EPA-44O/9-75-OO4
WATER QUALITY
MANAGEMENT PLANNING
FOR URBAN RUNOFF
U.S. ENVIRONMENTAL PROTECTION AGENCY
Washington, D.C. 20460
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EPA 440/9-75-004
December 1974
WATER QUALITY MANAGEMENT PLANNING
FOR URBAN RUNOFF
By
Gary Amy
Robert Pitt
Rameshawar - Sinqh
Westly L. Bradford
Michael B. LaGraff
Project Officer
James Meek
Chief, Planning and Standards Branch
Water Planning Division
Office of Planning and Standards
U.S. Environmental Protection Agency
Washington, D.C. 20460
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EPA/WPD Review Notice
This report has been reviewed by the Water Planning Division and
approved for publication. Approval does not signify that the contents
necessarily reflect the views and policies of the Environmental
Protection Agency, nor does mention of trade names or commercial
products constitute endorsement or recommendation for use.
During final review two portions of the method presented herein
appeared to warrant additional comment. The first concerns the selection
of a design storm. The method presented allows the selection of the
design storm without consulting local precipitation or hydrologic data.
The local applicability of calculated results could be improved if the
procedure included at least a brief survey of available precipitation
data. This review of available data could result in a more accurate
estimate of the volume of runoff as well as a better estimate of the
frequency of occurence and the reliability of the proposed stormwater
management system.
The second matter of concern involves the use of impervious area
runoff for the calculation of pollutant concentration. As is stated in
the manual, this assumption results in a "worst case" calculation since
pervious area runoff may provide additional water for dilution. This
additional volume from pervious areas should be considered particularly
in study areas with steep slopes, frequent rainfall events, and
relatively impervious soils.
The method presented here is intended to be used as a first
approximation of the magnitude of the stormwater management problem on
a local basis. Since the manual is applied to specific situations,
weak as well as strong assumptions will undoubtedly become apparent
and refinements of the method will be necessary.
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Abstract
This manual provides technical assistance to state and local
water quality management planners to enable them to quantify
within reasonable limits the urban non-point water pollution
problem in a local planning area without extensive data gen-
eration, and to make a preliminary evaluation of cost effec-
tive abatement and control practices. The manual prescribes
procedures for several levels of input, each requiring more
self-generated data, with increasingly sophisticated results.
A state-of-the-art and an extensive bibliography on urban
storm water runoff is presented in the appendix. A glossary
is also included.
The manual is not intended to be used as a basis for abate-
ment design but does provide a guide to data generation for
this purpose.
iii
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CONTENTS
Section Page
INTRODUCTION 1
Manual Format 5
I AN ASSESSMENT OF URBAN RUNOFF QUANTITY
AND QUALITY 1-1
1.0 General Considerations 1-1
2.0 Definition of a Study Area 1-3
3.0 Data Requirements 1-5
3.1 Study Area Characteristics. ...... 1-5
3.2 Determination of Contaminant
Loading Rates and Materials
Composition 1-7
3.2.1 Use of the Tables 1-7
3.2.2 Levels of Sophistication
in Using the Tables 1-10
3.3 Storm Event Characteristics and
Impervious Runoff Rate 1-18
3.3.1 Method of Computing Runoff
Runoff Rate From Impervious
Surface 1-18
3.3.2 Selection of Storm Event .... 1-19
4.0 Determination of Quantity and Flow
of Runoff 1-27
4.1 General Assumptions and Background. . . 1-27
4.2 Determination of Volume of Runoff . . . 1-28
4.3 Definition of a Hydrograph and
Unit Hydrograph 1-29
4.4 Development of Unit Hydrograph
for Entire Study Area 1-31
4.5 Development of Unit Hydrographs
for Impervious Area and Street
Surfaces 1-39
4.6 Modification of Unit Hydrographs to
Reflect Specific Runoff Rate 1-45
V
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CONTENTS
(Continued)
Section Page
I 5.0 Quality of Runoff 1-48
6.0 Quantity/Quality Hydrographs 1-55
6.1 Method of Routing Quantity/Quality
Hydrographs 1-55
6.2 Quality Composition of Runoff 1-60
7.0 Analytical Procedures for the Assessment
of Urban Runoff Quantity and Quality .... 1-64
7.1 Level I 1-64
7.2 Level II 1-65
7.3 Level III 1-66
REFERENCES 1-68
II EXAMPLE PROBLEMS II-l
Example 1 II-l
Example 2 II-4
Example 3 II-6
Example 4 II-6
Example 5 II-7
Example 6 11-7
Example 7 11-11
Example 8 11-14
Example 9 11-19
Example 10 11-20
III MISCELLANEOUS SOURCES OF URBAN
RUNOFF POLLUTION III-l
References 111-4
IV TREATMENT, ABATEMENT, AND DISPOSAL
OF URBAN RUNOFF IV-1
A. Evaluation of Urban Runoff Quality IV-1
B. Source Abatement IV-2
C. Technical Abatement. IV-5
Improved Street Cleaning Practices IV-6
Air Pollution Controls IV-12
VI
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CONTENTS
(Continued)
Section Page
IV D. Treatment IV-15
Storage and Treatment IV-15
Physical Treatment IV-19
Biological Treatment IV-22
Physical-Chemical Treatment IV-23
E. Alternate Methods of Disposal IV-24
Spray Irrigation IV-24
Infiltration Ponds IV-24
V STATE OF THE ART V-l
Characteristics of Urban Runoff V-l
Characteristics of Street Surface
Contaminants V-5
Impact of Urban Runoff on Receiving
Waters V-12
Control Measures V-13
References V-16
Appendix
A DATA ACQUISITION AND PROCESSING FOR
CONTAMINANT LOADING RATES AND MATERIAL
COMPOSITION A-l
Literature Sources A-l
Data Transformation and Augmentation A-8
Data Processing A-13
Conclusions Obtained in the Data
Processing A-37
Summary A-42
VII
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CONTENTS
(Continued)
Section Page=
Appendix
B SAMPLING PROCEDURE B-l
Locality Data Collection B-l
Hand Sweeping B-2
Hose Flushing B-2
Data Collection B-2
Sample Collection B-3
Sample Handling and Preparation
Procedures B-3
Solid Sample Preparation B-4
Appendix
C BIBLIOGRAPHY C-l
Information Matrix C-12
Appendix
D GLOSSARY D-l
VIII
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FIGURES
Number Page
1 Definition of a Study Area 1-4
2 Contaminant Removal as a Function of Runoff .... 1-22
3 Runoff Concentration as a Function of Runoff. . . . 1-24
4 1-Year 30-Minute Rainfall 1-25
5 Sample Study Area and Corresponding Hydrograph. . . 1-30
6 Definition of Unit Hydrograph Properties 1-33
7 Nomograph 1-34
8 Determination of T 1-35
R
9 Determination of T 1-36
B
10 Determination of W 1-37
5*J
11 Determination of W 1-38
f 5
12a Typical Urban Area 1-40
12b Impervious Network 1-42
12c Street Surface Network 1-43
13 Hypothetical Hydrographs for Urban Watersheds . . . 1-44
14 Modification of Unit Hydrograph to Reflect
a Specific Runoff Rate 1-46
15a A Sample Pollutograph 1-49
15b A Sample Loadograph . 1-49
16a Pollutograph and Loadograph Worksheet 1-51
16b A Sample Quantity/Quality Hydrograph Comprising
a Hydrograph, a Pollutograph, and a Loadograph. . . 1-53
17 A Hypothetical Study Area Consisting of Two
Subareas 1-56
18 Determination of Lag Time 1-58
19 Development of a Composite Quantity/Quality
Hydrograph by the Routing Techniques 1-61
20 A Sample Quantity/Quality Hydrograph Reflecting'
Several Quality Parameters 1-63
IX
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FIGURES
(Continued)
Number Page
21 Sample Study Area for Example 1 11-2
22 Unit Hydrographs Developed in Example 6 11-10
23 Pollutograph and Loadograph Developed in
Example 7 11-13
24 Study Area of Example 8 11-15
25 Quantity/Quality Hydrographs for Subareas of
Example 8 11-16
26 Composite Quantity/Quality Hydrograph
of Example 8 11-18
27 Unit Hydrographs Developed in Example 10 11-23
28 Hydrographs Developed in Example LO 11-24
29 Pollutograph and Loadograph Developed in
Example 10 11-27
30 Quantity/Quality Hydrograph Developed in
Example 10 11-28
31 Removal Effectiveness with Number of Passes. . . . IV-8
32 The Effect of Pattern on Residual Debris IV-10
33 Debris Pickup vs Brush Sweeper IV-10
34 The Effect of Sweeper Speed on Residual Debris . . IV-10
35 Comparison of Cleaning Performance of Motorized
Street Sweeping and Motorized Street Flushing. . . IV-11
A-l Climate Zone Codes for the Data Matrix and
Cities for Which Data Are Available A-5
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TABLES
Number Page
1 Characteristics of Stormwater Runoff 2
2 Typical Curb Mile Densities Arranged by
Land Use Typ«s 1-10
3 The Nationwide Mean Solids Loading Rate 1-11
and Composition 1-11
4 Mean Concentrations of Mercury and
Chlorinated Hydrocarbons in Street Dirt
from Nine U.S. Cities 1-13
5 Ninety-Five Percent Confidence Level 1-14
6 Ninety Percent Confidence Level 1-15
7 Eighty Percent Confidence Level 1-16
8 Values of the Runoff Coefficient k 1-19
9 Percent of Contaminants Removed from Urban
Land Surfaces by Runoff Rate/Duration 1-21
10 Lag Time Adjustment Factors . 1-59
11 Sample Row of Loading and Composition Data 11-5
12 Average Street Sweeper Removal Efficiency IV-6
13 Parameters Which Affect Street Sweeping
Performance IV-9
14 Nationwide Estimates of Particulate
Emissions, 1970 IV-13
A-l Data Acquired for the Analysis A-2
A-2 Codes for Independent Variables A-4
A-3 Climatological Categories A-6
A-4 Entire Matrix of Available Data A-10
A-5 Mean Chlorinated Hydrocarbon and Mercury
Concentrations in Urban Street Dirt from
Several U.S. Cities (The URS I Study) A-12
A-6 Land Use 10 - Open Space A-17
A-7 Land Use 20 - Residential A-18
A-8 Land Use 30 - Commercial A-19
A-9 Land Use 40 - Light Industry A-19
XI
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TABLES
(Continued)
Number Page
A-10 Land Use 50 - Industrial. A-20
A-ll Climate 1 - Northeast ..... ..... A-20
A-12 Climate 2 - Southeast A-21
A-13 Climate 4 - Southwest A-22
A-14 Climate 5 - Northwest A-22
A-15 Average Daily Traffic - Less Than 500 ........ A-23
A-16 Average Daily Traffic - 500 to 5000- A-23
A-17 Average Daily Traffic - 5000 to 15,000 A-24
A-18 Average Daily Traffic - Greater than 15,000 .... A-25
A-19 Landscaping Beyond the Sidewalk 1 - Grass A-26
A-20 Landscaping Beyond the Sidewalk 2 - Trees A-27
A-21 Landscaping Beyond the Sidewalk 3 - Landscaped
Buildings ......... ... A-27
A-22 Landscaping Beyond the Sidewalk 4 - No Landscaping. A-28
A-23 The Basic Statistics of the Available Data,
Including the Mean, Standard Deviation, Range,
and Number of Samples in the Set of All Data
and in 19 Subsets of the Data A-29
A-24 Means and Percent Standard Errors of the Means
of the Set of All Data and of 19 Subsets of the
Data. A-33
XII
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INTRODUCTION
The 1972 Amendments to the Federal Water Pollution Control Act specifi-
cally require that non-point sources of water pollution be considered in
the development of water quality management plans for both local and area-
wide planning. In the past, efforts to control water pollution were pri-
marily focused on point sources of water pollution such as effluent dis-
charges from sewage treatment plants and wastewater discharges from in-
dustries.
Significant gains have been made toward the control of point sources of
water pollution primarily because point sources are more obvious in nature
than non-point sources and are correspondingly easier to identify and char-
acterize. The water pollution potential of non-point sources was not fully
appreciated until several major studies revealed that, in many areas, urban
runoff can be a more serious source of water pollution than municipal sew-
age discharges. During the occurrence of some storm events, runoff from
street surfaces alone has been shown to contribute significantly more BOD
to receiving waters than the effluent from a city's sewage treatment plant.
The general quality characteristics of urban storm water runoff are de-
scribed in Table 1.
Although all surface waters have a natural self-purifying capacity for as-
similating water-borne wastes, an urban environment may overwhelm this nat-
ural assimilation process due to the vast amounts of pollutants entering
watercourses from a centralized urban area. Consequently, a systems ap-
proach must be adopted toward water quality management planning in order
to counteract the water quality impacts of urban areas. Such an approach
would focus upon a specified urban area and then proceed to identify and
characterize all sources of water pollution, point and non-point, within
the specified area.
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Table 1. CHARACTERISTICS OF STORMWATER RUNOFF
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
City
East Bay Sanitary
District; Oakland, California
Minimum
Maximum
Average
Cincinnati , Ohio
Maximum Seasonal Means
Average
Los Angeles County
Average 1962-63
Washington, B.C.
Catch-basin samples during storm
Minimum
Maximum
Average
Seattle, Washington
Oxney , England
Moscow, U.S.S.R.
Leningrad, U.S.S.R.
Stockholm , Sweden
Pretoria, South Africa
Residential
Business
Detroit , Michigan
BOD Total solids
(mc/f.) (mgAfj
3 726
7,700
87 1,401
12 260
17
161 2,909
6
625
126
10
1003 2,045
186-285 1 ,000-3 ,5008
36 14,541
17-80 30-8 ,000
30
34
96-234 310-914
Suspended
Solids Coliform Chlorides COD
(mg/£) (number/t) (mg/t) (mg/t)
16 4 300
4,400 70,000 10,260
613 11,800 5,100
110
227 m
199
26 11
36,250 160
2,100 42
16,100
40-200,000 18-3,100
240 ,000 29
230,000 28
b a
102-213 930,000
Maximum
b
Mean
Source: Water Pollution Aspects of Urban Runoff, APWA
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The purpose of this manual is to provide state and local water quality
management planners with a means of identifying and characterizing urban
runoff as a non-point source of water pollution without extensive data
generation. It is not intended to serve as a design manual nor as an ab-
solute source of information on the subject. Rather, it is intended to
give a general, first-glance assessment of the water quality aspects of
urban runoff within a specified urban area.
The analytical procedures described in this manual are identified accord-
ing to three levels of assessment: Levels I, II, and III. The level of
assessment selected by the user will vary according to the required accu-
racy and the effort to be expended. As one progresses from Level I to
Level III, the analytical procedures will require increasingly more effort
but will yield correspondingly higher degrees of accuracy.
Consistent with the idea of a "manual," numerous example problems have
been provided to illustrate analytical methods and procedures. In addi-
tion, an extensive annotated bibliography and an information matrix have
been included to provide additional information on specific aspects of
urban runoff.
A very sophisticated assessment of storm water runoff can be accomplished
by utilizing the U.S. Environmental Protection Agency's Storm Water Manage-
ment Model. This model is a computer system capable of simulating the
quantity and quality of urban storm water runoff. Ideally, the combina-
tion of this manual and the Storm Water Management Model can provide wa-
ter quality management planners with an entire spectrum of analytical
tools, ranging from simple desk calculations to sophisticated computer
techniques.
The initial construction of the manual was intended to provide an assess-
ment of total runoff flowing from an urbanized area. The model selected
for runoff quantity determination was developed for this purpose. However,
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the plasticity of data on the characteristics of runoff from pervious
areas required that at this time the manual be limited to the character-
ization of runoff from street surfaces only. The basic model was modi-
fied for this purpose rather than adapting a more limited street runoff
model. It is planned that as information becomes available, the manual
could be expanded to include a characterization and quantification of
runoff from the entire urban area.
Preliminary data have demonstrated that impervious surface contaminants
are, by far, the predominant pollutants associated with urban storm wa-
ter runoff. It should be noted that street surfaces serve as a vehicle
for transporting other sources of runoff to a storm drain. Contaminants
which originate from other sources often come to reside and accumulate
upon street surfaces by (1) wind actions which blow contaminants onto
street, (2) the evaporation of lawn irrigation runoff upon street sur-
faces, and (3) the evaporation of residual non-street storm water runoff
upon street surfaces. (This last factor is perhaps the most important
since a certain time of travel is required for non-street contaminants
to reach street surfaces. Often, a storm event will cease after non-
street contaminants have reached a street surface but before reaching a
storm drain and consequently non-street contaminants may eventually come
to reside upon a street surface either by deposition or evaporation.)
Therefore, contaminant loadings found upon street surfaces include non-
street contaminants.
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MANUAL FORMAT
In order for the manual to be used effectively, users should familiarize
themselves with its structure and content. The manual contains five prin-
cipal sections and several supplemental appendices.
Section I An Assessment of Urban Runoff Quantity and Quality
This section provides analytical procedures to pre-
dict the quality, quantity, and rate of runoff re-
sulting from street surface contaminant removal by
specified storm events.
Section II Example Problems
Example problems to demonstrate the concepts and
analytical procedures of Section I.
Section III Miscellaneous Sources of Urban Runoff Pollution
The preceding sections are concerned with urban run-
off resulting from rainfall. This section considers
other sources of urban runoff such as urban irriga-
tion and snowmelt.
Section IV Treatment, Abatement and Disposal of Urban Runoff
Methods for treatment, abatement and ultimate dis-
posal of urban runoff are discussed. Preceding
this discussion, general criteria is provided for
an evaluation of the water pollution potential of
urban runoff.
Section V State-of-the-Art
Provides background information regarding major rele-
vant studies conducted in numerous geographical loca-
tions since 1936. Emphasis is placed upon recent
studies which were used in the development of this
manual.
5
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Appendix A These appendices supply background information and
^ other materials essential for the use of the manual.
Appendix D
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INTRODUCTION
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Section I
AN ASSESSMENT OF
URBAN RUNOFF QUANTITY AND QUALITY
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Section I
AN ASSESSMENT OF URBAN RUNOFF QUANTITY AND QUALITY
1.0 GENERAL CONSIDERATIONS
This section provides a hierarchy of analytical methods to assess the
water pollution characteristics of urban runoff at several levels of
sophistication. The most accurate and definitive methods are the most
difficult to utilize while more simplistic methods are presented to al-
low the user to obtain approximate estimates.
The water pollution characteristics of urban runoff are related to both
the quantity and quality of runoff. The analytical methods presented in
this section will enable the user to assess the quality and quantity of
runoff as a function of time. Variations with respect to time are espe-
cially important if treatment, storage, or alternate methods of disposal
are under consideration, since it enables one to identify the most pol-
luted portion of the runoff.
Theoretically, the phenomenon of urban runoff can be described in a qual-
itative manner. Initially, contaminants accumulate upon urban land sur-
faces as a function of dustfall, street sweeping frequency, antecedent
rainfall characteristics, etc. When a rain event occurs, the energy of
dissipating raindrops dislodges contaminants from street surfaces, roof
tops, and other urban sources, causing some contaminant particles to be-
come suspended in solution. The soluble fraction of material goes into
solution. Subsequently, runoff transports contaminants across urban
land and into gutters and storm sewers, and eventually, runoff is dis-
charged into receiving waters from storm sewers. The pollutant con-
centration is greatest at the beginning of a rainfall event and as the
rainfall continues, subsequent runoff becomes less contaminated.
1-1
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Therefore, summarizing the chronology of the urban runoff phenomenon:
(1) Contaminants accumulate upon urban land surfaces.
(2) A rain event dislodges and removes some contaminants from
urban land surfaces.
(3) Runoff transports contaminants across urban land surfaces,
into gutters, and thru storm sewers.
(4) Runoff eventually discharges into a receiving water.
The analytical methods in this section will attempt to simulate the phe-
nomenon of urban runoff as described above. The ultimate product of this
simulation will be a graphical representation of the quantity and quality
characteristics of urban runoff originating from a defined study area un-
der specified conditions. The simulation will generally consist of the
following:
(1) Define ah urban study area.
(2) Describe the characteristics of the study area.
(3) Estimate the amount and composition of various contami-
nants found upon street surfaces within the study area.
(4) Select a certain storm event which will generate runoff
and remove street surface contaminants from the study
area.
(5) Develop a graphical representation of the quantity and
quality characteristics of runoff originating from the
study area.
The step-by-step analytical procedures for accomplishing the above simula-
tion are presented at the end of this section beginning with paragraph 7.0.
The procedures are identified according to three levels of assessment, each
yielding a correspondingly higher degree of accuracy.
Paragraphs 2.0 thru 6.0 of this section contain theory and support material
to be utilized by the analytical procedures of paragraph 7.0. It is recom-
mended that users of this manual make a concentrated effort to gain a basic
appreciation of paragraphs 2.0 thru 6.0 (and corresponding example problems)
before attempting the analytical procedures of paragraph 7.0.
1-2
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2.0 DEFINITION OF A STUDY AREA
The basis for defining a study area will vary according to the require-
ments of the user. One may assess the water quality of urban runoff ori-
ginating from an urban development, an urban watershed, or an entire ur-
ban community.
A study area may be defined as follows:
(1) An entire urban watershed (Figure la) .
(2) A portion of an urban watershed (Figure Ib).
(3) Two or more entire urban watersheds (Figure Ic).
Each individual watershed would be defined as a
subarea.
(4) Portions of two or more urban watersheds (Figure Id).
Each individual portion of a watershed would be de-
fined as a subarea.
(5) Two or more homogenous portions of an urban watershed
(Figure le). The study area would be divided into
individual homogeneous sections with unique charac-
teristics and each individual section would be de-
fined as a subarea. This approach should be used
when the characteristics of one portion of study
area dramatically differ from the characteristics
of another portion.
1-3
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(a) An entire urban watershed
(b) A portion of an urban watershed
(c) Two or more entire urban watersheds (d) Portions of two or more urban watersheds
(e) Two or more homogeneous portions of an urban watershed
high imperviousnes* ftr large slope
low imperviousness or small slope
Legend:
drainage channel
«»» watershed boundaries
Figure 1. Definition of a Study Area
1-4
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3.0 DATA REQUIREMENTS
The analytical techniques presented in this section are such that minimal
data are required. It was felt that inadequate or unavailable data should
not prevent a community from using the techniques of this section. There-
fore, most of the selected data parameters are readily available in most
communities and can be easily approximated in others when not available.
The data requirements for the analytical techniques within this section
are:
Study area characteristics
Contaminant loading and materials composition
Storm event characteristics and impervious runoff rate
3.1 Study Area Characteristics
Several physical characteristics of the study area are required as data by
the analytical techniques presented in this section. They include:
Size of study area (acres)
Amount of interconnected impervious area within
study area (acres)
Amount of street surface area within study area
(acres)
Length of main drainage channel (feet)
Average slope of main drainage channel (feet/feet)
If a study area is subdivided into two or more subareas, the physical char-
acteristics of each subarea (i.e., subarea size, amount of interconnected
impervious area within subarea, etc.) are required as data.
The amount of interconnected impervious area and street surface area can
be estimated from aerial photographs or engineering maps. The estimate
of interconnected impervious surfaces should not include rooftops which
drain directly onto lawns, or other impervious surfaces which are not
1-5
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ultimately connected to a street surface via other impervious surfaces.
The estimate of street surface area should include primary and secondary
streets but should not include driveways, alleys, and parking lots.
The main drainage channel of a study area can be a stream, a drainage
canal, or a large storm drainage pipe which traverses the study area and
receives drainage from tributarial sources. Within a given watershed,
smaller tributaries drain into larger tributaries which, in turn, drain
into even larger tributaries. This phenomenon implies a hierarchy of
tributaries which eventually drain into the main drainage channel which
is the ultimate vehicle for collecting and transporting drainage from a
watershed. In certain watersheds, the main drainage channel is easily
identifiable as either a stream or a drainage canal flowing along the
surface. However, in urban watersheds which are entirely serviced by an
underground storm drainage system, the identification of the main drain-
age channel requires further investigation. In such a situation, the
user may use a map of the storm sewer system of a study area in order
to determine the specific storm drainage pipe or pipes which serve as the
main drainage channel. Normally, if the main drainage channel is not
readily identifiable, it can be approximated by estimating the longest
distance of travel for runoff flowing thru the network of interconnected
impervious areas, from the most distant point within the network to the
point where collected runoff ultimately leaves the study area.
The average slope of the main drainage channel can be estimated from a
topographical map of the study area as follows:
Average Slope = Elevation Change of Main Drainage Channel
Length of Main Drainage Channel
The general approach for describing study area characteristics is demon-
strated in Example 1.
1-6
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3.2 Determination of Contaminant Loading Rates and Materials Composition
The following material presents procedures and data by which a user may
predict the rates of solid accumulation on urban streets and the composi-
tion of those solids. These data are based on an analysis of existing
published data. The procedures used in the acquisition and processing of
available data are described in detail in Appendix A. However, this sec-
tion as presented is complete and can be used directly by the user to ob-
tain the desired predictions. Appendix B describes recommended sampling
procedures if the user desires to substitute actual source data for the
data given in this chapter.
3.2.1 Use of the Tables
The basic tables consist of mean loading rates and solids composition in
the set of all data and in 19 subsets of the whole set. The original
basic statistics are shown in Appendix A/Table A-23. These statistics
are condensed into Appendix A/Table A-24 from which the loading and com-
position tables of this section are derived.
The loading rates are expressed as pounds per curb mile per day (Ib/curb
mi/day), whereas later procedures make use of loads in terms of total
pounds per acre of street surface. Therefore, it will be necessary for
the user to initially convert the loading rate to load/curb mi, and then
to convert load/curb mi to load/acre of street surface before proceeding to
the following sections.
Compute the load/curb mi by the following steps:
1. Calculate the load in Ib/curb mi as
Solids Load = Rate of Accumulation X
Equivalent Days of Accumulation
1-7
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The equivalent days of accumulation (EDA) factor is neces-
sary to account for residual amounts of pollutants left on
the street after the last sweeping. This residual is con-
sidered to accumulate since the last rain, heavy enough to
have removed a substantial fraction (90%) of the pollutants.
The following duration and intensities are considered heavy
enough to remove 90% of road surface particulates:
0.1 inch/hour for 300 minutes (5 hours)
0.33 inch/hour for 90 minutes (1-1/2 hours)
0.5 inch/hour for 60 minutes (1 hour)
1.0 inch/hour for 30 minutes (1/2 hour)
Each of these storms results in a total rainfall of 0.5 inch.
Therefore, the last substantial rainfall may be considered as
one resulting in 0.5 inch of rain falling within a period of one
to 5 hours. This relationship ignores the function of kinetic
energy loosening street surface particulates. Therefore, de-
viations from the model are more substantial at lower rainfall
intensities. At higher rainfall intensities, agreement with
the model is much better.
The EDA is determined using the following relationship:
EDA = (DR - Dg) (1 - Eg) + Dg
where
EDA = equivalent days of accumulation
D = days since last substantial rainfall
K
D = days since last swept (mechanically cleaned)
D
E = effectiveness of sweeping procedure.
o
1-8
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This relationship assumes that material began to accumulate on
a surface thoroughly cleaned by the last rain and that a single
sweeping operation was performed between rainfalls. Rainfalls
and more than one sweeping operation occurring since the last
substantial rain are not considered, but can be introduced by
multiple iterative process using appropriate rainfall removal
efficiencies and sweeping efficiencies. As previously indica-
ted, a total rainfall of 0.5 inches will remove 90 percent of
road surface particulates. Correspondingly, total rainfalls
of .27, .15, .08, and .02 inches will remove 70, 50, 30, and
10 percent, respectively. The efficiency of sweeping prac-
tices can vary substantially by sweeper type, operational param-
eters and street surface particulate parameters (such as wetness
and size distribution). In prior studies, URS has measured
street sweeper/flusher efficiencies in a range from 11 to 62 per-
cent removal. An average value of 40 percent is representative.
In evaluating the impacts of an assumed or hypothetical condi-
tion, it is best to ignore the rainfall effects and only use
the average street sweeping frequency. This procedure can also
be helpful in evaluating the effects of modifying the sweeping
programs of an area (see Section IV, part C.I.), if sufficient
operation parameter-effectiveness data is available.
2. Convert the solids load in Ib/curb mi to Ib/acre of street surface
as follows:
Ib / Ib \ / curb mi
acre of street surface \curb mi/ \acre of street surface
Equivalent curb miles per acre of street surface are shown in
Table 2, arranged by land use types. However, if actual values
are known, it is best to use known values.
1-9
-------
Table 2. EQUIVALENT CURB MILES PER ACRE OF
STREET SURFACE, ARRANGED BY LAND
USE TYPES
Equivalent Curb Miles
Per Acre
Land Use Type Of Street Surface
Open land . 53
General residential .54
General commercial .41
Light industrial .43
Heavy industrial .40
All land use types .46
The solids composition is expressed either as micrograms per gram (equi-
valent to micropounds per pound) or micrograms per kilogram, conveniently
referred to as parts per million (ppm) and parts per billion (ppb), respec-
tively.
Therefore, it will not be necessary to convert the solids concentrations
in the runoff water to metric units to obtain the concentrations of con-
taminants associated with the solids. The coliform concentrations are
expressed as numbers per gram of solids (#/g). Therefore, do not express
the coliform concentrations in the receiving waters as ppm or ppb, but as
number/100 ml.
3.2.2 Levels of Sophistication in Using the Tables
Level I. This level makes use of the tables in the simplest possible
manner to compute the solids concentrations and the associated contaminant
concentrations in the runoff water. Table 3 presents the nationwide means
of solids loading rate and composition. The percent standard error of the
mean indicates the degree of confidence that may be placed on the mean.
This parameter is explained more fully in Appendix A.
1-10
-------
Table 3. THE NATIONWIDE MEAN SOLIDS LOADING RATE AND COMPOSITION'
Lbs/Curb Mi/Day Concentrations in Micrograms per Gram of Dry Solid
Loading BOD5 COD QP04 TP04 NQ.j NH
;,. ORGN Cd Cr
156,
19,900, 140,000, 1,280, 2,930 804, 2,640o 2,950. 3.4a 211
D D D CD C D el ci
Concentrations in Micrograms per Gram of Dry Solid
#/Gram
Cu
Fe
Pb
Total Fecal
Ni Sr Zn Coliform Coliform
104a 22,000a l,810a 418o 35 21 370 2.5E6 1.7E5.
3. ci di Si Si cL SL C D
Percent Standard Error of the Mean Code: aO-9, b!0-19, C20-29,
d30-39, e40-49, f50-62.
-------
The set of data available for estimating the solids composition in mercury
and several pesticides is very small and unreliable. The means and stan-
dard deviations of that data are presented in Table 4. It is recommended
that the planner place very little confidence in these values; they are
presented primarily for informational purposes and use where it is neces-
sary to estimate loading for these pollutants.
Proceed as follows:
1. Determine the loading rate and composition directly from
Table 3. Attach the data from Table 4, if desired.
2. Convert the loading rate to load per acre of street
surface as described.
3. Proceed directly to the next section wherein the load
will be used. Carry the composition data along for
subsequent use.
* Level II. In this level, the user can take advantage of a more de-
tailed analysis of the available data on loading rate and solids compo-
sition. The set of all data was divided into a total of 19 subsets ac-
cording to methods outlined in Appendix A. Whenever the mean of any param-
eter, loading rate of composition, in any subset differs "significantly"
from the mean of that parameter in the set of all data, that number may be
substituted for the mean of the set of all data. The method for making
the determination of "significant" is described in Appendix A. Tables 5,
6, and 7 present those subset means which are different from the whole set
means at the 95, 90, and 80 percent confidence levels respectively. For
convenience of use, the line "All Data" is repeated in each table.
Proceed as follows:
a. Choose the level of statistical confidence permissible .in
substituting different values for the means of the set of
1-12
-------
Table 4. MEAN CONCENTRATIONS OF MERCURY AND CHLORINATED
HYDROCARBONS IN STREET DIRT FROM NINE U.S. CITIES
Concentrations in micrograms per kilogram of dry solid
Methoxy- Methyl
Eg Endrin Dieldrin PCB chlor DDT Lindane Parathion DDD
:
Mean 83 0.2 28 770 500 76 2.9 2 82
Standard
deviation 111 - 28 770 1,050 118 7.1 - 78
-------
Table 5. NINETY-FIVE PERCENT CONFIDENCE LEVEL
Land Use
Cllmata
Average Daily
Traffic
Mo. /day
Type of Land-
scaping
Beyond the
Sidewalk
Street Surface
Material
10
20
30
40
50
1
2
4,
5
<
>
1
2
3
4
1
2
Category
Open space
Residential
Commercial
Light industry
Heavy industry
Northeast
Southeast
Southwest
Northwest
500
500 - 5,000
5,000 - 15,000
15,000
Grass
Trees
Landscaped
buildings
Hard surfaces
Asphalt
Concrete
All data
Ibs/curb
ml/day Concentrations in mlcrograms per gram of dry solid "t Era" |j
Loading BODj COD OPO^ TPO4 IK>3 NH^ OrgN Cd Cr Cu Fe Pb Mn Ni Sr Zn TCOLI FCOLI
82,000. 1,800
' b a
74 58,700 6.430 3,400
c ' c a b
8.2E5
e
139. 870 21 260. 4.4E5
b c c b c
2,240 21 7.0E4.
a DO
50 470. 78. 57 15
c b a ba
30 34,500. 10 48O 6.8E5, 1.1E4,
c b caff
2S2b
9,500 419. 1,060 17. 3.4E5
c b c d d
3.8E5
e
8.3E5 7-4E4d
43b l.lE7b
461 16 7.1E5
b a c
2,470^ 1.770h
38b 72,000C 3,520b 2.600b 6,680b
156. 19,900. 140,000. 1,280. 2,930 804^ 2,640 2,950. 3.4 211 104 22.OOO 1,810 418 35 21 370 2.5E6 1.7K5.
b b bbc bcb a a aaa a a a a c b
Only those subset swans are shown which differ from the deans of the set of all data at the 95-percent confidence level (Student t ;. 2.25, Degrees of Freedom ;- 10). Tot»l
umber of permitted substitutions « 47. Percent Standard Error of the Mean Subscripting Code: a»0-9, b = 10 - 19, c = 20 - 29, d - 30 - 39, e = 40 - 49. f = 50 - 62.
Coliforo counts are expressed in computer notation, i.e., E3 = 10 .
-------
Table 6. NINETY PERCENT CONFIDENCE LEVEL
Land Us*
CllMte
Average Daily
Traffic
(to. /day
Type of Land-
scaping
Beyond th*
Sidewalk
Street Surface
Material
Iba/curb
mi/day Concentrations in micrograma per gram of dry aolld
Category Loading BOD. COD OPO. TPO. NO, Nil OrgN Cd Cr Cu Fe Pb
3 4 4 J 4
10 Open space
20 Residential 82,000 850 1.80O
' b b a
30 Commercial 74 58,700 269,000 1,580 6,430 3,440.
c c c c a b
40 Light Industry
50 Heavy Industry 1,160
c
I Northeast 5.97O 2.6. 139 870
ebb c
1 Southeast 103. 2,240 1,970 1,370.
b 'a 'a ' b
4 Southwest 50 470. 241 78 2,520.
c b a a ' b
5 . Northwest 30 246 34,500 2,600.
c s b b
< soo
500 - 9,000 9,500 83,000 419. 18,900 1,060
c c b a c
5,000 - 15,000
> 15,000 82,.
' d
1 Grass 1'370b
1 Trees 43.
b
3 Landscaped
buildings 93b
4 Hard surfacea 78,000 790. 461
CD b
1 Aaphalt 2,470^ 1>770b
2 Concrete 38. 72,000 3,520. 2,600k 8,680. 1.280
b c b a b c
All data 156,. 19,900. 140,000. 1,280. 2,930 804 2,640 2,950. 3.4 211 104 22,000 1,810
b b bbc b c b a a a a a
Only those subset neans sre shown which differ from the means of the set of all data at the 90-percent confidence level .(Student t ? 1.
number of permitted substitutions * 74. Percent Standard Error of the Mean Subscripting Code: a = 0 - 9, t = 10 - 19, c = 20 - 29, d
Vo 'cram
b b
Mn Ni Sr Zn TCDLI PCOLI
520
b
8.2ES
e
363 21 260. 4.4E5
a c b c
21. 7-OK4J
b d
57 15
b a
10 480 6.8E5, 1.1E4.
c a f f
252
17^ 3.4E5d
d
18
a
357 3.8E5
e
8.3E5d 7.4E4d
l.lE7b
l6. 7-1E5c
18
a
45
c
418 35 21 370 2.5E6 1.7E5
a a a s c b
.80. Degrees of Freedom a 10). Total
= 30 - 39, e « 40 - 49, f = 50 - 62.
Conform counts are expressed in computer notation, i.e., E5 » 10 .
Mote: Preliminary decisions to include or exclude values made on the basis of Table C-6. All values included were reaffirmed by checking the original data. In this check,
values were excluded if n < 10 and the diversity of the sample by the other independent parameters ma small, e.g., if all loadings for < 500 ABT came from Baltimore.
Climate 6 was rejected because it Is redundant to the Grand Mean.
-------
Table 7. EIGHTY PERCENT CONFIDENCE LEVEL
Land Use
Climate
Average Dally
Traffic
NO. /day
Type of Land-
scaping
Beyond the
Sidewalk
Street Surface
Material
Ibs/curb
mi/day
Category Loading BODj COD OPO4
10 Open space
20 Residential 14,000. 82,000. 850.
b b b
30 Commercial 74 58,700 269,000 2,250
c c c c
40 Light industry
50 Heavy Industry
1 Northeast 291e
2 Southeast 103. 29,100. 2,240
b b a
4 Southwest 50c 470b
5 Northwest 30c
< 500
500 - 5,000 9,500 83,000 741..
' c c d
5,000 - 15,000
> 15,000 82d
1 Grass. 32,400e
3 Trees 43b
3 Landscaped
buildings 93b
4 Hard aurfaces ia,700b 78,000c 790fc
1 Asphalt 2'470a
2 Concrete SSjj 72,OOOC 3,520b
All dats 156 19,900 140,000 1,280 !
'only those subset means are shown which differ from the mean of the set
number of permitted substitutions - 103. Percent Standard Error of the
Coll fora counts
are expressed in computer notation. I.e., E5 10 .
Concentrations in micrograms per gram of dry solid °^ 6ram ^
TPO4 NO3 NH^ OrgN Cd Cr Cu Fe Pb Mn Ni Sr Zn TCOLI FCOLI
550 1,800 93 1,430. 28
c a a b b
1,580 6,430 133. 3,440. 48. 520
c a b b b b
. 278. 28,600 1,160 570,. 8.2E5
b b c b e
5,970 2.6. 139,. 17,700. 87O 363 21 27. 260_ 4.4E5
ebb bcacbb c
1,970 137^ 1.370,. 21. 28 7.0E4
r b b b b d
241, 78, 2,520b 57,, 15, 5.7E6,,.
246 34,500 2,600. 10 480 6.8E5, 1.1E4.
a DO c a i f
l,210d 252b 6.9E4f
419 18,900 1,060 17 34 3.4E5
b a c a c d
".
357, 3.8E5e
1,370. 27. 8.3E5. 7. 4E4 .
b b d d
1.1E7
b
3.5E5.
a
481. 2,370. 16 7.1E5
b b I c
l,770b 18,
M. «c «.
of all data at the 80-percent confidence level (Student t z 1.39, Degrees of Freedom 2 10). Total
Mean Subscripting Code: a 0 - 9, b « 10 - 19, c = 2O - 29, d 30 - 39, e = 40 - 49, f » 50 - 62.
-------
all data and enter the appropriate table at the line labeled
"All Data."
b. Select a category of land use, climate, average daily traf-
fic, landscaping, or street surface material which best
applies to the area of study and move upward to the line of
data to the right of that category heading.
c. Substitute those values available in the row selected
for the corresponding values in the row entitled "All
Data." The resulting row of data may be used in all
later steps. Caution: It is not permissible to use
more than one row of substitutions at a time, i.e., to
use a BOD value for land use and COD for climate to
form a new row of loading rate and composition data.
Analysis of the data indicates that there is a relation-
ship between loadings by category but that this rela-
tionship is not valid when categories are mixed. It is
both proper and useful, however, to repeat the above
process to obtain several new rows of data to present
a range of composition and loading rates.
d. Attach the data from Table 4, if desired.
e. Convert the loading rate to load per acre of street sur-
face as described in the discussion for Level I. Carry
the composition data along for subsequent use.
Level III. This level makes maximum use of the available loading
rate and solid composition data. Alternatively, the published available
data may be replaced by site specific data available to the user. Recom-
mended procedures for conducting site specific tests are given in Appen-
dix B.
1-17
-------
Alternative A
The user has available site specific loading rate and solids
composition data. In this case, he may either proceed directly
to subsequent material or compare his data with the data pre-
sented in this manual to verify the analysis and field sampling
procedures. The data for comparison may be obtained by proce-
dures described in Level II of this section.
Alternative B
In this alternative, it is assumed that the user lacks site
specific data and wishes to examine the available published
data for source and reliability.
It is recommended that the planner turn to Appendix A which contains a
narrative description of the sources of data, the procedures for pro-
cessing the data, the procedures for grouping the available data by
categories for detailed analysis, the calculation of the statistical
parameters which describe the data and using those parameters to select
the data most applicable to the specific site.
3.3 Storm Event Characteristics and Impervious Runoff Rate
3.3.1 Method of Computing Runoff Rate From Impervious Surfaces
The determination of a specific runoff rate for an assessment of urban
runoff requires a knowledge of rainfall/runoff relationships, including
the rate of runoff which results from a storm of a specified intensity.
The method of runoff coefficient can be used to approximate the rate of
runoff resulting from a storm event. This technique provides the aver-
age rate of runoff from a storm, although it does not depict the varia-
bility of runoff rate during the storm.
1-18
-------
The method of runoff coefficient assumes that the rate of runoff is a
percentage of the rate of precipitation. If this is true, then:
R = kP
where R = runoff rate in inches per hour
P = precipitation in inches per hour
k = runoff coefficient (a dimensionless constant varying
from 0 to 1.0)
Values of the runoff coefficient, k, for impervious surfaces are given
in Table 8. These values of k can be used directly in conjunction with
the above equation when predicting the runoff rate from an impervious
surface. An example of the runoff coefficient method is given in Example
3 of Section II.
Table 8. VALUES OF RUNOFF COEFFICIENT, k
Impervious Surfaces Approx. k
Flat «2% slope) 0.80
Moderate (2 to 7% slope) 0.85
Steep (>7% slope)' 0.90
3.3.2 Selection of Storm Event
Theoretically, any storm event (and its resultant runoff) can be selected
for an assessment of urban runoff. A particular storm event can be char-
acterized by its duration, its average intensity, and its frequency of
occurrence.
