| 8.911
1,1 .1 ,1
2.23112.231 «.9fl| 8.911
31
DC
nr
IAVE.' FOR STATF
I BALTIMORE
1.'
1.'
I 0.0 120,
I . I
i i o.o i?o,
, i,
0?l
31
3'
31
MO
MO
MO
WASHINGTON 'nc MFTRP
IOTHFR URBAN APFAS
31 MD IAVE..' FOR STATF
0.0 113.271 4.8411.0.741
0.0 113.7?< a.41| 9.49I
0^0 llsTiM OoilO.a?!
0.0 M3.3M 4.70llOe'aP|
31
3'
31
31
31
V,
31
31
pA
PA
PA
PA
PA
PA
PA
pA
PA
PA
PA
pi
PA
| A! LFNTOWN
i AL THHNA
IH4RPISBURR
IJHHNSTOWN
ILANTASTER
1 PHILADELPHIA
IPTTTSBURGH
IRFAniNR
ISftRANITON
29
U.«0| 9
51531 9
5.5UI 9
fl.Ofcl
9.9RI .
l»l?l t
.ai -5.10111
?l.iai?l.ia 2.3" 8
31 PA
31
3!
31
31
31
VA
VA
VA
VA
VA
VA
VA
I ynpK
IOTHFR URBAN AREAS
IAVE." FOR STATF
,i_.-...-.------------
ILVNCHBFRG
t NFvJPQRT NFWS
8
8
8l67i'6;0pi 5I8UI 7
0*0 I1ol6?l 6^001 8
15.17113.431 3,'7SI 9
.221
.661
.531
.621
1531
.11 I
.63!
.6S I
.661
.231
*80I
I
.801
IPFTFPSBURG
31 VA
31 wV
31 '«iV
wv
wV
I
IRfiAWOKE
I WASHINGTON
IHTHFR URBANJ ARFAS
lAVt.' FOR STATF
. I -.--.„_.-- — -------
88'!13:72! 1:11! J-.WI
12161 121611 «:71' 5-gl!
7.9fl
H4.16
11.23111.2^1 -5.5«
16.00114.1?! C.O
10T63M3.89I «.
. 70!
I
3 i
31
3) wV
...l-.--
IHHMTTNGTOM
ISTEI'BENVII LF MFTRr,
IHTHFR IJRBAN AREAS
IAVE.' FOR STATE
. | -__,---_----- — ---•
.10.6', M3."«e| U.'lOl 9.70
P.1SI 0.0 I 0.0 I 8.1PI
6*061 0.0 I 0.0 I |«g|
1 §^.'4 I12l0fll 5^161 8^011
. fl.9flll2.'o
-------
( TABLE Iii-s
IEPAISTATEI
IREGI ID i
DEVELOPED POPULATION
(HUNTSVILLE
'MOBILE
IMHNTGOMERY ,
jo^iMiN
FOR STATE
A,L
AL
AL
AL
AL
AL
.FL IOPLANDO
EL IPENSACOLA
AREA*
a j
S!
si
a 1
ai EJ IWEST"PALM BEACH
aj EL IOTHFR URBAN AREAS
ai EL IAVE: FOR STATE
CnMRI5TORM||JN8EW| AVER |
I'liii
7.61 I
III !j|:|5| \:\k\ |:2?|
2*8 !.1S"?S! ?^5! sl?§!
RA i ALBANY'
PA • • -• —
GA
ISAVANNAH
IOTHFR URBAN AREAS
IAVE.' FOR STATE
lOWENSBORO
lOTHFg URBAN APEAS
at
«|
«l
a i
..i.
4 i
* i
ai
41
4 i
u\
41
u\
41
i
til
.-i.
41
41
4 I
ai
a i
41 TW IMEMPHls-
al TN (NASHVILLE
aj TN IOTHER URBAN AREAS
fL—il !AVE* FOR STATF
a I JAVEr"FOR"REGlf5N"""4"
"" ' •••••** ( HWMWW**M«"l«*">«WWMV«iHM
1 1.06111.06 I
•0.0 I 12.01
15.381 4.90J
9.81 I 1 1 .25 I
9.81 Ml.2^!
TT"T-!—i—!•
^ao|
!iHp!!i
*
a.n i
8.03I
J.'oi!
.
.5^1 9.991
STATE
"Ms'isiLnxi
MS ijACK'snN
MS IOTHFR URBAN AREAS
wr
WC
Nf
NC
NC
NC
NC
NC
SC IGREFNVILLF
sc IOTHFR URBAN AREAS
sc IAVE.' FOR STATE
IASHEVILLE
ICHA&LOTTi
I DURHAM
IFAYETTEVII.LF i
IGPEENSBORH
IHIGHPOINT
IRALEIGH
IWJLMINGTON
IWINSTON-SALFM
IOTHER URBAN"AREAS. i
FOR STATE I
12. 03113.ad
-----1.....I,
o.o iti.ooi
0.0 111.871
0.0 Ilir57l
I I
0.0 111.571
a.an i
••1—i
5.661
.!:1?i
"z^ifi
0.0
O.'O
0.0
0.0
0.0
O.P
:?i
7.901
491
2.-{?l
:
12.5? al6t sloa
1
1 9. 55
112. oa
11/i.O1?
iu.i«;
111.5*
5:33
3*57
5^60
0.0 ill.'56 I
---.i.-,..|,
" " 111.01 I
!H:WI
,ln- ,
0.0 111.2?)
-?:?!!
?:!!!
7.841
.'61 Ml.61 .
o.o 114Tia i
2.241 9.81 |
2.16H2.641
2.16ll"2."6a[
-— |—,—|
I 5.66! 7.861
5."66 | 7.'86 I
slssi
a!n i
a.a-;!
"a"9n!'
7.90 j
—i—i
R.16I
.-..-I
84
-------
TABLE IH-8'^DEVELOPED POPULATION WS™™'™^*^^™?™
_fATEj IIRBANI7EO AREA
JRFGI ID .._.___._.._; .
"ii""IL"iAHRnRA""
51 IL IBLOnHINGTflN
1l IL (CHAMPAIGN
il IL (CHICAGO
3AVENPORT MFTRH
DFCATUR
JHLTET
»EOPJA
IRnCKFQRD
I SPRINGFIELD
I COMPISTORMIU
l-o:rlT5:g"
I 0,0 113,3*
I P.O_I12.70
SEW I AVEFM
?1.
9.
SI
UI
IOTHFR URBAN APEA8
51 -IL IAVE.' FOR STATF
a
9.84
o.o
87l .0.0 I
0.0 U4.2UI
ils'?! 11. 2" I
19:911 7:201
9.9! I 7,"3fl
i e.201
I 0.0
I 7l8
I
I . !
4.75(10.88!
|!
I'
I!
51
..I..
I
51
||
51
51
...I.
||
..!i-
is
ii
II
51
51
. »i
5!
...I.
I!
11
51
I!
..!',
51
(ANDERSON .
(CHICAGO MFTRO
IEVANSVILLE
~ORT WAYNF
WDIANAPOLIS
AFAYETTE
,^JUTH BEND
iTERRA HAUTE
IOTHFR URBAN AREAS
IAVE.* FOR STATF
"IANN'ARBOR"""""""
IBAY CITY
(DETROIT
IFLINT
IGRAND RAPTDS
I JACKSON
IKALAHAZOO
ILANSING
(MUSKEGON
ISAGTNAW
IOTHPR URBAN AREAS
IAVE.* FOR STATF. '
"IDULUTH"""""""
IFARRO METRO
IMINNEAPgLIS
AREAS.
MN IAVE." FOR STATF
"OH" i AKRH""""
OH I CANTON
OH (CINCINNATI
OH (CLEVELAND
OH (COLUMBUS
OH I DAYTON
(HAMILTON
(LIMA
ILHRAIN
(MANSFIELD
I .4.051 .0.0
I 11.01 I 14.01
I 8^901.0.0
na.i6iia.ift
I13.«OI13.«0
114.501
0.0 I 4.051
4.101 8.8BJ
0.0 I 8.991
4^061 8.791
OH
M
n»
OH
„.. ISPRINGFIEID
OH (STEIIBENVILLE
OH (TOLEDO
OH (YnUNGSTOWN
OH (OTHER URBAN 4REAS
nH IAVE.' FOR STATE
Wl
w!
WT
Wl
Wl
WI
Wl
Wl
wt
w!
WI
IGREFN BAY
IKENDSHA
(LA CROSSE
IMADTSON
(MILWAUKEE
(OSHKOSH
ioTHF^URBAN AREAS
IAVE.* FOR
0.0 I
6.541
7^901
6.8UI
4.341
111. ,
I . 6.531 0.0 I
I 14.89111.93 I
112.01112.01 I
I13.43M3.43
I S.Sni .0
I.0.0 112.23
.0 112.
I 8.901 .0.
I 14.0*M~
114.08112.0^
112. 37112. 411
M2.3712.'4l
(10.71 I .0.0
112.841 11.91
l2.
I124fll
112.53114.43
!i2.53H"4.4*
4.071
Wt\ 7.27
llE:!t.
lll.lo !!•12 S»?5 S'ti i
l?J:Jprl:J? l:tt\ m\
I oHo I 9.791 0.0fel 9.79
I21.3?l 8.811 5.651 8.411
... I ..... | .-•.— mm^mm — - —
5 "5.17111.081 4.491 9.19
,..|.....|.....I —--I —.-I
85
-------
TABLE WI-8
!™!!T!?E|'
"l!""Ji"!f55!."SMITH
61 AR
61 AR
DEVELOPED POPULATION
URBANIZED AREA
. 61 AP
-- I ...,»
OTHFR URBAN APEAS
AVE." FOR STATF
61
61
6.1
61
61
61
6
LA
LA
LA
LA
w
LA
LA
OTHER URBAN
61
— •• I — .
61 MM
61 NM
.1TATF
.Si
61
61
61
61
I
61
-I.
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
61
6!
«• I *
61
I.
JOTHFR URBAN APEAS
STATF
NM
OK IL
OK OKLAHOMA CITY
OK ITULSA
OK JOTHER URBAN APEAS
-2!L!i)!E'"FOR STATE
~TX"I ABILENE "
TX IAMAPILLO
TX (AUSTIN
TV gPAUMONT
TX IBJWNSVILLE
~* (CORPUS CHPISTT
TX ir>ii i AC u i0'1
TX |
0.0 .
"o"o"i
0.0 I
0.0 I
0.0 I
o.o i
.....(
0.0 I
12.66) 4.6?!
To"o9|"fc;6^j
13.67 3:55
12.901 4,lfl|
-_" I "°*l
01
--------.—
2.231 3.741 6
3«0^l 42151 7
5.161 8
7 ."47
8:8
I .«
831
661
60|
TX HARLINGEN
TX HnysTON
TX (LAREDO
TX ILIJBBOCK
TX MCALLEN
X MiB'-AMO
Iv !2?B*SJRTHIJR.
TX SAN ANGELO
TX SAN ANTONTO
TX SHERMAN
I* TEXARKANA
TX TFXAS CITY
TX (TYLER
TX (WACO
TX (WICHITA FALL
TX JOTHFR URBAN*
TXjAVE.'jrOR STATE. .
! 8:8 i:
I 0.0 (•
I
I
I
I
I
I
I
I
I
I
I
0.0
0.0
2-2
0.0
0.0
o.o
0.0
2*2
0.0
o.o
0.8
o.
110.651
111.041
9.99)
-9.70
10.631
10.6^1
I ?.71
5.651 III
6.001 e:7,
5.651 7.53,
6.791 8.30
g T/l I - ~- !
6:
>
7.47J
§*§!
(Hi
!!2,
» J**
:»
6:74|
411*51
5.651
3^01
6.631
4.481
I
.001
.201
)
1:81
'irorsTuar??!
4.481 7.9fl|
*?65l"87s7!
...»|.....j
86
-------
TABLE III-8 DEVELOPED POPULATION DENSITY
!EPA|STATEJ URBANIZED AREA
IRP.GI
(..•I
I
'ID I
NSITY BY TYPE OF SEWERAGE SYSTEM
r^uxusmsK^v-
ii
7l I*
i CED AR'RAPIDS"""""
(DAVENPORT
j " O»ER$nNS/ACRE)
I. COMB I STORM|UNSEWI JMER_j
.. I ..«.. | mmm..m \ mmm.m. ( "•^~ ,
77,021
ii
IS«BL
uN AREAS
, , *,- ,„,... FOR STATE.
mm\mmmmm\mmmmmmmmmmmmmmmmmmt
71 KS (KANSAS CITY METRO
71 KS ITOPFKA
71 KS (WICHITA .
71 KS (OTHER URBAN AREAS
7J. KS JAVE," FOR STATE
11.82(11.8? 5.16
112.12112.1?! 4.91
I. 0.0 1 11.
, -93 1 11.
.
Ml
!.11.93M1.'50
71
Ii
71
— I-
71
?!
71
81
81
81
81
81
81
..I.
81
81
8t
81
-.1
81
81
I
COLUMBIA
KANSAS
SPRIN6F
ST..JO
1$
:PH
mmm-mm I
MO
MO
M6
MO I
MQ IST.I auis
MO IDTfipR URBAN AREAS
Mn IAVE.* FOR STATE
"NC"iLINCOLN"
NE I OMAHA
NE (OTHER URBAN AREAS.
NE IAVE.' FOR STATE
"*""IAVE?"FOR"""""""""'
2"69
I 0.0 112.08
I 5.351 0.0
I 9.721 O.jL
110.001 8.5?
I JO."00 I 8.52
mmm \ .-•-» | Hm*fmm
1.6.0 112.931
8.721
.... 8.041
fill! «-41'
5.291 8.45
mmm.mm \ mmm.mm \
3.85 7.051
! ?!! hli!
OTO I 5.351
9^531 9.641
7:491 8l6«5l
7."491 8.'651
mmmmm\mmm^mm\
4.591 8.481
4^061 8.791
I
en
en
en
CO
CO
"MT
MT
MT
IBOUIOER
(CnLnRADO SPRINGS
(DENVER
!5V5?!s%B.N
M4.'17I13.'41
0.
;;.'j;i:j.£l''
5"!"r"""!rT"'::
AVE:
AREAS
FOR STATE
(GRfAT FALLS
(OTHER URBAN AREAS
ia5:55iii:5^! !:!S! W!
!!l:il!i!:SII i:ttl »:H
!.19.<>3M2.84! H,b(t\ 8.91(1
,.. | mmmmm ( mmmmm \ --•»-» I "7 VS !
0.0 111.}* 5.6?. 8.17
I 0.0 ItO.l? 6.1* |-j£
I 0^0 110.571 5.86 8.46
MT IAVE.* FOR STATE __.
kin i c ADftn
? URBAN AREAS
FOR STATE.
I. 0.0 S°i)0.57l 5;86j 8.'46i
""i!8:lT!!8:i?!"I:!l!T:"
ft) NR IAVE.
. U0.67lfO.*67
•• I .-•» = - | 9m^mm
81
81
8
I
81
81
81
8 1
81
81.
81
. I
81
.• t
! -.80 I,
AREAS
SO UvE.* FOR STATE
.mmmImmmmmmmmmmmmmmmmm*
UT IOGDEN
UT '."^LAKE CITY 4Q
l!T (OTHER-URBAN AREAS
. UT IAVE.* FOR- STATE
...•. | mmmmm^mmmmmmmmmm,
MY IURB^N AREAS
WY IAVE.''FOR STATE
""""uvi:"FOR"REGln"£
!?2»'23ll'2.''23
mm | .«.««•- I •-•-•
(' 0.0 112.50
6."18| 9."06
T5IIT31
5.'28l 8.'32
•••••Immmmm
i:KI !:SJ
8i§5i f:U|
4.'37! 7.'9«
mm i mm>m.mm | mmm^mm \
161 5.621 8.161
U! 5.6?! 8.161
mm I mmmmm | mmm^mm \
,47881 8.561
••••• (•••••• |
87
-------
TABLE III-8
DEVELOPED POPULATION
URBANIZED AREA
91 *
91
...I.
91
91.
~9l'
91
91
9!
91
91
91
91
91
91
I!
91
91
91
91
91
91
*9 !"
91
91.
JURBAN'AREAS
IAVE." FOR STATF
DENSITY BY TYPE OF SEWERAGE SYSTEM
[DEVELOPS POPULATION DENSITY,
j f RUB I MVMMtj I . . I 2 Sit"? *• »•*. I
..
"*?" I PHO?NIX"
AZ I TUCSON
AZ JOTHER URBAN AREAS
FOR STATF .
I ..... ,
.lihl!!^:^!..^:!"!.!'501
« T ' b.' "U M "4l>
CA (MODESTO
CA IOVNARD"
CA
CA I
CA
CA
CA
CA
CA
CA
"HI"
HI
isTOCK^LEY
JOTHFR URBAN AREAS
[AVE." FOR STATE
I A A
\ III \Mi\
0.0
0.0
olo
111.29 I
I 9.871
I 9.801
111.061
10.981
frtfl
i:i«
7.48
6.68
MS
1^561
IP1.79I
*
•Wfl
9.93!
OTHER URBAN ARFAS
I | • - „..„-„ •r-tmo , y>0 | { } , 5 ^ | Q fl^j 9 7
-------
TABLE 'III-9
I 1
PAISTATEI
FGI I
VALUES OF
URBANIZED
•ITNTS
I
1) CT (BRIDGEPORT
II CT (BRISTOL
II CT IDANBURY
II CT IHARTFORD
{| CT IMFRTDEN
1 I CT INF* BRITATN
1 I CT INFW HAVEN
II CT INORU'ALK
II CT ISTAMFQRD
I I CT IWATFRBURY
1 I MF
1 I ME
..!...-.
1 1
11
11
1 I
1 1
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
~NH'
..i!
n
11
II Rl
...i—-,
1 I VT
..-(—.-
...I...-
21
?l
ILFWTSTON
(PORTLAND
I.»....------
(BOSTON
|BROCKTON
IFALI. .RIVER
IFTTCHBURG
ILAWRENCE
ILOWFLL
INF* BEDFORD
IPTTTSFIELn
ISPRTNGFIEID
I WORCESTER
. |. ..... — ---•
I MANCHESTER
(NASHUA
"(PRO"'IDENCF
" I URBAN AREAS
NJ
?l NJ
21 Nj
2 I N J
...|...-.
21 NY
§1 MY
NY
NY
NY
NY
NY
CITY
CITY
.
IATLANTIC
INFW YORK
IPHIl ADE
ITREK'TON
IVINELAND
I Al BANY
IBT'NGHAMPTON
(BUFFALO
INE* YORK CITY
21
2!
57
65
a3
,71 75
.71116
1 I
7|
55.61
66.51
ttfl.l I
57.61
f8 b" ' Xl j x2 j
£|Io"77| 2.'00| 2«.3irv,
1 1-1.06! 2.001 1^-S1!!'
i^lio-EI! 1:52! 3?
89'Io|-lI06J 2.001 11
.?I:3i:S:W 1:85! !j
8«:5|:8:»i! i:W
7*111-0.791 2.001 2?
. 5io
.1IO
aid
a2?l
30
92,
5B,
71,
60(
30
011
51
,?|\
.
19.7!-
76
. 8R
.91
.6I
47.ai 63.1
C- • W V 1 A *?
2.291 2«
....|———
tl 2.651 31
i! 2^01 •"'
2.00
2.00! . »-
2.001 22
2.00!
2.001
2:0^1
2.00!
2.001
-i"""
-0:791
-1*061
-0.61 I
-0.93!
-0.61 I
-1.061
-0^661
-0.6RI
29
?f
II
.710,
IBIO"
:3io!
:3io
.ti|0
,fl|0
.610
.6|0
6051
... -|
3U6!
a2JI
.531 I
,«57I
.351 I
.68fll
.56?)
126.
. 1?.
(SYRACUSE
IIJTITA
fl 23.5|-0.tttf 2.001 22.*»IO,
108 6 ! "29^9 I-C,9?I 2.29I 28.2'Oc _
730^9 I T5"5 I -1 "'0 B i "2?0 0 I "22^9 j 0^95 j
„..-. i.——1.-"2"2! II^II I III" i III3I i
"5H""l"T"6l-I 061 2*00| I5.«|0.58f»l
WMI iili "Bit*!
_1_i.-.--i----- I ----- I -----i
T08T3M2PT7I-0;93J |:05I 26.9]0.fl|5j
113,
^051 2(S-9|J|-2||j
1-0:79' |:0oo! h:6!S:26fli
fi
:?! 2i:?!:8:7§
2.00
2.00
89
-------
TARI F
I
III-9
I
VALUES nr
...|
31
...I.
31
mmm I i
31
2 I
... i.
31
31
3"
3!
31
31
li
31
31
"*3l"
31
3!
31
31
... I..
31
31
31
31
... i ..
iHARRISPlJRr,
..--i........
PA I Al
PA I Al
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
"VA"
VA
VA
ADELPWIA
IPTTT.sBl.JRGM
ISr.RAMTdK;
i WT.LKCS-BAPWF
VA |
VA |
VA .. _ |v
n!V ICWARLFSTO"'""'
wv isTEi'BENvii, I_F
a
59
.5
l-(
[Hl.««l 3?7o|Jo"740P|
1 07
51
10?
23
35
1 0 6
.0 I 124.-? j
--I—...|
.41 1 14.H
I? I 64*/j|,
.71 123.1 |,
.?! 32.«!•
.ft I 49.1 |.
.---.|.-...|...
•0.791 2.00| si
•0.3ft| 2.001 35
53,
11 ,
31'
"13*
61,
1 12.
15«:
30.
4 I
0 I
91
ft I
I-
65.B I-
20. ft I
65.0|
.
0.611
0.931
0.41 I
:??!
1.471
0.61 1
0.341
-0.51 1
73
79
22
0 I 22.ft j
41 83.7)
ft I 137.0 |
9 I 1 4 ? . «9 |
9 I 7ft." I •
ft I 45.s | ,
fr, 11 fe 7. a |,
-I.--..I.
«l 87lft|I
^1 32l?ll
.|----- | .
2.001
2.00)
2.001
2.00 I
2.001
2.00 I
2.00|
2.001
2.00 I
2.001
2.00|
2.001
"Ilaoi"
2.00|
32
.4(0
7^10
I
----I
I
il
,377
.
29.0)0.^51 1
..
15.0 IO.AOP !
0.771
0.61 1
.
1.11
0,41
2.601 IP!
1.84) 2«,
2.001 .19,
2.001 88,
...-.(....
2.071 ?1,
2.001 63,
2.00) 3«,
2.001 3",
010.1201
0 1 O
-I.
90
-------
TABLE; in-;
LEPA STATE
IREG IP
4
41-
. 41 '
41
41
'a!
f\ 1
41
41
41
41
4t
41
. 41
1 41
'i
F
F
F
F
F
"
L
? VALUES OF COF
' URBANI7EO ARE
BIRMINGHAM
GADSDEN
IHUNTSVILLE
ITUSCALOOSA .
IFT.LAUDERpALE
(GAINESVILLE
1JACKSQNV1LLF
IMIAM1
ORLANDO
1 PENSACOLA
1ST. PETERSBURG
1TALLAHASSEE
ITAMPA
1WEST PALM BEACH
10.1 10.720
9^910.730
.110.6651
0.48«;i
41
41
4i
49
41
4
41
41
41
41
tf I w
41
GA
GA
GA
GA
GA
SA
"KV"
KY
KY
KY
"MS"
I ALBANY
ATLANTA
AUGUSTA
41
41
41
41
4<
41
41
41
41
•jy
8!
NC
NC
NC
NC
COLUMBUS
MACON
SAVANNAH
HUNTINGTON*
(LEXINGTON
ILOUISVILLE
10WEWSBORO
'iBIL'nxi""""
1JACKSQN
'IASHEVILLE""""
I CHARLOTTE
1DHRHAH
1FAYETTEVILLE
a I
....I
53.0
83.1
94.4
73.5
68.7
24.4
I. 60.1 I
'"I?"!
I 51.01
2^.910.463
19.510*53?
13.010.670
49.0(0.266
8^310.541
lt410.511
5.510.
0
i"89"3iTT3"fi
I 30.31 44.1
NC
WC
1'
(RALEIGH
IWTLMINGTOM
1WIN8TON.SALEM
BSBVaB9BBJ
2.00
1.291
2.001
2.00
-0.571
:8:l7j f:§8 is:j
-0^611 f-oj'.i7.:?.
-o^ii'I^oo lf».7
91
-------
TABLE HI-9
1 - I
EPAJSTATEI
........immmmmmmmmmmmm
T I AiionoA
VALUES OF
URBANIZED AREA
."jAURORA
- BLOOMING!
: atg?M8N
I IS A UP Linn PSW>
— • w w* w 9« I u
t IfflW"
*u i gut. JLC I
L PEORIA
|L ROCKFORD
IL^ SPRINGFIELD . .
'U I iHMSSSSr™"*"™""'""''"''
K *NBFR30N
N CHICAGO
ki tptf«fctAr««.
51 OH
51. OH
i
COEFFICIENTS
x
VALUES OF
;**-1 »«••«.
i 62*
(-76.6
-!-«•»-1—.
I 95.7
M25*?
i 9?:?
-I--T--I— *..
I .64.4
1120.3
! ^"e- —•
§?.?I101.
ANNARBOR
GRAND RAPIDS
K4LAMAZOO
LANSING
I «£.*.! 6*:o
- f2-
65.91 87.9
MUSKEGON
FARSO METRO
I -33.
H47.1
108.4
125.6
42. S
533.9
180.?
100.?
10.458
10.443
MI
[JT .'JACKSON
M:
M:
M:
AKRON
CANTON
CINCINNATI
CLEVELAND
JJ|JJJLTON
LORAIN
MANSFIELD
fPBTNGFIELp
ISTEUBENVILLF
(TOLEDO
YOUNGSTOWN
IAPPLETON""""
DULUfH METRO
GREEN BAY
KEN03HA
I LA CROSSE
MADISON
MILWAUKEE
IgJS!"
92
-------
TABLE IH-9
I !
PAISTATEI
FGI ID i
VALUES OF f.OFFF
URBANl?En
61
61
61
"61
I!
61
*6I
..I
6!
61
AR IFORT SMITH
AR ILtTTLE 7?OPK
AR IPINF BLUFF
"LA"iBATON ROD^E
LA ILAFAYETTE
LA ILAKF CHARLES
I MONROE
(NEW ORLEANS
I, A ISHRFVEPORT
'"NM" i ALBUQUERQUE""""
'"OK"ILAWTON""
OK (OKLAHOMA CITY
HK ITULSA
.....i..............
IrI"TS VALHE*
a ! a'.!
....I....-I-
59.7! 84.5
11.61 l0.^
27.61 39.P
....|...-.
46.41 61.5
29.6! 01.«5
30.71 44.A
61
61
61
61
tl
61
61
61
61
61
6t
61
61
61
61
61
61
61
61
6(
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
IAMARILLO
(AUSTIN
I BEAUMONT
(BROWNSVILLE
(BRYAN
(CORPUS CHRISTT
(DALLAS
(EL PASO
I FORT WORTH
1GALVESTON
IHARLINGEN
(HOUSTON
(LARFDO
IL"BROCK
(MCALLEN.
(MIDLAND
(ODESSA
IPDRT ARTHUR
(SAN ANGELO
I SAN ANTONIO,
(SHERMAN
(TEXARKANA
(TFXAS CITY
OF COEFFICIENTS I
b ! «i I "2 ! 7 !
-.». | --«!— 1 ----- 1 — .--»
-0^51 I 2^001 ^"j'J-JJf.
°ol6*!"1^70!"2673I olop
. ' K .. - ! MWA*I *9 ^ A • A H ~> »f
o:
*5775!"75lfrl-
a,.... | .....
14.71 25.7
91.3(116.
1.
8.
68.01 90.a
.....)...-.
66.61 92.5
68.31 90.3
49.51 65.')
- - -'8.0
82.5110
24.01
2. 51
4.01 33. J
107. "
2.00
....I..«.—
0.791 1.87
• 0.41!
•1,11 (
!.;..i-
• l-.ioi
• 0,931
• 0.6AI
-Ull I
-0»41 I
87.ani
0. 6128
.«
240.6(284.6
130.71161.3l
91.PI116.5
32.81 46.9!
9.0| 21.6
36.9! 50.2
30.?! 42.01
25.11 40.5
16.41 25.9
12.7! 23.J
14:11 §6:?
12.71 24.0
l
.36
6
-
•1.111
•0:591
• 0.41!
• 0.581
•0.51 I
• 0,611
:o:4i i
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-0^61 I
0.41!
0.92!
0.01!
".Oil
29^1 S3I7I-0.59J
- - -• -" • '.I.06!
-0.41
2.00
2.00
2.06
>....
2.00
2.00
2.00
2,001
2.001
2.00!
Hi!
J m C. C !
2.611
2.001
2.00
2.00
2.00
2.00
2.00
^.00
l.OQ
21.010.516
».—.«i .«.<••
21.910.505
>.••>. |.«»».
14.010.560
13.610.635
16.710.578
• . —— — | .«• — !••
9.610.736
16.810.5751
28.910,00-
2.00
2.00
2,00
2.001
2^00
2.00
2.00
2.00
i i:JX
l»:tl«:M
!:?!S:r
o
5!
5:3 0
30.910.0
33.310.029
10.110.596
24:410.081
9:7(0.608
5.3(0.018
0.110.656
9:810.60?
29.110.399
9.610.6081
l?:8lO:529i
..«(B. | ..... I
93
-------
LE III-9 VALUES OF CHFFFlciFNTS
VALUES OF CHEFFIC
! a' j b I x I
-?-r-l—»—i
2.00.
2.001
«54|
oOOl
.001
URBANIZED AREA
683
.0110617
0.510
0.210
fit. 910
1 2 . fl I 0
10.610
CITY FTP
-=». (
.429!
.5161
21.010
26.9 I 0
""•?•» I «•«••«-
I :
...I.
81
81
J!
—i.
81
8!
•»l.
«l
>• I.
»}
*• I •
81
..
•-1.
61
HT IMIfcrWLLa
t 32.01 46,
UT JSALTULAKE
!'""] J "pT| j:pji 'poo
:2.:2!i.!;2|.«:!|J:H>
"5-§-! z:t;|jJiljfrjjli
•50 [.,39??["39?!
i"22r»i"'
li:tlJ:8lf
'-_ • ~_ —1""*«^» i
•001 22.9|n.5001
.— !-.«.«.. i Z3"I |
94
-------
TABLE
91
..I.
91
91
91
91
91
91
9!
1!
91
91
91
91
91
91
...I.
91
91.
III-9 VALUES OF COEFFirirNTS
! URBANIZED AREA I
1 I ---
! UPB AN'AREAS"""""""" j ^2:?'1*a '7'"
iPHOENIX""""" !11S'
I TUCSON 35.
OF COEFFICIENTS
.... | -.,— I ----- I ---•
i.odi j--22j_ i2:Ej2;tii
A7
IBAKFRSFIELD
IFRESNO
ILHS ANGELFS
IRODESTO
IOXNARD
ISACRAMENTn
! lh
8S
to
CA ISAN DIEGO
CA ISAN FRANCISCO
CA ISAN JOSE
CA ISANTA BARBARA
ii !8«5$o6°"
^ llT5?K^^LEY
HI
I 72.
I I!:
7 116.a -0.75
oi 5o:oi-o:s«i
61 9a..5l-0.93l
71 90.7 -0.8*51
Bl 66:? -0.611
o-Bii-IlQoiiqioTaao!
J.OOI
5:001
17.710.5591
22. 610. 501 I
1638161676^1-
I1l7.:6ll36.a|
1 5 •
26.
3!
ai .f i
57.81
ati
5RI
6H
|:8.o silflioosyj
2*00
il"!lll'l
j J2:i j ,«2:2
101 10
OR
OR -
OR I SALEM
.... |........—---•
WA (SEATTLE
WA (SPOKANE
WA ' '- "•
101
101
101
..«I •
101
101
101
.
-0.8SI 2.00
o
O
.
.
-0
-o
-O
-O.Sfll
..... |
...
.371
IB:I
35.1. . ^
"3a77in7360i
.
. no.
.3|ft.a05l
15.71
0.8? 20.at .
>..-.I.....I .....I
,__tf.I.....I..... I
- • 25.911" ""'
l85l
TSSI
'
"o ( 1 1 0 .«; j
2.131 2J.3 O.flao
2.00|_2l.aj0.513
"2^2? i "26^7 1 o"a7
95
-------
ABBREVIATIONS AND SYMBOLS
A
b
C
E
P
P
u
PD
PD
PD
PD
x
Coefficient
Adjusted coefficient
Total area of urbanized area, acres
Coefficient
Urban area with computed population density
Urban area with estimated population density
Population of urbanized area
Population served by combined sewer system, persons
Population served by storm sewer system, persons
Unsewered population, persons
Gross population density, persons per acre
calc Calculated average population density, persons per acre
Correlation coefficient
Percent of urbanized area
t tninte »
to cne integrated average PD
PD Corresponds
tfthff t Uppf /imit on x such ^at average PD corresponds
is sewered ^ ^^ PD 3nd P6rCent °f Urban area
Combined sewer area as percent of urbanized area
Undeveloped area as percent of urbanized area
Proportion undeveloped area
96
-------
REFERENCES
1. US Bureau of the Census, County and City Data Book, 1972, USGPO, 1972.
2. ' American Public Works Association and University of Florida,
"Evaluation of the Magnitude and Significance of Pollutxon Loading
frlm Urban StomSater^unoff - Ontario," Environmental Protection Ser-
vice and Ontario Ministry of Environment, Toronto, 19/b.
Washington, DC, 1968.
4. US Environmental Protection Agency, "1968 Inventory of Municipal Waste
Facilities - A Cooperative State Report, 1971.
5. American Public Works Association, "Problems of CombinecI Sewer Facilities
and Overflows," USEPA Report No. 11020 12/67 (WP-20-11), NTIS
PB 217 469, 1967.
6. National Planning Data Corporation, Ithaca, NY.
97
-------
SECTION IV
QUANTITY ANALYSIS
MODELING OF URBAN RUNOFF
98
-------
and ;design purposes. Hence, the ultimate goal of acquisition of
salient field data remains worthwhile and necessary. Throughout
this section, gaps in available data for input and calibration/
verification will be apparent. But the useful analyses which can
still be performed without these data should also be clear.
The modeling procedures developed for the nationwide assessment will
S discu-sse! L detail. Two levels of sophistication - ia the assess-
ment are considered: use of STORM for the development of the
"famet^rs used in the assessment methodology described in Section VI,
Ld use of a very simple runoff prediction technique f or the 248
urbanized areas of the nationwidei assessment itself. Preliminary to
both is a description of climato logical considerations that influence
selection of modeling sites and parameters.
PRECIPITATION ANALYSIS
Anv analysis of stormwater runoff must first examine the associated
rtLXS patterns and volumes. The intensity, duration, ^frequency
of the rainfall have profound effects on the amount of runoff produced
Precipitation patterns vary widely across the United States. '
variation is found not only in total annual volume, as showr in Figure
IV-1 Mean Annual Precipitation in the United States, in Inches, and
T^q-tAt^l .Boundaries. used for Nationwide Assessment, but also in the
seasonal distribution as shown in Figure 1V-2, Montti-to-M ont ^Variation
of Precipitation In the United States., Among the more dominant regional
characteristics are the dry summers"^ the West Coast the Abrupt changes
in the desert states such as Arizona, the peaks occurring in spring and
winter in the Central Gulf and Ohio Valley states, .and the uniformity of
monthly totals throughout tke year ,iu. .'tke New ; England , spates .
In order to analyze the effect that precipitation patterns have on
rSnoff patterns, and thus control alternatives, study areas were chosen
which reflected varying rainfall characteristics. The five cities
which were chosen are fisted in Table IV-1, PrfMp1f .f.1 on Characteristics
!SsLlvAreaS< along with the regions they represent and the mam char-
aeterislics which distinguish each city from the others .^her
discussion of precipitation characteristics is presented 3* Section VI.
RUNOFF ANALYSIS USING STORM
STORM was developed by Water Resources Engineers, Inc., for the Hydrologic
Engineering Center of the Corps of Engineers.8/9 The model was designed for
planning purposes, i.e., for long-term simulation of many storm events using
an hourly time step. For instance, the model has been used to simulate
runoff quality and simple storage-treatment options from a 63-year record
of hourly rainfalls in San Francisco.9
99,
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101
-------
TABLE IV-1. PRECIPITATION CHARACTERISTICS OF STUDY AREAS
Atlanta, Georgia
Region
Southeast
Characteristics
Large volume; peaks in
spring
Denver, Colorado
Rocky Mountain
Low volume
Minneapolis, Minnesota
Midwest
San Francisco, California West Coast
Washington, D. C.
Northeast
Large number of events;
uniform distribution
Dry summers
Large volume; peaks in
summer and winter
RUNOFF ANALYSIS USING SWMM
s
102
-------
The SWMM methodology, verification and usage are well documented in
the original final reports11/12/13'14 and in more recent publications.-13"10
and will not be described here. In general, the SWMM provides a com-
plete description (in both a spatial and temporal sense)' of flows and
pollutant concentrations from the point of rainfall, through the surface
and subsurface drainage network, through storage-treatment facilities,
and into the receiving waters.
During formulation of the research plan, it was felt that SWMM would
play the dominant role in fulfilling the modeling needs of the study.
This concept was later revised in light of altered techniques available
for accomplishment of project objectives. In particular, _it became
apparent that detailed modeling of a few cities would not suffice when
the nationwide assessment must encompass 248 urbanized areas. Conse-
quently, much simpler means had to be developed for the overall assessment.
In addition, the need for long-term simulation developed (e.g., simulation
periods on the order of years) for which the SWMM, at the time, was, un-
suitable. STOEM was later adopted for this purpose. Valuable runs of
SWMM were still made on catchments in all of the five,test, cities as .
described briefly in Volume III, but they served more to. enhance the
model's usefulness than to aid in the assessment. Hence, these results
are being incorporated into updated SWMM documentation (EPA Grant No.. ,
R-802411) and.are not presented here.
RUNOFF PREDICTION FOR NATIONWIDE ASSESSMENT
Form of Equation .'..,'.'..
As discussed, techniques for prediction of runoff quantities vary
from very simple methods of the Rational Method type to sophisticated
models of the nature of SWMM. The technique used in STORM is rela-
tively simple, relying on weighted average runoff'coefficients and a,,
simple loss function to predicthourly runoff volumes. , Nonetheless, ,
because of the nature of the continuous simulation involved, it is at
a considerably higher level, and therefore more complex, than earlier,
desk-top techniques.
.Due to the complexities and data requirements of STORM, it was riot
possible to run the model on all cities of the nationwide assessment,
or even a majority. Rather, it was run only on the five test cities
discussed earlier (plus the Des Moines example of Section VII). How-
ever, in its limited application, useful information was learned
regarding formulation of a simple runoff prediction method for appli-
cation to all the cities of the assessment. \ .
'Runoff is a function of meteordlogic, hydrologic, topographic and"
demographic factors. On an annual basis, many of the factors .may^be
considered constant, so that runoff is predicted on the basis of dif-
ferences between areas rather than reflecting seasonal variations
103
-------
a. year. Hence, the. prime meteorologic and hydrologtc factor
is annual precipitation, and other factors are incorporated into a
conversion to annual runoff.
These considerations led directly to the use of a simple runoff
coefficient method in which runoff is merely a fraction of rainfall.
This approach has been used successfully by Miller and Viessman17 for
runoff prediction on an individual storm basis in urban areas. This
equation was
AR - 1.165(1 - 0.17)(P - I )
(IV-1)
where AR = runoff, in.,
I - fraction imperviousness ,
P » precipitation, in., and
Ia = initial abstraction, in.
The recommended value of I which accounts for depression storage,
interception, etc., was between 0.10 and 0.15 in. (0.25 ~ 0.38' cm)
and the equation was deemed valid for a range of imperviousness between
35 and 80 percent. Extrapolation for use on an annual average basis,
however, may be questionable, particularly in the matter of how much
water should be abstracted out of the cycle on an annual basis. Hence,
an equation will be used that is similar in form to equation IV-1,
in S^Mn ,S,TC°nS SlTl **th thS STORM si*»^tion runs, described
in Section VI, on which the overall assessment is based.
C°efficient» C*>
between pervious
CR - 0.15(1 - I) + 0.90 I
• 0.15 + 0.75 I
(IV-2)
i,
ff °? imPerviousness and the coefficients 0.15 and 0.90
T USSd ** STOEM for runoff ^efficients from per-
-l a IV^^f eaS'/rPeCtiVely' N°te that in both equations
IV 1 and IV-2, the effect of demographic factors (e.g., land use
population density) is incorporated into the imperviousness, I?
°JafinSton> D(;>> the American Public Works Association
, and Stankowski (New Jersey), have developed equations to
predict imperviousness as a function of population density.^, 1 9
The imperviousness is to be estimated for the developed portion of the
urbanized area only. Also the weighted average imperviousness and
population density were calculated for nine Ontario cities.20 These
OH!* PS afe+.?10"ed °n Figure IV~3> Iiaperviousness as a Function of HO^T.
oped Population Density, along with the three estimating curves. Also a
tabulation was made of the Imperviousness due to streets alone for
104
-------
100
.persons/hectare
30 40 50 60 70 60
GRAHAM ET AL.,
WASHINGTON, D.C
NEW JERSEY,
567 MUNICIPALITIES
WASHINGTON, D.C.
O ONTARIO
IMPERVIOUSNESS DUE TO STREETS ONLY
0 5 10 15
DEVELOPED POPULATION DENSITY, PDd> persons/acre
Figure IV-3. Imperviousness as a Function of Developed Population Density
105
-------
I = 9.6 PD
(0.573-0.0391 log PD,)
'10
(IV-3)
where
I - imperviousness, percent,
u density in developed portion of the
urbanized area, persons per acre.
The simplified equation for estimating annual runoff (AR) is now
AR = (0.15+0.75 I/100)P (IV_4)
where AR - annual runoff, inches/year,
I s imperviousness j percent/from equation IV-3, and
P - annual precipitation, inches/year.
A comparison of STORM simulated runoff versus calculated runoff
for the five test cities using equation IV-4 indicated that the
average difference is about 0.3 inches (0.76 cm) per year. A
ofo r-fZT^r^111 ?* tot«*° «»«*««*«: indicated a difference
L ; V C 2? Cm) Per year-2° Th^» a correction factor was
added to equation IV-4 to reflect this difference. The final
equation is
AR - (0.15 + 0.75 I/100)P - 0.3
(IV-5)
Based on equation IV-5, wet-weather flow estimate^ were-inade for the.
248 urbanized areas for the combined, storm,- and-unsewered areas.
The results are shown in Table IV-3? 'Annual'Wet^.Weather Runoff. Pre-
cipitation data are from reference 2l1 ' ""
_Dry-Weather Flow Prediction
Dry-weather flow is predicted based on an average flow of 100 gallons
per person-day (379 litersper person-day). Upon multiplication^ by
population density and conversion to appropriate units
DWF = 1.34 PD.
(IV-6)
106
-------
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TABLE IV-3
I I I
IEPAISTATEI
RFGI ID I
i!
H
ANNUAL 'WET-WEATHER
URBANIZED AREA
CT I BRISTOL
CT IDANRURY
RUNOFF
TN/YRI
ANNL.l
PRECPI
"""mm I
42.01
43.01
42.01
II !B|B BL_
CT NORWALK
CT STAMFORD
CT WATERBURY
CT OTHER URBAN AREAS
II
l i
it
1!
1!
it
II
"ME"iLFWISTON"""""""""
ME (PORTLAND
ME OTHFR URBAN AREAS
.-mmltmm' F°R STATF
ILHWFLL
JHIS.5IBFOBD
MA
MA
MA
MA
MA
MA
W jftRgiSTig
JOTHFR URBAN AREAS
FOR STATE
MA
1 1
NH
«
RI
— .
1 1 "VT"
l
VT
VT
"
21
!!
21
mm | •
21
JOTHER URBAN AREAS
JAVE: FOR STATE
' I PROVIDENCE""""""*""
{OTHER URBAN,AREAS
[AVE.* FOR STATF
IURBAN'AREAS "
JAVE: FOR STATE
I 7 w ? ** ** ^ "•«•«••»• mtmmm mm mm m i
lAVEa POR REf5inKl i
NJ
IATLANTIC'CITY"""
Y9g.»<«ClTY METRO
1Rr'""*• METRO
J IVINFLAND
1 AVE." FOR ,.-,,„ IC
f"iALBANY""""""
K'Y
NY
21
21 NY
mm I....
NY IOTHFR, URBAN AREAS
JAVE." FOR STATE
••;«•• 1
42.01
4|.OI
43.01
42.01
44.01
-------
TABLE IV-3
It -I
I EPAiSTATE I
IRFG! ID I
ANNUAL WET-WEATHER.RUNOFF
URBANIZED AREA !iM!^!
IpREePI
WET-WEATHER FLO
(INCH
(WILMINGTON
(OTHER URBAN AREAS
1 31 DE
I 31 DE
! , 3! DE (AVE.* FOR STATE
| ... I mmmmm \:.mmmmmmmmmmmmmm
I 31 DC I WASHINGTON,D.C
I 3!. DC IAVE.* FOR STATE
(mmm
I
31
3(
3t
I
MD
MD
MD
I BALTIMORE Fvrtn
(WASHINGTON DC METRO
IOTHER URBAN AREAS
!AVE,* FOR STATE
| mmmmmmmmmm.mmmmmmmmm
(ALLENTOWN
(ALTHONA
ER
(E
IHARR
(JOHN
SBURG
TOWN
PA
LANCASTER
(PHILADELPHIA
(PITTSBURGH
(READING
ISCRANTON
(WILKES-B.ARRE
(YORK
lOTHFR URBAN. AREAS
(AVEl FOR STATE
LYNCHBERG
NEWPORT NEWS
NORFOLK
PETERSBURG
RICHMOND . ' . . .
WASHINGTON DC METRO
OTHER URBAN AREAS
AVEj FOR STATE
CHARLESTON'""-""""'"
HUNTINGTON ' ^.-
STEU8ENVILLE METRO
gm'S^N \nu
AVE.'FOR STATE
mmmmmmmmmmmmmmimmmtmm —
AVE. FOR REGION 3.
42.01
45.01
••«>«•• I
41.01
0,0s
16.11
16.01
18,2
43
44
42
4UOI
42.91
109
-------
TABLE IV-3 ANNUAL WET-WFATHER RUNOFF
(ES»AlSTATEl
! RF.G I . ID I
"RBANI7ED. AREA
41
41
41
41
41
41
I
41
8I
NGHAM
AL
AL
AL
AL
I MONTGOMERY
(OTHER URBAN AREAS
IPRECPI
"'"p!
«:8!
13:81
IAVE.*
STATE
COMR'("STORM7UNSEW| AJVER •
•""'-":;! -pi"-'
15.11
I-«i
!•'
... ..:=. :.-:.:_:.:::r^ \.
-
41
41
41
41
41
41
41
41
41
41
41
*4l"
at
41
41
41
41
41
I
41
(MIAMI
NVILLE
ORLANDO
1PENSACOLA
ft
FL
a
FL
FL (OTHER URBANAREAS
.--.| E* FDR STATE
"GA" ALBANY"""""""""""
GA (ATLANTA
GA (AUGUSTA
GA (COLUMBUS
G* IMACnN
GA (SAVANNAH
GA (OTHER URBAN AREAS
GA IAVE.' FOR STATE
"METRO""
JLoOfSviLLE
OWENSBORO
(OTHER URBAN AREAS
Pi
53.01
:§!
Ip'
11:8!
iS:i!
NC
NC
[ [AVE.' FOR STATE
i"iBiCnxi""""""""""""""
! (JACKSON
I (OTHER URBAN AREAS
| IAVE.' FOR STATE
" 1 ASHEVILLE"""""~"""""
(CHARLOTTE
(DURHAM
IFAYFTTEVILLF
IGREENSBORn
IHIGHPOINT
IRALFIGH
(WILMINGTON
WfRftfON-SALEM
OTHER URBAN AREAS
IAVE; FOR STATE
'JS^LESTON—"""'
'itLF
URBAN AREAS
48.0
I 47^1
I 39.01
I 49.01
i 33:8!
(.46.51
.)_....|
I 40.01
I 44.01
I 4i:oi
I 44.01
I «2.3I
! 58.01
4Mi
0 I
AVE." FOR STATF
I. 54.SI
!~4§Toi
I 43.01
I 43.01
I 47.51
I 42.01
I S*:8I
! i?:8l
I 46.01
I |
I. 46.01
|...*.),
I 47.01
! tl:tl
I 46.7,
TN IKNOXVILLE
TN (MEMPHIS
TN (NASHVILLE
TN IOTHFR URBAN AREAS
TN AVE.* FOR STATE
I 54.01
" fl-OI
48.01
45.0 I
48:31
. I. 48.:
.«».•.••«w..«
IAVE. FOR REGION. . « .
— — I | — ~ • • .- — -.-»,..». . -r . I « -T ' • U I , A >-' 9 i.)
Pi'OI 26.01
o.oi
o.o
o.o
2?.
0.0
0.0
0.0
0.0
• o.o
22.7
_j
21.71
.»..•i
-------
TABLE . IV-3 ANNUAL WET-WEATHER.RUNOFF
NNL.I
RECPI
IEPAISTATE
EGI ID
-3
URBANIZED AREA
WET-WEATHER FLOW
ClfiCHES PER YEJRJ^
CDMRI STORM IUNSEWI AVER
•I-
•(•
51 IL (AURORA
51 IL IBLODMINGTON
51 IL (CHAMPAIGN
51 IL (CHICAGO
51 IL (DAVENPORT MfTRO .
51 IL IDECATUR
51 IL IJOLIET
51 IL IPFQRIA
51 IL IROCKFORD
51 TL ISPRINGFIELD
51 IL IOTHER URBAN AREAS
51. . IL IAVE." FOR STATE.
34.01
,01
,0)
,01
37.01
33.01
35.01
36.01
0.0
0.0 .
i|:?
ll'.b
0.0
o'.n
35.01 16.61
15.0
15.6
15.8
10.1
13.21
0.01
14.01
0.0
16.0
14.3
11.61
10.Oj 12.61
10.91 13.61
13:61 13;6J
10.11 12.11
12.61 12.61
" 1H!
ll:'!
10.6
10^
35.01 16.61 11.61 10.31 13.4
• I.
• (•
51 IN
fl IN
I IN
tl IN
I IN
51 IN
51 TN
ll fN
§1 TN
| *N
. 51 . IN,
(ANDERSON
(CHICAGO .. .
IEVANSVILLE
(FORT WAYNE
(INDIANAPOLIS
(LAFAYETTE
IMUNCIE
I SOUTH BEND
ITERRA HAUTE
(OTHER URBAN AREAS
(AVET FOR STA.TE
36 0, 10.6,
41.01
34.01
40.01
35.01
3?.01
3*
.01
.01
.21
f'JI
5:8
i;!
0.01
14.71
0.01
1M\
15.01
11:11
0.0 10.61
16.1!
15.11 10
":«l
i.01
S^l
t
37.21 14.71 15.11 10.91 13.31
.1.
• I-
•(•
51 MI
(ANN ARBOR
(BAY CITY
(GRAND RAPIDS
(JACKSON
IKALAMAZOO
(LANSING
IMDSKEGON
ISAGTNAW
(OTHER URBAN AREAS
I
IAVE. FOR STATF
t:3!
MN
MN
MN
MN
MN
51 • MN
IDULUTH
IFARGO METRO
(MINNEAPOLIS
(ROCHESTER
(OTHER URBAN AREAS
IAVE." FOR STATE
5
I!
I
s!
5!
OH
OH
OH
OH
DH
OH
flH
OH
OH
OH
OH
OH
OH
OH
OH
OH
I AKRON
(CANTON
(CINCINNATI
(CLEVELAND
(COLUMBUS
IDAYTON
(HAMILTON
(LIMA
ILHRAIN
1 MANSFIELD
ISPRINGFIELD
ISTEUBENVILLE
I TOLEDO
IYOUNGSTOWN
(OTHER URBAN AREAS
IAVE.' FOR STATF.
. | •••!»••••• — —••• — ••«•
APPLETON
IDHLIJTH METRO
(GREEN BAY
IKENDSHA
ILA CROSSE
(MADISON
(MILWAUKEE
IOSHKOSH
IOTHFREURBAN AREAS
51 Wl
FOR,STATE..
