xvEPA
            United States
            Environmental Protection
            Agency
             Municipal Environmental Research EPA-600/9-78-017
             Laboratory         August 1978
             Cincinnati OH 45268
            Research and Development
Urban Stormwater
Management Workshop
Proceedings

Edison NJ
December  1,  1977



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                RESEARCH REPORTING SERIES

Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology.  Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:

      1.  Environmental  Health Effects Research
      2.  Environmental  Protection Technology
      3.  Ecological  Research
      4.  Environmental  Monitoring
      5.  Socioeconomic Environmental Studies
      6.  Scientific and Technical Assessment Reports (STAR)
      7.  Interagency Energy-Environment Research and Development
      8.  "Special" Reports
      9.  Miscellaneous Reports
 This document is available to the public through the National Technical Informa-
 tion Service, Springfield, Virginia 22161.

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                                             EPA-600/9-78-017
                                             August  1978
               URBAN STORMWATER MANAGEMENT
                   WORKSHOP PROCEEDINGS

                    Edison, New Jersey
                     December 1, 1977
                        Edited By

                      Richard Field
            Storm and Combined Sewer Section
              Wastewater Research Division
Municipal Environmental  Research Laboratory (Cincinnati)
               Edison, New Jersey   08817
   Based on Project Nos.  68-03-2617, R802411, R805238,
                         R804578, S804432
                     Project Officers

                      Richard Field
         Anthony N. Tafuri      Hugh E.  Masters
           Chi-Yuan Fan        Richard P. Traver
            Storm and Combined Sewer Section
              Wastewater Research Division
Municipal Environmental Research Laboratory (Cincinnati)
               Edison, New Jersey   08817
       MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
           OFFICE OF RESEARCH AND DEVELOPMENT
          U.S. ENVIRONMENTAL PROTECTION AGENCY
                 CINCINNATI, OHIO   45268

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                                DISCLAIMER

     This report has been reviewed by the Municipal Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved for publi-
cation.  Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.

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                                 FOREWORD

     The Environmental  Protection Agency was created because of increasing
public and government concern about the dangers of pollution to the health
and welfare of the American people.   Noxious air,  foul  water, and spoiled
land are tragic testimony to the deterioration of  our natural environment.
The complexity of that environment and the interplay between its components
require a concentrated and integrated attack on the problem.

     Research and development is that necessary first step in problem
solution and it involves defining the problem, measuring its impact, and
searching for solutions.  The Municipal Environmental Research Laboratory
develops new and improved technology and systems for the prevention,
treatment, and management of wastewater and solid  and hazardous waste
pollutant discharges from municipal  and community  sources, for the
preservation and treatment of public drinking water supplies, and to
minimize the adverse economic, social, health, and aesthetic efforts of
pollution.  This publication is one of the products of that research; a
most vital communication link between the researcher and the user
community.

     The workshop proceedings contained herein include discussions of
the urban stormwater management technology manual  update; new version
of the Storm Water Management Model  (SWMM); characterization of pollutant
accumulation rates in urban areas; and application of nonstructural con-
trol of urban runoff pollution.
                                     Francis T.  Mayo
                                     Director
                                     Municipal Environmental  Research
                                     Laboratory
                                    n

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                                 ABSTRACT

     The workshop on urban stormwater management technology was held on
December 1, 1977 at the offices of the USEPA in Edison, New Jersey.  The
purpose of the workshop was to exchange and disseminate the most up-to-
date research results and technical  information from projects sponsored
under the USEPA Urban Runoff Control Research Development and Demonstration
Program.  The proceedings contained herein represent the contributions
from participating lecturers and include the following topics:

     a.  Urban stormwater management and technology manual (update),

     b.  Comprehensive planning for control of urban storm runoff
         and combined sewer overflows,

     c.  Low cost-effective alternative and comparative analysis from
         208 areawide assessment study on combined sewer overflow and
         urban stormwater pollution control,

     d.  Statistical characterization of runoff loading rates and cost
         functions of control measures,

     e.  Dry weather pollutant deposition in sewerage systems and
         associated first flush combined sewer overflow pollution
         control by dry weather sewer flushing,

     f.  Nonpoint pollution abatement through improved street cleaning
         practices.

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                                 CONTENTS

Foreword 	 i"ii
Abstract 	  iv
Fi gures 	  vi
Tables 	 vii
List of Workshop Participants 	viii
Acknowledgments 	   x

1.  Urban Stormwater Management Program Workshop Introduction
      Richard Field 	   1

2.  State-of-the-Art of Urban Stormwater Management
      William G. Lynard and E. John Finnemore 	   5

3.  Summary Characterization of Urban Wastewater Management Options
      Stephan J. Nix, James P. Heaney, and Kevin Smolenyak 	  22

4.  Overview of November 1977 Release to SWMM
      Wayne C. Huber, Stephan J. Nix, Alan Peltz, and James P. Heaney   33

5.  Case Study:  Best Management Practice (BMP) Solution for a
    Combined Sewer Problem
      William C. Pisano 	  40

6.  Statistical Characterization of Urban Loading Rates and Cost
    Functions of On-Site Control Measures
      R. Berwick, J. Kuhner, D. Luecke, and M. Shapiro 	  53