The implications of selecting a specific storm event are two-fold; the
selected storm event will (1) generate a certain volume of runoff and
(2) remove a certain percentage of available street surface contaminants.
1-19
-------
The characteristics of the selected storm event (i.e., its duration,
average intensity, and frequency of occurrence) will greatly influence
the results of one's assessment. Therefore, the following material is
presented as general guidance for selecting a storm event.
« Basic Considerations. The pollution potential of urban runoff is
influenced by both its quantity and quality. In certain situations, a
large volume of moderately polluted runoff may contribute a greater pol-
lutant load to a receiving water than a smaller volume of highly pollu-
ted runoff even though the pollutant concentration is considerably
greater within the smaller volume. Consequently, this raises the ques-
tion as to which is more significant with regard to receiving waters;
pollutant concentration or pollutant load?
Generally, the assimilative characteristics of a receiving water will
determine which is more significant. Certain receiving waters are
more capable of assimilating a large volume of moderately polluted run-
off while others are more capable of assimilating a small volume of
highly polluted water.
The percentages of contaminants removed from street surfaces by var-
ious combinations of runoff rate and runoff duration are given in Table
9. Examination of the values within the table reveals that contaminant
removal is a direct function of the total inches of runoff. Consequent-
ly, a runoff rate of 0.5 inches per hour which lasts for 1 hour will re-
move the same percentage of contaminants as a runoff rate of 1.0 inches
per hour which lasts for 0.5 hours since, in both cases, 0.5 inches of
total runoff occurs.
Figure 2 describes street surface contaminant removal as a function of
total inches of runoff. The shape of the curve indicates that, as con-
taminant removal increases, the total inches of runoff increases expo-
nentially. Therefore, Figure 2 reveals that contaminants become in-
creasingly more difficult to remove (i.e., require increasingly more
1-20
-------
Table 9. PERCENT OF CONTAMINANTS REMOVED FROM STREET SURFACES BY RUNOFF RATE/DURATION
to
Ruflbff Rate
(in./hr)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.25
10.9
20.5
29.1
36.9
43.7
49.8
55.3
60.1
64.5
68.3
0.5
20.5
36.9
49.8
60.1
68.3
74.8
80.0
84.1
87.4
90.0
Runoff Duration (hr)
1.0 2.0 3.0 4.0 5.0 6.0
36.9 60.1 74.8 84.1 90.0 >90.0
60.1 84.1 >90.0 >90.0 >90.0
74.8 >90.0
84.1
90.0
>90.0
-------
90
80
70
60
03
S
50-
c
(Q
C
£
6 40<
30
20-
10-
0.1
0.2 0.3
Total Inches of Runoff
0.4
0.5
Figure 2. Street Surface Contaminant Removal as a Function of Runoff
1-22
-------
runoff) within the higher percentage ranges of contaminant removal. Con-
sequently, as the total inches of runoff increase, the total pollutant
load removed will increase while the average pollutant concentration will
decrease due to dilution from the additional increments of runoff re-
quired to remove contaminants within the higher percentage ranges.
Figure 3 describes average pollutant concentration as a function of
street surface runoff and contaminant removal. This curve confirms
that average pollutant concentration decreases as the total inches of
runoff increase.
Therefore, a storm event which produces a large volume of runoff will
contribute a greater pollutant load to a receiving water than a storm
event which produces a smaller volume of runoff. However, runoff from
the latter will contain a greater average pollutant concentration. It
is strongly recommended that the user keep the above phenomenon in mind
while selecting a storm event for assessment.
Basic Procedure. It is recommended that the user select a "typical"
storm event which has a duration of 30 minutes and would likely occur
several times a year in the geographical vicinity of the study area. A
storm with an occurrence frequency of six months or less would be appro-
priate (often a local weather bureau can provide this kind of informa-
tion) .
If local .sources of information cannot provide statistical storm frequency
data, it is recommended that the user select a one-year storm with a dura-
tion of 30 minutes, using the frequency curve given in Figure 4.
The general procedure for selecting a storm event is as follows:
(1) Select a "typical" storm event which has a duration of
30 minutes and would likely occur several times a year
in the geographical vicinity of the study area. If
such a storm event is indeterminable, select a 1-year
1-23
-------
2000-
1500-
\
oc
o
-8 .
1000-
I
500-
.2 .3
Total Inches of Runoff
10 20 30 40 50 60 70 80 90
Percent of Contaminant Removal
Figure 3. Runoff Concentration as a Function of Runoff (based upon an ini-
tial contaminant loading of 100 Ibs/acre of street surface)
1-24
-------
1 -YEAR 30-MINUTE RAINFALL (INCHES)
Figure 4. 1-Year 30-Minute Rainfall (Inches)
-------
storm with a duration of 30 minutes from the fre-
quency curve given in Figure 4.
(2) Using the method of runoff coefficient, determine
the runoff rate resulting from the selected storm
event.
Example 5 demonstrates the selection of a storm event.
1-26
-------
4.0 DETERMINATION OF QUANTITY AND FLOW OF RUNOFF
4.1 General Assumptions and Background
As stated previously, the procedures presented in Section I are intended
to yield a general, first glance assessment of the water quality aspects
of urban runoff. In light of this manual's intended degree of accuracy,
the authors have chosen to ignore runoff contributed by pervious surfaces.
Therefore, it is assumed that no significant amount of runoff will ori-
ginate from pervious surfaces during a storm event of short duration
(i.e., a storm of 30 minutes duration). Moreover, it is assumed that
the amount of rainfall during a storm event of short duration will not
exceed the infiltration capacity and surface retention capacity of per-
vious surfaces and, consequently, the pervious runoff that will occur
is minimal in comparison to impervious runoff.
A short duration, high intensity storm will result in greater quantities
of runoff from pervious areas; however, the quantity of runoff will cause
the pollutant concentrations to be diluted so that it would effectively
be less of a problem than using the above assumptions.
Since street surfaces have been shown to be the primary source of pollu-
tants associated with urban stormwater runoff, pervious runoff will es-
sentially dilute street surface runoff, whereby slightly polluted per-
vious runoff mixes with highly polluted street surface runoff. Since
pervious runoff will be ignored, the manual's assessment of urban runoff
becomes somewhat of a "worst case" assessment.
By dealing with a storm event of short duration, it has been demonstrated
that pervious runoff can be ignored without serious consequences. How-
ever, interconnected impervious areas other than street surfaces (i.e.,
driveways, parking lots, etc., which are ultimately connected to a street
1-27
-------
surface via other impervious surfaces) cannot be ignored. These non-
street interconnected impervious areas will contribute a significant
amount of runoff, even during storms of short duration. However, since
contaminant loadings are provided only for street surfaces and, more-
over, since street surfaces have been shown to be the primary pollutant
contributor, the assessment of urban runoff developed in this manual
will consider runoff from non-street impervious area as being unpolluted
and merely acting as dilution water.
For a more accurate assessment of pollutant concentrations, samples of
runoff from non-street impervious areas can be obtained and analyzed.
Samples should be taken from a point where runoff flows into the street,
as early in the storm event as possible after runoff begins to flow.
The impervious area could then be considered as a street surface and the
pollutant load determined from the samples converted from an aerial ba-
sis to curb mile basis (Table 2).
4.2 Determination of Volume of Runoff
An approximate assessment of urban runoff pollution requires a knowledge
of (1) the volume of runoff from impervious surfaces within a selected
study area during a given storm event, and (2) the amount of street sur-
face contaminants removed by runoff. Accordingly, the total volume of
impervious runoff from a study area may be estimated by the following
equation:
Volume of Runoff (cf)* = [Runoff Rate (in./hr)] x [interconnected
Impervious Area (acres)] x [l,8is]
* Equation is applicable for a 30-minute runoff duration.
1-28
-------
4.3 Definition of a Hydrograph and a Unit Hydrograph
A thorough assessment of urban runoff pollution requires more than just
an estimate of the impervious runoff volume from a study area. Since
the removal of street surface contaminants varies according to the rate
of street surface runoff, a method which describes impervious runoff as
a function of time is required for a thorough analysis.
The concept of a hydrograph can be used to describe runoff resulting
from a rain event. A hydrograph portrays the rate of runoff as a func-
tion of time. The runoff rate corresponds to a flow rate measured at a
designated inlet located within a particular study area. A sample study
area and its corresponding hydrograph are shown in Figure 5.
A hydrograph describes the rate of runoff from a study area during a
specific storm event. Unfortunately, an individual hydrograph is not
directly applicable to storm events with different intensities and/or
durations.
To portray the runoff characteristics of a given urban watershed for
storms of varying intensities with a single hydrograph requires a nor-
malization method to describe the general runoff characteristics of
the given watershed. The normalization method employed is that of
the unit hydrograph.
A unit hydrograph is defined as "the hydrograph of direct runoff re-
sulting from one inch of effective rainfall (rainfall which causes run-
off) generated uniformly over the basin area at a uniform rate during
a specified period of time or duration."
The concept of a unit hydrograph a,c >umes that (1) the effective rain-
fall occurs at a uniform rate, and (2) the effective rainfall is uni-
formly distributed over the watershed with respect to time and area.
1-29
-------
Sample Study Area With
Hypothetical Drainage Pattern
Assumed Main Drainage
Channel If There Is A
Defined Channel
.x* Assumed
Main DrainageN
"^^ /Channel If Plow Is
/_ Along Streets And
Gutters
Flow Monitoring Location
(Location through which all flow
from the study area passes.)
o
o
0)
CO
Q>
£
3
O
Time.
Figure 5. Sample Study Area and Corresponding Hydrograph
1-30
-------
4.4 Development of Unit Hydrograph for Entire Study Area
The following procedure provides a means of developing a unit hydro-
graph which portrays all runoff, impervious and pervious, from a study
area. This procedure will subsequently be modified in such a manner as
to ignore pervious runoff.
A method of synthesizing unit hydrographs for an entire study area is
2
given in a study by Espey who collected data from 11 urban watersheds
in Houston and 22 urban watersheds in different geographical locations
within the United States. Multiple linear regression analysis was used
to develop general equations which describe the 30-minute unit hydrograph
for urban conditions.
The problem of using statistics to synthesize a unit hydrograph is by
definition one of approximating the hydrograph shape. The approach
used by Espey was to describe the hydrograph in terms of typical hydro-
graph dimensions instead of attempting to derive an actual mathematical
function for the hydrograph.
To describe the hydrograph, the following parameters were chosen:
(1) T , the time of rise, defined as the time, in minutes,
R
from the beginning of surface runoff/to the peak runoff.
(2) Q, the peak discharge, defined as the peak value of dis-
charge, in cfs.
(3) T , the base time, defined as the time, in minutes, from
6
the beginning to the end of surface runoff,
(4) W the time, in minutes, between the points on the
O VJ
hydrograph when the discharge represented by Q is
OvJ
half the peak discharge.
(5) W71-i tne time, in minutes, between the points on the
o
hydrograph when the discharge represented by O is
three-fourths of the peak discharge.
1-31
-------
A summary of these parameters is shown on a typical hydrograph in Figure 6,
The statistical work by Espey produced the following equations which repre-
sent the 30-minute unit hydrograph for an urban watershed:
T = 13.12 L°'315 S-°-0488r0-490
R
Q = 3.54 x 104 T "ll10 (A/640)
H
T = 3.67 x 105 (A/640)1'14Q~1>15
3
A i r\*i. i (Yd
W = 4.14 x KT (A/640)i
-------
8
0)
4)
a.
.£>
O
TIME
Figure 6. Definition of Unit Hydrograph Properties
1-33
-------
s
.2-,
.1-
,01 -
.001-1
Reference
Ltne
80,000 -i
10,000-
1,000 -
300 -I
I
100 H
10 -
1 -
0.1-
0.5
Figure 7. Nomograph
Example:. Slope = 0.005
Length of Main Drainage Path = 3,000 ft
Impervlouiness = 50%
'nierefore ^
A
1.2
1-34
-------
3 I 56739!
? t 5 6 ? « t 1.
1,000 i
9
1
7
6
5
^
3
2
^^
'
c
3 100 -
9
<*
v -
CO 6
cn
s
£
3
*
i n .
IU H
.(
.._ -
------
)]
rr=
-
2
--
-
^^
_._
3
--
[
I
1
p-
-
...
--
--
i;
: rr
V-
r^
---
0
^
^TT
7
^
:
c
-
e
-
-
3
:.
a
.1
|fc
^SF^
^t
j
h
=^
=^=
^_
-.=.-
\
3
'^~-
\
S,
5
6
8
V
3
1
1
0
T.-T:^-
s
\.
S
i
V
s
\
3
k
\
V
4
^,
5
6
7
*
S
^2B
5
1
J
1'
D.
Q/A
Figure 8. Determination of T
R
-------
10,000
c
J. 1,000
co
100
z^?e
m
:r|£
-r-p
3 t ; 67891
^;
5 S 7 » 9 1.
3 4 567651
.01
.1
1.0
10.0
Q/A
Figure 9. Determination of T.
B
-------
co
1,000 ^
100 i
9
8
7
6
10
33=^=^=Es
I2i;d^: = 5i5
^*i
i 5 5 7 < 9 1.
**
^~
5 o 7 g a 1
67891
I 3 4 S o 7 I S 1'
.1
1.0
10.0
10.
07A
Determination of W
50
-------
1,000 T
r 100
m
oo
10
2 3 4 56759!
Is
^
i
.01
2 3
o 7 6 i i
.1
5 67831
1.0
i 3
i 67851
10.0
07A
Figure 11. Determination of W,
75
-------
The characteristics of the urban watersheds upon which Espey based his
equations were within the following range of data;
Area of Watershed: 8.2 to 59,000 acres
Length of Main Drainage Channel: 560 to 123,000 feet
Slope of Main Drainage Channel: .00047 to .146 feet/feet
Using the equations derived by Espey, one may synthesize a unit hydro-
graph to describe an entire study area. Although the required cal-
culations are reasonably simple, a certain degree of subjectivity will
be involved in actually drawing the shape of a unit hydrograph in terms
of the graphical variables (T , Q, T W w ). An example of unit
it D OU to
hydrograph synthesis using Espey's equations is given in Example 6 of
Section II.
4.5 Development of Unit Hydrographs for Impervious Area and Street
Surfaces
As presented in the previous section, Espey's equations enable one
to develop a unit hydrograph for an entire study area, comprised of run-
off originating from both impervious and pervious surfaces. However,
subsequent procedures require the development of two other unit hydro-
graphs; one which describes runoff from interconnected impervious sur-
faces and another which describes runoff originating from street sur-
faces within the study area. Consequently, the authors of this manual
have taken the liberty of adapting Espey's equations to satisfy the
needs of the manual as follows.
Figure 12a shows a typical urban study area comprised of about 15
acres of pervious surface and 15 acres of impervious surface, of which
about 10 acres are streets. Using Espey's equations, one can normally
develop a unit hydrograph for the entire study area and subsequently
modify it to reflect a specific runoff rate.
1-39
-------
Residential
Block
Total Area = 30 Acres
Total Impervious Area - 15 Acre*
Approximately
30 Feet Wide
Streets
Street Area * 10 Acres
Off-street Impervious Area
(Roofs, Driveways, Btc.)
IS Acres
Figure 12a. Typical, Urban Area
1-40
-------
It is assumed that the network of interconnected impervious surfaces can
be isolated from the study area and treated as a separate, although ir-
regular, watershed as shown in Figure 12b. Furthermore, it. is assumed
that, likewise, the street surface network can be isolated from the
study area and treated as a separate watershed as shown in Figure 12c.
Using Espey's equations, one can develop a unit hydrograph for the
watershed shown in Figure 12b by assuming that the area of the water-
shed is equal to the area of impervious surfaces (i.e., 15 acres) and
that the watershed has an imperviousness of 100 percent. The length
and slope of the main drainage channel are considered to be identical
to those of Figure 12a. This same approach can be applied toward the
development of a unit hydrograph for the watershed shown in Figure 12c,
whereby it is assumed that the area of the watershed is equal to the
area of street surfaces (i.e., 10 acres) and that the watershed has an
imperviousness of 100 percent.
At first glance, the above adaptation of Espey's equations may appear
to be a rather severe extrapolation of Espey's work. However, favorable
results can be obtained by using this approach. Figure 13 shows unit
hydrographs for the study area, impervious surface, street surface
and pervious surface runoffs. All of these, with the exception of
the pervious surface unit hydrograph, were developed by using Espey's
equations in the manner previously described. The pervious surface
unit hydrograph was developed by graphically subtracting the ordinates
of the impervious surface unit hydrograph from the ordinates of the
study area hydrograph.
A visual comparison of the unit hydrographs of Figure 13 indicates that
the unit hydrographs behave as expected. Both the impervious surface
unit hydrograph and the street surface unit hydrograph reach their
peak flow quicker and have shorter time bases than the study area unit
hydrograph. Furthermore, pervious runoff does not commence until about
1-41
-------
1143 Ft.
n
Impervious Area = 15 Acres
Figure 12b. Impervious Network
1-42
-------
1143 Ft.
/////////////////////////S
£
n
Street Area = 10 Acres
Figure 12c. Street Surface network
1-43
-------
50
Tt Timt in Minutes
Figure 13. Hypothetical Hydrographs for Urban Watersheds
1-44
-------
20 minutes after impervious runoff begins. Generally, it is believed that
the adaptation of Espey's equations for the development of impervious sur-
face and street surface unit hydrographs yields adequate results, in light
of the manual's intended degree of accuracy.
4.6 Modification of Unit Hydrographs to Reflect Specific Runoff Rate
As previously indicated, a unit hydrograph represents the direct run-
off resulting from 1 inch of effective rainfall (rainfall which causes
runoff), generated uniformly over an area at a uniform rate during a
period of 30 minutes. Once a unit hydrograph has been derived for an
area, the unit hydrograph can be modified to reflect a specific runoff
rate for the area under consideration, whether it be an entire study
area, the impervious area within a study area, or the street surface
area within a study area. This modification of the unit hydrograph
reduces its value as an indicator of the watershed hydrologic character-
istics but is is justified within the limits of accuracy of the procedure
as a whole.
The modification of a unit hydrograph to reflect a specific runoff rate
is a simple graphical procedure. Since a unit hydrograph represents 1
inch of runoff during an interval of 30 minutes, the implied rate of
runoff is 2 inches per hour. Accordingly, modification of the runoff
rate is accomplished by multiplying the ordinates of a unit hydrograph
by the ratio of: Specific Runoff Rate/2 inches per hour.
For example, in order to modify a unit hydrograph to reflect a runoff
rate of 1 inch per hour, multiply the ordinates of the unit hydrograph
by the ratio of 1 inch per hour/2 inches per hour = 1/2. This is shown
graphically in Figure 14.
1-45
-------
o
o
u
t>
CO
4)
O.
0)
4)
o
lo
2 Inches per Hour
I Inch per Hour
Time
14. Modification of Unit Hydrograph
to Reflect a Specific Runoff Rate
1-46
-------
Both an impervious area unit hydrograph and a street surface unit hydro-
graph can be modified to reflect the impervious runoff rate. This mod-
ification leads to the development of an actual hydrograph portraying
impervious runoff and another portraying street surface runoff during
a specific storm event. The actual hydrographs will be used in subse-
quent procedures.
1-47
-------
5.0 QUALITY OF RUNOFF
In addition to representing the flow of runoff as a function of time,
there is a definite need to express the quality of runoff as a function
of time. Appropriate graphical representations were felt to be a "pol-
lutograph" (shown in Figure 15a) and a "loadograph" (shown in Figure 15b) .
A pollutograph portrays pollutant concentration as a function of time
while a loadograph portrays pollutant load as a function of time.
The procedure to be utilized in developing pollutographs and loadographs
3
is based upon studies conducted by Metcalf & Eddy and URS Research Comr
4
pany. Both of these studies assumed that the amount of pollutants washed
off a street surface, dP, in any time interval, dt, is proportional to the
amount of pollutants remaining on the street surface, P:
which integrates to
P - P = P (1 - e~kt)
o o
where PQ = initial loading in pounds
P = pounds remaining after time, t
k = constant
t = t ime
P0 - P = pounds washed away in time, t
The constant, k, is a function of runoff. To determine k, it was assumed
that a uniform runoff of 0.5 inch per hour will wash off 90 percent of
the initial pollutant load in one hour (this assumption was verified in"
the URS study). This leads to the equation:
P0 - P - P0 (1 -
1-48
-------
s
c
*
a
1
Time
Figure I5a. A Sample Pollutograph
Time
Figure I5b. A Sample Loadograph
1-49
-------
The major limitation of the above relationship is that it can be applied
only to situations where the runoff rate is constant. Unfortunately,
this does not entirely hold true for street surface runoff during a par-
ticular storm event. The actual phenomenon is graphically portrayed in
a street surface hydrograph where there is a rising and receding limb at
the start and finish, respectively, of a rain event. After observing a
typical street surface hydrograph, it becomes obvious that runoff is not
constant at the start or finish of a rain event. Therefore, the previous
equation must be simplified according to the fundamental definition of
numerical differentiation:
P -P = AP = (4.6r P)At
o avg o
The above relationship is valid for a particular time interval, At. The
term, r , is the average street surface runoff within the time interval,
avg
P is the amount of pollutants at the start of the time interval, P is
o
the amount of pollutants at the end of the time interval, and P - P or
o
AP is the increment of pollutants removed during the time interval.
The above equation can be used in conjunction with the street surface
hydrograph and the impervious area hydrograph of a study area to devel-
op a pollutograph and a loadograph. Utilizing the accompanying work-
sheet (Figure 16a), the procedure is as follows:
(1) Utilizing the respective area hydrographs, select an ap-
propriate time interval At by dividing the time base of
both the impervious area hydrograph and the street sur-
face hydrograph into approximately 20 equal time in-
tervals which are convenient to use, such as 10 minutes,
20 minutes, 30 minutes, etc. A time interval of 10
minutes would be appropriate for a hydrograph with a
time base of about 200 minutes.
(2) Determine the volume of impervious area runoff and
the volume of street surface runoff within each time
interval (obtained from each respective hydrograph and
expressed as cubic feet per second) by the time inter-
val (expressed as seconds). The calculated volumes of
runoff will be expressed in cubic feet.
1-50
-------
FIGURE 16a.
POLLUTOGRAPH AND LOADOGRAPH WORKSHEET
1
At
(min)
2 3
STREET SURFACE RUNOFF
VOLUME ravg
(cu ft) (in./hr)
4
P
0
(lb)
5
AP
(lb)
6
ZAP
(lb)
7
TOTAL
VOLUME
(cu ft)
8
TOTAL SOLIDS
CONCENTRATION
(ing/ A)
i
01
-------
(3) Determine the average street surface runoff rate, ravg,
by dividing the volume of street surface runoff in each
time interval by the product of |;At (expressed as minutes)
times the amount of street surface within the study area
(expressed as acres) times the constant 60].
(4) The value of P0 for the first time interval can be de-
termined by multiplying the contaminant load (expressed
as pounds per acre of street surface) by the amount of
street surface area (expressed as acres). Values of PQ
for subsequent time intervals can be determined by sub-
tracting the P value of the previous time interval from
the PQ value of the previous time interval.
(5) Values of AP for each time interval can be determined
from the previously described pollutant removal equa-
tion.
(6) Add each calculated value of AP to the summation of
AP values.
(7) The concentration of total solids for each time in-
terval can be determined by dividing the value of AP
by the volume of impervious area runoff and multiply-
ing the result by 16,000. It should be explained
that runoff from total impervious area is used in
step 7 in contrast to steps 3 through 6 when only
the street surface runoff is utilized. The reason
for doing so is obvious because essentially all
urban contaminants are assumed to derive from
street surfaces and they are removed by street sur-
face runoff. These contaminants mix with the total
impervious runoff flowing through gutters and sewers
finally, neglecting the runoff contribution from per-
vious areas..
Using the above procedure, a series of values presenting the total solids
concentration and the accumulative solids load at corresponding time in-
tervals can be calculated and plotted against -time. The graphical plot
of concentration versus time represents the resultant pollutograph while
the graphical plot of load versus time represents the resultant loado-
graph. The combination of an impervious area hydrograph, a pollutograph,
and a loadograph superimposed upon the same coordinate system shall sub-
sequently be referred to as a quantity/quality hydrograph as shown in
Figure 16b.
1-52
-------
I
u.
j|
3
Pollutograph
! Loadograph
Hydrograph
S
ZJ
I
i
Figure I6b.
Time
A Sample Quantity/Quality Hydrograph Comprising
a Hydrograph, a Pollutograph, and a Loadograph
1
1-53
-------
A quantity/quality hydrograph describes runoff flow, pollutant concen-
tration, and pollutant load as a function of time. Since it is not
feasible to treat or store extremely large amounts of runoff resulting
from exceptionally severe storms, a quantity/quality hydrograph enables
one to focus attention on the most polluted portions of the runoff.
The development of a quantity/quality hydrograph is based on the assump-
tion that runoff will remove all size particles at approximately the same
rate (i.e., the rate of contaminant removal is independent of particle
size) even though each pollutant may not be equally distributed over all
particle size ranges. This assumption permits the development of the
quantity/quality hydrograph based on total solids concentration, which
can then be multiplied by a proportionality factor to determine single
pollutant concentrations. The alternative would be to develop quantity/
quality hydrographs for each pollutant under consideration which would
not significantly increase validity but would greatly add to the work
load. The assumption was supported by the results of the URS Research
Company study of the "Water Pollution Aspects of Street Surface Contami-
nants. "
An example of developing a pollutograph and a loadograph is given in
Example 7, Section II.
1-54
-------
6.0 QUANTITY/QUALITY HYDROGRAPHS
The procedures thus far presented enable the user to develop a quantity/
quality hydrograph for an entire study area or for individual subareas.
The ordinates of a quantity/quality hydrograph represent values of run-
off flow, total solids concentration, and accumulative solids load at
the inlet of the study area (i.e., the downstream location where the
main drainage channel intersects the perimeter,of the study area).
At this point, several more analytical tools are required for one to
complete an assessment of urban runoff. These analytical tools will
be provided within the following sections.
6.1 Method of Routing Quantity/Quality Hydrographs
No analytical techniques have thus far been presented to enable the user
(1) to combine the quantity/quality hydrographs of two or more subareas
into a single composite quantity/quality hydrograph representing an en-
tire study area and (2) to modify the time base of the study area quan-
tity/quality hydrograph such that it represents the quantity and quali-
ty characteristics of runoff at an outfall or point of discharge into a
receiving water (as opposed to the inlet of the study area). The fol-
lowing discussion describes a "routing" technique which enables the user
to accomplish both of these tasks.
Consider a hypothetical study area (Figure 17) consisting of two identi-
cal subareas. The respective inlets are identified as inlets 1 and 2
while D represents the point of discharge. The flow and quality charac-
teristics of runoff entering inlets 1 and 2 at time t can be expressed
as:
1-55
-------
Subarea 1
i
en
01
'"I
Inlet 1
Receiving Water
Subarea 2
~1
Inlet 2
Storm Drainage Pipe or Channel
Point of Discharge D
Figure 17. A hypothetical Study Area Consisting of Two Subarea*
-------
F = F + F
inlets, t l,t 2,t
O = Fl>tQl>t + F2,tQ2,t
^inlets, t F + F
J., t Z, t
Linlets, t = Ll,t + L2,t
where F = flow in cubic feet per second
Q = pollutant concentration in milligrams per liters
L = pollutant load in pounds
However, the instantaneous flow entering inlets 1 and 2 at time t will
not arrive at point D at the same identical time. The flow entering
inlet 1 at time t will arrive at point D at a later time than the flow
entering inlet 2 at time t. This phenomenon is obvious since flow must
travel a greater distance from inlet 1 to D than from inlet 2 to D.
Therefore, a time correction factor is required to compensate for this
lag.
"Lag time" will be defined as the time of travel for stormwater to flow
from an inlet to the point of discharge. Lag time, At, is primarily a
function of the distance of travel and the diameter and slope of the
storm drainage pipe or channel. The- nomograph in Figure 18 can be used
to estimate lag time.
The nomograph in Figure 18 is based upon a typical storm drainage pipe
or channel fabricated from ordinary concrete. If the pipe or channel
is fabricated from another material, the estimated lag time derived from
the nomograph must be multiplied by an appropriate adjustment factor
from Table 10.
1-57
-------
00
.X
r
CO
z
o
0
UJ
CO
UJ
s
-100,000
-50,000
-10,000
-5,000 ^
_^r |_
X^ C
Ul
-1,000 ^
-500 _
UJ
§
Ll_
O
-100 UJ
O
-50 |
o
-10
-5
-1
-100,000
- 50,000
-10,000
- 5,000
- 1 ,000
-500
-100
-50
-10
Figur* It. Determination of Lag Time
* If the depth of the water flowing in the pipe is at
least 3O% of the pipe diameter, the resulting esti-
mate of lagtime will at least be within + 2O%.
-------
Table 10. LAG TIME ADJUSTMENT FACTORS
CHANNEL MATERIAL ADJUSTMENT FACTOR
Plastic, glass, drawn tubing 0.69
Neat cement, smooth metal 0.77
Planed timber, asbestos pipe 0.85
Wrought iron, welded steel, canvas 0.92
Ordinary concrete, asphalted cast iron 1.0
Unplaned timber, vitrified clay 1.07
Cast-iron pipe 1.15
Riveted steel, brick 1.23
Rubble masonry 1.30
Smooth earth 1.38
Firm gravel 1.54
Corrugated metal pipe 1.69
Natural channels in good condition 1.92
Natural channels with stones and weeds 2.69
Very poor natural channels 4.62
Applying the concept of lag time to the hypothetical study area:
At = lag time from inlet 1 to D
At = lag time from inlet 2 to D
£t
Accordingly, the accumulative flow and quality of runoff at time t at the
point of discharge D can be expressed by:
FD,t = Fl,t - At + F2,t -
(F
v l,t - At
,} (Ql,t -
(Fl,t -
Atl}
At >
-t- fF * ) (Q A }
2 t " ""t 2 t "t
+ (F A )
v 9 f _ At
^> - c
Ll,t - Atx + L2,t - At2
1-59
-------
The above equations allow the user to combine individual quantity/quality
hydrographs of two or more subareas into a composite quantity/quality hy-
drograph representing the entire study area. A value of FD t> QD t» and
I, can be calculated for each time value, t, by substituting values of
D, t
F, Q, and L obtained from the subarea hydrographs into the above equa-
tions. The composite quantity/quality hydrograph can be developed by
plotting values of F_ , Q_ , and L^ versus time as shown in Figure 19.
Uj "C L/t "C Uj T
It may be required to plot many data points to obtain a high degree of ac-
curacy. However, if a rough approximation will suffice, a few data points
may adequately approximate the shape of a composite quantity/quality hy-
drograph.
An example of the above routing technique is given in Example 8, Section II.
The above application of the routing technique was concerned with com-
bining subarea quantity/quality hydrographs into a composite quantity/
quality hydrograph portraying the characteristics of runoff from an en-
tire study area at a point of discharge. However, if the assessment
conducted by the user does not require dividing a study area into sub-
areas, part of the routing technique can be used to simply modify the
time base of a study area quantity/quality hydrograph in order to por-
tray the characteristics of runoff at a point of discharge. The proce-
dure consists of using the nomograph shown in Figure 18 to estimate the
lag time from the inlet of the study area to the point of discharge.
Subsequently, the entire quantity/quality hydrograph is shifted along
the time axis in a positive direction for a distance equal to the lag
time.
6.2 Quality Composition of Runoff
The development of a quantity/quality hydrograph is based upon the re-
moval of solids from urban land surfaces by runoff. Consequently, the
quality ordinates portray the concentration and load of total solids.
1-60
-------
u
v
o.
4)
4)
JO
U
Pollutograph
Loadograph-
Hydrograph
ph
c u
O 4)
'+3 4-»
It
O Q)
O
co
i_
v
a
«
4)
J3
3
O
Pollutograph
Loadograph
o
a.
i/>
i/>
o
1
-o Quantity/Quality
g Hydrograph of Subarea 2
£ @ Inlet 2
Time
u
4)
to
l_
«
Q.
4)
1)
X)
3
U
Pollutograph^
C <-s
O i-
-1
Time
o Compos I te
§ QuantIty/Quality
Hydrograph of Entire
§ St-'jdy Area at
~" Point of Discharge
Figure 19. Development of a Composite Quantity/Quality
HydrograpH- by the Routing Techniques
1-61
-------
However, total solids is not a useful quality parameter for evaluating
the water pollution potential of urban runoff since the actual compo-
sition of the solids determines its pollutional characteristics.
Previous material provided means to estimate both a contaminant load
and its corresponding composition for a study area or a subarea. Data
regarding the composition of the contaminant load was generally expressed
in terms of micrograms per gram (parts per million) or micrograms per ki-
logram, (parts per billion).
In order to realistically evaluate the water pollution potential of ur-
ban runoff, the total solids ordinates of each quantity/quality hydro-
graph must be modified to reflect other quality parameters such as BOD,
COD, PO , NO , etc. The required modification consists of adjusting
4 o
the total solids ordinates by applying the composition data of a spe-
cific quality parameter as a proportionality factor. A sample quantity/
quality hydrograph modified to reflect several quality parameters is
shown in Figure 20.
The choice of which quality parameters to select for evaluation is de-
pendent upon receiving water characteristics and standards. It is the
responsibility of the user to designate which quality parameters to se-
lect for evaluation purposes. For example, one may select BOD as the
quality parameter for evaluating a receiving water with low levels of
dissolved oxygen while one may select PO and NO as the quality param-
4 3
eters for evaluating a receiving water which has a significant poten-
tial for eutrophication. The concept of modifying a quantity/quality
hydrograph to reflect other quality parameters is demonstrated in the
latter part of Example 10.
1-62
-------
600-
Accumulative
Pollutant
Load
300
Total
Solids BOD COD
Co
(me
nc. Cor
J/0 (mg
1200
-1000
-800
-600
-400
-200
c. Co
//) (m
-24
20
16
19
-8
-4
nc.
atf)
-36
-30
-24
-18
-12
- 6
Pounds
COD of Pounds Pounds
Total of of
(mgtf) Solids BOD COD
150,000
-3000
^00,000
-50,000
-4500
- 2000
- 1000
3000
1500
Figure 20. A Sample Quantity/Quality Hydrograph Reflecting Several Quality Parameters
-------
7.0 ANALYTICAL PROCEDURES FOR THE ASSESSMENT
OF URBAN RUNOFF QUANTITY AND QUALITY
7.1 Level I
A simple, first-glance assessment of the water pollution aspects of
urban runoff is to consider only (1) the volume of impervious runoff
and (2) the amount of street surface contaminants removed by street
surface runoff. In turn, the rate of runoff with respect to time is
ignored. The procedure is as follows:
(1) Describe the specified study area according to the
following characteristics: study area size (acres),
amount of interconnected impervious area within study
area (acres), amount of street surface area within
study area (acres), average slope of main drainage
channel (feet/feet).
(2) Determine an appropriate contaminant loading for
street surfaces within the study area.
(3) Select a storm event according to the criteria pre-
sented and compute the impervious runoff rate.
(4) The volume of runoff from the interconnected imper-
vious area can be estimated by the following:
Impervious Volume of Runoff (cf) = Runoff Rate (in./hr) X
Interconnected Imper-
vious Area (acres) X
1,815
(5) The percentages of street surface contaminants which will
be removed by various combinations of runoff rate and run-
off duration are given in Table 9. The amount of contam-
inants removed from the study area by the selected runoff
rate/duration can be estimated by the following:
Amount of Contaminant Removal (lb) = Contaminant Load Per
Acre of Street
Surface (Ib/acre) X
Amount of Street
Surface Area
(acres) X
Percent Contaminant
Removal/100
1-64
-------
(6) The average total solids concentration of runoff can be
described by the following expression:
,.,,,,.. j. j.. Amount of Contaminant Removal (Ib) , _, _
Total Solids Concentration = -- , -^ X 16.019
,n. Impervious Volume of Runoff (cf)
(7) The average concentration of other pollutants can be
determined by multiplying the average total solids
concentration by the composition data of specific
quality parameters.
A Level I assessment is demonstrated by Example 9, Section II.
7.2 Level II
An intermediate level of assessment can be used to investigate an entire
urban watershed or a portion of an urban watershed. The characteristics
of the study area are considered homogeneous and a single quantity/quality
hydrograph is developed for the entire study area. The procedure is as
follows:
(1) Describe the specified study area according to the fol-
lowing characteristics: study area size (acres), amount
of interconnected impervious area within study area
(acres), amount of street surface area within study area
(acres), length of main drainage channel (feet), and
average slope of main drainage channel (feet/feet).
(2) Determine an appropriate contaminant loading for street
surfaces within the study area.
(3) Select a storm event according to the criteria presented
and compute the impervious runoff rate.
(4) Develop a 30-minute unit hydrograph for both the inter-
connected impervious area and the street surface area
within the study area by using the equations derived by
Espey.
(5) Modify the impervious area unit hydrograph and the street
surface unit hydrograph to reflect the computed impervious
runoff rate.
J-65
-------
(6) Develop a corresponding pollutograph and loadograph
by using the appropriate procedure in conjunction with
the impervious area hydrograph and the street surface
hydrograph.
(7) Plot the impervious area hydrograph, pollutograph, and
loadograph upon the same coordinate system to produce
a quantity/quality hydrograph for the study area.
(8) Modify the total solids ordinates of the quantity/
quality hydrograph to reflect other quality parameters
of concern.
The procedure for a Level II assessment is demonstrated by Example 10,
Section II.
7.3 Level III
A thorough level of assessment can be used to investigate a study area
consisting of two or more urban watersheds, portions of two or more
urban watersheds, or two or more homogeneous portions of an urban water-
shed. The study area is divided into two or more subareas. A quantity/
quality hydrograph is developed for each subarea, and subsequently, the
routing technique is used to develop a single composite quantity/quality
hydrograph representing the entire study area. The procedure is as fol-
lows :
(1) Divide the study area into subareas.
(2) Describe each subarea according to the following charac-
teristics: subarea size (acres), amount of interconnected
impervious area within subarea (acres), amount of street
surface area within subarea (acres), length of main drain-
age channel (feet), and slope of main drainage channel
(feet/feet).
(3) Determine an appropriate contaminant loading for street
surfaces within each subarea.
(4) Select a storm event according to the criteria presented
and compute the impervious runoff rate for each subarea.
The impervious runoff rate for each subarea will be
1-66
-------
identical if the slope of each subarea is the same.
If the slopes differ, the computed runoff rate will
be highest for the subarea with the greatest slope.
(5) For each subarea, develop a 30-minute unit hydrograph
for both impervious area and street surfaces by util-
izing the equations derived by Espey.
(6) For each subarea, modify the impervious area unit hydro-
graph and the street surface unit hydrograph to reflect
the computed impervious runoff rate pertaining to each
subarea.
(7) Develop a corresponding pollutograph and loadograph
for each subarea by using the appropriate procedure
in conjunction with the impervious area hydrograph
and street surface hydrograph of each subarea.
(8) For each subarea, plot the corresponding impervious
area hydrograph, pollutograph, and loadograph upon a
coordinate system to produce a quantity/quality hydro-
graph representing each subarea.
(9) Modify the total solids ordinates of each subarea
quantity/quality hydrograph to reflect other quality
parameters of concern. If the contaminant load for
for each subarea is identical, this modification can
be made to the composite quantity/quality hydrograph
after it is developed in the next step.
(10) Transform the characteristics of all subarea quantity/
quality hydrographs into a single composite quantity/
quality hydrograph representing the entire study area
by utilizing the routing technique.
The procedure for a Level III assessment essentially consists of con-
ducting a Level II assessment for each subarea and, subsequently,
utilizing the routing technique to combine the subarea quantity/quality
hydrographs into a single composite one. Therefore, the demonstration
of a Level II assessment by Example 10 and the routing technique by
Example 8 should provide adequate insight into the procedure for a
Level III assessment.
1-67
-------
Section I
REFERENCES
1. Chow, Ven Te, Handbookxof Applied Hydraulics, 1964.
2. Espey, W.H. and D.E. Winslow, The Effects of Urbanization on Unit
Hydrographs for Small Watersheds, 1968.
3. Metcalf and Eddy, Inc., Storm Water Management Model, Vols. I-IV,
EPA, July 1971 - October 1971.
4. Sartor, J.D. and G.B. Boyd, Water Pollution Aspects of Street
Surface Contaminants, EPA, Research and Monitoring, Envir.
Protection Tech. Series, EPA - R2-72-081, Nov. 1972.
1-68
-------
Section II
EXAMPLE PROBLEMS
-------
Section II
EXAMPLE PROBLEMS
The example problems presented in this section will assist the user in
determining the water pollution aspects of urban runoff.
The following lists the example problems presented in this section:
Example 1: Characterization of a study area
Example 2: Determination of contaminant loading
Example 3: Determination of runoff rate by runoff
coefficient
Example 4: Determination of runoff rate by composite
runoff coefficient
Example 5: Selection of storm event
Example 6: Development of a unit hydrograph
Example 7: Development of a pollutograph and loadograph
Example 8: Utilization of routing technique
Example 9: A Level I assessment of urban runoff
Example 10: A Level II assessment of urban runoff
EXAMPLE 1
A 27-acre urban watershed is defined as a study area as shown in
Figure 21. Determine the following study area characteristics:
Average Imperviousness
Length of Main Drainage Channel
Average Slope of Main Drainage Channel
II-l
-------
Elevation 50 feet
Pervious
Impervious
Elevation - 10 feet
Main Drainage Channel
Figure 21. Sample Study Area for Example 1
Scale: 1"-200'
II-2
-------
Average Imperviousness
The hypothetical study area shown in Figure 21 consists of both imper-
vious and pervious surfaces. For the purpose of this example, imper-
vious surfaces will only include rooftops, driveways, and street sur-
faces. Furthermore, it is assumed that runoff from rooftops will
drain onto driveways which, in turn, drain onto street surfaces.
Only impervious area is considered in the computations, hence imper-
viousness is taken to be 100 percent. From Figure 21, it is estimated
that the study area includes about 13 acres of impervious surface area
out of a total area of 27 acres. Impervious area is comprised of two
parts, i.e., street surface consisting of 10 acres and others, 3 acres.
Length of Main Drainage Channel
Using the scale indicated in Figure 21, the length of the main drainage
channel is estimated to be about 1,200 feet.
Average Slope of Main Drainage Channel
The elevation varies from 50 feet to 10 feet over the length of the main
drainage channel. Accordingly, the average slope of the channel can be
estimated by:
50 ft - 10 ft 40 ft =
1200 ft 1200 ft
11-3
-------
EXAMPLE 2
Predict the load per acre five days after a rain and the composition
of solids present on an asphalt street in a new residential area in
Portland, Oregon, where the average daily traffic is less than 500.
Solution: Following the procedure given, the solution is obtained in
these steps:
1. Use only substitutions at the 95 percent confidence
level shown in Table 5.