'lAvir'FOR'RlGION* "5
1.0 I 12.6
.01 0.0
.01 15..0
28.01 0.0
P:?! iX:«
29. 7«V 14.5
15.61 I*.Z'..^^-
io:ai el
12.31 0.01
-- •• ig.S!
51
32.71 1«.B
12.9
111
-------
L
TABLE IV-3 AMNUAL WET-WEATHER RUNOFF
EPA
REG
""6
61
61
61
I
61
"~6t"
61
61
61
61
61
61
61
... I-.
61
61
61
61
61
I
61
I-..!
6
6
6
6
6
61
61
61
61
61
6
"AR" ! FORT'SMIT """""""
Ag 'LITTLE ROCK
AR IPTNF. BLUFF
AR IOTHER.URBAN AREAS
_AR IAVE.' FOR STATE
"tfiEWWEwF
t !i«HARLE*
LA I NEW ORLEANS
LA SHRFVEPORT
LA OTHER URBAN AREAS
(LAJAVE.* FOR STATF
OK IAVE.* FOR STATE
• 9 • f* I ^ —• ^ — —
TX
TX
i
61
6
6
6
6
6
6
6
6
6
61
61
61
61
61
..t\
61
T*
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
oeH""TT
IFHRT WORTH
I6ALVESTON
luAraiT
i i
TX
TX
TX
T.X
TEXARKANA
CITV
IWACn
(WICHITA FALL
OTHFR URBAN AREAS
.li.lJ!!' FOR STATF
.
35.31
112
-------
TABLE IV-3
EPAISTATEI
FGI. ID 1
ANNUAL WET-WEATHER
URBANIZED AREA
71 I A ICFDAR RAPIDS
71 IA IDAVFNPORT
71 IA IDES MOINE8
71 I A IDUBUQUE
71 IA ISIOUX CITY .
71 IA (WATERLOO
71 IA IOTHFR URBAN AREAS
.7! IA JAVE.* FOR STATF
71 K3 1 KANSAS CITY MFTRO
71 KS ITOPFKA
71 KS IWICHITA
71 KS IOTHER URBAN AREAS
J 1 ,
.71 KS lAVE. FOR STATF.
71 MO 1 COLUMBIA
71 MO (KANSAS CITY
71 MO (SPRINGFIELD
71 MO 1ST. JOSEPH
VI MO 1ST. LOUIS
J'l MO IOTHFR URBAN AREAS
71 MO !
71 NE 1
71 NE 1
7j NE I
71 NE 1
7!
81 CO 1
81 CO 1
81 CO 1
§i rn
1 CO 1
81 CO
81 MT
81 MT 1
81 MT 1
t 1
81 MT 1
81 NO 1
81 NO
81 ND
8> SD
81 SO
e! so
AVE.* FOR STATE
LINCOLN
DMAHA
OTHER URBAN AREAS
AVE.' FOR STATE
AVE. FOR REGION 7
BOULDER
COLORADO SPRINGS
DENVER .
OTHFR URBAN AREAS
AVE." FOR STATF
BILLINGS
GREAT FALLS
OTHER URBAN AREAS
AVE.' FOR STATF
OTHFR URBAN AREAS
AVE.* FOR STATE
SIOUX FALLS
OTHFR URBAN APF.AS
AVE.* FOR STATE
81 UT IQSDEN
81 UT IPROVO
81 UT ISALT LAKE CITY
81 UT IOTHER URBAN AREAS
81 UT
mmml «•••«.
81 WY
} , si . WY
\mmm 1 »aa,w-
1 81
AVE." FOR STATE.
URBAN'AREAS""""
AVET FOR STATE
AvirForTREGinT"!!
RUNOFF
N/Y8I
NNL.I
RFCPI
33.01
34.01
31.01
3
3
r-s
f:S
3J.3
34.0
34.0
33^0
33.0
37.0
34.0
41.0
35.0
1
7.0
6.P
36.8
27.0
26.0
26.5
26.5
31.9
1
i
i
9.0
3.0
4.0
4*5
14.5
:4*.o
14.0
21.0
21 . 0
. 21.0
m
§5:0
25.0
WET-WEATHER FLOW 1
CTNCHES PER YEAR) 1
. COMP STORM IUNSEWIAVER |
0.0 13.41 10.71
14.1 14.11 10.21
18.? 10. ?l 9.31
0.0 12.9| 11.21
0.0 10.71 .7.1!
0.0
18.0
18.0
14.! I
14.?
0.0
14.1
14.1
0.0
15.2
0.0
11.?
14, ^
14.?
14.?
Oe()
11.4
1 11.4
;I:ii l?^7J
i?.l
14.1
14.2
11:1
9.7!
To. 71
10.61
10.01
10.41
13.21 10.4!
15.7
11.6
17.2
0.0
l!:$
10.61
11.11
12.71
0.01
1217*1
12.7J
11.51 S.2I
11.41 7.61
11.51 7.7|
11.4 11.51 7.71
1. 14.0
12.6
10.81
1 0.0 8.0i 5.8-i
O.n 5.11 4.01
7.71 5.91 4.0|
t 5*,7 4. a 3.71
1 6.1
! 6..1
1 0.0
1 O.n
1 0.0
1 O.n
1 8.3
1 8.3
I. 8.3
1 10.4
I 10. a
15^01 0^
15.01 0.0
15.01. 0.0
15.01 0.0
15. ol. 0.0
17.41 7.5
5.8 4.01
5.8
4.01
5l7 4.9|
5.41 4.31
5.4
8:3
-2:2
4.31
«,.«!
il:l JrS!
10.4
2:1
6.3
T.,l
3.6
4.4
4.4
4.U
5.9 4.7
5.9
4.7
6.41 4.7
11.81
ll^!
If'?!
\\:i\
11.1 !
12.7)
12.31
11.41
12.11
12.11
12.5!
12.11
it:!!
13.5!
9.9|
3:?!
9.71
12.21
7.4
4l5|
5.11
4.31
5.11
5:o!
5.01
4*Ui
3:1!
5.31
"iiii
mmlil\
H3
-------
-IV-3, ANNUAL WET-WEATHER
URBANIZEH ARFA
•TABLE
f!!!l"!?!i._
i""9i""AK"jORBAN"AREAS""""
STATF
I TUCSON
(OTHER URBAN AREAS
aTATE
IOVNAR
SAN BERNANDTNO
ISAN DIEGO
ISANTA BARBARA
SANTA ROSA
SFARIDE
SIMI VALLEY
STOCKTON
OTHER URBAN AREAS
FOR
IS la
ID O
(OTHER URBAN AREAS
JAVE.' FOR STATE.
' LAS'VESAS" '
(RENO
(OTHER URBAN AREAS
lAVEj FOR STATF
j Avlr"FOR"REGinN"""«
OTHFR URBAN AREAS
101
101
101
OR
FOR STATF
! EUGENE '
(PORTLAND
, SALFM
OTHER URBAN AREAS
JAVE; FOR STATE
SEATTLE""""""""""""
SPOKANE
iT
..ACOMA
IOTHER URBAN AREAS
FOR STATE
.. mmmmm
AVEFoi?REGinN"
| mmmmmmmmmmmmmmmm
j .............__.__
THE IJ-
o
RUNOFF
TN/Yli
ANN"
PRE
23.0)
23.0! - 0.0
""jJToi""oTo
7.01 ~
5.51
5.5l'
™ ** ^ • | W ^- -. ^ I^
16.91 10.9
irroi"*oTo
11.01 0.0
11.01. 0.0
I 17.?
I 0.01
I 17.?|
39.31. 17.?|
12lo
30.3L 12.0
3.5
mmmm\mmmmm
15.91 11.9
3.9
>....
I?.?
•"mm|.....|..... .....
114
-------
where DWF = annual dry-weather flow, inches per year, and
PD, = developed population density, persons per acre.
a • ,
Results of these runoff calculations are shown in Table IV-4, Annual
Dry-Weather Flow.
Dry-weather flow and wet-weather flow for the developed portion of
an urbanized area with a precipitation of 15, 30, or 45 inches per year
are shown in Figure IV-4, Comparative Magnitude of Annual life.t»-and Dry-
Weather Flows. Note that dry-weather flow predominates: at" higher
population densities which have historically prevailed in cities. How-
ever, with the trend towards lower density urban living, wet-weather
flows take on greater relative importance. Indeed, they are larger
than dry-weather flows at the lower population densities.
115
-------
L,
TABLE IV-4.
ISTATE
RE6I. . ZD
•»•}••<*••
t "
II
J
,il.
TI *
ANNUAL DRY-WEATHER.FLOW
URBANXZE.D .AREA
R
DAN
•A -NEW'
IT I NEW
JPORT
1Y
A IN
.
CT NORWALK
CT STAMFORD
CT iWATERBl/RY
CT OTHER URBAN AREAS
L
I
I
I*
i
: I
: I
I
I
AVE.'. FOR STATE,
SHHF '
-if
II
.CT
"ME"
ME
ME
ME
"MA"
MA
•MA
MA
MA
MA
MA
MA ipmSFXELD"
MJ .'SPRINeFiELD
MA
MA
"NH"
NH
NH I
OTHER URBAN AREA'S.,. '
AVE.* FOR STATE. .
BOSTON"*""""""""""'"
$EK5?«E»
tIWIs
UNSEWjAVER
'>•••»• i
IP
!f:8:
tt:Z!
IIJI
».s| n.'i!
m^m]mmmmmj
w «I:!|.
E
I NEW BEDFORD
I
J
I WORCEST
OTHER URBAN AREAS
AVE * FOR STATE
OTHER URBAN AREAS
AVE.* FOR STATE.
.11. NH AVE.* FOR STATE. 41.01 12.j
"i ""«" ssSPSE?""!!!;" i?:?!"!?:!
i-,
RX
RX
imttm
VT
II VT
'"i!""—
I
.1.
OTHER URBA^N AREAS
AVE^FOR STATE.
URBAN"AREAST"""
AVE* FOR STATE
<
I
l"
2
m\
21
mm\
I
•il:
NJ
N
N
, NJ
•••.
NY
NY
NY
NY
NY
NY
NY
NY
NY
XLAOE
ENTON
NELAN
TY
.... __TY METRO
ELPHfA METRO
AT
NE
PH
VINELAND
AVE." FOR STATE
mmmttmmmmmmmmmmm
ALBANY
THER URBAN AREAS
AVE.* FOR STATE
AVE. FOR. RESIGN. . . 2, I,
116
-------
TABLE IV-4
tEPAlSTATE
IREGI ID
AMNUAL DRY-WEATHER
UMMIXED ARE*
FLO
OW
nRY-WEAtHER FLO
CO
fc
I DE
WILMINGTON I 45.0
OTHER URBAN AREAS J 45.01
AVE.' FOR STATE ... I, 45.01
'\
«:!
16.4
16.4
16.4
16.4
6.7
6.7
12
.0
.0
75
.9
t , 3
.
DC
WASHINGTON75rcT
AVE." FOR STATE .,
41.01
K41.0 I
42.?
42.2
19.1
19.1
0.0
0.0
26
26
1
MD
MD
MD
MD
BALTIMORE . I 43.01
WASHINGTON DC METRO I 4
OTHER URBAN AREAS I 4
AVE.* FOR STATE-
J:8!
i
0.0
0.0
0.0
,42.01, o.o
111
18.0
18.0
6.3
6.3
14
.01
.0
44.0
44.0
LANCASTER
PHILADELPHIA
BURG
..NC:/°*N
.HILA
PITTSBURGH
READING
fSCRANTON
IWILKE3-BARRE
1YORK
IOTHF.R URBAN AREAS
i .3! PA IAVE.* FOR STATE, . . i,4i.oi 20.4
4'
4'
4'
*«oi
42.01
3f.OI
39.01
40.01
41.01
^•*
28*,a
o.o
1U7
16.2
0.0
20.4
18.0
i:
6.
I:!
5.1
•
u
13.2
I.
?!
VA
VA
VA
VA
VA
V*
VA
VA
L
N
YNCHBEf
.RWPORT
NORFOLK
PETERSBURG
RICHMOND
ROANQKE
WASHINGTON DC METRO
OTHER URBAN AREAS
AVET FOR STATE
'
«:
18.7
J"2
«•!
.,,
•(,
sis
5.5
13.0
WV
wv
wv
wv
wv
w'v
CHARLSTON
METRO
AVE.* FOR STATE. f I, 41.0
mmm^mmmmmmmmmmmmmmmm\•*»"•j
AVE. FOR REGION 3.I,42.1
16,6
0.0
16. 2
. 16.2
m*mmm
18.1
6*3
8:8
2:8
'575
117
-------
ANNUAL DRY-WEATHER FLOW
URBANIZED ARFA ANN*?!
I •—" I
I EPA(STATE I
I REG I ID I
I 41 AL
I 41 AL
I 41
41
41
41
**' -.-— - n • •• « i i.j|^ u i *> i r
AL (MONTGOMERY
URBAN AREAS
. AL JAVE." FOR STATF
.4
41
41
41
41
41
. .
41
41
41
41
41
41
— I.
41
41
41
41
41
LF
FL
PC
FL
IWFST PALM BFACH
OTH^R URBAN AREAS
JAVE." FOR -STATF
»-"lALB4NY""" *
GA (ATLANTA
GA (AUGUSTA
GA ICDLUMBUS
GA IMACPN
GA (SAVANNAH
GA (OTHER URBAN AREAS
.£!.!„!• FOR ST!!F
"KY"
KY
KY
KY
KY
(LOUISVILLE
OWENSBORO
(OTHER URBAN AREAS
KY IAVE.* FOR STATE
rtRV-WEATHER FLOW i
„ (INCHES P?R YESR}
COMBjSTORMJUNSEWj AVER I
O.o
0.0
0.0
0.0
0.0
0.0
0.0
---.I-.--.
5.71 11.5
-«™. ..... I
14.9!
0.0
20^7
13.
32:?! lu-e
41.01
44.01
42^
42.31 16.2
48.0
43.0 I
43.01
47.01
42.ni
46.01 0.0
.... I.....
47.01 0.0
0.0
0.0
14. fl
15.41
15.0
15.1 I
15.l!
Bl
Jt _
OTHER"URBAN AREAS
IAVE." FOR STATE
(DURHAM
IFAYF.TTEVIILE
(GREENSBORO
IHIGHPOINT
(RALEIGH
41
• -!.
41
41
41
*{
41.
..I.
41
41
41
41
41
4>
..I-
41
..I.
SC
SC
SC
SC
SC
• ••••
TN
TN
TN
TN
TN
(OTHER URBAN AREAS
IAVE.' FOR STATE
' iCHARLESTON""""""""
(COLUMBIA
.-..^IL
(OTHER URBAN AREAS
FOR STATE
i CHATTANOORA"""""""
(NASHVILLE
(OTHER URBAN AREAS
TN IAVE.* FOR STATE
''
46.01
48.01
45.01 16l
48.31 16:3
4fl.3l 16.3
10.61
• I
49.61. 14.UJ 16.61 6.M Jl.ftl
118
-------
TABLE IV-4
1
EPAISTATE
Gl 10
51
51
I!
51
51
51
I!
51
51
51
51
II
51
51
1
IL
|
!L
I
T1
IN
IN
IN
IN
TN
. IN
IN
IN
TN
IN
ANNUAL nRY-WEATHER
1 URBANI7EH ARFA
1
(AURORA
IBLOOMINGTON
ICHAMPAIGN
ICHICAGO
IDAVFNPORT METPO
IOECATUR
i JOLTET
IPEQRIA
IROCKFORD
ISPRTNGFIELD
IOTHER URBAN AREAS
SAVE.' FOR STATE
1 ANDFRSON
ICHICAGO MFTRO
I-EVAMSVILLF
IFORT WAYNE
TNDTANAPCI IS
iCAFAYETTE
IMUNCIE
ISOUTH BEND
1TFRRA HAUTE
(OTHER. URBAN AREAS
i
FLOW .
1 TN/YPI
1 ANNL. 1
1PRECPI
1 34.01
1 36.01
1 37.01
1 33.01
1 34.01
1 37.01
1 33.01
1 35.01
1 36.01
1 35.01
1 35.01
! 35.01
1 36.01
1 33.01
1 41.01
1 34.0 1
1 39lO!
1 36.01
OR
(TN
COMP
0.0
0.0
!?:*
n.i
0.0
10. ft
O.n
15.?
26.8
26.8
18.8
1 " . 0
0^0
14.8
Y-WEATHER FLOW 1
CHES PER YEAR) 1.
STORM MJN3EWI AVER 1
18.81 5.51 11.51
18.01 6.01 12.11
17.11 6.9| 15.11
6.31 5.91 15.61
13.?.! 9.11 il.7|
0.0 1 11.11 11.11
16.9| 6.31 11.31
0.01 10.61 10.61
19;tl 5.51 12.01
15.21 7.ai 12. ?l
9.71 6.41 14.61
- j | j
9.7| 6.4| 14.61
IS^SI 5l6l U:9I
1 ^ • 0 1 5 » 5 1 1 1 • ft 1
1A n. 1 £1 & 1 1 ^ til
13:31 «:*i 12:31
S:OI 10.6! 11.21
13.01 9.21 10.91
16.8| 5.81 11.01
1 1 . 1
51 IN lAVEj FOR STATF I _37 .2 I _14.8 I _16.8 j __ %8 j _ll.n j
51
I!
51
51
51
51
Ml
MT
Hi
MI
Ml
MI
Ml
MI
MI
MI
MT
IANN ARBOR
IBAY CITY
IDFTROIT
iFLINT
IGPAND RAPTDS
I JACKSON
IKALAMAZOO
ILANSING
IMHSKEGON
ISAGINAW
IHTHFR URBAN ARFAS
I
31.0
28.01
31.0 I
30.01
31.01
34.0 I
3a.o
o.o i
11 .5!
20.0 I
16.1 I
i?:5!
28
31
I 0.01
15.01
0.01
L 2 . 0 I
01
0 I
o i
15.1 I
0*0|
16.01
16.1 I
18.1 I
O.Ot
16 . '1 I
15.91
17.ai
0.01
16. ?l
-u:l!
5.51
9.31
6.31
12.(
11 .M
I2:i!
10.71
10.a i
10.31
11!
i
51 MI IAVE.' FOR PTATF
I. 31.01 18.9) 16.21 6.31 12.9|
5!
51
I!
51
51
I!
I!
51
51
I!
5!
51
5
HN
MN
MN
MN
MN
IDHLUTH
IFAR'GQ METRO
IHINMEAPOLTS
I ROCHESTER
IOTH.FR URBAN APFAS
MN UVE." FOR STATE
,._..I-.-----------------1
OH I AKRON
'"' ICANTON
(CINCINNATI
(CLEVELAND
iCOLUMBUS
IDAYTON
(HAMILTON
ILTMA
ILORAIN
IMANSFIELD
ISPRTNGFIELD
ISTEUBENVII LF
29.01
21.01
25.01
29.01
26.01
96
12.21
10.61
12.61
10.61
I. 26.01
1
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
ITOLFOO
IYOUNGSTOWM
IOTHFR URBAN AREAS
i
51 OH IAVE. FOR STATF
3«.OI
38.01
3a.oi
32.01
36.01
35.01
ao.o i
36.01
35.01
43.0 I
ao.oi
•ao.oi
33.01
«2.0 I
37.21
51
51
i:
51
51
I
51
I APPLETON
ID'ILHTH METRO
IGREF.N BAY
IKENOSHA
ILA CROSSE
IMADTSON
MILWAUKEE
IOSHKOSH
IOTHEREUnBAN AREAS
W! 'IAVE. FOR STATE
Wl
wl
Wl
Wl
wl
wl
Wl
w!
wl
...j....-^_.-,----------.
...I._.--!---- — ....—
.37.2
32.01
31 .01
31.01
28.01
28.01
32.01
i.29.71 28.71 11.8 {__3;J;} .11:' |
119
-------
ANNUAL DRY-WEATHER
URBANIZED ARFA
TABLE IV-4
EPA I STATE I
RFGI . ID |
... I ----- I ........mmmmmmmmmmmm
61
i[
. 61
.«•« f i
61
61
61
61
61
61
61
A* 'LITTLE ROCK
AR PINE BLUFF
AR IOTHFR URBAN AREAS
AR IAVE; FOR STATE
"A" IE
LA ILAKE'CHARLES
LA I MONROE
LA (NEW ORLEANS
LA ISHRFVEPORT .
LA JOTHFR URBAN AREAS
.
61
61
61
•..I.,
61
61
61
61
. 61.
---!..
61
61
61
61
:
1
6j
6
6
6
6i
61
6
6
61
6
6
6
6
6
il
61
61
61
NM
NM
NM
• mm*
OK
OK
OK
OK
OK
'TX"
TX
TX
TX
FOR STATE
" i ALBUQUERQUE"""""""'
•OTHER URBAN ARFAS
.JAVE; FOR STATE
'!LAWTON""""""""""""
'" CITY
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
?!
TX
TX
TX
JOTHFR URBAN AREAS
JAVE^FOR STATE
" iABILENE""""""""""'
!J5*?!bLO
I BEAUMONT
HR^NSVILLE
CHRISTI
O
(FORT WORTH
IGALVESTON
JHARLINGEN
(HOUSTON
ILAREDO
ILUBBOCK
IflCALLEN
(MIDLAND
(ODESSA
IPflRT ARTHUR
(SAN ANGELO
ISAN ANTONIO
(SHERMAN
ITEXARKANA
CITV
FLOW
TN/YRI
(WACO
IWICMITA FALI
IOTHER URBAN~ARFAS
FOR STATE
..... .....
0.0
-=••• 0.0
48.0 9*3
48.01 9.3 15.fl,
•"-••-••••I——I
-*•
i:§
§:2
58.01
50.01
64.01
.45.01
56.01
56.01.
9.01
9.01
•j _• • w f
ij:«!
32.71
32.71
IP'"'
il-2'1
54.01
27.01
I|:§!
3S:?I
,0.0
0.0
0.0
0.0
0.0
0.0
o.o
0.0
0.0
0.0
0.0
0.0
0.0
S:
S:J
8.8
• mmm
6.8
20.8
10.4
II
25-1
C. •+ m J
10.8
16.2
16.2
7.0
17.0
.--.. |
li'M
i
8.8
0.0
7.7
7.6
7.6
6.21 11.0
>... \ mmmmm
5l6l I0l3j
5.S| 10.01
:
26.01
46.01
19.01
18.01
f?.-01
14.01
14.01
54.01
19.01
i2»oi
39.01
46.01
45.01
45.01
32.01
29.01
31.01
. 31.o!
"1575!"
120
-------
IV-4 ANNUAL
TABLE .
!EPA!STATE
IRFGI ID I _ ••___•
URBANIZED
I
I
I
I
I
I 71
71
It
IA
5S
It
ICFDAR RAPIDS
IDAVENPORT
IDES MOINES
FLOW I
. _.. YEAR; i
1STORMIUNSEWI AVER I
I..... I...— I —— I
_ _. 4Q/J,
«:«
10.6)
71 JA
I8«KLB8B»N
IAVE.* FOR STATF
' ""-•• ""^"^"'•"T^TTT^TT^^"^^ —r»** i t « A i «er a
KS (KANSAS CITY
KS. (TOPEKA
71 KS IWICWITA
7! KS IOTHER URBAN
71. KS IAVE.' FOR
~ (COLUMBIA
(KANSAS CITY
(SPRINGFIELD
1ST.JOSEPH
I'l*
10.21
10.21!
U'Z1
10.81
11.31
11.41
11.«l
5.21 9.51
7.71 10.21
6.31 10.11
' " Z.?l
BAN
iW 8:81 iS:«| i}:||
13:4 11.41 10.II 11.61
MO JAVE; FOR STATF
I ""7 l""" I LINCOLN"
I 71 NE (OMAHA
I 71 NE (OTHER URBAN-AREAS
I 71 NE IAVE." FOR STATF
I "" I """"" | AviT~FOR~REG ION""?
•—v I
-81
81
at
BI
si
8f'
CO I BOULDER
CO I COLORADO SPRINGS
CO (DENVER
.IOTHER URBAN AREAS
IAVE.". FOR STATF
CO
CO
CO
I!
8!
MT
MT
MT
MT
mm — •
ND
ND
iHk«NHu8
IOTHFR URBAN
i
UVE."
URBAN
.8
I...I
I 81
I 81 ..- . .
[ . 8i ND IAVE.' ^OR.^1*3^.
i ""5! ""so" i "=="""""""
| 81 SO II
SD IAVE.* FOR STATE
— — 1. — - - -
"
13.41 11.4
"""o7ol"l7T4i 6.21 11.4!
i2-°! J2'2!
19.0! 1«.01
.19.01 IB.01
5.01 16.U
25.o!. 16.4
I i!
UT
UT
UT
IPROVO
ISALT LAKE CITY
(OTHER URBAN AREAS
IAVE." FOR STATE
i URBAN"ARE"AS"""
JAVEJ FOR STATE
""5 I •"""" I A VE7"l?OR"RlGION"""
I 81 UT
I...)...•.
t 81 WV
I I
8!
I
6;4i 7;n
t 16.4J 7.1J
iWlTrifpfl:!!
ii:SI W\ K:H W\
15.ol O.o! 17.l! 5.9!
....(.--..
WY
-.)
8.
...
I
. 15. ol, 0.0
.-—- 1 -----
. 17.41 21. fl
..... I -----
15
.o!
!l
,2',
•-I
»:»!
10.71!
11.o!
121
-------
ANNUAL DRY-WEATHER
URBANIZED AREA
TABLE IV-4
EPAISTATEI
RFC I ID I
"~9I "*AK~ (URBAN*"7""""""""1""11"
91 A K I 1 \/F *
I '** | * v £ • r < .1 n DiHfc
91
91
...|..^» :"•«-• i u« i-ii»,ic. | V.OI, 0.
G I f» A lnilxe>n»Ar»w»-* ^mmmmmmm** I .....{....
JOTHER URBAN AREAS
IAVE." FOR STATE
FLOW
TN/YRI
ANNL..I
PRECPI
.....|,
30.01
I
COM* I STHRM | UN3EW
8"7s-|-r« 75
AVER!
.....(.....(...;.
0.0 16.81 5.3
O.Oi 15.51 7lfl
0.01 16.51 5.fl|
J 9.01, o.n
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
I
91
CA
It
«
CA
CA
CA
CA
CA
K
&
CA
CA
CA
CA
ILOS ANGELFS
I MODESTO
IOVNARD
•SACRAMENTO
ISALTNAS
(SAN
SAN
ise
JOTHFR URBAN AREAS
..i ~m }AVE- FOR STATF
"51 "HI" i •-"-•?•—— •
91 HI i
i i
2!..«.!AVE* FOR 8TATF
9l""NV~ """""
NV
NV
I?
ll
i
0.01
0.01
01
01
8
0
0
5j
0.0
o.oi
o.o
16. 5 5.8!
.
1 :
10.61
10.61
21 33.3
.....|.
23.01
23.01
23.o!
.
17.31
33.3
.1
0.01
0.01
o.o!
4.7
5.7
17.31 5.7.
lfl.2l""6*7o|
18.?| 6.01
10.01
II
13.«l
13.4 !
12761
12.61
6.0J 12.6
.!!..«.IA!E;.!2I!.!IfI!......! .!;!,'.i!:^ i6;«l *.ol io.«
101
101
101
-.1.
toi
101
101
101
.101
.•I.
101
101
101
101
ID
ID
ID
"PR"
OR
OR
OR
OR
"WA"
WA
WA
WA
[BOISE
IOTHFR
UVE; FOR STA.TF
1 EUGENE
!SP?LR^AND
OTHER URBAN AREAS
I .....I ..... I...
| }|.oi o.o! ja
— I-.
oi o;oi lallj
11. 01 0.0
"" ""
JOI
..i,
..i.
i
..i.
i
IAVE. FOR STATE
' iSEATTLE""""""
(SPOKANE
ITACHMA
JOTHER URBAN AREAS
F°R STATF
IAVE. FOR REGION 10
1J •••...—......«......,
IAVERAGE"FOR"THE"urs!
11........'...........,
38
3?
01
01
01
31
I
?
:
39. s, 17. 5
35.01
fi:!l
30.3!
ll'i
U'M
ill?
lit \
:«!
.AI
11. fl
16,
II:
16,
"*?
li
21
61
6.61
6.01
7.61
10
I
.1
•*l —
J!
81
01
.9
l6
6.2
-—I
*:JI
6.
1
'(.....(.....
I- 26.91 15.9
11.....|.....
i "33721"II75
15.? 17. fl 6.5
11
II
11
11
.«
:si
•:!
.«i
122
-------
persons / hectare
140
DRY-WEATHER FLOW
0 10 20 3O 40 50 60
DEVELOPED POPULATION DENSITY , PDrf .persons/acre
Figure IV-4. Comparative Magnitude of Annual Wet- and
: . Dry-Weather Flows
123
-------
ABBREVIATIONS AND SYMBOLS
AR
CR
DWF
P
PD
PD.
Wet-weather runoff, inches per year
Runoff coefficient
Abbreviation for dry-weather flow and dry-weather flow runoff,
inches per year
Imperviousness as a fraction or percent
Initial abstraction (loss) from precipitation, inches
Precipitation, inches per year
Population density, persons per acre
Developed population density, persons per acre
124
-------
REFERENCES
1. Bowers, C. E. , Harris, G. S. and Pabst, A. F., "The Real-Time
Computation of Runoff and Storm Flow in the Minneapolis-St. Paul
' Interceptor Sewers," St. Anthony Falls Hydraulic Laboratory Memo
No. M-118, University of Minnesota, Minneapolis, December iyt>».
2. Leiser, C. P., "Computer Management of a
Office of Research and Development, USEPA Report
NTIS-PB 235 717, July 1974.
-74-022,
1975.
•4. Huber, W. C. , "Modeling for Storm Water Strategies," APWA Reporter,
Vol. 42, No. 5, pp. 10-14, May 1975.
5. DiGiano, F. A. and Mangarella, P. A., eds. , "Application of Storm-
water Management Models," USEPA Report EPA-670/ 2-75-065,
'NTIS-PB 247 163, June 1975.
6. American Society of Civil Engineers, "^ban Hydrology Research, "
'Report, Engineering Foundation Research Conference Andover
New Hampshire, ASCE Urban Hydrology Research. Council, August 197.5,
7. McPherson, M. B. and Schneider, W. J., ^laofl to f^J^
Watersheds," Water Resources Research, Vol. 10, No. 3, pp. 434-440,
June 1974.
8. Hydrologic Engineering Center, Corps, of Engineers, "Urban Storm
Water Runoff: STORM," Generalized Computer Program 723-5b-L2i>^U,
May 1975.
9. Roesner, L. A. , et al. . "A Model for Evaluating Runoff -Quality in
Metropolitan Master Planning," ASCE Urban ^ater Resources Research
Program, Technical Memo No. 23, ASCE, 345 E 47 St., NY, NY 10017,
72 pp., April 1974.
10. Smith, G. F., "Adaptation of the EPA Storm Water Management Model ^
for Use in Preliminary Planning for Control of Urban Storm Runoff,
MS Thesis, Department of Environmental Engineering Sciences,
University of Florida, Gainesville, May 1975.
11. Metcalf and Eddy, Inc., University of Florida and Water Resources
Engineers, Inc., "Storm Water Management Model, Volume I - Final
Report," USEPA Report 11024DOC07/71, NTIS-PB 203 289, September
1971.
125
-------
***°Tt
NTIS-PB 203 290,
andTEddy'..onC" Universlty °f Florida and Water Resources
.
Listing, USEPA Report 11024DOC10/71, HTIS-PB 203 292, Sap tembef 1971 .
' • E"ber> "' C" et al- • "frban Stor™ater Management
Report 8m
Model
18.
Graham, -P. H. , Costello, L. S. and Mallon, E. J ., "Estimation of
Impervxousness and Specific Curb Length, for Forecasting StorLater
19.74d QUantlty' ***<*• Vol« "> No. 4, pp. 717^25,
wi S'/V "Magnitude and Frequency of Floods in New Jersey
with Effects of Urbanization," Special Report 38, US Geological
Survey, Water Resources Division, Trenton, NJ, 1974.
I"formtl°'> Center, Inc., Port Washix«-
126
-------
SECTION V
QUALITY ANALYSIS
•subcatchments within a city to a broad representation of
loads for an entire urbanized area, state, or region. It has been
necessary to consider the entire spectrum during the course of this
research. -. .
It is. unfortunate that perhaps the only consistent remark about
urban runoff quality analysis in general is that fata /^.^J^
of previous studies are so remarkably inconsistent. As discussed
in Volume III of this report, few studies have been made of charac-
teristics of street litter, and they offer a wide range of values
of concentrations and loads. Effluent data show a similar scatter.
However it is necessary that a decision be made regarding actual
vaSIs foJ use in the Salysis. This section will describe methods
used for predicting runoff quality, data required for their use,
and final results used in this study.
QUALITY PARAMETERS
Parameter Definitions
Urban runoff quality may be characterized by a variety of
that even at this juncture, a serious problem or def inition
arises because of various possibilities for analyzing and reporting
quality paramlters. The assurance that analyses have been performed
T^LTTn Standard Methods^ is not enough information For example,
solids are sometimes reported as "residue" instead of solids, and
^^
and PO leading to unrealistically low values if the reader mis-
thinks of tnem as total (soluble plus insoluble) concentrations
'127
-------
TABLE V-l. TYPICAL QUALITY PARAMETERS OF URBAN RUNOFF MODELS
Quality Characteristic
Solids
Oxygen Demand
Health Hazards
Representative Quality Parameters
— ——
Surface "Dust and Dirt"
Surface "Solids"
.Total Solids
Suspended Solids
Dissolved Solids
Volatile Solids
Settleable Solids
BOD, COD
Total Organic Carbon
Organic N, N02> NHg
Total Coliforms
Fecal Coliforms
Aquatic Growth Potential Ortho-
PO,
Total PO,
N02, N03, Total N
128
-------
**
pared to those that are soluble.
—
draw conclusions from all data considered together.
in this report, the solids relationship %2*£
fraction of suspended solids. Note
the size of total solids reported is imposed ^ megh screen)
openings in the sampling equipment (e.g., a quarter in
Similar diagrams may be prepared
shown in Figure V-2,
Parameters for Assessment
fcr purpo.es of the
Parameters Used in Nationwide Assessment.
F1,e-day BOB is used because of
role in water quality analysis
by the great difficulty ^
analyses. For instance, there is no
-e --
± tent laboratory
for laboratory comparison,
susceptible to
Hale sho^ that results
129
-------
z
o
or
o
Q_
UJ
ffi
CO
Q
Hi
o
CO
o
o
O
CO
5
o
UJ
o:
O
f
1 **"*
.5L"
f^m
r
i
i
CO
9
i
o
CO
0
Q
2
UJ
CL
CO
CO
o
-J
o
CO
UJ
• 1
DQ
"~^
UJ
H
h-
UJ
CO
o
UJ
— — x
LL.
10
o
l£
£
o
LL.
U- o"
O a
>s
§5
UJ
-------
TOTAL N
N02
N03
KJELDAHL N
ORGANIC N
Figure V-2. Relationships Among Nitrogen Parameters,
NH,
TOTAL P04
ORTHO-P04
(HYDROLIZED)
OTHER
Figure V-3. Relationships Among Phosphorus Parameters.
131
-------
TABLE V-2. QUALITY PARAMETERS USED IN NATIONWIDE ASSESSMENT
Note: All parameters (except suspended solids) are totals that '.".
include dissolved and insoluble portions, .and are usually,
determined as in Standard Methods.1 All are usually
reported in concentration units of mg/1 (equivalent to ppm)
Parameter
Abbreviation
1. Five-Day Biochemical Oxygen Demand
2. Suspended Solids .
3. Volatile Solids (Total)
4. Total Phosphate (as PO )
5. Total Nitrogen (as N)
BOD5 or BOD
SS
VS
PO, or TPO.
• 4 4
N -. .." .
are affected by the percent dilution and are generally not
reproducible.2 In addition, samples are affected by amounts of
heavy metals and other parameters present. Use of COD and/or TOG
avoids these problems for the most part, but their relationship
with traditional stream sanitation analysis (i.e., prediction of
dissolved oxygen) is unclear, and most people are used to thinking
StionT °f B°D' Xt ±S USed ±n this st"dF, realizing its limi-
The other four parameters are used because of general acceptance
and^availability of data. It should be borne in mind that many
options are available for modeling purposes, and the choice of
parameters is somewhat arbitrary. , . -
PREDICTIVE TECHNIQUES
Pollutant loads
The quality prediction techniques found in most urban runoff models
(e.g., SWMM, STORM) rely upon generation of an initial surface load
' of pollutants. This load is usually expressed in units of Ibs
Ibs/acre, Ibs/curb-mile, Ibs/day-acre, or Ibs/day-curb-mile (or
equivalent metric units). Normalized loads are, of course, multi-
plied by a unit of area, dry days, etc., to produce an initial'mass
132
-------
of pollutants at the start of the storm. Pollutants are then
=
utilized. in the development that follows.
Surface Accumulation Methods^
Both SWMM and STORM use this method for prediction of the total
soluble mass of pollutants available at the beginning of a storm.
I? is based upon the following equation, given in US customary
units
where
i'
dd,
total soluble pounds of pollutant j on urban
land use i at the beginning of the storm,
(V-l)
dd = pounds of accumulated dust and dirt on land
1 use i (or '^surface solids") per curb-mile per
dry day,
F . = soluble pounds of pollutant j per- pound of
ij3 dust and dirt found on land use i,
G
L,
D
= curb-miles per acre of land use i,
= area of land use i, acres,
= number of dry days since last storm, and
= total soluble pounds of pollutant remaining on
0 land use i at end of last storm.
133
-------
diysiSsLheeen^eLs°tf *?
This is due to the Set that in volt™
between storms is less than the street
generally ranges from one ?o too J
street sweeping is in^r^laS in
not
clean±nS 'Ption.
.intera"ival time
interval- The latter
are usually base
•.- -.*..
the 1969 APWA Chicago
Table v-3, Param^t^s for Surfa^ PolSt2t° f^ ' ^ ^ **™^
™v°*™wr^i^^^^
report allow the updating of all nara a°,CUmentf in VoluieZiroT-EhiS
described subsequently parameters of this table, as will be
effluent concentration ^of oJS^0?^"'?"1 tO Predicted
adds five percent of the SS ?on ? - P °rtlon) • For example, SWMM .
to obtain total BO?! on the basis" of T-K^ ^ SOlUble B°D -concentration
SWMM in San Francisco This ifh cal^ration of the original
° "
accumulatioa simply
srs
a
sufa" P°U«tant
where
PD.
PD
GL = 0.0782 - 0.0668 • 0.839 d
curb-miles per area, mile/acre, and
developed population density, persons/acre.
(V-2)
134
-------
Table V-3. PARAMETERS FOR SURFACE POLLUTANT ACCUMULATION USED
IN SWMM AND/OR STORM
Except as noted, values are for soluble portion and derived from the
1969 APWA Chicago study.7
__
Lau
1. Single- 2. Multi-
Parameter . Units family res. family res. 3.
Dust and dirt
loading, dd± lb/ day-curb-mile
kg/day-curb-km
Pollutant frac-
tionsb, F
i»3
SS (SWMM)
SSa (STORM)
Settleable Solids0
(SWMM)
Settleable S.olidsc
(STORM)
BOD 5
COD
Total P04
Total N
Grease3
Total Coliforms MPN/g
. • —
40.0
11.4
1f\
.0
0.111
0.1
0.011
0.005
0.04
0.00005
0.00048
0.001
1.3 x 106
121.0
34.4
i n
X . U
0.08
0.1
0.008
0.0036
0.04
0.00005
0.00061
0.001
2.7 x 106
' '
td Use
Commercial 4. Industrial 5.
___ •
174.0
49.4
1.0
0.17
0.1
0.017
0.0077
0.039
0.00007
0.00041
0.001
1.7 x 106
243.0
69.0
1.0
0.067
0.1
0.007
0.003
0.04
0.00003
0.00043
0.001
1.0 x 106
a
, Open
79.2
22.5
1.0
0.111
01
. J.
0.011
0.005
0.02
0.00001
0.00005
0.001
0.00
aAll values assumed.
fraction refers only to soluble fraction of dust and dirt (except for solids),
CA11 values assumed at 10% of value for SS.
135
-------
lenjth^olcjprirtroubleso^11 ^ res±dential areas» but the curb
-
To summarize, the surface accumulation methods are convenient for
tive StoTpOS™ ^ lllustrate the ^^ages between ™Sous fausa-
tive factors. The key missing factor is a link between the surface
both* ^ntiftntfi1^^ ^f ?aS beSn Ver±f±ed b^ -*-emeSf S
it?li-bf i, ^% accomplished, such a link must be hypothesized in
its mathematical formulation, as done in SWMM and STORM However
equation V-l is used in developments that follow to relate llalinL
between different land uses and pollutants; hence, the reason for"
Previous developments. The other side of the coin, that S
derivable from effluent data alone, will be discussed next?'
Effluent Concentration Method
rate
reP°rted -asured concentrations
Combined sewer discharges. If the flow
o S fl°W P°llut°8^Ph may be determined
e«l BOD), and integrated to produce the total mass
emission for the storm discharge. When distributed over the area of
e ^ ^^ by ^ number °f Preceding dry days,
ror ^SS ^'f" mass-BOD/^-day) may be determLed. Some
f ?f ValUeS directly' while others report a lesser
om « ™f inf°?nati011- In Senera1' the Surface loadi^ ^ay be deduced
from a measured average concentration and assumed runoff quantity
= P
CR
(V-3)
where
M = pollutant loading, mass/area-time,
P = precipitation, depth/ time,
c " rc • °ass of pollutmt
CR
p
runoff coefficient, and
water density, mass/ volume.
preceded
N
preci K
precipitation, P , may be given. Then,
o ™
total depth of
136
-------
CO
H
CO
p
^
^n
co
!=>
O
M
c
< O CU '
• to to
o —
O CJ
^d
'
tu
C K
° il
•H ^
0 M MJ
O OJ O
rJ «> 0
S
S 1
«
° .
0 rJ
(H CJ
M-l
0
O
01
. 10
0)
rH «
S
CD
M
O
to
CJ
rH
,-H
'B
^j - r-* oo CM ' '
. • • 1 • 1
CO
CO CO CO CJ;
CM CM tH 0 |
O O O O
_
o\ o •» co
°. ° s. 1 °. • t •
o o o o
CM o CO t^ ^ .
CM CO rH O rH
CT» CJN
"H ^ ° °. "~i
0 0 0 00
CM t- m m o
S 8 S , S S
o o o o o
m
• o <^ <^ ^ *°
^f •& CM CM - O
en
o CM r*^ r- ^
C^ fO i-H rH O -
o o o o o
^j ,_! CM .
•H
^
O
CO
. DC ' •'
a
•H
M
•p.
3. :
OJ
1..1975,
-Tindsor .
•0
•H "
O 4J
ta 0
0 t-l
& «
•H CJ
to ts
I-l
u -
•H 10
£2 fT
!=>
->,!tu
10 s
a) .*"!
4_) £-1
o
0) -
rH 0)
• rH i-l
O V<
0 3
to
to a
- TJ 4-1
13
0) 4J
J3 rH
.53
.H tO
,£1
3 *
fx en
cj a)
§ s
-H
o rt
Q) 4J
&o nj
oJ o
n
0) -
-------
M =
P • C • CR • p
o
N.
D
Fo^annual average confutations it may be assumed that,
and
where
P =
s n
D
CV-41
on an average
(V-5)
n
n
' average annual precipitation, depth/year,
average precipitation per storm, depth/storm,
average number of dry days between storms, and
average number of storms per year.
Equation V-3
e converted to
May-acreJ
10 lb
yr
CR • 62.
ft
acre
..ft
12 in.
or
where
365 day
M - 6.21 x 10~4 . P . c • CR
M = average surface loading, Ib/day-acre,
P - annual precipitation, In/yr,
C =
CV-7)
CR
runoff coefficient, fraction.
of
138
-------
generated by the simplest of methods, that of a runoff coefficient
with all of its well-documented errors.
nationwide assessment performed in this
In the same manner that surface accumulations could be considered
functions of population density and land use, so can surface loadings
derived from efrluent data. In particular, both the concentration and
runoff coefficient are clearly such functions; th* latt*£ j^Monal
.cussed in the previous section. In order to ascerta in f * j^10^.
relationship between the surface loadings and population density, avail
able data for the residential areas for which population density is given
have been tabulated. Derived surface loadings are *J*^ Table V-5,
Surface BOD Lo«din«e for Residential Areas *° "^j^f J™ *f g^V,.
Measurements. The cities included in the table all had data for^ resi
dential areas for which population density was specified and from which
surface loadings could be derived. The list is not meant to be exclusive
but represents data that were readily available during the study.
s^as
are omitted from subsequent analysis. The remaining data still show
Considerable scatter, but will be utilized to derive required relation-
ships.
LOADING PREDICTION FOR NATIONWIDE ASSESSMENT :>, ;
Form of Equation ._.-.-
Surface pollutant loads generated by the pollutant load estimating
equation will be assumed to "wash off" on an annual basis for purposes
of the nationwide assessment. Hence, they must be representative of
actual measured effluent loads. Moreover ,. they should be functionally^
related to causative factors in a reasonable manner, ^hey are expected
to be functions of land use and population density. In addition, the ;
extensive presentation of Volume II! showed geographical variations inv '
139
-------
Table V-5.
SURFACE BOD LOADINGS FOR RESIDENTIAL AREAS AS
DERIVED FROM EFFLUENT MEASUREMENTS
Note:
Surface loadings are taken directly from
the source if computed therein, or derived
from mass emission (e.g., Ibs/storm) data,
if listed. Otherwise equation V-7 is used
(for cities for which runoff coefficient
and BOD concentration are listed).
City
•^— • — _« «_
Tulsa
Bucyrus
Atlanta
Rosnoke
Milwaukee
-- Wash. D.C.
Dss Koines
Cincinnati
Durhaa
Seattle
Windsor
•^— «^VM^__H^_
Sit* OT ststio:
Site or
Station*
— — — — .^— ^____
3
5
8
9
11
13
15
8
17
23
Confed. Ave.
Blvd.
KcDan St.
Marian
Casplsn
Fed. Frls.
Trout Run
Hurray Run
24 St.
Havley Rd.
Cood Hope Run
B4
04
S-l
S-3
0-3
0-6
0-8
0-SA
Ht. Wsshlngton
E-l
W-l
W-2A
U-2B
K-l
Lov Dens.
Ked. Dens.
High Dens.
Labadie Rd.
a SS listed in «niir
Sever
System
— — — ^ — —
Separate
"
it.
it
Combined
Combihed
Separate
Separate
Combined
Separate
Combined
11 '
Separate
Combined
Separate
Separate
Separate
ii
it
Separate
— • _
Catchment Annual
Area Precip.
ac (ha) in. (cm)
•
550
507
197
211
64
815
212
74
179
614
378
1129
2421
968
954
517
1498
997
909
1034
495
265
105
222
315
356
4050
5600
1350
927
27
56
169
69
138
183
c
30
(223) 48 (122)
(80)
(85)
(26)
(330)
(86)
(30)
( 72) 35 ((!?)
• (249)
(153)
(457) 48 (122)
(980)
(392)
(386)
(209)
(606)
(404) 34 (86)
(368)
(419)
(200) 31 (79)
(107) 41 (104)
(43)
(90)
(128) 31 (79)
(144)
(1640)
(2267)
(547)
(375)
(11) 40 (102)
(23) 45 (114)
(68)
(28)
(56)
(74)
36 (01)
(12) 33 (84)
Runoff
Coef.
0.39
0.41
0.35
0.31
0.42
0.42
0.33
0.56
0.31
0:40
0.10
0.10
0.15
0.15
0.15
0.15
0.29?
0.35°
0.34b
0.36°
0.36b
BOD
Cone.
mg/1
120
107
108
210
84
286
7
20
26
49
48
63
69
95
68
77
25
61
38
51
71
— '
BOD
Surface Loading
Ib/ac-day
(kg/ha - day)
0.0381 (0.0428)
0.0901 (0.1012)
0.0417 (0.0468)
0.0899 (0.1009)
0.0544 (0.0611)
0.0963 (0.1081)
0.0679 (0.0762)
0.0688 (0.0772)
1.017 (1.142)
0.953 (1.070)
0.821 (0.922)
1.94 (2.178)
1.05 (1.179)
3.58 (4.019)
0.069 (0.077)
0.334 (0.375)
0.240 (0.269)
0.0363 (0.0408)
0.0428 (0.0481)
0.0233 (0.0262)
0.377 (0.423)
0.063 (0.071)
0.247 (0.277)
0.381 (0.428)
0.093 (0.104)
0.121 (0.136) 4
0.199 (0.223)
0.275 (0.309)
0.197 (0.221)
0.222 (0.249)
0.0904 (0.1015)
6.202 (0.227)
0.596 (0.669)
0.361 (0.405)
0.513 (0.576)
0.714 (0.802)
0.04 (0.045)
0.07 (0.079)
0.13 (0.146)
0.059 (0.066)
— ^
Population
Density
Persons/ac
(persons/ha) Reference
7.13 (17.61) 9
8.93 (22.06)
11.55 (28.53)
11.37 (28.08)
13.67 (33.76)
9.57 (23.64)
2.36 (5.83)
11.22 (27.71)
11.7 (28.9) 1"
9.1 (22.5)
. - 5.0 (12.4)
10.9 (26.9) 11
16.6 (41.0)
13.2 (32.6)
9.7 (24.0)
7.3 (18.0)
4.8 (11.9)
11.0 (27.2) 17
6.6 (16.3)
9.7 (24.0)
35.0 (86.5) 13
37.6 (92.9) M
43.6 (107.7)
52.6 (129.9)
• 7.4 (18.3) 15
, 5.3 (13.1)
7.5 (18.5)
8.3 (20.5)
10.9 (26.9)
10.9 (26.9)
9.0 (22.2) Ifi
14.9 (36.8) 17
2.6 (6.4)
11.0 (27.2)
13.4 (33.1)
4.2 (10.4)
11. Od (27.2) 1R
22.0° (54.3)
30. Od (74.1)
20.0 (49.4) 19
— • : ^
°V«lu« computed uaing lapervlousness.
"Hypothetical are'a based on measured data.
•UssoMd on basis of dwelling unite per acre.
140
-------
persons / hectare
o
1.0-
0.9 H
1
Ofi— 1
W.U 1
1
f\ f
O.7 —
Q o
O -a
CD i
i fl)
f* O
— o
'
o
Z 0.5-
o
1
0.4-
; 03-
0,2-
0.1-
0
20 40 60
O
V
WASHINGTON,
V DES MOINES
ATLANTA
BUCYRUS
MILWAUKEE
Till <5A
ROANOKE
CINCINNATI
DURHAM
SEATTLE
WINDSOR
A
A
A
•
O
B
0
10 QA
»
• H V O
^ T»V
^__^_^____— ^
, , r 1
0 10 20
.80 100 120 140
, ' t . i 1 1 — •—* ' 1
1;
•
SEPARATE COMBINED
D.C... A &
' 0 0
B D
y
O
y
•
»
u
Sr : :
'-••
.
O. A .......
. .
A
a
*
30 40 50 <
.2
.1
• -P
-0.9
f^ Q
-O.8
-O 7 5^
v/ » * ^\
f*\
0 T
CD a>
-0.6 i a
S t>
O)
I"*1
0.5
, -
•
0.4
-0.3
-0.2
-O.I
-0
30
DEVELOPED POPULATION DENSITY, PDd persons/acre
Figure V-4. Residential BOD Loadings vs
Developed Population Density
Data are from Table V-5.
141
-------
3=
it is also a function of
of street sweeping efficiency Thi
- -
• •« i J.T. •* *-**P-^^X* j. uu.&tu. L, J.4JJ.1O *
will thus be represented functionally as:
M- a • f (P) • f(PDj • f (N )
A z a 3s
(V-8)
where the coefficient a and functions f f and f are to be
below and N is the street: sm^em-ino- -tr-.-f-i~—4 Tt,^ 3 • , .. _
The procedure to be fol-
Precipitation Function
(V-9)
°f the
remaining
142
-------
7 loading. i ' Ki
. i 7 —— = v 2 • ^—.