7.  Interim Progress Report on Characterization of Solids Behavior
    in, and Variability Testing of Selected Control Techniques for
    Combined Sewer Systems
      William C. Pisano 	  80

8.  The Potential of Street Cleaning in Reducing Nonpoint Pollution
      Robert Pi tt 	  91

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                                  FIGURES

Number                                                                Page

 2-1       Inline Storage Effectiveness Regression Lines for
             Each Mode of Control, Seattle, Washington  	    11
 2-2       Preliminary Process Flow Diagram - Secondary Pilot
             Plant 	    16
 2-3       Preliminary Process Flow Diagram - Wet-Weather Pilot
             Plant 	    17
 2-4       Preliminary Process Flow Diagram - Sludge Pilot
             Plant 	    19
 2-5       San Francisco Pilot Plant Facilities	    20
 3-1       Storage-Treatment Isoquants for Minneapolis,
             Mi nnesota 	    24
 3-2       Application of Graphical Method and Expansion Paths 	    30
 4-1       Subcatchment Schematization During Simulation of
             Snowmel t 	    35
 6-1       Stormwater Runoff Process 	    53
 6-2       Raw Data:  Residential Loading Rates URS 	    57
 6-3       Steps in Constructing Stem-and-Leaf Display  	    57
 6-4       Residential Loading Rates 	    58
 6-5       Log-]Q (Residential Loading Rates) 	    60
 6-6       Residuals from Two-Way Fit of Land Use Versus
             Climatic Region 	    66
 6-7       Residuals from 2-Way Fit, Land Use vs Climatic
             Region 	    67
 6-8       Residuals from 2-Way Fit, Climate v_s_ Traffic 	    68
 6-9       Accumulation Load v_s_ Time 	    71
 6-10      Sutherland and McCuen Empirical Curves 	    72
 8-1       San Francisco Bay Area Showing the General Location
             of the City of San Jose 	    95
 8-2       Map Showing the Location of the Three Study Areas 	    96
 8-3       BOD Values as a Function of Incubation Time  	  106
                                    VI

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                                  TABLES
Number                                                               Page

 1-1        Project Title and Investigating Agency 	    3
 1-2        Common Project Objectives 	    4
 2-1        Levels of Stormwater Management Tools 	    7
 2-2        Summary of Principal Planning Guides 	    7
 2-3        Comparison of Typical  Values for Urban Stormflow
             Di scharges 	    8
 2-4        Summary of Legislative Programs 	   10
 2-5        Comparison of Physical Treatment Systems 	   11
 3-1        Parameters for Cobb-Douglas Production Function for
             Five Cities 	   27
 3-2        Results of System Optimization, Minneapolis,  Minnesota ...   31
 5-1        Summary of Sewer Flushing Potential  	   44
 5-2        Sewer Flushing Program Costs 	   48
 5-3        Overview of Overflow/Pollutant Reductions and Costs
             of Four Alternative Combined Sewer Management Plans ....   50
 6-1        Transformed Loading Data 	   62
 6-2        Loading Data Minus Row Median 	   62
 6-3        Loading Data with Row and Column Medians Subtracted 	   63
 6-4        Log-|o Loading Median 	   64
 6-5        Log-jQ Median Loading 	   64
 6-6        Estimates of Coefficients 	   77
 6-7        Restricted Regression Results 	   77
 7-1        Description of Flushing Segment Characteristics 	   84
 7-2        Average Pollutant Removal Characteristics - Phase I
             Flush Experiments 	   86
 7-3        Average Percentages of Pollutant Loads Removed per
             Flush for Each Pipe Segment 	   87
 7-4       Partial Summary of  Predictive Procedures for
             Estimating Daily  Dry Weather Sewage Collection
             System Deposition Loadings 	  90
 8-1        Accumulation Rates	   99
 8-2        Initial Test Phase - Street Cleaner Performance 	  103
 8-3        Street Surface Pollutant Removals Compared with
             Runoff Yields 	  109
 8-4        Runoff Water Quality Compared to Beneficial Use
             Criteria 	  HO
 8-5        Comparison of Urban Runoff and Wastewater Treatment
             PI ant Eff 1 uent 	  112
 8-6        San Jose Annual Street Cleaning Costs 	  114
 8-7        Preliminary Estimates of Cost Effectiveness for
             San Jose Street Cleaning Operations 	  114
 8-8        Estimated Costs for Treating Urban Runoff 	  116
 8-9        San Jose - Santa Clara Water Pollution Control Plant
             Effluent Conditions 	  117

                                   vii

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                      LIST OF WORKSHOP PARTICIPANTS

LECTURING PARTICIPANTS

Robert Berwick, Ph.D., Meta Systems, Inc., 10 Holworthy Street,
  Cambridge, Massachusetts 02138