2. Present a range of possible data based on the loading
rate only:
a. The area selected is residential. Move upward to
the residential land use category. No loading
substitution is permissible. One could substitute
other compositional parameters but we are looking
for a row that has a permissible loading substitu-
tion.
b. Portland is in the northwest. Move upward to
the northwest climate category. A loading sub-
stitution is permitted. After making all per-
missible substitutions into the row labeled
"All Data," the new row is shown in Table 11.
3. Since there are no other permissible substitutions of
the loading rate available under any other category
by which the site may be classified, present two
tables: the means of the set of all data with no
substitutions and Table 11.
4. The loading rate in Table 11 is lower than the mean of
all data and probably better represents the real situa-
tion in Portland, Oregon. For all later calculations,
use Table 11.
5. Convert the loading rate to load per curb mile as
follows:
30 Ib/curb mi/day x 5 days = 150 Ib/curb mi
II-4
-------
Table 11. SAMPLE ROW OF LOADING AND COMPOSITION DATA
Lbs/Curb Mi/Day Concentrations in Micrograms per Gram of Dry Solid
Loading
BOD.
COD OH)
TPO.
NO
NH,
ORGN CD CR
30
19,900 140,000 1,280 2,930 804 2,640 2,950 3.4 211
M
I
Ul
Concentrations in Micrograms per Gram ojf Dry Solid #/Gram
CU FE
PB MN NI SR ZN
TCOLI FCOLI
* * * * *
104 34,500 1,810 418 35 10 480 6.8E5 1.1E4
Note: Substitutions are indicated by an asterisk.
-------
6. Convert the load to load/acre as follows, using the
curb'mile density factor in Table 2:
150 Ib/curb mi x 0.54 curb mi/acre = 81.0 Ib/acre
EXAMPLE 3
An urban watershed consists of 200 acres of land with essentially all
impervious surface and a relatively flat slope.
a. What is the rate of runoff resulting from a storm with
an intensity of 0.5 in./hr which lasts for 1 hour?
R = kP
k = 0.80 from Table 8
R = (0.80) (0.5 in./hr) = 0.4 in./hr
b. What rainfall intensity will produce a runoff rate of
0.5 in./hr from the same watershed?
R = kP
P - £
k
_ (0.5 in./hr)
p " oTso = °'63 in-/hr
EXAMPLE 4
An urban watershed consists of 400 acres of land with a moderate slope.
The watershed comprises 200 acres of impervious surface and 200 acres
of pervious surface with lawns and heavy soils. What rate of runoff
will result from a storm with an intensity of 0.33 in./hr which lasts
for 45 minutes? (Only impervious area is considered as explained
earlier.)
k. = 0.85 from Table 8.
imp
R = kP
R = (0.85) (0.33 in./hr) = 0.28 in./hr
II-6
-------
EXAMPLE 5
Select a 1-year storm of 30 minutes duration for a study area in Portland,
Oregon and determine the runoff rate and duration resulting from the se-
lected storm event. The runoff coefficient for the study area is 0.9
considering only the impervious areas.
Solution
Figure 4 reveals that a 1-year storm of 30 minutes duration in the vicin-
ity of Portland, Oregon will contribute a total of 0.4 inches of rainfall.
Since this 0.4 inches of rainfall occurs over a 30-minute interval, the
average rainfall intensity of the storm will be 0.8 inches per hour.
The runoff rate of the selected storm event can be determined as follows:
R = kP
R = 0.9 (0.8 inches/hour)
R = 0.72 inches per hour
Therefore, the resultant runoff rate is 0.72 inches per hour while the
resultant runoff duration is 30 mrnutes. It should be noted that time-
base of the hydrograph will extend over a longer period than 30 minutes.
EXAMPLE 6
An urban study area consists of a rectangular parcel of land with a length
of 1.0 mile and a width of 0.5 mile. The length of the main drainage chan-
nel is 7,000 feet, the average slope of the main drainage channel is
2 ft/100 ft and the average imperviousness is 55 percent.
11-7
-------
(a) Develop a unit hydrograph for the impervious portion of the study
area by using Espey's equations.
Slope (S)
S =2 ft/100 ft = 0.02
Area (A)
(5280 ft) (2640 ft)
A
=
total 43560 ft^ acre
A = 0.55 x 320 = 176 acres
imp
Imperviousness (I)
I = 100%
Length (L)
L = 7,000 ft
Using Nomograph of Figure 7 in Conjunction with
Figures 8 thru 11
T = 27 minutes
K
Q = 259 cfs
T = 155 minutes
D
W = 37 minutes
OU
W = 25 minutes
5
The resultant hydrograph is shown in Figure 22a.
11-8
-------
(b) Develop a unit hydrograph for the street surface portion of the
study area assuming that street surfaces represent 35 percent of the
study area.
Slope (S)
S = 0.02
Area (A)
A ^ ^ = 320 X 0.35 = 112 acres
street surface
Imperviousness (I)
I = 100%
Length (L)
L - 7,000 ft
Using Nomograph of Figure 7 in Conjunction with
Figure 8 thru 11
T = 27 minutes
R
Q = 165 Cfs
T = 155 minutes
B
W =37 minutes
&u
W = 25 minutes
The resultant hydrograph is shown in Figure 22b.
II-9
-------
300
275
50 75
T, Time in Minutes
100
125
150
Figure 22. Unit Hydrographs Developed in Example 6
11-10
-------
EXAMPLE 7
Using the hydrograph developed in Example 6 (Figure 22), develop a
corresponding pollutograph and loadograph based on a contaminant load-
ing of 500 Ib/curb mile, if the length of all streets in the study area
is 30 miles.
The general pollutograph equation is:
AP = (4'6 ravg V At
where AP = Amount of pollutants removed during the time interval, At
P = Amount of pollutants remaining at the beginning of the
time interval, At
r = Average runoff rate within the time interval, A*
avg & "
Curb mile = 30 X 2 = 60, P (initial pollutant load) = 500 X 60
= 30,000 Ibs.
Values of total solids concentration and accumulative solids load are
plotted against time, and the resultant pollutograph and loadograph are
shown in Figure 23.
11-11
-------
POLLUTOGRAPH AND LOADOGRAPH WORKSHEET
STREET SURFACE RUNOFF
At
(min)
0- 12.5
12.5- 25
25 - 37.5
37.5- 50
50 - 62.5
62.5- 75
75 - 87.5
87.5-100
100 -112.5
112.5-125
125 -137.5
137.5-150
VOLUME
(cu ft)
16,900
72,000
122,500
102, 000
62,000
42,000
30,000
21,700
14,600
10, 200
6,100
2,900
r
avg
(in./hr)
0.200
0.850
1.446
1.204
0.732
0.500
0.354
0.256
0.172
0.120
0.072
0.034
P
o
(lb)
30,000
24 , 250
4,500
0
0
0
0
0
0
0
0
0
AP
(lb)
5,750
19,750
4,500
0
0
0
0
0
0
0
0
0
2AP
(lb)
5,750
25 , 500
30,000
30,000
30,000
30,000
30,000
30,000
30,000
30,000
30,000
30,000
TOTAT
X V J.^VI_I
VOLUME
(cu ft)
26,500
112,500
192,000
160,000
97,000
66,000
47,500
34,000
23,000
16,000
9,500
4,500
TOTAT QfVf TDfi
X \J -Lrll_i OV^J_i A-L^O
CONCENTRATION
(rag/*)
3,476
2,812
375
0
0
0
0
0
0
0
0
0
Following equations have been used in preparing the above table.
Volume = Q X (At) . X 60
avg . mm
r (in./hr) = ,A.;
avg (At)
Volume (cu ft)
. X A X (60.5)
mm acres
AP = (4.6 r P ) X
avg o
(At)
min
Cone.
60
X 16. 019
Runoff Volume from Total Imp. Area
-------
4000-
iJ 3000 -
9
O.
g
m
o
2000-
1000-
Loadograph
Pollutograph
40
1 I
60 8O
Time (minutes)
100
120
40,000
-30,000
I
-20,000
-10,000
a
s
Figure 23. P»llut°gr*Pfc »nd Loadograph Developed in Example 7
-------
EXAMPLE 8
A 300-acre parcel of urban land consists of 200 acres of industrial land
use and 100 acres of residential land use as shown in Figure 24. Quan-
tity/quality hydrographs which describe the two subareas are given in
Figure 25.
Using the routing technique described in Section I, combine the quan-
tity/quality characteristics of the two subareas into a composite quan-
tity/quality hydrograph representing the entire study area.
At = lag time from inlet 1 to D
At = lag time from inlet 2 to D
£i
From Figure 18 Nomograph:
At = 510 sec = 8.5 min
At0 = 300 sec = 5.0 min
The accumulative flow and quality of runoff at time t at the point of
discharge D can be expressed by:
F = F + F
D,t l,t-Afc 2,t-At
J.
(Q2,t-At>
-------
I
M
cn
Main Drainage Channel
1. Slope of Main Drainage Channel * .01
2. Hydraulic Radius of Main Drainage Channel
(equivalent to a pipe diameter of 36 inches)
0.75 feet
Figure 24. Study Area of Example 8
-------
300--
200-
-o
§
u
Q.
L-2000 !«
IV
o>
-1000
60
-500 |
o
ID
3
250$
|2
300 H
O
O
0)
200-
L.
v
o.
4)
0)
u
100-
100-Acre Residential Subarea
Hydrograph
-3000
-2000
-1000
o>
u
ft)
0.
V)
E
n>
o>
o
P
i
o
-750
-500
-250
^
3
10
20
60
Time
Figure 25. Quantity/Quality Hydrographs for Subareas of Example 8
11-16
-------
subarea 1 at time t - At and the flow and quality of subarea 2 at
time t - At into the equations. For example:
£i
@ time t - 15
FD,t=15 = Fl,t=6.5 + F2,t=10 = 16° + 13° = 29°
(Fl,t=6.5) (Ql,t=6.5) + (F2.t=10)
^15 " (Fl,t=6.5) +
V-15 = - (160)
(160) (2800) + (130) (1300)
(130)
_,. .
= 2130jngA.
= Ll,t=6.5 + L2,t=lO
In a similar manner, other values for the composite flow, pollutant concen-
tration, and pollutant load can be ascertained:
t
5
15
25
35
45
55
65
75
Fl,t-8.5
0
160
300
200
120
70
25
10
Ql,t-8.5
0
2,800
1,700
750
440
400
400
400
Ll,t-8.5
0
250
535
665
700
720
730
735
F
2,t-5
0
130
140
90
65
30
10
0
Q2,t-5
1,500
1,300
750
280
200
200
200
200
L2,t-5
0
88
155
185
187
189
190
191
D, t
0
290
440
290
185
100
35
10
T), t
0
2,130
1,400
600
360
340
340
400
Vt
0
338
690
825
887
909
920
926
The values of F , Q^ , and L^ are plotted against time to yield the com-
D, t *J11 * U, t
posite quantity/quality hydrograph representing the entire study area as
shown in Figure 26.
11-17
-------
500-
Loadograph
10
70
-1000
-750*
I
-I
500 «
-250
Flgur« 26. Composite Quantity/Quality Hydrograph of Example 8
11-18
-------
EXAMPLE 9
An urban watershed consists of 1,500 acres of impervious surface and
1,000 acres of pervious surface with lawns and sandy soils. The water-
shed is characterized by a steep slope and has an average contaminant
loading of 300 Ib/curb mile. A rainfall with an intensity of 0.32 in./hr
occurs for 60 minutes. What is the average total solids concentration of
the resulting runoff? What is the total load of solids removed by the
specified storm event?
Again using the impervious surfaces only,
k =0.90
imp
R = kP
R = 0.90 (0.32 in./hr) = 0.288 in./hr
From Table 9, it can be seen that a runoff rate of 0.288 in./hr which
lasts for 60 minutes will remove about 73 percent of the residing urban
land contaminants.
Volume of Runoff = Runoff Rate X Runoff Duration X Area X 3,630
Volume of Runoff = (0.288 in./hr)- (1.0 hr) (1,500 acres) (3,630)
Volume of Runoff = 1,568,160 cu ft
int. of Contaminant Removal = Contaminant Load X Area X (% Removal/100)
11-19
-------
Assume 600 curb miles in the watershed
Amt. of Contaminant Removal = (300 Ib/curb mile) (600) (0.73)
Amt. of Contaminant Removal = 131,400 Ib
Amt. of Contaminant Removal
Concentration = - volume of Runoff - X 16'°19
Concentration = ft X 16,019
Concentration = 1,342.3 mg/A
Therefore, the average total solids concentration of runoff is 1,342.9
mg/i while the total load of solids washed from streets is about 131,400
pounds .
EXAMPLE 10
It is proposed that a small artificial lake be created to store storm-
water runoff for subsequent treatment in an existing sewage treatment
plant. A 1-year storm of 30 minutes duration shall be the criteria for
design purposes. Stormwater runoff with a concentration of greater than
30 mg/H of BOD will be stored, while runoff containing less than this
concentration will be directly discharged into receiving waters. What
volume of water should the artificial lake be capable of accommodating?
The study area is located in Dallas, Texas and consists of 2.5 sq mi of
a generally flat urban watershed with an average imperviousness of 60
percent. The impervious portions of the study area consist of street
surfaces, parking lots, roofs, and others. The average contaminant
loading is 400 Ib/curb mile with a BOD composition of 30,000 milligrams
per kilogram. Forty percent of the total area is occupied by street
surfaces, consisting of some 150 miles of streets. Assume L = 15,000 ft
11-20
-------
and S = .025. The techniques of Level II are used in the following
assessment.
Determination of Runoff Rate
The selected storm event will be a 1-year storm of 30 minutes duration.
Accordingly, the resultant runoff can be determined as follows:
k. = 0.85
imp
R = kP
P = 1.0 in./0.5 hr from Figure 4
R = (0.85) (1.0 in./0.5 hr) = 1.70 in./hr
Therefore, the selected storm will result in a runoff rate of 1.70 in./hr
and a runoff duration of 30 minutes, neglecting the runoff from pervious
areas as usual.
Development of Hydrograph
Unit hydrographs for the study area can be developed by utilizing Espey's
equations, which may be solved graphically with the assistance of Figures
7 thru 11.
From Figures 7 thru 11:
L = 15,000 ft
I = 100%
S = .025
11-21
-------
Hydrograph Characteristics from Total Impervious Area
T = 34 min.
R
Q = 1,100 cu ft/sec
T = 210 min.
B
W = 48 min.
5U
W = 31 min.
I O
Hydrograph from street surface runoff will have the same above charac-
teristics except
Q = 736 cfs
The resultant unit hydrographs are shown in Figure 27.
The unit hydrograph must be modified to reflect the selected runoff
rate and duration. The unit hydrograph reflects a runoff rate of
2 in./hr and a runoff duration of 30 min, while the selected runoff
rate and duration are 1.70 in./hr and 30 minutes, respectively.
The duration of the unit hydrograph and the selected runoff duration
are identical; therefore, no modification of runoff duration is re-
quired. However, the unit hydrograph is modified to reflect the se-
lected runoff rate by multiplying the ordinates of the unit hydrograph
by:
Selected Runoff Rate _ 1.70 in./hr
2 in./hr ~ 2 in./hr = °'85
The resultant hydrograph, modified to reflect the selected runoff rate,
is shown in Figure 28.
11-22
-------
1200
1100
1000
lydrogiaph
Impervious
20 40 60 80 100 120 140 160 180 200 220 240
T, Time in Minutes
Figure 27. Unit Hydrographs Developed in Example 10,.
11-23
-------
1000-
900-
800-
pervious S irface
Hvdroi
;raph
TOO
B 600-
JS 50°-
o
400-
St
eet Si rface
Hydroj
raph
300-
200-
100-
20 40 60 80 100 120 140 160 180 200 220 240
T, Time in Minute*
#
Figure 28. Hydrographs Developed in Example 10
11-24
-------
Development of Pollutograph
Using the previously developed hydrograph (Figure 28), the corresponding
pollutograph and loadograph will be developed, based on a contaminant
loading of 400 Ib/curb mile. The pollutograph and loadograph will be de.-
veloped in a manner similar to that portrayed in Example 7.
P = 400 x 150 x 2 = 120,000 Ib.
Values of concentration and load are plotted against time, and the re-
sultant pollutograph and loadograph are shown in Figure 29.
Quantity/Quality Hydrograph
The developed hydrographs (Figure 28), pollutograph and loadograph
(Figure 29) are superimposed as shown in Figure 30 to yield the quan-
tity/quality hydrograph which represents the study area under the
specified conditions.
Ordinates which portray the concentration and load of BOD are also
included in Figure 30. These ordinates were developed by applying
the BOD composition data as a proportionality factor to the total
solids and ordinates. Since the composition of BOD within the solids
is 30,000 mg/kg or .03 kg/kg, values of BOD will be equal to .03 times
corresponding values of total solids.
From Figure 30, it can be seen that the resultant runoff will contain
less than 30 mg/^ of BOD at a time approximately 30 minutes after run-
off originates. The total load of BOD removed during this time period
is about 2,700 pounds. The runoff volume which occurs from the start
of runoff to a time 30 minutes after runoff originates is equal to the
area under the hydrograph curve from time = 0 to time = 30 minutes.
The runoff volume is graphically estimated to be about 500,000 cu ft,
or 11.48 acre-feet, which represents the volume of water that the ar-
tificial lake should be capable of accommodating.
11-25
-------
EXAMPLE 10
POLLUTOGRAPH AND LOADOGRAPH COMPUTATION
STREET SURFACE RUNOFF
i *
i i
I
to
as
At
(min)
0-10
10-20
20-30
30-40
40-50
50-60
60-70
70-80
80-90
90-100
100-110
110-120
120-130
130-140
140-150
150-160
160-170
170-180
180-190
190-200
200-210
VOLUME
(cu ft)
30,000
140,000
320,000
375,000
315,000
254,000
174,000
140,000
114,000
94,000
84 , 000
70, 000
58,000
45,000
35,000
32,000
24,000
18,000
13,000
8,000
3,000
r
avg
(in./hr)
0.077
0.362
0.826
0.968
0.814
0.660
0.449
0.362
0.294
0.243
0.217
0.181
0.150
0.116
0.090
0.083
0.062
0.046
0.034
0.021
0.008
P
o
(lb)
120,000
112,916
81,578
29,917
7,714
2,900
1,433
940
679
526
428
357
307
272
248
231
216
206
199
194
191
AP
(lb)
7,084
31,338
51,661
22, 203
4,814
1,467
493
261
153
98
71
50
35
24
17
15
10
7
5
3
1
ZAP
(lb)
7,084
38,422
90,083
112,286
117,100
118,567
119,060
119,321
119,474
119,572
119,643
119, 693
119,728
119,752
119,769
119,784
119,794
119, 801
119,806
119,809
119,810
TOTAL
IMPERVIOUS TOTAL SOLIDS
VOLUME CONCENTRATION
(cu ft) (mg/A)
45,
210,
480,
560,
470,
380,
260,
210,
170,
140,
125,
105,
87,
70,
54,
48,
36,
27,
20,
12,
5,
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
2,522
2,390
1,724
635
164
62
30
20
14
11
9
7
6
5
5
5
4
4
4
4
3
.4
.2
.1
.6
.4
.5
.0
.0
.4
.2
.0
.0
.2
BOD
CONCENTRATION
(mg/j0
75.7
71.7
51.7
19.1
4.9
1.9
0.9
0.6
0.4
0.3
0.3
0.2
0.2
0.2
0.15
0.15
0.1
0.1
0.1
0.1
0.1
-------
O
4*
3000-1
200,000
5
d
o
C
p
c
2000H
loooH
Pollutograph
+>
£
20 40 60
i
80
Loadograph
100,000
o
c
I
s-'
o
3
«
T3
H
rH
O
100 120 140 160
Time (minutes)
180 200 220 240
Figure 29. Pollutograph and Loadograph Developed in Example 10
-------
1000-
I
to
oo
20
40
60
80 100 120 140 160 180
Time (Minutes)
200
200
o
I
n
O
100
DO
o
-0
(4
O
-75 h
I
oa
g
bO
i-l
-50 £
H
5
c
o
+*
t
25
c
3
--0
-6000
-5000
at
O
H4000 3
£
-3000
-2000
-1000
-0
Figure 30. Quantity/Quality Hydrograph Developed in Example 10
-------
Section III
MISCELLANEOUS SOURCES OF URBAN RUNOFF POLLUTION
-------
Section III
MISCELLANEOUS SOURCES OF URBAN RUNOFF POLLUTION
Beyond the major source of urban runoff (i.e., stormwater drainage), there
exist several additional sources of pollution which have not been discussed.
These sources include: urban irrigation, cleaning, and snow removal. How-
ever, of these three sources, only snow removal through the use of deicing
compounds has been found to have a significant impact on urban water quality.
Irrigation (i.e., lawn watering, etc.) and cleaning (i.e., washing of cars
and driveways) have negligible effects because of the very small size of
their inputs when compared with other sources of runoff. Snow removal,
particularly in the northern snow-belt states, presents a somewhat larger
source of both runoff and additional pollutants in the form of deicing salts
and their additives.
The current amount of deicing compounds used in the United States has been
placed at 9 to 10 million tons of sodium chloride, 0.3 million tons of cal-
cium chloride, and 11 million tons of abrasives. This use is predominately
in the eastern and north central sectors of the country with 90 percent of
all chloride deicers being used in these areas. The leading states in total
use are Pennsylvania, Ohio, New York, Michigan, and Minnesota. With average
2
salting rates usually in the range of 400 to 1200 lb/mi/application, it is
not hard to see why they might add to the sodium chloride levels in adjacent
receiving waters. In addition to the amounts applied to roadway surfaces,
salt storage depots, which are necessary to sustain highway deicing operations,
often become a major contributing source of groundwater and surface water salt
contamination.
The pollutant loading on local treatment plants and/or nearby receiving
streams due to these operations is of such a magnitude as to become signifi-
cant in locales where deicing occurs on a large scale. In Milwaukee, the
daily chloride loads (in municipal sewage) were shown to be 40 to 50 percent
III-l
-------
higher for winter months as compared with summer months. During times of
2
heavy snow melt, these loads were threefold the normal summertime loads.
Indeed, recent studies have shown that 600 Ib of salt, when applied to a
one-mile section of roadway 20-ft wide containing 0.2 in. of ice, will
produce an initial salt solution of 69,000 to 200,000 mg/f in a temperature
range of 10°F to 25°F. Analyses of accumulated snow and ice deposits have
likewise shown them to contain up to 10,000 mg/f sodium chloride, 100 mg/f
oils, and 100 mg/f lead.
In addition to salt contamination, numerous special additives found in present
day deicers may create pollution problems more severe than those caused by
chloride salts. Sodium ferrocyanide, used to minimize salt caking, is quite
soluble in water and will generate cyanide in sunlight. Tests have shown
that a 15.5-mg/f solution of this additive produced 3.8 mg/f of cyanide in
2
30 min (the USPHS standard for cyanide in drinking water is 0.01 rag/f.).
Chromate and phosphate additives, used as corrosion inhibitors, have also
been found in high quantities. A study of snow-melt collections conducted
in the Minneapolis-St. Paul area showed values of up to 1.7 mg// hexavalent
2
chromium (the USPHS standard is 0.05 mg//).
The effects of these loadings, while not completely known, is becoming rela-
tively well recognized. The most noticeable effect of deicing salts is the
frequent damage to roadside soils, vegetation, and trees. Such drainage can
take the form of leaf scorch, defoliation, stunting, and ultimately, plant
die-off. However, in regards to man's use of water, the main effect of deicing
salts is an objectionable taste. While excessive salts can be harmful, health
hazards seem relatively rare. Serious groundwater pollution has, however,
occurred in many locations. In New Hampshire, more than 200 roadside wells
have been replaced at a cost of $200,000 due to salt contamination resulting
from highway deicing and leaching from salt storage piles. Some of these wells
o
had chloride concentrations exceeding 3500 mg/f . The USPHS standard for
chloride is 250 mg/f . A survey of northern states showed a total of 13 report-
ing injury to plants, while 12 reported water contamination as side effects
3
from the use of salts. Without some adoption of measures to mitigate these
III-2
-------
results, the continued intensive use of deicing salts will continue to cause
severe plant injuries and additive water pollution effects.
Many techniques and measures can be adapted to each specific locale which can
alleviate some of these problems, especially in the area of salt storage
depots. Because this problem is beyond the scope of this study, we recommend
that the reader consult the EPA study on the "Environmental Impact of Highway
Deicing" to obtain the necessary information.
III-3
-------
REFERENCES
1. Field, R., et al., "Water Pollution and Associated Effects of Street
Salting," National Environmental Research Center, EPA (May 1973).
2. "Environmental Impact of Highway Deicing," 11040 GKK 06/71 Storm and
Combined Sewer Technology Branch, Edison Water Quality Research
Laboratory, EPA (June 1971).
3. Hanes, R. E., et al., "Effects of Deicing Salts on Water Quality and
Biota Literature Review and Recommended Research," National
Cooperative Highway Research Program Report 91, Virginia Polytechnic
Institute and Highway Research Board (1970).
III-4
-------
Section IV
TREATMENT, ABATEMENT, AND DISPOSAL OF URBAN RUNOFF
-------
Section IV
TREATMENT, ABATEMENT, AND DISPOSAL OF URBAN RUNOFF
A. EVALUATION OF URBAN RUNOFF QUALITY
In order to determine the significance of non-point water pollution
originating from an urban study area, the user must evaluate the results
of his assessment of urban runoff conducted in Section I. Theoretically,
this evaluation should consist of comparing predicted pollutant concentra-
tions and loads against local, state, and federal water quality standards.
Unfortunately, standards which are directly applicable to the discharge
of urban storm water runoff into receiving waters are still in the develop-
mental stage and therefore, have not been established.
Consequently, the user may substitute standards which are applicable
to effluent discharges from point sources (i.e., sewage treatment plants,
industrial sources, etc.). Alternatively, the user may supply his own
criteria for evaluation until applicable standards are enacted. Such
criteria could be based upon the ambient water quality and pollutant assim-
ilation characteristics of a particular receiving water.
Based upon the evaluation, the user can determine if treatment, abate-
ment, or an alternative method of disposal is required. The following
material is intended to provide a basic insight into the alternatives
regarding treatment, abatement, and disposal of urban runoff. It is not
intended to serve as design criteria nor as an absolute source of informa-
tion on the subject.
IV-1
-------
B. SOURCE ABATEMENT
Although the accumulation of a certain amount of street surface contam-
inants in an urban environment is inevitable (e.g., pavement wear, dirt,
grease and oil from the undersides of motor vehicles), much of the
litter that reaches the street surface can be eliminated, or at least
effectively controlled at its source. This direct approach to reducing
the runoff pollution potential can best be accomplished through active
public education and through effective and enforceable regulations and
ordinances relating to street cleanliness.
In order to gain public support and cooperation in reducing street
litter, an effective education program is necessary to inform the public
of all the ramifications of street litter, including its effect on water
pollution. This information can be presented in well-organized school
programs; press, radio, and television messages; and lectures to local
civic organizations. A list of regulations and ordinances, possibly in
leaflet form, that pertain to litter control should be available so
that all citizens may understand what is expected of them. This list
or digest should be printed in all appropriate languages of the commu-
nity.
The local government can do its part by providing an adequate number and
variety of litter containers and by setting a good example. Litter con-
tainers should be kept in a presentable condition and should be emptied
frequently. Strict cleanliness practices should be followed by city
waste collection forces and other city departments which generate litter.
Street sweeping and sanitation equipment should be kept in attractive
condition to encourage community pride and respect.
Well-publicized cleanup campaigns can also provide motivation for the
public to clean up the premises and dispose of accumulated trash. In
many cities, spring cleanup days, in which free pickup of bulky refuse
IV-2
-------
is offered by the city, has been very popular. Guidance for the conduct
of community cleanup programs can be obtained from Keep America Beautiful,
Inc., 99 Park Avenue, New York, NY 10016.
The legal approach to the problem of street surface litter will usually
take the form of establishing definitive ordinances describing improper
littering practices and prescribing penalties for violation. Those
public officials responsible for maintaining clean streets should es-
tablish the antilitter ordinances and should be given adequate legal
authority to enforce these ordinances. In addition to the general anti-
litter ordinances, specific regulations should be directed to the fol-
lowing typical sources of litter in the urban environment:
garbage and refuse collection
open trucks
public litter receptacles
refuse dumping
building construction and demolition
street construction
sidewalk sweeping
vacant lots
parking lots and garages
drive-in restaurants
trailer courts and campgrounds
sports stadiums
auditoriums and exhibition halls
theaters
food handling establishments
pet control
IV-3
-------
distribution of handbills
posting of notices and political posters
street vending
garden refuse
scavengers
weed control
dead animals
produce markets
direct discharges into storm sewers
Typical and model antilitter ordinances published by Keep America,
Beautiful, Inc. and by AWPA in its book "Street Cleaning Practices" may
be used as check lists to determine the adequacy of existing local or-
dinances and to establish new litter control ordinances.
The effectiveness of local ordinances will depend on the enforcement.
Enforcement can often best be accomplished through municipal interde-
partmental cooperation. For example, the police department could en-
force ordinances pertaining to littering from moving vehicles, illegal
dumping of refuse, the storage of construction materials on sidewalks
and streets, etc. The fire department could enforce regulations per-
taining to storage of refuse which might create a fire hazard. The
health department could be concerned with the handling and storage of
refuse by food-handling establishments. Other city departments could
enforce regulations related to their functions. Enforcement by these
agencies should include both inspections and summonses.
An important aspect of litter ordinances and regulations is to establish
the responsibility for each source of litter. In many circumstances it
is impossible or impracticable to catch persons who actually do the
littering, but more responsibility could be placed on the owner of the
IV-4
-------
property where the litter occurs. This approach is common in many
European towns, where each storeowner or homeowner is responsible for
maintaining the sidewalk and curb fronting his property.
A major problem of litter control in most of the larger cities is parked
or abandoned cars. Parked cars make it impossible for street cleaning
equipment to clean all of the street. Part of this problem can be
solved by parking regulations which prohibit parking in certain areas
during specific hours. In residential areas, these parking restrictions
may apply only once or twice a week, while in commercial areas they
may apply every day.
The problem of abandoned cars is much more difficult to control, par-
ticularly in the very large cities. Abandoned autos not only interfere
with street cleaning operations but are a source of street litter.
Abandoned autos should be removed from the streets as soon as possible,
either by city forces or by a private contractor hired by the city.
The sale or auction of operative vehicles, and possibly the sale of
parts from a city-owned and operated junk yard, could help defer the
cost of removal.
Regulations should also be established that limit the use of chemicals
commonly used in the urban environment for weed and pest control.
Since the city is usually responsible for the use of large amounts of
these chemicals, regulations should be relatively easy to enforce.
IV-5
-------
C. TECHNICAL ABATEMENT
1. Improved Street Cleaning Practices
Street surface contaminants, which represent a major portion of urban
land contaminants, can be partially removed by street sweeping oper-
ations prior to being exposed to runoff. A primary method of abating
urban runoff pollution is to improve existing street cleaning practices.
Municipal street cleaning practices may be improved by (1) increasing
the frequency of street sweeping and/or (2) increasing the removal
effectiveness of street cleaning methods.
Motorized street sweepers are designed to loosen dirt and debris from
street surfaces, transport it onto a moving conveyor, and deposit it
temporarily in a storage hopper. The three major types of street
sweepers include the broom-type sweeper, the vacuum-type sweeper, and
a third type of sweeper which uses a regenerative air system to "blast"
dirt and debris from the road surface into a hopper.
Municipal street sweepers typically remove about half of the contami-
nants residing on street surfaces. The efficiency of contaminant re-
moval is related to the particle size distribution of the contaminant
material. Sweepers are most efficient in removing large size particles
while most pollutants are predominantly found in the small particle
size range of contaminant material. Street sweeper efficiency as a
function of particle size is described in Table 12.
Table 12. AVERAGE STREET SWEEPER REMOVAL EFFICIENCY
Particle Size Percent Removal
On)
<104 17
104 - 246 48
246 - 495 55
>495 67
-------
Street surface contaminant accumulation is a function of street sweeping
frequency, street sweeping removal effectiveness, and antecedent rain-
fall events. The accumulation of street surface contaminants can be
minimized by increasing the frequency of street sweeping operations.
Commercial areas are generally swept more often than other land use
areas, which explains the lower contaminant loadings found in commercial
areas.
The removal effectiveness of street sweeping operations can be improved
by sweeping an area more than once. Repeated passes over the same area
can effectively reduce the amount of contaminant material remaining on
a street surface. Figure 31 shows typical removal effectiveness vs the
number of passes of a street sweeper, based on a surface loading of
10 g/ft2.
The practice of repeated sweeping passes over the same area and the
practice of increased street sweeping frequency are essentially mu-
tually exclusive. Since there is only a finite number of available
street sweeping vehicles, an increased utilization of one practice will
result in a decreased utilization of the other. This phenomenon can
be partially circumvented by the acquisition of additional street
sweeping vehicles.
Besides increasing the sweeper frequency, street pollutant removal may
be improved by more efficient use of existing equipment and practices.
Table 13 shows some common parameters that affect street sweeping per-
formance. Figures 32 through 35 show how some of these parameters af-
fect removal effectiveness.
Vacuum-type sweepers have shown promise in achieving a fairly high re-
moval effectiveness, including the small particle size range of contam-
inant material, but their effectiveness falls off dramatically if the
streets are wet. On the contrary, broom-type street sweepers are es-
pecially prone to difficulty in removing small particle sizes.
IV-7
-------
^ 100
«J
i
£ 75
M
M
I 50
4-1
J!
H-
r 25
01234
Number of Passes (P)
Figure 31. Removal Effectiveness With
Number of Passes
IV-8
-------
Table 13. PARAMETERS WHICH AFFECT STREET SWEEPING PERFORMANCE
Fixed
Loading:
Surface:
Sweeper:
Mass level
Particle size
Uniformity
Type
Condition
Type
Controllable
Sweeper
operation:
PUB type
PUB rpm
PUB diameter
PUB strike
Forward speed
Number of passes
Gutter broom
Debris deflector
Untested
Operator skill
a. PUB - Main Pickup Broom
From Figure 35, one can see that a flusher removes more materials per
"unit effort" than a sweeper. It must be remembered that a flusher does
not remove the contaminants from the receiving water system, but actu-
ally discharges the pollutants during periods of no rainfall.
IV-9
-------
u
.a
t)
o
-------
o>
1
M
M
If
10*
10
1.0 =
10
-2
Surface: Asphalt
Initial Mass: 20 gm/sq. ft,
Sweeper
(Wayne 450)
* 6 8 10
Relative Effort
Figure 35. Comparison of Cleaning Performances
of Motorized Street Sweeping and
Motorized Street Flushing
IV-11
-------
2. Air Pollution Controls
Atmospheric dustfall can be a noticeable source of particulate contam-
inants that are washed off surfaces during rainfall events. Dustfall
results from the deposition of particulate matter from the atmosphere.
Some of this particulate material occurs naturally (fugitive dust from
soils and pollens) and is difficult if not impossible to control.
Other particulate material is caused by man-made processes. These in-
clude fugitive dust from disturbed soils (during and after construction)
and stockpiles; emissions from combusting materials, and emissions
from chemical reactions and grinding and pulverizing operations. Smoke,
made up of fine particles, is the most obvious particulate pollutant
associated with human activity.
Recent advances in stationary source controls for particulate matter
have resulted in removal efficiencies commonly greater than 99 percent.
Emissions from stationary sources are controlled by a large variety of
local, state, and federal agencies. These regulations are designed to
prohibit the accumulation of harmful concentrations and to prohibit
nuisance conditions. These particulate emissions can be controlled by
a variety of devices, including settling chambers, centrifugal sepata-
tors, inertial separators, liquid scrubbers, filters, and electrostatic
precipitators. The applications of specific devices for specific emis-
sions are best determined by a case-by-case study
Particulate emissions from mobile sources are small per emission device;
but due to the large number of devices, their contribution can be severe.
Table 14 shows the breakdown of nationwide particulate emissions for 1970.
IV-12
-------
Table 14. NATIONWIDE ESTIMATES OF PARTICULATE EMISSIONS, 1970
Source category
Transportation
Motor vehicles
Gasoline
Diesel
Aircraft
Railroads
Vessels
Nonhighway use of motor fuels
Fuel combustion in stationary sources
Coal
Fuel oil
Natural gas
Wood
Industrial process losses
Solid waste disposal
Agricultural burning
Miscellaneous
b
Forest fires
Structural fires
Coal refuse burning
Total
Emissions,
10"tons/year
0.7
0.4
0.3
0.1
0.1
a
Neg
0.1
0.1
6.8
5.6
0.4
0.2
0.6
13.3
1.4
2.4
1.5
1.4
Neg
0.1
26.1
Percent of
total
2.7
1.5
1.1
0.4
0.4
-
0.4
0.4
26.1
21.5
1.5
0.8
2.3
51.0
5.3
9.2
5.7
5.3
-
0.4
^Negligible (less than 0.05 x 10 tons/year).
Includes prescribed burning.
Source: EPA: Nationwide Air Pollutant Emission Trends, 1940-1970.
IV-13
-------
The emissions from mobile sources are controlled by state and federal
agencies. The EPA has not proposed any particulate emission controls
for automobiles, but has for other types of vehicles (heavy duty, air-
craft, vessels, etc.).
IV-14
-------
D. TREATMENT
The treatment of urban stormwater runoff has been approached in two
major ways over the past several years. One approach has seen the
development of varied means of storing the peak flows and discharging
them to conventional treatment plants during low flow periods. These
peak flows would normally be discharged untreated. The second approach,
which can be used in conjunction with storage, has seen the development
of specialized treatment processes to deal specifically with quantities
and types of pollutants found in stormwater runoff.
This section briefly outlines some of the common treatment and storage
techniques that have been used or show promise. This section is not
meant to supply sufficient information to design a complete treatment
scheme, but to give the planner a general idea of the methods available
and some costs.
1. Storage and Treatment
Although urban stormwater runoff is similar in many respects to raw
sanitary sewage and is amenable to many of the same treatment processes,
certain aspects of runoff create unique treatment problems. The main
problems encountered in treating urban runoff include (1) the intermit-
tent and random occurrence of the runoff event with correspondingly
high flow rates, and (2) heavy loadings of solids.
Perhaps the most serious problem involved in treating urban runoff is
created by high flow, rates. The hydraulic capacity of most treatment
plants is not sufficient to treat the total flow during high flow rate
periods of runoff. To overcome this obstacle, either the flow rate of
the runoff water entering the treatment process must be reduced, or the
hydraulic capacity of the treatment process must be increased, or the
detention time must be decreased. Usually a combination of those
approaches is the most desirable solution.
IV-15
-------
The following discussion on storage and treatment of stormwater runoff
is abstracted primarily from a recently completed EPA state-of-the art
document entitled, Urban Stormwater Management and Technology: An
Assessment, 1973.
a. Flow Rate Control
Several methods have been proposed for controlling the flow rate of
runoff waters into treatment facilities. The following is a brief
description of these various methods, including the advantages and dis-
advantages of each and construction and maintenance cost estimates
where available.
b. Storage Facilities
One of the most effective methods of controlling the flow rate entering
the treatment plant is to store the runoff during peak flows and dis-
charge it to the treatment facilities at a controlled rate during sub-
sequent low flow periods. Storage is perhaps the most cost-effective
method available for managing urban runoff, and it is the best docu-
X
mented stormwater pollution control measure in present practice.
Examples of runoff storage facilities include open ponds, steel and
concrete tanks, underground tunnels and caverns, existing sewers, and
underwater collapsible tanks.
Advantages of such storage facilities are: (1) they are generally simple
to design and operate; (2) they are relatively unaffected by rapid
changes in runoff flow rates and quality; (3) they frequently can be
operated in concert with existing waste water treatment plants; and
(4) they can serve as preliminary sedimentation basins, there reducing
the sediment handling requirements of subsequent treatment processes.
IV-16
-------
Disadvantages of storage facilities include: (1) their large size; (2)
their rather high construction costs; (3) the problem of removing solids
that have settled out; and (4) their dependence on other treatment fa-
cilities for final treatment of the retained water.
c. Open Ponds - The surface storage pond is by far the most frequently
proposed method of runoff storage. Varieties range from simple earthen
dams to concrete or asphalt lined basins.
An asphalt lined detention basin with a storage volume of 8.66 acre
feet was built as an EPA demonstration model in Chippewa Falls in
1969. Although the basin was built to receive overflow from a com-
bined sewer system, the same principles of operation apply to the de-
tention of flows from separate storm sewers. Captial costs for the
project, including the required additions to the capacity of the munic-
ipal treatment plant which finally treated the detained water, were
$6780 per acre of drainage area. Operation and maintenance costs for
the pond and associated facilities during the two-year test period
(1969 and 1970) were $7300 per year.
An example of a multiple use detention basin is contained in an apart-
ment development in Arlington Heights, Illinois, where stormwater run-
off from the site is temporarily stored in a depressed area that is
used as a tennis court during dry weather. The retained runoff is re-
leased into the area's drainage system only after the peak flow of the
uncontrolled stormwater runoff has occurred.
Such local storm retention ponds should be considered in the planning
of storm drainage systems for new communities and in the modification
of existing drainage systems. Such ponds will reduce the peak flows
of runoff resulting from urbanization. They also can reduce the needed
capacity (and thus the costs) of drainage pipes and treatment systems.
If storm retention ponds are constructed prior to the development of
of an area, they can also provide erosion and sediment control for the
area during the construction phase of the development.
IV-17
-------
d. Storage Tanks - Steel or concrete storage tanks can be used to detain
runoff in areas where surface ponds are physically or economically in-
feasible because of the large land requirements. Although construction
costs of tanks are high compared to the costs of surface ponds, tanks
do not require the algae and mosquito control measures that are often
necessary in open storage facilities; and they can be designed for
easier removal and disposal of settled solids.
e. Underground Tunnels - Due to the lack of usable surface area for de-
tention ponds or even storage tanks, underground tunnels or storage
caverns are being mined 200 to 300 feet beneath Chicago to provide
storage for combined sewer overflows. The underflow-storage tunnels
will have a total capacity of 18,000 acre feet, A 2.5-mile-long test
tunnel was mined at a cost of $1.5 million,
f. Use of Existing Sewers - With the use of a computerized sewer moni-
toring and control system, the city of Detroit can utilize existing
sewers in the downtown area for runoff storage. A complex array of
rainfall and overflow monitors coupled to a central computer can better
utilize the existing capacity of the sewers by moving high flows to
areas of the system experiencing low flows. The cost of the project
has been estimated at $2.2 million.
g. Underwater Storage - A rather unique solution to the problem created by
the lack of usable surface area is the use of underwater collapsible
tanks. In a pilot program, 100,000-gallon collapsible tanks were an-
chored offshore from Sandusky, Ohio, in Lake Erie. Stormwater over-
flows were diverted into the tanks and later pumped ashore for treatment.