"7 P. i=l i
(V-10)
„ Ib-BOD _
~
kg-BOD
where
Annual average BOD loadings for residential areas are now predicted by
M - 0.80 - P • f2(PDd) ' f3
-------
Population Function
density implied by equions d V
base slightly further it will L
increased popul^Ln d^ty*
area loadings. The data base can thin
. by the average loadings for separate S
from Atlanta, Bucyrus and DuSS JaS
jay be prepared. Finally, tS datf of
in Figure V-5, Normalized BOD
point has been
Ib-BOD/ac-day
is
population
toscbattder f 1 no
The concentration of stormwater pollutants is M/AR, or
M
where
I
I
0.096 PD
8. K[0.15 + 0.75 I]P
CO.573-0.0391 log PD )
nn
10
0.096 PD°'54
d
in the
or
(V-14)
(V-15a)
(V-15b)
where
_
AR
KtO.15 + 0.072 PD °'54]
PD
m
(V-16)
'd (V-17)
a = 0.142 = value at PDd = 0 (from open space value),
144
-------
Table V-6. NORMALIZED BOD LOADING DATA
Note: Values obtained from Table V-5, omitting data from Atlanta, Bucyrus and Durham.
Average
Loading
Ib-BOD
ac— dav City
(kg-BOD^
ha- day
Separate 0.0693 Tulsa
Areas (0.0778) .
Roanoke
Wash. D.C.
Des Moines
Cincinnati
Seattle
Windsor
Combined 0.271 Wash. D.C.
Areas
(0.304) Milwaukee
Des Moines
Loading
Ave. Loading
0.550
1.300
0.060'
1.297
0.785
1.390
0.980
0.993
0.524
0.617
0.336
0.909
1.342
1.746
1.305
0.577
1.010
1.876
0.851
0.911
1.405
1.391
0.734
1.014
0.727
0.819
Population
Density
Persons/ac (Persons/ha)
7.13
8.93
11.55
11.37
13.67
9.57
2.36
11.22
11.0
6.6
9.7
37.6
7.4
5.3
9.0
11.0
22.0
30.0
20.0
43.6
52.6
35.0
7.5
8.3
10.9
10.9
( 17.61)
( 22.06)
( 28.53)
( 28.08)
( 33.76)
( 23.64)
( 5.83)
( 27.71)
( 27.2)
( 16.3)
( 24.0)
( 92.9)
( 18.3)
( 13.1)
( 22.2)
( 27.2)
( 54.3)
( 74.1)
( 61.8)
(107.7)
(129.9)
( 86.5)
( 18.5)
( 20.5)
( 26.9)
( 26.9)
145
-------
3.0
persons / hectare
40 so so
K)0
120
140
SEPARATE
WASHINGTON D. C
DES MOINES •
MILWAUKEE
TULSA
ROANOKE
CINCINNATI
SEATTLE
WINDSOR
CALIBRATION POINT
COMBINED
= 0.142+ 0.218 POP'84
LAND USE = OPEN
20 3*0
-------
and developed population density will be used for consistency. Note
that, depending on the assumed value of m, the concentration of storm
water pollution will vary accordingly. Since no firm arguments can Be
made on the nature of the concentration function, it will Be assumed
that m is equal to the approximate exponent in the runoff equations or
m = 0.54. Thus, £2(PD.) =0.142 + b PD °'54. Lastly, all data points
with a PD . ranging2from 5 to 15 personsVr acre (12 to 37 persons/ha)
are averaged to obtain a calibrated value of f2(P$ = 0.895 at 10
persons per acre (25 persons/ha). This range xs chosen because data
from most cities fall within it. Thus, the final equation is
0.54
f2(PDd) = 0.142 + 0.218 PDd
where PD, = developed population density, persons per acre
d
(V-18)
The reasonableness of equation V-18 can be checked by estimating the
variation in concentration as a function of population density. From
equations V-ll and V-18, the annual BOD loading is
M - 0.80 • P • (0.142 + 0.218
'5
(V-19)
and annual runoff, AR, using the approximate New Jersey21 equation
(equation V-J.5b) for imperviousness is:
ad. annuaJ. runorr, .HJX, usj-ug UM.C a.VVi.\jj».
equation V-J.5b) for imperviousness is:
AR - [0.15 + 0.75(0.096)PDd°*54] • P
Thus,
M
AR
0.113 + 0.174 PDd
K[0.15 + 0.072
0.54
(V-20)
(V-21)
Using K = 0.227 to convert to mg/1 from the ratio of Ibs/ac-yr per in./yr
this ratio, which is plotted in Figure V-6, BOD Concentration Variation
Using Estimating Equation, shows concentration increasing with, population
density which does seem reasonable. The range of average annual concen-
trations 'is lower than values shown in Table V-5 since it represents the
average over the total residential area of a city. Unquestionably, the
data base for estimating pollutant loads is very weak, and the resulting
estimating equation, supported by such a weak foundation should be used
with extreme cautipn.
147
-------
15
CP
E
or
persons/hectare
20 40 60 80
100
120
-EQUATION V-21
—I—
20
0 10 20 30 40 50
DEVELOPED POPULATION DENSITY, PDd/ persons/acre
Figure V-6i. SOD Concentration Variation Using Estimating Equation
L48
-------
Adjustment for Street Sweeping
puted as
Po'
(1-e)]
(V-22)
where
N,
D
DD.
N
= total pounds of pollutant j on land use
i at the beginning of the storm,
= pounds of pollutant j per pound of dust
and dirt for land use i,
- number of days without runoff since the
last storm,
= total pounds of pollutant j remaining on
land use i at the end of the last storm,
- dust and dirt loading for land use i,
Ib/day = dd. • G_ , -A. from equation V-l,
J 0. Li, 3- J-
- number of days between street sweeping,
= number of times the street was swept since
the last storm, and
e = street sweeping efficiency.
Note that total pounds of pollutant Cnot soluble only) are used in the
equation! in keeping with ?he discussion following equation V-l.
s
n
149
-------
s Mo • T68' IOWa WaS Ch°Sen to demonstrate this contention.
Des Moxnes xs a moderately sized city with approximately 255,000 people
STO^vrT Average precipitation comparable* to the national aveJage?
STORM was run usxng the precipitation record for the year 1968 Pre-
vxously calibrated loading factors were also used. T£ street sweejin,
EO^Tl WaS IT ^^ the GffeCt °n the —P^ation of annuafaJerafe
JSL™ v 7 PSJ ed s°ildS concentration noted. The results are shown if
Figure V-7 Effect of Street Sw>^tog Frequency on Annual BOD Co^-
tration in Urban Stormwater Runoff - Des Moines, Iowa.' — -
The results show that there is a point after which the magnitude of
the street sweeping frequency has no effect on the computfd values of
average annual BOD and suspended solids concentrations In
where
Ng/20 If 0 <_ N ' '< 20 days
1 otherwise
Y = proportion of pollutant load remaining after
street sweeping, and
N,
(v_23)
s13 street ^Keeping interval, days.
Conversion for Alternate Land Uses and Pollutants
Different pollutants and land uses will generate different loadings
for at least three reasons. First the dust and dirt loadings for
different land uses differ. Second, the conversion factor of curb
length per area is different for different land uses. Third the
pollutant fractions (as a fraction of dust and dirt) are different
for dxfferent land uses. These factors are used to extend the
equations developed for BOD for residential areas to similar equations
for commercxal, industrial . and open land uses and for suspended solids,
volatile solxds, total P04 and total N.
It is assumed that fractions and ratios of pollutants as; they appear
in effluents will be, the same as those determined from analysis of
surface accumulation data. The parameters shown in Table V-7 Surface
Loading and Pollutant Fraction n.t-a are used for conversion purposes.
They are selected from the extensive survey material presented in
Volume III.
Where no data are available for pollutant
s Ss a fraction
n
of surface dust and dirt, use is made (as, a second choice) of similar
150
-------
O
•* I/BUI 'aoa
151
-------
Table V-7. SURFACE LOADING AND POLLUTANT FRACTION DATA
n°ted' a11 data are from Volume III,
l ^Dally DUSt and Dirt Accumulation
and Related Pollutant Concentrations for Select
Field Observations." Missing entries are not
given in original table or not used in analysis
BOD - ppm of
- £ _
day-curb mile
ppm of dd£
ppm of total solids (TS)a
Total P04 - ppm of dd<
• ppo of TS4
Total N - PPB of ddj
ppn of TSS
Suspended Sollds-ppm of TSa
Volatile Solids-ppn of TSa
'Values taken from Volume III, Table
Sum of K-N plus NO.J-N.
"Value of organic - N onlyl
62 "3 87.5 166
17 32 24.8 47
5260 3370 719Q
29840 83800
609200 582300
353000 367700
319
90
2920
25850
619500
306100
50
14.2
18990
1670
101 70C
453200
437500
159
45
170
664b
152
-------
ddi GL.i . i*BOD (v_24)
data developed for pollutants .as a fraction of total solids (TS).
The BOD data are first converted to other land uses using equation V-l
as indicated below, and using i = residential (res) land use as the
reference:
a(i,BOD) - a(res,BOD) *
where dd. = dust and dirt accumulation on land use i,
•*• lb/day-curb mile,
G . = curb miles per acre for land use i from
L>i Table V-4, and
F. = fraction of dust and dirt that is BOD on
i,BOD land use i.
For example, the parameter a for BOD for commercial land use for separate
areas is
166 x 7190 0.070
a(com,BOD) = 0.80 • 353455 ' Q.059
(V-25)
= BOD
- . kg-BOD
ac-in. ha-cm
where the number 353,465 is the average product of dd± • ?.. for j
and i = single and multi-family residential and is equal to
62 x 5260 + 113 x 3370
—
After determination of BOD for each land use, i, other quality param-
tteri,j!a?e computed on the basis of relative values of the fractions,
F. .. Thus,
aCU) - a(i,BOD) £**- . GT-26)
*i,BOD
For example, the parameter a for total P04 in commercial areas is
lb-PO,
a(com,P04) = 3.2 x U^ = 0.076 —^ .(V-27)
- °'034 ha^c^ •
For total nitrogen, N, in residential areas the calculation is similar but
includes the average product of dd± • Fi}j>
153
-------
a(res,N)
0.80 • 664 . ffi?
353,465
0.058 r^SrN
ha-cm
=
'
ib-N
ac-in.
(V-28)
"
a(com,SS) =3.;
582.300
83,800"
22
Ib-SS
ac-in.
(V-29)
9.8
ha-cm
Computations for combined areas are carried oni- -fr, M,
"
and
TABULATION OF NATIONWIDE BOD LOADS
In order to minimize the volume of material presented for each citv in
154
-------
Table V-8. POLLDTAH1 LOADING IACTORS FOE NATIONWIDE ASSESSMENT
ssa.
density.
Separate
Combined Areas:
where
•acre-yr
Land Uses;
M - pounds of pollutant j generated per acre of
land use i per year,
P = annual precipitation, inches per year,
PD = developed population density, persons per acre,
a,Bd = factors given in table below,
Y - street sweeping effectiveness factor, and
f (PD ) = population density function.
1=1 Residential
1=2 Commercial ...
j: 4 ^Developed, e.g., parks, cemeteries, schools
(assume PD, =0)
Pollutants: j = 1 BOD , Total .. .-
i = 2 Suspended Solids CSS)
j = 3 Volatile Solids, Total (VS)
j
j = 5 Total N
4 Total P04 (as P04)
Population.Function:
i = 1 f,(PD.) = 0.142 + 0.218 ..
i - 2,3 f,(PD°) =1.0
= 4 • • f> = 0.142 -
PD
0.54
0.442.
Pollutant,
Land Use, i
1. Residential 0.799
Separate 2. Commercial 3.20
Areas, a 3. Industrial 1.21
4. Other 0.1J-J
1, Residential 3.29
Combined 2. Commercial 13.2
Areas, 6 3. Industrial 5.00
4. Other O.4b/
67.2 38.9 0.139
91.8 57.9 0.312
120.0 59.2 0.291
11.1 10.8 0.0411
3. VS 4, P04 5. N
16 3 9.45 0.0336 0.131
22 2 14.0 0.0757 0.296
29 1 14 3 0.0705 0.277
2 70 2.6 0.00994 0.0605
0.540
1.22
1.1*
0.250
-, a function of street sweeping interval,
Street Sweeping: Factor Y is a function
' Ng, (days):
{a. 720 if o <_ N^ i
1.0 if N >: 20 d
<_'N <^ 20 days „ ,;
days
155
-------
Table V-9. COMPARISON OF BOD LOADINGS
Assume residential land use; PD = 10 persons/acre (24.7
persons/ha), P = 30 in./yr (76 cm/yr), and y = 1.
Separate Areas
Combined Areas
Dry Weather
DWF at 85% Treatment3
Ib/ac-yr
21
88
621
93
kg/ha-yr
24
99
697
105
Assuming 0.17 Ib-BOD/persons-day (0.08 kg-BOD/person-day)
(V-30)
The land use distribution fractions, w±, are given b.low from Table III-3:
Land Use
Fraction, w.
1
2
3
4
Residential
Commercial
Industrial
Open
-f-.
0.584
0.086
0.148
0.182
1.000
156
-------
When equation V-30 is applied to BOD loadings for separate areas, the
result is
M = 0.42 - P • (0.142 + 0.218
+ 0.46P
(V-31)
where
M = average annual BOD loadings over four land uses,
Ib-BOD/ac-yr,
P = annual precipitation, in./yr, and
PD = developed population density, persons/acre.
d
For application to combined areas, the result is
M
1.73 • P - (0.142+ 0.218 PDd°'54) + 1.9P.
(V-32)
These composite equations may easily be applied over the selected areas.
STresulL are shown "for combined, storm (separate) unsewered (using
loadings for separate areas) and total land areas in Table J^0' J^
wither BOD Loadings. BOD loadings are the only ones computed for the
sake of brevity. Values for other pollutants may be easily calculated
using Table V-8 and equation V-30.
Dry-weather flow loadings are computed simply -
density assuming average annual BOD generation of
(0.08 kg/person-day). Thus, BOD loadings are
.17 Ib/person day
(V-33)
where
average annual dry-weather flow BOD loading,
Ib-BOD/ac-yr. ,
However, this refinement was not included in the assessment.
•Values calculated using equation V-33 are presented in Table 7-11, -
weather BOD Loadings. As would be expected, the. most heavily ^a
Ireas! USEPA regions 2 and 3, have the highest wet-and dry-weather BOD
loadings. It may be also .noted that for the same population density,
areas with a large component of combined sewers produce higher loads.
157
-------
WET-WEATHEP BOD
URBANIZED AREA
TABLE V-10
I I I
IEPAISTATEI
I REG I ID |
I ""l I ""CT"| BRIDGEPORT '
1 1 CT (BRISTOL
1 CT IDANBURY
HARTFORD
MERIDEN
NEW BRITAIN
•NFW HAVEN
INORW/
II
it
i!
11
11
LOADINGS
iIK^"! ?FJow!*THER Bno i
£Hcl:-' fLBS/ACRE-YEAR)
..I 2!C I cnMR'8TORM|i|NSEW| AVER I
"! "|7o j 15777 "3&791 "307e "r:""'
38.7' ~-
.
30.4
2
2
CT INORWALK
CT I STAMFORD
CT IWATERBURY
CT OTHER URBAN AREAS
..El |AVE- FOR STATE
.
33.
ME
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
I
.-!.-.:Zu!*:''i ^7.81 31.si 56.
i tin « i 7c5T i ™T"7 . TTT ! T?"
-I
URBAN AREAS
FOR STATE
I BOSTON
BROCKTON
FALL RIVEt
TCHBUR.G
. .
I..... | .....
I 3.0ll6tt.-j
|F
I LOWELL
I NEW BEDFORD
IPTTTSFIELD
I SPRINGFIELD
[WORCESTER
OTHFR URBAN AREAS
0.01
MA IAVE.* FOR STATF
NH
NH
I?
RI
RI
VT
IOTHER URBAN AREAS
IAVE." FOR STATF
!-••—---«---.-.»....
I PROVIDENCE
IOTHER URBAN AREAS
JAVE.* FOR STATE.
' iUPBAN'AREAS~~""""~"
IAVE.' FOR STATE
JAVEr"FOR~REGinN~~~r'
'IATLANTIC'CITY"
(NEW YORK CITY METRO
A METRO
.
fl5.
I 16.01177.0
j 03. 61 152:9
I- 13.61152.9
(.....)..._.
J fO.01138.5
12.0I136>
I
36.01
38 .'I!
39:71
39.7 I
..... (.,
0.01
§:§!
0.0
32.11102.«
'js"?rii"f'
!?:"! I lilfl
ji:
28:
30.8
11:1
33?:I
56.9
31.61 56.9)
29*5 i "9377!
33.2
1.0
31.6
I.10.oll5?.9 Sa.ft! 28.8
, ( . o7oi""""~
I 35.01117.7) o.o!
["ir7r!"l976J"3877J
!~iI7o!'
31.0 I 91.0 |
— --• | ...__ i
28. 81 59. t
28.81 59:iJ
NJ
NJ
»••*•
NY
NY
NY
NY
NY
NY
NY
NY
NY
VINELAND
AVE.' FOR STATE
12.01 0.0
I 1|.0lt99:»|
i i?:oi 0:0
i 11.01 o:n
I-12.81160.1
I 36:o!l39:s
-!2:!'
27.9i"6875!
27.9! 68.5!
~627?!
ll-Ii
i||:lj
—«...
?9.6
I?:?
30.1
'Iz72l
J103.6J
JOTHFR URBAN AREAS
JAVE.' FOR STATE
|AVE7"FOR"REGION"""2"
37.'a I
39.2!
«....11
31.3
I 3«:i!i9o:'| 11:?! C0.3, ,,.v,
.38.11190.8 M.l! 26.s! 95.9 ('
j"lo75!T8778J"lo7T!"2972!"6p!
158
-------
TABLE V-10|
!EPA I STATE I
WET-WEATHER
URBANIZED AREA
LOADINGS
I1N/YRI
I»NNL.I
FCPI
WET-WEATHER BOO
CIRS/ACRE-YEAR5
. . .
•
So I OTHER URBAN AREAS
IHARRISBURG
54.21
32.71
45.1
ISCRANTON
WIUKES-BARRE
URBAN APEAS
FOR STATE
. . .
PETERSBURG
VA I RICHMOND
iB:»,
125.71
3?.3l04.1
31
..3.i.
31
i!
1!
I
i.»..
V ** in****'11*-"'*'
V*A ISl^lrON DC METRO
VA IOTHFR URBAN AREAS
VA IAVE." POR.f.J.*!!;
"wv"I CHARLESTON
SVV MMta' METRP
wv" j OTHER URBAN AREAS
FOR STATE,
mro. _
... i...». i -.«•••«——•«•"••"••'•
159
-------
TABLE V-10
IEPAISTATEI1
IRFGI IP I
WET-WEATHER BOO
URBANI7ER AREA
WET-WEATHER BOD
I
I
I
I
I,
I
I
I
I
I
I
I
I
I
41
41
41
41
41
I
41
41
41
41
41
ai
4!
41
41
41
41
41
41
AL IGADSDEN
AL IHUNTSVILLE
AL (MOBILE
AL MONTGOMERY
AL (OTHER URBAN AREAS
.*-.!!!!• FOR STATe
"pLV^LAUDERDArE
a IL...
FL (ORLANDO
PL PF.NSACOLA
R ITf,p?,t?5§3y»6
LOADINGS
IIN/YP.I
UNNL.l
^ IPKCC PI wW-T-I i o i i.rpcn
"'"II"o i ""o"!"""""
SI*8! 2*"
52.01 0.0
I 54*01
! 11*2! °'<('! *'«:
I 55.P| o.O ''
.^!-H:!L.2:Hl.*!:l}.fi-6i "u.ai
(JACKS!
(MIAMI
FL
FL
3.01 0.0
[1:8! 8:8
bO.OI 0^
51.0(190.0
i|.0 0.0
0*0
0.0
*
12:11 ":t'
_ «} FL AVE. FOR STATE [ 56 siJ90 0
i "4i""5A" ALBANY""""""""' !-rs--'—--
I 41 BA !Vi»M?A 48.01!52.0
41
41
41
41
41
41
41
I
41
GA
GA
GA
GA
GA
GA
GA
I AUGUSTA
I COLUMBUS
JMACON
(SAVANNAH
IOTHER URBAN ARFAS
i ? 7 " ii ' ' l f » V I -3*4 » C \ ** *> E
I 56.5H90.0I Sllol 40T>
51.01 40.fl
•————|mmmmm
li 0.0
M 35:4,
H 31.4
.0U66I9
39.01129.?
49.0(172.8
41.31
49.0
45:9 |
45.9J
I---I-----j »..«• STATE
i yi /i * '
i 52:01195:2!
46.51159:71
|. 46.51159.7
-I -----|.....
.
8.51
«0i4 ISril SSlS
lAVE.' FOR STATE
OTHER URBAN AREAS
I 43.3!15^;2|
. 42.3I153.?
AVE. FOR STATE
5
'JACKSON
OTHFg URBAN APEAS
I. 54. 5 I
'"48701
40.5| 42.9|'
NC IASHFVILLE
NC (CHARLOTTE
NC (DURHAM T
N t
NC
!OTHER URBiVAPEAS
AVE; FOR STATE
46.
! 11:81
4466:§!
46.7l'
'GREENVILLE
IOTHER URBAN
AVE. FOR STATE.
m
34.71 48.fl|
I 48.3|66.?
41
41
I
TN
TN
TN
TN
NASHVILL
OTHER URBAN AREAS
[*VE:_FOR STATE
"
J-C
-------
TABLE V-10
}ERAISTATEJ
I RFC! ID I
WET-WEATHER BOP LOADINGS
URBANI7ET) AREA I ANN1'
METRO
IDECATUR
IJOLIET
PEORIA
IE i&WTOItf AREAS
IL IAVE.' POR_STATE j J5.0
L22:: 1.5S:!l-5ii*!---«!
AREAS
H
MI
Ml
I DETROIT
FLINT
(GRAND RAPIDS
IJACKSON
(KALAMAZUO
IMUSKEGON
t
AREA.S
MI IAVE. FOR STATE
.... I .«.—.--——----—
MN
MN
MN
MM
,MTNNEAP~_
I ROCHESTER
ILTS
mm •
8'
ioTHER'URBAN AREAS
MM IAVE.' FOR STATE
._ | ........-—------'
JH (AKRON
H (CANTON
OH ICINnlNl^
OH ICLEVELANI
OH ICOLUM8US
PH (DAYTON^
OH (HAMILTON
OH (LIMA
OH LORAIN
OH MANSFIELD
gS IMBlllWftE.
8S IY^ITOWN rA,
OH I OTHER URBAN AREAS
OH IAVE." FOR.^ItI-—
W?
W
W
W
W
W
Wl ioSHKOSH
S! jg^lf URBAN AREAS
WI IAVE. P0".^*.!^..
"""*"iAVE?"FOR"REGION
.....Immmmmmmmmmmmmmmmm
(GREEN BAY
-
.01126.6
.01147.1
5loil31.0
. . o.o
.01110.9
:8!
I 26.01 90.
Soloitsol
37.21125.3
,31 ii
«:4| 5!:t|.
. ,.^.7iu6.? juii-SLiSl-SiiM
"i"i"32T7it2fl7r'
».«|
23.5 52.81
161
-------
J4BLE
V-10
6
6
61
AR
AR
WET-WEATHER P0f> LOADINGS
URBANIZED AREA '1^*1
IPRECPI
i^INEUiLUFFK
IOTHFR URBAN AREAS
WET-WEATHER BOO
'LBS/ACRE-YEAR)
T3TORMiuNSEwi AVER i
rt n i it 7 I "«?•?*!? !
—tj..f2.j*VE- FOR STATE
i
•-6:"> ii:f| i?.:;i !!;H
..i.;^::i:^^:j|.f2:!j.27*?|1 aa-1'
I 60.n l n it ""co""!T i T.'!'.*— \ T?"™
L22:2
j 575 '5575 1
' -"• 1 m"mmm \ mm*mm |
3.62
-------
TABLE V-10
I I I
I EPAiSTATE|
FG i D i
WET WEATHER
IJRBANIZEO ARFA
I TN
IAN
WEATHER BO
I
I
7! IA
IA ICFDAR RAPIDS
fA (DAVENPORT
IA IDFS MOINES
TA IDUBIIQUF.
I* !SIOUX CITY
if
AREAS
FOR STATE
0.0 I
Ikl I
0.01
0*01
11
/I ±f* i * v c. • r wi* «•' ' •* * •-•
•"'r'KriKlNSAs'ciTY"""""" '
71 KS I.TQPFKA,
ARFA8 I
I 31.3M38.Ij
i'piiipli
1 *i:8iiz?:SI
7! KS IAVE.*
71
MO 1KAN8AS CITY
FIE
FPH
MO I SPRINGFIELD
MO IST.JO
AREAS
7!
71 MH ininf]
71 MO IAVE.* run a . - • -
..)...-. 1———————'
i LINCOLN
7! Kl jS'THFJR. URBAN AREAS
71 NE IAVE.* FOR STATF
...I.....I...«--.......-...-»•
7) IAVE. FOR REGION 7
si cn
\ |i CCS
i PI co UVE.'
1 "" I "MT" i BT[r ING
Ii MMT
fil MT IAVE
I 37.01 0.0
I 34.01127.6
I 35"oiio7*.7l
I 37.01127.0
I 36.81125.?!
I I - I
I. 36.81J25.?
.!_....)..-»!•
I 27.01 O.o
I 26.01 96.P
I 26.51 96.PI
I 26.5J 96.81
.i.....|..... I
I. 31.9M21.71
r||"T9lol""oTol
I 13.01 0.01
27.71 25
2?*3I 17
27.21 24
26.31 23
26.?! 23
.....| —
29.61 25
29.71 25
26.6 23
27.91 24
27.9J 24
"5176!~26
26.41 25
':?i"ii:ii
«i 37.?!
• — . ««• £ >
?S
:? 48.01
•* 85*2!
.4| 37.5
.41^37.51
28
&?l !?
23.8. t«
23.81 18
'27^0l"Ii
*16l8l
•oli!?;?1,
:?| H:!!
70.3!
'IITj!
tt 8 , 3 I
.51
:I|
i
.9
AREAS
AREAS
61
1 i
14.51 50.?l
.1 '—"n|
i i^Ioi ol
I 14.01 0.
I 14.01 O.flj
9
13
10
10
I
41.81
—,.....|
.01 50.21
"!"u7(5i
.81 1?.3I
III 11.51
.11 13.41
.11 11.*l
.ii n.6i
ii:oi
01
ND
B
so
...
UT
B?
UT
UT
"WY
WY
... i .....
... t ..... i
IAVE.' FOR STATE __
"
I 21.01.73.51
• .....|..... I
, 11:8! $8:1
12.01 10.51 11.3
"l779l"T6Tll"l5To
17:91 16.11 I'.O
17.91 l*1^1,.^:!''
a i
1!
ei
ei
e i
IAVE.' FOR 5T4T^<.._...
"""IE"""
CITY A
IOTHFR URBAN AREAS
I AVE . r wi\ w i - • -
_ | ...__. — — - — — -•
URBAN AREAS
VE."
i..—. i -
17.01
13*.OI
15.01
15.01
I.25.01 90.1
.....I.....
- - • o.o
0.0
0.0
o.o!
. 15.01. 0.0
..... I .....
15.01 0.0
.xis.o! o.o
.....I .....
. 17.41.64.?l
21.9J_18,
21.3
10.91 12.01
10.9| 12.01
13.4i 10.9 12.01
~mmmmImmmmmI mm*mm \
12.91 11.31 12.01
12.91! 11.3 12.01
.....|..... I ..... I
13.71 11.51 13.01
.....j.....j.....I
163
-------
TABLE V-10
WET -WEATHER 800
URBANIZED AREA
IEPAISTATEI
IREG1 ID I
I "5 j "ZK" j URBAN-AREAS "——
I 9} AK IAVE.' FOR STATE
"5 •":?• ZZZZT.ZZ
.
!M,YR
WL
91
91
91
91
AZ jPHOENIX"
A7 I TUCSON
AZ JOTHER URBAN AREAS
AZ AVE.' FOR STATE
iiiifi.1-1 WET-WEATHER ROD i
jpfepj COM^^^^F.NslwrAVER !
I "30" j rri" i 'ir9i \mir.?, \ "26:? I
.!-2£i£|iii:2j.£Ii°' 21*2' 26<5'
! 9:°! °-"! 7,'<>! *:«! "l?!
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
>«••• | •
91
91
91
"51-
'!
91
"9l"
...I.
101
...I.,
101
101
101
101
ft I SHI 5BftCI8cn
CA (SANTA BARBARA
£A §*NTA ROSA
CA
CA
CA
101
101
101
to!
...i.
101,
mmmmm
HI
HI
HI
"NV"
NV
.NV
.(
26.2
mmmmm
86.0
•....
0.0
0.0
•r vr • w i
20.61
i 20:.6!
I .....I
' ".51
:J[
I a.oi
i..-.. i
i 12.a!
.....i.
2-31
9.^51
lil.9)
-i-I.
lfl.8
BB.7J 12.sj
,0.0
0.0
,0.0
• ... ^ _
iiiifj-si .iiiflij
11.51
"TS'Ti
16..7I
16.71 18.fl
•••I
ir:
IIII i 5JiE*5!l!22 JHE"U
39,
Si!
30
30
"26
• •••i
"s!
01
01
:S.I
.3!
:sl
0.0 9.3|
"•"•JsTSl
fiiil
.3^.0!
"SS's!
31146.0
127.3
56^8
103.Pj 32:i|
103.9
1167"
'J 32.1
» I m~mm»m
I 29.4
;ui
-I
.....
J36.A
I——.,
I 30.51
I-——I
3.?!
>—. |
10.91
..._.|
8.51
8y5 I
mmmm*'
"|«7Ti'
12 "4
23§:f
29^6
mmmmm
%3
J5:H
«.9|
•---.I
i«.?!
»..--j
I:S{
.-!:"1
'3372!
60,?|
iwr
53.81
.....
||:|l.;
25*31 48.'
.....)....»I
.....|
25.91
——I
164
-------
TAP!. F V-ll
EPA i
RFGI
"11"
11
11
!!
11
11
11
1!
1 I
\
URBANI7EO ARFA
nRV-MEATHEP "OP
TT
T.T
CT
CT
CT
CT
CT
CT
CT
' I BRIDGEPORT
IB"I«TOL
IHARTFORD
IMEP.TREN
INF.W RPITATM
INFW HAVFN
INHRWAIK
IWATFRBURY
URRAN A"FAR
MF
I A V E J FOR S t A T F
i
42.
4?*.
4?.
45.
*2-
45.
44.
45.
46.
oZj
0.
01 8?3.l
01 O.I
01 O.I
01 744.1
Ol?277.(
01 ~ '
JOTHFRAURBAK APFAS
I AVE." FOR STATF
1 I
1 I
1 I
1 I
1 I
1 I
1 I
t I MA
MA
MA
MA
MA
MA
•MA
MA
MA
MA
MA
Mi
IFALI RIVER
.
INFW BEDFORD
43
"44'
43
43
43
71 836.1
I -I
71.836.1
..I....*!
01 638.1
465.1
519.1
5 19.'I
.01
^51
. SI
iOTHFR' URBAN APFA?
l|
t!
IgH
v| H
MM
• '— — •
PT
PT
PT
• « «• i
VT
IAVE.* FOR STATF
I — — — — — ——— — — —— — — *• — — — "'
IMANCHESTE0
' 43
45
45
46
41
40
41
44
45
I 46
I 43
43
__ .--
.01 9*2.1
O.I
7«9.l
7M.
637.1
8A4.I
699.1
O.I
II
..I
I!
1!
?l
IHTHFR IIRHAN
I ,
I AVE. FOR
.!_.-.---------------•
IPOOV/IOENCF
IOTHFR URBAN AREAS
I AWE.' FOR ?TATE
.\.mmmmm.mmmm.mm.mm.m.
(URBAN AREAS
VT I AVE.' FOR PTATF
""" " "
MJ
Nj
NJ
N J
21
2"
21
I ?l
21
MY
NY
MY
MY
IPMII AOFLPWIA
ITREMTON
I AVE.* FOR STATF
BK TITY
| )-H C. •* I * ' " " "
IRHCMESTEP
(SYRACUSE
IIITITA
IPTHFP IJRBAN
I AVE." FOR STATF
.01
.01
.01
."I
.11
.01
.01
.01 ...
. 0 M 0 1 1 .
.t>\ 704.1
.61 704."
a?
41
41
:S! S18:.
.M 560.,
.0 I 560 . I
mm-*Immmmm]•
769.I 296.I
I!?:! IS:
8P.3.I 278.I
779.I 240. I
702.I 342.1
744.I 315.I
276.1 275.1
745^1 309.1
773ll 291.1
736.1 2S8.I
736.'! 288.1
"""o^'rioiii
.1
mmm'|.
"9821!!"
770.1
789.1
761.1
O.I
664.1
699.1
788.1
O.I
685.1
931.1
.1
931.1
— — -I
O.I
O.I
o!i
I
.... =n
5167 I
466.1
428.1
564.1
436.1
552.1
546.1
503.1
508.1
521.1
531.1
"HI I
MH:
226^ I
296.1
291.1
244.1
362.1
252.1
344.1
238.1
480.1
81:'
444.
.1
544.1
431.1
493.1
527.1
5S9.I
439.1
480.1
526.1
554.1
I
281.1 554.1
!»:!
481.1
481.1
368.1
40.01 984."! 72i;j 261.' 54fe.l
"'""""" 509.1
35.01. 544."
O.'l 480.'I 509.1
42,
•44,
3«
36
32
38
ilU
01
_.l -4,-
lll 517.1
9ll 601.1
~Tl 607.1
'8:1 664:! in:* 409-.\
214.'! 520."
'n 1*87371"""
•SiiSSh 5661:;
0 38791 1730.1
• '!•**• * * » .. f~ ^ i
,011045.
•ol ?38:
.-----•—•
-
1
i
1
,
1045.1
749ll
740.1
3
I
ll
272
601.
588.
494.
1533.
3* 1 ?4?8.M383;i 272.11533.
--L-|«---nl-"~""''""'™""*'"'fo™
~7«"ei52Rii Ml?6-l 2?9.l 979.
165
-------
TABLE V-ll
EPA I STATE I
REG I ID I
ORY-WEATHEP BOD LOADINGS
URBANIZED ARFA
ITN/Y9I
IANKIL;I
FOR
31
11
31
"31"
31
31
31
31
31
!!
1!
31
31
31
31
31
31
31
31
31
3!
31
DC
DC
"MD"
MD
MD
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
< -»'»•- h. ^ |
JPRECPI
!~?I-°"'
I 45.01
j.45.0 I
^RV-WEATHEP BOO
:V*S'A"F-y;EAR5
760
760.'
760; 309
760.1 309?
'
760.' 309.'
. - _. i OR oiAir
'BALTIMORE •"•-•---
'WASHINGTON DC MFTRO
OTHFR URBAN AREAS
~ -•- — ^ -iv
ill:
IAVE,
STATE
JHARRISBURG
IWILKE3-BAPRE
JHTHFR URBAN AREAS
FOR STATF
VA
VA
VA
VA
VA
VA
VA
VA
I
IffillPSEI
VA
"wv"
WV
WV
WV
WV
3! wv
.«• I .....
31
-.(.....
'WASHINGTON DC METRO
[HTHFR URBAN AREAS
IAVE; FOR STATF
IHUNTINGTON
!5IlF.lKILLfr METRn
IOTHFR URBAN AREAS
JAVE." FOR STATE
! 7r!r
-------
TAPLE V-ll
FPASTATFI
61 ID 1
DRY-WEATHER ROD
MRBANI7EH AREA
LOADINGS
ITN/YPI
DRY-WEATHER BOO
a^S/AeRE-YEARl
IANML .
IPREgpl COM^ISTHRMIUNSFWI
....-.,
AVER
41 AL (BIRMINGHAM
41 AL IGAI5SOEN
41 AL IHMNTSVK.LE
41 AL (MOBILE
41 AL (MONTGOMERY
4i AL ITHSCALOOSA
41 AL (OTHER URBAN
I
APFAS
41 AL IAVE. FOR STATE
I 53vOI
55.01
52.01
68.01
5fi-8!
81
I
.
53.
5«5
ij|f:
(hi 753ll
Oil 611.1
O.I —~ '
330.
246.1
230.1
267.1
3«5.l
417.1
302.1
0.1 732.1 302.
41
41
41
41
IS!
4 I
41
41
ai
I
41
FL
FL
FL
FL
EL"
El
FL
(GAINESVILLE
(JACKSONVILLE
IMIA"!
(ORLANDO
IPENSACOLA
1ST.PETERSBURG
IT»LlAHASSEE
(TAMPA
(WEST PALM BEACH
(OTHER URBAN AREAS
IAVE." FOR STATF
60
52
53
60
01
"1
01
01
1 51,01
63.01.
55.01
57.01
62:oi
56.^1
56.51
0.
0.
0.
0,
882.1
612
691.1
758.1
794.1
I 779.1
275.1
351.1
240.1
304.1
2?9.l
499.1
429.1
426.1
445.1
513.1
466.1
473.1
I 473.1
I.....I
524.1
493.1
443.1
O.I
°o:!
882.1
8*2." I
•IB: I
756.1
612.1
_ J b. % I
!H:I
lll:\
4 I
41
4 I
4(
41
4 I
1
41
-.1 •
4 I
41
a!
41
41
GA
GA
GA
GA
G»
GA
GA
"KY'
KY
KY
KY
I AI BANY
I ATLANTA
(AUGUSTA
(COLUMBUS
IMACON
I SAVANNAH
(OTHER URBAN APFAS
IAVE.' FOR STATF
49
44
52
46
01
01
01
51
6«7.l
O.I
9S5.
609. 1
til* I
478'.
533.1
812."l 264. j 533.'!
393.1
512.1
504.1
469.1
500.1
504.1
4 99., I
.
^7.1
748.1
MO.
699. 1
, I
550^1
$06.1
'85.1
?49:i
Ul KY
IHUNTINGTOM
(LEXINGTON
l|_nUTSVILLF
IOWENSBORO
IOTHFR URtfAN AREAS
IAVE." FOR STATE
I. 46.51 609." I 699." I 349
44.0 I
41.01
44.01
42.31
I
0. 714.1
883.
69?. 1
747.
683.1
697,
637.1
->56.l
347:1
273.1
499.1
..—-I
493.1
593.1
562.1
621.1
566.1
42.31 747.
i...;... I. — *
41
ai
41
MS IBTLHXI
MS IJACKSON
MS IOTHER URBAN APFAS
! MS IAVE.' FOR STATE
58,
! li:
! 54,
01
0 1
51
51
0
0.
0.
I 637."i 273.'! 566.1
I _.__., I--.--(-«--
6831
-
352.
?3?-'
465.1
508.1
i 7t*:i 350:1
! 7l9.'i 330.1 490.
a i
ai
ai
a!
41
ai
41
41 -
41
ai
NC
NC
NT
MC
Nf
NC
MC
• NIC"
MC
MC
(CHARLOTTE
(DURHAM
IFAYFTTEVILLF
IGREENSBOR"
IHIGHPOINT
IRALFIGH
48.01
43.01
43.01
47.01
42.01
46.01
46.01
IWINSTON-SALEM
(OTHER URBAN APFAS
NC lAVE." FOR STATF
I 52.
I 47.
I 46,
41
41
41
41
I
4 |
8C
sc
1 CWASLESTOM
ICOLUMBIA
AREAS
IAVE." FOR STATF
47.
47.
46.
46.
46,
0 I
01
0 I
01
o r
01
,01
,7)
,71
§;.
§:!
0.
0.
0.
O.I
O.I
0.1
0.
0.
0
0
852.1
686.1
631.1
694.1
778.1
593.1
747.1
873.1
693.1
718.1
226.1
355:i
412.1
347:1
286.1
4?0.l
331.1
222.1
3«8;l
339.1
465.1
508.1
491.1
483.1
499.1
460.1
718.1 339,'i 487.
.1
.....
4 I
41
41
41
41
T
TN
45
48
'\
» ft I
, ft I
.01
.31
72o:!
76§:
755.
IKNOYVILLE
IMFMPHIS
TN (NASHVILLE
TM (OTHER URBAN AREAS
i | I
41 TN IAVE." FOR STATF J
———i———••' •••^™">™""™Tsrzr.™—™Ti™ iflST i"Ztt^
/i IAVE FOR REGION 4 I. u^.o! ooj.
... I —— I —1 —— — ———I -«.— !——
684.1
710.1
694.1
697.1
697.'I
878.1
609.1
785.1
358.1
346.1
347.
35i:i
488.1
491.1
483.1
488.1
I
351.1 486.1
"3T67i""rr"
343.1
255.1
253.1 .,_.
277^ 491.1
7*5." I 377'l.a*iil
"766?!"304?I"507.I
...._|—————|—————|
167
-------
TARI.F V-ll
I I I
IEPAISTATFI
RFGI in i
...i.._._i
51
si
si
Tl
TL IBI
II.
Ppn
APFA
si
^.,
5
II
II.
Tl
!!
IL
IDAVFMPDRT MPT or
UPLIFT
IPPOPI4
I
s?o:i
51 II
SI
SI
S I
SI
«; i
q i
51
SI
51
SI
SI
SI
SI
SI
si
51
I
SI
"il"
SI
51
51
51
I
51
"si"
SI
SI
51
SI
SI
51
51
51
51
51
TM
T?
T«J
I*
TK1
1^1
TM
IJ"
i>
T"
" *'"
PTMFR
UVE.' FOP
i AMDFRsrt"
ICHIPAGP
IFIRT
I H'DTANAPPI
IL4FAYETTE
.
I SOUTH
HTHFR URBAN APF1S
IAVF.' FHR 0{ '
775.'!
I
i -
!!
Hill
2J5.I 7so.'| 200.1
5"4.
---..|
MM IHTHFB URBAM
MM AVE." FOR "5TATF
51
51
SI
51
51
5 i
I!
SI
51
I
Si
-I
PH
PH
PH
PH
PH
f)H
PH
PH
PH
PH
OH
OH
PH
P.H
76R."| 771.' 257.
ANTOK!
TNCI^
ICLFVELAND
I DAYTON
IHAMTLTON
ILTMA
9"8
:
?7a. I
\A'\
IMANSFIELD
ISPRTMGFIEI o
ISTE'IBENVII LF
IT"LPDP
i YPUMGSTP//KI
IPThFR IIRBAW
IAVE.' FPR STATF
I APPLETON --
OMLllTH METRP
BAY
:
:
: »t
an. 01 6A5.
O.I
37. ? 778
806
696
01
l?|=
2U7.I
*
"1:!
571
! •
531.*!
531 .'I
WI
rtl
3?
rtT
*I
WI
i LA rpnssF
IMADTSON
IMTLWAUKEf
IRACTME
IPTHF.R URBAN APFAS
<- • . " I u i
27.01 666.
32.01 865
31.01 603;
ffllftI« ut<"
fl.OI
l«:
603*
5.1
IAVE. FOR STATF
IAVE? FOR'PEGIPM""?
I
29.7l13?a
5577 I
l 507
358.1
2s?:!
351.1
---- •
?70.l
5?fl. I
lot:!
lll:\
522. I
57l7l
168
-------
TABLE V-ll DRY-WEATHER BOO
IEPA!STATEI URBANIZED AREA
RFCi ID i
"*~61"" AR" i FORT'SMITH
61 AR ILITTLE ROCK
61 AR IPINC BLUFF •
61 AR IOTHFR URBAN AREAS
61 AR IAVE. FOR STATE
mmm [mm mm m\ mmmm mm mm <* *<*»*•****'*•'
LOADINGS
ITN/YSI
IANNL.I
Y-WEATHER; BOO
BS/ACR£>YEAR)
i 43.0
I 49.0
I 52,0
I 48,0
I, 48,0
I.
!
I
61
61
LA
LA.
L*
LA
LA
LA
LA
6
LAKE CHARLES
0.
9.
I
AREAS
NM
NM
NM.
M«a **p
OK
OK
OK
OK
.
AVE. FOR STATE
50.0
64.0
45.01
56,01
I
IUNSEW
ammmm
, 430.
ALBUQUERQUE
OTHER URBAN AREAS
AVE, FOR STATE
mmmmmmmmmm***"*m*"»mm'
LAWTON
OKLAHOMA CITY
OTHER URBAN AREAS
1,56.01 0.
i I tsfffftm \ •»«>»*«
J 9.0! 0.
9.01 0.
405.
I
961.
786*
786?
I:
352.
408.
0»
356.
352.
352.
AVER
430.
491.
524.
482.
482.*
mmm.mm
mi
III:
1167^
499.
749:
749.
287. 506.
287.1 506.
61 OK.IAVE, FOR STATE
TX
TX
TX
TX
TX
,«, | ww*l»«t I m mm mf ft"" *"<"»'>•">"• m"
61 TX IABILENE
61 TX IAMARILLO
TX (AUSTIN
TX IBEAUMONT
TX I BROWNSVILLE
IBRYAN
CORPUS CHRISTI
DALLAS
EL PASO
FORT WORTH
GALVESTON
HARLINGEN
TX HOUSTON
TX LAREDO
TX LUBBOCK
TX MCALLEN
TX IMIDLAND
TX IODESSA
TX IPORT ARTHUR
TX 1SAN ANGELD
TX ISAN ANTONIO
TX ISHERMAN
ITEXARKANA
ITEXAS CITY
TX ITYLER
"X IWACO
"X IWICHITA FALL „„_
"X IOTHER URBAN AREAS
AVE. FOR STATE-
61
61
61
61
61
6!
61
6
I
61
6
J
6
61
.61 TX
"6 I*"*""
AVE* FOR REGION.
540
602
704^
,35.3J 675,
169
-------
TABL
I 1
IRFGI
I ...I
71
71
71
71
71
71
71
I
7'
...I.
71
71
71
71
I
71
"*7I~
71
71
71
I
71
*7l'
71
71
I
7 I
.. I .
7J
"l"
A I
8 I
fll
A I
I
A I
'A I "
fll
fll
I
F V-ll
I
STATEI
jn I
^RY-WE'ATWFR
ARFA
JA
TA I
i* IDHBIIOUE
TA isjnnx CITV
I* IwATPRLOQ
TA friTHFR URBAN ARFAS
T* AVE. FOR STATF
K* JOTHFP. URBAN
*S IAVF. FOR STATF
---- ---| ....
tl n I n n fi B i _ _ ** * I
1-j; ;sT;.rosEPH"i;
Mp 1ST.I GUIS
MH JHTHFP IIRRAM APFAS
.--.It**' Fnn STATF
VF •' -------..-.•
I TKJ/YPI
'
ROD
33.01
3?.01
31.01
33.01
25.01
32.01
31.31-
I
i i ---_. _ - , v ,
_31.3lt763.! 612.! 299.'
"' 734.| 734.
752.1 752*1
.?•! s??:
tir IPTHFR I'RPAN' APFAS
^F IAVE. FOR STATF
en
en
en
33.01
P377oj'
I 36>l
.36.A|
I
299.
290.
l
AVER
i BHU'DER
81:1
Ilfl
465. 537.1
.--:!!_f!2*! f-53-1 260-' 5ai.!
A I
A I
I
Al
~8l'
A I
I
81
--I.
81
PI
Rl
fll
81
.-I.
PI
I
81
.- I.
ai
-i.,
r.n
"MT"
MT
""5"
"sn'
JOTHFP IJRBAVJ ARFAS
i AVE. FOR STATF
I RTLl'lMSS
I6BEAT FALI S
IOTHFR URBAN AREAS
!AVE- rnr? STATF
IFAPRH " '
IDTHFR IJRRAM
• •^•^•"1 * * e- ^ i t» ~i / 0 |
"|i:8l""l:riJi:l
; f:oft!i!f»:l S3}-!
I 11.SI1238.I 798:!
.--:!il"2-J.I98-'
Tiroi""""* "—--'•
290. 555.
I AVE. FOR
""
IOTHFR URBAN A"F,AS
FOR STATF
UT
IIT
i!T
2UOI
21. o!
-----I
25.0 i
25To
0.1 657.1 364.1 525.1
"Sj?:! 662! |"s;:J-5K-1
^»AP I f* t* 3 I TO/ "*"-»•!
662.1 662.1
563
I
759.1
759 | 759;i 328:.
--:"!.!!!;,.!!!•' 328-'
|:oi
' 15.0)
15.0|
15.01
"1570!'
.15.01
"1774]
07[ 79071
,__0;(l_790.l^271.|
O.I 6937l"3497l
0.! 693.1 349.
516.
••.•a
497.
aas;
5062
9ft8.|
506.
506.
170
-------
TARI E
I
V-ll
HRBANl7En ARFA
IHADINGS
ITM/Y9I
FGI
P W» t •* •
9 I
I
9 I
.. I -.
9 I
91
91
I
9 I
.- I-
9 I
9 I
91
91
9 I
91
91
91
9 I
9 I
91
91
91
91
9 I
91
9 I
I
91
... I .
91
91
I
91
..-1.
9 I
91
91
91
...I .
9.1 .
...I.
1 01
1.1 I
AK
AK
• m •* «
A7
A7
A7
A7
»«•«•!
CA
CA
TA
PA
CA
PA
CA
CA
CA
CA
fA
CA
mm — •
NT
WT
""v*
MV
MV
MV
IU&PAM AREAS
I
IAVE. FOR STATF
' IPMQFNIX
IT'ICSON
IOTHFR URBAN
IAVF. FDR «TATF
" IBAKFRSFIELD""
IS' ANGELFS
.
IPRFCP
7.01
ll.o
*.n\
, „ /_w£ *THER Pnn |
fLBS/ACRE-YEAP) . I
B I STOR^IUNSEW | AVER I
..„|——-|-----|-----|
I 227.1 466.1
I I I
... I 2?7. I 466. I
-.1i_---.,|----- I -----i
O.I 777.1 246.1 484.1
O.I 715.1 340.1 518.1
Oil 761.1 268.1 492.1
O.I 7M.I 268.1 492.1
.„_„)--_-.,|-----|-----|
inYMARD
ISACRAMENTP
iSALINAS
ISAN BFRNAMDTMP
ISAM DIFGH
ISAN FRANCTSCn
ISAN JOSE
ISANTA BARBARA
ISANTA ROSA
ISFASIDF
ISIMT VAI.LFY
I STOCKTON
IHTHER URBAN ARFAS
IAVE. FOR STATF
" I HONOLULU
IOTHFR URRJM AREAS
IAVE. FOR STATF
-, | _...... ..----.
ILAS VFGAS
I RFNn
IHTHFR URBAN AREAS
IAVE. FOR STATF
"IAVE^'FOR REGION
I 15:51 8:
I'M 8!...8:
BS-.I 1??:!
I-WI:I
!:
I 25.01 O.I
I 14.01 O.I
I 17.?H539.I
I I - I
I. 17.211539. I
"*"!""3loi~""o'"l
I 23.01 O.I
! 23.01 0.
.-- | -----|--•—•I
I 4.0 I 0.1
I 7.01 893.1
I 5.51 893.1
I I . !
I 354.1
. _,l 342.1
798.1 262.1
798.1 262.1
"""I"I
841.1
841.! 277.
.-.*-|«----|
80UI 258.1
66lZl 326.1
777.1 277.1
• * I
555ll
617.1
617.1
,-.— |
583.1
583.1
583. I
...-.)
469. I
508.1
I
inTHFR URBAN APFAS I
'""l"l6"9n507. I
'7~7"l 263.1 601.1
l' n"R 1 PORTLAND
tn I in IAVE. FOR STATF
'?"l
101
1 VI
1 -M
I
1«l
"lOl
OR'IOTHFR URBAN AREAS
PR IAVE. FOR STATE
"~A"|SFATTLE
WA lsisCDKAMt
WA
I. ll.oi O.I
.-- i ----- | -,--—« l
I 3*.0I 748,'
I 40.61 81U
40.01
1
0.
810.
I
" " " I I
657.1 405.1
....-(..---|<
748.1 306.1
811,1 276.1
690.1 352.1
780.1 288..I
526.1
— •*!
502.1
52
WA
URBAN ARFAS
in!
I
WA IAVE. FOR STATF
.... I ...„... —--------
1 39.31 810. 780.
0.
774.
785.
785.
I
1.
8.1
'
I 39.01 774.
I 30.31 703.
!.30.3! 703.
„-.|.---.|----•I
10 I 26.91 734.1
288.
wmmmm
15?:.
290.1
302.1
302.1 525.1
528..