Richard Field, Chief, Storm and Combined Sewer Section, Wastewater
  Research Division, Municipal Environmental  Research Laboratory,
  Office of Research and Development, U.S. Environmental Protection
  Agency, Edison, New Jersey 08817

E. John Finnemore, Ph.D., Metcalf & Eddy, Inc., 1029 Corporation Way,
  Palo Alto, California 94303

James P. Heaney, Ph.D., Associate Professor,  University of Florida,
  Department of Environmental Engineering, A.P. Black Hall, Gainesville,
  Florida 32611

Wayne C. Huber, Ph.D., Associate Professor, University of Florida,
  Department of Environmental Engineering, A.P. Black Hall, Gainesville,
  Florida 32611

William G. Lynard, Metcalf & Eddy, Inc., 1029 Corporation Way, Palo
  Alto, California 94303

Daniel F. Luecke, Ph.D., Meta Systems, Inc.,  10 Holworthy Street,
  Cambridge, Massachusetts 02138

William C. Pisano, Ph.D., Technical Director, Energy & Environmental
  Analysis, Inc., 257 Vassar Street, Cambridge, Massachusetts 02139

Robert E. Pitt-, Environmental Engineer, Woodward-Clyde Consultants,
  3 Embarcadero Center, Suite 700, San Francisco, California 94111
PARTICIPANTS

D. Dean Adrian, Ph.D.
Director of Environmental
  Engineering Program
Department of Civil Engineering
University of Massachusetts
Amherst, Massachusetts 01002
Ralph W.  Christensen
Office of Great Lakes Coordinator
Region V
U.S. Environmental Protection
  Agency
230 South Dearborn Street
Chicago, Illinois 60604
                                   vm

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Francis Condon
Program Manager
Waste Management Division
Office of Air, Land and Water Use
Office of Research & Development
U.S. Environmental Protection
  Agency
Washington, D.C. 20460

Frank Drehwing
Vice President
O'Brien & Gere Engineers, Inc.
1304 Buckley Road
Syracuse, New York 13201

Chi-Yuan Fan
Staff Engineer
Storm & Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research
  Laboratory
U.S. Environmental Protection
  Agency
Edison, New Jersey 08817

Shinichi Hiromoto
Sewage Works Bureau of Kyoto City
Planning Section
12  Higashi Sanno
Higashi-Kujo Minami-Ku
Kyoto, Japan

George James
Clinton Bogart Associates
2083 Central Avenue
Fort Lee, New Jersey 07024

Thomas K. Jewell
Research Assistant
Department of Civil Engineering
University of Massachusetts
Amherst, Massachusetts 01002
Dennis Lai, Ph.D.
Clinton Bogart Associates
2083 Central Avenue
Fort Lee, New Jersey 07024

Hugh E. Masters
Staff Engineer
Storm & Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research
  Laboratory
U.S. Environmental Protection
  Agency
Edison, New Jersey 08817

Cornelius B. Murphy, Jr., Ph.D.
O'Brien & Gere Engineers, Inc.
1304 Buckley Road
Syracuse, New York 13201

Walter Su
Clinton Bogart Associates
2083 Central Avenue
Fort Lee, New Jersey 07024

Anthony N. Tafuri
Staff Engineer
Storm & Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research
  Laboratory
U.S. Environmental Protection
  Agency
Edison, New Jersey 08817

Richard P. Traver
Staff Engineer
Storm & Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research
  Laboratory
U.S. Environmental Protection
  Agency
Edison, New Jersey 08817

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                             ACKNOWLEDGMENTS

     I would like to thank the lecturing participants and the other
workshop participants.   Special  appreciation is to be given to
Chi-Yuan Fan of the Storm and Combined Sewer Section for his devoted
effort in planning and executing the workshop.   Finally, I acknowledge
the help of our secretaries, Elizabeth H.  Mohary and Marily A. Nelson,
for their valuable assistance in organizing the workshop and related
material.
                                            Richard Field

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               URBAN STORMWATER MANAGEMENT PROGRAM WORKSHOP

                                 Introduction
                                     by
                               Richard Field*

     Welcome to the Second Technical Interproject Workshop in the Urban
Stormwater Management Program.

     The principal purpose of these workshops is for an exchange of
project information and data, and to establish liaisons between principal
investigators on USEPA sponsored research projects, with similar objectives,
for the mutual benefit of their respective investigations and USEPA
personnel.  The workshop serves as an effective means for technology
transfer and provides impetus in surfacing the most up-to-date information
in the field.

     There is a crying need for (1) cost-performance data for full-scale
nonstructural urban stormwater pollution control measures or "best
management practices" (BMP's), and (2) for the appropriate planning
methodology to optimumly integrate nonstructural with structural control
on an area-wide basis.  A third very important need is for collection
and better evaluation of pollutant emission/water quality data.  We
have heard the cry for this information from 208 agencies, USEPA and
many others.  Today's session will  be aimed at broadening the technology
for these needs.