The project cost was $2 per gallon, but projected future costs were
placed at 40 cents per gallon.
IV-18
-------
The flow rate and runoff influent into treatment facilities can be in-
directly controlled by controlling the flow rate entering the city's
storm drainage system. An example of this approach to runoff control
is given below.
h. Rooftop Ponding - In an 80-acre urban renewal project in downtown
Denver, rooftops were constructed to hold up to 3 inches of rainwater.
This water was retained then released as surface runoff at a controlled
rate. Other rooftop storage schemes use the intercepted rainwater to
supplement the water requirements of an industrial complex.
2. Physical Treatment
Various methods of physical treatment are presently available that can
effectively be used for the removal of suspended solids from stormwater
runoff, but at the present time, physical treatment is generally less
effective in the removal of organics and nutrients than biological or
physical-chemical treatment.
The major advantage of physical treatment processes is their adapability
to automated controls and the rapid startup and shutdown that is often
required in runoff treatment. Recent improvements in physical treatment
processes have resulted in more rapid removal of suspended solids and
more effective removal of organics.
In addition to the conventional physical treatment processes used in
wastewater treatment (i.e., sedimentation; flotation; screening, and
filtration), more recent physical treatment processes have been de-
veloped that promise to be well suited for stormwater runoff treatment.
These techniques include dissolved air flotation, ultrahigh rate fil-
tration, swirl concentration, and a variety of screening techniques.
IV-19
-------
Dissolved air flotation has been shown to be a rapid and rather effective
technique for removing solids. In addition, organic removal has been
rather high for a physical treatment process. In tests, this process
has effectively removed approximately 40 percent of the organics and 60
percent of the solids from a combined sewer overflow under a surface
loading rate of 900 cu m/min/ha (3200 gpd/sq ft). The addition of
about 25 mg/f of chemical flocculate increased these removal efficiencies
by 30 percent. Probably the main drawback of the dissolved air flotation
process at the present time is the high cost. Construction costs of
$800///sec ($35,000/mgd) have been reported. However, a treatment
system for combined overflow and separate stormwater runoff in
Sacramento, California, which included dissolved air flotation was
determined to be the least-cost design. Capital costs for this proj-
ect were $530,000/f/sec ($12,000/mgd), and operating costs were esti-
mated at $6.87 per 1,000 ! ($26/1,000 gal).
a. Screening
A variety of screening techniques have been effective as primary treat-
ment processes. Ultrafine screens, rotary fine screens, and hydraulic
sieves are usually used as pretreatment units. The rotary fine screen
requires only one-tenth to one-twentieth the land use normally required
for primary treatment (sedimentation or flotation). Costs for rotary
screening have been reported at 5.8
-------
are required to better evaluate the efficiency of organic removal.
The average capital cost of these screening processes, based on a de-
sign flow rate of 17 //sec/sq m (25 gpm/sq ft) is approximately $297/
f/sec ($13,000/mgd).
A study has been begun recently to compare the effectiveness of three
screening devices (ultrafine screen, rotary fine screen, and stable
screen) operating on combined sewage overflows.
b. High and Ultrahigh Rate Filtration
Effective treatment of combined sewer overflow containing an average
of 250 mg/f suspended solids and 67 mg/£ BOD was obtained in a treat-
ment plant in Cleveland, Ohio, by adding a polyelectrolyte and then
filtering the influent through a deep bed, dual media filter at a
rate of 24 gpm/sq ft. This method resulted in a 93 percent reduction
of suspended solids and a 61 percent reduction in the BOD. Total
annual treatment costs (capital and operating) were estimated at $90,000
for a 25-mgd plant and $390,000 for a 200-mgd plant.
Successful results on a bench scale, have also been obtained from ultra-
high rate filtration of stormwater runoff. Filtration rates up to 30
f/sec/sq m (45 gpm/sq ft) through a dual media filter, following pre-
treatment through an ultrafine screen, have resulted in suspended
solids removal of 38 to 73 percent and organic removals of 8 to 36 per-
cent.
The advantages of these treatment processes are (1) high removal effi-
ciencies, (2) automated operation, and (3) limited space requirements
(as opposed to flotation and sedimentation).
IV-21
-------
c. Swirl Concentrator
Other recent developments in physical treatment processes include a
swirl flow regulator/solids separator with no moving parts. This low
cost method of solids removal has been thoroughly tested, and proto-
type demonstration units are being constructed.
3. Biological Treatment
Although biological treatment has been a popular and successful method
of treating domestic and industrial waste water, its application to
the treatment of stormwater runoff has the following serious drawbacks:
(1) the biomass used in the treatment process must be kept alive
during nonflow periods or developed for each runoff event; and (2)
once developed, the biomass is highly susceptible to washout by hy-
draulic surges or overload unless flow equalization is maintained.
However, recent developments in biological treatment may have applica-
tions to stormwater runoff treatment. Such biological treatment pro-
cesses include: (1) the contact stabilization of activated sludge; (2)
high rate trickling filtration; (3) bioadsorption using rotating bio-
logical contactors; and (4) oxidated lagoons of various types. The
first three methods must be operated conjunctionally with dry weather
flow plants to supply the biomass. Also with the exception of lagoons,
some form of pre-uriit flow equalization is necessary to prevent the
washout of the biomass.
Nearly 92 percent of the suspended solids and 83 percent of the organic
matter have been removed from combined sewer flows using the contact
stabilization method. Construction costs, excluding land, of the tested
project were $l,785/f/sec ($78,300/mgd) of capacity.
IV-22
-------
a. High Rate Trickling Filters
A high rate trickling filter process has treated flows up to 10 times
average dry weather flows. Removal efficiencies for both organic and
solids were approximately 65 percent. The cost of required plant ad-
ditions was $l,550/7/sec ($68,000/mgd) of capacity.
4. Physical-Chemical Treatment
Although at the present time construction costs are high, physical-
chemical treatment is becoming competitive in cost with biological
treatment. Physical-chemical processes are of particular impor-
tance to stormwater runoff treatment because of their adaptability to
automated operation, rapid startup and shutdown, and very good resis-
tance to shock loads.
Disadvantages of physical-chemical treatment, in addition to the high
construction costs, are high chemical requirements and problems of
sludge disposal.
In a recently completed pilot study for the EPA, a complete physical-
chemical process (comparable to secondary treatment) was developed and
tested. This technique, which incorporated chemical clarification fol-
lowed by adsorption on activated carbon, resulted in removal efficiencies
of 94 percent of BOD and 99 percent of suspended solids. It was deter-
mined that a 10-m.gd facility of this type could be developed at a cap-
ital cost of $1.8 million and an operating cost of 19
-------
E. ALTERNATE METHODS OF DISPOSAL
1. Spray Irrigation
The beneficial use of stormwater runoff as irrigation water deserves
consideration. Nutrients and organic material contained in stormwater
runoff are capable of stimulating plant growth. However, the signifi-
cant concentrations of heavy metals found in stormwater runoff may im-
pair its use as irrigation water.
In certain areas, legal restrictions are placed on the use of sewage
treatment effluent as irrigation water for crops intended for direct
human consumption. Although these restrictions may also apply to a
similar use of stormwater runoff, they do not apply to crops intended
for animal consumption.
2. Infiltration Ponds
Stormwater runoff may be discharged into infiltration ponds for the pur-
pose of groundwater recharge. A degree of treatment can be imparted
to stormwater runoff which is allowed to percolate through soil. Soil
particles are capable of filtering out residue and partially treating
stormwater runoff before it reaches the groundwater table.
IV-24
-------
Section V
STATE OF THE ART
-------
Section V
STATE OF THE ART
An extensive literature review has revealed that the significance of storm-
water runoff pollution was not fully appreciated until about the last ten
years. Although occasional studies revealing some of the water quality
aspects of stormwater runoff were conducted in the 1940s and 1950s, most of
the runoff studies during this period dealt with the quantitative aspects
of runoff.
The earlier runoff quality studies were usually concerned only with water
quality characteristics. This was usually accomplished by collecting and
analyzing water samples within the storm sewers or at the outfall. As the
significance of the problem became more apparent, studies were directed
toward defining and analyzing the sources of runoff pollution; establishing
relationships between water pollution parameters and land use, population
density, traffic volume, etc. ; using these relationships to develop methods
or models for predicting the pollution potential of a given area; and sug-
gesting and evaluating pollution control measures.
The following discussion lists some of the more significant and frequently
cited studies on urban runoff. This is not a complete list of urban runoff
studies; it is only a representative sample to indicate the type of work
that has been done in this area. A more inclusive annotated bibliography
and a cross-indexed information matrix has been provided in Appendix C for
further information or background on particular aspects of urban runoff.
1. Characteristics of Urban Runoff
One of the first published studies on the pollution potential of urban
stormwater runoff was conducted in Moscow, U.S.S.R., in 1936. BOD
o
samples taken in this study ranged from 186 to 285 mg/£, while suspended
V-l
-------
solids ranged from 1000 to 3500 mg/f.. Runoff samples taken from Leningrad's
cobblestone streets in ]
solids of 14,541 rag/f.
cobblestone streets in 1948-1950 contained BODg's of 36 mg/f and suspended
From 1945 to 1948 in Stockholm, Sweden, runoff samples were taken during sumr
o
mer storms. Most of the samples were taken from streets and parks. Test
results on these water samples revealed median values of 17 mg// BOD5>
188 mg/P COD, 300 rag/Jl total solids, and 4000/100 mf col i forms. Evidence
of the potential for shock pollution was revealed by individual samples
which ranged as high as 80 mg/f BOD , 3100 mg/f COD, 3000 mg/f total solids,
O
and 200,000/100 m$ for coliforms.
One of the earliest and most often cited studies in this country was conducted
3
by Palmer in downtown Detroit, Michigan, in 1949. Water samples taken at
catch basins indicated BOD,, levels on the order of 96 to 234 mg/f , 310 to 914
O
mg/f total solids, and coliform counts of 25,000 to 930,000 MPN/100 mf.
In 1954, runoff samples taken from a 611-acre (248 ha) estate in Oxney,
4
England, contained BOD "s up to 100 mg/J? and suspended solids up to 2045 mg/f> .
D
This study also revealed that the BOD tended to increase with the length of
O
the antecedent dry weather period. The pollution potential of the first flushes
of runoff was found to be not much greater than subsequent flow unless there had
been a long antecedent dry period. On comparison with a hypothetical combined
sewer system for the same area, it was concluded that the separate system re-
duced the BODg entering the receiving stream but increased the suspended solids
load six- or sevenfold.
In 1959 and 1960, in order to determine the effect stormwater runoff was having
on the aquatic vegetation of Green Lake in Seattle, Washington, runoff samples
were taken from surrounding street gutters. After analyses, the runoff was
shown to contain BODg's of about 10 mg/| , coliforms up to 16,000 MPN/100 mf,
nitrate nitrogen to 2.80 mg// , and soluble phosphorous to 0.78 mg/f . The
study recommended discontinuing most urban drainage to the lake.
V-2
-------
In 1961, surface runoff from residential and recreational areas in Pretoria,
South Africa, was sampled revealing the following results: 30 mg// BOD ,
o
29 mg/,P COD, 228 mg/i* dissolved solids, 5.4 mg/f total organic nitrogen,
and coliform counts of 240,000/100 ml. Analyses of runoff samples from
nearby commercial areas revealed slightly lower figures for all of the
above parameters except BODg, which was found to be slightly greater.
A more comprehensive study on the quality of stormwater runoff was conducted
7
by Weibel et al. between 1962 and 1964. Water samples taken from a separate
storm sewer system draining 27 acres (11 ha) of residential and light-commer-
cial urban land in Cincinnati, Ohio, revealed the following constituent aver-
ages: 227 mg/H suspended solids, 57 mg/j? volatile suspended solids, 111 mg/f
COD, 17 mg/^ BOD , 1.0 mg/f inorganic nitrogen, 1.1 mg/P total hydrolyzable
D
phosphate, and 1.7 rag// organic chlorine. The average coliform density in
90 percent of the samples was 2900/100 ml, but individual samples varied
widely.
The BOD and COD figures were, on the average, comparable to typical secondary
5
sewage treatment effluent, while suspended solids concentrations were gener-
ally comparable to raw sewage. The above-mentioned nutrient concentrations
exceeded the 0.3 mg/f inorganic nitrogen and 0.03 mg/f inorganic phosphate
indicated as threshold levels for algal blooms. The coliform density con-
siderably exceeded the criterion of 1000/100 ml commonly used as a maximum
for swimming waters in the United States. A further comparison of surface
runoff to domestic sewage is shown on the following page.
V-3
-------
Constituents
Suspended
solids
COD
BOD5
Total PC-,
4
Total N
Raw
Domestic
(Ib/day/acre)
1.5
2.6
1.5
0.19
0.23
Urban runoff loads as
sewage
(Ib/yr/acre)
540
960
540
68
82
percentage of
During runoff
2400
520
110
70
200
sewage loads
Annually
160
33
7
5
14
A stormwater sampling program was performed by Bryan in Durham, North
Carolina, between May 1969 and February 1970, to determine the quality of
storm runoff and the effect of land use on the quality. The 1067-acre
(433 ha) drainage basin studied comprised a mixture of land uses typical
of urban areas in North Carolina. The population density averaged about
9 persons per acre (22.5/ha).
The water quality characteristics of the sampled runoff were in general
agreement with the findings of other studies. The total solids contribu-
tion of the urban runoff was substantially larger than the expected average
raw sewage wastewater from the same basin. fhe BOD of the runoff was esti-
5
mated to equal sanitary effluent from a secondary treatment plant, while
COD was greater than raw sanitary effluent from a strictly residential,
average, urban area. The contribution of phosphates was nominal in com-
parison with domestic wastewater.
Obvious relationships between land use, population density, and other
numerical characteristics of the subbasins and the measured pollution
parameters could not be established because of the amount and type of
data generated.
V-4
-------
g
A detailed and comprehensive study by AVCO Corporation was conducted during
the fall of 1968 to determine the magnitude of stormwater pollution from
urban land areas in Tulsa, Oklahoma. Stormwater samples from selected test
areas were collected and analyzed for the usual pollution parameters. As a
result of these tests, statistical analytical procedures were developed to
relate runoff pollution to land-use variables.
The major sources of pollutants were considered to be material deposited on
impervious surfaces (street litter) and material eroded or picked up from
drainage channels. The largest amounts of pollutants were generated during
the season of greatest runoff.
The land surface characteristics which showed the highest correlation with
stormwater pollutant concentrations were (1) environmental conditions,
(2) geomorphic characteristics that affect drainage, and (3) the degree
of land development. A good prediction variable for bacterial pollution
parameters was found to be the general sanitary conditions of the site.
BODK concentrations decreased with increasing runoff flow, but the total
o
amount of BOD,, increased with the increasing flow.
o
In residential areas, the runoff pollution per unit area increased with
population density and/or the number of developed parcels. In commercial
and industrial areas, runoff pollution per unit area actually decreased
with the number of persons visiting the area. This was explained by sug-
gesting that, as the number of daily visitors increased, the degree and
frequency of maintenance operations increased.
2. Characteristics of Street Surface Contaminants
A study by the American Public Works Association was conducted in Chicago
during the summer of 1967 to determine the factors in the urban environment
which contribute to the pollution of stormwater runoff. Although several
sources of runoff pollution were recognized in the study, including street
V-5
-------
litter, catch basins, roof discharges, and various chemicals, only street
litter was extensively sampled and analyzed for its water pollution poten-
tial.
A significant aspect of the study was the calculation of street litter
accumulation rates for various land uses. This rate was found to range
from 0.5 to 8 lb/day/100 ft of curb. Average loadings varied from 2.4 lb/
day/100 ft of curb in single-family residential areas to 4.7 lb/day/100 ft
of curb in commercial areas.
Although the amount and composition of street litter was found to vary
with the day of the week, the season, land use, population density, and
pedestrian and vehicular traffic, the main component, by weight, was nearly
always the dust and dirt fraction (less than 1/8 in. in size). The total
dust and dirt fraction varied from 45 to 83 percent of the total litter.
The accumulation rate varied from 0.4 to 5.2 lb/day/100 ft of curb. The
accumulation rate by land use was as follows: single-family residential,
0.7 lb/day/100 ft of curb; multiple-family residential, 2.3 lb/day/100 ft
of curb; and commercial areas, 3.3 lb/day/100 ft of curb.
The water pollution potential of the street litter was based on an analysis
of the soluble portion of the dust and dirt fraction. Approximately 3 per-
cent of the total dust and dirt was soluble. The weighted average amounts
of the constituent pollution parameters were as follows: 5 mg/g BOD_,
5
40 mg/g COD, 0.48 mg/g total nitrogen, less than 0.05 mg/g phosphates,
more than 10 million/g total bacteria counts, more than 1 million/g coli-
forms, and 5400 mg/g fecal enterococci.
The most determinable measure of pollution potential was deemed to be the
BODg of the dust and dirt. Thus, based on a BOD- of 5 mg/g or 0.8 lb/day/
street mile, the average pollution potential of street litter was estimated
at 1 percent of raw sewage and 5 percent of secondary treatment effluent BOD,..
V-6
-------
The maximum peak pollution flush of stormwater runoff was also calculated
and compared to raw and treated sewage. It was assumed that the shock
loading effect of runoff would occur when all soluble contaminants in a
two-week accumulation are washed off the street within a single two-hour
runoff period. The BOD of the runoff generated under these conditions
o
was calculated at 11.2 Ib/mile of street. This level is approximately
equivalent to 160 percent of raw sewage BOD and 800 percent of secondary
D
treatment effluent during the two-hour runoff period.
Catch basins were also determined to be a significant source of shock pol-
lution. The solids that accumulate in catch basins take on the character-
istics of anaerobic sludge and the standing water becomes septic. This
water and some of the solids can be released into the storm sewer during
even minor rainfall or thaw. Water sample taken from a catch basin in a
residential area revealed a BOD of 60 ppm.
o
An extensive study on the water pollution aspects of street surface con-
taminants was recently conducted by URS Research Company for the Environ-
mental Protection Agency. In this study, samples of street litter were
collected from eight representative cities across the United States. Sample
collection methods included the use of a rain simulator to ensure the col-
lection of very fine, soluble materials that are often not picked up by dry
sweeping techniques. The samples were analyzed for the following pollutants:
BOD , COD, total and volatile solids, Kjeldahl nitrogen, nitrates, phosphates,
o
and a range of pesticides and heavy metals.
The results of this study were even more alarming with respect to the water
pollution potential of street runoff than the previous mentioned studies.
V-7
-------
Calculations based on a hypothetical but typical U.S. city* indicated that
the pollution load of street surface runoff during the first hour of a
moderate-to-heavy storm (brief peaks to at least 0.5 in./hr) would be many
orders of magnitude greater than the same city's untreated sanitary sewage
during the same time period. A comparison of the pollution load of runoff
with raw sewage and secondary treatment effluent is shown as follows:
CALCULATED QUANTITIES OF POLLUTANTS WHICH
WOULD ENTER RECEIVING WATERS IN A HYPOTHETICAL CITY
Settleable plus
suspended solids
BOD
5
COD
Kjeldahl nitrogen
Phosphates
Total col i form
bacteria (org/hr)
Street surface
runoff
(following
1-hr storm)
(Ib/hr)
560,000
5,600
13,000
880
440
4,000 x 1010
Raw sanitary
sewage
(Ib/hr)
1,300
1,100
1,200
210
50
460,000 x 1010
Secondary
plant effluent
(Ib/hr)
130
110
120
20
2.5
4.6 x 1010
Note: Since the above calculations were based only on a 5-day accumulation
of street litter, the above discharge of contaminated runoff could
conceivably occur many times in a year.
*The hypothetical city has the following characteristics:
Population - 100,000
Total land area - 14,000 acres
Land-use distribution:
residential - 75%
commercial - 5%
industrial - 20%
Streets (tributary to receiving waters) - 400 curb miles
Sanitary sewage - 12 mgd
V-8
-------
The loading intensities (Ib/curb mile) of street surface contaminants,
expressed as weighted average values for all samples, were found to be as
follows: total solids, 1400 Ib/curb mile; volatile solids, 100 Ib/curb
mile; BODg, 13.5 Ib/curb mile; COD, 95 Ib/curb mile; phosphates, 1.1 lb/
curb mile; nitrates, 0.094 Ib/curb mile; Kjeldahl nitrogen, 2.2 Ib/curb
g
mile; total coliforms, 99 x 10 /curb mile; and fecal coliforms, 5.6 x
g
10 /curb mile. Substantial quantities of organic pesticides were found
in most samples. The total amount for the cities tested was 0.001 lb/
curb mile. However,- the amounts varied considerably from site to site.
Both chlorinated hydrocarbons and polychlorinated biphenyl compounds (PCS)
were found rather consistently. Significant amounts of heavy metals were
also detected. Zinc and lead were the most prevalent, measuring 0.65 and
0.20 Ib/curb mile respectively.
Because of the consistent presence of heavy metals in amounts large enough
to interfere with BOD measurements, COD tests were considered to provide
5
a better basis for estimating the oxygen demand potential of street sur-
face contaminants.
A sizable percentage of the pollution potential of street debris was found
to be contained in the very fine silt-like fraction (< 43 p,). Although
this material accounted for only 5.9 percent, by weight, of the total solids
in street surface, it contained approximately one-fourth of the total oxygen
demand and perhaps one-third to one-half of the algal nutrients. This
material also accounted for more than half of the heavy metals and nearly
three-fourths of the total pesticides. These concentrations of pollutants
in the very fine material are of particular importance because conventional
street sweeping operations are rather ineffective in removing this material.
The quantity or loading intensity of material at a given test site was
found to be dependent on a number of factors, including surrounding land
use, elapsed time since the last sweeping or rainfall, volume and character
of local traffic, condition and type of street surface, season of the year,
V-9
-------
and public works practices. The overall weighted average loading intensity
for all samples was computed to be approximately 1400 Ib/curb mile. In
general, industrial areas contained substantially heavier than average
loadings, while commercial areas were considerably below the average.
Industrial areas averaged 2800 Ib/curb mile; commercial areas averaged
290 Ib/curb mile. Average loading intensities for residential areas were
1200 Ib/curb mile, but loadings varied widely from site to site. There
was a general tendency for newer, more affluent neighborhoods to be cleaner.
Asphalt streets were found to have loadings 80 percent heavier than con-
crete streets. Streets considered in poor to fair condition had loadings
about 2-1/2 times greater than streets in good to excellent condition.
A pattern in loading intensities across a typical street was also revealed.
Typically, 78 percent of the material was found within 6 inches of the curb
and over 95 percent occurred within the first 40 inches.
It was also determined that the rate at which rainfall washes loose partic-
ulate matter from street surfaces depends on three primary factors: rainfall
intensity, street surface characteristics, and particle size.
12
As a result of the findings in the above EPA study, an additional EPA study
was performed by URS Research Company to further define the character and
distribution of heavy metals contained in city street debris.
This project defined the breakdown of the particulates' compositions by
having mass spectrographic analyses performed on various samples. Using
these results, the heavy metals which were determined to have the greatest
water pollution potential (As, Cd, Cr, Cu, Fe, Pb, Mn, Hg, Ni, Sr, Ti, Zn
and Zr) were analyzed in each of about 75 samples collected nationwide in
10 cities in the previous study.
Other analyses conducted included: size affinities of the metals, solubili-
ties and toxicities of the road surface runoff mixture, and certain organic
V-10
-------
analyses on selected samples. Additional sampling was conducted on rural
road, highway, and airport surfaces; and particulates were analyzed for
the following common water pollution parameters: BOD , COD and nutrients,
o
plus selected heavy metals, for comparison with values representative of
normal city streets.
The loading (Ib/curb mile, kg/km) and strength (mg/kg) of the above heavy
metals were determined for various land-use categories. The three general
land-use categories used for comparison were residential, commercial, and
industrial; however, further breakdowns of each of these categories were
also defined and compared.
Industrial areas were shown to contain the greatest loadings of heavy
metals, while both industrial and commercial areas had the greatest
strengths, depending on the particular metal. Cities with high particu-
late levels of air pollution recorded high loadings of heavy metals.
The concentration of heavy metals in storm runoff was found to be 10 to
100 times greater than in sanitary sewage. On a slug load basis (Ib/hr,
kg/hr), the metal content of runoff was found to be 100 to 1000 times
greater than sanitary sewage. However, the metal content of storm runoff
is usually not sufficient to cause noticeable reductions in biological
treatment efficiency in plants handling combined sewage/storm drain systems.
The solubilities of heavy metals in a simulated receiving water environment
were shown to be low, most being less than 10 percent of the available
metal. However, copper, cadmium, lead and zinc were found sufficiently
soluble to cause toxic effects to certain aquatic organisms under selected
conditions. In most samples, more than half of all the heavy metals occur-
red as particles smaller than 495 jo,. The larger particles (> 246 jj,) were
found to be the most soluble.
V-ll
-------
An analysis of street surface contaminants found on rural roads and highways
indicated that the city street particulates have a greater pollution poten-
tial on a strength (mg/kg) basis. The BODg strength of city samples was
found to be an order of magnitude greater than the rural samples. The heavy
metal contents of airport surfaces were found to be quite similar to road
surface particulates.
Grease and oil were found to be the major organic constituents of the street
particulates, but no definite correlation was established between organic
strengths (mg/kg) and land use.
Highway deicing compounds were discussed as a source of urban runoff pol~
13
lution in an EPA report prepared by Edison Water Quality Laboratory in 1971.
High concentrations of highway deicing salts were shown to be common in urban
runoff waters during periods of snow melt. Concentrations of chlorides as
high as 25,000 mg// have been found in street surface drainage, and up to
2720 mg/f in storm sewers. Surface streams in urban areas also have been
found to contain up to 2730 rag// chlorides. The adverse impacts that deicing
salts have on small lakes and streams have been documented, but their influence
on major U.S. rivers appears,-at this time, to be relatively minor. Neverthe-
less, it is recognized that there are inadequate surveillance data available
to clearly define this area, and more information is necessary.
Deicing compounds often contain special additives with severe latent toxic
properties. Complex cyanides and chromate compounds used as additives have
been found in storm sewers. However, little is known of their fate and dis-
position or of their effects on the environment.
3. Impact of Urban Runoff on Receiving Waters
12
In a recently completed EPA study, copper, cadmium, lead and zinc contained
in street surface runoff were found to be sufficiently soluble to cause toxic
effects to certain aquatic organisms under selected conditions (such as soft
water).
V-12
-------
Bioassay tests conducted in aerated, moderately hard water indicated no toxic
effects on stickleback during a 96-hour period. It was determined that the
immediate toxic effects of road surface runoff are most likely due to extreme
oxygen demand rather than to effects from heavy metals. The most dramatic
toxic effects of metals will most likely occur when runoff is discharged into
quiescent water where it is allowed to accumulate to toxic concentrations.
About two-thirds of tWe five-day BOD values was exerted during the first day
of discharge of road surface particulates into a simulated receiving water
environment. Thus, very high BOD values can cause serious oxygen depletion
u
problems in the receiving water immediately following discharge.
The effect of discharge from a separate storm sewer system in Ann Arbor,
14
Michigan, was measured following a storm in 1964. The DO level in the
Huron River was depressed from about 10 mg/f to 2 mg/^. The depressed DO
level lasted about 24 hours after the end of the storm and affected the
river 2 miles below the storm sewer outfall.
4. Control Measures
A thorough discussion of control measures needed to reduce the pollution
potential of urban runoff is contained in the American Public Works Associ-
ation report. The following list is an outline of their major recommenda-
tions for effective control and reduction of runoff pollution.
1. Public cooperation in reducing the amounts of street litter
2. Street sweeping improvements for reducing accumulations of
urban street litter
3. Catch basin design and operation improvements for reducing
discharges of supernatant liquids and entrained sludge
4. Roof drainage controls to reduce overloading of combined
sewers
5. Improved regulation and enforcement procedures for reducing
urban littering
V-13
-------
6. Land drainage modifications : for reducing or eliminating the
runoff of polluted waste waters and to minimize the import of
waters containing snow and ice control chemicals
7. Reduction in indiscriminate use of chemicals for control of
unwanted pest infestations and the improvement of soil and
plant nutrient conditions, and
8. Management-labor relations practices to minimize waste col-
lection and other municipal sanitation work stoppages.
0
In the AVCO Corporation study, land-use practices such as improved urban
planning and land-use regulations were stressed as important control mea-
sures for urban runoff pollution.
As a result of tests performed on the effectiveness of public works prac-
tices during the EPA-sponsored study on street surface contaminants, the
following selected recommendations were made to improve the control of urban
runoff pollution: better training of cleaning equipment operators, proper
maintenance of cleaning equipment, improved street maintenance, auto parking
controls, and improved curb design. It was also recommended that public works
departments seriously consider eliminating catch basins from particular systems.
During this study, it was revealed that the efficiency of street sweeping pro-
grams in the removal of the dust and dirt fraction of street debris is low.
The overall removal effectiveness of dust and dirt was determined to be only
50 percent. The removal efficiency of conventional street sweepers was found
to be dependent on particle size. Sweeper efficiency ranged from 79 percent
for particles 2000 microns in size to only 15 percent for particles less than
43 microns. The removal effectiveness for larger litter (i.e., paper, wood,
leaves, etc.) ranged from 95 to 100 percent.
Increased removal effectiveness of present state-of-the-art street sweepers
was shown to be possible by decreasing the operating speed or conducting
multiple passes. However, to increase the overall effectiveness from the
normal (50 percent) to 70 percent would require twice the normal effort.
V-14
-------
A comprehensive report evaluating the various approaches to control and
treatment of stormwater runoff is currently being prepared by Metcalf
and Eddy under an EPA contract.
V-15
-------
REFERENCES
1. Shigorin, G. G., "The Problem of City Surface Run-Off Water,"
Vodosnabzhenie i Sauitarnaya Tekhnika (1956).
2. Akerlinch, G. , "The Quality of Storm Water Flow," Nordisk Hygienish
Tidskrift, Stockholm (1950).
3. Palmer, C. L. , "The Pollutional Effects of Storm-Water Overflows
from Combined Sewers," Sewage and Industrial Wastes, Vol. 22, No. 2
(February 1950).
4. Wilkinson, R., "The Quality of Rainfall Run-Off Water from a Housing
Estate," Jour. Inst. Pub. Health Eng., London.
5. Sylvester, R. O., An Engineering and Ecological Study for the Rehabili-
tation of Green Lake, University of Washington, Seattle, Washington
(1960).
6. Stander, G. J., "Topographical Pollution The Problems of the Water
and Sanitary Engineer," 40th Annual Conf. Inst. of Municipal Engineers,
Natl. Inst. for Water Research, South Africa (1961).
7. Weibel, S. R., R. J. Anderson, and R. L. Woodward, "Urban Land Runoff
as a Factor in Stream Pollution," Journal Water Pollution Control
Federation, Vol. 36, No. 7 (July 1964).
8. Bryan, E. H., Quality of Stormwater Drainage from Urban Land Areas in
North Carolina, Water Resources Research Institute of the University
of North Carolina, Report No. 37 (June 1970).
9. AVCO Economic Systems Corporation, Storm Water Pollution from Urban
Land Activity for the Federal Water Quality Administration, Publication
11034 FKL (July 1970).
10. American Public Works Association, Water Pollution Aspects of Urban
Runoff, Federal Water Pollution Control Administration, Water Pollution
Control Research Series WP-20-15 (January 1969).
11. Sartor, J. D., and G. B. Boyd, Water Pollution Aspects of Street Surface
Contaminants, Environmental Protection Agency, Research and Monitoring
Environmental Protection Technology Series, EPA-R2-72-081 (November 1972).
12. Pitt, R. E., and G. Amy, Toxic Material Analysis of Street Surface
Contaminants, Environmental Protection Agency, Municipal Pollution Control
Branch (1973).
V-16
-------
13. Edison Water Quality Laboratory, Environmental Impact of Highway Deicing,
Environmental Protection Agency, Water Quality Research, 11040 GKK (June
1971).
14. Burm, R. J., D. F. Krawczyk, and G. L. Harlow, "Chemical and Physical
Comparison of Combined and Separate Sewer Discharges," Journal Water
Pollution Control Federation, Vol. 40, No. 1 (January 1968).
V-17
-------
Appendix A
DATA ACQUISITION AND PROCESSING FOR
CONTAMINANT LOADING RATES AND MATERIAL COMPOSITION
-------
Appendix A
DATA ACQUISITION AND PROCESSING FOR
CONTAMINANT LOADING RATES AND MATERIAL COMPOSITION
1. Literature Sources
The objective of this stage of the manual is to determine the rates at
which solids accumulate on street surfaces and the chemical and bio-
logical composition of those solids as they relate to land use, cli-
mate, average daily traffic, and other common parameters. The method
for achieving this objective was not to design new experiments but to
extract the necessary data from available published literature. Ac-
cordingly, the URS staff conducted a search and analysis of this lit-
erature to obtain data which would fit into an idealized data matrix
or £ priori model.
Conceptually, rates of solids accumulation (loading rates) and the
composition of the accumulated solids should vary from place to place
as a function of certain independent variables such as land use, average
daily traffic, climate, season, population density, the fraction of the
land covered by solid surfaces, the median income of the population in
the watershed, and the type of landscaping in the watershed. It is con-
ceivable that, given enough data, an equation could be written relating
the loading rate and chemical composition to each of these independent
parameters.
It became apparent quite early in the analysis of the available litera-
ture that in no single publication were all of the desired independent
parameters presented along with the loading rate and composition. Of
the several parameters which could h&ve been included in an idealized
data matrix, relatively few were sought on a regular basis. These para-
meters are listed in Table A-l as independent and dependent parameters.
Some of the independent parameters are represented by numerical codes.
A-l
-------
Table A-l. DATA ACQUIRED FOR THE ANALYSIS
co
0)
p
i
ft
a
P<
c
ID
a
a
0)
ft
o
c
M
01
V
p
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2
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-M
C
ID
a
A
&
Q
Item
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17-32
33-34
35-42
Parameter Abbreviation
City
a
Climate C
a
Season S
n
Land use LU
Population density PD
Average age of the structures
in the area, in years AA
Fraction of the area covered
by impervious material IF
Average daily traffic
(number/day) ADT
a
Type of street surface S
a
Condition of the street C
Type of landscaping in the
area beyond the sidewalk A
Fraction of the area as
road surface FRS
Days since last rain R
Days since last swept S
3.
Method of cleaning M
Loading rate in pounds per
curb mile per day LOAD
Organic and inorganic constituents in ^ug/g
of dry solids. Self-explanatory as follows:
BOD5, COD, Ortho P04, total PO4, NOg, N^,
organic N, Cd, Cr, Cu, Fe, Pb, Mn, Ni, Sr,
and Zn.
Coliforms, total coliforms and fecal coli-
forms in numbers per gram of dry solids
based on the most probable number methods (MPN)
Pesticides and toxic organics: endrin,
dieldrin, PCB, methoxychlor, DDT, lindane,
methyl parathion, and DDD in /xg/kg of dry
solids.
For numerical codes see Table C-2.
A-2
-------
listed in Table A-2. The original intention in assigning these codes was
to represent the real variable (conceptually only) by a substitute (surro-
gate) variable which is proportional to the real variable. Unfortunately,
these particular independent parameters cannot be assigned surrogate vari-
ables that meet this criteria. For example, the increasing numerical codes
for climate and season do not necessarily go in any direction relative to
the loading rate or composition, Thus, these codes cannot be thought of
either as true continuous variables or as surrogate variables. The distinc-
tions between categories as represented by the codes are often gross; the
climatic distinction as shown in Figure A-l and Table A-3 is particularly
so, but the number of available data records would permit no further re-
finement. It is important to distinguish between areas which receive sub-
stantial amounts of snow from those which receive none, and between areas
where rain occurs frequently and uniformly throughout the year from those
where rains are seasonal. Basically, this is all that the climatic cod-
ing performs.
Approximately 150 publications were initially selected for review. Of
these, about half were deleted from the list because they were too
general in their approach to pollution from stormwater runoff. The re-
maining 70 publications were carefully analyzed and, finally, only 13
provided enough data to be included in the initial data matrix. These
sources provided 153 valid data records. None of the records provided
all of the parameters listed in Table A-l. The available data ranged
in quality and usefulness from a low, wherein the workers merely de-
clared that there were indeed materials on the streets which contained
a high pollutional potential, to a high, wherein the workers accurately
catalogued their test areas and carefully described their methods of
sample collection and analysis. Several publications that contained
data in a form otherwise consistent with that desired were rejected
from consideration because the collection and analysis methods either
were described incompletely or did not appear to be consistent with
those used in the bulk of the available data records.
A-3
-------
Table A-2. CODES FOR INDEPENDENT VARIABLES
Item
2
3
4
9
10
11
15
Parameter Subcategory Numerical Code
Climate Northeastern
(see Figure A-l Southeastern
and Table A-3) Midwestern
Southwestern
Northwestern
Season Annual
Winter
Spring
Summer
Autumn
Land use Open land
General residential
Low income/old/single
Low income/old/mult i
Median income/new/
single
Median income/old/
single
Median income/old/
multi
General commercial
Central Business
Shopping center
Light industry
Medium industry
Heavy industry
Types of street Asphalt
surface Concrete
Condition of the Excellent
street Good
Fair
Poor
Area beyond side- Lawns or grass
walk Trees
Landscaped buildings
Pavement or bare
Method of cleaning Swept and flushed
Swept only
No cleaning
1
2
3
4
5
0
1
2
3
4
10
20
21
22
23
24
25
30
31
32
40
51
52
1
2
0
1
2
3
1
2
3
4
1
2
3
A-4
-------
Figure A-l. Climate Zone Codes for the Data Matrix
and Cities for Which Data Are Available
-------
Table A-3. CLIMATOLOGICAL CATEGORIES
Code Description
1 Precipitation is not seasonal but relatively even year
round. Winters are cold with snow; summers are moder-
ate to hot and humid. Extremes in climate summer to
winter. Precipitation is about 40 inches/year.
Precipitation is about 40 inches/year divided about
evenly throughout the year. Summers are hot and humid;
winters are mild and wet with little snow.
Precipitation is less than 30 inches/year but about
evenly divided. Summers are hot; winters are arctic
and long. Suffers from great plains or tundra type
weather with wind and violent storms.
Precipitation is light and seasonal with about 10-20
inches/year, falling mostly in winter. Summers are
hot and dry; winters are moderate with rain. Snow
only in higher elevations.
Precipitation is about 40-60 inches/year and seasonal,
falling mostly in winter. Winters are cold but not
arctic. Frequent snowfalls but precipitation largely
as rain. Summers are moderate and dry.
National average climate. This is a meaningless cate-
gory and was employed only to give the computer a code
to reject average climate values into.
A-6
-------
Generally, analyses for nutrients and trace metals involved some form
of preliminary chemical extraction of either the dry street dirt or of
the whole runoff sample. The terminal analyses on the extract were
performed by accepted colorimetric or atomic absorption methods. The
BODg, COD, and pesticides analyses were performed either directly on
solids or on suspensions of the solids according to Standard Methods.
Methods of bacteriological analyses varied slightly between workers,
but the precision of the bacteriological analysis is notoriously poor.
The solids consist of the suspended load in those samples collected at
outfalls and of fine solids which pass a 1/8-inch screen in those
samples of dry dust collected directly from the street.
Four individual studies carried out for EPA provided the bulk of the
data. They are the Tulsa Study (Storm Water Pollution From Urban Land
Activity, AVCO Economic Systems Corp, 11034 FKL, 7/70); the Chicago
Study (Water Pollution Aspects of Urban Runoff, APWA for FWPCA, 1/69);
the URS I Study (Water Pollution Aspects of Street Surface Contaminants,
URS Research Company for EPA, 11034 FUJ, 11/72); and the URS II Study
(Toxic Materials Analysis of Street Surface Contaminants, URS Research
Company for EPA, 1/73). In the Tulsa Study samples were collected at
the main outfall from 16 watersheds within the city. The samples pre-
sumably contained only fine suspended solids and no *arge pieces. In
the Chicago Study, samples were collected by sweeping and vacuuming
only. This method is known to leave behind small particles in surface
interstices which can float out when wet. In both studies by URS
Research, street dirt samples were collected by both sweeping and
flushing.
Since no standardized techniques for collection and analysis of street
surface contaminants have been established, the variance in the data
collected from the open literature is expected to contain a large com-
ponent due to variations in methods. This variance component can be
neither eliminated nor evaluated at this time.
A-7
-------
2. Data Transformation and Augmentation
There were 14 independent and 27 dependent parameters recorded in the
data matrix. These parameters are described in Table A-l. The depen-
dent parameters were expressed by a variety of units in the literature
examined. These were transformed into units of parts per million (ppm)
of total solids for inclusion in the matrix. Therefore, the concentra-
tions are directly related to loading rates, that is, ppm of solids on
the street as expressed in any units desired (e.g., micropounds per
pound or micrograms per gram of dry solid). Loading rates were re-
corded in the literature by several units also. These were transformed
to pounds per curb mile per day. For most of the data, this transfor-
mation involved no guesswork,,or indefensible assumptions. However,
whenever loading rates were recorded in the literature as pounds per
acre of watershed per time unit, it was necessary to assume that only
street runoff contributed to this solids load because there was no way
to estimate the contribution from soil erosion. This latter contri-
bution is expected to be small except where active construction work
is in progress. Daily loadings on a per curb mile basis were obtained
by dividing the loading per acre by the curb miles per acre in the water-
shed. The latter figure, fortunately, was usually available in the
publication. This type of transformation was necessary for data from
the Tulsa Study and a few other smaller studies.
Very few of the literature sources provided an adequate cataloging of
the independent parameters. Population density, fraction of the area
covered by impervious material, average age of the structures in the
area, and the fraction of the area as road surface were not available
from enough sources to make them generally useful parameters. Data on
the condition of the street, the street surface material, cleaning
method, and days since the street was last swept or rain had fallen were
generally available only from the URS Research studies. These parameters
were used for separate analyses described later. Those parameters that
were consistently available in the literature sources were land use,
A-8
-------
climate, season, and type of landscaping in the area beyond the side-
walk. Average daily traffic (ADT) was determined from traffice maps
obtained from the traffic engineer's office of the city of interest.
Occasionally, if these parameters were not explicitly stated in the
publication, they were obtained from a picture of the area or from a
verbal description given over the telephone by the city engineer.
In those areas where the study was conducted on a whole watershed, the
ADT was calculated by a circuitous method described as follows. A unit
area was selected for the given city. It was then assumed that people
enter and leave the area ultimately by major arteries and that one ar-
rival or one departure constituted one ADT unit. The total traffic
into and out of the area was determined and expressed as ADT per acre
of area. Finally, ADT was expressed as number per day per curb mile
of road by dividing the ADT per acre by the highway density in curb
miles per acre. This unit is different from the regular ADT; but the
unit length, the curb mile, is a standard unit used in the daily loading
values and was believed to be acceptable for categorizing a given water-
shed area as lightly or heavily traveled. These values are recorded
without distinguishing marks in the data matrix.