*530~.l
527.1
504.1
525.1
I AVERAGE'EPR'THF""
:": I *33Ta ! TO""*
"5.o7:| "iss: i
"i"a.1
171
-------
ABBREVIATIONS AND SYMBOLS
i
AR
a
b
BOD
BOD5
3
C
COD
com
CR
i
i
e
DWF
Coefficient
Area of land use i .
Wet-weather runoff, inches per year
%£?£ Lo"-S>loadlns £actor f°r sep°r«* «»*-«-.
Coefficient
Biochemical oxygen demand
Biochemical oxygen demand at five days
loading factor for combined sewered areas
a'° 9
Concentration, mass pollutant per total mass or mg/1
Chemical oxygen demand
Abbreviation for commercial
Runoff coefficient
Pounds of accumulated dust and dirt (or "surface solids") on
land use i per curb-mile - dry day
•Dust and dirt loading for land use i, pounds per day
Street sweeping efficiency
tier flow and dry-weather flow runoff,
inches per year
o£ p°llutant
of pouutant
poiiutmt
function of
of
of
JSlutantl ^^ aUd d±rt ^ ^^ ^ ± that C°nSiStS °f
Length of curb per area of land use i, curb-miles
per acre
172
-------
Y
I
K
m
M
M
M
M
n
N
N.
D
P
P
s
PD
PDd
P04
r
res
Street sweeping factor
Imperviousness as a fraction or percent
Conversion factor
Coefficient
Pollutant loading, pounds per acre-year
Pollutant loading averaged over different land uses,
pounds per acre-year
Pollutant loading in combined sewered areas, pounds per
acre-year ,'-
Pollutant loading under dry weather conditions, pounds per
acre-year . . ,
Pollutant loading in separate sewered areas, pounds per
• acre-year
Number of storms per year, also number of times street swept
since last storm
Total nitrogen '
Number of dry days since last storm
Street sweeping interval, days
Precipitation rate, inches per year
Mass of pollutant on surface at end of previous storm, pounds
. Mass of pollutant j on surface of land use i at beginning of
storm, pounds
Precipitation depth during one storm, inches
Population density, persons per acre
Population density in developed area, persons per acre
Phosphate or total phosphate
Correlation coefficient
Abbreviation for residential
Water density, pounds per cubic foot
173
-------
ss
TOG
TPO,
TS
VS
w.
Suspended solids
Total organic carbon
Total phosphate
Total solids
Total volatile solids
Fraction of total area consisting ,0f land
use i
174
-------
REFERENCES
1. American Public Health Association, American Public Works Associ-
ation, Water Pollution Control Federation, Standard Methods for
the Examination of Water and Wastewater, 13th Edition, American
Public Health Association, Washington, DC, 1971.
2 Colston N. V., "Characterization and Treatment of Urban Land Run-
off," USEPA Report EPA-670/2-74-096, NTIS-PB 240 987, December 1974.
3. Metcalf and Eddy, Inc., University of Florida, and Water Resources
Engineers, Inc., "Storm Water Management Model, Volume I - Final
Report," USEPA Report 11024DOC07/71, NTIS-PB 203 289, September 1971.
4. Huber, W. C., Heaney, J. P., et al., "Storm Water Management Model
User's Manual Version II," USEPA Report EPA-670/2-75-017, March 1975.
5. Hydrologic Engineering Center, Corps of Engineers, "Urban Storm
Water Runoff: STORM," Generalized Computer Program 723-58-L2520,
May 1975.
6 Roesner, L. A., et al., "A Model for Evaluating Runoff-Quality in
Metropolitan Master Planning," ASCE Urban Water Resources Research,
Program,• Technical Memo No. 23, ASCE, New York, NY, April 1974.
7 APWA, "Water Pollution Aspects of Urban Runoff," USEPA Report
11030DNS01/69 (WP-20-15), NTIS-PB 215 532, January 1969.
8 Graham, P. H., Costello, L. S. and Mallon, H. J., "Estimation of
Imperviousness and Specific Curb Length for Forecasting St°rmwater _
Quality and Quantity," JWPCF, Vol. 46, No. 4, pp. 717-725, April 1974.
9 AVCO Economic Systems Corporation, "Storm Water Pollution from Urban
Land Activity," USEPA Report 11034FK07/70, NTIS-PB 195 281, July 1970.
10. Burgess and Niple, Ltd., '-'Stream Pollution, and Abatement from Com-
bined Sewer Overflows, Bucyrus, Ohio," USEPA Report 11024FKN11/69,
NTIS-PB 195,162, November 1969.
11. Black, Crow andEidsness, Inc., "Storm and Combined Sewe*
Sources and Abatement, Atlanta, Georgia," USEPA Report
NTIS-PB 201 725, January 1971.
12 Haves, Seay, Mattern and Mattern, "Engineering Investigation of
Sewer Overflow Problem," USEPA Report 11024DMS05/70, NTIS-PB 195 201,
May 1970.
13. Rex Chainbelt, Inc., "Screening/Flotation Treatment of Combined
' Sewer Overflows," USEPA Report 11020FDC01/72, January 1972.
175
-------
'
"'
"
18. Cornell, Rowland, Hayes and MerrvfiPlH w-rn r »* TT
Management Program Vol ™a Jafrry?lejd-Hill, Inc., "A Water Resources
2°' TaSn%J' A' TV"1"1' B- G-, "Urban Stormater Management and
eSSmen" "IS-PB
Survey Water Resources Division Trenton! £,1974?
, Ottawa, Ontario, April 1971
23. American Public Works Association and
of
and Ontario Miniatr
Report WPD 03-76-, Januar 19^?
Seminars,"
176
-------
SECTION VI
OVERALL COST ASSESSMENT
This section develops and applies a methodology to estimate the cost of
controlling pollution from urban storm-related discharges nationwide.
Costs of controlling combined sewer overflows, stormwater runoff, and/or
providing tertiary treatment are compared.
I
BACKGROUND
In 1967, the APWA conducted a survey to gain information related to wet-
weather pollution. 1 All urban communities with a population greater than
25,000 persons were involved. Results indicated that approximately $56
billion (1974 dollars) would be needed to complete separation of all
eSting combined sewers. An additional $34 billion (1974 Collars) in
plumbing changes on private property would be required to ef feet the
separation, These costs do not include any indirect costs brought about
by the disruption in a dense urban area which would occur if the
separation actually took place.
trolling wet-weather pollution could be assessed. Due to a lack of
^^
.
.and/or Control of Itormwater) of the 1974 Needs Survey were greater than
tSe cost of all other categories combined. Reported costs included con-
struction of storm sewers and elements of flood control.
The next part of this section presents a general methodology for deter-
- mining wet-weather pollution control costs. Then, a procedure is
'described for determining the relationship between storage, treatment,
and pollutant control for control of wet-weather flows. Generalized
predictive equations are developed based on relatively intensive studies
of five cities: Atlanta, Denver, Minneapolis, San Francisco, and
Washington. Knowing this "production function" one can determine _the
optima! combination of storage and treatment by combining this information
177
-------
assessment. Results are presented for all
1. urbanized areas in the US,
•»
2. states, and
3. USEPA regions.
METHODOLOGY
There are several economic theories which, when applied to environ-
O^f ^Hr^^^r^' aSS±St ±n thS decisi°n-making process.
One such theory is production theory, which provides techniques that
aid in evaluating items such as the optimal size of a reservoir for
water supply and flood control, or a wastewater treatment plan? for
pollution control. When the cost of inputs such as the reservoir
of oJtnuTf P I5 ^^ then the C°St °f achieving a desired level
of output (e.g., water supply or pollution control) may be determined.
In stormwater management, the inputs may be in the form of a storage
capacity and a treatment rate. Storage is expressed in terms of
Sini°L?ia T °Ji inCh<:S °Ver a Certaln area' typically the watershed
being analyzed. The unit for treatment is either million gallons per
day or inches per hour, using the same area as storage.
When the degree of wet-weather control is considered as a single
output, it can be expressed either in terms of the percent of the run-
tf aS^fS °ri number °f overflows per year. This is with respect
to quantity only and is therefore dependent upon the input storage
capacity and treatment rate.
* ±t: '±S feaslble to use a graphical
method to find the optimal combinations. Isoquants can be constructed
which represent equal levels of output for different combinations of
input (see Figure VI-1, Determination of Least-Cost Combination of
iBEgs). For example, each isoquant could represent a specific percent
of the runoff treated for different combinations of storage and treat-
ment. Isoquants have the following properties:3
1.
2.
Two isoquants cannot intersect. Intersecting
isoquants would imply two different levels of
output from the same input.
Isoquants slope downward and to the right
because as one input increases it takes less
of the other input to achieve the same level
of output.
178
-------
UNIT COST OF INPUT !
UNIT COST OF INPUT 2
ISOQUANT
EXPANSION RATH
ISOCOST LINE
INPUT 2
Figure VI-1. Determination of Least-Cost Combination, of
Inputs .
179
-------
3. Isoquants are convex to the origin because of
the decreasing ability of one input to be sub-
stxtuted for another to obtain a given level of
output. This is known as the principle of
diminishing marginal rate of substitution.
Also on Figure VI-1, a series of parallel lines has been constructed
These lines represent combinations of input 1 and input 2 which
to produce a desired level of output is the point whe?e the ifocost
output ^ n§ent t0 ^ 1S°qUant reP— nting the desired level of
isoquants
XS Called the expansion path. After the
determlned» the optimal combination of input
ass
th±S info™ati°»> the isocost line my be
minimize
subject to
where
Z =
•cs_ o
total control costs,
storage costs,
(VI-1)
c,j,(T) = treatment costs.
180
-------
s
T
R,
f(Ri;S,T)
= storage volume,
= treatment rate,
= percent pollutant control, and
= production function relating the level
of pollutant control (R.,) attainable
with specified availabilities of storage
(S) and treatment (T) .
The next three subsections describe
the available storage/treatment options - their costs
* and effectiveness;
the production functions for evaluating tradeoffs
* between storage and treatment; and
the solution to the optimization problem yielding the
* optimal expansion path for any city.
Given this information, the final assessment is presented.
CONTROL TECHNOLOGY AND ASSOCIATED COSTS
A wide variety of control alternatives are available for improving
the quality of wet-weather flows.4/5/6 Rooftops and parking lot
storage, surface and underground tanks and storage in treatment units
are the flow attenuation control alternatives. Wet-weather quality
control alternatives can be subdivided into two categories: primary
devices and secondary devices. Primary devices take advantage of
physical processes such as screening, settling and flotation. Secon-
dary devices take advantage of biological processes and physical-
chemical processes. These control devices are suitable for treating
stormwater runoff as well as combined sewer overflows. However, the
contact stabilization process is feasible only if the existing waste
treatment plant is of an activated sludge type. The quantities of wet-
weather flows that can be treated by this process are limited by the
amount of excess activated sludge available from the dry^weather plant.
At the present time, there are several installations throughout the US
designed to evaluate the effectiveness of various primary and secondary
devices. A summary of the design criteria and performance of these
devices is presented in Table VI-1, Wet~Weather Treatment Plant Per-
formance Data. Based on these data, the. representative performance
of primary devices is assumed to be 40 percent BOD removal efficiency
and that of secondary devices to be 85 percent BOD removal efficiency.
181
-------
Table VI-1. WET-WEATHER TREATMENT PLANT PERFORMANCE DATA
Device Control Alternatives
Primary Swirl Concentrator3'15
Microstrainer0
•
Dissolved Air Flotation
w/ Chemical Addition*3
Sedimentation6
Representative Performance
Secondary Contact Stabilizationf
Physical-Chemical8
Representative Performance
Reported
Design Criteria . BOD5 Removal
gpm/sq ft (1/min-m2) Efficiency, n
60.0 (2,448.0)
20.0 ( 816.0)
2.5 ( 102.0)
0.5 ( 20.4)
0.25 - 0.50
0.40 - 0.60
0.50 - 0.60
0.25 - 0.40
0.40
0.75 - 0.88
0.85 - 0.95
0.85
1, 1976.7
bSullivan, 1974.8
°Maher, 1974.9
jLager and Smith, 1974.5
S
Performance data based on domestic wastewater treatment.
fAgnew et al.. 1975.10
ff
Estimate based on performance of these units for domestic .wastewater.
182
-------
"Storage" devices will typically be used in conjunction with the
above "treatment" devices. ,The two purposes are interrelated.
Wastewater detained a sufficient time in a storage unit will undergo
.treatment. On the other hand, treatment units also functxon as
storage units in that they equalize fluctuations in influent flow and
concentration. DiToro presents approaches for evaluating the equali-
zation and treatment which occur in both of these units. The STORM ,
model, which was used in this assessment, assumes the configuration
for storage and treatment shown in Figure VI-2, Storage-Treatment
nnnflpiiratlon Used in STOEM Model. No treatment is assumed to occur
in storage and "treatment" is assumed to be complete removal of all
pollutants routed through treatment. Thus, for the, purposes of this
assessment, no treatment is assumed to occur in storage and control
costs are assigned accordingly. This assumption tends to underesti-
mate the costs of storage since all provisions for solids handling
are included in treatment.
Cost data for installed wet-weather treatment devices are listed .Ln
Shi P VT>2 Installed Costs for Wet-Wither Treatment Devices. - Since
quality control devices are presented in Table Vi.
leather Control Devices. These costs
eather Contro evces.
visions for sludge handling, engineerinJTcontingencies and land costs.
^•r^eS'. thlbreakeven pipe length, L, is found
Two plants One plant + pipeline
s(10)Z + s(10)Z = s(20)Z + K(10)y(D
where s, z, K and y = coefficients.
(VI-2)
183
-------
o
H
OT
0)
.3
g
•H
4J
Cfl
o
o
4J
0)
£?
O
-------
Table VI-2. INSTALLED COSTS FOR WET-WEATHER TREATMENT DEVICES
Annual Cost: $/yr
Control Device
Swirl Concentrator0
. d
Microstrainer
Dissolved' Air Flotation
Contact Stabilization8
Capacity
mgd (m3/d«y)
6.8 (26,400)
7.4 (28,700)
25.0 (96,900)
20.0 (77,500)
Amortized Capital3'
5,600
14,230
71,706
120,000
Operation and Maintenance
2,100.
3,895
16,700f
24,000
Total
7,700
18,125
88,406
144,000
aBased on 8 percent interest for 20 years.
instruction cost. Does not include sludge handling costs.
°Field, 1976. 7
r, 1974.9
8Agnew et al., 1975." Operation and maintenance costs based on 960 hours of operation.
185
-------
TSble .VI-3. COST FUNCTIONS FOR WET-WEATHER CONTROL DEVICES
Device
Secondary
Storage
Amortized Capital Operation and Maintenance
Total
Control Alternative
CA = IT"
or lSm
1 . m
OM = pTq
or sS
Primary
Swirl Concenttaforc>d'e
Micros trainer6'
Dissolved Air Flotation
Sedimentation3
1,971.0
7,343.8
8,161.4
32,634.7
0.70
0.76
0.84
0.70
f
493.0
1,836.0
2,036.7
8,157.8
q
0.70
0.76
, 0.84
0.70
s
2,464.0
9,179.8
10,198.1
40. 707. S
z
0.70
0.76
0.84
n 7n
Representative Primary Device Total Annual Cost ? $4.000 per mgd ($1.05/m3/day)
Contact Stabilization8
Physical-Chemical6
Representative Secondary Device Total Annual Cost = $15,000 per mgd ($3.93/m3/day)
.High Density (15 per/ac)
Low Density (5 per/ac)
Parking Loth
Rooftop'1
51,000.0
10,200.0
10,200.0
5,100.0
Tk - Wet-Weather Treatment Rate in mgd; s
Storage Volume in mil gal
lz
Benjes et al.. 1975. l
ftager and Smith, 1974. 5
*Haher, 1974. 8
gAgnew et al.'. 1975. 10
and Robbins, 1975. 13
.0T e5onomles of scale beyond ioo mgd, (378.500 m
- gross population density, persons/acre. <
J
e
.One agd « 3,785 m3/day.
One nil gal - 3,785 m3.
1.00
1.00
1.00
1.00
Representative Annual Storage Costj ($ per ac-in) - $122 e0
186
-------
Unfortunately, data on the number and flow rate of stormwater . •
discharges in urban areas could not be found. Thus, it is not
possible to determine the optimal mix of treatment plants and
pipelines. Therefore, :the representative treatment costs shown
in Table VI-3 were based on using relatively small plant sizes.
Cost data on detention basins built in the Chicago'area for tern-
porary storage of runoff are listed in Table VI-4, Capital Cost of
Storage Facilities. Costs of storage tanks built for the purpose
of wet-weather quantity and quality control as well as for dry-
weather quantity control are also included in this table. Due to
the wide variations in these figures, an attempt was made to verify
these costs using excavation costs as the &asis. St°raSe «>s
based on unit excavation costs are listed in Table VI-4.
cost of equalization and the estimated costs of rooftop and
lot storage basins for sewage treatment plants are also shown m^
Table VI-4. Lastly, analysis of recent estimates of storage costs
developed by Benjes et al. indicates the following unamortized capi-
tal cost C ($ x 106) as a function of storage volume, S (mil gal):
Unit Cost @ S =10 mil gal
$/gal ($/liter)
Type
Earthen
Concrete w/o Cover
Concrete w/ Cover
Equation
C
C
C
= 0
= 0
= 0
.025
.350
.400
S0.73
S0.58
S0.79
$0.013 C$0.0034)
$0.133 ($0.0350)
$0.250 (.$0.0660)
The data indicate wide variation in the costs of storage. Thus, the
relatively simple relationship shown in Table VI-3 was used. Annual
storage costs are estimated as a function of gross. P°P^ion Density,
The curve was derived using an unamortized capital cost of $0.10 per
gallon ($0.026/liter) for PD = 5 persons per acre C12.4/ha) and $0.50
per gallon ($0.132/liter) for PD = 15 persons per acre (37.1/ha).
RELATIONSHIP BETWEEN STORAGE/TEEATMENT AND PERCENT POLLUTION CONTROL
Use of STORM
STORM was used to evaluate various storage/treatment options Jor «m-
trolling stormwater runoff pollution. This model assumes that the
study area can be characterized as a single catchment from which hourly
runoff is directed to storage .and treatment.
STORM uses a simplified rainfall/runoff relationship, neglects the
transport of water through the city and assumes a very s^a^ion-
ship between storage and treatment. However, these Ji^Ufxcations
are essential if one hopes to do a continuous simulation. The contin
simulation approach was used because no general concurrence exists
187
-------
Table VI-4. CAPITAL COST OF STORAGE FACILITIES*
.Capacity •
mil gal (1000 m3)
Capital Cost
$/gal (S/liter^
Storage Reservoirs
Hillside Park
Heritage Park
Oak Lawn
Middle Fork North Branch
Wilke-Kirchoff
Melv'ina Dutch
Oak Mill Park
Dolphin Park
Average
Storage Tankse
Cottage Farm, Boston0
Spring Creek, New Yorkc
Chippewa Falls, Wisconsin0
Humboldt Avenue, Milwaukee0
Seattle, Washington
Hhittier Narrow, Columbus0
Average
Based on Excavation Costsf
$2/cu yd ($2.62/cu m)
$5/cu yd ($6.54/cu a)
Equalization Basins for Dry
Weather Sewage Treatment
11.4 ( 43.1)
36.5 (138.0)
7.8 ( 29.5)
195.5 (740.0)
32.6 (123.0)
53.8 (204.0)
25.1 ( 95.0)
(204.0)
53.8
52.1 (19710)
1.3 ( 4".9)
10.0 ( 37.8)
2.8 ( 10.6)
4.0 ( 15.1)
32.0 (121.0)
4.0 ( 15.1)
9.0 ( 34.1)
0.01
0.01
0.02
0.02
0.03
0.03
0.02
0.01
(0.003)
(0.003)
(0.005)
(0.005)
(0.008)
(0.008)
(0.005)
(0.003)
Earthen
Earthen
Earthen
Earthen
Earthen
Earthen
Earthen
Earthen
Basin
Basin
Basin
Basin
Basin
Basin
Basin
Basin
0.019 (0.005)
5.21"
2.33
0.29
0.55
0.25
1.70
(1.38)
(0.62)
(0.08)
(0.14)
(0.07)
(0.45)
1.72 (0.45)
•0.01 (0.003)
0.025 (0.007)
Covered Cone. Tanks
Covered Cone; Tanks
Asphalt Paved Basin
Covered Cone. Tanks
In-line
Open Concrete Tanks
Earthen Basin
Earthen Basin in Rock
PlantsS
h
Other
Parking Lots
Rooftops
i
3
io
i
3
10
.0
.0
.0
.0
.0
.0
( 3.
(11.
(37.
( 3.
(11.
(37.
8)
4)
8)
8)
4)
8)
0.22
0.10
0.
0.
0.
0.
0.
0.
06
39
28
25
10
05
(0.
(0.
(0.
(0.
(0.
(0.
(0.
(0.
06)
03)
02)
10)
07)
07)
03)
02)
Earthen
'Earthen
Earthen
Concrete
Concrete
Concrete
Basin
Basin
Basin
Basin
Basin
Basin
Source: Metropolitan Sanitary District of Greater Chicago.
Also used for stormwater treatment.
Includes pumping station, chlorination and outfall facilities.
eSource: Lager and Smith, 1974.5
Soil Conservation Service, Gainesville, FL
8Source: Anon., "Flow Equalization-Plus for Wastewater Treatment Plants'"
Civil Engineering, Vol. 45, No. 9, September 1975, pp. 66-68.21
Source: Wiswall and Robbins, 1975.13
188
-------
of pollutants discharged to the receiving waters.
As described in the User's
on the composite runoff cof ^^^fore the. runoff coefficient
The depression storage .^f J^J^he Amount of depression storage
is applied to the precxpxtatxon. . T^amoun,t o^ P faction of
runoff is sent to storage. If ^e^unot^ occurs. when runoff falls
to exceed the storage "P^^Hs depleted at the excess treat-
below the treatment rate then storage xs aep d runof£ and
ment rate. The hourly °^^£t£~*f tS entire record of rain-
runoff that has overflowed is tabulatea ror overflow events
fall, included in the outpus the annu^n^ber^ ^^
for different storage capacxties
and treatment rates.
tion III). The percent imperviousness and ^8thoJt^SggStantowski:8
Were found by their relationship to^ P°P^^ndgSr ^ lejgth density
equation for imperviousness and APWA a e^ion to evaporation
(Volume IIS described in the two praams J^xouB; ^^^.16
rates for each month are from a report by Jhornthwa
The depression storage ; is assumed to be 0 01 inches ^
all cities. A summary of xnput data f or all ot tn ^ data .
in Table VI-5, STOEMInput Data f or S^^gM^^^^
for the study areas are shown xn laoxe vx o, r^^^ - fi - __
Areas.
Hourly precipitation data were »^y!£v y (ary SS
ser,lce in Ashevil le Morg Carol^ ^Sd*™ the five test cities.
£,D3r££S)7£r«Sy "70 to Bece?er 1972, of data »ere
obtained for all stations in the United States.
189
-------
Table VI-5. STORM INPUT DATA FOR STUDY AREAS
Study Area:
Area:
Depression Storage:
Atlanta
278,400 ac (112,800 ha)
0.01 in. (.0.025 cm)
n «, e^aP°ration rates for each month, Jan-Dec, in in/day (cm/day)
.0.01 0.02 0.04 0.07 0.10 0.11 0.10 0.08 0.06 0.04 0020 01
(0.03) (0.05) (0.10) (0.18) (0.25) (0.28) (0.25) (0.20) (.0.15) (0.10) (0.05) (003)
Study Arear
Area:
Depression Storage:
Denver
187,500 ac (75,900 ha)
0.01 in. (0.025 cm)
Daily evaporation rates for each month, Jan-Dec, in in/day (cm/dav^
0'° O'O 0.01 0.02 0.04 0.07 0.09 0.08 6.06 005Co3 0 01
(0.0) (0.0) (0.03) (0.05) (0.10) (0.18) (0.23) (0.20) (0.15) (0.13) (0.08) (O.'S)
Study Area:
Area:
Depression Storage:
Minneapolis
461,400 ac (186,700 ha)
0.01 in. (0.025 cm)
Daily evaporation rates for each month, Jan-Dec, in in/day (cm/day)
Study Area;
Area:
Depression Storage:
Study Area:
Area:
Depression Storage:
San Francisco
435,800 ac (176,400 ha)
0.01 in. (0.025 cm)
Washington, DC
316,800 ac (128,200 ha)
0.01 in. (0.025 cm)
190
-------
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w
<1
>-<
\=>
H
W
p*
O
1*4
^
^
3
4«J
CO
o1 o" o o o
,-j cs i~< oo i~»
^ ^ VO f} °2
s d. ^ ci • .s
'
00 CTl O l~^ ^
"~j "1 "^ .
VO IT) O t^>
iH •-< i-H
r-, oo t~* os o
CO CO CO CO -5T
o o o o o
cy> -* CO CTi CTi
CM CO CM CO CO
0 0 Op O '
X~N /^N
o" S >* «£> o
. • • • •
CO 00 -* i-H O
_4 cri r^. vO T— 1
In "-*
^^^^
5 S CM ^ °
• • • • *
^] rH CM CM •*
(y, O I-H ' r-. 0%
VO VO I-* ^O ^
°* "^ 2 rH H
r-1 I—I
O
o o
o
CO CO «
•H -H 0
i— ) cj o
0 0 W
nj PJ TO oo
*J jjj S fe -S
5 t»i l"^ i~t
^ ? c 0 CO
1j S 'rl TO TO
<1 O 53 CO I3B
: 0
0
rH
H
O
Oi
D
0
O CO
H -H
>>. CO
H rH
1 .TO
O TO
iT> P5
rH 0
• H
O CO
ii a
o
Pd M
O 1*1
rt ,fl
191
-------
°f
apparent
tO ada1uate1!' determine
the effectiveness of
'
for each city was expensive and time
consuming
STORM RESULTS
1. treatment efficiency, and
2. variable concentration due to first flush effects.
Adjustment for Treatment Efficienr.v
192
-------
200
.2
cm / hr
.3
.4
.5
MINNEAPOLIS
WASHINGTON, D. C.
ATLANTA
DENVER
SAN FRANCISCO
Figure VI-3
.06 .08 .10 .12
RAINFALL, in/ hr
Average Twenty-Five Year Rainfall
Duration for Each Study Area
193
-------
cm / hr
SAN FRANCISCO
WASHINGTON, D. C
0 .02 .04 .06 .08 JO .12 .14 .16 :i8 .20
RAINFALL, in/hr
Figure VI-4. Selected One-Year Rainfall -Duration for
Each Study Area
194
-------
10
J M M 0 S N
-24
716 E ATLANTA
-8
0
o
10
5
0
n
n
J M M J S N
-24
-16
8
O
I SAN FRANCISCO
10
5
J M M J S N
-24
-16
-8
0
| DENVER
a:
10
5
0
J M M JSN
-24
^-8 *
0
WASHINGTON
-24 ;" ' ' •' \'
~I6E MINNEAPOLIS
-8°
J M M J S N
MONTH
FigureVI-5. Monthly Rainfall Distribution for
Study Year for Each Study Area
195
-------
treatment efficiencies, in terms of BOD, removal, were derived for
primary and secondary devices. These vllues are as follows!
Treatment Device
Primary
Secondary
Assumed Efficiency, n
(BOD5 Removal)
°-40
0.85
deS±reS 25 percent BOD5 remov*l with a primary device
percent of the runoff volume must be processed wherels only
6 rUn '-
thonfio
then 62
is'seltctef hus t e
is selected. Thus, to convert percent runoff control isoauant^ m
percent pollutant control isoquants, one uses ^quants to
Rl
R " — • 0 <. R < 100
11 — —
(VI-3)
Adjustment for First Flush
remaxning and that a uniform runoff of one-half inch, per hour would
wash away 90 percent of the pollutant in one hour." If a first flush
is assumed, then storage and treatment can be operated more ef Ac-
tively because of the greater relative importance of capturing the
initial runoff. The first flush is accounted for by defining tfuTout-
put in terms of pollutant control directly. aermxng the out-
Mathematical Representation of Isoquants:
The storage/treatment isoquants are of the form: ;
,-KS
where
T = Tl'+ (T2 ~
T = wet-weather treatment rate, inches per hour,
Tl " treatment rate at which isoquant becomes
asymptotic to the -ordihat'e, inches per
hour, -
T2 = treatment rate at which isoquant intersects ',
the abscissa, inches per hour,
(Vl-4)
196
-------
S = storage volume, inches, and
K = constant, inch. .
A relatively large storage reservoir is required to operate the
treatment unit continuously. Thus, first flush effects would be
dampened out and the effluent concentration from the reservoir
should be relatively uniform. Thus, if sfcormwater entering the
treatment plant has a relatively uniform concentration, then T^
can be found as follows (using 8,760 hours per year):
(VI-5)
where AR = annual runoff, inches per year, and ' . :
R = percent runoff control.
By relating the parameters T, , T -^ and K to the level of control
R, one equation was developed for each of the five cities. The
T -T and K terms versus R were found to be of the following general
•Fr*i*m *
hR
form:
T - T =
12 1
K
de
(VI-7)
Based on this analysis the following general equation for the isbquants
is obtained:
aR + be
—•FT?
hR-(de r )S
(VI-8)
The values of parameters a, b, h, d and f for various cities are
presented in Table VI-7, Values of Parameters and Correlation Coeffi-
cients for Isoquant .Equations for Percent BOD Control Without First Flush
and Table VI-8? Values of Parameters and Correlation Coefficients for
Isoquant Equations for Percent BOD Control With First Flush. The
correlation coefficients: for the, equations, for the four cities are also
shown in these tables. In general, the fits are excellent.
The results for the five cities are shown in Figures VI-6, Storage-
Treatment Isoquants for Percent BOD Removal with First Flush - Region I -
San Francisco. VI-7, Storage-Treatment Isoauants for Percent BOD Removal
with First Flush - Region II - Denver. VI-8, Storage-Treatment Isoquants
for Percent BOD Removal with First Flush - Region III - Minneapolis,
VI-9, Storage-Treatment Isoquants for Percent BOD Removal with First
Flush - Region IV - Atlanta, and VI-10, Storage-Treatment Isoquants
for Percent BOD Removal with First Flush - Region V - Washington. DC.
Each figure shows the isoquants calculated by the isoquant equation.
Also shown are some actual -data points for a treatment rate of 0.01
inches per hour and varying amounts of storage.
197
-------
Table VI-7. VALUES OF PARAMETERS AND CORRELATION COEFFICIENTS
FOR ISOQUANT EQUATIONS FOR PERCENT, BOD CONTROL
WITHOUT FIRST FLUSH
Note: Values are for developed portion of test
cities and for n = 1.0.
a b
in.hr^a R)"1 in.hr"1
Test City
San Francisco
Denver :
Minneapolis
Atlanta
Washington, DC
(cm hr" )
0.0000107
(0.0000272)
0.0000064
(0.0000163)
0.0000120
(0.0000305)
0.0000185
(0.0000470)
0.0000197
(0.0000500)
(cm hr"1)
0.0021466
(0.0054524)
0.0012194
(0.0030973)
0.0012909
(0.0032789)
0.0022832
(0.0057993)
0.0020464
(0.0051979)
h • ' d f Correlation
(% R)"1 : in."1 (% R)-1 T.-T =becR
— 1 2 !
(cm X)
0.0377090 108.5330 0.0335173 0.9821
(275.6738)
0.0397305 119.8106 0.0279204 0.9931
(304.3189)
0.0487845 191.2782 0.0322136 0.9963
.(485.8466)
0.0486532 112.2002 0.0348027 0.9905
.(284.9885)
0.0567454 117.5456 0.0398007 0.9925
• (298.5650)
Coefficient
K=de"fR
-0.9888
-0.9791
-0.9913
-0.9712
: -0.9759
Table VI-8. VALUES OF PARAMETERS AND CORRELATION COEFFICIENTS
FOR ISOQUANT EQUATIONS FOR PERCENT BOD CONTROL
WITH FIRST FLUSH
Note: Values are for developed portion of test
cities and for n = 1.0.
Test City
San Francisco
Denver
Minneapolis
Atlanta
Washington, DC
a
in. hr~1(% R)"1
(cm hr"1)
0.0000107
(O; 0000271)
0.0000064
(0.0000162)
• 0.0000120
(0.0000304)
0.0000185
(0.0000469)
0.0000197
(0.0000500)
'•* i
b
in.hr"1
(cm hr"1)
0.0021654
(0.0055001)
0.0013631
(0.0034622)
0.0013656
(0.0034686)
0.0025864
(0.0065694)
0.0018959
(0.0048155)
. .- .
h
(% R)"1
0.0388910
6.0439822
0.0481981
0.0468175
0.0487876
d
in:1
(cm )
211.2763
(536.6418)
184.9639
(469.8083)
241.6141
(613.6998)
190.2240
(483.1690)
228.8434
(581.2.622)
f
(% R)"1
0/0320226
0.0279177
0.0301648
0.0312484
0.0339322
Correlation
2 1
0.9893
0.9903
0.9956
0.9857
0.9933
— — -^— — ^— ^— __
Coefficient
V A ~fR
K»de
-0.9898
-0.9926
-0.9958
-0.9899
-0.9896
198
-------
.00
.90
.80-
.70-
.60-
.50-
V)
..40H
LJ
CC
T, cm/hr
.01 02
T, cm/hr
000 004 .008 .012 .016 .020 -
• *. '. !'• .*!
.15-
-000
002 .DO 4 .006
T, in/hr
.010
TREATMENT ,T, in/hr
,020
- 1.2
- 1.0
ANNUAL RUNOFF = 9.37in.
.030
Figure VI-6.
Storage-Treatment Isoquants for Percent B'OD Removal , • '
with First Flush - Region I -: San,Francisco _
199
-------
.90
.00
.80-
.70-
.60-
.50-
.00-
.000
T, cm /hr
.01 .02
Tf cm/hr
.000 .094 .OQ8 .012 .016
.000
.002 .004 .006
T, in/hr
ANNUAL RUNOFF = 5.59 in.
.010
TREATMENT,!, in/hr
.020
.020
-.40
rl.2
k 1.0
-.80
Figure VI-7. Storage-Treatment Isoquants for Percent BOD Removal
with First Flush ^- Region II - Denver
200
-------
.00
.90
.70-
.60-
,50-
c
tO
T, cm/hr
,01 .02
T, cm/hr
.000
.15-
,004 .008
' —L
.016 ,020
' L
.000 .002 .004 .006
T, in/hr
ANNUAL RUNOFF = 10.5O in..
.00-
.000
.010
TREATMENT ,T, in/hr
.020
008
.00
ri.2
-1.0
.00
.030
Figure VI-8. Storage-Treatment Isoquants for Percent BOD Removal
with First Flush - Region III - Minneapolis
201
-------
.90
.00
.00
.000
.01
T, cm/hr
.02
T, cm/hr
.000 .004 .008 .012 .016 .020
002 .004 ,006
T, in/hr
ANNUAL RUNOFF = 16,18 in.
.010,
TREATMENT, T, in/hr
.020
.00
.030
Figure VI-9. Storage-Treatment Isoquants for Percent BOD Removal
with First Flush - Region- IV - Atlanta
..202
-------
T, cm/hr
T, cm/hr
.00
.90
.80-
.10
.004
.000
.01
.000 ,004 .008 ,012 .Oj6 .020
. 002 .004 .006
T, in/hr
ANNUAL RUNOFF - 17.22 in
.010
TREATMENT, T, in/hr
.020
.00
.030
Figure VI-10. Storage-Treatment Isoquants for Percent BOD Removal
with First Flush - Region V - Washington, DC
203
-------
(VI-9)
The optimal expansion path can be found using
CT
•cs
where cg = unit cost of storage,
CT = unit cost of treatment, and
MRSgT = marginal rate of substitution of
storage for treatment.
The values of cg and CT were presented in Table VI-3.
Analysis of the figures indicates -that if c /c < 25, then treatment
alone should be used. From Table VI-3,
°T CT
CS 122 e°-16(PD) '
For primary treatment, CT - $2,610/acr^nch. Thus, even at zero
population density, c /c - 21.4 so that the optimal policy is to
use treatment only. For secondary treatment, letting c /c = 25 and
knowing that c - $9,800/acre~lnch, yields ?
122 e
0.16CPD)
or
PD » 7.29 persons/acre.
If PD is higher than about 7.5, then the relative cost of storage is
such that it is again optimal to use treatment only. Using 7.5 per-
sons per acre as the cutoff, then some.of the 248 cities, would use
treatment only for the secondary; control level. The, remaining
cities would select a anix of storage and treatment.
It is simple to find the optimal expansion path graphically for the
five test cities. Unfortunately, these results need to be'extrapolated'
to the other 243 cities. It appeared that an analytical approach would
provide a more general and consistent procedure. Thus, the isoquant
parameters were adjusted based on the runoff in the city under consid-
eration relative to the reference city, i.e.,
204
-------
let AR. = annual runoff in city i; i = 1,2,...,248
AR = annual runoff in test city for region j;
j j = 1, 2, 3, 4, 5,
Then, the isoquant coefficients are :
AR.
(8.76 x 10 ).
AR.
CVI-10)
(VI-11)
(VI-12)
AR
' and
(VI-13)
CVI-14)
where a..,:b.., h.., d , and f.. are parameters for city i in region
j and b.-J h.^Jd.,^nd K are the0parameters for the test city in region
j. The-'tesl ciries are-1 denoted as follows: .;, •
j = City
1 San Francisco
2 Denver
3 Minneapolis
4 Atlanta
5 . Washington, J)C.
Wet-Weather Quality Control Optimization •,",.• •' '
The wet-weather optimization problem, assuming linear .costs, may be ,
stated as follows: . , .--.;-..-
minimize
Z = cgS
205
-------
subject to
- T1)e
T,S >_ p.
Solving this constrained optimization problem yields
S* = max I- In -
. •-.
- T^J, OJ
(VI-16)
where
and
S* * optimal amount of storage, inches,
(T2 ~
(VI-17)
where T* - optimal amount of treatment, inches per hour.
Note that T* is expressed as a function of'S*, so it is necessary to
find S* first. Knowing S* and T*, the optimal solution is
where
Z* = CgS* + cTT* , , ,.... (yi-18)
Z* = total annual cost for optimal solution, dollars
per acre. ,
Data needed to estimate T , T and K have already been presented in
the previous subsection.
o
For a primary device, c = $4,000/mgd = $2,610/a6t^:t]:lch « $1.05/m—.
riour dav
For storage cost,
cs($/acre-inch) = 122 e0-16(PD)
(VI-19)
where
PD
gross population density in persons per acre.
The above optimization procedure was programmed to generate curves,
e.g., Figure VI-11, Control.Costs for Primary and Secondary Units in
Storm Sewered Areas. Atlanta^ .<^m^ng p0^OT; r^lll1tnnt rcmo--cd
versus total annual costs for primary and secondary treatment in con-
junction with storage. Note that,, for wet-weather control, marginal
costs are increasing because of the disproportionately larger sized
control units needed to capture the less frequent larger runoff volumes,
206
-------
CO
tfl
0)
13
0)
O
4-J
C/3
CO
•u
•H
a
§
OT
3
O
M-l
CO
O
CJ
H tO
O 4J
•M CO
ti H
O 4J
O -<4
•
rH
M
0)
60
•H
e
m
'S1SOO 1VHNNV "1V101
207
-------
"
in
The^curves shown in Figure VI-11 were approximated by functions of the
Z* = ke
SR,
where
Z*
k,3
total annual cost for optimal solution,
dollars per acre,
parameters,
RX = percent pollutant removal, 0 <_ IL <_ R , and
Rj_ = maximum percent pollutant removal.
(VI-20)
Estimating Number of Overflow Event
the nUmber of ^erflow events per year
-
208
-------
TABI F vl-9 ANHHAI. CMMTPIH COST? - rnMBi*;Er» AREAS
EPAISTATEI itRBAMlTri ARE* 1 pEF 1 ' 'C'1£P8."
(PEG' ID 1 IfTYl k ' g 25%
1
:
:
!
1
1 CT IBRIOGEPnnt
I CT (BRISTOL
1 CT IDANBljPY
1 CT IHARTFOPD
I CT IMERIDE^
1 CT INFW PR1TAIN
1 CT INEW HAVF*'
1 CT INTRWALK
1 CT ISTA^FDpn
i CT I«ATERBURY
1 CT (OTHER URBAN /i?F*S
1 CT IAVE. FOP STA1F
1! ME ILEWlSTflN
1 ME (PORTLAND
1 MF (OTHER URBAN ATEAS
1 1 ME 1 AVF.. FOR STATF
11 MA IBOSTtiN
SI MA (BROCKTON
1 MA (FALL RIVE1!?
1 MA (FITCHBURT,
H MA (LAWRENCE
11 MA (LOWELL
11 MA INFw BERFOI'D
1 ! MA IFITTSrjEI r»
11 HA I SPRl^GFIF.I P
Jl nA ! WORCESTER
11 MA (OTHER URBAN AI^FAP
11 MA IAVE. FOR ?T*TF
1 I MH iMANCHESltr-
11 MH |NASHUA
H MM IOTHEP URBAN. AFFAS
i i
11. NH IAVE. FfjP STATE
11 RI IPROVIDENCF
it RI IOTHFR URBAN ARE*?
1 1 RT IAVE. For? STATF
VT IU&BAK: AREAS
i i
1 1 VT IAVE. FOR Sf*TF
1 1 i AVE. FOR RgPirw i
21 NJ IATLAMTIC CITY
21 Nj IK'EW YORK CITY MET!M.<
21 MJ 1 PHILADELPHIA MfTRI.i
21 NJ IVlNELAWD
21 MJ IAVE. FOR STATE
21 NY IAL3ANY
§1 MY |BIMGHAMPTnM
1 NY (BUFFALO
21 NY (NEW YORK CITY
81 NY 1 ROCHESTER
|l NY (SYRACUSE
21 NY IUTICA
21 MY (OTHER URBAN-APEAS
21 «IY IAVE. FOR STATF
21 IAVE. FOR BEGIMM 2
r
F,
I
l\
5
c
5
r,
5
5
5
5
' 5
5
5
olu i
o.u
10.65
0> 1
?9.6l
""n^o i
10.48
10.5^
10.56!
iilc
c.C i
»:<••
(>:c
i' : r, 4 ?> i
7.4? 0.04?
4.91. I n.041 I
5.69 (1.041
5.6° P. 041
7 Q "^
0 * 0
17131
9.t()
-------
TABIE e
"c"
DC
IflTHER ORHAM A--5i-:4;
lAVp. rnp STATF
I WASHING ri'lM.n i* ^
I - ...
IAVE. FOR .STATC
nr MFT'JO
MO IHTHFP ijR^Av
••ID IAVE. -FOR srArc
I AI.LEMTOUN
I Al T"riMA
I ERIE
IJOHMSTOHM
(LANCASTER
IPHH.AOEI PHJA
IPlTTSBURGH
1 READING
!if^ES-BAPRL
?A
PA
PA
tt
«
PA
PA
DA
OA
PA
OA
VA (.NEWPORT nr»'ti
VA IMDRFQLK ft
VA (PETERSBURG- *
VA I RICHMOND
VA IROANQKE
VA I WASHINGTON rc ...
V'A IOTHFR URPAV APfAp
VA IAVE. FOR RTATF
STATF
WV lUHEFflKG"11" "F1r"'
fV I OTHER UPRA\ AfFAp
_*'V JA^E. FPR STATE
IAVE. FOR'PECI"'?""";!"
>*»n<. I •*•••• — — — — •*««»«•.»-. — — _
CnNTRQI. COST
rS/ACRE)
* i S
5!
J 2,,,
;i 29.1
«4l
R«.
I I II
I2B."710.0571 1!9.!
. I— .,-.1 I—... |.
i! O.IJ IM.O I 0,1
» Oi'.' I'.'.O I Oil
i O.'J |n.r, I o.|
I . i i |
i O.M l".0 ' O.I
• I ----- I ----- I -----j.
jl11.51 In.0431 ^u.)
!' e.r?T!i).o42| 25!"
22.1
490.
""o7
C.
0.
p,
To"7
P . U 1 (' . 0
i'
'l 75!l
i
.-112:
5, «.
si o;
si o:
Ji!p;
! p,
.
isTi
24.,
I
51 6
I 6
J.*:
Ill
00|{i.C46l 44. i
lr» i d.ofln i""n . l'
0 Ki.O I O.I
0 I I-. r, | O.I
'.! 1 'i. 0 ! 0.!
24.1
0?H'.041 14.1
73 I''.040 I 10.1
33IC.042I 18.1
01'('.C«2I 17.I
01 I c.0421 17.!
2?l"7o48l""a7l'
i 9 jf 1(543 I
1°IH.04?| 24.1
0
«i
70
16%7:
60.
30.
27l
51.
48.
374
374.
_, 374.
""7 I 35467
>....(....«
O.I 0.
O.I 0.
0 . 0 .
O.I
"29471
0.
"4537
!Z2:| ill:
193. I 294.
34611 5351
II«:!"IT$:
203.1 311.
O.I 0.
447.1 7ii.
(447.l 711.
'"i71 "1277
0.1 0.
0. 0.
O.I 0.
254.1 390.
l«2.l 293.
415.1 646.
199.1 305.
199.1 305.
107. 162i
74. 110.
144.1 219.
136.1 206.
_136.'j 206.
"4157l"&767
210
-------
TABLE vT=9 ANM.-AL r.n\r«tjL CUSTS •
i i i
EPA (STATE! URBANIZED APFA IpfF
REGI 10 1 IrTY
ai AL IBIRMINGHAM
ai AL IG40SDEN
41 AL IHUNTSVIILE
41 AL (MOBILE
41 AL MONTGOMERY
ill AL ITUSCALCIPSA
41 AL (OTHER URBAN 4FEA?
II
41 AL IAVE. Fen STATE
tt! FI. IFT LAUDERHAI, F
al -PL (GAINESVILLE
41 FL IJACKSOMVILt t"
41 FL IMIA^J
41 FL (ORLANDO
41 FL IPENSACOI.A
41 FL IST.OETERSP.lmr;
41 FL ITALLAHAS3FE
41 FL IT*MPA
4' FL (WEST PAI.M BPAt>
41 FL'I OTHER L'KPAN! 4RFAS
1 1
41 FL IAVE. FOR PTATF
41 GA (ALBANY
41 G* (ATLANTA
41 GA (AUGUSTA
41 GA 1 CnL''MRiig
41 GA 1 n»ACnN
41 GA ISAVAWAH'
al GA IOTHF.R URBAN A&FAS
II
41 GA IAVE, FOR STATF
ai KY (HUNT INC TON «FT»"
41 KY ILEXl'iGTO'. STSIE
41 SC ICHARLEST'"1'"'
41 SC (CniUMBlA
41 SC I6REENVII I.F.
41 SC 1 OTHER URBAN A Rf .»..
U
.'1
4
14
4
'»
4
U
l-\
<\
i\
4
4
4
4
k
0.0
0.0
o.u
O.U
O.U
0.0
0.0,
0.0
o.'o
olo
18.99
0.0
O.d
O.U
0.0
0.0
IP. 99
li'.bl
l6!4?
10.4?
7.1s
O.U
U*fi3
9.2'B
0.0
bio
0.0
O.U
olo
O.U
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
n.9?
0.0
12. 8*5
KEn AT
•T-20
~CS.
B
(i.O
o.O
?:S
o.O
o.C
n. 0
(i.O
''.0
c.c
t'.O
o.O
0.041
0,0
o.O
0.0
0.0
n. r,
'i.r.4i
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211
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• U.F-M I O I A
REG I!
»4»| «•«<
IV t •<
-P ANNUAL C'lNrR'U
E [ URBANIZED ARP.A
" """ """"'
C"STS - CHMPIHED ARFAS
I I EON VT-20 I
CORPS.
CONTROL COST
CRE)
I CHAMPAIGN
(CHICAGO
!!?*VENPQ|U
IDECATUR
UOLIET ... -'
IPFORIA
IROCKFORD
(SPRINGFIELD
i OTHER URBAN; APP;»S
AVE. FOR STA-TF. *
IN
! CHICAGO .
lEVANsVILLF
IFHRT WAVNF
! INDTANftPm'r
(LAFAYETTE
IMUNCIE
.I SOUTH DENH
TERRA HAUTE
I OTHER URBAN
JAVE. FOR STATF
i AN"AR,ROR
I BAY CITY -
I DETROIT
"
-
ISLAND RAPIDS
I JAC*SON
IKALAMAZHO
[SAGINAW
IOTHER URBAN APFAS
JAVE. FOR STATE
"'DULUTH
i MINNEAPOLIS
ROCHESTER
OTHER URBAV! ARCAS
JAVE. FOR STATE
""""""""""""
I CANTON
IClNCINKJAfl
CLEVELAND
-
•HAMILTON
OH IVOUNeSIOWM
OH IOTHER URBAN APEAS
OH JAVE. FOR STATE
"WI'UPPLETON"
wi
}§HA'
IWof888E
51
KI I RACINE
WI [OTHER URBAN AREAS
^Wl^jAVE^rOR STATE —
„!,. - (S/ACRE) • |
Dl-l—i—.j.-J—|.SSL|.2£i.U75x j 8?x
"" • - oa| o.l ni ft
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li'sTriioToirr
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31 13.91? I/I.C41 I
3 11 /1. 12 I (!. C 41 I
31 8.3410.0391
31 >6.3*!O.C39|
31 0.0 n.0 I
I 10.04 I('.C40 I
! I I
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7&: 242:i Hi:fills:
76.1. 242.1 771.
35; I
20*1
106.1 29;
7-5.1
7-5.1 204!
75'. I 204.
Id.C I
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1225.
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185:1
438. I
462.1
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305.
3| 6.1610.0391
31 0.0 10.0 I
31 8.67KV.040I
30
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' • "sla2 I "o4?. I "23" I "kZ~ | "16?! "25!! !
"021" ft'tQ i 1-51 *.« " I 12.Z«! ?21*!
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-»t 7.7? I o. 040 I
31 0..0 10.0 I
I 7.56|(i.C40l
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:
122.
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229.
.:—!»!:!*i2:222j..!i:i_-«ii 153'' ??9*'
31 c.J2 I o.O I 0. 0 . l"""o" I ""*™o"* I
..Ihi-SS S-JISi! 59- 122-! ?2Z: 602.*!
li «.0010.0391
3117.4310.0421
3117,0810.0421
31 0.0 I 0.0' I
1.116166 11«:041 I
35:i5|ti:o56|
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9;61|0.039
^110.3110.0401
3112.9610.0401
111.2610.041)
22:
it:!
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18:
3
226,
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130. 365. 550:I
s?j: aS5|:Li5J:l
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ll.26lo.C4ll 31.1 87,1
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242.
10.041 32.1 88. 246.
9 #VE. FOR REGION 5 15 06
..I.....|mmm*mmmmmmmmm2™ ^|_„ |iSl.Z
10.041
0.040
0,0 .
0.0501
g.o i
8:849!
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130S.I
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690.
212
-------
TABI P Vl-9 AMM.JAL CM'irtJ'M C'l?
if !
IEPAISTATEI . MR6AN.'17t~v 4 TX lAVt. FOR STATE
, ; 61, IAVE. FOR PEGIiU 6
rS • C'VlPI' fi AT
1 r>i-.; VT-20
?TV| kC''C
-~H\'".v
•'U 0.0
ill 0.0
1 5.5?
1
1 5.5?
"I P . 0
a i o.o
41 n . 0
fll n.O
41 O.U
••fll 0.0
1 0.0 -
1
1 0.0
21 0.0
1 0.0
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3 1 0.0
31 0.0
31 0.0
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1 0.0
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31 0.0
31 Olo
31 IB. ft?
31 0.0
3 ! 0.0
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31 0.0
3li7l77
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' 31 0 1 0
•31 0.0
31 0.0
31 0.0
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31, 0.0
31 0.0
'31 0.0
31 oTo
31 0,0
310.0
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110.34
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0.
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0.
0.
0.
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277.
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96.
0.
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96.
0.
0.
0.
0.
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0.
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0.
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187?.-
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0.
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0.
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371.
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0.
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141.
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1 J665*
1665.
608.
