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

-------
     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

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             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

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            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.

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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

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           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

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                                             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 .

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                                                  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.

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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.


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         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

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     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

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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

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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

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       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





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     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

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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

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   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

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         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

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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)
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.£>



O
                                     TIME
            Figure 6.  Definition  of  Unit Hydrograph Properties
                                      1-33

-------
    s
  .2-,
  .1-
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         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!
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Figure 8.  Determination  of  T
                              R

-------
      10,000
     c

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         100
                                      z^?e
                                       m
 :r|—£
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                                           Figure  9.   Determination  of  T.
                                                                          B

-------
co
      1,000  ^
        100  i

             9

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                                                                   67891
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                                                          07A

                                                      Determination of W
                                                                        50

-------
      1,000  T
    •r 100
                                   m
oo
        10
                                                2    3   4   56759!
                                                                           Is
                                                                                  ^
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       1.0
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                                      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

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   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

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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

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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

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     •  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

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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

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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

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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

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^  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

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    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

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 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

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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

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    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

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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

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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

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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

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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

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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

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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

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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

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 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

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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
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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

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   Section V
STATE OF THE ART

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                                  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

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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










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i
ft
a
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•a
a
0)
ft
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c
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e
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-M
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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

-------
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BALTIMORE
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MED/NEW/SING
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CENT.BUSi
SHOP. CENTER
LIGHT INC.
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                                             Table A-4,'  ENTIRE MATRIX OF AVAILABLE DATA

-------
CITT
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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
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24
135
63
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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
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7'
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12"
121
170
9!
66
94
121
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120
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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
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61
111
'700O
23000
?1000
16000
44000
'4000
26000
23000
17000
?1000
23000
15000
15000
20000
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15000
14000
1"000
15000
25000
34000
'2000
15000
13000
15000
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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
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'1000
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11000
25000

5000
'4000
'2000
27000
37000
'3000
59000
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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
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?=00

0
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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
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7
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30
37
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7
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170
18
23
39
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20
19
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20
10
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17
13
13
12
15
11
15
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21
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76
20
20
7
20
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33
41
24
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24
23
5
6
25
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34
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38
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21
9
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13
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4
13
5
6
3
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78
110
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37
38
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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















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3.1E4

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I'9E5
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1'8E4
6-7E5
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5-8E4
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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
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3-5E4
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1-3E5
2-3E5
1-6E5
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3-1E5
1-6E5
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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
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                                                                                                           2600
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22
    Z.5W 1.QE3
    a.»E» t.JES
 I
M
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                                                           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
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2 0
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1 2
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1 3
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4 1
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8900
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5860
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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
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3
a
a
3
3
3
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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
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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
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16
a
9





















160
90
83
170
120
.IS
120
180
170
91
66
9*
iao
79
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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
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35
6
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28
33
81
a*
76
18
7
80
9
33
33
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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
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1-OE6
3-OE6
3.9E3
2.6E6
a.!E6
4.0E4

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to
cm
DURHAM
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DURHAh
TUUSA
TUUSA
TUUSA
TUUSA
TULSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
TUUSA
BAUTIM6RE
ATUANTA
TUUSA
BAUTIMeRE
BAUTIM8RE
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ATUANTA
ATUANTA
ATUANTA
ATUANTA
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TUUSA
TUUSA
TW-SA
TUUSA
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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
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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
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.23
.11
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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!
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LIES


4.ZE3
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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

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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

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                                  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.

                                      C-3

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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.
                                      C-4

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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.
                                      C-6

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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.

                                      C-7

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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.
                                      C-8

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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.
                                      C-9

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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.
                                     C-10

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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-bis—fp-
      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
 8. DISTRIBUTION STATEMENT

  RELEASE TO  PUBLIC
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21. NO. OF PAGES

  247
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