     The project titles and names of the investigating agencies involved
in this workshop are indicated in Table 1-1.  Guest speakers will represent
these projects as will be described a little later.  The common project
objectives and their interrelationships are presented in Table 1-2.  This
is a simple matrix to follow.  As you can see, and may have anticipated,
all the projects relate to the basic objectives of this workshop.

     The first speakers will  be Dr. John Finnemore and Bill Lynard,
Project Managers from Metcalf & Eddy, Inc., Palo Alto, California.
They will discuss results from the completed "Urban Stormwater Manage-
ment and Technology Update" project, the plan of the new project on
cost-effectiveness and impacts assessment, and the operational program
of the San Francisco stormwater treatment pilot plant.  Their presentation
should be of great interest.   We have just received the published update
SOTA report which is appropriate to hand out at this workshop.

*Chief, Storm & Combined Sewer Section, Municipal Environmental Research
Laboratory (Cincinnati), U.S. Environmental Protection Agency, Edison,
New Jersey 08817
                                      1

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     Next, Drs. James Heaney and Wayne Huber will present the new features
of the SWMM program.  These include a revised storage/treatment block to
predict dry and wet-weather flow treatment performance and sludge handling
facility requirements and costs.  As you all know, the University of
Florida was involved with the original SWMM development and has been
retained for its improvement since 1974.  In addition, they also developed
"desk-top" analysis methods for preliminary screening procedures and
comparative evaluation of storage-treatment and other management practices.
The desk-top procedures are in two USEPA reports that have been used by
208 planning agencies.

     The third speaker will be Dr. William Pisano who will discuss his
involvement in area-wide wastewater management planning projects in
Massachusetts.

     The first lecture after lunch is on the Meta Systems' project.
Drs. Dan Luecke and Bob Berwick will present the preliminary results
of their study on urban pollutant loadings, runoff control options
and related costs, and storm event analysis.  The basic objective of
this project is to develop and evaluate pollutant production, flowrate
and costs for stormwater pollution control in new residential developments.
The project began July 1977.

     Afterward, Dr. William Pisano will present his second lecture on
his study on dry-weather pollutant deposition in sewerage systems and
upstream source control by sewer flushing.  The project involved a vast
field sampling and analysis program for sewer deposition and flushing,
and resulted in published methods for predicting the total daily mass
of pollutant deposition accumulations.

     The last speaker will be Mr. Robert Pitt of Woodward-Clyde Consultants'
San Francisco office.  He will present interesting results of his work
in the City of San Jose on a demonstration project which includes full-
scale street cleaning equipment tests for runoff pollution control,
analysis of particulate routing and mass balances in storm drainage,
and economic evaluation of control alternatives.'

     Each lecture will be one hour long, which includes 45 minutes for
presentation and 15 minutes for questions and answers.  We will collect
the lunch sandwich selections now before we proceed with the lectures.
Lunch will be served from 12:15 pm to 1:00 pm in this room.

     Thank you and have a fruitful meeting.

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              Table 1-1.

Investigating Agency

1.  Metcalf & Eddy
Project Title and Investigating Agency

  Project Title and Number

  "Assessment of the Cost-Effectiveness and
  Impacts of Completed and Operating Stormwater
  and Combined Sewer Overflow Remedial  Systems
  for Future Guidance" (68-03-2617)
2.  University of
    Florida
  "Comprehensive  Planning  for  Control  of Urban
  Storm Runoff and  Combined  Sewer  Overflow"
  (R-802411)
3.  Meta Systems
  "New Residential  Developments  and  the  Quantity
  and Quality  of Runoff"  (R-805238)
4.  Northeastern
    University/EEA
  "Characterization  of  Solids  Behavior  in,  and
  Variability  Testing of  Selected  Flushing
  Techniques for  Combined Sewer  Systems"  (R-804578)
    City of San Jose'/
    Woodward-Clyde
  "Demonstration  of  Non-Point  Pollution  Abate-
  ment  Through  Improved  Street  Cleaning  Practices"
  (S-804432)
    Monroe County/
    O'Brien & Gere
  "Combined  Sewer  Overflow Abatement  Program  -
  Rochester,  New York"  (Y-005141)

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                   Table  1-2.  Common Project Objectives
               Meth for Integrated         Nonstructural        Collect/
               Struct/Nonstruct            Control/BMP          Evaluate
Project        Control  Optimization        Performance          Field Data
1.  (M&E)               X                        XX


2.  (UF)                X                        XX


3.  (MS)                X                        XX


4.  (EEA)               X                        XX


5.  (W-C)               X                        XX


6.  (O&G)               X                        XX

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                              STATE-OF-THE-ART  OF
                          URBAN  STORMWATER MANAGEMENT

                                       By

              William  G.  Lynard,  P.E.*  and E.John  Finnemore,  P.E.*


Within  the  last decade,  a  concentrated effort  has been made  by  tne  EPA,
local,  and  private  agencies  to  investigate  the effects and impacts  of
stormwater  and combined  sewer overflows on  the receiving water  environment.
Over  this period, a greater  awareness  of the adverse  stormwater  contributions
to  the  aggregate  quality of  the  surface waters of the nation have come  to  the
forefront;  and as the goals  of  clean water  and restoration rise, and as
increasingly  effective coutermeasure implementation is achieved, the role  of
noncontinuous stormwater discharges has become increasingly  important.