To facilitate the operation of reading the data into the computer, the
total coliform (TCOLI) and fecal coliform (FCOLI) numbers were repre-^
sented with the exponential notation, e.g., 2.0 E6 is equivalent to 2.0
x 106.
Chlorinated hydrocarbon and mercury analyses were available only in the
URS I study. Since they were so few in number, these data were separated
out of the data matrix and treated individually. All of the data avail-
able except the chlorinated hydrocarbon and mercury analyses are shown
in Table A-4. The chlorinated hydrocarbon and mercury concentration
data are given in Table A-5 along with the basic statistics of these data
sets.
A-9
-------
ICOLI TCOLI
I
M
O
cm
DURHAM
DURHAM
DURHAM
LAWRENCE
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
ANN ARBBR
ANN ARBBR
ANN ARBBR
GRAND RAPI3S
GRAND RAPIDS
GRAND RAPIDS
CINCINNATI
CINCINNATI
TUSCBN
TUSCBN
TUSC8N
TUSCBN
TUSC8N
TUSCBN
CINCINNATI
ANN ARBBR
CHICAGB
CHICAGB
CHICAGB
CHICAGS
CHICAGO
CHICAGB
CHICAGO
CHICAGB
CHICAGO
CHICAGB
CHICAGO
CHICAGB
CHICAGS
CHICAGB
CHICAGO
CHICAS8
CHICAGO
CHICAGB
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
SAN JOSE I
PHOENIX I
MILWAUKEE
BUCYRUS
BALTIMORE
SAN JOSE II
ATLANTA
TULSA
PH9ENIX II
SEATTLE
L8U/9LD/SING
LOvJ/OL'VMULT
MED/NEW/SING
MED/8LD/SING
MED/8LO/MULT
CENT.BUSi
SHOP. CENTER
LIGHT INC.
MED* IND.
HEAV.Y INOt
C
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IP ADI S
.29 2900 1
.34 4200 1
20 2000 1
.38 2500 1
.37 5600 2
.37 5600 8
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10000
10000
14000
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.37 5600 2
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7950
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11140
800
1000
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1980 :
5860 Z
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14070
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.30 10100 1 1
.55 10600 2 1
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.84 6600 1 =
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LOAD
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39fl
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170
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860
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280
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296
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BODS
5300
18900
167000
175000
46200
76000
78900
18500
30700
81300
75000
88300
109000
assoo
5050
4030
14540
2950
1720
9100
2180
2810
4770
2900
8030
6380
2880
3840
9430
1940
8880
3220
5800
89300
11600
83800
67300
35000
19000
39300
24000
Z5300
25000
39600
32100
18200
43900
17000
10000
4400
8100
61000
8900
4500
43000
11000
10000
10000
28000
18000
7100
8300
8600
15000
11000
3700
COD
65500
61600
611000
525000
423000
400000
586000
489000
252000
'00000
312000
101000
147000
201000
224000
497000
26700
24800
66700
23000
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50700
29500
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68000
30000
91000
58000
38000
32000
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21000
73000
60000
17000
OP04
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5050
1810
26*0
5790
650
950
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240
760
4390
610
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87
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63
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20
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109
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14
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1600
3800
8800
1800
3300
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1600
3000
2400
1600
1500
?700
2600
1700
3000
770
340
100
180
1000
750
620
1700
3000
1100
1100
1100
1100
1100
1100
1000
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1400
1400
1400
TP04 N03 HH4
, 212
11601
8720 5390
1810 4940
1920 188*0
196fl 3280
3110 2630
2620 650 595
8770 801 730
8130 441
4850 4400
5440 16000
273"
9*0
1020
350
430
790
8300 3620
8510 630 660
480
323
457
431
325
523
523
773
377
405
845
670
961
"523
482
410
356
3600
450
80
89
38
45
55
37
130
59
64
64
64
64
64
600
600
78
78
72
ORGH
6670
11*80
6"80
9000
10=500
535
365
9250
12POO
8COO
450
490
3600
8800
1600
2700
1800
1900
1800
1600
1100
1800
1900
3100
1700
1400
2300
2300
530
890
1900
1800
1VOO
2000
3200
2000
2200
8200
?800
2200
8200
1600
1600
1600
1600
1600
CD
0.7
0-4
0.6
3.5
7.2
2-7
8.7
8.0
4.3
1.1
1.7
0.7
1-1
8.5
3.6
3.3
a.a
3.1
6.5
3.7
4.4
3.8
4.7
CR
a
3
5
304
211
147
180
273
845
220
112
141
343
808
188
192
183
175
847
805
288
844
304
CD
11
B
0
71
121
161
90
161
75
120
91
67
8°
110
90
81
94
70
88
850
130
87
151
FE
3100
2500
2600
24000
P1000
1POOO
P1000
?*000
25000
24000
12000
15000
29000
24000
20000
18000
20000
24000
24000
23000
22000
23000
40000
PB
117
137
66
47
17
15
2700
1500
S30
890
2200
3400
660
740
680
2100
1500
1°00
1200 -
1600
1900
3600
3600
8800
1400
470
«H HI
8
4
6
460 96
540 15
280 28
470 17
480 31
460 87
350 19
340 9
490 11
460 38
*20 38
330 28
420 36
370 13
330 14
420 46
370 57
490 44
420 35
870 33
SE z«
57
81
8?
8 360
14 3*0
28 880
33 853
21 640
16 400
4 330
63 840
16 810
11 *80
86 370
19 360
32 890
18 S30
12 350
80 640
16 *00
2* 400
24 880
17 390
L\Ml*l- * w«
1.1E5
2»8E6 5.2E5
1-6E6 8.6E5
2-5E5 8.0E3
8>6E7 1.7E7
2'4E7 7.5E5
3«*E7 8>2E6
1'5E7 3.5E5
1«OE7 1.0E6
3-3E7 4.6E5
3.5E6 9.7E3
5.8E5 3.2E*
1>OE6 *.OE3
3«8E5 8.7E*
1>1E6 1.3E5
1.1E6 4.1E4
1-3E6 7.0E5
1-2E6 5.7E5
2-6E6 6.1E5
1.6E6 1.9E5
3.1E6 8.7E5
5-6E6 4.5E5
4.1E6 4.0E5
1-OE6 1.8E6
3.0E6 1.3E6
3-9E5 1.5E*
2»6E6 5.3E5
2.1E6 1.0E6
3.2E5 ».2E3
1«6E6 6.9E*
1.5E6 4.8E*
-4.1E5 1>3E4
5-SE6 5.5E*
4.0E6 5.8E5
7-7E5 8.9E3
6-3E6 1.8E*
9.6E6 7.0E3
3.0E6 7.0E3
6.4E6 LIE*
8.BE6 5.0E2
6-OE5 3.8E3
8>4E4 6.3E3
8-1E6 1.3E4
*>OE4 5.7EO
1-OE5
8.6E» Z.OE2
1.6E5 1.5E*
1-1E6 8.5E5
1.8E5 5.SE3
7-7ES 9.1E3
8.1E5 8.3E*
1.5ES U8E*
4.2E5 1.4E*
LIES 3. IE*
1.1E5 1.1E5
2-OE6 5.2E5
3.7E5 6.9ES
2.3ES J.3E3
1.3E* 4.5E2
Table A-4,' ENTIRE MATRIX OF AVAILABLE DATA
-------
CITT
SAN
SAN
SAN
SAN
SAN
SAN
SAN
PHOE
JOSE I
JOSE I
J8SE
JOSE
JOSE
JOSE
J6SE
NIX
PHOENIX
PHOE
PHOE
PH8E
NIX
NIX
NIX
PHOENIX
PH8E
PHOE
NIX
NIX
MILWAUKEE
MIL
MIL
0
1.3
2-t
1.6
°.3
'8
0.
1-1
0.3
0.
0.8
6.4
?!
0.3
0.
3.4
1-3
1.6
J.3
1-5
1-4
325
325
295
320
325
300
2R5
203
159
215
238
1 = 0
16"
208
256
130
141
153
125
191
177
17 =
12R
13?
139
17H
33°=
15°
?1C
290
123
215
356
264
750
290
345
295
75
245
310
430
306
194
IB?
127
207
100
16?
585
275
1S6
15D
24
135
63
13*
74
185
165
111
193
71
1!8
310
233
23 =
250
254
266
247
239
8*
96
3-"
in
80
87
67
150
3°
140
53
6°
2=;
100
3«
83
170
t2i
7'
RIO
12"
121
170
9!
66
94
121
7°
1?"
l?i
120
I?"
291
211
28n
21i
151
131
34
51
84
96
71
9?
151
71
30i
30
140
190
3R
160
71
66
96
66
110
64
9=
74
5'
46
9=
121
3'
81
100
67
48
?ic
61
111
'700O
23000
?1000
16000
44000
'4000
26000
23000
17000
?1000
23000
15000
15000
20000
?4COO
15000
14000
1"000
15000
25000
34000
'2000
15000
13000
15000
"POO
43000
20000
'"000
'4000
1C000
11000
40000
'3000
31000
'5000
^3000
401QO
11000
'6000
30000
'3000
1'ODO
'?000
P4000
13000
?0000
12000
16000
72000
14000
20000
17000
1400
11000
8800
15000
MOO
'1000
'0000
11000
25000
5000
'4000
'2000
27000
37000
'3000
59000
""000
"POOO
'7000
2*00
'000
2100
3500
7600
2000
3500
1200
3700
= 70
3600
3200
1600
2500
1200
790
580
= 70
470
2700
"00
660
360
350
430
1600
780
?60
1700
1000
730
1500
5700
2100
ijoog
1800
310
5700
600
3 = 00
5100
0009
2700
1500
280
480
3 = 00
'000
740
= 40
1400
1100
= 70
230
1300
2400
1100
65
340
2000
?20
?=00
0
'too
1700
1700
3000
'C00
?600
3300
4700
1100
45n
450
350
470
410
500
600
320
280
680
440
380
360
430
330
280
230
250
290
300
39n
270
310
420
371
490
621
470
29n
430
ISO
270
770
500
830
681
1600
560
230
470
500
540
490
450
210
280
280
290
300
1100
240
430
520
100
250
160
440
180
700
370
450
420
280
460
410
430
490
460
441
430
440
490
85
80
100
110
93
110
93
0
0
25
5
6
7
6
1
33
0
26
18
30
37
21
30
13
36
7
35
6
45
55
2
18
51
7
37
12
14
120
30
75
83
140
120
93
9
7
18
12
19
84
12
32
1
0
29
10
24
26
11
3
0
7
170
18
23
39
40
29
39
39
40
20
19
17
S
20
10
0
8
17
13
13
12
15
11
15
12
21
24
76
20
20
7
20
9
33
33
41
24
17
24
23
5
6
25
33
34
33
38
28
21
9
15
13
13
18
3
4
13
5
6
3
14
78
110
6
37
38
93
77
25
23
12
15
25
15
10
13
8
9
15
0
16
320
370
260
600
tio
350
450
290
210
330
490
335
720
230
210
300
21C
?5o
210
650
320
370
220
190
110
390
200
ito
630
760
730
490
1000
510
780
410
300
RIO
210
420
510
380
340
280
270
180
1100
320
320
880
310
350
180
220
420
190
360
160
350
250
130
290
400
360
150
460
660
460
*10
500
390
480
9.9E4
8'OE4
3.1E4
2-7E4
l.OES
5.3E5
I'9E5
9-1E4
1'8E4
6-7E5
4-OE4
5-8E4
?'OE5
1-3E6
1-8E5
2-OE5
1-7E5
8-3E4
1-4E5
1-4E6
6«5E5
9.4E6
5-6E5
1-2E!
7.3E5
2-OE5
1-9E5
2-5E4
»>6Et
3-5E4
4-2E4
1-3E5
2-3E5
1-6E5
6.4Et
3-1E5
1-6E5
l'BE5
3ME6
2.6C
3.6E3
S.5E3
1.5E3
3.6E3
9.0E2
6.7E3
6.7E1
6.1E2
6.SE4
1.2E4
4.6E3
2.2E2
7.2E3
5.7E3
5.3F2
9.3E2
5.5E2
3.4E5
5.2E5
3.8E1
9.1E5
1.1E4
5.4E3
2.7E4
2.2E3
1.1E4
1.4E?
8.8E1
7.2E1
3.2E3
3.6E4
2.4E2
S.OE3
1.5E3
9.1E3
Table A-4 (continued)
-------
Table A-5. MEAN CHLORINATED HYDROCARBON AND MERCURY CONCENTRATIONS IN
URBAN STREET DIRT FROM SEVERAL U.S. CITIES (THE URS I STUDY)
Concentration
City Hg Endrin
San Jose I 330
Milwaukee
Bucyrus
Baltimore
San Jose II 14
Atlanta 53
Tulsa 59
Phoenix II 24
Seattle 75
Mean 83
Standard 111
Deviation
Range 14-330
N 6
2.2
0
0
0
0
0
0
0
0
0.2
0-2.2
9
in Micrograms/kilograms of Dry
Methoxy-
Dieldrin PCB dhlor- DDT Lindane
12
3.8
12
3.0
4.4
55
74
26
59
28
28
3 . 0-74
9
1300
1300
470
1000
100
150
200
71
2300
770
770
71-2300
9
0
3100
1200
170
0
0
0
0
0
500
1050
0-3 100
9
120 19
0.4 1.2
43 0
30 0
28 0
30 0
39 0
14
380
76 2.9
118 7.1
0.4-380 0-19
9 7
Solid
Methyl
Parathion DDD
22
0
0
0
0
0
0
0
0
2
0-22
9
73
0.2
61
!
100
20
79
100
37
270
82
78
2-270
9
A-12
-------
3. Data Processing
a. Preliminary Evaluation of Data Obtained by URS Research Company
In an earlier study of trace metal concentrations in street surface
materials, the URS Research Company had collected raw data in the form
of solid load in pounds per curb mile and corresponding days since last
the street had been swept or since a substantial rain had last occurred.
The usual method for expressing these two data is as a ratio of pounds
per curb mile per day since last cleaned by either sweeping or rain,
whichever is less. However, theoretically, there is no justification
for this type of transformation. Conceptually, it is probably valid
to assume that solids accumulate on streets by some process at a con-
stant rate (C), and are removed from the streets by a different set of
processes at a rate proportional to the existing load (L). These con-
cepts may be expressed mathematically by the differential equation
dL
dt = C -
where k is a removal constant. The theoretical load of solids on the
street at any time (t), starting with a clean street, is expressed as
r
L =
dt
From the last equation, it is clear that when t is small, the load in-
creases at a constant rate C; and as t approaches infinity, the load
approaches assymptotically to the value C/k. Thus, the ratio of L/t
(pounds per curb mile per day) appears nowhere in the theoretical treat-
ment. Since the raw data of L and t were available from the URS study,
it was decided to attempt to find out whether the data would approximate
the conceptual model.
A-13
-------
First, the days since rain had fallen or the street had been swept were
transformed into equivalent days of accumulation (EDA) by the formula
EDA = (days since last rain - days since last swept)
X (1 - sweeping efficiency) + days since last swept
The assumption inherent in this formula is that the last rain cleaned the
street thoroughly. This is a poor assumption, but no data were available
from which to estimate the fraction of material washed off by each rain.
Generally, it is assumed that a rain intensity of 0.5 inch/hour gets 90
percent of the solids. The days since last rain figure is the days
since a rain of this intensity. Obviously, whenever rain had fallen
fewer days previous than the street had been swept, the days since last
rain was EDA.
Attempts were made to fit EDA and load data by linear regression, poly-
nomial regression, and a theortical curve. To remove the effects of
different land use and climate, the data were separated wherever there
were enough data records to allow it. No statistically significant
relationships could be found between EDA and load in any land use or
climatic category. To include potential effects of ADT on the load, these
data were included as log ADT and log (ADT x EDA) in a second attempt
to find relationships. Attempts were, made to fit load with log ADT and
log (ADT x EDA) by linear and polynomial regression and with a theoret-
ical curve. No significant relationships were observed. The best cor-
relation coefficient obtained was 0.29. This was obtained with a linear
regression between load and log (ADT x EDA) in residential areas in the
east, and suggested that load decreased with increasing log (ADT x EDA).
After this abortive attempt to verify the theoretical relationship with
data from the real world, the loading data was transformed as usual into
the ratio Load/EDA and expressed as pounds per curb mile per day. The
results of this preliminary attempt to extract information from data
taken from the real world is indicative of the complexity of real systems.
A-14
-------
The variance in the loading rate data exceeds the variance of our repre-
sentation of any single independent parameter as far as can be determined.
Therefore, the probability that any combination of independent parameters
can describe or predict the loading rate (i.e., will co-vary with the
loading rate) is small. The only recourse to extracting information
from the data by covariant analyses is by a factor analysis technique.
Unfortunately, there are not enough -complete data records to permit this
kind of study. Furthermore, most of the independent parameters as con-
ceived in this work are not continuous and cannot be mixed with contin-
uous variables in a covariant analysis.
b. Grouping the Data
Since no continuous surrogate variables could be defined to represent the
independent parameters, generally one cannot expect to find continuous
relationships between the independent and dependent parameters. Since
the chances of discovering useful relationships by multiple regression
or curve fitting techniques appeared to be poor, an alternative method
was adopted for detecting systematic changes in the dependent with
changes in independent parameters. The procedure was to (1) group the
data records according to the independent parameters, (2) calculate the
mean and other basic statistics of the dependent variables so grouped,
and (3) test the differences between these means for statistical signif-
icance by the student t and test. This procedure for grouping the data
records provides the mechanism for both detecting significant variations
in the grouped data and presenting the available information which is
readily usable by the urban planner.
The data grouping operation was performed by computer. The entire matrix
in Table A-4 was broken up into 19 primary categories by Land Use, Climate,
ADT, and Type of Landscaping Beyond the Sidewalk. There were enough data
to allow a breakdown by season also, but it was thought to be redundant
with climate and so was not considered. The computer sorted the matrix
into groups to these four parameters, according to the codes shown in
A-15
-------
Table A-2. These submatrices are shown in Tables A-6 through A-22,
Data from URS Research Company's earlier studies that had collected
data on the condition and type of street surface was grouped by hand
into two street surface types. These groups are not shown.
Each column of data was first scanned by eye and extreme values were dis-
carded in pairs a high and a low. The total number of points dis-
carded did not exceed 10 percent of the number available. A single
point was discarded only when it was one to two orders of magnitude
different from the bulk of the remaining data in the column. The mean,
standard deviation, range, and number of data points were calculated
and recorded. Standard deviations for means of less than 5 data were
considered unreliable and not recorded. The same calculations were
performed on the set of all data. These basic statistics are listed in
Table A-23.
These basic statistics are useful for testing whether the analysis of a
single sample is significantly different from the mean of the published
analyses. But in its present form, Table A-23 is much too complex and
cumbersome to enable a planner to use it to predict loading rates and
composition or to make decisions about whether a mean value in a par-
ticular subset is significantly different from the mean of the set of
all data. To simplify the presentation of these data, the standard
*
error of the mean (cr) was calculated as
or cr = ~ if i?
-------
cm c
GRAND RAPIDS 1
CINCINNATI
TULSA
0 10
0 10
0 10
9 20
.9
.37
.11
5600
300
0
08
OHM TTO4 K03
22300 252000 5»«0 UOOO
12 18200 90900 1700
12800
1700
CD
0-6
CR
5
CD
9
FB
2600
rt
15
SI
f>
ZH TCOLI VCOU
22
Z.5W 1.QE3
a.»E» t.JES
I
M
-J
Table A-6. LAND USE 10 - OPEN SPACE
-------
00
CITY
DURHAM
DURHAM
DURHAM
LAWRENCE
ANN ARBOR
ANN ARBOR
ANN ARBOR
GRAND RAPIDS
TUSCON
TUSCON
ANN ARBOR
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
SAN JOSE I
PHOENIX 1
MILWAUKEE
BUCYRUS
BALTIMORE
SAN JOSE II
ATLANTA
TULSA
PHOENIX II
SEATTLE
LOW/OLD/SING
LOW/OLD/MULT
MED/NEW/SING
MED/6UD/SING
MED/BLD/MULT
SAN JOSE I
SAN JOSE I
SAN JOSE I
PHOENIX I
PHOENIX I
PHOENIX I
PHOENIX I
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
BUCYRUS
BUCYRUS
BUCYRUS
BALTIMORE
BALTIMORE
BALTIMORE
BALTIMORE
SAN JOSE II
SAN JOSE II
SAN JQSE 1 1
ATLANTA
ATLANTA
ATLANTA
ATLANTA
TULSA
TULSA
TULSA
PHOENIX II
PHOENIX II
PHOENIX II
PHOENIX II
SEATTLE
SEATTLE
SEATTLE
SEATTLE
C S
2 0
E 0
a o
3 0
1 2
1 3
1 4
1 0
3
1
0
3
3
3
3
3
3
3
1 3
1 3
1 3
1 3
1 3
1 3
2 0
2 0
S 0
2 0
2 0
2 0
2 0
2 0
4 4
4 1
1 2
1 2
2 2
4 2
1 I
4 2
5 3
6 0
6 0
6 0
6 0
6 0
4 4
4 4
4 4
4 1
4 1
4 1
* 1
i a
1 2
1 2
1 2
1 2
1 2
1 2
2 2
2 E
2 E
2 2
4 E
4 2
4 2
E 2
E 2
2 2
2 2
2 2
2 2
E 2
4 E
4 2
4 S
4 2
5 3
5 3
5 3
5 3
LD
20
20
20
20
20
20
20
EO
20
20
20
20
20
20
ao
20
ao
20
20
20
ao
ao
ao
80
80
80
80
80
80
80
80
80
80
20
80
20
ao
ao
20
20
20
20
21
aa
83
84
as
88
83
81
22
23
85
21
82
23
25
at
23
24
82
23
24
25
ai
22
23
21
22
23
25
21
23
25
21
22
23
25
21
22
24
25
PD AA
9.7 10
10.5 10
8.2 10
10
15
50
50
20
80
15
ao
50
ao
10
10
ao
7.1 5
8.9 50
11.5 20
11.3 20
13.6 EO
9.5 50
E.3 10
11. E 20
IF
.29
.34
.20
.38
.27
.30
.32
.37
31
.41
.23
.38
ADT
8900
4200
2000
5500
5600
5600
5600
5600
10000
10000
5600
800
1000
700
1980
5860
5860
1828
a4o
14070
3600
laoo
17300
1680
3800
14500
8300
6100
3800
3200
8000
10200
10400
11400
10100
1SOO
10900
10400
7BOO
7200
11400
11700
5600
8000
8500
1400
10800
9400
12050
300
8700
11100
10300
11000
14000
4000
300
16000
300
300
300
6700
700
700
8000
9400
1E050
300
300
300
300
300
5000
700
16800
2700
11100
10300
11000
6700
6900
3200
12300
S
1
1
1
1
1
E
8
1
1
1
1
1
1
1
1
1
1
1
8
1
1
1
1
1
8
1
1
1
1
1
1
2
1
1
1
E
2
1
1
1
1
1
1
2
1
C A
1 1
1 1
1 1
1 1
4
4
4
4
8
a
*
i i
2 2
2 4
3 3
2 2
2 2
2 3
2 3
1 3
l a
1 2
2 2
a 3
1 1
2 2
1 1
1 2
3
3 a
i i
1 a
i
i
1
1
i
i
i
i
i
i
2
1
8
2
0
2
1
3
1 1
2 1
3 1
0 1
0 1
1 3
1 1
0 1
0 1
1 1
2 1
1 1
1 1
1 1
1 1
3 1
3 1
2 1
2 1
3 1
1
1 1
2 1
3 1
1 3
1 1
2 3
FRS
20
84
13
19
80
24
88
37
48
20
22
R
1*
12
0
2
26
59
2
9
60
12
ja
13
ia
12
la
la
la
0
o-
0
0
8
a
a
26
86
26
26
59
59
59
a
2
2
a
9
9
9
60
60
60
60
12
ia
ia
12
S
i
3
3
3
3
3
3
3
3
7
7
3
7
6
7
4
1*
8
1
7
3
7
6
7
9
1
13
5
4
1*
1
81
88
H
2
2
2
8
2
2
2
2
2
2
2
2
2
2
2
a
a
i
8
i
i
8
1
a
8
2
2
a
8
8
8
8
2
2
2
2
a
1
1
1
1
8
2
8
1
1
1
1
1
1
1
a
8
2
8
1
1
1
1
LOAD
400
600
390
810
170
19
38
181
1*8
81
68
181
135
1*8
19
20
96
153
60
88
38
35
a*
33
H
28
70
92
8700
690
860
860
880
372
659
*is
70
85
84
77
838
18
3*
103
93
40
770
950
205
950
100
67
93
33
11
8
3
295
31
165
13
69
17
27
18
6
8
39
45
88
12
BOD5
5300
18>00
18600
30700
21300
23500
1780
9100
S180
2810
»770
2900
2030
6320
81(0
3tkO
9*30
19*0
3880
11600
67300
19000
39300
8*000
85000
32100
43900
17000
10000
4400
2100
61000
8900
*500
»3000
11000
10000
10000
22000
12000
7100
COD
63500
61600
200000
318000
18300
53100
50700
89500
61300
32600
3*000
45600
8*600
2*500
72(00
32100
3*600
91700
510000
11*000
898000
277000
ao7ooo
192000
158000
3*000
46000
18QOO
21000
20000
68000
30000
91000
58DOO
38000
32000
86000
40000
8*000
OP04
650
950
820
520
»50
610
20
70
1*2
21
63
30
2*
73
ao
8*
109
28
30
2800
3300
1600
3000
3400
1500
8600
3000
770
3*0
100
1000
750
680
1700
3000
1100
1100
1100
1100
1100
1100
TP04 M03 HH4
212
11600
E6ZO 650 595
2770 800 730
2110 440
2730
940
2510 630 660
325
523
523
773
377
405
845
670
961
585
523
482
356
1600
450
80
89
38
45
55
37
130
59
6*
6*
64
64
64
OHOI
535
365
450
2200
a7oo
1900
1800
1600
1200
3100
1400
2300
2300
530
890
1900
1800
1100
2000
3200
2000
aaoo
2200
2200
2200
2200
CD
0.4
3.5
7.2
E.7
E.7
8.0
4.3
1-1
1.7
0.7
1-1
2.5
3.6
3.3
2.8
3.1
3.5
3.5
4.5
4.0
5.5
8.8
6.0
4.8
1.4
0.6
2.3
3.0
E.6
1.6
8.8
6.1
5,6
5.2
6.0
2.0
5.4
0.
0.
1-0
1.3
2.4
1.1
0-3
C.
0.8
0.
3.4
1.3
1*6
at
3
30 4
Ell
147
180
E73
E45
220
112
141
243
208
188
192
183
175
325
325
295
2o3
159
215
238
130
141
153
125
132
138
178
210
290
120
215
295
75
245
182
127
186
150
24
185
165
111
193
233
239
250
254
CD
8
71
120
160
90
160
75
120
91
67
89
110
90
81
94
79
88
96
33
150
39
140
53
83
170
120
72
91
66
94
120
120
1EO
190
130
34
53
150
70
160
71
66
99
74
52
46
80
100
67
48
FE
2200
24000
21000
18000
S1000
24000
25000
24000
12000
15000
29000
24000
20000
18000
20000
24000
27000
23000
21000
23000
17000
21000
E3000
15000
14000
18000
15000
13000
15000
22000
19000
24000
15000
18000
»8000
11000
E6000
24000
13000
20000
17000
1400
81000
20000
11000
25000
27000
37000
23000
59000
PB
117
137
66
17
2700
1500
830
890
8200
3400
660
740
620
8100
1500
1900
1200
1600
1900
3400
2000
aioo
1200
3700
970
3600
790
580
970
470
350
430
1600
1700
1000
730
1500
5700
600
3900
280
480
1100
970
230
340
2000
220
8900
1700
3000
8500
2600
WH
460
5*0
880
470
480
460
350
340
490
460
420
330
420
370
330
450
450
350
380
880
680
440
280
830
250
290
420
370
490
290
430
150
270
560
230
470
210
280
430
sao
100
700
370
450
480
430
490
460
440
HI
4
96
15
88
17
31
87
l!
a
32
38
as
36
13
1*
85
80
100
0
0
as
5
33
0
26
IB
13
36
7
45
55
2
18
120
30
75
9
7
38
1
0
11
3
0
7
39
40
89
39
SR
8
1*
88
33
81
16
4
63
16
11
26
19
32
18
12
19
17
5
17
13
13
12
21
2*
76
20
33
33
*1
24
83
5
6
88
81
9
3
4
78
110
6
85
83
12
15
13
8
9
ZN
21
360
3*0
880
250
6*0
*00
330
2*0
210
*80
370
360
290
530
350
320
370
260
890
810
330
»90
300
aio
aso
810
190
110
390
630
760
730
»90
810
810
480
870
180
350
180
820
350
850
130
290
4.60
660
460
*10
TCOLI
8.6E7
8i*E7
1-1E6
1-1E6
1.3E4
H8E6
8.666
1-6E6
3tlE.6
5.6E6
*>1E6
1.0E6
3.066
3.9E5
S-1E6
1-5E6
5.5E6
7t7E5
6.3E6
9!6E6
6.4E6
6.065
8-1E6
4.0E4
l.OE!
8.6E*
J.6E5
! 1C*
1-2E5
7i7E5
8-1E5
1.5E5
4.EE5
lilES
9.9E4
8.0E4
1.0E5
5.3E5
1.9E!
9.1E*
8iQE5
lt3E6
1.8E5
1-*E6
6>5ES
9.*E»
2.0E5
1.9E!
2.5E*
B.6E*
1 *6E5
6.4E4
3.1E5
FCOLI
HIES
1.7E7
7i5E5
1.3E5
*<1E*
7.0E5
S.7E5
6<1E5
J.JE5
2.7E5
4.5E5
4.0E5
l.2E»
1.3E6
I.SE*
1.0E6
*
-------
cm*
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
TUSCON
TUSCON
CINCINNATI
CHICAQO
CHICASO
CHICAGO
TULSA
TULSA
CENT* BUS*
SHOP*CENTER
SAN JOSE I
SAN JOSE I
PHOENIX I
PHOENIx I
MILWAUKEE
MILWAUKEE
BALTIMORE
BALTIMORE
SAN JBSE I I
SAN JOSE I I
ATLANTA
ATLANTA
TULSA
TULSA
PHOENIX II
PHOENIX II
SEATTLE
SEATTLE
C I
1 3
1 4
1 1
1 2
1 3
1 0
4 3
4 1
1 0
1 3
1 3
1 3
2 0
2 0
6 0
6 0
4 4
4 4
4 1
4 1
i a
1 2
2 a
2 2
4 2
4 2
2 2
2 2
2 2
2 2
4 2
4 2
5 3
5 3
U7
30
30
30
30
30
30
30
30
30
30
30
30
30
30
31
32
31
32
31
32
31
32
31
32
31
32
31
32
31
32
31
32
31
32
FD AA
» 20
9 20
9 20
9 20
9 20
9 20
9 20
20
20
20
4.0 5
4.3 20
IT
.37
.37
.37
.37
.37
.37
.37
.55
.74
ADT
5600
5600
5600
5600
5600
5600
14000
14000
5600
7950
20400
13500
10600
2600
15500
18800
3600
13000
11900
15000
6000
20800
14200
27000
23600
13000
14100
34100
18000
5000
11900
15000
16000'
26500
5
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
1
1
c
1
1
1
1
1
1
I
1
2
1
1
2
1
2
1
0
2
0
0
2
1
1
0
2
2
2
1
2
2
A RS
1
1
1
1
1
1
1
1
1
4
4
4
3 .20
2 *5l
4
4
4
4
4
4
4
4
4
3
4
4
4
4
4
1
4
4
4
4
I
13
18
12
12
0
0
26
26
59
59
2
2
9
9
60
60
12
12
S M
1 2
1 2
1 2
2
2
1 2
13 2
1 2
7 2
1 1
4 1
2
2
1 1
14 1
1
1
2
2
1
1
LOAD
32
13*
326
71
46
HI
296
122
21
26
26
S3
260
30
5
4
20
3
60
2J5
20
4
3
16
16
KBS
167000
17BOOO
46200
7*000
71100
75000
109000
5050
4030
1*540
21300
25300
8300
1600
COD
611000
525000
423000
400000
536000
489000
101000
147000
497000
26700
2*800
66700
163000
2*7000
30000
21000
opt>4 not
6670
5050
1810
26*0
5790
4««0
170
230
4390
38
27
1*2
3200
1600
1000
1000
103 HH4
2720 5390
1810 4940
1920 1880
I960 3280
3110 2630
4400
1020
350
2300 3620
480
323
457
600
600
OROl
6670
11880
6920
8000
10500
9250
8800
3600
1900
1600
16QO
CD
6.5
3.7
2.6
5.0
6.6
2.0
3.9
3.2
as.
3.7
4.9
3.1
5.3
0.
1.6
9.3
6.4
2.3
1.5
Cfc
247
205
320
325
190
168
190
177
356
264
310
43o
ao?
loo
135
63
71
266
247
CD
88
250
110
80
69
25
310
120
290
210
84
96
300
30
96
66
99
210
63
IE
24000
23000
16000
44000
15000
15000
25000
34000
40000
asooo
30000
23000
20000
12000
11000
8800
5000
32000
42000
n
3600
3600
3500
7600
3200
1600
2700
2200
5700
2100
5100
9999
3900
2000
1300
2*00
0
3300
4700
n
420
370
470
410
380
360
300
390
770
500
500
540
280
290
250
160
280
430
440
n
46
57
110
93
6
7
30
37
51
7
83
140
18
12
29
10
170
39
40
SK
20
16
20
10
15
11
20
7
25
33
15
13
13
5
37
38
25
15
0
ZM
4*0
400
too
410
336
720
650
320
1000
510
510
380
1100
320
420
190
400
500
390
TCOLZ
Z>IC6
1.6S6
3.4E7
1.SE7
3*566
5.SES
1.0E4
1.6E6
3'OEt
1>U5
2>OE6
3. IE*
1«8E*
6.7EB
2.0EB
li7ES
5.6E5
3.55*
4t2E*
1.6E5
ItlES
rau
».tES
J.4ES
it£E4
1.5EB
J.7EB
3. IE*
4.0E3
4.»E4
7.0E3
ltl«
I.2E3
»iOES
4.7C3
SE2
7.IE3
S.tES
I* IE*
t.»K
5.0E3
1.5E3
to
Table A-8. LAND USE 30 - COMMERCIAL
OPO4 TP04 H03
BRAND RAPIDS
TUSCON
TUSC8N
TULSA
LIGHT IND.
SAN JBSE I
PHOENIX I
BALT1M8RE
SAN JBSE II
ATLANTA
TULSA
PHOENIX II
SEATTLE
1
«
4
2
6
4
4
2
4
2
2
4
S
0 40
3 40
1 40
0 40
0 40
4 40
1 40
a 40
2 40
2 40
2 40
2 40
3 40
5600
12000
12000
0 2 1
8300
7250 2
12300 1
9900 2
7a50 2
5000 2
700 2 1
12300 1 1
10000 2 2
48
13 2
12 10 2
26 3 1
59 2
a 30 i
9 1
60 2
ia i
57
946
131
41
87
204
1850
12a
4
59
201000
224000
39600 222000
15000 73000
240
760
2700
1400
430
790
7?
1500
1600
3.4 300
11.
CD
16
130 22000 2800
87 24000 aooo
208 IOC 20000 2500
8.2
3-7
1.5 162 HO
280 31000 9999
3Q6 71 17000 2700
16000 740
138 110 15000 1100
3-1 188 120 24000 alOO
1*4 339 Ho 27000 1100
MS
490
500
430
830
490
300
440
460
490
HI
a
44
110
6
37
120
19
24
IB
ao
SR
24
0
15
34
13
6
93
15
16
ZH
57
400
350
230
780
340
320
360
360
480
TCOLI
1.0E7
3.3E7
2.8E6
3.7E5
4.0E*
8.3E*
1.2E5
1.3E5
3.1E6
FCOU
1.0E6
4.6E5
5.0E2
6.9ES
6.7E1
5.7E3
3.8E1
8.SE1
9.1E3
Table A-9. LAND USE 40 - LIGHT INDUSTRY
-------
cm
CHICAGO
CHICAGS
TUUSA
TULSA
TULSA
MEO. I NO.
HEAVY IMD«
SAN J6SE I
PHOENIX I
MILWAUKEE
MILWAUKEE
BUCYRUS
BUCYRUS
BALT1M9RF
SAN J6SE I I
ATLANTA
ATLANTA
TUCSA
PHOENIX II
C
1
1
2
2
6
6
it
4
1
1
1
2
2
4
2
2
2
4
S
3
3
0
0
0
0
0
4
1
2
2
2
2
2
2
2
2
2
2
LB
50
50
50
50
5C
51
52
51
51
51
52
52
51
52
51
52
51
51
51
PD AA IP ADI S
20 11140
15 9800
5 5 .30 10100
3.8 20 .51 10200
3.2 1C .24 6600
9100
9800
7200
17200
12000
8000
300
5000
11000
21000
7200
5000
5000
5000
17200
C t FRS R
2 4
3 I
1 4 -15
1 4 -23
2 4 .23
4
4
1 4 18
04 12
24 0
24 0
0 1 2
34 2
24 26
01 26
1 4 59
I 2
3 1 2
2 1 9
14 60
S
1
8
8
8
4
4
10
7
H
2
2
2
2
2
2
2
1
1
2
1
1
1
2
LOAD
284
536
419
88
25
264
61
130
51
1500
500
800
57
16
19
150
31
18
BODS
2950
2820
5800
23800
35000
11000
3700
COD
23000
31800
49000
168000
267000
60000
17000
OP04 TP04 H03 HH4
30 431
1* 410
1600
1800
2500
1400 72
1400 72
ORGS
490
1600
1800
1600
1600
CD
3.2
4-7
?>2
1-7
6-3
1.6
4.7
"C
8.8
6.8
4.0
6.4
C.
0.3
CR
244
304
285
256
179
128
335
159
290
345
194
585
275
74
310
CU
"7
150
67
38
120
170
120
79
210
150
92
190
38
64
32
FE
23000
40000
26000
24000
25000
15000
43000
20000
26000
53000
22000
72000
14000
8100
22000
PB
1400
470
3500
1200
660
360
780
260
1800
310
1500
940
1400
65
1700
MN
420
870
600
330
270
310
620
470
680
1600
'450
1100
240
180
400
SI
35
33
93
1
21
30
35
6
12
14
93
84
12
26
23
SR
24
17
8
ia
ao
9
24
17
33
38
18
14
77
10
ZH
280
390
450
210
370
aao
200
140
410
300
280
880
310
160
150
ICOLI
s'.aes
2*6E6
3.2E5
4* 1ES
4.0E6
a.3E5
1-SE*
2t7E*
5 .BE*
1 '*E5
7-3E5
2.3E5
FCOLI
a.7E»
5*3E5
*.a&3
1«3E*
5t aE5
1.3E3
4.5E2
3.6E3
6.1E2
5.3E2
9.1E5
7.2E1
Table A-10. LAND USE 50 - INDUSTRIAL
to
o
CITY
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
ANN ARBOR
ANN ARBOR
ANN ARBOR
GRAND RAPIDS
GRAND RAPIDS
GRAND RAPIDS
CINCINNATI
CINCINNATI
CINCINNATI
ANN ARBOR
CHICAGO
CHICAGS
CHICAGS
CHICAOS
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHJCASO
CHICAGS
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
CHICAGO
MILWAUKEE
BUCYRUS
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
BUCYRUS
BUCYRUS
BUCYBUS
BUCYRUS
BUCYRUS
1
1
1
1 0
1
1
1
1
3 30
4 30
1 30
2 30
3 30
2 Bo
1 3 ao
ao
0 40
o ao
10
0 30
0 10
0 30
o ao
30
30
30
50
80
ao
ao
3 20
ao
ao
80
ao
ao
3 ao
s ao
3 ao
3 so
ao
80
ao
PD AA
9 ao
9 SO
3
3
3
3
1 3
1 3
1 3
1
1 3
1 3
1 3
1 3
1 3
1
1
1
1
i a
i I
i a 22
i a as
a 8s
31
i a 3a
i a si
i a sa
l a 21
i a si
i a a*
i a si
i a sa
9 ao
9 ao
9 ao
so
ao
so
ao
10
15
so
50
ao
ao
15
ao
so
ao
10
10
15
SO
.37
.37
.37
.37
.37
.37
.37
.37
5600
5600
5600
5600
5600
5600
5600
5600
5600
5600
5600
5600
300
5600
5600
7950
20400
13500
111*0
800
1000
700
1980
5860
5860
1828
240
1*070
3600
1200
17300
9800
1680
10100
1200
1*000
4000
300
16000
6000
80800
12000
8000
300
300
300
300
sooo
a
a
a
a
a
a
8
a
i
t
i
i
2
1
1
a
l
i
i
i
i
i
i
i
l
l
i
i
l
i
i
a
a
l
a
a
3
a
a
a
a
i
i
t
a
3
a
i
3
1
a
0
8
a
i
3
0
0
0
3
1
1
1
1
1
*
4
*
*
4
*
1
1
1
*
*
*
*
*
1
a
*
3
a
a
3
3
3
a
a
a
i
3
1
R
0
a
0
0
0
0
0
0
0
0
a
a
a
e
a
5
1
1
1
1
1
3
3
3
3
3
3
3
3
7
7
3
7
7
7
6
7
9
1
7
8
a
H
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
LOAD
32
170
13»
386
71
284
19
38
181
1*8
SI
68
121
13S
1«S
19
ao
96
536
153
2700
690
193
13
0
770
8*0
3D
51
1500
9BO
805
950
500
BOO
BODS
167000
17SOOO
46800
TtOOO
78900
18500
30700
81300
75000
22300
109000
23300
5050
4030
14540
2950
17SO
9100
aiso
asio
4770
8900
8030
63ZO
2280
32»0
9*30
1940
2820
32tO
4400
aioo
COD
611000
sasooo
483000
400000
586000
469000
252000
497000
86700
24800
66700
23000
18300
53100
50700
29500
61300
33600
3*000
45600
24600
8*500
72800
32100
31900
3*600
18000
a tooo
OP04 IP04
6670
5050
1810
2640
5790
650 2680
950 3770
220 2130
*MO
5440
4390
610 2510
a?