213
-------
r
.TABLE VT-9 ANNUAL CONTROL Cl'STS - CHMPIN'EO ARPAS
IEPAISTATEI URBANI7EO ACJEA
IREGI ID 1
71 IA
?! IA
1! 8
71 14
KS
71 KS
71 (C9
71 KS
71 KS
71 MO
71 MO
71 MO
71 MO
71 MO
MO
71 ME
71 ME
71 ME
7"
1! ®
fll CO
81 CO
81 CO
81 CO
81 MT
81 MT
• 1
81 MT
81 ND
81 ND
81 3*
1
ICEDAR RAPIDS
IDAVENPORT
IDES MOINES
IDUBUQUE
ISIOUX CITY
IWATERLOO
IOTHER URBAM ARFAS
IAVE. FOR STATF
ITOPEKA
IWICHITA
IOTHER URRAM A&EAS
i
IAVE. FOR STAT^
(KANSAS CITY
ISPRINGFIEI.D
IST. JOSEPH
1ST. LOUIS
IOTHER IJRPA.N AREAS
SAVE. FOR STATF
IOMAHA
IOTHER URRAN ARTAS
IAVE. FOR STATE
(COLORADO SPRIGS
IDFNVER
1 PUEBLO
IOTHER URBAN AREAS
IAVE. FOR STATF
BILLINGS
1 GREAT FALLS
IOTHER IJRBAM AREAS
IAVE. Ff)R STATE
IOTHER URBAN AREAS
IAVE. FOR STATH
ISIOUX FALLS
OTHER URBAN ARC4S
^81 SO JAVE. FOR STATF
81 UT IOGDFN"" """
g UT IPROVO
si UT ISALT LAKE CITY
8 UT IOTHER URBAN A°EAS
I...).....
i
1 81 WY
... I.....
81
WMM«WM *
URBAN A«iAS
AVE. FOR $T»Tr
1,'EFl "'COEFSJ
" ll 9*6l!o°§40
312510710:056
3 0.0 0.0
CONTROL COS
CS/ACRE)
1 25% 1 50% 75%
T
65X
i "26- i "7?: "tiJF 'ill'
I lOlJl 405: 162&: 283$:
310.010.0 1 O.I 0 . 0 1
3j?il:{i5 !i:g55| ,8:j 3ei: ,548:
12/1. 05l". 055
31 9.53 0.040
3i o!o"i6:o
1 9.68 ".040
1 9.68 0.040
31 P.O 0.0
3II4;ol 0.041
31 o.O M'.O
31 7^69 o:G39
I fi.19 0.039
1 8.19 d.039
31 C . U o.O
3i °.98 0.041
1 9.9810.041
1 9.98 0.041
l\ O.U ",0
21 P.O 0^
2119.37 0.051
211/1.42 0.041
115.04 0.045
1 15.04 (1.045
21 0.0 10.0
21 0.0 O.C
1 0.0 0.0
1 0.0 0.0
31 /I. 93 10. 039
/l.°3 0.039
31 7.36 0.040
1 7.36 0.0401
1 7i36 C',040
gjTo'o'lo.O "l
I 0.0 0.0 1
21 0,0 10.0 1
1 0.0 0.0 1
1 fl . 0 0.0
21 o7o 0.0
1 0.0 0.0
llSr?? (U044
1 96.1 385. 1542.
26,! 70. IS?!
27,1 73. 199.
0. 0, 0.
26TI 7\, 195:
j 26. j 71. 193.
O.I 0. 0.
39^ 109. 306:
O.I 0. 0.
10 ?tt <,«
ii:l 13: id:
22. j 59. 158.
1 0,1 0. 0.
28.1 77,1 214.'
..!.:!..!!:! 22"-
8« °-i °r
! rf: Hli ill:
1 «6.j 141. «34.
«6. 141.1 434.
2" 2*1 °«
8: 8: 8:
£- .0.1 o.
n:! "";: -•;«:
13.1 35.1 94:
..ihl.Jh! M-
18:! 12:! 13i:
20.1 54.1 148.
O.I O.I "~0^l
8:| 8:1 8:
O.I 0.! 0.
o.i o.i °°or
O.I O.I 0.
""II^i "SIT rilST
U« 1
1 0 e
2688?
26B8.
2P2.I
297e
1 Os
2B7.
2S7.
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ill:
235.
322°
322:
322.
349.
0.
0.
1468.
438,
684.
684.
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0.
140.
140.
140.
221.1
221 J
221.1
0,1
2*1
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0.
O.I
&&3 1
214
-------
TABLE VT-9 ANMIJAL -Ci fiTO' •!.. C"?
EPA 1 STATE 1 URBANIZED A»FA.
RFGI 10 1 • - .
91 AK 1 URBAN 4REA<3
91 AK IAVE. FQ3 STATE .
91 AZ 1 PHOENIX
91 AZ 1 TUCSON
91 AZ 1 OTHER -URPAM .'.t-'FAS
II
91 A 7 IAVE. FOR STATF
.91 CA IBAKEPSFIKlD
91 CA IFR.ESNO
91 CA ILnS ANCELFS
91 CA IMODESTO
91 CA IfJXNARD
91 CA (SACRAMENTO
91 CA ISALtNAS
9 1 CA ISAN rjFRNANrT',0
91 CA ISAN DIF.GO
91 CA ISAM FRA>4C15i;n
91 CA ISAKi JOSE
,91 CA 1 SANTA BARBARA
91 CA 1 SANTA ROSA
9i CA ISEASTOE
9' CA ISTMI VAI.LFY
91 CA 1 STOCKTON
91 , CA IHTHFR IIRPA": Ar?rs«
1 '!
91 CA IAVE. FOR PTATr
91 Hi 1 HONOLULU
91 .Hi InTHFR .||?RAK- VF"''
II
91 HI. IAVE. FOR STA ru
91 NV ILAS VEGAS
91 MV IREMO
91 MV lOTHF" USBANi AI»EA3
1 1
91 MV IAVE. F'.IR STATr
• 91 • IAVE. rOR RFT.TO'J O
101 p IB'MSC
toi ID IOTHFR URBAN; A^EAS
101 in IAVE. FUrt P'fArF
TSl OR 1 EUGENE
10,1 OR IPORTLAN'0
101 OR 1 SALEM
101 nR IOTHF.R UR^AN AT is
.1 i
101 OR IAVE. FOIJ STATH
Toi WA 1 SEATTLE
101 *A ISPHKANE
10! W* TAC"MA
101 WA' (OTHER UR3*N /.QFAS
.11
101 WA IAVE. FOR STATfT
Tot IAVE. FOR REGION To
i IAVERA^F. FOR THF u.s.
TS - CHM3I
rn.-i v
t»EF COF
pTV fc
1 i /( . fi 3
2 O.U
?. o^u
0.0
0.0
1 O.D
1 0.0
ti o.o
t o.u
1 7*29
1.1 olo
1 O.U
1 0.0
i 27>y
3 o.u
I o:u
1 0.0
1 ' O.U
1 O.U
1 ->r'^r,
i
1 O.U
0.')
O..IJ
ij1 p.°,
J2/I.76
Zl 0 . 0
O.U
O.U
T T«.7|
I j tug
17.94
Hl3ll69
1 l!:iS
11.22
113.17
npn AR
T-20
FS.
B
C.04C
(i.O
(i.O
n.O
0 . 0
(i.O
(i.O
n.O
0,0
»i.f.
"lo
n.O
c.O
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• Ho
•'.C-
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n'.n
'1.0
c.O
(|.03
-------
.TABLE Vl-10
EPA!
RFC I
•T.« I
1!
i!
1!
I!
CT
CT
I
CT
CT
CT
• MM«
MF
ME,
URBANI7F..D
.1—.-
(BRIDGEPORT
(BRISTOL
I-DANBURY
I HARTFORD
IMERIDEN
•••"•• BRITAIN
(STAMFORD
IWATE9I3IJRY
IOTHFR U^B*^• ATtEAS
[AVE. FOR ST,*.TF
I LFuilST",""••"""-•"•
I PORTLAND
IOTHFR URBAN
1 1
.. i
I!
i:
1 1
1 1
ti
] i
!"
M 1 ••
1 j
I
1!.
1 1
MF
~",l"
MA
MA
MA
MA
,MA
MA
MA
HA
MA
"A
MA
"NH"
WH
MH
WH
«...
RI
PT
RI
'VT"
VT
PTATF
I BOSTON
I BROCKTON
IFALI- RIVER
JOTMFR URBAN A"FAS
JAVE. FOP .«!./•!F
'IHANCHESTE!?
I OTHER UR3AN
FOR STATF
I OTHER 'JP?Ah;
"AVE. FOt? STATF
i URBAN" AREAS"*"""""
AVE. FOR .STATE
II
I!
21
NJ
NJ
wj
NJ
MJ
NJ
"NY"
NY
NY
MY
NY
NY
NY
NY
NY
JAVE.
'IATLANTIC'CITY"
NEW YORK CITY
ADEI.PHIA
'VINELA'JO
JAV£. FOR STATF
i ALBANY""""""""
IBINGHAMPTON
I BUFFALO
YORK CITY
IUTICA
IOTHFR URBAN AREAS
FOR STATF
.£!-....!*y.Ei FOR REc;inN 2
- STORM AREAS
PEF!
rTY!
CON
D9T
51 9.4610.043)
pill.75(0.043)
51 9.0710.0431
5110.6510.0431
5110.39lo.043l
5 fi.35 I 0.042 I
51 9.6110.0431
51 3.2610.0391
"
! 25% I 50%' r?5X I
5llo:48Jo:6'43i
I 9.3610.0431
I 9.36|o.043
pi cTo i".o
5 0.0 10.0 I
0.0 I 0.0 I
I I I
i o.o -IM.O l
---!.._..|.....|
5.115.23
5M0
5
28.1
12:!
lh\
24^1
Z5:l
28.
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H
81.
8:
19'
234:
4:1
85% I
358.1
469.1
339.1
416.1
395.1
303.1
358.)
«?.!
i'.0«.SI 27.1 79.1 230.1 352.1
.:£--!. JZ:L.Z!:i_230'' 352.l
0.1
§:
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1 10. 043
0.1
II
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O.I
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O.I
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j §:2 io-rr! 35:! * »j
51 ) I.OP It'.0431 33!l 97! pflg
51 7.9PIO.&42! 23!l 65!) JR8*
p !8:g5l3:H '8:! 8§:,' 25§:
51 8.62IO.O«2I 25!l 71! an**
5 I C . 0 I 0 '. 6
5l 8.6210.
j 1 4 . 01 I
114.01 |f>.G44i
p!~oio"iu"rc"~r'
5 0.0 10.0 I
I 0.0 lo.c |
I 0.0 I 0.0 I
0.
203.
381.
127.1 381.
..... | .
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0. 0.
0. O.I
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0.0 I (.' . 0
I
l^'ill-i'
....I:!.1!*'!
""oil """or!1
o.l o.l
»....i.....i,
107.1 318.1
657.
385.,
406.1
3B4.I
O.I
isl'l
ill:
O.I
310.1
592.1
592.1
....(
O.I
O.I
Oil
I
....If
297.1
297.1.
297.1
• MMMHI |
O.I
O.I
P!14.09|(l.c _.
51 0.0 iO.O !
51 8.091 o.0*»2 I
5 I 7.ft7| t
Jl3.54J,
'51107721?
p 0.0 10.0 I
51 5.15 I 0.041 I
5j29.8Jlo:o55j :
i\ 8!lPl()!c4l|
51 9.3010.0431
121.90 I 0.0551
I I I
. 121.9010.0551
••I .....|.....j..
I16.7PI0.051!
102.
12J«
0.
67.
64.
122.
MMMMM
94.
.»!:
'B:
335,
335.
B»»»MM
216.
301.
382.
0.
i?42!
I 364.
| mmtmmm
I 27?'
!i9oi:
8!l
1313!
1313.
.....
786.
0.
293.
279! I
565.1
.....
432.1
178:1
3m:\
! 3SZ.-I
I 2271.j
2271.1
.....
liShl
216
-------
TABIF Vl-10 ANNUAL COM RrH.
I '
PAiSTATE I
FG|__ID_I
"3I""DE t
3! DC I OTHER UPBAM
.2L.2E.
3. DC,
31 DC
"31MD"
3< MD
31 MD
31 MD
..i-----
3! PA
31 PA
31 PA
31 PA
31 PA
3! PA
31 PA
31 PA
§1 • PA
31 PA
31 PA
31 PA
31 PA
. fCT, STATF
I WASHI~!cTo»»r»Tc.
FOR *TATF
IBALTIMORF
IWASHIMOT'.U' OC "fTWf.1
IQTHFR URBAN A&FAS
I AVE. FOR STATF
IAI..TUQN.A
II.ANCASHT
(PHILADCI P
I PITTSBURGH
I READING
ISCRAMTCiN
'
31 PA
UI78A-M
I AVE. POT STATf-'
31
31
31
31
31
3!
.-!.
31
31
31
...|.
3t
...I.
VA
VA
VA
VA
VA
VA
VA
I
U'^fiAN
VA iAVE. rnn STATF
wv
wv
wv
wv
wv
wv
ISTE'.'RENVIi.l.r yrTR;l
I WHEELING
I OTHER URBAN AT AS
I AV£. FOR STATIC
AREAS '
VT-20 I
FFI CHEFS. j
IILJu-S-JU-USSL
0.9ai().C«3l 29.
CONTROL COST
CiR/ACRE)
.
I 9l94IO.O*m
i 11 .S6H'.P4'.ll
. i ..... I ----- I
5110. 9310
!l0.96|0.043 I
"siTusl! JU043Y
.
?l RllOUi.Ctt?
ci 1 0.0 (.' . 0 I
-._.
51 0.0 If . 0 I
5 II C. 6* It' •
HI 13. 5« I d.
.
I 11.7«IO.C«3I
5 I 10.KM 0.043
5! 0,0 in.O I
rt I 0.0 IO.C I
I 9.61 It}.043 I
I I -I
29.
29.
35.
• »*•
3?.r
32.
II"
i
?§:
22-
?2;
20!
34.
34,
• •»•«••
0,
31,
UO,
"0.
30,
23
35
• MM<
29
0
1*
28
28
"II
52L
""B".
84.1
S4,l 244.
75X
"2441
85X
9 Wfl*B
374.
370.
374.
105.1 311.
31:1 2-79;
95.1 279.
'7?:!
till
58.1
67.1
86.1
248.1
67.1
O.I
70.1
58.1
101.
101.
""o"
91.
103.
103.
103.
0.
5?:!
81. I
81.1
Tool i
294.
203.
168.
278.
166.
151:
!«:
203'.
164.
305.
305.
""""
267.
359.
2541
306.
306.
M»«W
249.
S-
0.
144.
235.
235.
481.
mm^mm
427.
430^
430.
"4531
310.
257.
429.
252.
294.
384.
1317.
294.
0.
311.
250.
474.
474.
• «•.—••
0.
409.
3ft
1%
471
47S
473
"381
0
0
219
360
360
"461
217
-------
T4PLE VT-10 ANNUAL Cf]NT,7!)L
...I.
41
41
41
41
41
41
41
••«•» | •
41
41
41
41
41
41
4 I
41
41
41
41
I
41
41
41
4 I
41
41
41
41
—.-(-,
4 I
41
41
4 I
4 I
I
41
...I..
4 I
41
41
I
41
.-I..
41
41
41
41
41
4 I
41
41
STATE
URBANIZES
AL
At
at
AA(:
AL-
AL J
FL
I
FL
ft
FL
FL
"GA"
GA
GA
GA
GA
RA
RA
(BIRMINGHAM
IGADSDEN
IMQBTLE
I MONTGOMERY
ITL/SCAI.OOSA
OTHEP UR3AM
{JAcKpViiTE
!ORLANDO
MEST PAI.M 3&ACH
OTHER U984.-J Ar*r
Fdi?
lALBAMy""1
(ATIAMTA
I-AUG'JSTA
'COL'IMBUS
IOTHFR
IAVE. FPI
RTATF
KV
KY
I OTHER
A.~<(TAS
MR
58
we
MC
NC
MC
(AtfE. FOP S'TiTf
I BT" "i
For?
NC
MC
VC
IHlGHPQi»jf
'RALEIGH
A u *\ - .
inaA'j A'jfr*s
FOR STATF
41
41
41
41
.41
-I.
41
41
41
§c (CHARLESTON! ""'
SC (COLUMBIA
|C IGPEENVIILE
SC OTHER IJR'JAN APEAS
_SC^JAVE. FDR STATE
TN (MEMPHIS
TN INASHVII IF
TN IOTHER UR'SA
FOR STATF
}__«{ ..|AVE. For? REGIPN 4"
CilSTS - STOPM AREAS
!
15. 2fM 0.040
lHfe,i9
1ST,
J14.92lo.04d
14.9? 1 0.040
!ifl
&!
11:1
40.1
•40.1
""3*71'
34^1
Si:!
ft:!
j!f:|
"U040J 5o!l
(1.50X_| 7SX I 85% I
"ii" rr?s"! "*$?•!*«* •'
a2"' J}?« 3??. 460.1
iii; 302. 451;
HI. I 388: j 57$: |
" 302. 450.1
, ;-• 199. 295.1
110,1 298.1 445.1
110.i
...... I
IO.
I IP. 37 l!i.r,4oi
''! o.o K
it i " ' *
•;l 10.0 II..,.
'"I 11 .6310.040
'[i 12.03 I 0.040 I
I .57(11.0401
5IJl.83lo.044l
^1 P.47|(i.c4i|
! 10.97(0.0431
10. 97o. 043
lt 13.631 0.04
I 13.6*10.040 I
I I I
^113.6510.040!
"I 16.6°I 0.041
'•I10.1PI0.04C
«I P.9410.039
f|ll.35 I 0.0401
'f 1?.31 10.0401
"I fl.74 I 0.039 I
'
-------
T\PI £ vT-lo ^N'l'JA' i~<":TRCil Cnf.rS - STOP" ARfrAS
I 1 " ' , -
;PAI STATE' I'RBANlZEn ARFA
SI 11 IAURHRA
SI IL 1 B '.00 MI Ml rfi
si IL IOECATU.<
51 IL I.IOLTFT
S' TL IPFDOIA
SI IL IROrKFORO
SI T L ISPRTfcGFlH 1".)
S! TL U'lTHFR '.'R3A>< AKTA9
1 I
^ 1 TL lAVf:, rr-n STATF.
SI TK' 1 A^OFRSPN.
si Tw ICHICAGP HFTRP
51 IN | FVAMRVT| 1 r
51 TM i FORT :-IA\ "i .
5 ! T-M 1 Ik-ir>I ANAPOi T*
Si TK> ii AFAYFT IF
-. si IN IM'.ujCTF
C | T N! 1 S 0 ' ' T H 1 * *-" '• '• lx
SI TK1 lTFF;r'A HAlJTE.
e; i .f-t IPTMF? i,:p;-AM A'r-fis
| |
si TK-i i A"r.. Fnr, STATF
51 MI 1 A*'".' ARnrR
si M T i B 'w t'liY
5 1 M J I n F T R T 1 T
si MI iFfrt
si' "i ii;i*A''-r> PASTES
Si MT i JACT.ON
SI ,-tT i KAl As. A 7 HO
SI "T 'L'^iSJNC
SI «T ' Mlls^FG^-N
SI MI I s A r, T v 4 v,
SI Ml IMTHFP Uri'^VT-PO 1 CONTROL COPT
•fvl k t 7 1 25% 1 50%
3 1 12.?-C 1 0.041 1 36, 1 ^8.
?H?.5?I 0.0411 3^.1 95.
3! M .8!' |*«.0«OI 32.1 «6.
31 7*lM''Io39| 19*1 5U
3 1 0 . 0 1 c . f. I O.I 0 .
3110. ?4ir.. 0401 23,1 76.
31 0.0 1 0.0 1 C.I 0.
"* 1 1'UOS 1 (1.041 I 39. I 109.
31 q.lfi.i.cflCI 25.1 66 4
1 O.PM".C39I 15.1 ait
! 1 1 1
31 0.0 Id.O 1 O.I 0.
311?. ""10.0411 35.1 °6«
3 1 0 . 0 1 (i . 0 1 0,1 0.
31 13. 1? IM.041 I 36.1 101,
311 •5.951,.. cMj 39.1 106.
31 2.17 1 -).r,3f» 1 5.1 13.
31 8.3'«l(-.-039! 22.1 59.
3 1 0 . y - 1 " • f- ' O.I 0 .
31 4;p7l'.:o39l 24^1 63.
111*631-1.0411 32.1 88.
1 1 i 1
31 R.Otlc.OUOl ?2.l 5fl.
31 0.0 I".G 1 O.I 0,
31 8. 801'!. 0401 24,1 65.
31 8.61 lti.0401 23.1 63.
3110.331(1.0411 30.1 82.
31 0.0 10.0 1 0,1 0.
3110. 09|(i. 0401 ?7.l 75.
31 8.671(1.040! 23.1 64.
311^ 46i'l.040i 29.1 79,
310.010.0 1 3.1 0.
1 9.0/JI -i.O-aoi 25.) 67.
Ill
1 •?. 0410. 0401 25.1 67.
31 8.4?!0.040I ?3.l 62.
3! 7l72lo!o40l 2l!'l 57^
3 ; A. 72 10. o39i ta. i ae.
1 7. '361". 0401 21.1 570
III)
I 7.66)0,0401 21.1 57.
3113. 68| --), 041 I 33.1 104.
71 l'l.2'5l(!.0«l I 59.1 109.
31 8. (Ml 1 i!.039| 21-,! 57.
3117 4310.04?) 50.1 l^Sf
31 I7.(j8|:i.0«2l a8.l 137.
3111*911". 0401 33.1 90,
3) 16.66lo.041 1 47.1 130,
3)0.010.0 1 O.I 0 ,
3)10. SOKi. 0"OI 29.) 76,
31 9-OOKI.039) 24,t 63.
31 «.4H |n.r,39| 22.) 6n.
3) 0.0 10.0 1 0.! 0,
31 9.,i7lc. 0401 25.) 67.
3)j?. 961'!. 0401 35. i 96.
1 14.21 td.041 ! 40.1 112.
1 ! 1 1
llfl.2Uti.OMI "0.1 It?.
31 11. 401 ('.CM 1 32.1 88.
31 6.6«|n.030| 1P.I 48.
31 &*.44H>.0319| i7*j a6.
3i 6*4210.0391 17,1 45.
3! 7.901(1.0*01 21.1 57.
3Hl.04iu.oail 30.1 84.
31 3.56!«.038I 9.) 23.
31 5.f
-------
TaBLE VT-lO A.JMUI r't-Tani r.
II i ' " ''
lEOAISTATEl
I RFC I TD I
61 AR IF!lP,T S'lTTw " """
6' AR JLITTLE nOCK
61 Ap I PT\'F C
61
61
6|
f> I
61
61
61
61
61
61
?x I&I.K1;-
TV IRRVA'. "' "
TX IC^R^US rrtiiT«TT
TX IOALIAS "
TV I EL PASri
TV IFHPT iv.'ipri-
Tj[ !PJLyE3ioJ
TX
i. LE,1'
* I
?l-,
*L,
TV
TX
• • • i- • *i ^ i n i v
H 3tt ^^Ln
TX
TV
TV ITev«<5 PT'.TY
TV ITYLF.R
TV lv»A(-''1
TV IWIC^ITA FA"t
TX IDTHFo !JU3A'j APFA1?
I ~ •'
TV "AVE. FOR STATF
ARtAS
I
• .
•; l J.u7i '
12. 62|(i.
| 13.25l'i.0401
"! 13.92ld.040l
« I in!?1 n '--'
O.I
35:1
3i:i
CONTROL COPT
CS/ACl?E)v
50X .j 75% I
"^iilii
"2.1
1 !10.651•
• 29.87 In^0*2i
"29.A7lo.042l
38.1
29:1
126:1
?o
'I
86.1
""I"!'
..12:1.
til'!
-ii-. 273:I
368:|tg?|:i
?.^m.\ f??:i
S"'.! 7t7.l
85X
""""
390.
337.
372.
372.
• •»*»•»«
S'5.
.-I:,1..!!;1
3IH;i^i/.:o4tj
17 I 46 «?X
Ii*! 2?:I 236;
1 I I
I I 1 .161(1.040 I
31 77(161"""""
. .
3! IP. 62 I o.
31 li.3«l|i.040l
.
'1.1
84.
,~.l
42*1
•3 i «.2«?l-i.o3RI
JM 1 .7-) I (i.c^oi
it' 'I.'13IM.039|
3 'i.a3id.r,«0l
^ «.*7ln.o39l
3 ^. SO ln.039,1
31 3.2) I'
.
31 11.80 10.
..
1 6.39|o.r39l
I 10.21 K'.C«OI
i ! ' i ;
J IP. 21 IC.040 I
.11:!.
IB^I
9<; .i
30.1
°*!
n:
31.1
12^
i?:i
llll
7.1
^Ti
34.1
!!•!
II: i
8".. .-...,
"sH!"""""'
1
1096.!
.-.-»)
139.1
139.1
139.1
....«!
HI
347.1
pifrf-iil:
,202.1
272.1
5flT. 1
310. I
398. I
88.1
95;
If;] 'l-:l
livi ill-!
67»l 17!:
§§«! ?3?:i
§3. 223:1
72. 196.1
»J:MMI
>62.l
344. I
332:!
292.1
178.1
310.1
.I6:
1*0*5"
208. 310.1
••»-» nmmmmj
292. i4«0.l
220
-------
T&^LE vl-to AiJ'lUAi. rt.vrrr?Ml CM.S
EPA! STATFI H3RANi?F.n AREA
REG1' IP 1 , ' - .
71 IA inAvESPORI
7! I A IQF.S HCVINE'*
7! IA IDilBU'llJF.
71 i A isrnuic CTTV
71 I A 1 rfATFRLO')
71 I A IQTHFi} GR3«V ASF AS
• ! -. I ' .
71 ' TA IAVE. FUR STATF
71 • -KS' IT'IPFKA .; * ' • '' .
71 : 'KB l/Jir>UT4
71, KS inTHFP I.RMAJJ • ARFA.S
• • i i '•' .
71 KS 1 A\'F. FC.IT STATF
71 ,,.Mn (COLUMBIA . • .
71. ' wn tgplm-GFtF^r '
'! i MO i ST jnstPr
• ?i --Mn isOouis
•"•VI Mp IOT,HF7 lirj,ja,v AGFA'S
1 I'
71 An i AVF,, 'rrirt STATF. ,
- 7 i- ME I LT;.iCOi.M
71.",. *.F. inTHF.5 LlTbAN- APE.AS
• - r • • i '
71 .."iE l-AVE. rti'R «*TA.TF'.-'
71 - ". • u"I. FOR t?f.'c-inv . 7
• » i en .1 B^ULO'EI?
, « ! 'en 1 DENVER • ' •--.''.
£1 CF1 1 pTHFr?1' u'rjlitf: A."FA$
1 1 - " '
81 ..CO 1 A-VE. FflR STATF
PI »T I8-ILI.IKT.S
'.- 81 " MT ''GR'pAT F;A! IS'
• ' ;',-•'•' T j ,,' ,. ,'F
••.. s i-.--;:J!n IFA^GO ••-.. .,
31 Kjn inTriFi* • 'inp/.M APFA.^
: I , 1 " •=•:-•. •..".•
• •. fl" -»|D 1 AVF.. FHR. STATF
S1 ; sii 'iio'iv- FAi.i.s
"': 6 1 ' so IOTHFR ur>hiN' ARFAS •
j» 1 SI IAVE. FOR STATE..
HI UT 1 OGOB"! '
: .Ri ijT ippnv'n
• ,-91 • LIT-- | SALT l.AKf CITV
- " ' f ' ' ' ' ' 1 ' ' "
81 I'T lAVE. FOP STATF
PI wV HIPBAN' ARF«CS
| I
g' WY IAVE. F0,7 STATK
°"B i "" "IAVE. FHR PFGIII^ 5
TS -
PEP
STflR" Ar'fc'AE
k °| "e.
5i R-4U i'l.OCC
3
3
^
3
3
3
;S
' -
3
•
.
p
?
?
. 3
3
?
3
9.6! ld.0401
3.6 11 ".03 7
7.1(H'ii-03e»
P. 3 (I 10. 040
7.64 1 '1.039
h.97 1 0.039.
c.,971 0.039
o*93 1 o .0"0
7.VU 1 0.040
1
o.f>7 1 0.040
\ 1 . 6 1 ( o . 0^ C
/I /j1^ 1 n ^ Q3 ^
r . o I o . iy •
0.0 1 0 . 0 .
6 94IM.039
1
6.94 lfi.039
P.7gi;;.c4o
9:.2.a,i.n .0.41
9.24IO,04il
7.73 10.040
12.39 1 0.03?
«:60 1 'UOi/
9.0710.038
1
25%
, 23.
• 26.
,1:
. 13:
. 19°
•19.
27.
li:
23.
32.
ii:
0.'
0 .(,.
18.
.18.
IP:
I . :3rj*
'
25.
21.
32.
1 17.
12^
24:
1
0.07(0.0381 24.
6.46I0.03PI 17.
6. 4" I 0.037 I )6i.
6. 42 1C. 03 7
<>.4H! 0.037
4.9310.039
4. "3 10.03"
/! .""3 In. 039
7.361 0.040
7.3610.040
7. 361". 040
Ic.42ln.o3R
'9:401 o:o38
1
Q. 49 | n,038
O.Q38
1
7.50! 0.038
g"?q 1 n.038
1 16.
16.
H:
1 13.
i 20 :
I ?0 .
1 22!
! 2§:
i
1 25.
19.
19.
1 22.
" ' ($/A
50%
61.
' 71,
11:
50.
50.
70.
73.
57,
63!
, 63.
97.
0-*
• • 0.
49,
49,
»*:
70,
70.
56.
43:
66.
29,
62.
. 62.
42.
41.
42.
4.2.
- 35:
35.
54.
54.
54.
70.
58,
63;
•64,
64.
49.
49e
56.
^in
163.1
19-1.1
60.1
133.1
\lh\
134.1
134.1
199. j
!?<>:!
170.1
0. I
'
|
'130.1
V\\°\
194.1
1
194. j
152.1
fn§:l
iiim\
161.1
•1.61.
108.
1 n6«
1 06.
'94.
. 94:
«4.
Us:
148.
181.
151.
164.
166.
166.
126.
126.
85X
242.
2R5.
87.
1
-------
AM!UAI. f.P.VT!9M|_
""EANlZr.r 4(?FA
TAB'f Vl-10
itPAISTATEI
IRFPI i" i
~~51 ~~ AK" i UPBA "AREAS""""""'
91 AK IAVf. FOR .STtIF
""9i""AZ"iPHOENIX
91 AZ ITUC-SPf:
91 AZ iriTHEp Ur>BA\- APEAR
^91 AZ IAVE. FOR STATF
CMSTS - STORM ARFAS
1 ! f*K VT-?0 I
I»EF| CHEFS. |
9'
9 !
91
91
9«
91
5!
?!
91
91
9i
-------
TABLE Vl-11
PAISTATEI
F.G 10
ANNUAL CONTROL COSTS -
URBANIZED AREA J»EF
UNSEWFRED AREAS -_M_Bril
CONJROL
6 "
J:_.
P9">* I wwwww»w »»»*••
CT BRIDGEPORT
CT BRISTOL
CT DANBURY
CT HARTFORD
CT MERIDEN
CT NEW BRITAIN
CT (NEW HAVEN
CT INORWALK
CT (STAMFORD
CT IWATERBURY
CT IOTHER URBAN AREAS
CT UVE. FOR STATE
ME
ME
ME
ME
I
,EWISTON
I URBAN AREAS
VE. FOR STATE
iolTON*"""""1"
ROCKTON
MA IP
MA IS. „
MA IWORMuu.u.-. _._
MA IOTHER URBAN AREAS
MA IAVE. FOR STATE
"NH" i MANCHESTER""*"
NH UVE. FOR STATE
•••••••*••
IPROVIDENCE
R URBAN
RI
R! IOTHER URB
RI IAVE. FOR STATE
__.. I mmm*mmmm9mm
VT IURBAN AREAS
VT UVE. FOR STATF
mmmmm I •••• — •• — • — — <••••••• — -••
FOR REGION
NJ
N5
NJ
NJ
NJ
INEW YORK CIT
(PHILADELPHIA METRO
(TRENTON
IVINELAND
FOR STATE
NY
.NV
NY
NY
NY
i ROCHESTER"™
iSYRACUSE
lOTfiER URBAN AREAS
2 . NY IAVE. FOR STATE
m
21
••«•••<••*»••<••
IAVE. FOR REGION
.
mmm I ••••• I ••-•-•-••••
3.2610.039
2!B3«).0|9
3.6elo.040
3.80(0,040
«<•••• I -•••••-
2.85 0.039
2:8510.039
2.85(0,039
._»_•> I ««•-
2.74I0.03B
ra.i
,mmmm\
223
-------
TABLE VI-1J
ANNUAL criNTRHL
AREA
ICPAISTATE
IRFGI ID ,
" "51 ""DE" I "~~M "" •
31 DC [OTHER URBAN AREAS
-.2i..25.if!!: Fn" STATF
3| DC •"•-••- - m
CrisrS .
"jgfjgSo-
APEAS
ipEFl -"cni
21! !...k.. I ..p.. i —
5 I "ISo I "0391 ""To"
3.6oio:o39i IS;
!-2:!2!!!:£22Li2:
I 5(0-0 IM _ n l n
"N
I 26.
I 26.'I
1ST
75X I 85X I
.---(.-...I
68.1
101,
FOR
31
31
31
31
I!
33!
I - - I • I u • I
°«0 I O.I o.l
_26i, i oo. p 101.
""\""'\ o"Ti
o.! o.l
MD
MB
DC ..w ,
BAN AREAS
STATE
.......
ENTOWN
is
PA
(LANCASTER
®*""
ibTHE
R URBAN AREAS
3j PA IAVE. FOR STATE
I
3
yA IROANOKE
WV IHUNTlNGTON
! 31
WV
AREAS
-•-,!_— i.2:".j°:0 ' 0-!
i! ]:oII8:81S! 2-! |?-| *f-j' jg."
' is:i If:! gg'j
jJ:ff|n.C39I 9.J_ 23.1 61.1 90.1
! II*! Si*!*"^?!
IS' ??.• H0:i
in* 6,2« '5.1
i|» 2?. 71.1
II' Si- 135:1
II: Z5- US'!
I
I 2.7llo.o39J
i
5
1:818:8381
3.3910.0391
*:}5 8:840!
?
9!
.-i2:..22:L
°-' 5§:l
52:1
24: i B:l
B:l
'73-:!
73.1
^.^ ...OJy e. 22.1 5?:i «5
.--!.2:i2 £:22! ...2: M5!-| ".I B5
51 *.46lii-nTO a tT"—-"-—.-
II
3'
0. I
nsli
. i — -,,_,. >^ i^ LJ n IY Mr«t.MO
..21..-m.\t~ll F°R STATE
I.. I j""t« rin<
-
Irfef&l
8:1
IS:!
I*:!
26.1
5-7:i
'5.1
?.!
!j * - • j "-"• I OO.
-i222L.i2:L.2*:L *!•
3 • I
- *"* I
96. I
O.I
101.'I
101.1
224
-------
TABLE Vl-jl
AISTATEI
Gl ID
ANNUAL CONTROL
URBANIZED AREA
C"STS - UNStrWFRED AREAS
41
4!
41
41
41
41
41
41
41
A
A
A
AL
(BIRMINGHAM
IGADSDEN
IHUNTSVILLE
I MO
MO
INTGOME
ISCALOO
RV
URBAN
AREAS
AL IAVE.
...<•• I
tL
FOR STATF
FL i JACKS~5Nvi
ft l8itH»o
F[ IPENSACQU
FL
ft
41 FL
BA
SA
5A
4
4 .
41
41
41
Si
AVE. FOR STATE
• »••«•»•§»— — «••""•
ALBANY
ATLANTA
AUGUSTA
It !S^E«BAN AREAS
GA UVE. FOR STATE
41 KY
41 KY
41 KY
4! KY
... I ...<;.
fll MS
41 MS
41 MS
. U\ MS
~*4l"*NC"'
41 NC
41 NC
41 NC
41 NC
41 NC
ii i
41 NC
41 NC
"3 I ""siT
41 K
4! SC
-Sr-TN
I L.C- A * '^ w 1 \J1V
(LOUISVILLE
lOWENSBORO „,...
IOTHER URBAN AREAS
IAVE. FOR STATE
I ......-...-—.«—-'
IBILOXI
loTHER URBAN A"EAS
FOR STATE
ASHEVILLJ
CHARLOTTE
,.EVILLE
INSBORO
IHI8HPOINT
(RALEIGH
I UTMSiflN^SAt-PM
IOTHER URBAN AREAS
IAVE. FOR STATE
'iCHARLElTON*""
igTHER'uHBAN AREAS
IAVE. FOR STATE
, I..........--••-"-
- gOOGA
41 TN
!... ....
«l
I mmm I ••••
AREAS
AVE. FOR STATF
AVEI"?OR"RIGION^
3.3310.037
?
4.9510.03
4.9510,038
3.
3.04M).039
nieioZpHi
S;64lO.O|7
4l94|olo|8|
4^6910.0381
4.6910,038
:5:
0.038.
.«.•. I
0.0
0.0
0,0
0,0
114.1
225
-------
ANNUAL CONTROL COSTS •
TABLE Vl-11
ICPA!STATE
I REG I ID .
""I!""IL"!AURORA——
UNSKWFRED AREAS
—"
URBANIZED AREA
HICAGO
(DAVENPORT MFTRO
'
IROrKFORD
•• — . — - •-i^'Grli.t.u
IL IOTHER URBAN AREAS
IL IAVE. FOR STATF
IN IEVAN3V
N
(SOUTH BEND
TERRA HAUTE
IAVE. FOR STATE
IMUS
JSAGINAW
OTHER URBAN AREAS
IFARGO METRO
!MINNEAPOLIS
BAN AREAS
JAVE. FOR STATE
[CANTON
CINCINNATI
IYOUNSSTOWN
OTHER URBAN AREAS
51 OH
..I....
IAVE. FOR STATF
r*-.-=—• •——
OTHER URBAN AREAS
IAVE. FOR STATE.
•--. i».«.,, i ..•.
3.08 0.037 8.
226
-------
Vl
CPA STATE
IRC6I ID
°6!<*"'AR
61 AR
tl A"
f AR
61 AR
°6I°"LA°
61 LA
61 LA
6! LA
61 LA
61 LA
61 LA
61 LA
"'("NM"
61 NM
11 ANNUAL CONTROL
! URBANIZED
B f («J » «B ^ «* «» «D ^ S" ITO «3 OJ «t W »• — — •"
I FORT SMITH
I LITTLE ROCK
IPINE.BLUFF,
(OTHER URBAN AREAS
IAVE. FOR STATE
'!BATON'ROUGE""""""'
ILAFAYETTE
(LAKE CHARLES
(MONROE
(NEW ORLEANS
iSHREVEPORT
(OTHER URBAN AREAS
IAVE. FOR STATE
|SBSD5iiyriREw"
FDR STATF
11 BKK
I! RK
61 n«
"6("°TX°
61 ;?
61
6!
61
61
61
It
61
61
61
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
IOKLAKOMA CITY
inTHER* URBAN AREAS
IAVE. FOR STATE
(AUSTIN
(BEAUMONT
BROWNSVILLE
(CORPUS CHRISTI
(DALLAS
(EL PASO
)RT. WORTH
61 TX
ll TX
I 8
ISiLVESTON
(HARLINBEN
(HOUSTON
ILAREDO
ILUBBOCK
IRCALLEN
I MIDLAND
(ODESSA
(PORT ARTHUR
jI'AN ANfO
SHERMAN
I^NA
(SAN ANGELH
SAN ANTON]
SHERMAN
8
ITYLEF
WACO
IwICH]
IOTHEF
,B.JHITA FALL
(OTHER URBAN AREAS
TX IAVE. FOR STATE
AREAS
25% j
II: I
5£*J
•-•-•j
96,
"
• w;i 100:
39,1 idOa
• ma oao* | C9a3®WC»
it:i 35: ill:
16^1 tlO^I 1041
16IJ HI.! iO|6
Q» I 0«
11:1 If:
15.
(ti»t5?i» j
i!:!
I6l!
II
IS
ll
,_ 0,036.
o:!|!li:0o!7!
l:W8:flI.
3*5310.0||I
5^2210.037
2^2110.037
1*9810.037
!:ZS!S:81J
i:HI3:m
3
i:jl
•I
I:ii
i.
"Ǥll
8:«p
8:W
8:i!2
7i
3122 Oi
3.'22 I 0.037
[4;
14.1
»»-r
«:!
4.
n«B«9«»
to.
7.
*l
i!
i
I
i«:
U:l
13.1
8.
37,
9B»6*caer
?:
^5Q
so oses
II
85S !
BOB iBSfB*" |
ifli.f
isg.i
taa*!
I«&8|
ia&J
it ^a ma ea ta J
140*1
16S.I
151*1
15«.«
eai
t«:.l
isaj
0099(903 I
30,
30,
i 21.1 30.!
•». 21,,
qifBCBcnfix J
&?.
ll
fl:
?i:
isj 46.
vwocara \ r>«>9a>Q
pi' '
II*
5S»!
75* *
66. t
66 J
227
-------
TABLE VI-11
CPA
REG
STIADTE!
ANNUAL CONTROL COSTS -
URBANIZED AREA IoEF
. ' - ITTY
UNSEWERED AREAS
CONTROL COST
CS/ACRE)
50X . 75!! I
, II
i II. i
!l
!
ICEDAR RAPIDS
IDA
IDEl
DUBUQUE
yENPORT
El MOINES
OUX CITY
TERLOO
HER URBAN AREAS
IA AVE. FOR STATE.
"5-
••*!
KS
KS
!i
1 KANSAS CITY METRO
TOPEK
WICHI
OTHER
CHITA
URBAN
AREAS
IAVE. FOR STATE
> I <
71
71
71
MO
MO
MO
MO
MO
HO
(COLUMBIA
NSAS C
RI
. J
T'L
IOTHER URBAN AREAS
AS CITY
INGFI
OSEP
OUls
IKAR
'SPRINGFIELD
!§}•«
PH
MO IAVE. FOR STATF
! I
NE
WE
NE
7! NE:
'LINCOLN
IQWAHA
I OTHER URBAN AREAS
IAVE. FOR STATE
IAVE. FOR REGION
§1
I
§1
I
81
81
O
O
co
I BOULDER
(COLORADO SPRINGS
mm
?OTHER URBAN AREAS
IAVE. FOR STATE,
81
SI
8!
.2!.
BJ
MT
MT
LINGS
ER URBAN AREAS
AVE. FOR STATE
NO
ND
ND
OTHER URBAN AREAS
FOR STATE
8
8
8!
•<• i >
81
81
gj
8
•|
8
..I.
.SI.
85
so
so
mmmm
UT
H?
UT
UT
mmmm
WY
WY
SIOUX FALLS
JOTHER URBAN AREAS
JAVE. FOR STATE
I »•••••<•••*••• wwnvapM•»*•*•
lOGDEN
IPROVO
I SALT LAKE CITY
OTHER URBAN AREAS
IAVE. FOR STATE
I mmmmmmmmm^mmmmm•-•t.n
IURBAN AREAS
AVE. FOR STATE
AVir"FOR"REGION"""l"
i | «n«pVe*MwwMWMmg»wva»^«
228
-------
TABLE Vl-11
!EPA!STATE! -
IRF-SI ID I
ANNUAL CONTROL COSTS - UMSrWFRED AREAS
URBANIZED AREA
IttEFl
CTVI
25X t 50%
COST
75% I 85*
•t 1 URB AN AREAS
91 AK IAVE. FOR STATE
II 2.98
I 2.9B
0,037
0.037
, , 19. i
•••?»
48.1 69.1
48 J. 69.1
"Soil
91
91
91
VAZ I PHOENIX
AZ I(!)TH!R URBAN AREAS
AZ
AVE. FOR STATE
1.2S
0.035
0,036
0.036
0.036
91
91
91
Ii
I!;
i!
91
91
91
91
91
•CA
CA
CA
£A
•CA
CA
CA
•CA
-CA
PA
i
•«
es
CA
BAKERSFIELD
FRESNO •
LOS ANGELFS
I MODESTO -
IQXNARD
_,.-NANDlNO
I SAN DIEGO
SAN FRANCISCO
I SAN JOSE
I9ANTA BARBARA
I SANTA'ROSA
(SEASIDE;
SIMI VALLEV
STOCKTON.
THER URBAN .AREAS
91. CA IAVE. FOR STATE
0,0371
10,037
l,5B o.O
3.47IO«0
1,56 0.0
2*P8!"«2l§
1.69 0,037
t:W °'°-*Z
s,* o y
2:41
1:18
1.69
0.0|7
o':8f
0,02
0,01
0.03
0,0|
0.037
I!
AREAS
91 HI IAVE. FOR STATE
2.60
2.60 0,037
j.7.
4|.|. 61,j
«2.
61
91
91
91
9!
NV
NV
NV
OTHER URBAN ARRAS
AVE. FOR STATE :
0.50 0.035
1.231O.Q36
0.7010.036
0.70 I 0.0361 ; 2.
•I-
10.
10.
10, I
?!:!
....\t%
91. ,.. fAVEa FOR
1.66 0.037
• woiwv I «wweaf J
2*5510,0361
I:55|o:o36l
2.55J6,03>
;Ui;
38.
i«i
101
il
I8
ID
BOIS
OTH
? URBAN AREAS
FOR 9TATF
ai
,61.!
''6.'
"!;6»
16.
38. 55.
"lit OR
1II' OR
181 OR
10 j OR
"lit -OR IAVE. FOR STATE.
.4.74 0,
TfiER URBAN -AREAS
ft. 72
8:§I|
8:8!?
0.037
50 „ I
77. UZ.
77.]-:
IS! SS jIMM
1i':f,SlJ.opRAURBAN AREAS
10IwA IAVE. FOR STATE ,
S-JX
0,037
0.038
0.037
0,037
0,037
ii.
,-
.51:
27,
66.1
68.
99.
oi
v. FOR ReoN 10
„— I •
«.2lio,037
,'.:
i27.
69.
100,1
vRACE FOR .THE .U^S* 1 ...
f. r~ i *s '- -.?*• '"»>
iYi'^i* :-'-y^
229
-------
Because of this definition, the number of overflows may increase
with an increase in treatment rate as shown in Figure VI-12, Effect
of Storage and Treatment Capacity on Number of Overflow Events"If
the treatment rate is high enough to deplete storage after the first
overflow, then the event is over. When storage is utilized later a
new event starts, and any overflow occurring in this event is consi-
dered separate from the first overflow. Thus, the number of overflow
events was increased from one to two events, even though the treat-
ment rate was increased.
The number of overflow events appears to provide a more meaningful
parameter if the event is defined differently than the definition used
by STORM. The overflows shown in case 2 in Figure VI-12, should be
considered as a single event since they occur so closely together. The
event would then be redefined to be a separate overflow event if there
was a specified length of time between it and the overflows preceding
and following the event.
Determination of a National Precipitation Event Definition
A methodology is presented by which, separate precipitation events may
be defined on a national scale. Several valid, but totally different,
alternate approaches will be briefly discussed and evaluated with regard
to each one's usefulness in deriving a national event definition. The
main portion will be devoted to the derivation of a precipitation event
definition for the five test cities. Each, city has precipitation
records which are representative of the climatologic region in which it
is located. From the results for each region,a national precipitation
event definition will be formulated for the contiguous United States.
Given an event definition the relationship between tKe mimB'er of oyer-
flow events and volume of runoff controlled will be presented.
Meteorologic studies are one approach which may provide a useful
insight into the definition of a national precipitation event. Since
the atmospheric mechanisms for the cooling that creates saturation
are of three types — cyclonic, orographic, and convective — an event
definition could be based upon storm origination (e.g., all
precipitation resulting from one frontal movement would be considered
a single event even if the front became stationary with extended periods
of zero precipitation). This would dictate'an event duration range of
from an extended period (i.e., 12 to 72 hours) for a cyclonic storm to a
short period (i.e., seldom more than one hour) for a convective
storm. 7 This causal concept would not be applicable for even a local
precipitation event definition since localities are subjected to both
cyclonic and convective storms. Even a twofold definition — one each
for cyclonic and convective origination — is inappropriate as cyclonic
storms may be divided into frontal and nonfrental varieties. Over-
shadowing the possibly solvable problem of regionalizing cyclonic and
convective precipitation events is the problem of identifying all future
230
-------
0£' 02' Of DOS' ,0
I -I - U - L
0£- 02" 01'
Q)
1
S3
ci
, o
-P
•rl
a
rt1
cu
cd
u
S
rt
(U
g.
60 ••
rt to
V< ,-u
o 0
u
w
O <+-)
Q) M
<-H
w.o
r-i
I-l
•H
or so'
or so1
S3HONI
39VM01S
231
-------
storm types in order to determine the number of events. These
difficulties make the envisioned national precipitation event
definition an unattainable entity via this approach.
^
A second approach to this event definition is a statistical one.
The^examination of the probability distribution of storm character-
istics is accomplished first by separating a time-series of point
rainfall observations into events which are statistically independent
by use of the rank correlation coefficient.18/-19 Then, combination
with a significance test allows selection of a limiting time interval
(i.e., a minimum interevent time) between nonzero rainfall pulses,
such that the hypothesis of independence may be rejected with a chosen
level of assurance for nonzero pulses which are closer together. This
time between independent precipitation events has been observed to
follow closely the Weibull distribution.18/19 Use of this approach
would be applicable for a specific region at best. Extensive studies
would be required to determine the parameters of the Weibull distri-
bution. Furthermore, comprehensive validity of this technique is open
to question as it was developed through studies of convective storms
only in northeastern North America and Tucson, Arizona.18/19
A final approach could be one which established a minimum number of
consecutive dry (i.e., no measurable precipitation) hours that must
precede and follow individual storm events. This rather elementary
concept is probably the best current approach to providing a useable
event definition that can readily be applied on a national scale. This
dry hour or zero precipitation approach is presented next.
In general, the following procedures were utilized to develop the
event definition for each of the study cities. A one year precipitation
record that approximates the average rainfall distribution was obtained
for each city. Precipitation events were tabulated by varying the
number of zero rainfall hours necessary to divide two separate events.
A "minimum interevent time" is defined as the minimum number of
consecutive zero precipitation hours which must occur between two
separate storm events. By varying the "minimum interevent time" the
number of separate storm events generated is tabulated. The results
are shown in Figure VI-13, Effect of Minimum Interevent Time on the
Annual Number of Storm Events. Based on a qualitative analysis, a value
of 12 hours is chosen for the national precipitation event definition.
Using this event definition and the results of the STORM analysis,
one can derive the relationship between percent runoff control and
number of overflow events per year. The results, shown in Figure Vl-14,
Relationship Between Percent Runoff Control and Annual Number of Over-
flow Events, can be used to transform the final estimates to a base of
events per year. The no-first-flush isoquant parameters can be used
to estimate the level of runoff control.
232
-------
HV3A d3d S1N3A3 dO
233
-------
100
REGION
SAN FRANCISCO. I
• DENVER........ IE
— MINNEAPOLIS IH
ATLANTA EC
:— WASHINGTON, D,C 2Z
Figure VI-14.
% CONTROL OF VOLUME,R
Relationship Between Percent Runoff Control
and Annual Number of Overflow Events
234
-------
OVERALL COST ASSESSMENT
Overall Results
General results thus far are summarized in Table VI-12, General Information,
Table VI-13, Land Use by Type of Use, Table VI-14, Land Use and Population
by Type of Sewerage System, Table VI-15, Quantity and 'Quality of Sewage
and Stormwater Runoff, and Table VI-16, Annual Control Costs per Unit of
Developed Urban Area.
The only remaining problem is to estimate the nation-wide costs for 25, 50,
75, and 85 percent control. As a first approximation, assume that an overall
25 percent control level is achieved by 25 percent control on the combined
(A..), storm (A?) and unsewered (Aj areas at annual unit costs ($ per acre)
of C , C9, and C«, respectively. Thus, the approximate total annual costs,
TAG, are
(TAG)
(C2)
(C3)
(VI-21)
From Tables VI-14 and VI-16, we obtain
(TAC)9, - 47(2.248 x 1Q6) + 32(5.987) x 106) +8(7.393 x 106)
Likewise,
$356.4 x 10 /hr.
(TAG)
50
$1,065 x 10
CVI-22)
(TAC)?5 = $3,210 x 10°
(TAC)QC = $5,029 x 106.
OJ
Recall that -the cost of wet-weather control using secondary facilities is
6R, '
Z* = ke -1 ..-.'. (vi-24)
s
annual cost for optimal solution, dollars per acre,
k, 6 = constants, and
R = percent BOD removal (0 ± RI <_ 85) .