A series of state-of-the-art documents, developed by Metcalf &  Eddy, to
provide current assessments, problem identification, characterization,  and
control/countermeasure implementation  practices have been published by  the
EPA.  In 1974 "Urban  Stormwater  Management  and Technology: An Assessment,"
EPA-670/2-74-040, presented  a comprehensive investigation and assessment of
promising,  completed,  and  ongoing  stormwater projects, representative of the
state-of-the-art  in abatement theory and technology.  The second of the
series, "Urban Stormwater  Management and Technology:  Update and Users'
Guide," EPA-600/8-77-014 (just  released), is the  subject of  part of this
paper;  and  is a continuation and reexamination of the state-of-the-art  of
storm and combined  sewer overflow  technology.   Recommendations  from this
study have  identified the  need  for more detailed  investigation  of the most
promising structural  control facilities and of the state of  technology  for
source  control mitigation  practices, termed "Best Management Practices"
(BMPs)—the subject of the current study and third in the series of
technology  assessments.

To  complement and put into   practice the results of these studies,  a pilot
plant testing program has  been  initiated as a  part of a wet-weather Step 1
facilities  plan for the  City and County of  San Francisco.  This  project
features the most current  structural mitigation and management  concepts
developed for the control  of combined  sewer overflows.

URBAN STORMWATER MANAGEMENT AND  TECHNOLOGY:  UPDATE AND USERS'  GUIDE

The unquestioned need  for  solutions to urban runoff pollution have  spawned a
major research and  development effort  resulting in the evolution and
""Project Engineer and Project Manager, respectively, Metcalf & Eddy, Inc
Western Regional Office, Palo Alto, California.

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application of a new technology which emphasizes time and spatial effects and
total system consciousness.  Solutions are found not only in improved
hardware and process operations, but even more so in the stressing of
management practices that limit the spread of the problem and attack it at
its source.  Assessment of the urban runoff problem is referenced to the
developing national data base, then localized through site selective
monitoring and analysis, and is quantified as to potential source and
magnitude using modeling techniques or simplified desktop procedures.

Control of urban runoff pollution is separable into two categories of
countermeasure implementation:  nonstructural or low-structural  source
controls/BMPs, and structural alternatives.  BMPs focus on source abatement,
whereas, structural alternatives roughly parallel conventional wastewater
treatment practices of end-of-pipe correction.

Approach to Stormwater Management

The basic approach concept may be viewed as a four step process:
(1) quantifying the need, (2) selective field monitoring, (3) cost-
effectiveness assessment, and (4) impact simulations.  However,  surface
runoff generated problems and appropriate mitigation measures are difficult
to assess because:

     •    The events are irregular and unpredictable
     •    The impacts are likely to be highly time and location  variable

     •    Other discharges or conditions tend to mask actual results
     •    Relatively little usable local data are available and  new data are
          extremely time consuming and costly to obtain
     •    Mitigation measures are largely conceptual and effectiveness is
          ill defined

User assistance tools and planning guides are available where gross
assessments of relative loads, sources, and their impact on water quality
are required.  User assistance tools range from desktop procedures to highly
complex digital computer simulations.  These tools have been characterized
into four categories and are described in Table 2—1.

Among the user planning guides published since January 1974, which are
designed to aid managers, three have been noted for their fuller treatment
of the five components summarized in Table 2-2.

Characteristics of storm flow pollutants are also of particular  interest to
the designer-manager implementing urban runoff mitigation measures.  These
include:  (1) source of pollutants, (2) discharge loadings, (3)  process
residuals, and (4) receiving water impacts.  A summary of typical values of
stormwater quality parameters is shown in Table 2-3.

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             Table  2-1.    Levels  of Stormwater  Management  Tools
Analysis
level Model type
I Desktop
Model
complexity
Low to
medium
Purpose of model
Problem assessment,
preliminary planning,
alternative screening .
Model characteristics
Ho computers. Equations,
nomographs based on sta-
tistical analyses of many
years of records.
Continuous     Low to       Problem assessment ,
simulation     medium       planning, preliminary
                           sizing of facilities
                           (particularly storage) ,
                           alternative screening.
                           Assess long-term
                           impacts of designs.
Single event   Medium       Analysis for design,
simulation     to high      detailed planning
Operational    Medium       Real-time coverage  of
                           sewerage systems
                                                                 Program of few hundred to
                                                                 few thousand statements.
                                                                 Uses many years of rainfall
                                                                 records with daily time
                                                                 steps, or worst 2 years with
                                                                 hourly time steps.   May
                                                                 include flow routing and
                                                                 continuous receiving water
                                                                 analysis .

                                                                 Program to over 10,000 state-
                                                                 ments.  Higher modeling pre-
                                                                 cision, from rainfall through
                                                                 sewers , possibly to receiving
                                                                 waters.  Short-time steps and
                                                                 simulation times .  Fewer
                                                                 a] ternatives to be evaluated.