1*8
30
ao
70
1*2
21
63
30
a*
73
ao
8*
109
a*
;*
30
100
ito
H03
2720
1810
1920
1960
3110
650
BOO
440
4400
16000
2300
630
480
323
457
431
325
523
523
773
377
405
845
670
961
525
523
482
410
356
20
89
HH4
5390
4940
1880
3280
2630
595
730
3620
660
ORGN
6670
11880
6920
8000
10500
535
365
9250
12800
8800
450
530
890
CD
0.7
0>4
0.6
2.7
2.7
4.2
1.4
0.6
2.3
3.9
3.2
6.3
1.6
3.0
a. 6
1.6
4.7
4.0
at
9
3
5
1*7
180
130
1*1
153
ias
190
177
179
188
isa
138
178
335
159
CD
16
a
9
160
90
83
170
120
.IS
120
180
170
91
66
9*
iao
79
FE
3100
2200
2600
laooo
a 1000
15000
1*000
18000
15000
25000
34000
22000
15000
13000
15000
22000
43000
20000
PB
47
17
15
830
890
790
5SO
970
*70
a700
2200
660
360
350
430
1600
780
860
MH
280
»70
280
230
250
890
300
390
870
310
420
370
»90
620
»70
HI
a
*
6
88
17
33
0
26
11
30
37
21
30
13
36
,7
35
6
SR
28
33
81
a*
76
18
7
80
9
33
33
*>
E*
17
ZH
57
81
22
280
850
300
810
250
810
650
320
370
220
190
110
390
200
1*0
ICOLI
2.8E6
1.6E6
2.5E3
3.5E6
5.8ES
1.0E6
3.2E5
flCt
1.1E6
1.3E»
1.2E6
2-6E6
1.6E6
3.1E6
5>6E6
*ilE6
1-OE6
3-OE6
3.9E3
2.6E6
a.!E6
4.0E4
9.9E*
8'OE*
3. IE*
2.7E*
FCOLI
5.2E6
S.6E8
».OE3
9.7E8
i.«e»
*
-------
>
to
cm
DURHAM
OURHAr
DURHAh
TUUSA
TUUSA
TUUSA
TUUSA
TULSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
BAUTIM6RE
ATUANTA
TUUSA
BAUTIMeRE
BAUTIM8RE
BAUTI"BRE
BAUT1M8RE
BAUT1M9RE
BAUTIMBRE
BAUT1MBRE
BAUT1M8RE
BAUTIMBRE
ATUANTA
ATUANTA
>TUANTA
ATUANTA
ATUANTA
ATUANTA
ATUANTA
ATUANTA
ATUANTA
TUUSA
TUUSA
TW-SA
TUUSA
TUUSA
TUUSA
TUUSA
C
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
8
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
S
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
LD
20
20
20
50
30
20
30
20
50
20
20
20
30
20
to
20
10
20
20
20
2C
22
23
21
25
31
32
to
51
52
21
22
23
31
32
40
52
25
51
21
23
25
31
32
40
51
PD
9.7
10.5
8.2
.5
4.0
7.1
3.8
8.9
3.2
11.5
11.3
13.6.
4.3
9.5
0.0
2.3
.9
11.2
AA
10.
10
10
5
5
5
20
50
20
20
20
20
20
50
10
10
20
IP
.29
.34
.20
30
.55
.27
51
.30
.24
.32
.37
.31
.74
.41
.46
.23
.11
.38
ADI
2900
4200
2000
10100
10600
3800
10200
14500
6600
8300
6100
3800
2600
3200
0
8000
0
10200
10900
7200
7200
6700
700
700
8000
14200
27000
9900
11000
21000
300
300
300
14100
34100
5000
5000
300
5000
5000
700
16800
18000
5000
700
5000
S
1
1
1
1
2
1
1
2
1
2
1
2
2
1
1
1
1
2
1
2
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
2
2
1
2
2
2
C
1
1
1
1
1
1
1
2
2
1
1
1
3
1
1
1
1
1
0
0
0
0
2
2
0
1
1
1
1
0
2
3
3
3
2
2
2
£
1
2
A
1
1
1
4
3
1
4
2
4
1
2
3
2
2
1
1
1
2
1
1
1
3
1
1
1
4
3
4
4
1
1
1
I
4
4
1
1
1
1
1
1
1
4
1
1
1
FRS
20
24
13
15
20
19
23
20
23
24
28
37
51
42
48
20
08
22
R
26
2
9
26
26
26
26
26
26
26
26
26
2
2
2
2
2
2
2
2
2
9
9
9
9
9
9
9
S
4
14
1
13
5
4
1
4
3
4
4
14
1
21
1
14
30
10
28
7
H LOAD
400
600
390
419
46
60
88
22
25
32
35
24
41
33
37
41
12
28
1 260
1 220
1
1 100
1 67
1 93
1 33
1 5
1 *
1 87
1 57
1 16
1 295
1 31
1 165
1 60
1 215
1 1850
1 130
1
1
1 13
1 *9
1 17
i 20
i
1 122
1 31
BODS
5300
5800
29300
11*00
23200
67300
35000
19000
39300
2*000
25300
25000
39*00
32100
18200
43>00
61000
»SOO
43000
COD
65500
49000
163QOO
91700
168QOO
510000
267000
114600
298000
277000
247QOO
207QOO
222000
192000
90900
158000
20000
30000
91000
OFO4
1600
3200
2800
1800
3300
2500
1600
3000
240O
1600
1500
2700
2600
1700
3000
1000
620
1700
IP« H03
212
38
55
37
QRGN
490
3600
2200
1600
2700
1800
1900
1800
1600
1900
1200
1900
3100
1700
1400
1900
1100
2000
CD
8.0
1.1
1.7
8.8
6.1
5.5
5.2
25.
3.7
8.2
8.8
6.8
0.
0.
5.3
0.
1.5
6.4
0.4
1-0
1.3
2.4
1.6
9.3
2.8
0.
CR
273
220
112
210
290
120
215
356
264
760
290
345
182
127
207
100
162
585
275
186
150
24
135
63
138
74
CD
160
120
91
120
120
120
190
290
210
280
210
150
150
70
300
30
140
190
38
160
71
66
96
66
110
64
Fl
24000
24000
12000
19000
24000
15000
18000
40000
23000
31000
2BOOO
53000
24000
13000
20000
12000
16000
72000
14000
20000
17000
1400
11000
8800
15000
8100
PB
117
137
66
2200
660
740
1700
1000
730
1500
5700
2100
9999
1800
310
280
480
3900
2000
740
940
1400
1100
970
230
1300
2400
1100
65
KB
480
350
340
290
430
150
270
770
500
830
680
1600
210
280
280
290
300
1100
240
430
520
100
250
160
440
180
HI
31
19
9
45
55
2
18
SI
7
37
12
14
9
7
18
12
19
84
12
3t
1
0
19
to
24
26
SR
21
4
63
24
23
S
6
25
33
3*
33
38
3
4
13
5
6
3
.,
78
110
6
37
38
93
77
ZH
440
330
240
630
760
730
490
1000
910
780
300
270
180
1100
320
320
880
310
ISO
180
220
420
ISO
360
160
ICOLI
3.2E5
l!sE6
4-1EB
SiSE*
4.0E6
7i7E5
6.3E6
9i6£6
3.0E6
6.4E6
3.8E6
6.0E5
8.4E4
S.1C6
l.OE!
1.6E5
1.166
I.OEB
2>OES
1.3E6
1.8E5
2.0E5
1.7ES
8.3E4
1-4S5
1.4U
6.5E!
9.4^6
5.6ES
1.2E5
7.3E!
ICOLI
LIES
4.ZE3
».9E»
».IE4
I.JE»
SOEfc
B.2E5
2*9E3
1»2E*
7.0M
7.0E3
LIE*
5.0E2
3.IE3
6.3E3
1 36*
l.SE»
t.SES
l.*E3
fr«6C4
1.8E4
4,»E3
C«2CE
7.tE3
8.7E3
9. jet
f.lCt
5t 9C2
3*4C^
S.tEB
3.«E1
f.lEB
Table A-12. CLIMATE 2 - SOUTHEAST
-------
to
to
CITY
TUSCBN
TUSCBN
TUSCBN
TUSC8N
TUSC8N
TUSCBN
SAN JBSE I
PH6EN1X I
SAN J6SE II
PH8ENIX II
SAN JBSE I
SAN JBSE I
SAN JBSE I
SAN JBSE I
SAN J8SE I
SAN JBSE I
SAN JBSE I
PHBENlX
PHBENlX
PH9ENIX
PH8ENIX
PH8ENIX
PH8ENIX
PHOENIX
PHBENlX
SAN JBSE II
SAN JBSE II
SAN JBSE It
SAN JBSE II
SAN JBSE II
SAN JBSE II
SAN JBSE II
PH8ENIX II
PH8ENIX II
PH8ENIX II
PH8ENJX II
PH8ENIX II
PHSENIX II
PH8EN1X I!
PH8ENIX It
OP04 TP04 S03
ZH TC01I FCOII
4 3
4 1
4 3
4 1
4 3
4
4 1
4 2
4 2
4 2
4 ^
4 2
4 2
4 2
4 2
4 2
4 2
4 2
4 2
4 2
: i
20
20
30
30
40
40
20
20
20
20
21
22
23
31
32
40
51
21
22
23
25
31
32
40
51
21
22
23
31
32
40
51
21
22
23
25
31
32
5?
10000
10000
14000
14000
12000
12000
10400
11400
10400
11400
9400
12050
300
3600
13000
7250
7200
2700
11100
10300
11000
11900
15000
12300
17200
9400
12050
300
23600
13000
7250
7200
2700
11100
10300
11000
11900
15000
12300
17200
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
I
2
1
2
2
0
2
2
1
1
0
1
2
1
2
1
2
1
3
1
2
?
1
1
1
2
2
1
1
4
4
1
1
1
1
1
1
1
4
4
4
4
1
1
1
1
4
4
4
4
1
1
1
4
4
4
4
1
1
1
1
4
4
4
4
14
12
59
60
12
13
12
13
18
13
18
12
12
12
12
12
12
12
12
59
59
59
59
59
55
59
60
60
60
60
60
60
60
60
2
6 2
2
2
2
2
2
2
2
2
2
8 2
1 2
7 2
3 2
1 2
13 2
10 2
8 2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
200000 520
312000 450
101000 170
147000 230
201000 24Q
224000 760
70 17000 34000 770
92 10000 46000 340
860 8900 68000 7SO
11000 58000 3000
70
85
24
21
26
131
61
77
238
18
34
26
53
41
130
11
8
3
20
3
204
19
27
IB
6
8
4
3
4
IS
2730
940
1020
350
430
790
3600
460
45
130
2300
2300
1BOO
3200
3.5
7.2
4-3
0.7
3.5
3.5
4.5
2.6
5,0
3.4
2.2
4.0
5.5
a. s
6.0
6.6
2.0
11.
1.7
6.0
2-0
5.4
4.9
3.1
3.7
4.0
1.1
0.3
0.
0.8
6.4
3.1
0.3
304
211
245
141
325
325
295
320
325
300
285
203
159
215
238
190
168
208
256
295
75
245
310
430
306
194
185
US
111
193
71
188
310
71
120
75
67
88
96
33
110
80
87
67
150
39
140
53
69
25
100
38
130
34
53
84
96
71
92
99
74
52
46
99
120
32
24000
21000
25000
15000
27000
23000
21000
16000
44000
24000
26000
23000
17000
21000
23000
15000
15000
20000
24000
48000
11000
26000
30000
23000
17000
22000
21000
20000
11000
25000
5000
24000
28000
2700
1500
3400
620
2400
2000
2100
3500
7600
2000
3500
1200
3700
970
3600
3200
1600
2500
1200
5700
600
3900
5100
9999
2700
1500
340
2000
220
2900
0
2100
1700
460
540
460
490
450
450
350
470
410
500
600
320
280
680
440
380
360
430
330
560
230
470
500
540
490
450
700
370
450
420
280
460
400
96
15
87
11
85
80
100
110
93
110
93
0
0
25
5
6
7
6
1
120
30
75
83
140
120
93
11
3
0
7
170
11
23
8
14
16
16
19
17
5
20
10
0
a
17
13
13
IS
15
11
15
IS
28
21
9
15
13
13
IB
23
23
12
15
25
15
10
360
340
400
210
320
370
260
600
410
350
450
290
210
330
490
335
720
230
210
810
210
420
510
380
340
2SO
350
250
130
290
400
360
150
8.667
2«467
3.4E7
li5E7
1.0E7
3i3E7
2. 654
1«2E»
5.3E?
1.9E5
9.164
1.864
6.7K5
4.0E4
5.8E4
2.0E5
1.9E5
2.5E4
S.6E4
3.5(4
4.2E4
lt3H
2.3ES
1.TE7
7.5E!
S.2E6
3-SE5
1.0E6
4.4E5
I.OE2
5.8E3
S.SE3
1.3E3
3.6E3
9.0E2
6.7E3
6.7E1
6.1E2
LIE*
5.4E3
S.7E4
2.SE3
l.lEt
1.4M
!!"
7. It!
Table A-13. CLIMATE 4 - SOUTHWEST
cm
SEATTLE
SEATTLE
SEATTLE
SEATTLE
SEATTLE
SEATTLE
SEATTLE
SEATTLE.
C S in
5 3 20
5 3 21
5 3 IS
5 3 24
5 3 25
5 3 31
5 3 32
5 3 40
ADI S
11700
6700
6900
3200
1 1
1 3 1
1 1 3
2 1 1
12300 1 2 3
16000 1 2 4
26500 1 2 4
10000 224
K
12
12
12
12
12
12
12
12
S H
1
1
1
1
1
1
1
1
LOAD
39
45
22
J2
16
16
5»
BODS COD OP04 TPO* N03
10000 38000 1100 59
ZH TCOLI PCOLI
2000
1.1
0*
3.4
1.3
1.6
2.3
1.5
1.4
243
233
239
250
254
266
247
239
89
80
100
67
48
210
63
110
29000
27000
37000
23000
59000
32000
42000
27000
2100
1700
3000
2500
2600
3300
4700
1100
460
430
490
460
440
43Q
440
490
32
39
40
29
39
39
40
20
11
13
g
9
15
0
16
480
460
660
460
410
500
390
*eo
7-7E!
1 t6ES
6t4E4
3«lt5
1.6ES
1.8E!
3-1E6
>ilE3
1.PE3
(4E2
5.0E3
1.5E3
S.1E3
Table A-14. CLIMATE 5 - NORTHWEST
-------
CITT
CINCINNATI
CHICA08
TULSA
TULSA
SAN JOSE I
MILWAUKEE
BUCYRUS
BUCYRUS
BUCYRUS
BUCYRUS
SAN J8SE 11
ATLANTA
ATLANTA
ATLANTA
ATLANTA
c
1
1
a
a
4
1
1
1
1
1
4
a
2
2
a
8
0
3
0
0
4
2
2
a
2
a
2
a
2
2
2
va
10
20
to
10
23
13
21
23
24
51
as
ai
aa
23
25
TO AA
5 2o
20
0.0 10
.9
300
210
0
0
300
300
300
300
300
300
300
300
300
300
300
2
a
i
2
l
i
i
i
l
i
2
i
i
2
1
1
1
3
0
0
0
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
R
12
0
2
2
2
a
59
2
a
2
2
s
3
7
14
j
21
28
H
2
2
2
2
2
2
2
2
1
j
1
1
LOAD BODS COD OPO4 T
135 6380 45600 73
57 39AOO 222000 '2700
12 1S200 90900 1700
2*
to
950
aos
950-
500
3
a93
31
165
PO* HO3 HB4 OKGH CD
*4O 160OO 12BOO
670
19OO
1700
4.5
0.6
3.0
2.6
1.6
4.7
5.4
0«
C.
CR
295
153
132
138
178
335
245
tea
127
CD
33
120
91
66
94
120
53
150
70
ra
aiooo
laooo
13000
1SOOO
22000
43000
26000
2*000
13000
n
2100
970
350
430
1600
780
3900
280
480
HH
350
250
420
370
490
620
*TOv
210
280
HI
100
26
13
36
7
35
75
9
7
s»
5
76
33
33
41
2*
9
3
*
CM
260
250
190
110
390
200
»20
270
ISO
TCOLX 1
Z>SE9
S.tE*
2.SE*
8»tC*
9ite»
a.oes
1.3E6
l.«ES
Ptxmx
».oe»
s:;n
4*3E3
li»E3
6.8E»
LIE*
4.6E3
to
CO
Table A-15. AVERAGE DAILY TRAFFIC - LESS THAN 500
CITY
DURHAM
DURHAM
DURHAP
LAKRENCE
CHICAGO
CHICAG6
CHICAGO
CHICAGO
CHICAG9
CHICAGO
CHICAGO
CHICAGO
TULSA
TULSA
TULSA
TULSA
BUCYRUS
MED/NEW/S1NG
MED/OLD/SING
SAN J8SE !
PHOENIX I
MILWAUKEE
BALTIMORE
BALTIMORE
TULSA
TULSA
PHOENIX II
SEATTLE
C
a
2
2
3
1
1
1
1
1
1
1
1
2
2
a
a
i
6
6
4
4
1
2
s
a
a
4
5
S
0
0
0
0
3
3
3
3
3
3
3
3
0
0
0
0
2
0
0
4
1
a
2
2
a
2
2
3
LD
20
20
20
20
20
20
ao
20
20
ao
20
ao
20
ao
30
ao
20
S3
24
31
ai
aa
23
24
23
40
21
24
PD AA
9.7 10
10.5 10
8.2 10
10
15
50
50
15
20
10
20
7.1 5
13.6 20
4.3 20
9.5 50
IF
.29
.34
.20
.38
.27
.31
.74
.41
ADT
2900
4200
2000
2500
800
1000
700
1980
1828
3600
1200
1620
3800
3800
2600
3200
1200
2500
1400
3600
2700
4000
700
700
700
700
2700
3200
S
1
1
1
1
1
2
1
1
1
1
1
1
2
1
2
2
1
2
C
1
1
1
1
1
a
2
3
2
1
1
2
1
1
3
2
2
3
1
0
2
1
3
1
A FRS
1 -20
1 .24
1 -13
1
1
a
4
3
3
2
2
3
1 -19
3 .37
2 .51
2 -42
1
1
1
4
1
4
1
1
1
1
1
1
R
z
13
12
0
26
26
9
9
60
12
S
1
3
3
3
3
7
7
7
8
6
13
5
M
a
2
2
2
2
2
2
2
2
2
a
2
1
1
1
1
2
1
LOAD
too
600
390
aio
19
32
121
1*8
121
19
ao
153
60
24
41
33
690
418
21
77
93
67
93
69
122
27
22
BODS
5300
12900
i7ao
9iOO
2180
2810
2030
3240
9430
3220
ll&OO
aiooo
25300
25000
2100
12000
COD
65500
61600
18300
53100
50700
29500
3*000
2*500
72800
3*600
91700
277000
247000
207000
2tOOO
40000
OP04 TP04
212
20
70
1*2
21
24
24
109
30
2800
2400
1600
1500
180
1100
1100
N03
11600
325
523
523
773
845
525
523
356
89
64
64
ot.au
2200
1600
1900
i?00
890
2200
2200
CD
2.7
3.3
2.8
2.6
4.0
1.4
6.1
5.5
1.3
2.8
1.1
1.3
CR
180
192
183
320
203
1*1
290
120
150
138
185
250
CU
90
81
94
110
150
170
120
120
71
110
99
67
FE
21000
18000
2QOOO
16000
23000
14000
24000
15000
17000
15000
21000
23000
PB
117
137
66
890
1200
1600
3500
1200
580
1000
730
970
1100
3*0
2500
UN
470
420
370
470
320
230
430
150
5ao
440
700
460
NI
17
36
13
110
0
0
55
a
i
24
11
29
SR
33
32
IS
ao
17
a*
as
5
110
93
as
9
ZN
250
290
530
600
aso
210
760
730
180
360
350
*60
TCOLI
1-1E6
lilE6
li3E6
1-2E6
3.1E4
1KJE6
3>OE6
2tlE6
1.5E6
9.6E6
3.0E6
6'4E6
4.aE5
1.0E5
6.5E5
1.2ES
2-OES
FCOLI
LIES
1.3E5
4. IE*
7.0EB
S.7E5
2i7E5
1.ZE6
i. SEA
1.0E6
4.8E4
7.0E3
7.0E3
LIE*
l.*E*
5.SE2
3.8E1
1.1E*
Table A-16. AVERAGE DAILY TRAFFIC - 500 to 5000
-------
CITY
CINCINNATI
CINCINNATI
CINCINNATI
ANN ARBOR
ANN ARBOR
ANN ARBOR
GRAND RAPIDS
GRAND RAPIDS
GRAND RAPIDS
CINCINNATI
TUSCON
TUSCON
TUSC8N
TUSCON
TUSC8N
TUSCBN
CINCINNATI
ANN ARBOR
CHICAG9
CHlCAGB
CHlCAGB
CHICAGO
CHlCAGB
CHlCAGB
CHlCAGB
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
SAN J?SE !
PHBENIX I
MILWAUKEE
BALTIMflRE
SAN J9SE II
ATLANTA
. TULSA
>> PHBENIX M
I SEATTLE
M LOw/OLD/SlNG
rf* L9W/6LD/HULT
MED/OLO/fULT
LIGHT IND.
MEOt IND*
HEAVY INDt
SAN J6SE I
SAN J9SE I
SAN JBSE I
SAN JBSE I
SACJ J6SE I
PH9ENIX I
PHOENIX I
PH8ENIX I
PH8ENIX I
PHBENIX I
MILWAUKEE
MlLVrAUKEE
MILWAUKEE
MILWAUKEE
BUCYRUS
BALTIMORE
BALTIMBRE
BALTIMBRE
BALTIM9RE
BALTIMBRE
SAN JOSE 11
SAN JBSE II
SAN JOSE U
SAN J6SE II
SAN JSSE II
ATLANTA
ATLANTA
ATLANTA
ATLANTA
TULSA
TULSA
TULSA
PHBENIX II
PHBENIX II
PHBENIX II
PHBENIX II
SEATTLE
SEATTLE
SEATTLE
SEATTLE
C
1
1
1
1
1
1
1
1
1
1
1
4
4
4
4
4
4
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
4
4
1
2
4
2
2
4
5
6
6
6
6
6
6
4
4
4
4
4
4
4
4
^
4
1
1
1
1
1
2
2
2
2
2
4
4
4
4
4
2
2
2
2
2
2
4
4
4
4
4
5
5
5
S
3
4
2
2
3
4
0
0
0
0
3
1
3
1
3
1
0
0
3
3
3
3
3
3
3
0
0
0
0
0
0
0
0
0
4
I
2
2
2
2
2
2
3
0
0
0
c
c
0
4
4
4
4
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
UJ
3C
30
3C
30
20
20
20
40
20
10
30
20
20
30
30
10
10
30
20
30
30
50
20
20
20
SO
50
30
50
20
50
20
20
20
20
20
20
2C
20
20
20
20
20
2C
21
22
25
10
51
52
21
22
3?
40
51
22
23
25
31
10
21
31
51
52
52
22
25
31
to
51
21
32
1C
51
31
51
21
32
51
22
23
25
31
10
21
?2
25
10
PD M
9 20
-9 20
9 20
20
9 20
9 20
20
20
20
20
20
50
15
.5 5
1.0 5
3.8 so
8.9 50
3.2 20
11.5 2o
11.3 20
2.3 lo
11.2 20
IF
.37
.37
.37
.37
.37
.30
.55
.51
.30
.21
.32
.37
.23
.38
ADT
5600
5600
5600
5600
5600
5600
5600
5600
5600
5600
10000
10000
11000
11000
12000
12000
5600
5600
7950
13500
11110
5860
5860
11070
9800
10100
10600
10200
11500
6600
8300
6100
8000
10200
10400"
11400
10100
10900
10100
7200
7200
11400
11700
5600
8000
10800
8300
9100
9800
9100
12050
13000
7250
7200
11100
10300
11000
11900
12300
11000
6000
12000
8000
5000
6700
8000
14200
9900
11000
9400
12050
13000
7250
7200
14100
5000
5000
5000
5000
5000
5000
11100
10300
11000
11900
12300
6700
6900
12300
10000
S
2
2
2
2
2
2
2
1
2
1
g
1
2
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
I-
c
1
1
1
1
1
1
1
2
2
2
2
1
3
1
1
1
2
2
1
1
1
1
1
2
1
2
1
2
0
2
2
1
1
0
2
2
3
1
0
0
2
2
1
2
1
2
1
1
2.
3
3
2
2
1
2
2
1
3
1
2
2
,
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
4
1
4
4
4
4
2
2
3
1
4
3
4
2
4
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
4
4
4
1
1
4
4
4
1
1
1
4
4
1
4
4
4
4
3
1
4
4
4
1
1
4
4
4
4
1
1
1
1
1
1
1
1
1
4
4
1
3
3
4
PRS
15
20
23
20
23
24
28
20
22
H
11
12
0
26
59
2
9
60
12
12
13
18
13
18
12
12
12
12
12
0
0
0
0
2
26
26
26
26
26
59
59
59
59
59
2
2
2
^
9
9
9
60
60
60
60
60
12
12
12
12
S
1
1
1
3
3
3
6
7
4
11
1
7
3
1
10
7
1
8
8
1
1
1
3
1
1
30
10
7
H
2
2
2
2
2
2
2
2
2
1
2
1
1
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
2
2
2
2
2
1
1
1
1
1
1
1
2
2
2
2
2
1
1
1
1
LOAD
32
170
131
71
281
81
62
118
536
119
46
88
22
25
32
35
11
28
70
92
2700
260
860
220
372
659
916
261
70
85
26
131
61
238
18
31
26
11
103
260
51
1500
800
100
33
5
87
57
11
a
3
201
19
60
1850
150
13
31
IB
6
8
4
1
39
15
12
59
BODS
167000
175000
46200
76000
78900
18500
30700
21300
75000
109000
23500
5050
14510
2950
1770
2900
2280
2820
5800
29300
23200
67300
35000
19000
39300
32100
43900
17000
10000
1100
61000
8500
4SOO
13000
11000
10000
10000
22000
7100
15000
11000
3700
COD
611000
525000
123000
400000
586000
489000
200000
312000
101000
147000
201000
224000
197000
26700
66700
23000
61300
32600
21600
31800
19000
163000
168000
510000
267000
1HOOO
298000
192000
158000
31000
16000
18000
20000
68000
30000
91000
58000
38000
32000
26000
21000
73000
60000
17000
OPO4 TP04 W
6670 2
3050 1
1810 1
26*0 1
579Q 3
650 2620
950 2770
220 2130
4050 1
520 2
150
170 1
230
210
760
1390 2
610 2510
38
142
30
63
30
20
14
1600
3200
1800
3300
2500
1600
3000
2600
3000
770 3
310
100
1000
750
620
1700
3000
1100
1100
1100
1100
1400
1400
1400
>3 HH4 OBGH
20 5390 6670
1C 4940 11880
20 1880 6920
>60 3280 8000
10 2630 10500
50 595 535
Co 730 365
(1C
CO 9250
30
20
50
30
Co 3620 8ROO
30 660 150
80
57
3l
77
05
61
10
190
3600
1600
2700
1«00
1900
1800
3100
14CO
»OC 230C
5- 2300
20 530
38 1900
45 laoO
55 1100
37 2000
30 3200
59 2000
64 220C
64 2200
64 2200
7J 1600
^^ 1600
7a 1600
CD
0.7
0.1
0.6
3.5
7.2
2.7
8.0
4.3
1.1
1-7
0.7
1.1
2-5
3.6
3.1
4.4
3.2
4.7
3.5
3.5
5.0
3.1
2.2
5.5
8.8
6.0
6.6
11.
1.2
3.9
6.3
1.6
1.0
8.8
5.2
25.
8.2
8.8
6.0
2.0
3.1
3.7
4*0
5.3
1.5
6.1
0.1
1-0
9.3
C«
C.3
C.
0-8
3-1
0>
3.1
1.6
1.4
CK
9
3
5
301
211
147
273
245
220
112
141
243'
208
188
175
288
241
301
325
325
325
300
285
159
215
238
190
208
130
190
179
128
159
210
215
356
760
290
295
75
130
306
194
207
162
585
275
186
63
74
165
111
193
188
233
239
254
239
ra
16
8
9
71
160
160
75
120
91
67
89
110
90
79
130
87
150
88
96
80
87
67
39
110
53
69
100
83
810
120
170
79
120
190
290
280
210
130
34
96
71
92
300
110
190
38
160
66
61
71
52
16
120
80
100
48
110
FE
3100
2200
2600
2100C
21000
18000
24000
25000
24000
12000
15000
29000
24000
20000
24000
22000
23000
10000
27000
23000
11000
24000
26000
17000
21000
23000
15000
20000
15000'
25000
22000
15000
20000
19000
1BOOC
10000
31000
25000
18000
11000
23000
17000
22000
20000
16000
72000
11000
20000
3800
8100
20000
11000
25000
21000
27000
37000
59000
27000
FB
47
17
15
2700
1500
830
2200
3100
660
710
620
2100
1500
1900
1900
2800
1100
170
2100
2000
7600
2000
3500
3700
970
3600
3200
2500
790
2700
660
360
260
1700
1500
5700
9999
1800
5700
600
9999
2700
1500
3900
710
940
1100
1100
2100
65
2000
220
2900
2100
1700
3000
2600
1100
M
160
540
280
480
460
350
310
190
460
120
330
330
190
120
870
150
150
110
500
600
280
680
410
380
130
280
300
270
310
170
290
270
770
830
680
560
230
510
190
150
580
300
1100
210
130
160
180
370
150
120
460
130
190
140
190
HI
8
1
6
96
15
22
31
87
19
9
11
32
38
28
14
44
35
33
85
80
93
110
93
0
25
5
6
6
33
30
21
30
6
45
IB
51
37
12
120
30
140
120
93
18
19
81
12
32
10
26
3
0
7
IB
39
10
39
20
SR
8
11
28
21
16
1
63
16
11
26
19
12
21
21
17
19
17
10
0
8
13
13
12
15
15
21
20
20
9
17
21
6
25
31
33
28
21
13
13
18
13
6
3
11
78
38
77
23
12
15
15
13
8
16
ZH
57
21
22
360
310
280
610
400
330
240
210
480
370
360
350
too
280
390
320
370
110
350
150
210
330
190
335
530
300
650
370
220
110
630
490
1000
780
410
BIO
210
380
340
280
1100
320
880
310
350
190
160
950
130
290
360
160
660
410
SO
TCOLI
J.8E6
1.6E6
8.6E7
S.1E7
3.4E7
1.5E7
1.0E7
3.3E7
3'SE6
1.0E6
3.2E5
2.6E6
1.6E6
4.1E6
S.6E6
3.2E!
1.6E6
4.1E5
5.5E6
4.0E6
7.7E5
6.3E6
6.0E5
S-1E6
4.0E4
1.0E5
2.6E4
1.6E5
l!aE5
7.7E5
2-1ES
1-5E5
LIES
3.7E5
2.3E!
1.2E1
9.9E4
3.1E4
2.7E*
5.3E5
1.9E5
6.7ES
4.0E4
5-8E4
2.0E5
8.3E4
1.1E6
7.3E5
1.9E5
2.5E4
8.6E4
3«5E»
1.3ES
6\E1
3-1E5
3.1E6
FCOL1
5.2EB
2.6E6
1.7E7
7.5E5
8.EE6
3. SEE
1.0E6
1.6E5
5.7E5
4.0E3
2.7E4
6.1E5
t.9E5
4.0E5
5i3E5
4.EE3
6.9E4
1.3E1
E.5E1
5.2E5
2.9E3
1.2E4
3.8E3
1.3E1
5.7EO
2.0E2
1.5E4
2.5E5
5.8E3
9.1E3
1.ZE1
2.1E4
6.9E5
1.3E3
4.5E2
2.6E1
3.6E3
2.5E3
1.5E3
6.7E3
6.7EJ
4.1E2
2.ZE2
5.7E3
S.3E2
9.3E2
9.1EB
5t»E3
2.7E1
8.2E3
8.4EI
I.2E3
3<6Efc
2.1E2
9.1E3
Table A-17. AVERAGE DAILY TRAFFIC - 50OO to 15,000
-------
to
Ul
CITI
CHICAGO
CHICA39
CENT'BUSi
SHOPiCENTER
PH8ENIX I
PHBENIX I
MILWAUKEE
MILWAUKEE
BALTIMORE
BALTIMORE
SAN JOSE II
ATLANTA
TULSA
TULSA
PHBENIX II
PHOENIX II
SEATTLE
SEATTLE
C 8
1 3
1 3
6 0
6 0
4 1
4 1
1 2
i 2
2 2
2 2
4 2
2 2
2 2
2 2
4 2
4 2
5 3
5 3
W
30
20
31
32
32
51
25
32
32
52
31
32
25
31
32
51
31
32
OPO4 TPO4 H03
ZN TCOLI FCOLI
eo
10
20400
17300
15500
18800
15000
17200
16000
20800
27000
21000
23600
34100
16800
18000
15000
17200
16000
26500
2
1
2
1
1
2
1
1
1
1
1
I
2
1
0
2
^
0
0
2
0
2
2
1
1
2
2
4
2
4
4
4
4
1
4
3
1
4
4
1
4
4
4
4
4
12
12
0
0
26
26
59
2
9
9
60
60
12
12
1
3
13
8
9
7
4
4
14
2
2
2
2
2
2
1
1
2
1
1
1
2
2
1
1
326 4030 24800 27
96 19*0 32100 2B
296 8300 30000 1000
122 8600 21000 1000
S3
130
770
30
4
16
20
215
17
20
3
IB
16
16
323
482
600
600
1600
1600
6.5
3.7
2.0
1.7
2.3
3.2
3.7
6.8
4.9
0-
2.4
1-6
6.4
0.3
2.3
1.5
2*7
205
16«
256
125
177
264
345
310
100
24
135
71
310
266
247
88
250
25
38
72
120
210
150
84
30
66
96
99
32
210
63
24000
23000
15000
24000
15000
34000
23000
53000
30000
12000
1400
11000
5000
22000
32000
42000
3600
3600
1600
1200
470
2200
2100
310
5100
2000
230
1300
0
1700
3300
4700
420
370
360
330
290
390
500
1600
500
290
100
250
280
400
430
440
46
57
7
1
18
37
7
14
83
12
0
29
170
23
39
40
20
16
11
12
20
7
33
38
15
5
6
37
25
10
15
0
640
too
720
210
210
350
510
300
510
320
220
420
400
150
500
390
5.8ES
3.9E5
LIES
2«OE6
BiOE*
1.8E4
1.7E5
9.4E6
5.6E5
4.2E4
2-3ES
1.6E5
liBES
3.2E4
1.SE4
LIES
S.2ES
9.0E2
7.8E3
3.4E5
5iEE5
1.4ES
7.ZE1
5.0E3
ItSES
Table A-18. AVERAGE DAILY TRAFFIC - GREATER THAN 15,000
-------
to
cur
DURHAM
DURHAM
DURHAM
LAWRENCE
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
CINCINNATI
TUSC8N
TUSCAN
CINCINNATI
CHICAGO
CHICAOS
TULSA
TULSA
TUISA
TULSA
TULSA
SAN JOSE I
PHSENIX I
MILWAUKEE
BUCYRUS
BALTIMBRE
S*N JOSE I!
ATLANTA
TULSA
PHSENIX II
SEATTLE
LSW/BLO/SINO
L9W/BLD/MULT
MEO/NEW/SING
MED/8LD/SINQ
WED/BLD/MULT
»AN JOSE I
IAN JOSE I
CAN JOSE I
PHBENIX I
PHBENIX I
PHBENIX I
PHBENIX !
MILWAUKEE
MILWAUKEE
MILWAUKEE
BUCYRUS
BUCYRUS
BUCYRUS
BUCYRUS
BALTIMBRE
BALTIM9RE
BALTIMBRE
BALTIMORE
SAN J6SE I)
SAN JOSE II
SAN JBSE II
ATLANTA
ATLANTA
ATLANTA
ATLANTA
ATLANTA
ATLANTA
ATLANTA
TULSA
TULSA
TULSA
TULSA
TULSA
TULSA
PHBENIX II
PH6CNIX II
PHBENIX II
PHBENIX I!
SEATTLE
SEATTLE
C S
2 0
2 0
2 0
3 0
1 3
1 »
1 1
1 2
1 3
1 0
1 0
1 3
4 1
1 0
1 3
1 3
2 0
2 0
2 0
2 0
2 0
* *
4 1
1 2
1 2
2 2
4 2
2 2
2 2
4 2
5 3
6 0
6 0
6 0
6 0
6 0
4
4
4
4
4
4
4 1
1 2
1 2
1 2
1 2
1 2
1 2
1 2
2 2
2 2
2 2
2 2
* 2
4 2
4 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
* 2
4 2
4 2
4 2
5 3
5 3
UI PD
20 9.7
20 10.5
20 8.2
20
30 9
30 9
30 9
30 9
30 9
30 9
10 9
30
30
30 9
20
50
20 7.1
20 11. S
40 0.0
20 2.3
10 .9
20
20
20
20
20
20
20
20
20
20
21
22
23
24
25
21
22
23
it
2'2
23
25
21
23
25
21
23
24
51
23
24
25
52
21
22
23
21
22
23
40
52
25
51
21
23
25
32
40
51
21
22
23
25
21
24
U IT ADI
10 .29 2900
10 .34 4200
10 .20 2000
.38 2500
20 .37 5600
20 .37 5600
20 .37 5600
20 .37 5600
20 .37 5600
20 .37 5600
20 .37 300
14000
14000
20 .37 5600
10 BOO
15 9800
5 .27 3800
20 .32 8300
10 .46 0
10 .23 8000
.11 0
10*00
11*00
10100
1200
* 10900
10400
7200
7200
11*00
11700
5600
8000
2600
1*00
10800
9*00
12050
300
2700
11100
10300
11000
14000
300
16000
300
300
300
300
700
700
8000
21000
9400
12050
300
300
300
300
5000
5000
300
5000
5000
700
16800
5000
700
5000
2700
11100
10300
11000
6700
3200
S
1
1
1
1
2
2
2
2
2
2
2
2
1
2
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
I
1
1
1
1
2
1
1
1
1
1
2
1
1
2
1
1
1
1
1
I
1
1
1
1
2
2
2
2
2
I
1
1
1
1
2
C
1
1
1
1
t
1
1
1
1
1
3
1
1
1
I
1
2
1
2
2
0
2
1
I
2
3
0
0
0
1
0
0
o
1
2
1
1
1
3
3
3
2
2
2
1
2
3
1
2
3
1
A res a
1 <20
1 »2*
1 >13
1
1
1
1
1
1
t
I
1
1
1
19
.2*
*8
20
08
1*
12
0
1
1
1
1
1
1
1
1
1
1
1
1
1
I
t
1
1
I
1
I
1
>
1
1
1
1
I
1
1
1
1
1
1
1
J
1
1
1
1
1
1
1
1
2
26
59
2
9
60
12
12
13
12
12
12
12
12
0
0
o
2
2
S
2
26
26
26
26
59
59
59
2
2
S
2
2
2
2
9
9
9
9
9
9
60
60
60
60
12
12
S
1
6
7
4
1*
8
1
7
)
7
7
9
13
5
4
*
1*
1
21
30
10
28
7
M
2
2
2
2
2
1
2
1
1
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
2
2
2
1
1
1
1
1
1
1
1
1
t
J-
1
2
2
2
2
1
1
LOAD BODS COD OPO4 TfO» 103 HH4 OBOJ CD
400 6300 65500 tit
600
390
210 11*00 61600 11600
167000 611000 667Q 2720 5390 6670
175000 525000 5050 1810 4940 11SBO
46«00 423000 1810 1920 18SO 6920
76000 400000 26*0 I960 3280 8000
78*00 526000 579fl 3110 2630 10500
75000 489000 4iSO 4400 9250
22300 252000 5**0 16000 12800
101000 170 1020
1*7000 230 350
32 10*000- 497000 4390 2300 3620 8800
|9 1720 18300 20 325
536 2120 31800 14 410
60 11*00 91700 2BOO 2200
32 19000 11*000 1600 1900
57 3*600 222000 2700 1900
*1 31100 192000 2600 3100
12 1(200 90900 1700 1700
70 17000 3*000 770 3600 2300 3.5
92 10000 46000 3*0 *5o 2300 7'2
2700 4*00 18000 100 So 530 2.7
690 2100 21000 180
260 61000 20000 1000
860 8900 68000 750
220 4500 30000 620
43000 SIOOO 1700
11000 58000 3000
10000 38000 1100
372 10000 32000 1100
65* 22000 26000 1100
418 12000 40000 1100
1100
7100 24000 1100
70
8$
2*
77
238
18
34
103
40
770
950
205
950
500
67
93
33
16
11
8
3
295
31
165
1850
150
13
6*
17
122
31
27
18
6
8
3*
22
89 890 2.7
38 1900 8.0
45 1800 4.3
55 1100 1.1
37' 2000 1.7
130 3200 0-7
59 2000 1.1
64 2200 3.5
64 2200 3.6
64 2200 3.3
64 2200 2.8
64 2200 3.1
3.5
3.5
4.5
4-0
5.5
8.8
6.0
4.2
0.6
2.3
3.0
2.6
1.6
4.7
6.1
5.5
5.2
6.8
6.0
2.0
5.4
0.
0<
1.5
6*4
0.4
1-0
1-3
2.4
9.3
2.8
0.
1.1
0*3
0.
0«8
0.
1.3
at
304
211
147
180
273
245
220
112
141
243
208
188
192
183
175
325
325
296
203
159
215
238
J30
153
125
132
138
178
335
290
120
215
345
295
75
245
182
127
162
585
275
186
150
24
63
138
74
185
165
HI
193
233
250
CD
71
120
160
90
160
75
120
91
67
89
110
90
SI
94
79
88
96
33
150
39
140
53
83
120
72
91
66
94
120
120
120
190
150
130
34
53
150
70
140
190
38
16.0
7i
66
66
110
64
99
74
52
46
80
67
FE
24000
21000
18000
21000
24000
25000.
24000
12000
15000
29000
24000
2QOOO
18000
20000
24000
27000
23000
21000
23000
17000
21000
23000
15000
18000
15000
13000
15000
22000
*3000
2*000
15000
18000
53000
48000
11000
26000
24000
13000
16000
72000
14000
20000
17000
1*00
8800
15000
8100
21000
20000
11000
25000
27000
23000
FB
117
137
66
2700
1500
830
890
2200
3400
660
740
620
2100
1500
1900
1200
1600
1900
2*00
2000
2100
1200
3700
970
3600
790
970
»70
350
430
1600
780
1000
730
1500
310
5700
600
3900
280
»80
7*0
9*0
1*00
1100
970
230
2*00
1100
65
3*0
2000
220
2900
1700
2500
wi
*60
5*0
280
470
480
460
350
3*0
*90
»60
420
330
420
370
330
460
450
360
320
280
680
*»0
2BO
250
2*0
420
370
»90
620
*30
ISO
870
1600
660
«0
*70
210
£80
300
1100
2*0
430
920
100
160
»*o
180
700
370
»50
420
*30
- *60
HI
96
15
22
17
31
87
19
9
11
32
38
28
36
13
1*
16
80
100
0
0
26
5
33
26
IB
13
36
7
35
55
2
18
.1*
120
30
75
9
7
19
8*
12
32
1
0
10
z*
26
11
3
0
7
3*
29
SH
8
1*
28
33
21
16
*
63
16
11
26
19
32
18
12
1*
17
_S
17
1?