The cost of wet weather control in terms of pounds of pollutant removed, w,
is - „
where
Z*
S
(VI-25)
where w = pollutant removal, Ibs/acre-yr, and
M = pollutants available, Ibs/acre-yr.
235
-------
Table VI-12. GENERAL INFORMATION
Total Urbanized Area
Total Population :
Average Population Density'
Average Precipitation
29.037 x 106 ac (11.751 x 106 ha)
149.366 x 106 persons
5.14 persons/acre (12.7 persons/ha)
33.4 inches/year (84.8 cm/year)
Table VI-13. LAND USE BY TYPE OF USE
-Use
.106 Acres (ha)
Undeveloped
Residential
,Commercial
Industrial
Other '
Total '
13.409 ( 5.426)
9.120 ( 3.691)
1.337 ( 0.541)
2.324 ( 0.941)
2.847 ( 1.152)
29.037 (11.751)
Table VI-14. LAND* USE AND POPULATION BY TYPE OF SEWERAGE SYSTEM
Undeveloped
Combined
Storm
Unsewered
e - •
.
6
f: 10° Acres (ha)
13.409 ( 5.426) •'•?.
,- 2.248 ( 0.910).
5.987 ( 2.423)
7.393 ( 2.992)
. ',' Developed ;
; ' Population
Density"
g '"' Persons/
10 Persons ,.; Acre;, (ha)
,o o : ;
37.606 16. 7.! (41. 3)'
77. .853 13.0 (32.1)
33.906 ' 4.6, (11.4)
29.037 (11.751) 149.366
9.6 (23.7)
236
-------
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•^^
•C/V
• ..
- . -A, ^j
W
' §
^"M
1"
P^
1
O
Ed
CP t>
rH W
t>
prj
CU O
iJ3 EH
cd H
EH S3
*T"J r™^
Crf
S
rH
rH CO
1 EH
. > CO
o
CU O
rH
O - - 1
Id o
H B
U H
o
U
cu ~i
CO.
-' r^
in
oo
.in
o
m
m
CM
CU
4-S
CO
cU
60
cd
cu
•cu
CO
t4H
O
•
p»
. £
CO CO CO
*^" **d~ co
0 0 O
• • *
o o o
in CM rH
O 00 CO
« • ' »
vo
O r>» in
m CM
X~N X™\ X"~X
co* CM . . sr ,.
i-~ co m
CO CM
V»X N*^ **^^
H ,-<5- CM :
m cjx CM
rH • •• . ' ••• '
^~S '^N ST**
i — ! CO
vb' a\ , Oi
rH f"*^ rH
rH . '
V^ V-^ -' N,-''
r-~ CM , 03 .
•
a>
lea
•j^
HH
0
o
HI
H
^>
CU
rH
I
rC?
'T
-tr^
jw*-
iH
•s
C8
237
-------
The marginal cost of BOD removal is
d7* 100g w
s _ 100 0 k ~1
dw
M
(VI-26)
Given these convex cost functions, the optimal mix of control of storm
runoff from combined,storm,and unsewered areas is found by equating
" C?7?S; ^-^ equati°n VI~26 with the subscript (1) denoting
, (2) denoting storm,and (3) denoting unsewered, yields
100
ioo elW;L 100
M, 100 B^kL I
Ml
M
(VI-27)
100
100
,:f marginal costs for, say, 50% BOD removal are compared, one obtains
MC - 100(0.048)0.4.0$ 100(0.04a) (0.5)
1 136.6 e
= $5.44/lb BOD ($li;99/ke.,SOj))
MC - 100(0.043)qp.82) 100(0.043)(0.5)
2 30.5 e
- $13.10/lb BOD ($28.85/kg BOD)
MC = 100(0.038) (3.^1') 100(0.038) (0.5)
3 25.9
= $3.25/lb BOD ($7l.i5/kg JBQD.)
(VI-28)
(VI-29)
(VI-30)
Ihis result indicates that, to achieve 50 percent control, storm sewered
areas should be controlled less intensively due to their relatively
.;igh marginal costs and unsewered areas should be controlled more
intensively because of their relatively low marginal cost.
The r.orrect solution can be found by solving for w. and w0 as functions
of " i.e., ± j
Wl = a!2
b!2w2
. 85
(VI-31)
238
-------
W3= a32 + b32w2 0
(VI-32)
where
Ml 02 k2 "l
•*• i,, r f \ f—=-^ ( \
;~ -Ln L *. Q Mi, MM ->
!2
M
k2 M3
•v
b
32
M
are as
defined earlier.
The total wet-weather pollution load, WP (pounds/year), is
WP = 2 M^ (VI-33)
- 4-1.
where M, = annual pounds per acre from i area, and
4-T-l . . - "
A. = area of ± area, acres. .•••••
Let p denote the proportion of WP that one wishes to control. Then, the
optimal removal, w|, for a given p is found by substituting equations
VI-31 and yi-32 into VI-33, or . . - "
L, + w,A« (VI-34)
p (WP) = w^ +
p(WP) = Ca'+
^ b32VA3
"•W2-
!- a12Al - a32A3
(VI-35)
(VI-36)
where
Knowing w$
and VI-327
= optimal annual pounds per acre: removed "from
the ±th source area.
one can find w* and w* by substituting into equations VI-31
' ' x ' J
239
-------
The optimal percent control of the ith source for control level, p, in
100(w*)A
terms of w* is
M.
0
(R*) < 85.
i p
(VI-37)
The optimal percent control for 25, 50, 75, and 85 percent is shown in
Table VI-17, Optimal Percent Control For Specified Overall Percent
Control. Knowing (R-) , one can find the cost per acre by simply
substituting into equation VI-24, i.e.,
3R-,
(VI-38)
to obtain the optimal annual cost per acre as shown in Table VI-18, Optimal
Annual Cost per Acre for Specified Percent Control. Thus, the optimal annual
control costs are shown in Table VI-19, Optimal Annual Control Costs. These
costs are approximately 17 percent less than the approximate values reported
earlier for 25, 50, and 75 percent control, i.e. equations VIr21 and. VI-22.
Tertiary Treatment vs Wet-Weather-Treatment
The optimal mix of tertiary treatment and wet-weather control can be
found by equating the marginal cost of tertiary treatment with the
marginal cost of wet-weather pollution control. The estimated total
annual incremental cost of a tertiary treatment plant is:20
°'787
= 87,000 D
where C = total annual incremental cost of tertiary
treatment plant, dollars per year, and
D
(VI-39)
= plant size, mgd.
Assume a 25 mgd plant. Then, C = $1,096,000 per year. The plant
is assumed to increase the BOD removal from 85 percent to 95 percent
or about 0.017 pounds (7.71 g) per capita-day or 1,550,000 pounds
(704,000 kg) per year for the city of 250,000 people. Thus, the
unit cost of tertiary treatment, c , is $0.71 per pound ($1.55 per
kg) of BOD removed.
Equating the marginal cost of wet weather control to the unit cost of
tertiary treatment yields
"tert
IQOg k
M
IPO e
M
(VI-40 )
240
-------
TABLE Vl-17
DEPCF^T
IEPA
IRfG
"t
• «•
1
• M
1
"T
• •
i
STATE
.
"CT"
""ME"
""MA"
""H"
25X '
cuvniSTOrt"iuNSE:wi
5l7o!~"I°! ?.P.6j
__.._ I-__,-|.....I.
27.91 n.OI 3.01
CT.NTRCt FOP SPECIFIED fiVFRALL PERCENT CONTROL
OPTIMAL PERCENT cnNTR°L , 85X
rn^BiSTOPMiu^sE^! COMBiSTORMIUNSEWI COMBiSTORMIUNSEW
*f ;. i -..;-1 ...i-1 ...1-1 ...i-1 —.-. i "—••• i "'•••" \ mzz"n
•>?.8l O.Ol 28.71 77.71 0.0 54.5I 85.0 0.0 85.0
I I _.•)*._..(•*• I «••»*• I •«• w I •«••»• I «•»••€• I mmmmm |
"•^•"*(~~~*~W?'TT -.! „.. -* t A. •» i *«- MI «e 'A i Be rtl
°2&7i; I ""7*0! "52?"! "5O! "5*71 j "5572! ^75711 "5775 j jji7o I ^57o j "es7o! "5i7o i
mmmmm \ »«•«»•-» I mm~m,m | *•*•••'*•? . ***"T*?T.IItT**AiJT a! A A I C * 51 AC ft I « rt i ft** - fl
j O.Ol 85.0
2
"5
» a
• <•
3
"5
*3
NJ
»••••
NY
mmm,
"E"
•..•
DC
"MD"
till
I57o i ""-I.7o i "Ji?771 "«e75 j
i
s^?!""^^"??^^
2578l*2?"T[""o7o|'
'"o7o!"l6l7l"4672l
p*
*3l""vA
"5i""wv'
• I.
.1.
61.1}
no i
?fi.ll 70.21 77.91
"2774["ft53oi°7875l
"2775j"i«"ot"si.Oj^
"279r"o7or72°0|'
.....1 ..... I.....I.
40 41 73.01 0.01
63.0 85.01
•mmmm\mmmmm\«
54.31 85.01
..... 1..... 11
I I
I
..... I..... i .»«>•»
85.01 95.01 85,0
..... j ...=?. I .«.»•
85.01 85.01 8S.O
..... I .....I.»=»•I
82.81
,mmmm I <
70.8
0.01
5s7oi
85.01 85.0
.....I.....
85.01 85.0:
..... I ...t*«
O.Ol 85.0
89.0
»<*m9m
. £i£
"3i7o
32.2! 6.91 «2;a|_
4iir6!""5!!3l"367il
,..._|...r.i....>1.
27.21 0.01 6.51
55.M 33.51 70.31 83.31 62.4! 85.01
.... I ..... I ..... I ..... I ..... I .....
69.21 ?7.8l 63.51 «5.0I^61.5 I^BS.O!
'^275 i ""5761 "?57"i! "77721 "55751 "55751
65.Oi 85.01 85.0
»mmmm\mmmmm\mmm»*
85.01 85.Oi 85.0
"6l"oi"857oic"ei,o
I
41 AL
tmmImmmmm
41 FL
i..I.....
at GA
• mm Immmmm
KY
MS
...
NC
a i sc
>.i....
41 TN
..)....
mmmmm \ m-mmfim \ mmmm_m , — _ — . — . - .
37.81 0.91 21.41 62.31 23.4 07.4
.._._!.....!....-!-....!--..-I««T-T!"
44.01 .6.61 37.31 68.41
""o7o! "117" * "55751 "o7o i
.....|.....!...^.'
0.01 11.7! 34.71
"o7i)i"T577i"5375i
.....i.....;...*-i
39.01 1.21 31.91
,..2:2!
i..2:2!
u*h2!
•mmmm\«
23.4J
tmmm** I '
30.61
'3671 j'
"55781'
...-!,
25.21
64.01
,.-..(<
60.41
immmm | i
?".5i
'587ll
0.01
.....I<
B5.0I
....11
85.01^
..1-1.
0.01
..— I.
0.01
....(
85.01
mmmmm\
85.0
"5570
.....
85.01
m«mmm]
85.01
•....I
85.01
mmmmm
85.0
85.0
ummmm
85.0
"557o
"5i7o
85.0
9mmmm*
85.0
>....I
85.0<
mmmmm
85,0
ammmm
85.0
*8i7o
mmmmm
85.0
"5173
.....
• 0...
,25s2
"5i7S
^I57o
m<*m*»m
85.0
"5i7o
mmmmrm.
85.0
241
-------
TABLE VI-
EPAI STATE
RPGI 10
•61 AR
.£\1.±L
..*!..?-.
61 OK
61 TX
71 IA
7' K9
7 1 MO
71 ME
81 CO
81 "T
SI NO
81 SD
._£!..£-
81 WY
1
91 AK
.-.2L.1L
91 CA
91 HI
91 NV
1
10« ID
-1SL.21L
:»!..!£.
17 npTiMAf PFRr-.ENr cr-A'tnni ^np. SPEC
OPTIMAL PER
25x i •so*
COMB STORM IUNSF!-)! -'COMB I 3TOPM 1 U*pSrh
55.0
0.0
0.0
0.0
25.7
I?. 7
l.aj 19.31 PO.l
13.6! 54.31 0.0
7.ai aj.ei o.o
5.81 39.51 0.0
10. ?I 39.1
|
15.71 36.5
38.21 4.?1 ?7.1
31. «
27.7
•
o.ot a . a
0.01 it*. 7
i
2a.6l--9.6J M. ?
!UO
si. a
a« .8
0.0
0.0
.36.1
0.0
16.7:1 36,. /
15.51 32.4
9.51 35.5
' 6.9; /M.,9
13.01 36.5
.1 ' "
0.31 39.0
3.6*1 ai..6
19.71 13.41 47. 6
0.0
10.61 46. '0
30,21 2.5 1 35^6
,
o.o
1
_--_,"£?
31.91 O.'oi 27.7
3Z;2I 0.01 .zl.B
44.9
*0.9
6?. ft
•^6.3
53.5
a5,a
0.0
76.1
6d.9
0.0
0.0
.-
ft(>,2
0.0
rt .1 .7
0.0
54.1
0.0
57.0
«•!*
?r,.5\ ud.b
37.71 PI. 6
31.51 ft7.5
.-29.4J 65.5
3/1.3
41.4
2fl.7
?rt.l
P0.3
•Jfl.U
a 1.3.
'iO.l
33.6
'50...9
37.4
2ft. «
^2.8
38.6
3!3.0
27.0
at.O
?2.6
65.3
63.9
53.3
?9.e
56.3
70.9
62.3
5R.3
61,4
67. fl
/.?.!
64.9
67.4
<'4.«
71.9
61^7
59.1
54.1
IFIT^ OVERALL PERCf
:ENT CONTROL
75«
COMB 1 STORM IUNSEW
B5.0
0.0
0.0
0.0
69.2
53.7
"85.0
81. S
76.8
73.6
0.0
85.0
85.0
0.0
0.0
85.0
0.0
69.9
0.0
80.1
o.b
56.8
71.1
64,4
61.7
64.9
1
73,5
54.4
"9.2
4a.a
67.3(
67.9
67.6
61.8
64.3
64.6
56.4
65.1
71.1
68.1
53.5
1
65.6
81.61 47.2
T5*Ti 50. 7i 82. 5i 44.3
T7.5
85.0
85.0
85.0
85.0
85.0
80.8
55.4
83.4
1
85.0
85.0
85.0
85.0
85.0
85.0
65.0
65.0
85.0
85.0
85.0
84.5
80*1
76.6
'NT .CO
COMB
85.0
0.0
0.0
0.0
85.0
85.0
85.6.
85.0
85.0
85.0
0.0
85.0
85.0
o.o:
0.0
85.0
^ITPOL
STORM JUN8EW
85.0
' 85.0
85.0
85."0
85.0
85.0
85.0
85,0
85.0
85.0
• 85.0
85.0
85. '0
85.0
85.01
85.0
85,0
85,0
85,0
85,0
85,0
85.0
85.0
85.0
85,0
85.0
85.0
85.0
85.0 85,0
85.01 85,0
0.0 85.0 85.0
85.0
0.0
85.0
0.0
85.0
85.0
85.0 85, (5
85.0
85,0
85.0
?5,0
85.0 85.0 85.0
85.0
85,0
95.0
242
-------
TARI.E Vl-18 OPTIMAL AN'JIIAl.
EGI 10
..I...-.
1 I ME
II, MH
cn*3
7fll
'HIT
751
i
n.
'"o"
"o"
'""
'IT!
0.
10.
"'4.
..--
• •*••>
JO.
°t.
CHNTUOL
COST P||xACRr , ?5?
a^.j n,
30:
771
P2«r
"in
"29!
cn^s
37"7
"39!
355?
1471
• •••*•»
396.
TQ^MI
o
UMSFWI rOMBlSTORMIUNSEW.!
"B"l~51o!!"3527 "92.1!
771
297.
"""o.
107.
"'!;
III!
Ill:
IsoT
21 NJ
... I...—
21 NY
... I ..»•»-
I
_..(-...-
31 DC
""ii"""
"°5l"~PA"
""ii"vA"
...I..-—
3! 4V
l6
*
46.
"I^"
"liT
"22'
"If
1S7.I «-
37.1 206.1 360
T2.
lot.
III5I
90.1
""si!
...: i
loi.!
.-.!-..--
41 AL
..-I .....
41 FL
""3|l"GA"
"«r"xY°
...I..---
SI ' MS
...!-.-->
a i NC
°""4i""sc
... i ..->.
41 TN
51 IL
"si""i5
„.!„,.-
51 MI
"i|~~MM
0
"Tol
•»•«•«»
47
0
n
.....
"~66
"3!
"30
""SO
?t.
*!• «•«•
"•»» •
11.
"71"
MO
0
"o
'M
is
...
•--
""9
"7i
"Iop"t""H6
"~*!'iII*I
"ToiTj""??
46
"l9
"II
"Ifl
350.
!•»••*•••
»•» 1« M ^
0
""""
'337"
°667
°233
"285
"l2
"306
"45?
B4.I 350.1
t64
131
71?
127.1
•.•^1
113.
Hill
*8«"!l
.....I
133.1
7I«j|
7511
.l
52.1 305.1 364
• •MB I •••OWBI I M«*
-------
TARJ.P VT-
1
EPA 3TATE
RFGI Id
61 I,'
6
6
7
7
7
1 OK
1 TX
1 I*
i pn
8
8
8
8
SO
I'T
HI
To
ID
18 MPTTMAi AM.'.'JAJ C"ST
1 M 25* __ ;[
45.
0.
".
0.
82.
49.
'4'1.
2».
31.
45.
n.
37.
«4.
n.
0.
62.
0.
63.
°.
17.
0.
63.
40.
13.
53.
7.
U.
15.
U.
10.
0.
0.
13.
12.
9.
11.
1?.
15.
Ifl.
ir.
3.
0.
12.1. tl'7.
?p:? ACT FnR S"(;CI
PTI,UU .Ak'M'JAL COST
'ST04''1 1-'IMSE^ ! CDMR
35.
:54.
93.
i-'SiU!*:
CpWTRHL
( 85X
141.
0.
0.
0.
1665.
268«.
287.
?35.
322.
684.
0.
72"
0.
0.
0.
1201.
0.
7257
0.
37?.
1096.
139.
347.
310.
198.
"?54.
193.
290.
236.
72o.
?I7.
22i.
183.
426.
146.
138.
30.
66.
75.
73.1
82.
147.
53?
48.
62.
62.
62,
50?
691:
112. 25.
170. 40.
314. 61.
60. 72.
119. 55.
509.1 469. 112.
314.
432. 94.
244
-------
TABLE V.T-1
1 ) 1
IE.PAISTATE
IRFGI TD
IH
1 11
1 11
1 1 1
,...,-
ITL *E
1 21
l .21
PT
MF
MA
MM
E
DC
PA
VA
11 V
-.3 3
AL
FL
. r,A
KV
MS
NC
«|C
TM
PS il
IL
TN
Ml
MN
OH
VI
HS
9 MPTIMAI 1 r.|:JA' C!'K|THOl. Ci'Si
nPrlMAi. AKVi'iA!. C'i'jT-^n1. cnsn
'J.6I 13.51
0:. 9. 1 ' ? . T 1
. £:-!!. .2S:i'!.
1.71 s.aj
n.21 0.51
12.51. 35.^1
37.')| 1
-------
TABLF VI-19 OPTIMAL ANNUAL CMNTRDL COSTS
|EPA!STATE!°PTIMM """
IRFGI ID 1 25* 1
61 LA
TL "EG 6
71 IA
71 KS
'71 MCI
fr"PE""7
81 CO
81 MT
8j ND
81 IJT
81 WY
TL PEG 8
91 NV
10j ID
~Toi~~o5~
T"u7s7~"
97oT
0.61
3.01
1'->.5I
32.0!
3-. U 1
.
III!:!!"
""11761 "
... 2:2 L
0.51
0.31
0.41
1.21
0 . .5 i
._
1.01
1.1,
0.2,
2«.5l
0.4!
— sril-
296.71 a
' 50X 'l
5.01
27.71
1.51
7.91
U3.5I
•55.51
a. ii
6.51
16.21
4.01
6.21
1.3.
0.81
1.01
3.11
0.71
13.11
0.81
?.6I
59.21
?.6I
0.41
65. ?!
l.Ol
TJI.9I
'26741*
7S5 1 *5"< 1
T3. 31 24. s!
38.71 152.21
3.91 6. At
21.51 40.lt
lle.il 206.?!
2U6.7I 430.71
23.11 48.?!
I'.'Tl 30.31
43. j?l 71.51
10.71 "s.l!
17.01 28. 9J
3.21 5.01
2.11 3.41
2.71 !i.7l
fl.ll 1U. 31
LSI 2.91
35.0! 59721
2.11 fl.OI
6.71 "T.5!
I7?.at 287.61
7.9| 13.51
l.II 1.91
190. Tl 318.51
?.5I 3.91
39.2! 69.21
"697?l"r2o79l
«5. 712725. a!5o29. 01
246
-------
or
w* =
M
1003
r
L
^ert^,
1000 (k)J
(VI-41)
where
w* = optimal pounds of wet-weather pollution to control
prior to using tertiary treatment, pounds/acre-yr.
The optimal percent, control in .terms of R^ is ..
c ert.(M)
R* - max X-g In [1QQg (k)L: 0) .
The overall average BOD loading per acre, M (pounds/acre-yr), is
' — WP 6.81 x 106
(VI-42)
(VI-43)
where
tot
Atot 15.63 x.lO
M = 43.6 Ib Bpp/ac-yr (34.0 kg BOD/ha-yr)
i ,
= total developed area in US, acres.
0.047JR.,
The overall cost function for the US is Z = 5.37e ;
'r 1' •; 0.71(43.6) . nl
Rl = max [07047 ln 100(0.047)(5.37)' UJ
Thus,
(VI-44)
R* =
4.3%.
Thus, for these assumed conditions, approximately '-"4 percent of the wet-
weather pollution should be controlled prior to initiating tertiary
treatment. While these results are for one specific set of assumptions,
they do suggest that it is highly desirable to do this tradeoff analysis
before committing a community to tertiary treatment.
Potential Savings Due to Multipurpose Planning
The cost of wet-weather quality control can be reduced by integrating
this purpose with dry-weather sewage treatment plants and/or storage
facilities for stormwater quantity control. Therefore, it has been
suggested that flow equalization be considered as an alternative to
conventional design.21 The storage volume needed for dry-weather
flow equalization is estimated to be 10 to 20 percent of the average
dry-weather flow.22 Integration of wet-weather quality control with
dry-weather control affords the opportunity for equalizatxon of dry-
weather flow since the wet-weather quality, in general, must be
247
-------
Z£ - cost of dry-weather control at a secondary plant,
dollars per year
= amortized capital costs and annual Q & M costs
118,000(D
55,000 D
°'83
CVI-45)
°
allo = mgd 3nd a Per caP±ta sewa§e
gallons per day, computing Z£ and dividing by area A - D
of
where
Also,
PD
Z, = 15.6 PDj
d
annual cost of dry-weather quality control,
dollars per acre, and
"d - developed population density, persons per acre.
Z2 = annual cost of wet-weather quality control,
dollars per acre
where all terms are as defined earlier,
and
annual cost of wet-weather quantity control
dollars per acre,
(VI-46)
(VI-4 7)
(VI-48)
248
-------
where V = storage volume required for wet-weather quantity
control, inches,
= CR'(i), with
CR/ = runoff coefficient in developed state minus runoff
coefficient in undeveloped state, and
i = 24-hour rainfall for design frequency, inches.
If dry-weather quality control and wet-weather quality control are
integrated, then
Z = joint annual cost of two purposes, dollars
per acre,
=15.6 PD + cE + cS*
(VI-49)
CT.[ TL - E
where E = excess capacity per total area, inches ,per
hour = 1.535 x 10~4PDd assuming E' = 10 mgd
generated using 100 gal/capita-day and
converting from mgd to ac-in./hr; ,and
c = annual cost of treating E at dry-weather
plant,
= $1,960 per acre-inch/hour (assumed using
$3,000 per mgd from analysis of treatment
costs).
If wei^weather quality control is integrated with wet-weather quantity
control, then
Z = ioint annual cost of two purposes, dollars per
23
" acre,
-KS*,
CTIT1 + (T2 -
where
S* = max (V, | In [^- K(T2 - T^ J , 0) .
(VI-50)
(VI-51)
It is assumed that dry-weather control cannot be integrated with wet-
weather quantity control. Therefore,
249
-------
i,- ~ joint annual cost of two purposes, dollars per
acre,
Zl + Z3
= 15.6 PD , + c0V.
d S
(VI-52)
(VI-53)
If all three purposes are integrated, then
Z123 S 15'6
cgS*
-KS*
(VI-54)
To determine the potential savings in wet-weather quality control due to
integrating this purpose with the other two purposes, it is first neces-
sary to apportion the total cost among the three purposes. For cost
allocation, the use of facility method, most commonly applied in the
wastewater field, cannot be applied in this case because there is no sin-
gle facility utilization parameter that is common to all three purposes.
Hasan has demonstrated that the alternative.cost method is appropriate
for cost allocation.** , Briefly, the alternative cost'method assigns
each purpose its separable cost plus a portion, of the joint or non- • ':'
separable cost on the basis;,of; alternative cost. , These .costs site "defined
as follows: , '. . .• ' .•> > ...... .- ... ; ;. .,„-
Then,
where
SC^ - separable cost ,to purpose i • ', < Y ....
- cost of the total project minus cost of the project
with purpose i excluded, and
NSC s non-separable cost
= total project cost minus sum of the separable costs
to all purposes.
+± = SC^ + Y±(NSC) (VI-55)
ij> » cost allocation to purpose i, and .*•....,,
Y£ = fraction of the non-separable cost assigned- to i. -
In light of the above discussion, • the cost assigned to stormwater quality
control, 2, (purpose 2) is as follows: .'.". ,
(VI-56)
250
-------
where
SC
NSC
Z
13
3
i=l
SC
(VI-57)
(VI-58)
Z12+Z13+Z23 ~2Z123'
Z2 - SC2
3
2 + z3) - z sc.
(VI-59)
Z2 " (Z123 Z13)
ZJ -
123
Z23}
CZ123 Z13)
(Z123 ~ Z12)]
(vi-eo)
Then, the potential savings are Z2 ~ ^2*
The above procedure was used to derive Figure VI-15S Cost Allocation Factors
for Five Cities, for the two year storm. The sensitivity analysis
reflecting the variations in cost allocation relationship for various
frequency storms for region III is presented in Figure VI-16, Effect of.
Design Storm and Number of Purposes on Cost Allocation Factor for Various
Levels of Control. Based on the results in Figure VI-15, the following
cost allocation proportions can be used for all urbanized areas.
% Pollutant Control
25
50
75
85
Cost Allocation Factor,
0.46
0.50
0.70
0.80
Potential Savings Using Best Management Practices
In addition to using storage-treatment devices to control wet-weather pol-
lution, other options, popularly called best management practices (BMP s),
are available. In a related study, Heaney and Nix evaluated the-savings in,
control costs if BMP's are used in conjunction with storage-treatment.
The procedure was applied to Anytown, U.S.A., a hypothetical city of one
million people whose characteristics were determined based on average values
251
-------
to
-------
253
-------
derived in this study. The results, shown in Table VI-20, Comparison of
Annual Cost of Optimal Control Strategy for Anytown, U.S.A." Using Storage-
Treatment Alone and in Combination with Best Management Practices, indicate
significant savings could be realized by using BMP's in conjunction with
storage-treatment devices. These results can be incorporated into the final
assessment by multiplying the storage-treatment costs by the % savings shown
below.
% Pollutant Control
25
50
75
85
% Savings Using BMP's
54
37
37
39
SUMMARY
assessment is to evaluate the cost of controlling
™ < w*fc-weather Pollution, emanating from the 149 x 10?
, § ? Urban areaS ±n the United States' **iable procedures
S St0rTater P°Hution are not yet available. Thus, a con!
™ TJ °f devel°Pment^ effort was expended in devising such
procedures. Major results are presented, by item, in the nex^f ragraphs ,
1. Total Single Purpose Control Costs
Relatively detailed studies in five cities provided
the basis for evaluating storage-treatment alter-
natives for wet-weather control. One year of hourly
data was simulated to predict the performance
of various storage-treatment configurations.
These results were put in analytical form to
expedite extrapolation to the other 243 urban-
ized areas and other urban areas. These
results are combined with data on the cost of
storage and treatment to derive the optimal
mix of storage and treatment to use to obtain
a given level of control. The final result is
shown in Figure VI-17, Storm Water Pollution Control
Costs for the United States. A striking feature
of curve A is that it bends upward (convex) indicating
increasing incremental costs (particularly at higher
levels of control) . The primary reason the curve has
this shape is due to the disproportionately larger
amounts of storage and treatment required to control
the larger storms.
254
-------
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255
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5400
EQUATION: 1-
89.9aO.046R,
SINGLE PURPOSE : STORAGE - TREATMENT
ONLY
MULTIPLE PURPOSE : PORTION OF STORAGE
TREATMENT COSTS ASSIGNED TO OTHER
PURPOSES
SINGLE PURPOSE : STORAGE-TREATMENT
AND BEST MANAGEMENT PRACTICES
SINGLE PURPOSE : STORAGE-TREATMENT
ONLY - RESULTS FOR COMBINED
SEWERED AREAS
to
a
O 2400
U.S. URBAN POPULATION = 149 XIO
U.S. DEVELOPED URBAN AREA = 15.6 X I06 flc
10
2O 30 40 50 60 7O 80 90
% BOD REMOVAL , R
Figure VI-17. Storm Water Pollution Control Costs for
the United States
256
-------
2. Total Multiple Purpose Control Costs
Significant savings can be realized if one
integrates dry and wet-weather treatment and
storage for quality as well as quantity control.
Curve B in Figure VI-17 indicates the cost
ofTtormwaterVality control in an integrated sys-
tem. This result suggests that the potential
savings are significant enough to.warrant further
study in evaluating stormwater systems.
3. Total Costs of Storage-Treatment and Best Management
Practices
Potentially significant savings can be achieved by
re?ebc>?
high pollutant loading rates and sewer flushing where
heavy deposition occurs in combined sewers appear to
be especially promising.as options to storage-treatment.
Furthermore, a significant portion of the costs of
BMP's could be allocated to other purposes.
4. Tertiary Treatment vs Stormwater Quality Management
A comparison of the marginal costs of tertiary-
treatment of sewage for further BOD control with.
initiating control of wet-weather quality indicates
that one should initiate some level of wet-weather
quality control prior to using tertiary treatment.
Of course, a different result would occur if nutrient
control is used instead of BOD control. Nevertheless,
the relatively low marginal costs of wet-weather con-
trol at low levels of control indicate that it should
be given serious consideration as an alternative to
tertiary treatment.
5. Comparison of Control Costs with Other Studies
Previous studies have indicated that stormwater control
is quite expensive. Table VI-21, Control Costs Reported
in Other Studies indicates that the 1974 Needs Survey
estimated total control costs as $266 billion whereas the
more recent National Commission on Water Quality estimate
is $278 6 x 109 25' 26 This assessment indicates initial
capital'costs ranging from 2.5 billion for 25 percent con-
trol to 41.9 billion for 85 percent pollutant control, a
small fraction of the above estimates. Two reasons why
the estimates vary by such a large amount are discussed
below.
257
-------
Table VI-21. CONTROL COSTS REPORTED IN OTHER STUDIES
1974 Needs Survey
National Commission
on Water Quality0
Combined
Sewers3
Stormb
Sewers
Total
Combined
Sewersdj'
Storm
Total
266.1
79.6
199.0
278.6
estimates -*ns various assumptions regarding design storm,
cEstimate includes collection sewer costs
.STS.'c.r^.STS?;^ *t*A* «"1SSi°'> ;« »«~ Q-Uty, Washing-
eUsed 2 year, 1 hour .design'1 "storm."
258
-------
A. Collection System Costs -
9
The NCWQ estimate includes $84 x 10 for
constructing storm sewers. This study did not
view storm sewers as chargeable to pollution
control.
B. Choice of a Design Storm -
The NCWQ studies used control of the two year,
one hour storm as the basis for their mean esti-
mate of control costs. The concept of a design
storm was not used because it was felt that a
continuous characterization in terms of percent
of the runoff which could be treated (or events
per year) was more appropriate since no accepted
design event condition exists which also speci-
fies a design antecedent dry-weather period.
Also, sizing storm water quality control units
to treat runoff from storm intensities associ-
ated..with less frequent events, e.g., two
year - one hour storm, requires relatively
large treatment and storage capacities'.''
Using 25 years of hourly rainfall data for
San Francisco, CA, and Atlanta, GA, the
relationship between percent of precipi-
tation "treated" and the design storm was
determined. Figure VI-18, Percent of Total
Precipitation Volume vs Rainfall Intensity -
Atlanta, GA (1948-1972), indicates the per-
centage of total volume less than or :equal
- to a given value. For example, approximately
65 percent . of the total- precipitation volume
occurs from rainfall rates of 0.30 inch per
hour (0.76 cm/hour) or less. Thus, a treat-
ment unit of this capacity could treat the
entire precipitation during those hours. It
could also treat a portion of the remaining
more intense storms. The additional volume
treated equals the number of hours during
which the precipitation'was greater than
the indicated treatment rate times the
treatment. From Figure VI-19, Percent of
Total Precipitation Hours vs Rainfall Inten-
sity - Atlanta, GA (1948-1972), approximately
five percent '• of the rainfall hours exceed 0.30
inch per hour (0.76 cm/hour). The average
precipitation in Atlanta is 47.1 inches per
year (120 cm/year) occurring during an average
259
-------
Npiiviidioaud-. iN30d3d
260
-------
p-
to
.
(NJ
04
OJ
00
00
to
a 3
H
to
1*2
•o
fl)
4-1
OJ
4-)
M
H
•a
m
cti
to
ft)
4-1
cd
4J
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H
o
4J
PM
§
s30N3a,anooo Noiiviidioaad Ivioi
261
-------
of 537 hours per year. Thus, the additional
percent volume treated during intensities
greater than 0.30 inch per hour (0.76 cm/hour)
is 100[0.05(0.30 in./hr)(537 hr/yr)/47.1 in./yr],
or 17.1 percent.
Thus, the total percent of volume treated
with a unit capable of handling precipitation
of 0.30 inch per hour (0.76 cm/hour) is about
65 + 17 =-82 percent. In general, to find
percent treated use:
PT = Fra + (100 - F )(I )(N WP
B - v v rc'v TMiNTOTAL;/
where PT = annual overall percent of
,. precipitation treated,
XR = rainfall intensity, in./hr,
IT = design treatment rate, in./hr,
•Fg = percent volume for all I _< I
, ...Fc = percent of total rain-
fall hours with I <_ I ,
total number of rainfall
hours per year, and
P = average annual precipi-
* tation, in./yr.
Note that the result is conservative (the
estimate of PT is slightly low) since the
frequency analysis has been performed using
precipitation volumes and hours rather.than
runoff-volumes and hours. ' This was done to
insure: comparison with the NCWQ study in '
which only precipitation frequencies are '
reported.
The-general results for Atlanta, GA, are, shown
in Figure VI-20, Overall Percent Precipitation
Control vs Rainfall Intensity - Atlanta, GA
(1948-1972) -. Also shown on Figure VI-20 are
the intensities for storms ranging in frequency
from two weeks to 25 years.- Treating at the
intensity equal to the two week storm would
prpcess over 80 percent of the rainfall volume.
(VI-61)
N,
TOTAL
262
-------
lOdlNOO
263
-------
Frequencies larger than a month or two ap-
proach total capture of the volume. These
results indicate that for the NCWQ study,
virtually all of the volume would be processed
with a control unit operating at an intensity
associated with the two year - one hour storm.
However, one could control nearly as much
precipitation by using a much smaller size con-
trol unit. Alternatively, the marginal gain
from using these much larger units is quite
small. For comparative results, the same
three figures were generated using 25 years of
hourly data for San Francisco. The results
are presented in Figure VI-21, Percent of Total
Precipitation Volume vs Rainfall Intensity -
San Francisco, CA (1948-1972). Figure VI-22,
Percent of Total Precipitation Hours vs Rainfall
Intensity - San Francisco, CA (1948-1972). and
Figure VI-23, Overall Percent Precipitation
Control vs Rainfall Intensity - San Francisco
CA (1948-1972). - — '
sj£
sr ES
Improved estimates for a specified city can be obtained using Seal data?
S™?^1
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ABBREVIATIONS'AND SYMBOLS
a Coefficient, inches per hour
A Catchment :or 'sewerage area, acres
Al Combined sewered area, acres
A2 Storm sewered' area, acres
A3 Unsewered area, acres
Total developed area, acres
Annual runoff, inches per year "
Coefficient, inches per hour
Biochemical oxygfen demand --•„•;••••
Five-day biochemical oxygen demand
tot
AR
b
BOD
3
c
tert
CS
CT
Jtert
CR
CR-
Coefficient,' percent"1 '? ' ' ^'' '' : '"" "''''''''•':'""'' :
Coefficient, percent R '"'
Unit treatment cost for excess capacity, annual dollars per
acre-inch/hour „
Unit incremental cost of tertiary treatment, annual dollars
per pound
Unit cost of storage, annual dollars per acre-inch
Unit cost of treatment, annual dollars per acre-inch/hour
Unamortized capital cost, dollars, also annual unit costs,
dollars per acre ... u :
Total annual incremental cost of tertiary treatment plant '
dollars '
Gross runoff coefficient
Net runoff coefficient = developed runoff coefficient minus
undeveloped runoff coefficient : - ../'. ...i •'. ;
Coefficient, inch"1 ;•' ,,, •••.•>. •-:.-; :: : ,; ; ,. , ;
268,
-------
D
E'
.E
ENR
n
f
h
i
I
k
K
1
L
m
mg
mgd
M
Actual sewage flow, mgd ,,
Excess capacity at sewage treatment plant, mgd" ,. !? ' -,
Excess capacity at sewage treatment plant per total area,
inches per hour
Engineering NewsRecord Cost Index
Treatment plant efficiency ' ,,.•:•••-
Coefficient, percent R .
Production function relating percent pollutant control (RI> to
storage (S) and treatment (T)
Percent of annual precipitation volume for which 1^ _< 1^
Percent of total annual precipitation hours for which IR j£ ;IT
Coefficient . • '. ' :
Fraction of non-separable cost assigned to purpose i ;
,2 '£:- . - *•• * " ->' -
Coefficient ....'... '
24-hour rainfall for design frequency, inches
Percent imperviousness ...
-Rainfall intensity, inches per hour ;
Design treatment rate, inches per hour
,'"..' ?t '• ' =''.'.- - ' '•- '"•'•'"> " *"• '•--•'' ' ''•'' • '" ' ' •/,,'"--•-• .'• ..--.-
Coefficient
Coefficient
" Coefficient ,-: .., . ' ,
.Pipe length L . : ..'•- .
Urban land use
Million gallons
Million gallons per day • •
Annual pollutant loading, pounds per acre-year
269
-------
ST
M
MC
MRS
NSC
NTOTAL
OE
P
P
PD
PDd
PT
*i
q
R
R,
R|
s
S
S*
S2
SCi
ss
Average annual pollutant loading, pounds per acre-year
Marginal cost of pollutant removal, annual dollars per pound
Marginal rate of substitution.of storage for treatment
Non-separable cost, annual dollars per acre
Total number of precipitation hours per year
Number of overflow events per, year . -
Coefficient
Annual precipitation, inches per year
Gross population density, persons per acre
Population density in developed portion of urban area,
persons per acre
Annual overall percent of precipitation control (treated)
Annual cost assigned to purpose i, .dollars per year
Coefficient
Percent runoff control = percent of annual runoff volume
passing through treatment unit
Percent pollutant control .(removal)
Maximum percent pollutant control (removal) .
Optimal percent pollutant control prior to using tertiary
treatment
Fraction of total wet-wea'ther pollution load controlled
(removed)
Coefficient
Storage volume, inches
Optimal storage volume for wet-weatherpollution control, inches
Maximum of S* or V, inches
Separable cost assigned ta'purpose i, annual dollars per acre
Suspended .solids .
270
-------
T
X*
TAG
v
V
w
w.
w*
TO
y
z
Z
Z*
Z
Zi
Treatment rate, inches per hour
Optimal treatment rate for wet-weather pollution control,
inches per hour - r"' ' ' '**
Treatment rate at which isoquant is parallel to the ordinate,
inches per hour ,
< * - .•,-.. '.-"•>*> >•'•' 3 *J fi i"*""' '-'' • '.-" • / • - '
Treatment rate at which.isoquant intersects the abscissa,
inches per hour • ; ,. .= - - .
Total annual costs, dollars per year'--''"- "'
Coefficient r . -'.
Volume of storage required for wet-^weather quantity control,
inches
Pollutant removal, pounds per acre-year
Pollutant removal from type of sewered area'- ±$ pounds per
acre-year »
Optimal pollutant removal from type of sewered area i,
pounds per acre-year - =;.-'•.•« : t.-- . •••;.-.•
Optimal annual pounds per acre of wet-weather pollutants to
control (remove) prior to using tertiary treatment, pounds
per acre-year • • • • - ' ''"~J.. • '
Total annual wet-weather pollution load from developed
urbanized area, pounds per year , --,.. i '=-
Coefficient'- ' - ' ' •':' ' '' ~ >'••"-'•-••'.I -^.»^~ "-^•"•"•- '
Coefficient - •••-"-' --'.••''>••" s ;-'-^' m. -:- :;:' \ ' '/"
Total annual cost, dollars per acre
Optimal total annual cost, dollars per acre
Annual cost for primary control unit, dollars per acre
Annual cost for secondary control unit^ dollars per acre
Annual cost of dry-weather quality control,--dollars
Annual cost of dry-weather quality control, dollars per acre
•Annual cost of wet-weather quality control', Jdollars per acre
Annual cost of wet-weather quantity control,;dollars per acre
271
-------
"13
J123
Annual cost of dry-weather quality and wet-weather quality
control, dollars per acre
Annual cost of dry-weather quality control and wet-weather
quantity control, dollars per acre
Annual cost of wet-weather quantity and quality control,
dollars per acre
Annual cost of dry-weather quality, wet-weather quality, and
wet-weather quantity control, dollars per acre
272
-------
REFERENCES
1. American Public Works Association,/'Problems of Combined Sewer
Facilities and Overflows, 1967," USEPA Report No. 11020 12/67
NTIS-PB 214 469, December 1967.
2. American Public Works Association, "A Study of the Costs Associ-
ated with Meeting the Requirements of PL 92-500'," Draft Report
to National Commission on Water Quality, 1974.
3. James, L. D. and Lee, R. R., Economics of Water Resources Planning,
McGraw-Hill, Inc., NY, 1971.
4. Field, R. I. and Struzeski, E. J. , Jr., "Management and Control of
Combined Sewer Overflows," JWPCF, Vol. 44, No. 7, 1972, pp. 1393-
1415.
5. Lager, J. and Smith, W., "Urban Stormwater Management and Technology:
An Assessment," USEPA Report EPA-670/2-74-040, NTIS-PB 240 697,
December, 1974.
6. Becker, B. C., et al., "Approaches to Stormwater Management," Hittman
and Associates, USDI Contract 14-31-001-9025, 1973.
7. Field, R. I., "Treatability Determinations for a Prototype Swirl
Combined Sewer Overflow Regulator/Solids-Separator," Proceedings
Urban Stormwater Management Seminars, Atlanta, GA, November 4-6,
1975, Denver, CO, December 2-4, 1975, USEPA Report WPD 03-76-04,
pp. II-98-II-111, January 1976.
8. Sullivan, R. H., et al.. "The Swirl Concentrator as a Grit Separator
Device," USEPA Report EPA-670/2-74-026, June 1974.
9. Maher, M. B., "Microstraining and Disinfection of Combined Sewer
Overflows - Phase III," USEPA Report EPA-670/2-74-049, NTIS-PB
235 771, August, 1974.
10^ Agnew, R. W., et al., "Biological Treatment of Combined Sewer
Overflow at Kenosha, Wisconsin," USEPA Report EPA-670/2-75-019,
NTIS-PB 242 120, .April, 1975.
11. DiToro, D. M., "Statistical Design of Equalization Basins," JEE
Div., ASCE, Vol. 10, No. EE6, pp. 917-933, December 1975.
12. Benjes, H., et al., "Estimating Initial Investment Costs and
Operation'and Maintenance Requirements of Stormwater Treatment
Process, USEPA Cont. 68-03-2186 (unpublished), 1975.
13. Wiswall, K. C. and Robbins, J. C., "Implications of On-Site
Detention in Urban Watersheds," ASCE Hyd. Div. Conf., Seattle, WA,
1975.
273
-------
14.
15.
16.
17.
18.
19.
20,f
21.-
22.
23.
Hydrologic Engineering Center, "Urban Stormwater Runoff: STORM,"
US Army Corps of Engineers, Generalized Computer Program 723-58-
L2520, 1975.
Stankowski, S. J., "Magnitude and Frequency of Floods in New Jersey
with Effects of Urbanization," Special Report 38, USGS, Water
Resources Div., Trenton, NJ, 1974.
Thornthwaite, C. W. and Mather, J. R. , "Instructions and Tables for
Computing Potential. Evapotranspiration and the Water Balance,"
Drexel Institute' of "Technology, Publications in Climatology, Vol. 10,
No. 3, Centerton, NJ, 1957.
Eagleson, P. S., Dynamic Hydrology, McGraw-Hill Book Company, NY, 1970.
Grace, R. A, and Eaglespn* ••?>: S. , .."The Synthesis of Short-Time-
Increment-Rainfall. Sequences,," MIT Dept. of Civil Eng., Hydrodynamics
LaK Report 91, Cambridge, MA,; May ,1966..
Sariahmed, A. and Jcisiei, C . C., "Synthesis of ' Sequences of ^Summer
Thunderstorms ; Volumes for the Atterbury Watershed in the Tucson
Area," Prpc. lASH Symp. Use Analog Digital Computers in Hydrol.,
Vol. 2, ..pp. 439-447,, .Tucson, 1968. , •,..,-'.•
Hasan» ;,?,?.».. in;te8rat?d Strategies for Urban Water Duality Management,
PhD Dissertation, University .of .Florida, Gainesville,* 1976.
• it. t" y »• • ' '
» # . . -, f ,„, '< t' „ ^ . J. ,V _',?' T- . ,r\ ' t>. ^jS " '' ' Jl ,' , " ; * ' '.'•"-',, '. "" '. •; ''
. Anonymous,-. "Flow; EquaMzation'-Plus. for Wastewater Treatment, Plants?"
Civil Engineering, t. Vol. 45, No. 9, pp. 66-68, September 1975.
Anonymous, J'FlowiU Equalization," USEPA Technology Transfer, 1974.
•Battelle-Nprthwestj, ^fevaluation of. Municipal Sewage Treatment
Alternatives" : Council _ on Environmental Quality, NTIS-PB 233 489,
1974. ' - ,•;'•'-,.•.*' .". '. ,- . .. ',- ..''....' ' . ." ' . ":"
24.
25. ,
26.
27.
Heaney;; J.-P., -and S. ,J, Niic, "Storm Water Management Model: Level I—
Comparative Evaluation of Storage-Treatment and Best Management Prac-
tices," USEPA, Cincinnati, OH., 1977 (in press).
.US Environmental Protection Agency, "Cost Estimates .for Construction
of.Publicly OwnedrWastewater Treatment "Facilities^"^1974 Needs
Survey,.- February 1975. ;,< ,. • : ; , . :,.:, ., ,'.," ..-,•,' ,
•.Staff .Report to -the National Commission 6n Water Quality, USGPO, 1976.
:Heaney, J.,^P., Huber,' -W. C., and Nix, S. J., 'Storm Water. Management
Model: Levelil^-Preliminary-Screening'Procedures," USEPA Report EPA-
600/2-^76-2 75»-; October -1976. ; : ^ ,.:,-::. .
.274
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SECTION VII-
IMPACT OF URBAN WATER POLLUTION CONTROL
ON RECEIVING WATER QUALITY
PROBLEM DEFINITION
Unquestionably, the nationwide commitment toward control of water
pollution and the efficient use of available water resources has led
to the need for comprehensive and integrated river hasiti planning... An.
average annual precipitation of 30 inches (762 mm) constitutes the
basic source of water supply for the conterminous US, an area of
3,022,387 square miles.(7,827,982 sq kms).1 This amount corresponds to
approximately 4,300 billion gallons per day (16.3 x 10y cu m/day).
Surface runoff, interflow and groundwater flow result in an average
annual streamflow of about 1,200 billion gallons per day (4.54 x 10y
cu m/day).2 This is a simplified measure of the total available fresh-
water supply. Estimates of water use in the United States for 1970
indicate that 370 billion gallons per day (1.40 x 109 cum/day) were
withdrawn to satisfy off-channel demand. Of the. total withdrawn, 87
billion gallons per day (0.-33 x 109 cu m/day) were estimated to be con-
sumed — that is, water made unavailable f6r further possible withdrawal
by natural evaporation, incorporation into crops and manufactured
products, and other causes. For the 5-year period from 1965 to 1970,
withdrawals increased by 19 percent and consumptive water use increased
12 percent. It is not difficult to visualize that if withdrawals
in 1970 were already 31 percent of the total available supply,_and
consumption was 7 percent, strict water management practices will ..b.e.
absolutely necessary to cope with an increasing water demand upon a
fixed water resource. The quality of a scarce freshwater supply will
have an added significance for obvious reasons.
The water quality cycle is a dynamic system existing within each phase
of the hydrologic cycle. The amount of pollution entering or leaving
a water body is determined by the quantity of .flow and concentration of
pollutants in each of the hydrologic components of the physical system.
The retention of pollutants in the water body is riot solely a function
of the quantity and quality of hydraulic flows, since it also depends
upon the location of the pollutants within the water body. The pollu-
tants exist in the water, bottom sediments, and the aquatic life. When
area sources such as bottom sediments are the greatest pollutant contri-
butors, it may be necessary to consider treatment of the water body
itself; for example, stream aeration. Thus, one of-the initial steps in
the planning process is water quality problem identification.
275
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Urban areas represent the centers of most intense human activity.
Urban land use is small within a river basin, as evidenced in Table
VII-1, Land Use in the United States.3 Point discharges resulting
from commercial, industrial, and residential wastes, generally enter
the receiving stream within relatively short distances of each other,
and in some cases all such wastes are processed by municipal facili-
ties and discharged to the water body at one location. Thus, these
continuous waste products are concentrated within a relatively small
volume of the receiving water. Intermittent precipitation falling on
urban areas becomes contaminated as it enters and passes through or
within the manmade environment. The first quality degradation occurs
When the rainwater comes into contact with pollutants in the air.
Next, the surface runoff passes over ground and building surfaces,
carrying suspended sediment 'from erosion sites and dissolving other
impurities. Finally, the stormwater runoff comes into contact with:
1) solid residues deposited from earlier storms throughout the con-
veyance system and appurtenances; and 2) dry weather 'flow (DWF) in
combined sewer systems. This storm runoff is well mixed with:sanitary
sewage under conditions of turbulent flow in a combined•sewer'system,
and it eventually discharges to the receiving stream. The degradation
undergone by urban stormwater passing through the surface runoff phase
of the hydrologic cycle can be several orders of magnitude greater than
that experienced by rainwater during the precipitation phase.4 The
pollutants either decompose (nonconservative), accumulate (benthic
deposits), or are carried further downstream Conservative, suspended
and dissolved matter). ,
The essence of a rational water quality and quantity management program
is the decision making process. The high cost of pollution-;.control
facilities, in terms of both energy utilization and financial burden,
obligates the planning agency to select the optimal strategy for areawide
wastewater management. Such a process must focus on a systematic pro-
cedure that identifies and defines: 1) the cause/effect relationships
of the physical environment,' 2) the economic realities of control alter-
natives; and 3) the benefits to be derived from implementation of these
controls. A preliminary analysis that provides an approximation of
system responses to proposed treatment measures should aid the selection
of the best strategy for restoration of water bodies to accepted water
quality standards. Such an analysis must never be interpreted as other
than a guide to be tempered by professional judgment. The mathematical
models applied need not incorporate all phenomena but rather should be
relevant to the problem under consideration. The problem of specific
interest is to assess the separate and combined effects of the major
urban sources of water pollution upon the quality of the receiving waters.