                                                                 Uses telemetered rainfall data
                                                                 and feedback from sewer system
                                                                 sensors to continually make
                                                                 short-term predictions of sys-
                                                                 tem responses, and so produce
                                                                 control decisions during storms .
              Table  2-2.    Summary  of  Principal  Planning  Guides
                     SWMM:   Level I   Preliminary   Water Quality  Management    Areawide Assessment
                                                  Planning for Urban Runoff   Procedures Manual
Prepared by



Release date

Reference No.
                     Screening Procedures

                     University of Florida



                     October  1976

                     1
Complexity  level (s)   I-low

Coverages
  Discharge quality
  and quantity
  Control  alter-
• Receiving
  water impacts
• Control costs
  and benefits
• Example
  applications
        Yes


        Yes


        No


        Yes


        Partial
                                     URS Research Company



                                     December 1974

                                     2

                                     II-medium
                                                  General discussions , only
                                                                             EPA Municipal Environ-
                                                                             mental  Research
                                                                             Laboratory

                                                                             July 1976"

                                                                             3

                                                                             Ill-low to high
                                                                             Yes

                                                                             Not clear

                                                                             Yes

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 Table 2-3.  Comparison of Typical  Values for Urban Storm Flow Discharges3

                                 Kjeldahl  Total                      Fecal
                  TSS  VSS  BOD  COD nitrogen nitrogen    PO4-P   OPO4-P  Lead coliforms
Background
levels
Stormwater
runoff
Combined
sewer overflow
Sanitary
sewage
5-100
415
370
200
... 0.5-3
90 20
140 115
150 375
20
115
375
500
	 0.05-0.5b 0.01-0.2°
1.4 3.1 0.6
3.8 9-1 1.9
40 40 10
<0.1
0.4 0.35 14,500
1.0 0.37 670,000
2,500,000 —
7 °'17 50,000,000
       a. All values mg/L except fecal coliforms which are organisms/100 mL.
       b. NO3 as N.
       c. Total phosphorus as P.

Often overlooked in countermeasure  planning  is  the impact of residual
sludge/solids from  stormwater  treatment  processes.  The relatively high
loadings of highly  inorganic solids may  cause major treatment and disposal
problems, especially  if solids are  returned  to  dry-weather treatment
facilities for processing.

Stormwater and Combined Sewer  Overflow Control  Measures

Best Management Practices.  Nonstructural  and  low-structural source controls
offer considerable  promise as  the  first  line of defense to control urban
runoff pollution.   For  developing  areas,  BMPs are implemented through
planning, legislation,  and enforcement with  goals of maximizing detention-
percolation, avoidance  of over-development or land misuse, and minimizing
impacts of construction activities.   For developed areas, sound maintenance
and operation practices are required  for (1) litter and chemical use control,
(2) street cleaning and repair,  (3) catchbasin  and collection system
maintenance, (4) runoff flow controls, and (5)  public support and
involvement.  BMPs  have decided  benefits over structural alternatives
including lower cost, earlier  results, and an  improved and cleaner
neighborhood environment.  The greatest  difficulty, however, is that the
action-impact relationships are  almost totally  unquantified.

In planning, the concept of preventing and reducing the source of stormwater
pollution best applies  to developing  urban areas, for these are areas where
man's encroachment  is yet minimal,  or at least  controllable, and drainage
essentially conforms  to natural  patterns and levels.   Such lands, in
consequence, offer  the  greatest  flexibility  of  approach in preventing
pollution.  What is required,  therefore,  is  to  manage development in such a
way that a runoff regime may be  retained close  to natural levels, thereby
minimizing the need for expensive  downstream structural controls.  Effective
land use planning can be used  to control  the type and mix of land activities
to meet water quality standards.  Land use planning elements include:

     •    Utilization of greenways  and detention ponds

     •    Utilization of pervious  areas  for  recharge
                                      8

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     •    Avoidance of steep slopes for development

     •    Maintenance of maximum land area in a natural undisturbed  state

     •    Prohibiting development on floodplains

     •    Utilization of porous pavements where applicable

     •    Utilization of natural drainage features

Construction controls such as minimizing the.area and duration of exposure,
protecting the soil with mulch and vegetative cover, increasing infiltration
rates, and construction of temporary storage basins or protective dikes to
limit storm runoff can significantly reduce receiving water impacts  caused by
erosion.

Proper maintenance and cleanliness of the entire urban area can have a
significant impact on the quality of pollutants washed from an area  by
stormwater.  Cleanliness of an urban area starts with control of litter,
debris, and agricultural chemicals, such as pesticides and fertilizers.
Regular street repair and sweeping can further minimize the pollutants
picked up in stormwater runoff.  Proper use and maintenance of both
catchbasins and the collection system can improve control of pollutants
by directing them to treatment or disposal.