13
12
21
76
ZO
33
33
41
2*
23
5
6
38
28
21
9
3
*
6
3
1*
78
110
6
38
93
77
25
13
12
16
13
*
a
360
3*0
380
260
6*0
too
330
1*0
210
480
370
160
990
S30
350
320
370
ws
no
«0
330
4*0
300
850
210
1*0
no
1*0
200
760
730
*?0
300
10
210
*80
370
180
320
880
Ji'o
390
iso
220
1*0
360
160
350
»SO
130
1*0
»60
«60
TCOLI FCBU
ItlEB
2.8E6 i.tES
1.6E6 t.6E*
2>SES OC1
3.4E7 IttEl
1.SE7 JtSES
1.1E6 t.JES
2.6E6 HEB
1.5E6 ».8E4
7.7E5 ti*C9
g.8E6 tOEZ
6.0E5 J.8E3
«.*g* 6.3E3
4.014 B»TEO
1-OE5
J.6E4 tiOEE
1.6E8 l.BE*
If IE* «.»E6
1-2E5 6.8E3
7.7E5 I.1E3
2.1ES 2.3E*
1.5E5 IttE*
4.JE5 l.»E»
LIES 1* IE*
»t*C» |.*E1
8>OE*
ItOES
5.3E6 tilES
1.9CB J.SE3
9.1E4 3.6E3
2.0EB 6.IE*
1.3E6 IrM*
1.815 t.6M
8.3E* B.7E3
1.4EB OKI
1.4E6 ».SE«
6.SE5 Bl«Et
*i*E> »»»E6
1.2E5 1.IE1
7.3EB filEI
2-OEB HIE*
1 >*EB *»»E3
2.5E* i.n»
8.6E* t«IE3
1.6ES KIM
Table A-19. LANDSCAPING BEYOND THE SIDEWALK 1 - GRASS
-------
OP04 CTO4
TUSCAN
TU8C8N
CHICAGO
CHICA68
CHICA08
CHICA08
CH1CAS8
TULSA
TULSA
TULSA
TULSA
TULSA
4 3
4 1
1 3
1 3
1 3
1 3
1 3
1 3
2 0
2 0
2 0
2 0
2 0
20
20
20
20
20
20
20
20
20
20
30
20
20
8.9
11.3
4.3
9.5
11.2
15
20
20
20
10
10
50
20
20
50
20
.30
.37
.74
.41
.38
loooo
10000
1000
5860
5860
3600
1200
17300
14500
6100
2600
3200
10200
2
1
2
1
2
2
2
1
1
2
2
1
1
3
1
2
2
2
2
2
2
2
2
2
2
2
2
2
20
28
51
42
22
3
3
3
7
r
3
2
2
2
2
2
2
32
81
62
IS
20
96
22
35
41
33
88
5100
»770
2900
32*0
9*30
1»40
473QO
31300
29300
23000
43900
200000
312000
33100
61300
32600
24500
72800
32100
510000
298000
247000
207000
158000
520
450
70
63
30
54
109
28
3300
3000
1600
1500
3000
H03
2730
9»0
523
377
405
525
523
482
2700
1800
1900
1200
1400
TCOU
S.&K7
l'.lE6
l'.6K6
1-OE6
3-OE6
3.9E5
5i5E6
6-3E6
3.0E6
6.4E6
8.1E6
J.TE7
7.8E5
ES
»it
t.l
l»3E*
J.SE4
8i5E»
litC*
7.QE3
1.1E4
1.3E4
Table A-20. LANDSCAPING BEYOND THE SIDEWALK 2 - TREES
CITT
C»ICAOB
CHICAG8
CHICAGO
CHICAGO
CHICAGO
TUUSA
TULSA
BALTIMORE
BALTIM9RE
SEATTLE
SEATTLE
C S UJ
1 3 20
1 3 20
1 3 20
1 3 20
1 3 20
2 0 3c
2 0 20
2 2 22
2 S 32
5 3 22
5 3 25
PD AA
50
15
20
50
20
4.0 5
13.6 20
IF ADT S
1980
1828
240
14070
1620
.55 10600 2
.31 3800
6700 1
27000 1
6900 1
12300 1
C
3
2
2
1
2
1
1
0
1
2
A
3
3
3
3
3
3
3
3
3
3
3
FBS
20
37
R
26
26
12
12
S H LOAD
3 2 148
3 2 121
3 2 135
3 2 148
7 2 153
46
24
1 1 100
4 1 4
1 45
1 12
BODS
2810
2030
6320
2280
3220
29300
24000
COD
29500
34000
45600
24600
34600
163000
277000
OP04
21
24
73
20
30
3200
2400
TBM H03
773
845
670
961
356
3600
1600
8.8 210 120 19000 1700 290 45
3.7 .264 210 23000 2100 500 7
3.4 ' 239 100 37000 3000 490 40
1.6 254 48 59000 2600 440 39
24
33
630
510
660
410
1>2E6
3.1E6
5.6E6
4tlE6
2.1E6
1>6E6
9.6E6
6.4E4
3.1E5
reoti
5.7E5
2.7E5
4.5E5
4.0E5
1.0E6
6.9E4
7.0E3
3.6E4
2.4E2
Table A-21. LANDSCAPING BEYOND THE SIDEWALK 3 - LANDSCAPED BUILDINGS
-------
to
00
CITY
ANN ARB8R
ANN ARBOR
ANN ARBOR
BRAND RAPIDS
BRAND RAPIDS
BRAND RAPIDS
TUSCBN
TUSCBN
ANN ARB8R
CHICAS8
CHICAG8
CHICAGB
CH1CAS8
CHICABB
TULSA
TULSA
TULSA
CENT. BUS.
SH6P. CENTER
LISHT INO-
MED. IND.
HEAVY IND.
SAN JOSE I
SAN JBSE I
SAN JBSE I
SAN JBSE I
PH8ENIX I
PH8ENIX I
PHBENIX I
PHBENIX 1
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
MILWAUKEE
BUCYRUS
BALTIMBRE
BAL TIMBRE
BALTIMORE
SAN J9SE II
SAN JBSE II
SAN JBSE II
SAN JBSE II
ATLANTA
ATLANTA
TULSA
PHBENIX II
PHBENIX II
PHBENIX II
PHBENIX II
SEATTLE
SEATTLE
SEATTLE
C
1
1
1
1
1
1
4
4
1
1
1
1
1
1
2
2
2
6
6
6
6
6
4
4
4
4
4
4
.4
4
1
1
1
1
1
2
2
2
4
4
4
4
2
2
2
4
4
4
4
S
S
S
S
£
3
4
0
0
0
3
1
0
3
3
3
3
3
0
0
0
0
0
0
0
0
4
u
4
4
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
LD
20
20
20
40
20
10
40
40
20
30
30
30
SO
20
50
50
SO
31
32
40
51
52
31
32
40
51
31
32
40
51
22
31
32
51
52
52
31
40
51
31
32
40
51
31
32
31
31
32
40
51
31
32
40
PD U IE APT
5600
5600
5600
5600
5600
5600
12000
12000
5600
20 7950
20 20400
20 13500
20 11140
50 700
5 5 .30 10100
3.8 20 .51 10200
3.2 20 .24 6600
15500
18800
8300
9100
9800
3600
13000
7250
7200
11900
15000
12300
17200
4000
6000
20800
12000
8000
5000
14200
9900
11000
23600
13000
7250
7200
14100
34100
18000
11900
15000
12300
17200
16000
26500
10000
S
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
C
I
1
2
2
2
1
1
2
2
1
2
1
2
1
1
0
3
0
£
2
2
3
0
2
2
I
2
1
1
0
2
2
1
1
1
2
2
2
A
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
PIS K
15
.23
23
13
18
13
18
12
12
12
12
0
0
0
0
0
2
26
26
26
59
59
59
59
2
2
9
60
60
60
60
12
12
12
S
1
1
1
1
3
1
13
10
1
3
4
1
14
H
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
2
2
2
S
1
,1
2
2
2
S
1
1
1
LOAD
I'D
134
326
71
284
121
419
88
25
296
122
946
264
21
26
131
61
26
53
41
130
93
260
30
Si
1500
800
5
87
11
3
204
19
60
215
20
4
3
4
11
16
1
59
BODS
lasoo
30700
21300
23500
5050
4030
14540
2950
2180
5800
23200
35000
' 8300
8600
15000
11000
3700
COD
201000
224000
26700
24800
66700
23000
50700
49000
168000
267000
30000
21000
73000
60000
17000
OP04
650
950
220
240
760
610
38
27
142
30
142
1600
1800
2500
1000
1000
1400
1400
1400
TP04 H03
2620 650
2770 800
2130 440
430
790
2510 630
480
323
457
43!
523
600
600
72
72
72
ZK TCOLI FCOII
595
730
535
365
450
490
1600
1800
1600
1600
1600
1600
1600
0.7
0.4
0.6
6.5
3.7
4.4
3.2
4.7
2.6
5.0
3.4
2-2
6.6
2.0
11.
1.7
1.4
3.9
3.2
6.3
1.6
4.0
25.
8.2
8.8
4.9
3.1
3.7
4.0
5.3
0.
1.6
6.4
3.1
0.3
2.3
1.5
1.4
9
3
5
247
205
288
244
304
320
325
300
285
190
168
208
256
141
190
177
179
128-
159
356
760
290
310
430
306
194
207
loo
135
71
188
310
266
247
239
16
8
9
88
250
130
87
150
110
80
87
67
69
25
100
38
170
810
120
120
170
79
290
280
210
84
96
71
92
300
30
96
99
120
32
210
63
110
3100
2200
2600
24000
23000
22000
23000
40000
16000
44000
24000
26000
15000
15000
2QOOO
24000
14000
25000
34000
22000
15000
20000
40000
31000
25000
30000
23000
17000
22000
20000
12000
11000
5000
24000
22000
32000
42000
27000
47
17
15
3600
3600
2800
1400
470
3500
7600
2000
3500
3200
1600
2500
1200
580
2700
2200
660
360
260
5700
9999
1800
5100
9999
2700
1500
3900
2000
1300
0
2100
1700
3300
4700
1100
420
370
490
420
870
470
410
500
600
380
360
430
330
230
300
390
270
310
470
770
830
680
500
540
490
"450
280
290
250
280
460
400
430
440
490
8
4
6
46
57
44
35
33
110
93
110
93
6
7
6
1
0
30
37
21
30
6
51
37
.!
140
120
93
18
12
29
170
18
23
39
40
20
20
16
24
24
17
20
10
0
a
15
11
15
12
24
20
7
20
9
17
25
34
33
15
13
13
18
13
3
37
25
15
il
0
16
57
21
22
640
400
400
280
390
600
410
350
450
335
720
230
210
210
650
320
370
220
140
1000
780
410
510
380
340
280
1100
320
420
400
360
150
500
390
480
1.0E7
3<3E7
3.5E6
5.8E5
1.0E6
3<2E5
1>3E6
3>2E5
4.U5
4iOE6
1'IES
2>OE6
3t7E5
2>3E5
1.2E4
3.1B4
2.7J4
!«»
6.7ES
4.0C4
5.8E4
2.0E5
lt7ES
5.6E5
3.5E4
4t2E4
1.3EB
2.3EB
1.6E5
I.SES
3.1E6
1.0E6
4>6E5
9.7E5
3.2E4
4.0E3
2.7E4
7tOE5
4.JE3
1.3E4
5.2E5
lilE5
5.2E5
6.9E5
1.3E3
4. SEE
llekl
6.7E3
6.7E1
6.1E2
*.iez
7.IE3
S.tES
l>l£4
1.4E2
8»VC1
7.2E1
5.flE3
1.5E3
Table A-22. LANDSCAPING BEYOND THE SIDEWALK 4 - NO LANDSCAPING
-------
Table A-23. THE BASIC STATISTICS OF THE AVAILABLE DATA, INCLUDING THE MEAN (X), STANDARD DEVIATION (o),
RANGE (R), AND NUMBER OF SAMPLES (N) IN THE SET OF ALL DATA AND IN 19 SUBSETS OF THE DATA8
to
lb»/curb Kl/diy
CMciorr 1
10 I
It
n
20 I
o
R
"
« » *
=> <7
0 *
z
-------
Table A-23 (continued)
CO
o
Iba/curb ml/day
Category
».
a
o
LU
cc
UJ
oe
15H I
0
R
X
1 *
0
R
R
2 X
0
1
*
3 *
0
1
X
4 X
0
*
R
Loading
280
343
12-
950
12
140
155
20-
600
24
146
211
5-
946
61
82
104
3-
326
17
196
245
8-
950
55
37
20
19-
96
11
93
56
4-
153
10
121
151
4-
800
40
BOD,
21,600
-
6.320-
39.600
4
9,500
8,520
1,720-
25,300
16
27,400
26,000
2,900-
10,400
38
5,720
-
1,940-
8,600
4
32,400
37,900
2.100-
16,700
29
23,000
21,500
2.900-
67.300
10
9,990
11,600
2 ,030-
24,300
7
13,700
10,200
2.180-
35.000
17
COD
153,000
-
45,600-
252,000
4
83.000
83.200
18,300-
277,000
16
163,000
165,000
18,000-
526,000
42
26,980
-
21,000-
321,000
4
154,000
172,000
.8,300-
526,000
31
141,500
108,000
24,500-
312.000
12
86,900
96,900
24,600-
277,000
7
78,000
71,100
21.000-
224,000
13
Concancratlona In Mlcrogrm par Cra of Dry Sollda
OPO^
1,500
-
73-
2,700
3
741
950
20-
2,800
15
1,340
1,250
30-
5.050
43
514
-
27-
1,000
4
1,720
1,490
100-
5,790
28
1,140
1,120
28-
3,300
12
824
1,370
20-
3,200
7
787
590
30-
1,800
17
TPO,
5,440
-
-
1
212
-
-
1
2,960
1,070
2,130-
4,850
5
-
-
-
-
3,300
2,860
212-
5,440
3
_
-
-
-
-
-
-
-
2,510
-
2,130-
2,770
4
KOj KH4
8,335
-
670-
16,000
2
419
269
64-
845
11
836 2,640
979 1,820
37- 595-
3,600 3,390
37 9
501
-
323-
600
4
1,010 3,620
1.320 1,340
37- 1.880-
3,600 5,390
25 6
540
186
482-
940
7
721
230
356-
961
5
461 662
232
72- 595-
790 730
16 3
OtjH
5,470
-
1.700-
12.800
3
1,515
146
890
2.200
7
2,900
2,430
490-
9,250
30
1,600
-
0
2
3,900
3,240
890-
11,900
26
1,800
580
1,200
2,700
5
2,600
-
1,600-
3,600
2
1,200
594
365-
. 1.800
11
Cd
2.8
2.0
0 -
5.4
8
2.9
1.6
1.1-
6.1
12
3.1
2.5
0.0-
9.3
54
3.1
2.1
0 -
6.8
16
3.9
5.9
0.0-
6.<
50
_
-
-
-
4.6
-
1.6-
8.6
4
3.6
2.1
0.4-
34
Cr
198
76
132-
295
9
196
62
138-
120
12
213
80
9-
430
54
203
93
24-
345
16
197
71
63-
345
31
_
-
-
-
242
-
210-
264
4
228
87
430
35
Cu
69
37
33-
150
9
107
31
67-
170
12
107
62
9-
300
56
102
69
25-
250
16
96
39
33-
190
53
.
-
-
-
120
-
48-
210
*
116
77
16-
300
35
Fa
21,700
9.300
13,000-
43,000
9
11,900
3,500
14,000-
23,000
12
22,500
10,000
2,600-
59,000
56
22,900
13.400
1,400-
53,000
16
11,040
8,500
6,100-
53,000
51
_
-
-
-
34,500
-
19,000-
59,000
4
22,000
9,320
2,600-
42,000
36
Pb
1,210
1,180
280-
3,900
9
1,060
925
66-
3,500
15
1,010
1,480
47-
5,700
54
2,230
1,530
470-
5,100
15
1,370
980
117-
3,900
S3
.
-
-
-
2,350
-
1,700-
3,000
4
2,370
1,720
17-
5.700
34
Ho
384
'130
210-1
620
9
415
140
150-
700
12
442
172
160-
1,100
55
357
105
100-
500
15
406
157
150-
1,100
51
.
-
-
-
430
-
290-
500
'
446
154
250-
870
35
XI
26
23
7-
75
8
17
18
0-
35
11
38
35
0-
140
58
28
23
0-
83
15
27
25
0-
96
49
.
-
-
-
33
-
7-
45
4
41
34
4-
120
34
>T
19
15
3-
33
1
34
32
5-
110
12
18
10
4-
63
51
ie
11
5-
38
15
24
20
3-
93
51
.
-
-
-
22
-
8-
33
3
16
8
0-
37
33
In
252
100
110-
420
9
418
198
180-
760
12
375
167
57-
780
54
389
160
150-
720
16
348
168
110-
880
53
.
-
-
-
552
-
410-
660
4
385
162
37-
720
34
No. /pa.
IColl*
1.3E6
2.0E6
8.4E4-
5.6E6
8
2.1E6
2.5E6
1.0E5-
9.6E6
17
3.1E6
7.1E6
2.5E4-
3.4E7
57
3.8E5
5.4E3
1.8E4-
2.0E6
12
8.3E5
1.6E6
4.0E4-
9.4E6
40
1.1E7
6.4E6
3.9E5-
8.6E7
13
3.5E6
3.0E6
3.1E5-
9.6E6
8
7.1E5
1.1E6
1.8C4-
4.0E6
28
FColl*
6.9E4
1.6E5
3.0E2-
4.5C5
«
3.4E5
4.6E5
5.5E2-
1.3E6
16
1.7E5
2.6E3
6.7E1-
9.1E5
5)
1.4E3
2.1E3
1.2E2-
S.2E5
11
7.4E4
1.4E5
3.8E1-
3.3E3
38
3.5E5
4.9E5
7.0E3
1.3E6
12
3.5E5
3.4E5
7.0E3
1.0E6
t
1.6E5
2.8E5
7.2E1-
9.715
28
-------
Table A-23 (continued)
>
co
IWcurl «l/d«r
Category *
1
CJ
oc.
- £
_ >; 2
UJ
or
t
CO
_j
-------
indicates a range of ten percentage units. These transformations are
carried over into Table A-24, which shows the means and the subscripts
described. It is intended here that the percent standard error of the
mean represents the confidence that may be placed in the value of the
mean for predictive purposes. Subscripts are not given for mean values
of less than five data points.
Table A-24 is constructed so that the total degress of freedom available
in making a statistical test of significance exceeds ten. The Student
t probability distribution is relatively constant for degrees of free-
dom of 10 or greater, so all discussion,which follows is based on 10
degrees of freedom.
Table A-24 may be used directly for detecting functional relationships
between dependent and independent parameters by the simple mechanism of
the Student t test for significance, described in the next section.
For example, referring strictly to the Loading column in Table A-24,
the following statements can be made: (1) commercial and residential
area means are different at the 97 percent confidence level, (2) com-
mercial and light industrial area means are different at only the 73
percent confidence level, (3) commercial and. heavy industrial area means
are different at the 95 percent confidence level, and (4) the commercial
mean differs from the mean of the set of all data at the 98 percent
confidence level. These four statements were proved within two minutes
by the author, using a hand electronic calculator and a plot of the
Student t distribution. Obviously Table A-24 is an extremely useful
and versatile tool in predicting functional relationships between the
dependent and independent parameters in this study.
The information in Table A-24 is presented in a more useful fashion in
the text as Tables 5, 6, and 7. These tables show only those subset
means which differ from the means of the set of all data at the 95, 90,
and 80 percent confidence levels, respectively. These tables enable the
A-32
-------
Table A-24.
MEANS AND PERCENT STANDARD ERRORS OF THE MEANS (Subscripts )
OF THE SET OF ALL DATA AND OF 19 SUBSETS OF THE DATA
Land Use 10
20
30
40
50
Cllnate 1
2
4
5
. Average Daily <
*l Traffic
CO No. /day
co
>
Type of Land- 1
scaping
Beyond the
Sidewalk 3
Category
Open space
Residential
CoBBerclal
Light Industry
Heavy industry
Northeast
Southeast
Southwest
Northwest
500
500 - 5,000
5.000 - 15,000
15.OOO
Crass
Trees
Landscaped
buildings
Ibs/cur
i/day
Loading
12
149b
74
389°
203c
291c
103W
D
50c
30C
280.
a
140
c
146b
82d
196b
43K
b
93b
b
BODj
20,300
14,000.
b
58,700
27,300
12,100^
29,1OOC
29,100b
11,700.,
D
10,000
21,600
9,500
' c
27,400.
b
5,720
32,400C
23,000.
' d
9,990
Concentrations in nlcrograms
COO
171,000
82,000.
D
269,000
C
180,000
88.OOO
e
159,000c
172,000b
139,000c
38.OOO
153,000
83,000
' c
163,000,.
b
26,980
154,000C
142,000
c
86,900e
OP04
1,700
850b
2,250
1,275
l,250d
990.
a
2,240.
470b
1.100
1,500
793.
d
l,340b
514
l,720b
1,140
c
824f
TP04 "°3 ""4
5,440 16,000
2,000 550 660
4,850 1,580 3,620b
430
246
8,390. 1,010. 2,640
O DC
212 43
l,160d
59
5,440 8,330
212 419b
2,980,. 836. 2,640
b be
501
3,500 l,010c 3,620b
540
b
721b
OrgN
7,250
1,800
6,430b
1,750
l,420b
5,970
1,970.
2,400.
b
2.OOO
5,470
1,515
c
2,900.
b
1,600
3,900b
1,800.
b
2,600
Cd
0.6
3-°b
4.2
4'°c
3'9b
2-6b
4'°b
3.8b
1-'b
2.8
c
2.9.
b
3.8
3'1b
3.9C
4.4
per gran of
Cr
5
192.
225
288.
d
278b
139b
234b
241a
246
198b
196
a
215a
203b
197.
242
Cu
9
93a
133b
128b
107b
"b
137b
78.
96c
89b
107
a
107.
102.
D
96a
120
dry solid
Fe
2,600
20,600.
23,300b
21,800.
28.6OO.
b
17,700b
20,300b
22,600.
34,500b
21 , 7OO
b
18,900
' a
22,500
a
22,900.
b
21,000.
34 , 500
So./gnm
Pb
15
l,430b
3,440b
2 , 780 .
a
1,160
c
870
c
l',370.
D
2 , 520.
D
2,600b
1,210.
d
c
2,010,_
D
2,230b
l,370b
2,350
Un
392.
397.
490b
570b
363.
395b
446.
455.
384.
o
b
442
a
357.
406.
430,
Nl
6
28b
48b
41d
37c
21c
21b
57b
35a
26d
17.,
d
38b
28
c
27b
33
Sr
21b
18b
27d
23c
27b
28b
I5a
10c
19d
37
c
18
a
18b
24b
22
Zn
22
350
520
368b
317h
260b
439b
357.
480
a
252b
418
tr
375
a
389.
D
348.
552
TCOLI
1.
I.
1.
2.
8.
1.
»
5_
6,
1.
2.
3.
3.
8.
1_
3.
6ES
7E6
8E6
»E6,
2E5
e
,8E6.
b
.9E6c
,7E6d '
. 8E5f
,3E6f
. 1E6
c
,lE6d
8E5e
3E5d
, 1E7
b
. 5E6
a
FCOLI
7.1E3
1.6E5
c
1.9E5.
a
1.5E5f
1.8E5
4.4E5
7.0E4d
5.2E5f
l.lE4f
6.9E4f
3.4E5.
d
1.7E5
c
1.4E5
e
7.4E4.
d
3.5E5
e
3.5E5.
d
4 Hard surfaces 121C 13,700b 78,000C 787fa 2,510 461b 662 l,200b 3.6b 228. 116b 22,000. 2.370b 446. 41b 16. 385. 7.1ESe 1.6E5d
Street Surface 1 Asphalt
Material
2 Concrete
All data
150 23,000 150,000 2,470 212 11,600 1.770,. 3.7 217 107 22,400 2,000. 414 43. 18 380
bcca baaaa babaa
38. 72,000 383,000 3,520. 5,150 2.600 3,620 6,680. 3.5 173. 97. 22,800 1,28Q 342. 24 45 292.
b c b o DbcbbD cbcc D
156 19,900. 140,000. 1,280. 2,930 804. 2,640 2,950. 3.4 211 104 22.000 1,810 418 35 21 370 2.5E6
b b 'bbc b'cb a a aaa a a a a c
'Percent Standard Error of the Mean Subscripting Code: a = 0 - 9, b » 10 - 19, t -20-29, d » 30 - 39, e = 40 - 49, f « 50 - 62.
Collform counts are expressed in computer notation, i.e., E5 = 10 .
-------
planner to find quickly the best data available in predicting loading
rates and chemical composition. The use of these tables is described
in detail in the text.
An urban planner may be concerned whether or not one mean value is signi-
ficantly different from another. Table A-24 is constructed so that he
may perform a Student t test of significance, some example results of
which were mentioned in the last section.
The table is constructed so that (1) the standard error of the mean falls
into a group which has numerical limits as narrow as feasible while still
permitting efficient presentation, and (2) standard errors of the mean
are presented only for those values where the number of samples was
five or greater, so that the degree of freedom used when consulting
tables or graphs of the Student t distribution is always greater than
ten. All discussion is based on 10 degrees of freedom.
The Student t is performed as follows:
Definitions
cr= standard deviation
*
cr = standard error of the mean
cr= :-2- for n >10 and for n<10
Vn~ Vn^l
*
%cr = percent standard error of the mean; this is the
value indicated by superscript in the tables
%
-------
First, compute the standard error of the difference between any two means,
x and x , as
i. ^
*2
°2
Then the Student t is defined as
xl ~ X2
Student t = =
The probability that the Null Hypothesis (that x - x = 0) is correct
1 2
is given in tables of the Student t distribution listed in any good
statistics text or in Standard Mathematical Tables (Chemical Rubber
Publishing Company, Cleveland, Ohio).
To assist the planner in learning and using this technique, two sample
problems are presented:
Problem 1:
Question: Is the mean COD concentration in solids found
in heavy industrial areas significantly different
from that found in residential areas?
Given: (1) Heavy Industry COD = 88,000
(2) Residential COD = 82,000b
Subscript e = 40-50%, average 45%
Subscript b = 10-20%, average 15%
*
cr = 80,000 x 0.45 = 36,000
o-0 = 82,000 x 0.15 = 12,300
2t
o- =/3$,0002 + 12,300 = 38,000
a> ^
A-35
-------
Consulting tables of Student t with 10 degrees of freedom, the probabil-
ity that the Null Hypothesis is true is >0.90. Therefore, there is no
significant difference between these mean values. Using the original
data, Student t is calculated as 0.048 and the same conclusion is ar-
rived at: that the difference in the means is not significant.
Problem 2:
Question: Is the mean Pb concentration in solids from areas
with ADT>15,000 larger than from areas with ACT
500-5000?
Given: Pb @ ADT = 500-5000 = 1060
c
Pb @ ADT>15,000 = 2230
Subscript c = 20-29% average = 25%
Subscript b = 10-19% average = 15%
0 = 1060 x 0.25 = 265
CT2 = 2230 x 0.15 = 334
_. , . .
Student t
=\265+ 334 = 427
2230 - 1060 1170
= - = ^427
Consulting tables of the Student t distribution with 10 degrees of
freedom, the probability of the Null Hypothesis' being correct is
0.020. Therefore, the difference is significant at the 98 percent con-
fidence level. Using the original data, Student t is calculated as
2.53 and the confidence level of the difference is actually 97 percent
rather than 98 percent. The decision remains the same, however, The
difference in the means is highly significant.
A-36
-------
4. Conclusions Obtained in the Data Processing
Table A-24 provides the mechanism for detecting significant functional
relationships between independent and dependent parameters. Some of
these relationships observed by the authors are put forward here. It
should be realized that these conclusions are derived from data which
represent the combined effects of natural physical processes and man's
activities. Sweeping the streets, for example, logically reduces the
apparent loading rate recorded here but does not change the rate of the
natural accumulating process, a parameter which cannot be measured by
the methods used in this study. These conclusions represent the state
of the nation resulting from normal mean living conditions. Unexpected
findings are noted with an asterisk (*).
a. Loading Rates Are Lowest in:
1. Commercial areas, probably because they are swept frequently
2. Northwest
3. Areas with highest traffic, probably because the removal pro-
cesses (primarily traffic generated winds) are more active
*4. Tree covered areas; unexpected because it was thought that
leaves contributed substantially to loading rates
5. Concrete surfaces
b. BODg Concentrations Are Lowest in:
1. Residential and heavy industrial areas
2. The southwest, probably reflecting the lack of lush vegetation
relative to the East Coast
3. Areas with moderate ADT
4. Areas with landscaped buildings, probably reflecting better
maintenance
5. On asphalt road surfaces
A-37
-------
c. COD Concentrations Are Lowest in:
1. Residential and heavy industry, whereas it is highest in
commercial areas; the latter may be due to oil from many
parked cars on the street
2. Not significantly different climatologically
3. Areas with moderate traffic (500-5000 ADT)
4. Areas with landscaped buildings
5. Not significantly different between street surface types
d. Qrtho Phosphate Concentrations Are Lowest in;
*1. Residential areas; an unexpected finding considering the wide-
spread use of fertilizer on lawns
2. The southwest (highest in southeast), probably reflecting the
difference in vegetation or fertilizing practices
3. Areas with moderate traffic (500-5000 ADT)
4. Areas with no landscaping, probably because no fertilizer is
used
5. On asphalt surfaces
e. No Conclusions Can Be Obtained From the Total Phosphate Data
f. Nitrate Concentrations Are Lowest in:
1. Heavy industry areas
2. No significant differences climatologically
3. No significant differences between traffic densities
4. Areas without landscaping
5. No significant difference between street surface types
g. No Conclusions Can Be Obtained from the Ammonia Data
A-38
-------
h. Organic Nitrogen Concentrations Are Lowest in:
1. Heavy industry areas
*2. The southeast; and unexpected finding since the southeast con-
tains the most lush vegetation
3. Areas with moderate traffic (500-5000 ADT)
4. Areas without landscaping
5. Asphalt surfaces
i. Cadmium Concentrations Are Relatively Uniform in All Categories
j. Chromium Concentrations Are Lowest in:
1. Residential areas
*2. The northeast; unexpected since the major source of chromium
on streets was thought to be chromate salts added to deicing
salt as a metal preservative
3. No significant differences with traffic density
4. No significant difference with landscaping
5. On concrete surfaces
k. Copper Concentrations Are Lowest in:
1. Residential areas
2. The southwest
3. Areas with light traffic «500 ADT)
4. Areas with grass landscaping
5. Not significantly different between street surface types
1. Iron Concentrations Are Lowest in:
1. Not significantly different with land use types
*2. Lowest in the .northeast (highest in northwest)
3. Not significantly different with traffic density
A-39
-------
4. Not significantly different with landscaping type
5. Not significantly different between street surface types
m. Lead Concentrations Are Lowest in:
1. Heavy industry areas, low also in residential areas; this
probably reflects low vehicular traffic
*2. The northeast (highest in the northwest); this may reflect
the inhomogeneity of sampling sites
3. Areas with light and moderate traffic
4. Areas with grass landscaping
5. Concrete road surfaces
n. Manganese Concentrations Do Not Vary Greatly Between Categories
o. Nickel Concentrations Are Lowest in:
1. Residential areas
2. The northeast and southeast
3. Areas with moderate traffic (500-5000 ADT)
4. Areas with grass landscaping
5. Concrete road surfaces
P. Strontium Concentrations Vary Only Slightly Between Categories
q. Zinc Concentrations Are Lowest in:
*1. Heavy industry areas
2. The northeast
3. Areas with light daily traffic «500 ADT)
4. Not significantly different between landscaping types
5. Concrete surfaces
A-40
-------
r. Total Coliform Counts are Lowest in:
1. Heavy industry areas
2. The northwest
3. Areas with heavy vehicular traffic (>15,000 ADT)
4. Areas with grass or no landscaping (and highest in tree
covered areas)
s. Fecal Coliform Counts Are Lowest in:
*1. No significant differences between land use categories;
unexpected because generally it is thought that pet feces
cause higher fecal coliform counts in residential areas
2. The northwest
3. Areas with heavy traffic (>15,000 ADT)
4. No significant differences with landscaping
Generally the trends observed were expected,or, at least, not unexpected.
In eight cases the data indicated that preconceived notions were not sup-
ported by observations.
A-41
-------
5. Summary
The URS staff has assembled all presently available data on the rates of
accumulation of solids and the concentrations of various constituents in
those solids that collect on street surfaces. These data have been
0
grouped and processed into a form which should be useful to an urban
planner in predicting the pollutional load from areas projected for ur-
ban development.
The range and scatter in the available data are extreme. Both the
sampling variability and the complexity of natural systems contribute
to this extreme variance. Attempts to test conceptual models with the
available data have met with absolutely no success because the models
are much too simple to adequately describe the conditions in the real
world. The available data records are not sufficiently complete to per-
mit sophisticated analyses of covariance.
The data and calculations presented here represent the state-of-the-art,
as viewed by the participants in this study, on the subject of loading
rates and chemical composition of solids on streets. The results pre-
sented here are probably adequate for most urban planning operations.
As the nation becomes more concerned in the future over nonpoint sources
of pollutants, more sophisticated models of accumulation rates should be
developed and tested. The methods of sample collection and analysis
should be standardized. Controlled experiments should be carried out in
an effort to identify sources of solids loading. These adjustments in
the state-of-the-art will permit refinements in the methods of handling
data as presented here.
A-42
-------
Appendix B
SAMPLING PROCEDURE
-------
Appendix B
SAMPLING PROCEDURE
In many instances, local agencies may want to conduct sampling and ana-
lytical programs of their own. These data, if handled properly, may be
used in analyzing the areas' runoff problems instead of using values
presented in the manual. This would be desirable in most instances, but
especially in areas or under specific conditions that were not analyzed
in great detail under any studies used in preparing this manual.
Additional information is presented in the following discussion to help
these local agencies design and carry out sampling programs of their own.
The first question to be answered concerns the type of sampling to be
conducted. Two general types have been used in the studies referenced
in this manual. These are the street surface collection method and the
runoff collection method. Both methods require a substantial amount of
additional information concerning the area. The street surface method
has an advantage of being able to conduct sampling without having to
wait for rain and the elaborate rainfall data required for the runoff
sampling method.
The following list briefly describes the procedures which may be used in
a street surface sampling program.
LOCALITY DATA COLLECTION
After setting up traffic control in the chosen test area, information
should be gathered as to: location, date, land use, parking and traffic
characteristics; street, gutter; and curb composition, condition and
texture; test area and description of the adjoining area. At this time
photographs of the area should be taken. Street cleaning and rainfall
history of the test site is highly desirable.
B-l
-------
HAND SWEEPING
Hand sweeping, or dry solids collection, should utilize a standard stiff
bristled broom, sweeping toward the curb while moving laterally along
the street. After concentration in the gutter, samples should be col-
lected by whisk broom and dustpan and placed in clean paint cans.
HOSE FLUSHING
Flushing should be conducted after sweeping to remove adherent soluble
films and otherwise nonsweepable material. The downslope gutter should
be dammed with sandbags to create a collection area for flushing water.
A small vacuum collector can be constructed using 5-gal paint cans and
connected to an industrial wet/dry vacuum cleaner. The test area is
first slightly wetted to facilitate removal of soluble materials.
Flushing should be commenced at the road crown, using a garden hose and
spray nozzle connected to fire hydrants. All water is collected by the
vacuum box and measured. The samples then are mixed by vigorous stirring
and split to a 1-gal volume. If pesticide analysis is to be conducted,
an additional sample should be taken in quart size glass containers.
Plastic gallon bottles may be used for all other samples.
Collecting rainfall runoff samples will give a definite indication of
the concentration of contaminants in the runoff, but collecting repre-
sentative samples can be difficult. Relating these runoff concentrations
to other instances (differing rainfall intensity patterns, etc.) can be
nearly impossible. In some cases, this procedure may be preferred, and
the following general instructions may be helpful.
DATA COLLECTION
Land use patterns within the watershed must be clearly defined. Per-
centages of vacant land and special sources must be identified and
B-2
-------
located. Accurate distribution of rainfall intensity is also required.
Street cleaning and rainfall history of the area is highly desirable.
SAMPLE COLLECTION
Sample collection should begin at the onset of a rain event and continue
for as long as practicable (at least several hours). Accuracy would be
enhanced by collecting discrete samples based on volume of flow and not
by time. Besides collecting samples, flow data should be compiled. If
pesticide analyses are to be conducted, the sample containers should be
glass.
SAMPLE HANDLING AND PREPARATION PROCEDURES
All solids and liquid samples should be immediately shipped from the test
sites to the laboratory. Upon receiving the shipment, the samples must
be placed in a cold room maintained at 5°C. The solids can be stored in
new unlined metal paint cans, while the liquids can be stored in plastic
containers. All samples designated for pesticide analysis must be col-
lected and stored in glass containers.
All individual solid samples should be dried under heat lamps (less than
100 F) and weighed.
Size classification of solid samples is performed by standard sieve anal-
ysis. The dried solid sample to be analyzed is placed on top of the
2000-micron screen in the nest of 5 screens (sizes 2000 microns, 840 mi-
crons, 246 microns, 104 microns, 43 microns and the pan). The screens
are then placed in a roto tap unit and agitated for a half hour. The
screens are then removed and the material on each screen weighed.
B-3
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Special sample preparation should be used for the heavy metal analysis
of the liquid samples. All liquid samples can be cotton filtered prior
to analysis to remove large settleable solids.
SOLID SAMPLE PREPARATION
Prior to chemical analysis aliquots of each solid composite sample can
be taken and placed in a blender with a known amount of distilled water
(varied according to strength of pollutant) and homogenized.
B-4
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Appendix C
BIBLIOGRAPHY
-------
Appendix C
BIBLIOGRAPHY
ANALOG COMPUTER SIMULATION OF THE RUNOFF CHARACTERISTICS OF AN URBAN WATERSHED,
V.V.D. Narayana, J.P. Riley, and E.K. Israelsen, Utah Water Research Lab.,
1/69.
"Application of an electronic analog computer to the evaluation of the effects
of urbanization on the runoff characteristics of small watersheds," V.V. Dhruva
Narayana and J. Paul Riley, THE USE OF ANALOG AND DIGITAL COMPUTERS IN
HYDROLOGY, Proceedings of the Tucson Symposium, 1:38-48.
"An approach toward a physical interpretation of infiltration-capacity,"
Robert E. Horton, SOIL SCIENCE SOCIETY PROCEEDINGS, 5:399-417, 1940.
"Assessing the quality of urban drainage," Warren Viessman, Jr., PUBLIC WORKS,
10/69.
"How to determine average intensity of rainfall for use in the rational
formula," H.M. Gifft and George E. Symons, WATER AND WASTES ENGINEERING,
44-45, 12/68.
"The bacteriological aspects of stormwater pollution," E.E. Geldreich, L.C.
Best, B.A. Kenner, and D.J. Van Donsel, JOURNAL WPCF, 40(11,1): 1861-1872,
11/68.
"Bacteriological comparison between combined and separate sewer discharges in
southeastern Michigan," R.J. Burm and R.D. Vaughan, JOURNAL WPCF, 38(3):
400-409, 3/66.
BANK AND SHORE PROTECTION IN CALIFORNIA HIGHWAY PRACTICE, California Div. of
Highways, 11/70.
THE BENEFICIAL USE OF STORM WATER, C.W. Mallory, Office of Research and
Monitoring, EPA, R2-73-139, 1/73.
BIBLIOGRAPHY OF R & M RESEARCH REPORTS, Office of Research and Monitoring,
EPA, R5-73-012, 1/73.
Biospherics Incorporated proposal.
"Calculation of water pollution by surface runoff," N.A. Pravoshinsky and
P.D. Gatillo, WATER RESEARCH, 2(l):24-26, 1/68.
CHARACTER AND SIGNIFICANCE OF HIGHWAY RUNOFF WATERS, A PRELIMINARY APPRAISAL,
Robert 0. Sylvester and Foppe B. DeWalle, Washington State Highway Commission,
12/29/72.
C-l
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"Characterization, treatment, and disposal of urban stormwater," S.R. Weibel,
R.B. Weidner, A.G. Christiansen, and R.J. Anderson, PROCEEDINGS, 3RD
INTERNATIONAL CONFERENCE ON WATER POLLUTION RESEARCH, Munich, 1:1966.
"Chemical and physical comparison of combined and separate sewer discharges,"
R.J. Burm, D.F. Krawczyk, and G.L. Harlow, JOURNAL WPCF, 40(1):112-126,
1/68.
CHEMICAL TREATMENT OF COMBINED SEWER OVERFLOWS; Study of flocculent treatment
and disinfection of Milk River Pumping Station combined sewer overflows at
Grosse Pointe Woods, Michigan, Dow Chemical Co. for Water Quality Office,
EPA, 9/70.
CHOICE OF ADJUSTMENT TO FLOODS, Gilbert F. White and John Eric Edinger, U. of
Chicago, Dept. of Geography, Res. Pap. No. 93, 1964.
COMBINED SEWER OVERFLOW ABATEMENT ALTERNATIVES, WASHINGTON, D.C., Roy F.
Weston, Inc. for Water Quality Office, EPA, 8/70.
COMBINED SEWER OVERFLOW ABATEMENT TECHNOLOGY; A compilation of papers
presented at t
Chicago, 1970.
presented at the FWQA "Symposium on Storm and Combined Sewer Overflows,"
COMBINED SEWER OVERFLOW SEMINAR PAPERS, FWPCA, Storm and Combined Sewer
Pollution Control Branch, 11/69.
COMBINED SEWER OVERFLOW STUDY FOR THE HUDSON RIVER CONFERENCE, Alan I.
Mytelka, L.P. Cagliostro, D.J. Deutsch, and C.A. Haupt, EPA-R2-73-152,
1/73.
"The combined sewer problem and standards of water quality," Gordon McCallum
and Leo Weaver, SUMMARY OF PROCEEDINGS, INTERSTATE CONFERENCE ON WATER
PROBLEMS, Dayton, Ohio, 12/65.
COMBINED SEWER TEMPORARY UNDERWATER STORAGE FACILITY, Melpar for FWQA, 10/70.
"Combined wastewater overflows," C.K. Chen and W.W. Saxton, JOURNAL WPCF,
45:434, 1973.
COMMUNITY ACTION GUIDEBOOK FOR SOIL EROSION AND SEDIMENT CONTROL, M.D. Powell,
W.C. Winter, and W.P. Bodwitch, National Assoc. of Counties Research
Foundation, 3/70.