Oxygen concentration is considered the key to the quality of natural water
bodies, although it certainly is not the only water quality indicator.5
Thus, the relative impact of these wastewater sources is appraised by their
effect on the dissolved oxygen concentrations downstream from the urban area.
276
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0 generate stormwater runoff pollutant loads and dry-
weather sanitary flow pollutant loads;
simulate the pollutant removal efficiency of various
treatment schemes; various
0 simulate the conveyance system, including mixing
in combined sewers of wet- and dry- weather pollutants;
mix the various pollutant inflow combinations with
• pollutants already in the receiving water (.from
upstream sources); and
m predict the oxygen balance of the polluted waters
downstream from the waste sources,
data
of cost
or cost
0
quantity and
the
effecvene alternative *• ^ighed on the basis
effectiveness: the annual total cost and the water
,
to Des Moines, Iowa, is presented. Computed values for dissolved oxveen
are compared with field measurements. dissolved oxygen
METHODOLOGY
Characterization of Wastewater Discharges and Polluted Wa
n faraferf e the strength of wastewater discharges and the
quality of polluted waters, large and diverse numbers of chemical
dSSSid blT°i:glCs1' 3nd bact--l°S-al methods of anal^JT^ been
developed., The most common parameters measured are listed in Table
I ' Poll-utl°n and Contamination Indices. The bacteriological nroce-
ofrthea::terw£hdbt0 ^^^ P°tentISr^th haZards fromL"t Lotion
of the water with human or animal feces. The sampling technique
°f — tion'should bl tailored to
278
-------
Table VII-2. POLLUTION AND CONTAMINATION INDICES
Physical Parameters: , .. .':
Temperature
, Turbidity , ' '
Color . ' -•'-••
Chemical Parameters:
Oxygen Demand ' . .
Biochemical Oxygen Demand (BOD)
• Chemical Oxygen Dei-tan d (COD) • .
Total Organic Carbon (TOC)
Nitrogen Compounds
Organic • ' . - ••"- •
Nitrite
Nitrate . . .- --.
Phosphorus Compounds
Ortho Phosphorus
Poly Phosphates .• • .J •;.: ;
Total Solids
"'' .Dissolved ' .
. Suspended ' ;
Volatile and Fixed
Settleahle .. .-. ,..-)•"• , -,'• ...
Chlorides .
Sulfates '•.''.',.
PH ' • '-•- - '-•-•- " '••
Alkalinity
Hardness
Heavy Metals
Lead
: Copper ' -
Zinc
Chromium ,
Mercury
Biological Parameters: • • •• .•••••>
Plankton . . , ,;•
Periphyton
Macrophyton ,
Macroinvertebrates
. Fish Bioassays
Bacteriological Parameters: ,. ;,
Total Coliform Count
Fecal Coliform •'•"•'
Fecal Streptococci
Total Plate Count
279
-------
S? r C°nS^tU1ents that affect ^e distribution of dissolved
f a na WatSr SyStSm are WSl1 docu*ented. The oxygen
™f or SK;iSraSe,treatment PlaUt effluent> Polluted stormwaLr
runoff or industrial wastes is exerted by three types of materials:5/6
1. carbonaceous organic matter oxidized by
heterotrophic bacteria for energy and cell
2.
synthesis;
organic nitrogen compounds hydrolyzed into
ammonia-nitrogen (NH -N), then oxidized by
autotrophic bacteria (Nitrosomonas europaea)
to nitrite-nitrogen (NO -N), further oxidized
by Nitrobacter winogradskvi to nitrate-nitrogen
(N03~N); and
3.
certain chemical reducing compounds (ferrous
iron, sulfite, and sulfide) which will react
with molecularly dissolved oxygen.
l OXygen.demand ls the response of aquatic biota to an adequate food
supply and is commonly referred to as the biochemical oxygen demand (BOD)
at°ry,T techni«ue is an empirical bioassay-typ! procedure : '
T^r 7 micr°bial llfe in an ^cubated bottle is measured with
If r ^ a SPSf f±ed temPerature' The actual environmental con-
temPer^ure changes, biological population, water movement,
rn L^ +?"*, ^ aer°bic and ^aerobic processes cannot be faithfully
reproduced in the laboratory. Thus, the "bottled" system, on a kinetic
comparison, is completely accurate in representing itself but may be
relatively unreliable as a representation of the source from which the
sample was taken. The basic assumption that consumption of DO is an
absolute and complete parameter of biological decomposition in the BOD
bottle constitutes a simplification of complex interactions.
evorv. hSVe been developed to measure the oxygen demand
exerted by organic matter. The chemical oxygen demand (COD) and total
organic carbon (TOG) tests are more -precise chemical methods, but the
S arS n0t accurate if the organic material measured is not
°rganic matter act"ally being utilized by the micro-
evn "S^- MUCh Can be d°ne t0 lmpr°Ve the '-curacy of the
a natural XoS»S f^1™ Water from the receiving stream, thus introducing
rSe °rganisms into the bottle system. With all of
Procedure is still considered to be the best
evaluatln§ the e«ect of waste inputs on the oxygen balance of a
Model Overview
*.^~ ~~~t T J •"•'"* aLC used to simulate the hypothetical response of
the receiving water to the separate and combined effects of BOD waste inputs
280
-------
from: 1) upstream sources, 2) dry-weather urban sources, and 3) wet-
weather urban sources. The general concept is illustrated in Figure
^ ifled Configuration of Mixed Waste Inputs to Receiving -
--1-*1- *"* P-.I. • "• ' "™ . "" " ~__ *—,-r « •#- fiff, T.T-1 ~\ 1
VH-1,
iimpllJ
The urban community served by a separate sewer system will con-
runoff and municipal sewage through conduits which are
(Q ) and the flow (DWFCMB) intercepted for treatment by the DOT ^^
iri identical Any degree of treatment desired may be imposed at both
III JwF and the SwJ treatment plants. The concentration of the combined
BOD inputs in the receiving water is given by:
BOD =
m
(VII-D
where BOD = mixed BOD concentration in receiving water,
m mg/1,
BOD = mixed BOD concentration from sources upstream
u of urban area, mg/1,
BOD = BOD concentration of dry-weather flow treat-
d ment plant effluent, mg/1,
BOD = BOD concentration of wet-weather flow treat-'
w ment facility effluent, mg/1,
Q = upstream flow, cfs,
Q = DWF treated effluent, cfs, and
0 = WWF treated effluent, cfs.
The technique for calculation'of the quantity and quality of storm-
water and combined sewer overflows is discussed in further detail
lubsequently The BOD concentrations of the DWF and WWF treated effluents
are given by:
[BOD,, • DWFSEP + BOD
DWFCMB ](l-Rd)
BOD.
DWFSEP + DWFCMB
(VII-2)
BOD =
w
[BOD • QQ + BOD • Q 1(1-R )
S S _*-• v^
Q + Q
x
(VII-3)
281
-------
Q«,BOD$
URBAN AREA
SEPARATE
SEWER
SOURCE
UPSTREAM
SOURCES
DWFSEP, BOD*
URBAN AREA
COMBINED
SEWER
SOURCE
Qu,BOD0
RECEIVING WATER
BOD
m
Figure VII-1. Simplified Configuration of Mixed Waste
Inputs to Receiving Water,
282
-------
where
BOD,
BOD concentration of municipal sewage, mg/1,
BOD = mixed BOD concentration in the combined sewer,
mg/1,
BOD = BOD concentration of urban stormwater runoff, mg/1,
s
DWFSEP = DWF contribution from separate sewer area, cfs,
DWFCMB = DWF contribution from combined sewer area, cfs,
Q = urban runoff carried by the separate storm
sewer, cfs,
Q = combined sewer flow:, cfs,
c
R = fraction removal of BOD achieved by the
DWF treatment facility, and
R = fraction removal of BOD achieved by the
w
WWF treatment facility.
The initial conditions of BOD in the river are defined by equation VII-1,
and the hypothetical impact on the oxygen balance of the receiving stream
is estimated by using simplified mathematical modeling approaches. The
total hours of runoff-preducing rainfall throughout the year are separated
into storm events by defining a minimum interevent time. The procedure
is discussed in detail subsequently. For a given'storm event, the runoff
and pollutant loads are summed and the critical. DO deficit is estimated
as a function of several stream parameters: temperature, flow, oxygen, :..,,:,
concentration, deoxygenation and reaeration rates," and BOD concentrations.
The minimum DO is calculated subsequently and a frequency analysis is '
performed. Stream velocity is computed as a function of the discharged and/
the time and distance to each critical deficit point are obtained for each,
event. ....... ...
The options used for the simulations include:
1. five inflow combinations:
a. river flow + DWF
b. river flow + DWF + separate flow
c. river flow + DWF + combined flow
d. river flow + separate flow + combined flow
e. river flow + separate flow + combined flow
+ DWF,
2. four DWF treatment rates (variable),
3. three WWF treatment rates (variable), and
283
-------
4. three fractions of measured upstream flow
may be investigated. , ... •
Item 4 is included as a model option to investigate whether the relative
impact of urban stormwater runoff is most significant in the upstream
portions of river basins. This effect may.be simulated by simply reducing
the upstream flow to any desired fraction of its actual measured-value.
Thus, discharge into a dry river bed may be studied.
Technique for Calculation of Urban Runoff Quantity and Quality -
This section briefly .describes the methods used to generate storm runoff
and pollutant concentrations. The Hydrologic Engineering Center model, '
STORM , is utilized to obtain hydrographs and pollutographs for Des Moines
for the year 1968 on an hourly time step. , •,
Urban Runoff Quantity ,-- . '.. ,: ^
As described in Section VI, STOSM computes urban runoff as a function of
land use and rainfall/snowmelt losses:
AR
u
CR (P - f )
uv u u''
(VII-4)
where
ARu = urban area runoff, in./hr,
CR - composite runoff coefficient dependent
on urban land use,
pu ~ hourly rainfall/snowmelt in inches over
the urban area, and
fu m available urban depression storage, in.
A maximum depression storage of a hundredth of an inch (0.25 mm) is assumed
for Des Moines, Iowa. The hourly urban runoff values, expressed in cfs,
are saved in a file for later recall by the simplified mathematical model.
Urban Runoff Quality — • "
The basic water quality parameters modeled by "STORM are suspended and
settleable solids, BOD, total nitrogen (N), and total phosphate (PO ). It
is important to emphasize that the BOD values are expressed in terms of the
standard BOD5 test: incubation at a temperature of 20°C for 5 days. These
values represent most of the carbonaceous oxygen demand exerted by organic
matter present in the urban runoff, and include the BOD contribution from
suspended and settleable solids. The BOD loading rates generated by STORM
284
-------
are based on land use and other factors such as number of- dry days without
runoff since the last storm and the street sweeping intervals.
However, these loading rates were calibrated against yearly averages
and single storm values obtained from a detailed study of Des Moines
by Davis ,and,.Borchardt. 9 The hourly BOD,, pollutdgraphs, in pounds
per hour and mg/1, are also saved.in a-dxgital computer file for later
.recall tby the receiving, water simulation.1 - •'• : '
Definition of an Event
As stated previously., rainfall input to STORM is prepared as a sequence
of consecutive hourly values (including zeros for no measurable preci-
pitation) . -.These inputs are- used by STORM to' generate the corresponding
series of hourly urban,runoff. The basic approach to define a wet-
weather event is to analyze the hydrdlogic time series and establish the
minimum number of consecutive dry-weather hoursr (DWH) that separates
independent storm events. The independence of these storm events is not
strictly climatological and is discussed later. The dry-weather hours
refer to periods during which no runoff was produce'd. . thus, depression
storage and evaporation rates must be satisfied before any runoff is
generated by STORM. , ' • ' • '"' • ; ""'• f ; : "
Two techniques are used to analyze the hydrologic time series: 1) an
analytic approach, autocorrelation, and 2) a" graphic procedure. The
precipitation time series is presented in Figure VII-2, Point Rainfall
for Des Moines, Iowa. The abscissa represents the 10-month period, in -.
hours, from March 1 to December 30, 1968. An examination of the rainfall
record provides considerable insight as to rhe storm.groupings, their
intensity and duration, and frequency of occurrence. The-broken line on
the abscissa indicates dry-weather periods cit least 9 hours in length.
Figure VII-2 provides necessary info-.m-iti' .1 to; apply both techniques
and define a minimum interevent t3it.c. .. . ,
For hydrologic processes, it is practical .to,estimate the autocorrelation
coefficients by an open-series approach:-10; -11
""' 'J'" - ' VXiXi+k
x=l
"n-k „ , fn-k 12"
Z-o- . I V -ir \
X. , L X. j
Li-i x n~k 1 1-1 XJ J
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n-l
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k
r
,i=
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=k+l x
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V V
i
,i=k+l .
I' ( -
T~k.U
n -.2-
E__ \
X.
=k+l X-J .
0.5
-
Cvn-f
where
' ;r'T (k) = "sampie estimate of ,lag-k autocorrelation
coefficient for hydrologic process I,
; x'. - discrete data series (observations) of
" "-•'• ..... 'hydrblogic process I, for i = l,2,...,n,
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n = total number of data points or observations,
and
k = number of hourly lags.
A plot of the serial correlation coefficients, r(k), against the number
of lags, k, is called a correlogram. The technique of autocorrelation
analysis is essentially a study of the behavior of the correlogram of
the process under investigation.12 The correlogram shape, or curve
joining each point to the next, is henceforth referred to as the auto-
correlation function. An analysis of the precipitation time series of
Figure VII-2 results in the curve shown in Figure VII-3, Lag-k Auto-
correlation Function of Des Moines, Iowa, Hourly Rainfall. At a lag of
zero hours, the correlation of the discrete open series is unity because
this point on the c^rve represents the linear dependence of the data
series on itself. The number of observations (including zero values)
totals 7,372 consecutive values, and lags up to 720 hours were investi-
gated. The first minimum of the autocorrelation function occurs at a lag
of 10 hours, "and the value of the function is also zero at this point.
The physical interpretation is that periods without rainfall- for at least
10 hours separate uncorrelated, and therefore independent, storm events.
Actually any point of the autocorrelation function which lies outside of
the 95 percent tolerance limits indicated in Figure VII-3 suggests a
significantly non-zero correlation between storm events at that particular
time lag. The Des Moines rainfall record obviously exhibits nonrandom
behavior at lags of 377 hours (a, 16 days) and 421 hours (^ 18 days) in
particular. The tolerance limits for a normal random time series of n
values, and an open-series approach at a 95 percent probability level, are
given by: -1 ° .
TL (95%)
-1 ± 1.645
n-k
(VII-6)
where TL(95%) = tolerance limits at a 95% probability level.
As the number of lags, k, increases the tolerance limits diverge. However,
the divergance is not noticeable for large, n. Values of the autocorrelation
function between lags of 100 to 300 hours-and 500 to 720 hours fell between
the 95 percent tolerance limits and are not shown in Figure VII-3.
Similarly, autocorrelation analysis was performed on the sequence of hourly
runoff values generated by STORM from the rainfall input. The lag-k serial
correlation coefficients, r (k), are plotted against the number of lags in
Figure VII-4, Autocorrelation Function of Hourly "Urban Runoff for Des
Moines. Iowa. The analytic technique establishes that the minimum interevent
time of consecutive DWH that separates "independent runoff events is 9 hours.
Examination of Figure VII-4 reveals that the. runoff time -series is not
purely random either. Linear dependence is observed at time lags of 377
hours (<\, 16 days) and 436 hours (^ 18 days), as expected, because of the
high correlation between rainfall and runoff processes.
291
-------
1.0-
O.8.
0.6-
0.4J
0.2
0,0
-0,2-
-0.4-
95% T. L.
95% XL.
0.0 10
20 3O 40 , 50 60 7Q • 80 ., 90 ,•
LAG K, hours
100
Figure VII-3. Lag-k Autocorrelation Function of Des Moines, Iowa
Hourly Rainfall, 1968. ; •"' - -••-•
292
-------
V. f. -
O.I -
Of) —
H
Oo
r 3(
95% T. L. j\ 1
/ A /"V ' V\ A N
V /~ \ ;V V
95% T. L.
DO 310 320 330 340 350 360 370
\j \/VV
380 ' 390 4(
DO
hours
!. 400 410 420 430 440 . 450 460 470 480 490
500
LAG . K, hours ,
Figure' VII-3 (continued) . Lag-k Autocorrelation Function of
Des Moines, Iowa, Hourly Rainfall,
1968. '
293
-------
-0.2-
-0,4-
MINIMUM INTEREVENT TIME:
9 HOURS OF DRY WEATHER
95 % T. L.
0-0 10 20 30 40 50 60 7O SO 90 100
... • . LAG K, hours
\
Figure yiI-4. Autocorrelation Function of Hourly Urfita Runoff
for Des Moines, Iowa, 1968.
294
-------
0.2
0,2-r-
0.1-
^'o.o-
-0,1-
95% T. L.
±^S£gIXL-ALV ~7ZSHZ
T. L.
_L
-0.2-
,
400 410
Figure
420 430 44O 45O -460 470 480 490 500
LAG K, hours
(.continued} > Autocorrel'ati-pn SunctiDn of
Urban Runoff for Des Moines, Iowa,
1968.
295
-------
Slight differences are observed between the correlograms of Figures
VII-3 and VII-4. These are due to the fact that depression storage
and evaporation rates must be satisfied before runoff is generated by
STORM. Thus, the digital simulation of the runoff process By STORM
acts as a filter and has a slight smoothing effect.
The graphic procedure requires the number of dry-weather hours immediately
preceding each hourly runoff occurrence. These values are determined
directly by the chronological record provided by STORM of all the runoff
events it^generates from the input rainfall. If a hydrologic model such
as STORM is not available, a close approximation may be obtained by assum-
ing that the same numbers of DWH precede the rainfall and runoff events.
Thus, the information provided by the precipitation records or the rainfall
time series (such as Figure VII-2) is sufficient. A plot of the number of
wet-weather events obtained by varying the minimum interevent time is shown
in Figure VII-5, Definition of a Wet-Weather Event for Des Moines by Graphic
Procedure. It is evident that a time value exists after which an increase
in the minimum interevent time does not result in a correspondingly signi-
ficant reduction in the number of storm events. The graphic procedure
selects a period of 8 consecutive DWH as the minimum interevent time. The
result is remarkably close to .that obtained by the analytic technique,
autocorrelation. In some cases, however, the graphical approach may not
exhibit a curve with such a well-defined transition point. It is then
necessary to apply classical statistical techniques to investigate the
sequential properties of the hydrologic series.
Based on the above analyses, a wet-weather event and its duration are
defined in the mathematical model as follows:
1. Any runoff occurrence having nine or more DWH
preceding it denotes the beginning of the
event (see below).
2. The event continues as long as all of the
subsequent runoff occurrences have a DWH value
immediately preceding them equal to or less than
eight hours.
3. The event runoff duration (in hours) is equal to
the sum of all the runoff occurrences in (2).
4. The actual event duration (in hours) must be
determined by examining the date and hour of the
first runoff value and the date and hour of
occurrence of the last runoff value within the
event.
The hourly urban runoff and associated pollutant loads within each event
(including DWF pollutant loads during DWH periods less than nine hours
duration) are summed, average conditions are determined, and the model
proceeds with the receiving water analysis.
296
-------
100-
QL
UJ
>-
cc
UJ
Q_
UJ
cc
UJ
UJ
UJ
O
d
z
80-
60-
40-
0 20H
DES MOINES, IOWA
PRECIPITATION YEAR OF RECORD = 1968
SELECTED MINIMUM INTEREVENT TIME'
8 HOURS OF DRY WEATHER
40 60 80
MINIMUM INTEREVENT TIME, hours
100
Figure VII-5. Definition of a Wet-Weather Event for Des Moines by
Graphic Procedure
297
-------
Separate Storm. Combined and Dry-Weather Loading
All of the following methodology can be used regardless of the technlaue
*!Sf V^r116^.6 St°rm rUn°ff aUd qUallty' as l^g /as these values <
pertain to the entire area being modeled. :, '' . •••-.• -•,•:..
Separate Storm Flows and Loadings — ,'
°f
fl°W W BOD' loading^ made on the basis of
S£Parate ^d: combined sewers. Runoff from
separate sewered areas is thus (refer to Figure VII-1) :. ,. ',. '.,
Q -^
s A.
.(vir-7)
where Qg = stormwater flows from separate sewered
areas, cfs,
Ag - area served by separate sewers, acres,
Qt - total (storm plus combined) urban runoff, cfs, and
At = total area of catchment, acres.
The concentration of BOD in separate storm sewers, BOD , is simply the
hourly value computed by STORM, BOD? (mg/1), for the tStal urban runoff.
Dry-Weather Flow and Loadings —
Dry-weather flow and BOD loadings are assumed known from data on point
sources in the area. Thus, Q represents the flow Ccfs) into receiving
waters of treated wastewater, and BOD represents the BOD concentration
at 68 F (20°C) for 5 days, mg/1. The amount of treatment-can be varied
in the analysis, as stated earlier.
Combined Flows and Loadings — . - '
Dry-weather flow (DWF) is assumed to cause only a negligible increase in
llow in a combined sewer'during a storm event. However, two factors
related to DWF may increase significantly the BOD concentration of the
combined sewer storm water:
1. the BOD strength of the municipal sewage with ' : '''
which it mixes; and
2. the BOD exerted by sediment accumulation in
each section of the sewer under DWF conditions
which is subject to the "first flush" effect
induced by the initial runoff.
298
-------
To incorporate the "first flush" effect, it/is assumed that the hourly
in-sewer sediment build-up is constant over consecutive dry-weather
hours. This assumption is reasonable although*it is evident that particle
size and specific gravity, depth of flow, and the slope of the conduit
are important factors affecting deposition. Data,collected by Davis and -
Borchardt9 at Various combined sewer overflow stations in Des Moines, Iowa,
support the first flush theory. BOD and total suspended solids (TSS) con-
centrations decreased with time with little or no relation to the flow
pattern. Furthermore, pollutographs.(BOD vs time,, and. TSS vs -time) for
these stations seem to indicate that the flushing occurs mostly during the
first hour of runoff generated by the storm event. . ,
Thus, the mathematical model computes the sewer solids build-up that occurs
during the consecutive DWH, then the BOD load contribution from these
solids is lumped into the first hour of runoff. The first flush BOD load
is given by ,
FF = FFLBS • DWH • A
•(VII-8)
where FF = first flush BOD.load, Ibs/hour,
FFLBS = first flush factor, Ibs/first flush hour
per DWH-acre, .
DWH = number of dry-weather hours preceding
each runoff event, and -
A = area served by combined sewers, acres.
The first flush factor, FFLBS, must be determined from
1. the total flow generated by the combined sewer f
area (including dry—weather flow contribution)
during the wet year; '
2. the difference in annual average concentration
between BOD (excluding factor FFi and the.
measured annual average value; and , .
3. the total number of DWH. for the. entire, .year
under study. , .'
An example of this calculation is presented in the application of the
model to Des Moines, Iowa, ,-••••'
Apportionment of the total flow; on the basis, of relatj&re. area g£yes.f
299
-------
(VII-9)
where
Qc - combined sewer overflow rate, cfs.
I - factor to convert FF • from lb/hr to cfs ^mg/l:^ 4.
45
EFFECT ON STREAMS
Introduction
,-... . j '..rt.i
• s- !. ? r r ':
are determined for a large SmLr ff J *§ minimum D0 concentrations
££*• ^-^ "s ^si^ars^^r sr1-:r
earlier. The construction of * detailed -•—-^ . • "pi-i-ons ,given
to
severely limited their own applicability?
have consequently
°f th->ypieal of models for interim planning,
Temporal steady-state conditions prevail,
where all system parameters, and .' inputs, v are. 'con-
st^at with, respect .to time; However, a
2.
Stream system parameters (such as river flow
velocity, depth, deoxygenation rate/and
reaeration rate) are spatially constant'along
the flow axis throughout each time'step.
300
-------
3. All waste inflows occur at one point on the
receiving stream.
4. The effects of various natural biological
processes (algal photosynthesis and respiration,
benthal stabilization) are incorporated into
a background quality which is reflected by DO
deficit (if none, by saturation), upstream, from
the waste inflow point. Any benthic buildup is
incorporated into the BOD decay rate.
5. Waste treatment facilities operate at constant
efficiencies, independent of hydraulic and ,
organic loadings, for the entire period of
simulation. ' '-'••'•
Initial Conditions
Initial conditions of BOD in the river are defined b,y equation
In subsequent equations, the mixed BOD concentration in the river _will be
denoted by L . Thus, , -
• •" : " "; ' ' "' ' ' L = BOD -.-.'.
,, • < .-•-•.• •',•;.'." '•'..'••-• '•'' ' ' • O - • m ' .'
The assumption that all waste inflows occur at one point; is not unreasonable
for Dea Moin.es ;.• but in some locations the distribution of inflows along i%
the river may need:.to be considered. It is important to emphasize that all
of the-BOD contributors in equation VII-1 represent BOD^ values. Thus, the
mixed BOD concentration in the-river, BOD , is also in terms of the standard
BOD test...The^ultimate first-stage (carbonaceous) demand;is related to the
BOD- ; value by:., ,;:,. -••'•• : ' : ._
BOD,
-5K,
'(VII-12)
1-e
where (L ) = ultimate first-stage BOD demand, mg/1, and
• '• A -I
K = first-order BOD decay rate constant, day .
Through verification analysis the value of K^ was determined to , - 0.7
day"1. Thus, for Des Moines,
CL0-)C
(VII-13)
and the conversion "'is unnecessary.,* .Of course, generalizations cannot be made
because the decay rate may vary considerably for different river systems.
301
-------
The other initial condition required is the initial oxygen deficit
™
.D =
D Q
(VII-14)
where
Do = initial DO deficit, mg/1, and
D
u
DO deficit in receiving waters upstream
of inflow point, mg/1.
Oxygen Balance of Polluted Streams ' • " • ' •''"
Pollutant transport processes in a s'tream system may be adeauatelv
where
concentration of water quality parameter
(pollutant), M/L3,
time, T, . ••-..'•
(VII-15)
-E9x - mass flux due to longitudinal dispersion along
the flow axis, the x direction, M/L2T,.
UC = mass flux due to advection by the fluid con-
taining the mass of pollutant, M/L2T,
S = sources or sinks of ±he substance -C, M/L3T,
U = flow velocity, L/T, and
E - longitudinal dispersion coefficient,, L2/T. ,
The equation assumes no diffusion of pollutants through the river
boundaries (other than what is included in the source-sink term) and
i-L * fu±ted t° predict concentrations relatively far downstream from
soL^S ° WS8te inJection' Since critical DO deficits usually occur
some distance downstream from the waste source, equation VII-15 is
particularly well suited for such predictions. ' - .
°f dissolved o^^n in the stream are atmospheric
ygen pr°ductio11 ^'Photosynthesis. The major sinks
Carbonaceous oxygen d«mm,d (CBOD), nitrogenous oxv^en d^Sf
302
-------
(NBOD), benthai demand, and respiration of aquatic plants. All stream
system parameters are assumed spatially constant along the flow axis,
and .by substituting the various sources and sinks'of'Db into"equation
VII-15 the following expression is obtained: va; ';
2
gx^"
S.
(yn-16)
- K-L - K N
1 n n
P - R - B
where
C = concentration of DO. in the stream* mg/1 ,
• 2
E = longitudinal dispersion coefficient, ft /sec?
U = freshwater stream velocity, ft/see, ,
K_ = atmospheric reaeration coefficient , hours ?,.
,.:..•'. .• - "-. .-. ,' >...?.L • .. •'!>'.» i-;'J" '•••''• '
C ' = dissolved oxygen saturation, mg/1,
S I ' ..''.- •"'•'- ..-••-• -;. . ....... . ..
C -C
s
dissolved oxygen deficit, mg/1 = D,
K.. = deoxygenation Constant ,of carbonaceous BOD,
hours" ,
L = remaining carbonaceous BOD concentration, mg/1,
K = oxidation coefficient of nitrogenous BOD, hours
n
N = remaining nitrogenous BOD concentration, mg/1,
n .> . . • •;-... . -.._''•>•; -i ' • .-.,;• - ;'".
P = oxygen production rate by algal photosynthesis,
mg/1-hour,
R = algal respiration rate,: mg'/l-hour, arid
B = benthal demand of bottom deposits,1 mg/1-hour.
For freshwater streams, the advective flux is significantly larger
than the mass flux due to longitudinal dispersion.13 For steady-
state analysis, all1 system parameters are assumed constant in time,
Since.it is ;,desired , to,,solve, for the DO deficit and v
; : !; ,"'!f;' 0;-.E.";OJ' 3^='~^"".- .^rV:V'-'.'^
equation Vli-16 reduces to an ordinary .differential equation:
(VII-17)
0 = u- + K0D - K-L
' • dx 2 1
C N + P -;R <-' B>
. n n , . e
(yn-i'8l
303
-------
measured upstream o deficit
oxygen by these processes after
1. the amount of organic and ammonia' nitrogen
present in all wastewater inputs is
BOD
of
0 = U^+K2D-
U
(VII-19)
- -K,t
)•+ D e 2
(VII-20)
where D - DO deficit, mg/i,
^ ='deoxygenation coefficient, hours'1,
K2 = reaeration coefficient, hours"1, and
t = elapsed time, hours.
The critical (maximum) deficit is found through differentiation to be
^L
Dc = -Kf e
(VII-21)
304
-------
where D = critical (maximum) oxygen deficit, mg/1, and
c
t = elapsed time at which critical deficit
c occurs, hours.
The value of t is given by
where
f = self-purification ratio
(VII-22)
The minimum DO level is calculated as
C . = C - D
mm s c
(VII-23)
where C . = concentration of DO at maximum
mn deficit, mg/1, and
C = saturation concentration of DO, mg/1.
s
The saturation concentration is determined from the regression relation-
ship developed by ASCE,11*
2 3
C = 14.652 - 0.41022T + 0.0079910T - 0.000077774T
s
(VII-24)
where T = water temperature, °C,
Values of K- and K,,
The deoxygenation coefficient, K., represents the loss of DO in_Jhe
water due to reduction of BOD. XA calibrated value of 0.7 day is used
for this simulation for K^ at 20°C (68°F). A temperature correction and
conversion to hour" gives:
K1(T) =
,T-20
(VII-25)
A variety of formulas exists for prediction of the reaeration coefficient
K , almost all of which depend upon velocity, U, and depth, H. The
equation'of Langbein and Durum15 was chosen because it is most closely
related to subsequent procedures used to obtain U and H.
305
-------
K
2.303
where -,,K2-=reaeration coefficient at 20°C, day"1,
u •'" stream velocity, ft /sec, and' '
H
stream depth, ft.
(VII-26)
. The problem lies in obtaining values of U and H, since the stream-
flow varies with time. In the absence of measurements, or if the
data cannot be obtained in an expedient manner (as in the ensuing
application to the.Des *foines River), an approximation can be made
based on the, work of Leopold and Haddock^ ln which they show strong
correlations between velocity vs flow and depth vs flow, namely:
U
H
0,
(VII-27)
(VII-28)
where
"l' a2
s t reamflow, cfs, and
,regression coefficients.
The nofiel presentlylutilizes coefficients which were determined for
the Kansas Rxver System in Kansas and. Nebraska, for which ,
c^ - 1.60
-------
Total Volume of DO Deficit
One reason for determining DO levels for several inflow options, is to
attempt to establish the relative effect of one source versus another.
Unfortunately, the critical deficit, equation VII-21, is nonlinear in
terms of the initial BOD, L , thus effects cannot be separated directly.
One measure of the relative°importance is the integral, or summation of
the deficit equation VII-20 over all time,
D dt =
D + L
o o
CV1I-31)
where V may be interpreted as the total volume of deficit, with units of
mg-hours/1. Values of ^ are displayed By the model for each, inflow
combination.
Presentation of Results
The impact of urban runoff on the receiving waters, is evaluated in terms
of violations of potential DO standards, i.e., by the number of times
predicted DO levels fall below a specified value. The receiving program,
running on an hourly time step for rainy events only, maintains a tabu-
lation of the frequency of DO values within specified intervals, .from which
the cumulative,relative frequency may be plotted, as sketched in Figure
VII-6, Hypothetical Results of Simulation. For example, from the figure,
the percent of wet-weather events during which a DO standard of, say, 4 mg/1
is violated may be readily obtained. Different input options will produce
different curves in Figure VII-6 and they may be compared in this manner.
Curves are also developed for dry-weather periods and a cumulative annual
frequency is obtained. . .,,
In addition, the total "volume" of DO deficit, equation VII-31, may be
compared 'for each option. This will give some indication of the relative
impact of one option versus another, although the results have no special
physical significance.
APPLICATION TO DES MOINES, IOWA /'
General Description .
; * . :
The City of Des Moines, Iowa is located near the confluence of the Des
Moines River and the Raccoon River as shown in Figure.VII-7, Map of
Des Moines Area. It contains approximately 200,000 people out of the total
of 288,000 for the metropolitan area.9 The mean annual precipitation is
31.27 inches (795 mm) which is approximately equal to the United States
average. Based on an extensive sampling program, annual pollutant unit loads
upstream from the city were determined (see Table VII-3, Pollutant Unit Loads
307
-------
100
•% of Wet-Weather
Events with DO
Greater than
Indicated Value
DO; mg/1 —•—fe.
Figure VII-6. Hypothetical Results of Simulation
Table VII-3.
POLLUTANT UNIT LOADS FOR DRAINAGE AREA2
ABOVE DES MOINES, IOWA
(Davis and Borchardt, 1974)9
Des Moines
River
Drainage Area, acres (ha) 3,738,000
(1,512,769)
Unit Average Annual Runoff, 0.42
acre-ft/acre (ha-m/ha) (0.13)
Unit BOD, Ibs/acre (kg/ha)
Unit N03, Ibs/acre £kg/ha)
»
Unit P04, Ibs/acre (kg/ha)
a
On an annual basis.
13.40
(15.02)
3.75
(4.20)
0.54
(0.61)
Raccoon River
2,202,000
(891,149)
0.40
(0.12)
6.93
(7.77)
3.74
(4.19)
0.42
(0.47)
Total
5,940,000
(2,403,918)
••' 0.41
(0.12)
11.01
(12.34)
3.75
(4.20)
0.50
(0.56)
•308
-------
S.DJ ^ --
K) HBANKENYx,
\ 1 WINDSW
V. LHEIOHTS
Figure VII-7. Map of Des Moines Area (Davis and Borchardt, 1974)'
309
-------
for Drainage Area Above Des Moines, Iowa). The estimated annual loading
from the urban area's 45,000 acres of separate sewer and 4,000 acres of
combined sewer systems is shown in Table VII-4, Summary of Present Am,,,?i
Metro Area Discharges. —^~ :—— • —
f} upstream drainage area for the Raccoon and Des Moines
nnn ^"^contributions are: 65,225,000 pounds of BOD
,000 kg); 22,222,000 pounds of NO. (10,080,000 kg); and 2,940,000
pounds of PO (1,334,000 kg). The urban3area loadings (when added to
upstream values) represent, respectively: 15 percent, 3 percent and 51 '
£vernL?f *£ t0tf B°?' N°3' and P°4 »Ma Ioadin8s to the Des Moines
River below the metropolitanJarea. Tne Davis and Borchardt report esti-
mates made_from river sampling data taken below Des Moines indicate
J5? SJ 1f^nt8 fVSoagf annual r±Ver loadinss: 70,000,000 pounds of BOD
(31,751,466 kg); 25,400,000 pounds of NO (11,521,250 kg); and 7,950,000
pounds of PO (3,606,059 kg). These figures reveal that: (1) 6 610 000
pounds of Soft (2,998,246 kg) are "lost" in transit through the urban
,*1? (2> by Contra5t 2,474,360 pounds of NO,
addition 'I'40? POUndS °f P°4 <869'718 k§) are gained5 in
addition to the measured urban sources.
Davis and Borchardt offer some explanations:
The "sometimes" decrease in organic load through, the
metro area may be attributable to treatment realized
in the low head impoundments at Scott and Center
Streets on the Des Moines River and just below Fleur
Drive on the Raccoon. To some extent these impound-
ments may be serving as intermittent sedimentation
and stabilization units.
All BOD data, including that used from the two other
studies, were obtained from unfiltered samples.. HOWT
ever, since the analytical technique was the same for
all samples, the relative magnitude of the data should
not be affected.
There has been some speculation that treated wastewater
effluents may exert an antagonistic or retardant effect
on the BOD exertion rate of the receiving stream. If
true, this may be due to surfactants or to the, expected
lower exertion rate of the. effluent. In this- regard,
the decreased BOD in 4 or 5 measurements between R-5
and R-6 is of interest. Increased loads between the
summation of R-4 and R-9 versus R-5 are likely due to
raw and combined sewage bypassing the intervening area.
Another, and probably the most practical, possibility for
the discrepancies is the fact that the data are biological
and biochemical in nature and such data do not always
provide predictable comparative summations.-
310
-------
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cociCTlS^}i3VDCi'H'> f^l° ^ <^
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co"i-T
o -* o o o -* o C-? o r? o S o Co"
S S O-^f OlO Oc^ OvO Ocn SS
ot^o coo^ nr^. cri-* ooo r->vo VOH
^ S iQiS 0°" °^-* COr-T.<^>^ Co"oT
^^ "^ IO OJ O M
^
^^
ililllllllil §3
SIS Jo^ ^SS "^^ ^^ ocT o 5. "*^J, •-fv-' ^OM «OH
CN tH r-Tio"
»-! «0 CM CO O VD 10
r~< *~1 , CNJ to \O
ro
T3
to
0)
oO
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H i i I B 1 ^
CD t-l — i __. °
_LI r~' '"i ^O ^ rn
« Q; o^ • • -3
£s S M ** Q
1 5 5 ! i i i i
sKS^SRl ^
gl^tOrscOH^ ,_,
•g ^ -H 0 0 CD 2
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s
60
4J S
•s «s & .« '*•
o 2M g» ft -g
0 4J >, fl H
-------
The sampling stations (Numbers 4, 5, 6, 9) are shown in Figure VII-S,
Location Map: River Sampling Points. Intervening creeks, such as Beaver
Creek, which carries nutrient loads of 2,860,000 pounds of NO per year
'(I 297 274 kg per year) and 390,000 pounds of PO, per year (176,900 kg
per year), may be an answer to observed differences in the nitrate loads.
However, the phosphate totals are still unbalanced and the cause unresolved.
As stated earlier, the relative importance of a city is expected to increase
as the size of the upstream drainage area decreases. To simulate this
effect the model application to Des Moines investigates the response of the
receiving water when upstream river flow, Q , is reduced to various fractions
of measured flow. This option and others used for the Des Moines simulations
are summarized in Table VII-5, Options Used for Des Moines Simulatxons.
Thus, there is output for 5 • 4 • 3 • 3 = 180 combinations for each of four
different combined area fractions. Conditions particular to Des Moines, Iowa,
and the Des Moines River are investigated as well as the response of the
receiving stream to hypothetical situations.
Data Sources
The data may be broken into categories describing needs for the runoff
simulation. All land use, population density, areas, curb lengths etc.,
were obtained from data prepared by APWA for STORM simulatxons (Volume III) .
Hourly rainfall values for the year 1968 were obtained from the Natxonal
Weather Records Center at Asheville, North Carolina. The area served by com
bined sewers, AC = 4,000 acres, is given on p. 2 of the Davis and Borchardt
report.9 Dry-weather flow values are taken from Table 5, p. 52. Receiving
water upstream flows, temperatures, BOD and DO levels are taken from
pp. 285-308. Total urban runoff (Qt) and its BOD concentration (BOD ) are
l basis. BOD , BOD, Q,
. .
obtained from the STORM simulation on an hourly basis. BOD, Q, BOD
d,
g
BOD , BOD and 0 are computed from input data and appropriate mixing
equationsWby the simplified mathematical model described in .this, section.
The first flush factor, FFLBS, was determined as follows:
DWH/year = 6,993 hour
8
Total flow combined = 1.55 x 10 cf/yr
(4.39 x 10b cu m/yrl,.
Mixed concentration of storm water (from STORM) plus DWF = BODc = 62 mg/1.
The annual average BOD concentration in the combined sewer was measured by
Davis and Borchardt9 to be 72 mg/1.
BOD difference = 10 mg/1
- 0.0006243 lbs/ft3
313
-------
LEGEND
A louo Slalf Unlvtrtily
Enfinttrlag Rasaarch Institut*
D Slolt Hyglinic Laboratory
# This Project
© USGS Strtamflow Station
Figure VII-8. Location Map: River Sampling Points
(Davis and Borchardt, 1974)9
314
-------
TABLE VII-5. OPTIONS USED FOR DES MOINES SIMULATIONS
The following five inflow combinations (M^ are used:
1. River flow + DWF •
2. River flow + DWF + separate flow
3. River flow + DWF + combined flow
4. River flow + separate flow + combined flow
5. River flow + DWF + separate flow + combined flow
and the following four DWF treatment rates (J) are used:
1 0% (no treatment)
2. 30% (primary)
3. 85% (secondary)
4. 95% (tertiary)
and the following three WWF treatment rates (L) are used:
1. 0% (no treatment)
2. 25%
'3. 75% *
and the following three river flows (K) are used:
1. 0% of measured flow
2. 25% of .measured flow
3. 100% of measured flow '
and the fraction of combined area is varied, four times:
1. 0% of total urban area
2. 8.16% of total urban area (existing conditions)
3. 50% of total urban, area
4. 100% of total urban area
Parameters J, K, L and M were used as subscripts in the
computer output and aerve to aid in labeling the various
combinations.
315
-------
FFLBS =
BOD load = (0.0006243 Ibs/cf)(1.55xl08 cf/yr)
= 96,766.50 Ibs/yr (43,892.50 kg/yr)
that can be attributed to first
flush effects
96,766.50 Ibs/yr
(6993 DWH/yr)(4000 acres)'
= 0.0035 Ibs/DWH-acre
= 0.0039 kg/DWH-ha.
This factor, as demonstrated earlier, is then used in equation VII-8 to
estimate the first flush BOD load, FF, during the first hour of. runoff
generated by each storm event. , •'• •
Special Problems
application to Des Moines, various problems were encountered
revolving around the critical deficit (D ) and critical time (t ) equations?
equations VII-21 and VII-22, respective^. Due to the- large number of con!'
*™t!!?! ^TlSlm^at^' including dry watercourses in which, the waste inputs
constituted the only flow, situations were encountered in which:
1. the deficit load ratio, R , was undefined
'" ' •'" Ko'" L~ ' '" (VH-32)
• - '' • , • o
because both DQ and L were equal to zero;
2. the self-purification ratio, f, was equal . .
to one, causing equation VII-22 to be undefined-
and :."•:>, . .. ..
3. values of RQ were such that negative values of
t were obtained. , : .
Mathematical analysis led to the incorporation of certain modifications
and safeguards. Thus, equations VII-21 and VII-22 were defined only for:
2. L ^ 0, and
3. 0 < R < 1/f.
— f\ ^—
316
-------
Otherwise, in order, if
1. f = 1, then
R -1
o
D = L e
c o
2. f 1 1, LQ = 0, then
D = D
c o
3. f 1 1, L0 = 0, RQ > 1/f, then
D = D .
c o
(VII-33)
(VII-34)
(VII-35)
These equations, obtained by taking limits, are not^particular to Des
Moines and are applicable to any receiving stream.
17
Verification Analysis
An important part of the total effort required to develop a mathematical
model of water quality in a stream is devoted to verification and improve-
ment of model accuracy. The verification procedure recommended for
steady-state water quality models includes:
1. examination of model output using preliminary
coefficients on a diverse set of data (dif-
ferent waste loads and temperatures under
conditions of high and low flow, and variable
initial stream quality);
2. assessment of the closeness of fit of observed
field data to computed values;
3. adjustment of the model coefficients until the
desired accuracy is obtained; and
4. achievement of a mathematical abstraction that
reasonably reproduces observed stream response
and establishes the necessary validity for
planning purposes. .. v . .._ ,.
The verification procedure was preceded by calibration of the urban
runoff BOD loading rates for Des Moines, Iowa, as computed by STORM.
The dust and dirt surface loading factors were adjusted to obtain an
, annual average BOD,, concentration of 53 mg/1 for urban stormwater run-
off. The above concentration was the average value determined by the
field monitoring program in the separate sewer system.y The developed
mathematical model, as discussed in the methodology, simulates the mixing
317
-------
of stormwater runoff and sanitary sewage in the combined sewer system.
S££eT£a£ ?? 5 "nCentf*i0n of c°^i-d sewer, overflows wal
computed to be 75 mg/1, including the effects of first flush. The
determin*d fy the field monitoring program in the
The
e montorng pr
combined sewer system was determined to be 72 mg/1.
verJf B°D reactlon "efficient, KI, was refined during the :
verification process to a final value of 0.70 day"1 (at 20°C) . The
model, of course, converts to units of hour-1 and adjusts for temperature
c±±t-Tf i°n V^~25' Th£ atmosP^ric reaeration coefficient?^? is
e±SonevTTn^rnSly f a fUnCti°n °f stre-^ and temperatur^ by"
equation VII-30; therefore, no adjustment was necessary. Measured and
R±VerS are comPared in Figure
C°^elation between the calcu-
-f l is quite good. The point corresponds to
mpling location no. 6 as shown previously in Figure VII-8.
fofefch ^F±8U^ VII~9 3^ rainfa11 and ave^ge total river flow values
for each wet-weather event (as defined in Figure VII-4). Differences
between measured and computed DO concentrations may be attributed to such
X?S aS! K(1? the t±me °f day durlng which the ^le -s taklrca)
variaSonfL Sr6; 8?°? ^^ ** ,laboratory anal^i« ^nd the temperature
variations in the receiving water during the day; and (3) a lack of data
on photosynthesis, algal respiration, a?d benthlc demand The tif scale
-
Again, it should be reemphasized that thes,e. DO -values are not
the minimum DO's resulting from maximum deficits. The maximum deficits
occur much further downstream (10 - 30 miles or 16 - 48 £ Tand water
quality standards are violated much more frequently.
Further verification of the model can be achieved by simulating the
stream response to hydrologic and waste inputs for, say, the year 1969
However, such a time-consuming task was no? deemed 'justifiable ?n view'
of the accuracy already obtained and the study objectives.
Results
Carolina>> the total precipitation
HPM Iowa, during 1968 was 27.59 inches (701 mm).
STORM computed a total runoff of 10.28 inches (261 mm) over a watershed
Scienf otn ?7aCr™ (19'60° ?°' f°r m °Vera11 Urb- area runof? "Sel-
recorded f ™ \ • l*ll ™* " dayS ^ tKe year durln^ **<*• rainfall was
recorded, from which 58 wet-weather events were defined. The results arp
presented in the form of minimum DO frequency curves for thewet-weather
and dry-weather periods throughout the calendar year. The reader is
modeled in'tf D° ^ VII~5 ^ * ^™ °f th^ multit"de "
modeled in the Des Moines application.
318
-------
O.O-i I
". 0.5-
d i.o-
J£
2 1.5-
<
01 2.0 J
14-
12-
_ 10-
•*N.
f 8-
J 6-
d 4-
2
0
1 MIT 1
» '. f
ISA •
V
\\
\'/
\
hi i i ' 1
MEASURED FLOW
+ URBAN RUNOFF
i-O- ... 4 , .
-1 •• ' ,..-" •'
-<* e'*-'' '•"-'.
: , - O •. •'• -
- 3 •• • • • •
-4
--^0' :•-•
>-5
f—
•
— 2000
— 4000
- ., „
J-6000
- ; -
jf-8000
^ i
^-topoo
- •' -,• .
^-12,000
. ^--.,
•'-. -
i
;, i • '• '
0-
1
- _^«1* -
10
*• =
*T-
:"?f"
,5
iU...
cc
Ul
S
e>
3
•^•100
— 200
— 300
o
I
10
[
50
MARCH
10
Figure VII-9.
IOO
20
150
DAYS
200
250 30O
DEC.,
1968
30
40
50
and
319
-------
1. combination M = 5, all waste inputs; .
2. secondary treatment (85 percent BOD removal)
of DWF, J = 2;
3. no stormwater treatment, L = 1;
4. river flow 100 percent of measured flow,
K = 3, and
5. the fraction of combined area is 8.16 percent
of the total urban area.
Figure VII-10, Minimum DO Freouenr.v Tu «~r,
Des Moines Riv^^ -Mlu-trntr-. ..71 _ , rT sinS on
,
320
-------
lOO-i—:.—.
PRECIPITATION YEAR OF RECORD • 1968
DWF TREATMENT RATE' 85%(SECONDARY)
WWF TREATMENT RATE • 0% (NO TREATMENT)
RIVER FLOW = 100% (OF MEASURED FLOW)
COMBINED SEWER AREA •• 8.16% (OF TOTAL URBAN AREA)
INFLOW COMBINATION
RIVER FLOW + DWF
RIVER FLOW + DWF ••• SEPARATE FLOW
RIVER FLOW + DWF 4-COMBINED FLOW
RIVER FLOW + SEPARATE FLOW_4- COMBINED^FLOW
Y\r
2.0 4.0 6.0 8.0 10.0 12.0
DISSOLVED OXYGEN CONCENTRATION, mg/l
14.0
Figure VII-10. Minimum DO Frequency Curves for Existing Conditions
in the Des Moines River
321
-------
PRECIPITATION YEAR OF RECORD • 1968
DWF TREATMENT RATE> 85 % (SECONDARY)
WWF TREATMENT RATE: 0% (NO TREATMENT)
RIVER FLOW =100% (OF MEASURED FLOW)
INFLQW COMBINATION' ,
, RIVp FLOW + DWF t COMBINED FLOW.+ SEPARATE FLOW
COMBINED AREA» '
— ^ 0% (OF TOTAL URBAN AREA)
8.16% (OF TOTAL URBAN-AREA)
50% (OF TOTAL URBAN AREA) :
100% (OF TOTAL URBAN AREA) • •'
2.0
DISSOLVED OXYGEN CONCENTRATION, mg/j
KO
Figure ;VII-11., Mnimiun;Dp Frequehcy Curves for Varied'
•Percent of Combined Se^er Area
322
-------
100 n
K 90
UJ 80-
70-
Q
UJ
x
UJ
co 50-
UJ
40-1
cr
UJ
UJ
I-
UJ
20
10
0
PRECIPITATION YEAR OF RECORD = 1968
• DWF TREATMENT f?AT.E • 85.% (SECONDARY) . ,
Xx WWF TREATMENT 8ATE ;07o (NO TREATMENT)
\.s\ COMBINED AREA ' 8:I6%!(OF TOTAL URBAN AREA)
X._ \ \ . ; '; , ' ' . -
'"-A \ ' INFLOW COMBINATIONS1 • - r
\\ \^ RIVER FLOW t DWF + SEPARATE FLOW* COMBINED FLOW
\'V \ RIVER FLOW; "• '. .
""--.. \ \ 0% (OF MEASURED FLOW) ' /
\\ \ 25% (OF.'MEASURED FLOW) ,
\\ \ "lOO % (OF MEASURED FLOW)-•'
0
T
2.O 4.0 6.0 8.O. !0.0 :.I2,C
DISSOLVED OXYGEN CONCENTRATION,,mg/1
.~:f:V' •"T~-"1
'.t2.0; :•• 14.0
Figure VII-12. Minimum DO Frequency..Curves. .'for JVaried
Percent of Actual Measured Upstream
River Flow , :;
323
-------
100
PRECIPITATION YEAR OF, RECORD- 1968
COMBINED AREA '8.16% (OF TOTAL URBAN AREA)
WWF TREATMENT RATE = 0% (NO TREATMENT) -
RIVER FLOW • 100% (OF MEASURED FLOW)
INFLOW COMBINATION =,
, , RIVER, FLOW + DWF + SEPARATE FLOW + COMBINED FLoitf
DWF TREATMENT RATE ' C . .
a ** ' ' 'f,
"••-•"- 30 % (PRIMARY) , £
-85% (SECONDARY) ...•'£
— .-—9.5 % (TERTIARY) ••«•••" -
—-—INDICATES EVENTS EXCEEDING \
DESIRED D.O. LEVEL ' " ;:
Ul
* .oJ
5§
0-
"
''•••.
• 1 • ' • T7~I — "^ 1 — i —
0 2.0 4.0 6.0
8-° IQ.O f|2.0
DISSOLVED OXYGEN CONCENTRATION, mg/l
Figure VII-13. Minimum DO Frequency Curves for Varied
DWF Treatment ' - •-•'•-.•• - ' ..- -..-,.>•.