Program success is dependent on legislation or ordinances, to force  or
encourage conformance with the intended BMP, and a concerted effort  to
monitor compliance and educate not only those who will bear the
responsibility of regulation, but the public as well.  Legislation has
been implemented successfully in several communities for surface runoff
control as summarized in Table 2-4.

Structural Alternatives.  Structural alternatives involve storage
(volume sensitive) and treatment (rate sensitive) options and balances.
A substantial arsenal of devices has been developed through research and
demonstration projects and much is being learned with respect to their
feasibility, efficiencies, costs, and problems.  Cost-effective
applications of structural alternatives may be implemented through
multiprocess or integrated treatment installations, or both.
Multiprocess operations are characterized by the optimization of storage
and treatment to control both quantity and quality.  Optimization can
also be accomplished by integration of a wet-weather treatment process
with a dry-weather treatment facility where either excess dry-weather
capacity is utilized for stormwater treatment or where the wet-weather
facility provides an added measure of control for the dry-weather
treatment operation.

Storage facilities are frequently used to attenuate peak flows, thereby
reducing the size of facilities required for further treatment.
Storage, however, with the resulting sedimentation that occurs due to
increased detention times, can also be considered a treatment process.
Storage can be implemented through (1) inline storage, where the unused
volume in interceptor and trunk sewers is used to store runoff; and
(2) offline storage, where structures independent of the
interceptor/trunk sewer system are used to store runoff.

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                    Table  2-4.    Summary of  Legislative  Programs
                                                Description of legislation
    Denver Urban
    Renewal Authority
    Naperville,
    Illinois
    Joliet,  Illinois
    Albuquerque
    Metropolitan
    Arroyo Flood
    Control Authority
    Arvada,  Colorado
    Boulder,  Colorado
     Metropolitan
     Sanitary District
     of Greater Chicago
     Montgomery County,
     Maryland
     Fairfax County,
     Virginia
     Springfield,
     Illinois
Requires private developers to pond rainfall on rooftops and in plazas of
all new and renovated construction.  The design criteria for plazas require
a runoff rate of 1 in./hr and a water depth of 0.75 in. during the 10 year
rain.   The values for rooftops are 0.5 in./hr and a depth of 1 in. for the
10 year storm or 3 in.  during a 100 year rain.

Plumbing, sewer, and water ordinance requiring that runoff release rate be
regulated by the safe capacity of the receiving water, but no more than
0.15 in./hr.  Storage must be designed for the 100 year storm.  The ordi-
nance is applicable to all new subdivisions and compliance is required for
approval of development permits.

Ordinance similar to that of Naperville.  Requires runoff to meet a variety
of criteria:  (1) runoff rate shall not exceed historic values, (2) allowable
runoff rates are prorated on the basis of stream capacity, and (3) runoff
rate shall not exceed that of 2 year storm with a runoff coefficient of 0.3
unless facilities can handle the flow.  The ordinance is enforced for 10
acre residential areas and 5 acre nonresidential developments through the
issuance of building permits.

Requires stormwater detention for all new developments such that downstream
drainage facility capacity is not exceeded or the rate of runoff does not
exceed the natural rate of flow.  Compliance is required for building per-
mits and subdivision plat approval.  In addition,  a developer not in compli-
ance can be sued as creating a. public nuisance.

Requires detention for runoff greater than predevelopment rates for new con-
struction.  If a developer chooses not to provide the detention he is
assessed a one time fee that reflects the cost the city will pay to develop
a drainage system.  If detention is provided, no fee is assessed.

Monthly drainage fee that is assessed against all property in the city on
the basis of surface area and runoff coefficient.  Efforts to retain runoff
onsite will result in lower monthly charges.

Requires provision for stormwater retention before granting sewer connection
permits to new developments.  The maximum release rate is computed by the
Rational Formula with a 3 year rain and a coefficient of 0.15.  Storage must
be designed for the 100 year storm.

The State of Maryland has classified sediment as a pollutant under its Water
Pollution Control Act and Montgomery County's program is an example of the
result. The recommendations of the ECS on erosion control must be met to
obtain clearing and grading permits in the county.  Detention ponds are part
of the requirements for approval.

The county has a history of runoff control similar to that of Montgomery
County. Erosion and sediment control has been mandated during construction
since the late 1960s.  Temporary detention ponds were used at most sites and
permanent detention must be evaluated for all new developers.

Sewer ordinance for combined sewer areas that has decreased runoff by a suc-
cessful campaign to disconnect sewer downspots from the sewer system.
     in.  x  2.54 = cm
     acre x 0.405 = ha
An  inline,  computer  controlled  storage  system  in  Seattle  [4]  has
effectively demonstrated  the use  of  such  a  system,  under  varying degrees
of  control,  for  reducing  overflow  volumes.   The  increased storage
effectiveness  as  a  result  of increased  system  control  is  shown  in
Figure  2-1.
                                                  10

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          50 r
                           LOCAL CONTROL
                           STATIC REGULATOR
                           LOCAL CONTROL
                           DYNAMIC  REGULATOR
                   0. 10
        Mgal X 3785= m3
         in. X 2.54= cm
                           0.20
  0.30      0.40
TOTAL RAIN,  in.
                                                   0.50
                                                           0.60
                                                                   0.70
              Figure 2-1.  Inline  storage effectiveness regression
                            lines for each mode  of control,  Seattle, WA.