"Computer simulation of urban storm water runoff," Carl W. Chen and Robert P.
Shubinski, JOURNAL HYDRAULICS DIVISION, ASCE, HY2:7924, 2/71.
"Conservation programs in the urban fringe," John W. Neuberger, JOURNAL OF
SOIL AND WATER CONSERVATION, 216, 11-12/69.
C-2
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"Chapt. VIIIC. Control of sediment in the urban environment," Harold P.
Guy, TREATISE ON URBAN WATER SYSTEMS, Colorado State U., 7/71.
"Deep tunnel storage may solve city storm water problem," ENVIRONMENTAL
SCIENCE AND TECHNOLOGY, 3(3):209-211, 3/69.
"Development of a simulation model for stormwater management," J.A. Lager,
R.P. Shubinski, and L.W. Russell, JOURNAL WPCF, 43(12):2424-2435, 12/71.
"Discharge from separate storm sewers and combined sewers," W.J. Benzie and
R.J. Courchaine, JOURNAL WPCF, 38(3):410-421, 3/66.
'"Drainage of airport surfaces - some basic design considerations," Stifel W.
Jens, ASCE TRANSACTIONS, Paper No. 2348.
"Drainage of roads and paved surfaces," M.J. Hamlin and F.D. Hobbs, INSTITUTE
OF PUBLIC HEALTH ENGINEERS, 69(2):122-141.
"Dustfall as a source of water quality impairment," R.E. Johnson, A.T.
Rossano, Jr., and R.O. Sylvester, JOURNAL SANITARY ENGINEERING DIVISION,
ASCE, SA1:245, 2/66.
EFFECT OF URBAN GROWTH ON SEDIMENT DISCHARGE, NORTHWEST BRANCH ANACOSTIA
RIVER BASIN, MARYLAND, F.J. Keller, U.S.G.S. Prof. Paper, 450-C, C129-131.
"Effect of urbanization on storm water peak flows," Pedro C.C. Da Costa,
JOURNAL SANITARY ENGINEERING DIVISION, ASCE, SA2:187, 4/70.
EFFECTS OF DEICING SALTS ON WATER QUALITY AND BIOTA; Literature review and
recommended research, R.E. Hanes, L.W. Zelazny, and R.E. Blaser, National
Cooperative Highway Research Program Report 91, 1970.
"Effects of urbanization on storm water runoff quality: A limited experiment,
Naismith Ditch, Lawrence, Kansas," E.E. Angino, L.M. Magnuson, and G. F.
Stewart, WATER RESOURCES RESEARCH, 8(1):135, 2/72.
THE EFFECTS OF URBANIZATION ON UNIT HYDROGRAPHS FOR SMALL WATERSHEDS, HOUSTON,
TEXAS, 1964-67, W.H. Espey, Jr., and D.E. Winslow, Office of Water Resources
Research. (Also APPENDICES - DATA COMPILATION)
"The effects of urbanization on water quality," B.C. McGriff, Jr., J. ENVIRON.
QUALITY, l(l):86-88, 1972.
"Elemental composition of suspended particulate matter in metropolitan New
York," Norman L. Morrow and Richard S. Brief, ENVIRONMENTAL SCIENCE AND
TECHNOLOGY, 5(9):786-789, 9/71.
ENGINEERING INVESTIGATION OF SEWER OVERFLOW PROBLEM; A Detailed investigation
into the cause and effect of sanitary sewer overflows and recommended
remedial measures for Roanoke, Virginia, Hayes, Seay, Mattern & Mattern for
FWQA, 5/70.
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"Environmental effects of highways," Melvin E. Scheidt, JOURNAL SANITARY
ENGINEERING DIVISION, ASCE, SA5:17, 10/67.
ENVIRONMENTAL IMPACT OF HIGHWAY DEICING, Edison Water Quality Lab for Water
Quality Research, EPA, 11040 GKK, 6/71.
ENVIRONMENTAL IMPACT OF URBANIZATION ON THE FOOTHILL & MOUNTAINOUS LANDS
OF CALIFORNIA, California Div. of Soil Conservation, 11/71.
EROSION AND SEDIMENT CONTROL ON URBAN AND CONSTRUCTION SITES; An annotated
bibliography, ASAE Pollution by Sediment Committee (SW-224) of the Soil
and Water Div., 1972.
"Erosion rates and control methods on highway cuts," E.G. Diseker and B.C.
Richardson, TRANSACTIONS OF THE ASAE, 153, 1962.
"Erosion, runoff and revegetation of denuded construction sites," L.D. Meyer,
W.H. Wischmeier, and W.H. Daniel, TRANSACTIONS OF THE ASAE, 14(1):138-141,
1971.
EVALUATION OF STORM STANDBY TANKS, COLUMBUS, OHIO, Dodson, Kinney and
Lindblom for Water Quality Office, EPA, 11020 FAL, 3/71.
"Experience with the evaluation of urban effects for drainage design,"
Donald Vansickle, EFFECTS OF WATERSHED CHANGES ON STREAMFLOW, ed. by
Walter L. Moore and Carl W. Morgan, Center for Research in Water
Resources.
"Firestone installs pollution control," OIL AND GAS JOURNAL, 68(35):79,
8/31/70.
"Five cities try to stop storm pollution," ENGINEERING NEWS-RECORD, 67,
1966.
"Freeway runoff to be treated," PUBLIC WORKS, 102(5) :104, 5/71.
"Graphic analysis of roadway runoff," J.B. Wolfson, CIVIL ENGINEERING - ASCE,
64-65, 6/71.
GUIDELINES FOR EROSION AND SEDIMENT CONTROL PLANNING AND IMPLEMENTATION,
Maryland Dept. Water Resources and Hittman Associates for Office of Research
and Monitoring, EPA, R2-72-015, 8/72.
HIGH RATE FILTRATION OF COMBINED SEWER OVERFLOWS, Hydrotechnic Corp. for
Office of Research and Monitoring, EPA, 11023 EYI, 4/72.
"Hydraulics of runoff from developed surfaces," Carl F. Izzard, PROCEEDINGS
OF THE 26TH ANNUAL MEETING, HIGHWAY RESEARCH BOARD, 12/46.
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HYDROGRAPH SYNTHESIS FOR WATERSHED SUBZONES FROM MEASURED URBAN PARAMETERS,
J.B. Evelyn, V.V.D. Narayana, J.P. Riley, and E.K. Israelsen, Utah State
U. , 8/70.
HYDROLOGIC DESIGN, Chapt. G200 - 1969 edition, Storm Drain Design Manual,
City of Los Angeles Bureau of Engineering, 1970.
HYDROLOGIC EFFECTS FROM URBANIZATION OF FORESTED WATERSHEDS IN THE NORTHEAST,
Howard W. Lull and William E. Sopper, U.S.D.A. Forest Service Res. Paper
NE-146, 1969.
HYDROLOGIC EFFECTS OF SUBURBAN DEVELOPMENT NEAR PALO ALTO, CALIFORNIA,
J.R. Crippen and A.O. Waananen, U.S.G.S. Water Resources Div., 1/69.
HYDROLOGY FOR URBAN LAND PLANNING - A GUIDEBOOK ON THE HYDROLOGIC EFFECTS
OF URBAN LAND USE, Luna B. Leopold, U.S.G.S. Circular 554, 1968.
"The hydrology of urban runoff," A.L. Tholin and Clint J. Keifer, JOURNAL
SANITARY ENGINEERING DIVISION, ASCE, SA2:47, 3/59.
A HYDROMETEOROLOGICAL STUDY RELATED TO THE DISTRIBUTION OF PRECIPITATION
AND RUNOFF OVER SMALL DRAINAGE BASINS - URBAN VERSUS RURAL AREAS, R.G.
Feddes, R.A. Clark, and R.C. Runnels, Texas A and M U., Tech. Rept. No. 28,
6/70.
HYPOCHLORITE GENERATOR FOR TREATMENT OF COMBINED SEWER OVERFLOWS, Ionics,
Inc. for Office of Research and Monitoring, EPA, 11023 DAA, 3/72.
"The influence on flood peak discharges of some meteorological, topographical,
and hydraulic factors," Peter 0. Wolf, International Assoc. of Scientific
Hydrology, General Assembly of Toronto, Tome 111:26-39, 1958.
INVESTIGATIONS OF POROUS PAVEMENTS FOR URBAN RUNOFF CONTROL, Franklin Institute
Research Labs for Office of Research and Monitoring, EPA, 11034 DUY, 3/72.
"Lead in a suburban environment," P.R. Atkins, JOURNAL OF THE AIR POLLUTION
CONTROL ASSOCIATION, 19(8):591-594, 8/69.
"The measurement of the effects of building construction on drainage basin
dynamics," D.E. Walling and K.J. Gregory, JOURNAL OF HYDROLOGY, 11:129-144,
1970.
"Mercury and other metals in urban soils," David H. Klein, ENVIRONMENTAL
SCIENCE AND TECHNOLOGY, 6(6):560-562, 6/72.
"A method of computing urban runoff," W.I. Hicks, Esq., ASCE TRANSACTIONS,
Paper No. 2230.
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MICROSTRAINING AND DISINFECTION OF COMBINED SEWER OVERFLOWS, Cochrane Div.,
Crane Co. for FWQA, 11023 EVO, 6/70.
"Modeling the runoff characteristics of an urban watershed by means of an
analog computer," J. Paul Riley and V.V. Dhruva Narayana, EFFECTS OF
WATERSHED CHANGES ON STREAMFLOW, WATER RESOURCES SYMPOSIUM NO. 2, Austin,
Texas, 10/68.
A MULTI-PHASIC COMPONENT STUDY TO PREDICT STORM WATER POLLUTION FROM URBAN
AREAS, AVCO Economic Systems Corp. for Office of Water Resources Research,
EPA, 12/70.
NON-POINT SOURCE POLLUTION FROM AGRICULTURAL, RURAL, AND DEVELOPING AREAS,
Hearings before the Subcommittee on Conservation and Watershed Development
of the Committee on Public Works, H.R., 92nd Cong., 8/72.
"Pesticides and other contaminants in rainfall and runoff," S.R. Weibel,
R.B. Weidner, J.M. Cohen, and A.G. Christiansen, JOURNAL AWWA, 58(8):
1075-1084, 8/66.
"Physicochemical and microbiological properties of urban storm-water run-
off," G. Soderlund, H. Lehtinen, and S. Friberg, FIFTH INTERNATIONAL WATER
POLLUTION RESEARCH CONFERENCE, San Francisco, 1970.
"Pollution abatement through sewer system control," W.T. Eiffert and Paul J.
Fleming, JOURNAL WPCF, 41(2):285-291, 2/69.
"Pollution control measures for stormwaters and combined sewer overflows,"
D.D. Dunbar and J.G.F. Henry, JOURNAL WPCF, 38(l):9-26, 1/66.
A PRELIMINARY ANALYSIS OF THE EFFECTS OF URBANIZATION ON WATER QUALITY,
T. Kawashima, T.R. Harmer, and R.E. Coughlin, Regional Science Research
Institute, Philadelphia, Pa., 1970.
PROBLEMS OF THE SOIL MANTLE AND VEGETATIVE COVER OF THE STATE OF CALIFORNIA,
State Div. of Soil Conservation, 1/71.
PROCEEDINGS OF A SEMINAR ON URBAN HYDROLOGY, The Hydrologic Engineering
Center, 9/70.
PROCEEDINGS OF THE NATIONAL CONFERENCE ON SEDIMENT CONTROL, Washington, D.C.,
U.S. Dept. of Housing and Urban Development, Environmental Planning Div.,
9/69.
A PROGRAM IN URBAN HYDROLOGY. PART II: AN EVALUATION OF RAINFALL-RUNOFF
MODELS FOR SMALL WATERSHEDS AND THE EFFECTS OF URBANIZATION ON RUNOFF,
P.B.S. Sarma, J.W. Delleur, and A.R. Rao, Purdue U. Water Resources
Research Center Tech. Rept. No. 9, 10/69.
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PROPOSED COMBINED SEWER CONTROL BY ELECTRODE POTENTIAL, Merrimack College
for FWQA, 11024 DOK, 2/70.
Protecting steep construction slopes against water erosion," N.P. Swanson,
A.R. Dedrick, and A.E. Dudeck, HIGHWAY RESEARCH RECORD, 206:46-52, 1967.
QUALITY OF RUNOFF FROM DIVERSIFIED URBAN WATERSHEDS, Vishu V. Dharmadhikari,
M.A. Thesis for U. of Arizona, Dept. of Civil Engineering and Engineering
Mechanics, 1970.
"Quality of stormwater drainage from urban land," E.H. Bryan, Water Res.
Bull. 8(3):578-588, 1972.
QUALITY OF STORMWATER DRAINAGE FROM URBAN LAND AREAS IN NORTH CAROLINA,
Edward H. Bryan, Duke University, Durham, North Carolina, for Water
Resources Research Institute, University of North Carolina, Raleigh
Report No. 37, 6/70.
"Quantity of water from rainfall," W.A. Hardenbergh, WATER SUPPLY AND
WASTE DISPOSAL, Chapt. 2, 8-39.
"The rational method," Capt. Charles C. McCloskey, III, THE MILITARY
ENGINEER, 1-2/72.
"Real-time computer control of urban runoff," James J. Anderson, JOURNAL
HYDRAULICS DIVISION, ASCE, HY1:153, 1/70.
REPORT ON PROBLEMS OF COMBINED SEWER FACILITIES AND OVERFLOWS, 1967, FWPCA,
12/1/67.
"Roadside sediment production and control," E.G. Discker and B.C. Richardson,
TRANSACTIONS OF THE ASAE, 4:63-68, 1961.
ROTARY VIBRATORY FINE SCREENING OF COMBINED SEWER OVERFLOWS, Cornell, Howland,
Hayes and Merryfield for FWQA, 11023 FDD, 3/70.
"Runoff - a potential resource," Eric F. Mische and Vishnu V. Dharmadhikari,
WATER AND WASTES ENGINEERING, 8(2):28-31, 2/71.
"Runoff forecasting by variable transformation," Mikio Hino, JOURNAL
HYDRAULICS DIVISION, ASCE, HY4:871, 4/70.
"Runoff from combined rural and urban areas," L.H. Watkins, RIVER ENGINEERING
AND WATER CONSERVATION WORKS, Chapt. 7:111-121, 1966.
"Runoff volumes from small urban watersheds," Clayton R. Miller and Warren
Viessman, Jr., WATER RESOURCES RESEARCH, 8(2):429, 4/72.
SCREENING/FLOTATION TREATMENT OF COMBINED SEWER OVERFLOWS, Ecology Div.,
Rex Chainbelt Inc. for Office of Research and Monitoring, EPA, 11020 FDC,
1/72.
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SELECTED URBAN STORM WATER RUNOFF ABSTRACTS, Franklin Institute Research Labs
for FWPCA, 6/69.
SELECTED URBAN STORM WATER RUNOFF ABSTRACTS, Franklin Institute Research Labs
for Water Quality Office, EPA, 11024 EJC, 7/70.
SELECTED URBAN STORM WATER RUNOFF ABSTRACTS, July 1970 - June 1971, Franklin
Institute Research Labs for Water Quality Research, EPA, 11024 FJE, 7/71.
SELECTED URBAN STORM WATER RUNOFF ABSTRACTS, July 1971 - June 1972, Mrs.
Dorothy A. Sandoski for Office of Research and Monitoring, EPA, R2-72-127,
12/72.
SIMPLIFICATION OF INTEGRATED STORMWATER PLANNING FOR MODERN MULTIPLE LAND
USE IN URBAN AND SUBURBAN DEVELOPMENTS, Brian M. Reich, Unpub. Research
Project Tech. Completion Rept., Pennsylvania State U., Institute for Research
on Land and Water Resources, 3/31/70.
"Simulation of runoff from urban watersheds," V.V. Dhruva Narayana, J. Paul
Riley, and Eugene K. Israelson, WATER RESOURCES BULLETIN, 7(1): S4, 2/71.
SIMULATION OF WATER QUALITY IN STREAMS AND CANALS; Program documentation and
users manual, Texas Water Development Board, 9/70.
"Some effects of urbanization on runoff as evaluated by Thornthwaite Water
Balance Models," Robert A. Muller, PROCEEDINGS 3RD ANNUAL AMERICAN WATER
RESOURCES CONFERENCE, Urbana, Illinois, 11/67.
"Source control of urban water pollution," James P. Heaney and Richard H.
Sullivan, JOURNAL WPCF, 43(4):571-579, 4/71.
"Spatially variable discharge over a sloping plane," G.H. Keulegan,
TRANSACTIONS, AMERICAN GEOPHYSICAL UNION, 956-968.
"Stilling pond storm overflow studies," Y.R. Reddy and John Pickford,
JOURNAL SANITARY ENGINEERING DIVISION, ASCE, SA4:609, 8/72.
STORAGE AND TREATMENT OF COMBINED SEWER OVERFLOWS, Office of Research and
Monitoring, EPA, R2-72-070, 10/72.
STORM AND COMBINED SEWER DEMONSTRATION PROJECTS, January 1970, William A.
Rosenkranz, Div. of Applied Science and Technology, FWPCA, 1/70.
STORM AND COMBINED SEWER POLLUTION SOURCES AND ABATEMENT, ATLANTA, GEORGIA,
Black, Crow and Eidsness, Inc. for Water Quality Office, EPA, 11024 ELB,
1/71.
"Storm sewer design by the inlet method," Albert B. Kaltenbach, PUBLIC WORKS,"
86, 1/63.
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"Stormwater disinfection at New Orleans," Edgar H. Pavia and Crawford J.
Powell, JOURNAL WPCF, 41(4):591-606, 4/69.
"Storm water for fun and profit," John R. Sheaffer, WATER SPECTRUM, 2(3):
29-34, Fall 1970.
STORM WATER MANAGEMENT MODEL, Vols. I-IV, Metcalf and Eddy, Inc. for EPA,
7/71-10/71.
STORM WATER POLLUTION FROM URBAN LAND ACTIVITY, AVCO Economic Systems Corp.
for FWQA, 11034 FKL, 7/70.
STORM WATER POLLUTION, NEW ORLEANS, LOUISIANA; Final report on the sources
of storm water pollution by sewage from sanitary sewers, Sewerage and
Water Board of New Orleans, 7/70.
STORM WATER POLLUTION, NEW ORLEANS, LOUISIANA; Supplementary report covering
repair work.
STORM WATER PROBLEMS AND CONTROL IN SANITARY SEWERS, OAKLAND AND BERKELEY,
CALIFORNIA, Metcalf and Eddy, Inc. for Water Quality Office, EPA, 11024
EQG, 3/71.
STORM WATER RUNOFF FROM AN URBAN HIGHWAY DRAINAGE SYSTEM, K. Morrison,
J.B. Kim, F.W. Ellerman, J.I. Chang, and H.H. Liu, D.C. Dept. of Highways
and Traffic, Final Research Rept., 8/25/71.
"Storm water to cool?" WATER AND WASTES ENGINEERING, 8(9):12, 9/71.
STREAM POLLUTION AND ABATEMENT FROM COMBINED SEWER OVERFLOWS; A study of
stream pollution from combined sewer overflows and feasibility of
alternate plans for pollution abatement in Bucyrus, Ohio, Burgess &
Niple, Ltd. for FWPCA, 11/69.
STUDIES OF STORM AND COMBINED SEWER POLLUTION OF THE SOUTH RIVER DRAINAGE
BASIN, CITY OF ATLANTA, GEORGIA, Black, Crow and Eidsness, Inc. for FWPCA,
draft rept.
A STUDY OF METHODS USED IN MEASUREMENT AND ANALYSIS OF SEDIMENT LOADS IN
STREAMS, Federal Inter-Agency Sedimentation Project, rev. 4/69.
A SUMMARY OF METHODS FOR THE COLLECTION AND ANALYSIS OF BASIC HYDROLOGIC
DATA FOR ARID REGIONS, U.S.G.S. Water Resources Div. Open File Rept.
"Surface runoff determination from rainfall without using coefficients,"
W.W. Horner and S.W. Jens, ASCE TRANSACTIONS, Paper No. 2153.
SURFACE STORM WATER RUNOFF AND DRAINAGE STUDY FOR ESSEX COUNTY, NEW JERSEY,
Essex Co. Dept. of Planning, Economic Development, and Conservation,
12/69.
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"A systems study of storm runoff problems in a new town," Charles W. Mallory
and John J. Boland, WATER RESOURCES BULLETIN, 6(6):980, 11-12/70.
THESAURUS OF WATER RESOURCES TERMS; A collection of water resources and
related terms for use in indexing technical information, U.S. Bureau of
Reclamation, 1971.
TOXIC MATERIALS ANALYSIS OF STREET SURFACE CONTAMINANTS, Robert E. Pitt and
Gary Amy, URS Research Company for Municipal Pollution Control Branch,
EPA, 1/73.
"Treatment of urban runoff," F. Condon, APWA REPORTER, 40(3):8-11, 3/73.
"Treatment of urban stormwater runoff," F.L. Evans, III, E.E. Geldreich,
S.R. Weibel, and G.G. Robeck, JOURNAL WPCF, 40(5,2):R162-R170, 5/68.
UNDERWATER STORAGE OF COMBINED SEWER OVERFLOWS, Karl R. Rohrer Associates,
Inc. for EPA, 11022 ECV, 9/71.
"Urban effects on quality of streamflow," E. Gus Fruh, EFFECTS OF WATERSHED
CHANGES ON STREAMFLOW, WATER RESOURCES SYMPOSIUM NO. 2, Texas, 10/68.
URBAN GROWTH AND THE WATER REGIMEN, John Savini and J.C. Kammerer, Geological
Survey Water-Supply Paper 1591-A, 1961.
"Urban hydrology," W.J. Bauer, THE PROGRESS OF HYDROLOGY, Vol. 2, Illinois
University, 7/69.
"Urbanization and the water balance," Andrew M. Spieker, PROCEEDINGS OF THE
SYMPOSIUM ON WATER BALANCE IN NORTH AMERICA, Banff, Alberta, Canada,
American Water Resources Association, 6/69.
URBAN DRAINAGE PRACTICES, PROCEDURES, AND NEEDS, Herbert G. Poertner, Robert
L. Anderson, and Dr. Karl W. Wolf, American Public Works Association, 12/66.
"Urban land runoff as a factor in stream pollution," S.R. Weibel, R.J.
Anderson, and R.L. Woodward, JOURNAL WPCF, 36(7):914-924, 7/64.
URBAN RUNOFF, M.B. McPherson, ASCE Urban Water Resources Research Program
Tech. Mem. No. 18, 8/72.
"Urban runoff adds to water pollution," ENVIRONMENTAL SCIENCE AND TECHNOLOGY,
3(6):527, 6/69.
"Urban runoff by road research laboratory method," Michael L. Terstriep and
John B. Stall, JOURNAL HYDRAULICS DIVISION, HY6:1809, 11/69.
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Urban runoff by road research laboratory method," discussions by Leaonard
H. Watkins, Franklin F. Snyder, Harvey W. Duff, and George C.C. Hsieh,
JOURNAL HYDRAULICS DIVISION, ASCE, HY7:1625, 7/70.
"Urban runoff by road research laboratory method," discussion by D. Earl
Jones, Jr., JOURNAL HYDRAULICS DIVISION, ASCE, HY9:1879, 9/70.
URBAN RUNOFF AND BASIN DRAINAGE STUDY, LAKE WASHINGTON (CEDAR RIVER) AND
GREEN RIVER BASINS, SEATTLE, WASHINGTON, Seattle District, Corps of
Engineers, 7/72,
URBAN RUNOFF CHARACTERISTICS, Division of Water Resources, Dept. of Civil
Engineering, U. of Cincinnati, Interim Rept. of the EPA Water Quality
Office, 11024 DQU, 10/70.
URBAN RUNOFF IN LAKE COUNTY, ILLINOIS; A REASSESSMENT OF THE ROLE OF URBAN
RUNOFF IN THE POLLUTION OF LAKE MICHIGAN, State of Illinois, Lake Michigan,
and Adjoining Land Study Commission Rept. to the Governor and the 77th
General Assembly, Vol. II, 10/71.
URBAN SOIL EROSION AND SEDIMENT CONTROL, National Association of Counties
Research Foundation, FWQA, 15030 DTL, 5/70.
URBAN SPRAWL AND FLOODING IN SOUTHERN CALIFORNIA, S.E. Rantz, U.S.G.S.
Circular 601-B, 1970.
"Urban storm drainage criteria manual from Denver," Elmer L. Claycomb,
CIVIL ENGINEERING - ASCE, 40(7):39-41, 7/70.
URBAN STORM RUNOFF AND COMBINED SEWER OVERFLOW POLLUTION, SACRAMENTO,
CALIFORNIA, Environgenics Company for EPA, 11024 FKM, 12/71.
"Urban storm runoff relations," W. Viessman, Jr., W.R. Keating, and K.N.
Srinivasa, WATER RESOURCES RESEARCH, 6(1):275, 2/70.
"Urban stormwater quality and its impact on the receiving system," Edward
H. Bryan, PROCEEDINGS OF THE SOUTHERN WATER RESOURCES AND POLLUTION
CONTROL CONFERENCE, 20:4/71.
VARIATION OF URBAN RUNOFF WITH DURATION AND INTENSITY OF STORMS, D.M. Wells,
T.A. Austin, and B.C. Cook for Office of Water Resources Research, 8/71.
WATER POLLUTION ASPECTS OF STREET SURFACE CONTAMINANTS, James D. Sartor and
Gail B. Boyd for Office of Research and Monitoring, EPA, 11034 FUJ, 11/72.
WATER POLLUTION ASPECTS OF URBAN RUNOFF, APWA for FWPCA, 1/69.
WATER QUALITY CHARACTERISTICS OF STORM SEWER DISCHARGES AND COMBINED SEWER
OVERFLOWS, V. Kothandaraman, 111. State Water Survey, Urbana, Circular 109,
1972.
C-ll
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INFORMATION MATRIX REPORT CODE REFERENCES
1. 'Application of an electronic analog computer to the evaluation of the
effects of urbanization on the runoff characteristics of small watersheds,"
V.V. Dhruva Narayana and J. Paul Riley, THE USE OF ANALOG AND DIGITAL
COMPUTERS IN HYDROLOGY, Proceedings of the Tucson Symposium, 1:38-48.
2. "Assessing the quality of urban drainage," Warren Viessman, Jr., PUBLIC
WORKS, 10/69.
3. "The bacteriological aspects of storm-water pollution," E.E. Geldreich,
L.C. Best, B.A. Kenner, and D.J. Van Donsel, JOURNAL WPCF, 40(11,1):
1861-1872, 11/68.
4. "Bacteriological comparison between combined and separate sewer discharges
in southeastern Michigan," R.J. Burm and R.D. Vaughan, JOURNAL WPCF, 38(3):
400-409, 3/66.
5. THE BENEFICIAL USE OF STORM WATER, C.W. Mallory, Office of Research and
Monitoring, EPA, R2-73-139, 1/73.
6. "Calculation of water pollution by surface runoff," N.A. Pravoshinsky and
P.D. Gatillo, WATER RESEARCH, 2(1) -.24-26, 1/68.
7. CHARACTER AND SIGNIFICANCE OF HIGHWAY RUNOFF WATERS, A PRELIMINARY
APPRAISAL, Robert 0. Sylvester and Foppe B. DeWalle, Washington State
Highway Commission, 12/29/72.
8. "Characterization, treatment, and disposal of urban stormwater," S.R.
Weibel, R.B. Weidner, A.G. Christiansen, and R.J. Anderson, PROCEEDINGS,
3RD INTERNATIONAL CONFERENCE ON WATER POLLUTION RESEARCH, Munich, 1:1966.
9. "Chemical and physical comparison of combined and separate sewer discharges,"
R.J. Burm, D.F. Krawczyk, and G.L. Harlow, JOURNAL WPCF, 40(1):112-126, 1/68.
10. "Deep tunnel storage may solve city storm water problem," ENVIRONMENTAL
SCIENCE AND TECHNOLOGY, 3(3):209-211, 3/69.
11. "Development of a simulation model for stormwater management," J.A. Lager;
R.P. Shubinski, and L.W. Russell, JOURNAL WPCF, 43(12):2424-2435, 12/71.
12. "Dustfall as a source of water quality impairment," R.E. Johnson, A.T.
Rossano, Jr., and R.O. Sylvester, JOURNAL SANITARY ENGINEERING DIVISION,
ASCE, SA1:245, 2/66.
C-13
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13. EFFECT OF URBAN GROWTH ON SEDIMENT DISCHARGE, NORTHWEST BRANCH ANACOSTIA
RIVER BASIN, MARYLAND, F.J. Keller, U.S.G.S. Prof. Paper 450-C, C129-131.
14. "Effect of urbanization on storm water peak flows," Pedro C.C. Da Costa,
JOURNAL SANITARY ENGINEERING DIVISION, ASCE, SA2:187, 4/70.
15. EFFECTS OF DEICING SALTS ON WATER QUALITY AND BIOTA; Literature review arid
recommended research, R.E. Hanes, L.W. Zelazny, and R.E. Blaser, National
Cooperative Highway Research Program Report 91, 1970.
16. "Effects of urbanization on storm water runoff quality: A limited
experiment, Naismith Ditch, Lawrence, Kansas," E.E. Angino, L.M. Magnuson,
and G.F. Stewart, WATER RESOURCES RESEARCH, 8(1):135, 2/72.
17. "The effects of urbanization on water quality," E.G. McGriff, Jr., J.
ENVIRON. QUALITY, l(l):86-88, 1972.
18. "Elemental composition of suspended particulate matter in metropolitan
New York," Norman L. Morrow and Richard S. Brief, ENVIRONMENTAL SCIENCE
AND TECHNOLOGY, 5(9):786-789, 9/71.
19. ENVIRONMENTAL IMPACT OF HIGHWAY DEICING, Edison Water Quality Lab for
Water Quality Research, EPA, 11040 GKK, 6/71.
20. EVALUATION OF STORM STANDBY TANKS, COLUMBUS, OHIO, Dodson, Kinney and
Lindblom for Water Quality Office, EPA, 11020 FAL, 3/71.
21. "Five cities try to stop storm pollution," ENGINEERING NEWS-RECORD, 67,
12/66.
22. "Freeway runoff to be treated," PUBLIC WORKS, 102(5):104, 5/71.
23. HIGH RATE FILTRATION OF COMBINED'SEWER OVERFLOWS, Hydrotechnic Corp. for
Office of Research and Monitoring, EPA, 11023 EYI, 4/72.
24. HYDROGRAPH SYNTHESIS FOR WATERSHED SUBZONES FROM MEASURED URBAN PARAMETERS,
J.B. Evelyn, V.V.D. Narayana, J.P. Riley, and E.K. Israelsen, Utah State
University, 8/70.
25. INVESTIGATIONS OF POROUS PAVEMENTS FOR URBAN RUNOFF CONTROL, Franklin
Institute Research Labs, for Office of Research and Monitoring, EPA,
11034 DUY, 3/72.
26. "Lead in a suburban environment," P.R. Atkins, JOURNAL OF THE AIR POLLUTION
CONTROL ASSOCIATION, 19(8):591-594, 8/69.
27. "Mercury and other metals in urban soils," David H. Klein, ENVIRONMENTAL
SCIENCE AND TECHNOLOGY, 6(6):560-562, 6/72.
C-14
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28. "A method of computing urban runoff," W.I. Hicks, Esq., ASCE TRANSACTIONS,
Paper No. 2230.
"T
29. Pesticides and other contaminants in rainfall and runoff, S.R. Weibel,
R.B. Weidner, J.M. Cohen, and A.G. Christiansen, JOURNAL AWWA, 58(8):
1075-1084, 8/66.
30. QUALITY OF RUNOFF FROM DIVERSIFIED URBAN WATERSHEDS, Vishu V. Dharmadhikari,
M.A. Thesis for U. of Arizona, Dept. of Civil Engineering and Engineering
Mechanics, 1970.
31. QUALITY OF STORMWATER DRAINAGE FROM URBAN LAND AREAS IN NORTH CAROLINA,
Edward H. Bryan, Duke University, Durham, North Carolina, for Water
Resources Research Institute, University of North Carolina, Raleigh
Report No. 37, 6/70.
32. "Some effects of urbanization on runoff as evaluated by Thornthwaite
Water Balance Models," Robert A. Muller, PROCEEDINGS 3RD ANNUAL AMERICAN
WATER RESOURCES CONFERENCE, Urbana, Illinois, 11/67.
33. "Stilling pond storm overflow studies," Y.R. Reddy and John Pickford,
JOURNAL SANITARY ENGINEERING DIVISION, ASCE, SA4:609, 8/72.
34. STORM AND COMBINED SEWER POLLUTION SOURCES AND ABATEMENT, ATLANTA,
GEORGIA, Black Crow and Eidsness, Inc. for Water Quality Office, EPA,
11024 ELB, 1/71.
35. "stormwater disinfection at New Orleans," Edgar H. Pavia and Crawford
J. Powell, JOURNAL WPCF, 41(4):591-606, 4/69.
36. STORM WATER POLLUTION FROM URBAN LAND ACTIVITY, AVCO Economic Systems
Corp. for FWQA, 11034 FKL, 7/70.
37. STORM WATER RUNOFF FROM AN URBAN HIGHWAY DRAINAGE SYSTEM, K. Morrison
et al., District of Columbia Department of Highways and Traffic, Washington,
D.C. , 8/71.
38. TOXIC MATERIALS ANALYSIS OF STREET SURFACE CONTAMINANTS, Robert E. Pitt
and Gary Amy, URS Research Company for Municipal Pollution Control Branch,
EPA, 1/73.
39. "Treatment of urban runoff," F. Condon, APWA REPORTER, 3/73.
40. "Treatment of urban stormwater runoff," F.L. Evans, III, E.E. Geldreich,
S.R. Weibel, and G.G. Robeck, JOURNAL WPCF, 40(5,2):R162-R170, 5/68.
41. "Urban land runoff as a factor in stream pollution," S.R. Weibel, R.J.
Anderson, and R.L. Woodward, JOURNAL WPCF, 36(7):914-924, 7/64.
C-15
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42. URBAN RUNOFF, M.B. McPherson, ASCE Urban Water Resources Research Program
Tech. Mem. No. 18, 8/72.
43. URBAN RUNOFF IN LAKE COUNTY, ILLINOIS: A REASSESSMENT OF THE ROLE OF
URBAN RUNOFF IN THE POLLUTION OF LAKE MICHIGAN, State of Illinois, Lake
Michigan, and Adjoining Land Study Commission Rept. of the Governor and
the 77th General Assembly, Vol. II, 10/71.
44. "Urban storm runoff relations," W. Viessman, Jr., W.R. Keating, and K.N.
Srinivasa, WATER RESOURCES RESEARCH, 6(1):275, 2/70.
45. WATER POLLUTION ASPECTS OF STREET SURFACE CONTAMINANTS, James D. Sartor
and Gail B. Boyd, URS Research Company for Office of Research and
Monitoring, EPA, 11034 FUJ, 11/72.
46. WATER POLLUTION ASPECTS OF URBAN RUNOFF, APWA for FWPCA, 1/69.
47. WATER QUALITY CHARACTERISTICS OF STORM SEWER DISCHARGES AND COMBINED
SEWER OVERFLOWS, V. Kothandaraman, 111. State Water Survey, Urbana,
Circular 109, 1972.
C-16
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Appendix D
GLOSSARY
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Appendix D
GLOSSARY
1. Ammonia Nitrogen (M.)' - a form of nitrogen which is an essential
nutrient to plants (can cause algal blooms if all nutrients are
present in sufficient quantities). A product of natural decompo-
sition of fecal matter, urea and other animal protein.
2. Average Daily Traffic (APT) - an average value for the daily
vehicular traffic on a specific roadway.
3. Biochemical Oxygen Demand (BOD ) - the amount of oxygen required
by bacteria in a five day period while decomposing organic matter
under aerobic conditions.
4. Cadmium (Cd) - an element of high toxic potential when taken by
mouth and possible association with renal arterial hypertension
at sublethal levels.
5. Chemical Oxygen Demand (COD) - a determination of organic material.
The sample is "completely'' oxidized by chemical methods, instead of
the incomplete oxidation by bacteria in the BOD test.
o
6. Chromium (Cr) - a toxic element when present in the hexavalent
chromium ion form.
7. Copper (Cu) - an essential and beneficial element in human metab-
olism, but quantities above 1 mg/f tend to impart an undesirable
taste to drinking water.
8. Cubic Feet Per Second (cfs) - one cubic foot volume of water passing
a point per unit second.
D-l
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9. DDD - an organochlorine insecticide [1,l-dichloro-2, 2-bisfp-
chlorophenyl) ethane].
10. DDT - an organochlorine insecticide closely related to DDD [1,1,1,-
trichloro-2, 2-bis-(p-chlorophenyl) ethane].
11. Dieldrin - an organochlorine insecticide of fairly complex molecu-
lar structure.
12. Dissolved Oxygen (DO) - the amount of oxygen dissolved in a liquid.
If at equilibrium with the overlaying atmosphere, it is then con-
sidered saturated. Must be present to support aerobic aquatic life.
Is consumed naturally in the trself-purifying'r process of waterways.
13. Endrin - an organochlorine insecticide which has the same struc-
tural formula as dieldrin and is its isomer.
14. Equivalent Days of Accumulation (EDA) - a measure of the relative
days of accumulation of pollutants on a street surface as a function
of rainfall and sweeping history and respective removal efficiencies.
15. Fecal Coliforms (FColi) - indicators of recent fecal pollution in
water supplies by pathogenic bacteria, expressed as number of
organisms per gram of solids of street surface material.
16. Iron (Fe) - an element that imparts a bitter taste to water and a
brownish color to clothing laundered in such water.
17. Lead (Pb) - a highly toxic heavy metal when ingested for either
brief or prolonged periods (cumulative poison).
18. XinQane - an organochlorine insecticide which is the gamma isomer
of benzene hexachloride.
D-2
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19. Loading - the average amount of total solids found on street surfaces
per unit distance per day (i.e., pounds/curb mile/day).
20. Manganese (Mn) - an element which produces a brownish color in
laundered goods and impairs the taste of beverages, including
coffee and tea.
21. Mercury (Hg) - a heavy metal which can cause severe neurological dis-
orders when it is ingested in large quantities. (Particularly dan-
gerous because small quantities can be concentrated by aquatic or-
ganisms which are frequently eaten by man) .
22. Methoxychlor - an organochlorine insecticide [1,1,l-trichloro-2,
2-bis (£-methoxyphenyl) ethane],
23. Methyl Parathion - an organophosphate insecticide [0,0-dimethyl
0-p-nitrophenyl phosphorothioate].
fi
24. Micron (fJi) - a unit of length equal to 1 x 10 meters.
25. Micrograms Per Gram (/x.g/g) - the micrograms of a specific pollutant
found per gram of total solids.
26. Micrograms Per Kilogram (/zg/kg) - the micrograms of a specific
pollutant found per kilogram of total solids.
27. Milligrams Per Liter (mg/f) - the milligrams of a substance per liter
of water on a dry weight basis.
28. Most Probable Number (MPN) - a statistical indication of the number
of bacteria present in a given volume (usually 100 mf).
D-3
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29. Nickel (Ni) - an element which is very toxic to most plants (espew
cially agricultural crops) but less so to animals.
30. Nitrate (NO ) - a form of nitrogen which is an essential nutrient to
-^. i J^
plants (can cause algal blooms if all other nutrients are present
in sufficient quantities). Product of bacteria oxidation of other
forms of nitrogen, from the atmosphere during electrical storms and
from fertilizer manufacturing.
31. Organic Nitrogens (OrgN) - '"original" form of nitrogenous nutrients.
Gradually converted to ammonia nitrogen and to nitrites and nitrates,
if aerobic conditions prevail. An indication of algal activity po-
tential of a water.
32. Orthophosphate (OPOryl - a measure of the total inorganic phosphate
content. Other inorganic phosphate forms (polyphosphate) convert
to this form after several hours to several days. Inorganic phos-
phate is an important nutrient to plants, and excessive amounts
can cause algal blooms if the right conditions prevail.
33. Polychlorinated Biphenyls (PCB) - organochlorine compounds of a
pesticidal nature which are usually used for industrial purposes
(such as plastic manufacture).
34. Pounds Per Curb. Mile (Ib/curb mile) - a measure of loading intensity
of a pollutant per unit distance.
35. Strontium (Sr) - the radioactive form of this element (Strontium-90)
has a tendency to accumulate in bone structures and is a
well-recognized health hazard.
D-4
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36. Total Conforms (TColi) - bacterial indicators of less recent
pollution and/or the existence of defects in water treatment or
distribution (these bacteria are of nonfecal origin usually orig-
inating in soil).
37. Total Phosphorus (TPO ) - shows all types of phosphorus occurring
in a water system (ortho-, poly- and organic) and is a good indi-
cator of the potential biological productivity of the water.
38. Zinc (Zn) - an essential element in human health which, when pres-
ent in excess, can impart a milky appearance and metallic taste
to water supplies. Is toxic to many organisms in large quantities.
D-5
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA 440/9-75-004
3. RECIPIENT'S ACCESSION'NO.
\. TITLE AND SUBTITLE
Water Quality Management for Urban Runoff
5. REPORT DATE
December 1974; Issuing Date
6. PERFORMING ORGANIZATION CODE
'. AUTHOR(S)
Gary Amy, Robert Pitt, Rameshawar-Singh,
Westly L. Bradford, Michael B. LaGraff
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
URS Research Company
155 Bovet Road
San Mato, California 94402
10. PROGRAM ELEMENT NO.
1BB034/ROAP 21 ATB/Task 76B
11. CONTRACT/GRANT NO.
68-01-1846
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Protection Agency
Office of Planning and Standards
Washington, D.C. 20460
13. TYPE OF REPORT AND PERIOD COVERED
.Final _____
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This manual provides technical assistance to state and local water quality
management planners to enable them to quantify within reasonable limits the
urban non-point water pollution problem in a local planning area without extensive
data generation, and to make a preliminary evaluation of cost effective abatement
and control practices. The manual prescribes procedures for several levels of
input, each requiring more self-generated data, with increasingly sophisticated
results.
A state-of-the-art and an extensive bibliography on urban storm water runoff
is presented in the appendix. A glossary is also included.
The manual is not intended to be used as a basis for abatement design but does
provide a guide to data generation for this purpose,
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COS AT I Field/Group
Drainage, *Water Pollution, *Surface
Water Runoff, *Runoff, *Water Quality,
*Storm Sewers, Hydrology, Hydraulics,
*Mathematical Models
Drainage Systems, Water
Pollution Controls, *
*Urban Hydrology, Runoff
Pollution Loads
0808
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RELEASE TO PUBLIC
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Unclassified
21. NO. OF PAGES
247
20. SECURITY CLASS (Thispage)
Unclassified'
22. PRICE
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