324
-------
IOO-
0 90-
Q
ui. so-
ts
0 70-
8
UJ 60-
§ •
to 50-
UJ
gj 40-
I 30
UJ
* 20
H
UJ
^ 10
sS
0
^...-.v.— .;— ^. _ PRECIPITATION YEAR OF RECORD « 1968
x :\. j \ DWF TREATMENT RATE = 85% (SECONDARY)
V , "•••"—•^ \ \ COMBINED AREA ' 8.16% (OF TOTAL URBAN AREA)
\ \
. ./",-^..-\:
\ ' RIVER FLOW-' 100% (OF MEASURED FLOW)
\ INFLOW COMBINATION' , , | . .
\ RIVER FLOW + DWF + SEPARATE FLOW + COMBINED FLOW
1 \ \ i • . • - •, • •
\
\
L_1JN
t ' 1 ' •
0 2.O '
V WWF TREATMENT RATE'
\ . 0% (NO TREATMENT)
\ V 25% .
\ V. 75%
\ \ \ INDICATES EVENTS EXCEEDING
\ \ \ ^ DESIRED D.O. LEVEL,
\ \ \
\\ \
\ \ \ ,
. \\ \ •..:. ' ; . ,
\\ \
\ \ \
^-4 \ •.•"' •' .'."••
^\.'\ \ • ...-:'-.'•
^i-^-X,...-^ .;.-.... -: , , . .
~~^\ • - , ' • •••
..-, p- , --.. V | ^—^
.0 6.0 8.0 10.0 12.0 i4.0 , .
DISSOLVED OXYGEN CONCENTRATION, mg/I
Figure VII-14. Minimum DQ Frequency Curves for Varied
WWF Treatment ,
325
-------
\
\
PRECIPITATION YEAR OF RECORD'1968
INFLOW COMBINATION'
RIVER FLOW* DWF+COMBINED FLOW +• SEPARATE FLOW
COMBINED AREA,' 8.16% (OF TOTAL URBAN AREA)
RIVER FLOW- 100% (OF MEASURED FLOW)
DWF TREATMENT RATE'
95 % (TERTIARY)
; 85 % (SECONDARY)
85 % (SECONDARY)
85 % (SECONDARY)
\ 3O% (PRIMARY)
0% (NO TREATMENT)
WWF TREATMENT RATE-
0 % (NO TREATMENT)
75%
25 %
0 % (NO TREATMENT)
O % (NO TREATMENT!
0%(NO TREATMENT)
V INDICATES EVENTS EXCEEDING DESIRED D.O. LEVEL
4.0 6.0 8.0 10.0 12.0 14.0
DISSOLVED OXYGEN CONCENTRATION, mg/l
Figure VII-15. Minimum DO Frequency Curves for Varied Treatment
Alternatives
326
-------
1. 'for all of the precipitation events defined
by the model, upstream; 'river"' flow was on .'';;'
''••'• ; ;' the; average" 50 percent pf ,'t;he total river ',/ '
'•''' flow5 and • ••','•''' •"*.•
2. this percentage ranged from as low as 6 : /
percent to as high as 97 percent of total
•'•'•''••' '"' river flow. '' *"!"...',-'•' ,'.'..,.. .'.••'.-•
For the Des Moihes application,.Vand the .particular rainfall year selected
(196.8.),v urban runoff seems to.:be the,key factor in receiving water i;:,
critical-DQ levels. However, an- urban'-area located very/far upstream .in
a river basin would have a mo're detrimental impact on wliter quality down-
stream from':the; urban area than if the same'urban are6 was located on 'a'"'
higher order stream within the network. • ',-
Figure VII-13 shows the effect of varying the degree of treatment of DWF
while holding the other parameters constant. It-can be inferred that „,.
there is no significant improvement of stream, water quality (DO) by up-
grading DWF treatment from secondary to tertiary' during p.eriods of wet
weather. However, it is clear that,the improvement in minimum DO
levels by upgrading DWF treatment from primary to secondary is probably
worthwhile: 7 percent more wet-weather events would, exceed a.JDO. >v ;,. ;
value of 4.0 mg/1. "Examination of,rFigure VlI-14 feveals,,,that critical.
DO levels are improved appreciably with 25 percent" treatment of WWF
and markedly with 75 percent treatment ;bf WWF, while providing secondary
treatment of DWF. The minimum DO frequency curves 'in Figure VII-15
compare f our;. treatment alternatives to.. reduce water .pollution during '
periods of urban runoff: , ...-.'--i ••.•-vr'' :.'..-:*
1. 95 percent treatment of DWF and no treatment
of urban runoff,
2. 85 percent treatment of DWF and 25 percent
treatment (BOD removal) of WWF,
3. 85 percent treatment of DWF and 75 percent
treatment of WWF, and
4. - 85 percent treatment of DWF and no treatment
or urban runoff.
The zero treatment and primary treatment curves are also shown for com-
parison, but are not considered acceptable alternatives. I.t appears
that options 1 and 4 above result in comparable critical DO levels in
the receiving stream. However, options 2 and 3 result in much more im-
proved critical DO levels. An economic evaluation of these treatment
alternatives, on an annual basis, is presented in a later subsection.
327
-------
, appr°prx ate to examine the results of applying the model to
™ J hr°UghoUt the y£ar durin§ ^ich no urban runoff was produced.
Dry weather was experienced for approximately 300 days throughout 1968.
The model Was applied to these periods using a daily time step. This
modification is certainly justified since conditions are more truly
steady-state than during periods of precipitation and subsequent runoff
for example, waste loadings (DWF treatment plant effluent) and river flow
day' F°r the dry-weather simulation
°n the average 94 Percent of total r^er
™ rr n°m TT PrCSnt t0 "'6 Percent- The results are shown in
Figure VII-16, Dry-Weather Minimum DO Frequency Curves for Varied DWF
S^S6nt f-!erna^ves- A remarkable 97 percent of the dry-weather days
exceed a minimum DO concentration of 4.0 mg/1. Upgrading of DWF treatment
becomes meaningful only if stream DO standards areset higher than T£
mg/1. From Table VII-3, it is clear that the Des Koines River in particular
carries a high BOD load upstream of the Des Moines urban area. ?Ms
SPtnfDW tre!^ J^8 f ^weather Period« onl7, a significant increase
tL ^f f*atmenVate !S n0t reSUlt ±n a corresP°nding increase in
the critical DO levels,, as shown in Figure VII-16.
To maintain the proper perspective, it is desirable to view the effects
of urban runoff on an annual basis, not just during periods of wet
weather. The frequency curves shown in Figures VII-15 and VII-16 are
combined by weighting on the basis of the number of rainfall days and dry.
W^thf davs *n ^e year. The composite totals are presented in Figure
!**"^;/Tf „ mUm D° Frequency Curves- *°r example, a given stream
standard of 4.0 mg/1 is exceeded 90 percent of the time for existing
conditions in Des Moines, Iowa, throughout the year 1968. A significant
amount of treatment (75% BOD removal) of WWF in addition to secXdary
treatment of DWF results in critical DO levels such that the same stream
standard is exceeded 97 percent of the days in the year. Annual DO
duration curves tend to mask the impact of shock loads of organic pollu-
tants discharged during periods of urban runoff. A few extended violations
of stream DO standards may cause anaerobic conditions resulting in fish
kills and proliferation of undesirable microorganisms.
The integral of the DO deficit equation over all time, equation VII-31
has been suggested as a measure of the relative effect of one waste '
source versus another. Denoted as V, the volume of DO deficit this
«S£ter ^ r^^f f°r eaCh treatment °Ption during both wet- and dry-
weather Periods. The average values obtained are given in Table VII-6
f±C't' ^ ind±Cate the same ™»^ of the treat-
. ' ae e same ™»S of the t
ment alternatives as suggested by the curves in Figure VII-17, from a
water quality viewpoint. This implies that the integrated DO deficit
V, may provide, a simple method of comparing the impact upon receiving'
waters of alternative input configurations. However, interpretation
of the numerical value of V, in an absolute sense, remains ambiguous.
328
-------
iuu-
_? OO —
O 9O
UJ 80-
o
O 70-
z
o
UJ 60-
o
X
UJ
5O-
co ou
1
^40-
UJ
I
^ 30-
UJ
*
>20-
o
3? 10-
O-
<
^^v^^*
••.]
J
; _.
D 2.0 4
_.
N^^-.:
V \
XA
3
i
.0
•j>
\\
. :\
\ \\
• \ ii
\\"^
\ \ V.
. \ 'A
\ \ ''•
\\ '
\\
\\
v
\
\
i
— i <~
6.0
DRY WEATHER DAYS OF 1968
WASTE INPUT--
URBAN DWF+ UPSTREAM SOURCES
RIVER FLOW = IOO%, OF MEASURED FLOW
DWF TREATMENT RATE =
QC oi fTPRTIARYl
QE Cy ^OCr*AKinADV^
-\ 30% (PRIMARY)
\\ 0% (NO TREATMENT)
\\ INDICATES EVENTS EXCEEDING
V\ DESIRED D.O. LEVEL
•. \
': \
': \
\ I
,\\\
\^\
^k v "••V
\>^>x
^^ ":^»s;^>.^
^>*v'\
%
%,„
8.0 IO.O 12.0 14.0
DISSOLVED. OXYGEN CONCENTRATION, mg/l
Figure VII-16.
Dry-Weather Minimum DO Frequency Curves for
Varied DWF Treatment Alternatives
329
-------
0
SIMULATION PERIOD'1968
WASTE INPUT' UPSTREAM SOURCES + DWF +
SEPARATE SEWER FLOW* COMBINED SEWER FLOW
RIVER FLOW = 100% OF MEASURED FLOW
COMBINED SEWER AREA = 8.16% OF URBAN AREA
• DWF TREATMENT RATE=
95 % (TERTIARY)
85 % (SECONDARY)
— 85 % (SECONDARY)
85 % (SECONDARY)
30 % (PRIMARY)
0 % (NO TREATMENT)
WWF TREATMENT RATE'
0 % (NO TREATMENT)
75%
25%
0 %(NO TREATMENT)
0%(NO TREATMENT)
0%(NO TREATMENT)
— INDICATES EVENTS EXCEEDING DESIRED D.O. LEVEL
2.0
4-° 6.0 8.0 10.0 12.0
DISSOLVED OXYGEN CONCENTRATION, mg/l
Figure VII-17. Annual Minimum DO Frequency Curves
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ECONOMIC EVALUATION OF TREATMENT, ALTERNATIVES
Alternatives
alternatives to existing conditions are considered in this
1. upgrading the existing DWF treatment facility
from^a high-rate trickling filter plant to a
tertiary treatment process , or
2. providing two separate levels of urban stormwater
runoff control
i. 25 percent BOD removal, or
ii. 75 percent BOD removal,
while maintaining existing DWF removal
efficiencies .
Upgrading DWF Treatment
To achieve a tertiary treatment configuration, the Des Moines DWF
treatment facility must undergo an intermediate-staga modification to
SSr^J ?J *?' A Si^lif^d Profile of the existing DWF treatment
SOT* I characteristics is presented in Figure VII-18, Existing
f! *™CeS^r0file- m&n aU ac£ivated «^dge uSit is . added, the
trickling filter acts as a roughing filter. The capital costs of this
iSr"? di^e-stafe grading are evaluated separately and must be added
Xater to the capital costs associated 'with a tertiary treatment
capability. A profile of the* trickling filtration/activated sludge
1STSh?^,^ ^S^ro-lQ, Trickling Filtration /Act ±v*t*A M
^5 d°llarS 'CENR 2200^» ^r a design flow of 35.3 mgd . '
cu m/day), the intermediate-r-staga capital costs are approximately 4 18
Aeration Tank(s)
Sludge Recirculation
Clarifier Modifications
$1,560,026
574,746
88,000
Total •
supervision
$2,222,772-
-. engineering design, bonding, and construction
The unit processes added to achieve tertiary treatment (95 percent BOD L
removal or better) are shown in Figure VII-20, Added Tertiary Treat^V ;
Unit Processes. It is assumed that disposal of chemical sludge is' By —
incineration. Cost figures were obtained from a report by Battelle-Pacific
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Northwest Laboratories.19 Capital cost changes due to variations in
plant size are approximated through use of the exponential rule:19
1. if plant size changes by a factor of X,
the cost will change by a factor of XN
where N varies from 0.0 to 1.0, and
2. an average exponential factor of N = 0.6
is used, for wastewater treatment facili-
ties and equipment designed for plants with
100 mgd (378,500 cu m/day) flow or less, as
in the case for Des Moines, Iowa.
Capital costs presented in the Battelle Report are expressed in 1973
dollars (ENR-1900). Capital costs presented in this section have
been updated by multiplying the base cost by the ratio of the current (197C>)
ENR index (2200) to the 1973 index. Local cost multipliers were not
available to adjust national average costs to figures reflecting the
price structure likely to prevail in Des Moines. Thus, for example,
a land value of $1000 per acre ($2500 per ha) has Been assumed.
Operating costs may vary significantly from the estimates provided in
this section as a result of local differences in costs for power, fuel,
chemicals, labor, transportation, supervision and maintenance. An
exponential rule is used to adjust approximately for variations in plant
size as follows:
1. N = 0.58 for labor and supervision,
2. N = 0.55 for electrical, and
3. N = 1.00 for chemicals and fuel,
for flow rates up to 100 mgd (378,500 cu m/day). Total annual costs
are then calculated by adding amortized capital costs to the operating
and maintenance costs.
Capital costs are shown itemized in Table yi.I-7, Capital Costs:'for
Tertiary and Intermediate-Stage Treatment. These, figures do not include
costs of primary and secondary treatment, as they pertain to already
existing conditions in Des Moines. Operating costs are listed in
Table VII-8, Operating Costs for Tertiary and Intermediate'stage • Treat•=•
ment. The following assumptions are-made;
1. overall costs of labor are $8 per man hour
for an 8 hour day,
2. power costs are $0.025 per kilowatt-hour,
336
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TABLE VII-7. CAPITAL COSTS FOR TERTIARY AND INTERMEDIATE-STAGE
TREATMENT, 35.3 MGD (1.55 cu m/sec)
Item
1.
2.
3.
4.
Liquid Treatment
Liquid Disposal
Chemical Sludge
Organic Sludge
Land for (1.)
(3.)
(4.)
Base Cost
$4.7 x 106
0.043 x 106
1.9 x 106
1.0 x 106
12,000
21,000
4,100
r*
Scale Factor, ENR
XN Ratio
(3.53)0'6
2.13
2.13
2.13
2.13
2.13
2.13
2200
1900
1.16
1.16
1.16
1.16
1.16
1.16
Adjusted
Cost
$11.60 x
0.11 x
4.69 x
2.47 x
0.03 x
0.05 x
0.01 x
io6
io6
io6
"I
106
106
10°
TERTIARY CAPITAL EXPENDITURE
Intermediate-Stage Cost
TOTAL CAPITAL EXPENDITURE
Amortization: 20 years at 8%
Amortized Annual Capital Cost =
$ 18,960,000
+ 2,222,772
21,182,772
$2,157,508
See definition in text.
Reference 19 (Strategy no. 9) for 10 mgd plant.
337
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TABLE VII-8.
OPERATING COSTS FOR TERTIARY AND INTERMEDIATE-STAGE
TREATMENT, 35.3 MGD (1.55 eu m/sec)
Item
Base Costb
Scale' Factor,
X
.N
Adjusted Cost
1. Base Labor
(i) Liquid Treatment 214,912
(ii) Chemical Sludjge 233,600
(iii) Organic\Sludge ""151,840'
2. Electrical Power,, ,r" ,*"• '^.,, ' 7.
(i) ' Liquid', Treatment" ',..' 197,100
(ii) ChemiQal Slu'dgeJ I'T. 29,337
(iii) Organic Sludge 16,516'
3. Chemicals _. , ,
'(i) Liquid Treatment V. J ,,"".,.
a) Lime,:' ,''"",'' * 153,300
b) CO ./ *,..; . -.19X450
c) ci , :,,;v;:,/.'.; "is, 396"
d) Polymer f I., ,','.", '.".' 15,330
(ii) Organic Slu"dge J" I,' "'"
• .(Polymer) ',. ^•"•Jf7.''r1^00Q'.
4. Fuel ,/ ' ,,\ , . [., V~7"" " /'", ',' ,/.
(i) Liquid .Treatment, ,- ,3,164
(ii) Chemical Sludge ' 109,208
(iii) Organic Sludge ,. 40,150
3.53
2.08
2-Q8
2^0,8
o;58
(3, .53)
2.0Q
2 ... 00 '
2.00
j(3.53)-
3^53
' ,"3.53 .
3.53
3.53^
.,3.53
sO.55'
3-53
3.53
3.53
$446,653/yr
485,492 ,
.315,57Q, ,rf.
394,423,
.-', 5-8»7 0.7.'
', .33,051
,541,149
..682,879
64,938
.54,115;
,515,380
,12,756
385,504
1.41,730,
..; TOTAL ANNUAL CpST . $•.' 4,132,347
a • • .•.•'"•
See definition in;text. V;
Reference 19 (Strategy no. 9) for 10 mgd plant.
338
-------
3. chlorine costs are $0.12 per pound
($0.26 per kg),
4. polymer costs are $2.00 per pound
($4.41 per kg), ;
5. lime costs are $0.02 per pound ($0.04 per kg),
6. C0? costs are $0.02 per pound ($0.04 per kg),
• ' and
7. fuel costs are $0.11 per therm ($0.11 per
1000 kg-caloriei.
Transportation costs are not included since the sludge is assumed to be
disposed on site by incineration. The total annual cost (including
amortized capital cost) is $6,289,855. This figure represents a cost
of $0.49 per 1000 gallons ($0.13 per cu m) for conversion to activated
sludge from high-rate trickling filtration, the intermediate-stage
and addition of tertiary treatment with incineration of chemical and
organic sludges.
The tertiary treatment configuration shown in Figure VII-20 constitutes
a rather complete and sophisticated process profile. Battelle-Pacific
Northwest Laboratories recommends this strategy as an alternative to
existing secondary activated sludge plants which are required to provide
additional organic and nutrient removal.19 Furthermore, such a process
configuration will produce a higher quality effluent than any of the
other strategies considered.19 However, examination of Tables VII-7 and
VII-8 reveals that the incremental cost of providing such a facility is
quite high. If organic waste removal is the primary consideration, a
lower cost control strategy may be considered.: activated sludge^coagu-
lation-filtration (Battelle Northwest, 1974; Strategy 8) ,19
Assuming that the intermediate-stage conversion has been accomplished (see
Figure VII-19), an alternative to complete tertiary treatment is shown
in Figure VII-21, Activated Sludge-Coagulation-Filtration Process Profile.
This control strategy compares favorably with the more advanced option
in BOD removal capabilities. Treatment plant efficiencies vary consi-
derably with the input flow rate and waste concentration. Battelle
assumed a medium strength sewage of 200 mg/1.19 For 10 mgd (38,000 cu m/day),
100 mgd (380,000 cu m/day) and 1000 mgd (3,800,000 cu m/day) plant sizes
BOD removal efficiencies up to (1) 99 percent for tertiary treatment, and
(2) 98 percent for activated sludge-coagulation-filtration are given.
In general terms, any type of post-secondary treatment (other than
chlorine disinfection) is classified as tertiary treatment in the water
pollution control literature. The option depicted by Figure VII-21 has
been assumed as a type of tertiary treatment and exclusively referred to
339
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as such in the analysis presented in Section VI. The more descriptive
title is used in this section- to distinguish the process from the more
advanced profile of Figure VII-20. It is appropriate to emphasize that
the type of post-secondary DWF process adopted by a municipality will
depend on such local factors as sewage characteristics, energy resources,
and receiving water quality. ... |
'- ' t
The incremental cost of providing the facilities of an activated sludge-
coagulation-filtration system may be estimated by:20
C - 87,000 D
tert ' p
0.787
(VII-36)
where
tert
D
= annual cost, dollars, including amortized capital
costs (20 years, 8 percent) and operating and
maintenance costs, and
= plant size, mgd. ' , .
Total annual costs of both types of post-secondary treatment are summarized
in Table VII-9, Dry-Weather Flow Costs for Advanced Treatment. The
amortized capital cost of intermediate-stage conversion has been added to
the cost computed by equation VII-36 for the activated sludge-coagulation-
filtration design. The large difference in annual costs is due to:
1. the added expense for chemicals during ammonia
removal, and
2. added fuel costs during incineration of chemical
and organic sludges,
for the complete tertiary treatment configuration.
Control of Urban Runoff
The methodology for assessing the cost of providing storage/treatment
facilities for wet-weather flows is presented in Section VI. A basic
assumption is that a secondary treatment technology is applied to achieve
four levels of control for the" total BOD generated by urban runoff: 25,
50, 75 and 85 percent BOD removal. Various control alternatives were
evaluated: dissolved air flotation, biological treatment, physical-
chemical treatment and sedimentation.21
The cost of wet-weather control for the storm sewered'area of Des Moines,
Iowa, may be estimated by (Table VI-10):
0.
ST
3.61 e
037^
(VII-37)
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where Z = annual cost, dollars per acre of storm sewer
area, and
R = percent BOD control,
and
0 < R < 85.
(VII-38)
The cost of>wet-weather control for the urban area served by combined
sewers may be approximated by (Table VI-9):
where
: 0.056^
ZCQ - 25.07 e
Z ?=• annual cost, dollars per acre of combined
sewer area. • ;
(VII-39)
These equations yield unit costs, not total costs, ;but they are derived _
by a procedure that estimates the total developed acreage of an urban
area (see Section' -VI). The average annual runoff associated with
equations VII-37 and VII-39 is 11.2 inches (284 mm), computed from a mean
annual precipitation of 30.37 inches (771 mm). The length of the preci- •
pitation records is 30 years (1931-1960).22
The cost figures for 25 percent and 75 percent BOD removal of WWF are
presented in Table VII-10.. Wet-Weather Flow Control Costs. These values
.rare based on a total drainage area of 49,000 acres ,(19,600 ha), of which :
45,000 acres (18,000 ha) are served by separate sewers and 4,000 acres
(1,600 ha) are served by combined sewers. The annual runoff for 1968
for Des Moines, Iowa, is 10.28 inches (261 mm); thus, the unit costs ob-
tained from equation's VII-37 and VII-39' are slightly high but an ^ adjust-
meht is not considered necessary.' The total annual costs shown in Table
VII-10 include amortized capital :costs and annual:; operating and maintenance
' COStS'. . ........ ' ;' ;_ ' ,.' '. -.•'-• .'.
•Tradeoff in Alternatives ,
To view the control strategies in the proper perspective, the status quo
.:conditions are included as a base:for comparisons. Any alternative plans
that depart from this base must be justified on their cost-effectiveness.
Thus, the cost figures shown in Table VII-11, DWF Tertiary Treatment
vs WWF Control,.represent the additional expense incurred in providing
storage/treatment beyond that already available with secondary treatment
of DWF and no control of urban runoff. Figures VII-15, VII-16 and VII-17
:Show the effects of various control strategies upon the minimum DO con-
centrations of the. Des Moines River.
343
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The Des Moines River stretches for 200 miles (322 km) from the^City
of Des Moines to its junction with the Mississippi River and in
general the river is wide and swift with occasional deep holes and
f broad'flood plain. According to the State Hygienic ^oratory,
bottom material is composed of silt deposits, sand ?ra-f *n^ubble
providing numerous habitats for fish and other aquatic life.Recre
ational activities such as fishing and boating are quite heavy The
entire reach is classified as warm water "B" stream by the Iowa Water
Quality Standards,23 such that the Absolute minimum dissolved oxygen
?evel specSied 1^ 4.0 mg/1. The Iowa Standards also require a minimum
of5.0 mg/1 during at least 16 hours per day.2" Thus, taking 4.0 mg/1
as the standard or basis for water quality comparisons, the different
control options may be judged by the following criteria:
1. total annual cost, and
2. .violations of the minimum allowable dissolved
oxygen level.
T.KIo VTI-12 Control Hosts vs Violations of the DO Standard, summarizes
tne cost-effectiveness of two advanced waste treatment options two wet-
weather control options, and existing DWF secondary treatment facilities,
Sfcomparat^e purposes! two additional treatment conditions which are
not presently acceptable by government regulation are presented.
Examination of Figures VI1-15, VII-16, vII-17 and Table VII-12 reveals
that: " •
1. since both types of tertiary treatment remove
essentially the same amount of BOD, option 1
is justified over option 2 only t&ea.mitrwat
removal is.necessary;
2. option 4 is preferred over any form of advanced
waste treatment;
3 option 3 is attractive because it causes the
least amount of damage to the receiving stream,
but it is the most expensive alternative;
and
4. any reduction in the degree of DWF treatment
for existing conditions, option 5, results in a
substantial deterioration to receiving water
dissolved oxygen levels and must be weighed
against the savings incurred.
345
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Again, the issue of shock loads is important, and favors high levels
of WWF control.
The reader should be cautioned that advanced tertiary treatment is
rarely imposed just to improve the BOD removal capabilities of
existing facilities. It is usually designed specifically for nitrogen
and/or phosphorus removal. For the heavy precipitation months of
June, July and August, 1968, Davis and Borchardt9 reported the following
nutrient concentrations at a point approximately 5.5 miles (9.0 km)
downstream from the confluence of the Raccoon and Des Koines Rivers:
1. total organic nitrogen ranged from 1.6
to 3.7 mg/1,
2. nitrate nitrogen ranged from 0.2 to
7.8 mg/1, averaging 3 to 4 mg/1, and
3. orthophosphate (OK),) ranged from 0.6
to 1.8 mg/1, averaging slightly over
1.0 mg/1.
Since most of the urban runoff would overflow untreated to the
receiving water, any program of advanced treatment given to all urban
DWF would be relatively ineffective. It would also be questionable
whether a level of WWF control consisting of secondary treatment (such
as that evaluated for 75 percent BOD removal) could reduce nutrient
levels in the Des Moines River and Red Rock Reservoir to inhibit aquatic
plant growth. Davis and Borchardt9 observed high algal densities in
both the Des Moines and Raccoon Rivers, and they also state that nutrient
concentrations are almost always present at levels reported by Sawyer
to be sufficient for nuisance algal growths: 0.3 mg/1 for inorganxc
nitrogen (NH,, NO,, NO ) and 0.015 mg/1 of inorganic phosphorus. Further-
more, since nitrates are abundant in groundwater and the surface and
subsurface hydrologic systems are not independent of each other, nutrient
control seems highly complex and improbable.
The total annual precipitation for the year 1968 was 27.59 inches (701 mm),
The frequency and intensity of precipitation over an urban area has a
direct bearing on the magnitude of stormwater pollution and, consequently,
dissolved oxygen levels in the receiving water. In the selection of the
"best" control strategy, other factors may become important, such as:
1. recovery of receiving waters from shock loads
during runoff periods,
2. local and regional water quality goals,
3. public willingness to pay the costs associ-
ated with each level of control, and
347
-------
4. consideration of alternate use of WWF
facilities as DWF treatment facilities
during periods of no urban runoff.
;
OTHER RECENT RECEIVING WATER IMPACT STUDIES
.
simulate the effects of a oin r™ ? T D^vls:ion> "». applied, to
dissolved oxygen, were predicted for over 40 miles (64 km) downstream
348
-------
and the minimum dissolved oxygen concentration was computed to be
1.4 mg/1. ...
Hydrocomp International (Palo Alto, California) conducted dynamic
simulation of a large watershed in the South Platte River basin for
Black & Veatch and the Denver Regional Council pf Governments.28
The 750 square mile (194,256 ha) area included metropolitan'Denver
within its boundaries. Simulations of streamflow and stream water
quality conditions for given precipitation, wastewater loadings, and
diversions (irrigation) were performed for each of several proposed
wastewater system configurations. The water quality calibration
process to adapt the Hydrocomp Simulation Program (HSP) to actual
environmental conditions was hindered by the lack of a continuous
data base within the designated study area, a problem prevalent through-
out the nation. An analysis of a 20-year simulation for one plant con-
figuration indicated that the calendar year 1954 represented one of the
most critical periods from a water quality standpoint. It had the
lowest annual streamflow for the 20-year historical record, and some of
the lowest stream.-dissolved,oxygen concentrations were predicted.
The Hydrocomp study did not specifically address urban stormwater control
facilities as a treatment alternative. Five' levels of wastewater treat-
ment (DWF) were tested for the critical year: .(1) secondary, (2)
secondary with filtration, (3) secondary with nitrification, (4) tertiary
and (5) zero pollutants discharged. Daily duration curves were computed
for minimum dissolved .oxygen for each DWF. treatment level. Of the five
levels, the effluents from secondary treatment with nitrification,
tertiary treatment, and absolute treatment (zero pollutant discharge from
DWF sources, financially prohibitive) did not exceed the stream standards
imposed by the regulatory agency. Treatment levels 3 and 4 were thus
considered the only acceptable options from an effluent viewpoint. Three
possible treatment plant discharge configurations were tested at these
two treatment levels. The resulting minimum daily DO duration curves
(such as Figure VII-6) were practically identical.
From the DO duration curves.for zero pollutant discharge from dry-weather
sources, some interesting inferences may be drawn ori the effects of urban
runoff. The average annual precipitation over the study area for the
period 1935-1974, was 14.58 inches (370 mm), less than half the national
average.29 The entire modeling area may be classified as semi-arid. The
total annual precipitation for the critical year 1954 was 7.51 inches
(190 mm), or about one-fourth of the total annual precipitation recorded
in 1968 over Des Moines, Iowa. The minimum ^daily DO duration curve for
the zero pollutant discharge (DWF) treatment level was entirely above the
minimum DO standard, 5.0 mg/1, for the reach of the South Platte River
below .the Denver metropolitan area. This curve represents the minimum
daily DO, concentration history of the receiving stream-resulting exclusively
349
-------
f rt ^ rUB?ff ^^ inpUt> ThUS' the Urban runoff Pollutant
load for the drought year 1954 was insufficient to drive the critical
DO levels below the fish and wildlife standard. The cost of storm-
water control would not be justified.
aaul, °f the effects of urbanization.
aquatic ecology and hydrologic regimes for the Office of Water
Research and Technology, Department of the Interior. 30 Urbani2ation
affects flood frequency, flow duration, total volume of runoff, and
has an impact because of additional pollutant load on stream BOD, DO
and nutrient concentrations. Computer simulation was employed to
generate synthetic data series in the absence of historic series for
three hypothetical cities representing the Pacific Northwest, eastern
slope of the Rocky Mountains, and the central Atlantic states. Of
particular interest is a comparison among these regions of the effect
of urbanization on: (1) surface runoff quantity (Table VII-13
in Surface Runoff) , and (2) pollutant loadings (Table VII-U
in Annual BOD Mass mBrh»rpp) . The hypothetical watersheds
. waerses co
of a drainage area of 60 square miles (155.4 sq km) having all of the
i£LT?1 'Jf racteristics of ^e test watersheds (for which HSP was
regionally calibrated) , including impervious area. For the urbanized
case, the lower half of each watershed was assumed to be intensively
urbanized with 35 percent imperviousness and directly connected to the
Iw™ ir^ ^ hydrol°g±c P"^ °f study extended from 1948 to
J.y/2. The average percent increase of annual runoff due to urbanization
tne S^ffT \V°r th^ ar±d Wate-bed. •*>* the very wet va2»£ed?
the Pacific Northwest, high soil moistures resulted in almost as much
runoff from rural land as from impervious (urban) areas. A substantial
increase in peak flows was observed for one year return period events
Again, the percentage increase in peak flows due to increased impervious-
ness was observed to decrease from arid to wet regions. For the 25-year
peak f lows, _ the percent increase due to urbanization was slight. This,
of course, is largely due to the magnitude of these events. It is
interesting to note from Table VII-14 that the annual BOD mass discharge
was increased at least 100 percent by urbanization.
CONCLUSIONS
A methodology has been
importance of separate,
generated by the urban
*
in the Des Moines River
treatment alternatives
uous model which shows
presented in Section VII to assess the relative
combined, and DWF sewer runoff as waste sources
environment. The effects of WWF and DWF pollutants
°f ^ Urban Stream are P^ented on a frequency
to Des Koines-, Iowa, demonstrated clearly the
n rUn°ff P°llution °n critical DO concentrations
The cost-effectiveness of various wastewater
can be determined realistically only by a contin-
the frequency of water quality violations in the
350
-------
Table VII-13. INCREASE IN SURFACE
(Hydrocomp, 1975)30:;;
Basin ... .•
Eastern Slope Rocky Mountains
Central Atlantic,
Pacific Northwest
Mean Annual Precipitation
in (mm)
18.2 ( 462,0)
43.5 (1105.0)..
56.2 (1427.0)
%. Increase,- Annual .Runoff Z., Increase -Beak
" VV. ..'.'" . . .. '. -. -,1 yr-2^.^.
" ; ], 91'.'. ... '... .... •. 2800 -...9
,;.., •- '.;.. \,.. 53.-. •* , •' ~ "- ' 1460 ^ 4-
'Fjows
Table. V-II-14.
INCREASE IN ANNUAL.BOD MASS DISCHARGE'
(Hydrocomp, 19-75)30- •: -;:r.-:v; ••'.-• • - -
Basin
Rural BOD
.lbs/yr (kg/yr)'
'' tfrban BOD '
lbs'/yr (kg/,yr) ,
Urban/Rural Ratio
Eastern Slope Rocky Mountains
Central Atlantic
Pacific Northwest .
0.65 x 105 (0.29 x-105)
1.30 x 105 (0.59 x 105)
2.10 x 105 (0.95 x 10 ) ;
2.1 "x 105'(6::95 x 105) : '
3.0 .x_' lg5; (1.36 x 105)
4.4 x'lO5 (2TOO x,105)-; -'
-•-• ' 3.2
351
-------
For 1968 65 days of rainfall were recorded .over Des Moines, Iowa,
interevent f? ^^^ events — ***»* according to a minimum
interevent time of nme dry-weather hours. The annual precipitation
total was 27.59 inches (701 mm), producing 10.28 inches (261 *£ of
urban runoff. For existing treatment facilities, 42 percent of the
wet-weather events were predicted to violate a 4.0 mg/1 DO standard,
as well as two percent of all the dry-weather days. Thus, the model
predicted that violations of such a standard would occur 33 days out
of the year. The highest control strategy would reduce the number of
Sfectivf8^' atVan lncremental cost °f $9 million. The most cost-
effective alternative seems to be 25 percent control of WWF and
secondary treatment of DWF, at a cost of $816,000 per year, and
resulting in stream standard violations 26 days out of the year.
The methodology that ha,s been applied to the Des Moines urban area
should serve as a decision-making tool for planning purposes only.
^ eX1St thSt Sre better SUited for the inten? of design and
SP tS °f ^ USS °f ^P1"^ mathematical modeling
? agreement was achieved with field measured data through
analysis, and the primary purpose of the modeling effort
was accomplished. The reader should review the numerous assumptions
S£?rf ^ m 1U^al Part °f m°del devel°P^nt and application and
should understand that the final numbers obtained are intended as a
guide for screening alternatives.
352
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ABBREVIATIONS AND SYMBOLS
B
BOD
BOD
BOD
BOD
BOD
m
BOD
BOD
BOD
u
BOD
B-,
C
C
w
"min
utert
CBOD
Area served by combined sewers, acres
Area served by separate sewers, acres
Total area of catchment, acres
Urban area runoff, inches per hour
Regression coefficients
Benthal demand of bottom deposits, mg per 1-hour
Biochemical oxygen demand, mg/1
Standard BOD test, 5 days at 68°F (20°C), mg/1
Mixed BOD concentration in the combined sewer, mg/1
BOD concentration of wet-weather flow treatment facility
effluent, mg/1
BOD concentration of municipal sewage, mg/1
Mixed BOD concentration in receiving water, mg/1
BOD concentration of urban stormwater runoff, mg/1
Hourly BOD concentration of total urban runoff, mg/1
Mixed BOD concentration from sources upstream of urban area,
mg/1
BOD concentration of treated wet-weather effluent, mg/1
Regression coefficients
Concentration of water quality parameter, M/L
Concentration of dissolved oxygen (DO) in the stream, mg/1
Concentration of DO at maximum deficit, mg/1
Saturation concentration of DO, mg/1
f - •%
Conversion factor, pounds per hour to mg/1 • cfs
Annual cost of activated sludge-coagulation-filtration system,
dollars
Carbonaceous biochemical oxygen demand
353
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COD
CR
u
D
D
c
Du
Do
DP
DO
DWF
DWFSEP
DWFCMB
DWH
E
ENR
f
fu
FF
FFLBS
H
k
K
n
Kl
K2
L
L
Chemical oxygen demand .' . ,"
Composites-runoff coefficient dependent on urban land use
Dissolved oxygen deficit = O . - .C, mg/1 .•,;.,
s
Critical (maximum)''deficit, mg/1 ." , ,
DO deficit in receiving waters upstream of, inflow-point, mg/1
Initial DO deficit, mg/1- ,-". -;...-.- - ... ..,.,..
Size bf activated sludge-coagulation-filtration plant, mgd
Dissolved oxygen -..•
Dry weather flow, cfs '; ,: , -,.,-... ^t^yy,
Dry-weather flow contribution from separates, sewer area, cfs
DWF, contribution--from--combined- sewer area, cfs f
Number of ,• dry-weather hours-preceding each rurioff-event
r\
Longitudinal dispersion coefficient, feet . per second
Engineering Kfews. R^c.ord Cost Index , ,
•Self-purification rat-id,;K'/K ••-...
' 21
Available .urban depression storage, inches ... -•..-•
First flush BOD load:|- pounds per hour ,-..-, ••.-.••
•First flush .fa'ctbr', pounds/hour per DWH-acre, >.
Stream depth, feet
Number of hourly lags
Oxidation coefficient of nitrogenous BOD, hours
Deoxygenation constant of carbonaceous BOD, hours
Atmospheric reaeration coefficient, hours""1"
Remaining carbonaceous BOD concentration, mg/1
Mixed BOD concentration in the river, mg/1
Ultimate first-stage BOD demand, mg/1
354
-------
n
N
n
N
NBOD
OP04
P
P
t
Q
Q,
u
R
R
R
R
o
rQ(k)
Rw
S
T
t
t
Total number of data points or observations of a hydrologic
process
Remaining nitrogenous BOD concentrations mg/1
Exponent in scale factor
Nitrogenous biochemical oxygen demand
Orthophosphate
Oxygen production rate by algal photosynthesis, mg/1-hour
Hourly rainfall/snowmelt in inches over the urban area
Streamflow, cfs
Combined sewer flow, cfs
DWF treated effluent, cfs
Urban runoff carried by the separate storm sewer, cfs
Total (storm plus combined) urban runoff, cfs
Upstream flow, cfs ,
Wet-weather flow (WWF) treated effluent, cfs
Percent BOD control (removal) by wet-weather storage-treatment
Fraction removal of BOD achieved by the DWF treatment facility
Algal respiration rate, mg/1-hour
Sample estimate of lag-k autocorrelation coefficient for
rainfall
Deficit load ratio = BO/LQ
Sample estimate of lag-k autocorrelation coefficient for runoff
Fraction removal of BOD achieved by the WWF treatment facility
• ' 3
Sources and sinks of the substance C, M/L T
Stream temperature, °C .
Time, hours or days
Elapsed time at which critical deficit occurs, hours or days
355
-------
TL(95%)
TOG
TSS
U
¥•
WWF
x
xl
X
zco
ZST
Tolerance limits at a 95 percent probability level
Total organic carbon
Total suspended solids
Flow
Volume
velocity in stream, feet per second
of
DO deficit, mg-hours/1 or mg-day/1
Wet-weather flow, cfs
Distance downstream, feet or miles
Discrete data series (observations) of a hydrologic
process
Scale factor
Optimal annual/cost of wet-weather control for combined
sewered areas, dollars per acre comoxneci
Optimal annual cost of wet-weather control for
areas, dollars per acre
storm, sewered
356
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REFERENCES -.. _ . • '... . ' ' '• ; '* "'' ' '
1. Geraghty, J. J., et al., Water Atlas of the United States, Water
Information Center, Inc., Port Washington, NY, 1973.
• . .•:: ','•:." -IB-,1':'
2. Murray, C. R. and Reeves, E. B., "Estimated Use of Water in the •
United States in 1970," USGS Circular 676, 1972.''"
3. US Department of Agriculture, "Major Uses of'Land and Water in
the United States with Special Reference to Agriculture: Summary ,
for 1964," Econ. Rept. 149, 1968. 'f^r-ty.
4. Medina, M. A., Jr., "Data- Needs for Stbrmwater Treatment and
Control," Proceedings of Third Annual Environmental Short Course,
.,. Florida Engineering Society, Lake Buena Vista, Florida, October,
1974.
£,
5. Liptak, Bela G., Environmental Engineers' Handbook, Vol. 1: Water
Pollution, -.Se.ction-2.10, Chilton Book Company';'Radnor^, PA, 1974.
6. American Public Health Association, Standard Methods for the
Examination -of Water and Wastewater, 13th Edition; Washington, DC,'
1971. : •'•''•••
7. Nemerow, N. L., Scientific Stream Pollution Analysis, McGraw-Hill
Book Company, NY, 1974.
8. Hydrologic Engineering Center, Corps of Engineers, "Urban Storm-
water Runoff: STORM," Generalized Computer Program 723-58-L2520,
1975. ' '
9. Davis, P. L. and Borchardt, F., "Combined Sewer Overflow Abatement
Plan," USEPA Report EPA-R2-73-170, April, 1974. .
10. Yevjevich, V., Stochastic Processes in Hydrology, Water Resources
Publications, Fort Collins, CO, 1972. •
11. Fiering, M. B. and Jackson, B. B., Synthetic Streamflows," Water
Resources Monograph 1, American Geophysical Union, Washington, DC,
1971.
12. Quimpo, R. G., "Autocorrelation and Spectral Analyses in Hydrology,"
J. Hyd. Div., Proc. ASCE, Vol. 94, No. HY2, pp. 363-373, March 1968.
13. Hydroscience, Inc., "Simplified Mathematical Modeling of Water
Quality," USEPA, March 1971.
14. ASCE, Committee on Sanitary Engineering Research, "Solubility of
Atmospheric Oxygen in Water," JSE Div., Proc. ASCE, Vol. 86, No. SA4,
pp. 41-53, July 1960.
357
-------
:'
17.
Arbabi M. et_al._, "The Oxygen Sag Equation: New Properties and a
sr? s:
USEPA Technology Transfer, October 1971.
19. Battelle-PacificNortlwest Laboratories, "Evaluation of
" — °f -ironmental
20. Hasan, S .M. Integrated Strategies for Urban Water Quality
Management, PhD Dissertation, University of Florida, Gainesville, 1976,
d
' W' G. , "Urban Stormwater Management and
wT °f IOWa'" Climates of th* S.^go Vol. II
Water Information Center, Inc., Port Washington, NY, 1974. '
23. State Hygienic Laboratory, "Des Moines River - Limnology Study,"
Report submitted to the Department of Enyirornnental Quality Jd the
Iowa Water Quality Commission, April 1974. 7
24. State Hygienic Laboratory, "Water Quality Survey of the Des Moines
October S?Sf SUbmltted t0 the Iowa Dilution Control CommissS"
25. Sawyer, C. N. , "Basic Concepts of Eutrophication," JWPCF Vol 38
No. 5, pp. 737-744, May 1966. ' '
26-
2?*
28.
controi'M
; ?ro«r & Eidsness, Inc., and Jordan, Jones & Goulding, Inc.,
Poxnt Pollution Evaluation - Atlanta Urban Area," Report to
US Army Corps of Engineers, Savannah District, GA, January 1975.
Black and Veatch, "Volume II - Hydro Quality Model Report, "
Denver Regional Council of Governments, Water Quality Management
Program Project No. IGA-00034, May 1974. • cigemenc
358
-------
29. Private Communication, National Oceanic and Atmospheric Admini-
stration, Asheville, NC, September 1975.
30. Hydrocomp, Inc., "Evaluation of the Effects of Urbanization on
Aquatic Ecology and Hydrologic Regimes," Office of Water Re'search
and Technology, Contract No. 14-31-001-4203, July 1975.
31. Texas Water'Development Board, "DOSAG-I Simulation of Water Quality
in Streams and Canals, Program Documentation and User's Manual,"
Austin, Texas, NTIS-PB 202 974, September 1970.
359
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SECTION VIII
GLOSSARY
Antecedent conditions—Initial conditions in catchment as determined from
hydrologic events prior to storm.
Biological treatment processes—Means of treatment in which bacterial or
biochemical action is intensified to stabilize, oxidize, and nitrify the
unstable organic matter present. Trickling filters, activated sludge
processes, and lagoons are examples.
Catchment—Surface drainage area.
Combined sewage—Sewage containing both domestic sewage and surface water
or stormwater, with or without industrial wastes. Includes flow in heavily
infiltrated sanitary sewer systems as well as combined sewer systems.
Combined sewer—A sewer receiving both intercepted surface runoff and
municipal sewage.
Combined sewer overflow—Flow from a combined sewer in excess of the inter-
ceptor capacity that is discharged into a receiving water.
Conservative—Non-interacting substance,' undergoing no kinetic reaction;
examples are salinity, total dissolved solids, total nitrogen, total
phosphorus.
Convective Precipitation—Precipifai-f nr, caused by lifting due to convective
currents, as in thunderstorms.
Cyclonic Precipitation—Precipifai-lnr, caused by lifting associated with
junctions of different air masses, as for instance, with most warm and '
cold fronts.
Depression Storage—Amount of precipitation which can fall on an area
without causing runoff.
Detention—The slowing, dampening, or attenuating of flows either enter-
ing the sewer system or within the sewer system by temporarily holding
the water on a surface area, in a storage basin, or within the sewer
itself.
360
-------
Domestic sewage—Sewage derived principally from dwellings, business _ -
buildings, institutions, and the like. It may or may not contain ground-
water.
Economies of scale—Unit costs decrease as output increases.
Equalization—The averaging (or method for averaging) of variations in
flow and composition of a liquid.
Expansion path—Locus of points connecting numerous isoquants indicating
the optimal combination of inputs.
First flush—The condition, often occurring in storm sewer discharges and
combined sewer overflows, in which a disproportionately high pollutional
load is carried in the first portion of the discharge or overflow.
Frequency diagram—Curve which relates the number of occurences of events
to their magnitude.
Initial abstraction—Initial precipitation loss including interception
and depression storage.
In-system—Within the physical confines of the sewer pipe network.
Interception—Initial lost of precipitation due to vegetation.
Isocost Lines—Lines of equal cost.
Isoquants—Curves representing combinations of the inputs yielding the same
amount of output.
Non-conservative—substance undergoing kinetic interaction, assumed to be
a first-order reaction;' examples are biochemical oxygen demand (BOD),
coliform bacteria, dissolved oxygen (DO).
Orographic Precipitation—Precipitation caused by lifting of an air mass
over mountains.
Orthophospate—Phosphate that appears as PoJ, HP04 or H2P04, i.e. is hydroli-
zable. Creates a growth response in algae.
Physical-chemical treatment .processes—Means of treatment in which the _
removal of pollutants is brought about primarily by chemical clarification
in conjunction with physical processes. The process string generally in-
cludes preliminary treatment, chemical clarification, filtration, carbon
adsorption, and disinfection.
Pollutant—Any harmful or objectionable material in, or change in, physical
characteristic of water or sewage.
361
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Precipitation event—A precipitation event terminates if zero rainfall
has been recorded for the previous specified time interval. -
Primary treatment—Process which removes about 35% of the biochemical
oxygen demand of the waste. . , , .: ..
Retention—The prevention of runoff from entering the sewer system by
storing on a surface area or in a storage basis.
Runoff coefficient—Fraction of rainfall that appears as runoff after
subtracting depression storage and interception. Typically accounts for
infiltration into ground and evaporation.
Sanitary sewer—A sewer that carries liquid and water-carried wastes from
residences, commercial buildings, industrial plants, and institutions,
together with relatively low quantities of ground, storm, and surface
waters that are not admitted intentionally.
Secondary treatment—Process which removes about 85% of the biochemical
oxygen demand of the waste.
Sewer—A pipe or conduit generally closed, but normally not flowing full,
for carrying sewage or other waste liquids.
Sewerage—System of piping, with appurtenances, for collecting and con-
veying wastewaters from source to discharge.
Storm flow—Overland flow, sewer flow, or receiving stream flow caused
totally or partially by surface runoff or snowmelt.
Storm sewer—A sewer that carries intercepted surface runoff, street wash
and other wash waters, or drainage, but excludes domestic sewage and in-
dustrial wastes.
Storm sewer discharge—Flow from a storm sewer that is discharged into a
receiving water.
Stormwater—Water resulting from precipitation which either percolates
into the soil, runs off freely from the surface, or is captured by storm
sewer, combined sewer, and to a limited degree,sanitary sewer facilities.
Surface runoff—Precipitation that falls onto the surfaces of roofs,
streets, ground, etc., and is not absorbed or retained by that surface,
thereby collecting and running off.
Tertiary treatment—Process which removes about 96% of the biochemical
oxygen demand of the waste. •
Urbanized area—Central city, or cities, and surrounding closely settled
territory. Central city (cities) has population of 50,000 or more.
Peripheral areas-with population density of 1,000 persons per acre or
more are included.
362
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Urban runoff—Surface runoff from an urban drainage area that reaches a
stream or other body of water or a sewer.
Wastewater—The spent water of a community.
363
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TECHNICAL REPORT DATA
Iflease read Instructions on the reverse before completing)
EPA-600/2-77-064
3. RECIPIENT'S ACCESSIOWNO.
NATIONWIDE EVALUATION OF COMBINED SEWER 'OVERFLOWS AND
URBAN STORMWATER DISCHARGES
Volume i'f; Cost Assessment and Impacts
5. REPORT DATE
March 1977..C.Issulag Date
i. PERFORMING ORGANIZATION CODE
James P. Heaney, Wayne C. Huber, Miguel A. Medina, Jr.
Michael P. Murphy, Stephan J. Nix and Sheikh M. Hasan
8. PERFORMING ORGANIZATION REPORT NO.
Department of Environmental Engineering Sciences
University of Florida
•Gainesville, FL 32611
10. PROGRAM ELEMENT NO.
1BC611
68-03-0283
12. SPONSORING AGENCY NAME AND ADDRESS
Municipal Environmental'Research Laboratory—Gin.,OH
Office of Research and Development
US Environmental Protection Agency
Cincinnati, Ohio 45268
13. TYPE OF REPORT AND PERIOD COVERED
Final. 6/75-12/76
4. SPONSORING AGENCY CODE
EPA/600/14
Richard Field, Storm and Combined Sewer Section, Edison, NJ . (201) 321-6674
. FTS 340-6674 ''
*n asses^en^ has been made of the quantity and quality of urban storm flow
emanating from combined sewers, storm sewers, and unsewered portions of all 248
urbanized areas and other urban areas in the United States. Available control alter-
uS6L^ I fso"a*ed costs were als° determined. Continuous simulation runs
using one year_of hourly data were made to determine the attainable level of pollu-
tion control with a specified availability of storage volume and treatment ra?e in
five cities: Atlanta Denver, Minneapolis, San Francisco, and Washington, DC. This
procedure was used to derive generalized equations relating pollution control to
wSgL^l-ref ^nt' ,™T *6SUitS W6re combined int° * simple optimization model
which determined the optimal mix of storage and treatment for any feasible level of
control for any city. Then the nationwide assessment is presented. - The results :
S? 09? !-??"UaV°Ssr ranging £r°m $297 million £or 2S Percent Pollution control to
§5,022 million for 85 percent pollution control. The corresponding initial capital
investment ranges from $2,476 million for 25 percent control to $41 968 million for
SL?**?6? ?ontro1- ^f5* costs can be reduced significantly if stormwater pollution
control is integrated with dry-weather quality control and wet-weather quantity
control. Also, the relative impact of wet-weather versus dry-weather flows is
illustrated for a case study of Des Moines, Iowa.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Combined sewers
Water pollution
Cost analysis
Mathematical models
Water pollution control, Water
pollution effects, Water pollu-
tion treatment,'Urban runoff
pollution, Separated sewers,
Urban drainage, Des Moines, San
Francisco, Denver, Minneapolis,
DC' Urban"
13B
RELEASE TO PUBLIC
19. SECURITY CLASS (ThisReport)'
UNCLASSIFIED
21. NO. OF PAGES
380
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
orm 2220-1 (9-73)
364
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t~"^ o a- 5
-i § » 3
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