Both  physical and biological treatment systems have  been demonstrated
throughout the United  States either with pilot scale or full-scale
prototype facilities.   Of these processes, physical  treatment  systems
have  demonstrated best the capability of controlling high and  variable
influent  concentrations and flowrates, and operate  independently  of
other treatment facilities.  A comparison of typical ranges of pollutant
removal and average  construction  costs for several  physical treatment
alternatives is shown  in Table 2-5.

           Table 2-5.  Comparison  of Physical  Treatment Systems
Physical unit process
Sedimentation
Without chemicals
Chemically assisted
Swirl concentrator
Screening
Dissolved air flotation
High rate filtration"3

Suspended
solids

20-60
68
40-60
10-90
45-85
50-80

BOD5

30
68
25-60
10-50
30-80
20-55

COD

34
45
—
22
55
40
Percent
Settleable
solids

90-95
50-90
10-95
93=
55-95
reduction
Total
phosphorus

20
—
13
55
50

Total Kjeldahl
nitrogen

38
—
16
35
21

Average cost,
$/Mgal da

23,000
23,000
4,500
19,000
34,000
58,000
   a.  ENR 2000.

   b.  Process efficiencies include both prescreening and dissolved air flotation with chemical addition.

   c.  Prom pilot plant analysis.

   d.  Includes chemical addition.
                                      11

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The range of removals by physical processes is influenced by the quality of
the influent storm flow, the specific type of process equipment used, and
type and amount of chemical additives used (polyelectrolytes and chemical
coagulants).

Portions of the work on which this presentation is based were performed
pursuant to Contract No. 68-03-2228 with the Environmental Protection Agency.

CURRENT STORMWATER PROJECTS

Stormwater and Combined Sewer Overflow Assessments

Experience in stormwater management to date has shown that there are no
absolutes with respect to design and performance criteria.  Generalizations
to provide national coverage on applicability greatly overextend a very
limited data and experience base.  Given the broad, comparative information
on control measures in two EPA state-of-the-art reports, planners and
designers next will need more in-depth evaluations (performance, cost
effectiveness, operating problems, environmental and social impacts) of the
most promising alternatives, as demonstrated in real, problem solving
applications.

A project has been initiated with the EPA which will be focused to provide
summaries of essential data, professional judgments as to applicability, and
recommendations as to the most promising approach methodologies.  Recognizing
that the technology base and demands are continuously changing, the primary
goal of this project will be to improve and accelerate the transfer of the
most promi-sing technology to the potential user.  The project will therefore
be selective in its content.

Objectives.  The objectives of this project are to evaluate, for future
guidance, the successes of the most promising completed and operating urban
stormwater .pollution control measures in fulfilling their problem solving
objectives, including cost effectiveness and environmental and social
impacts, and to give priority to advancing our presently lagging knowledge of
nonstructural and low-structural measures (BMPs).

Needs.  Estimated nationwide costs for treatment and control of combined
sewage and urban stormwater have been estimated in the $10 billions [5, 6,
7].  However, these costs and studies were based on simplified assumptions.
To establish better figures and estimates, it is necessary to conduct a cost-
effective analysis based on constructed and operating stormwater
control/treatment facilities.  Of particular interest is the evaluation of
the effect of the stormwater control/treatment facilities on pollution
abatement and water quality improvement in the receiving system.
Furthermore, the effect of such facilities on local planning and
implementation of other storm runoff correction measures within the local
area should also be evaluated and could incorporate a more sensitive
construction and operating cost analysis.
                                      12

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Method of Approach.  A selected number of combined  sewer  overflow/stormwater
and flood/erosion control projects will be  reviewed  in  the  following  three
categories:

     1.   The effectiveness of completed and operating  nonstructural/low-
          structural source control programs (BMPs)  will  be evaluated  for
          selected projects or studies determined to be most promising  in
          stormwater pollution abatement.   In-depth  act ion-impact analysis
          will be emphasized.  These projects will  be selected  from the
          following source control areas with emphasis  on
          multipurpose/benefit application:  (a) natural  drainage, (b)  porous
          pavements, (c) onsite/source storage, (d) maintenance practices
          (street sweeping), and (e) erosion/flood  control.  Assessment of
          actual or apparent benefits with  respect  to effectiveness, costs,
          local and environmental impacts will be made.

     2.   The most promising, new and ongoing BMP projects will also be
          selected for in-depth case studies.  Projects of this type
          resulting from Section 201 and 208 plans  currently being
          implemented will be considered in this category.  The basis and
          reasons for their preferred and successful selection  at the
          respective sites will be investigated, and their apparent benefits
          (single and multipurpose) will be assessed.

     3.   Selected completed and operating  combined  sewer overflow treatment
          facilities will be evaluated from a treatment systems approach.
          The projects will be selected for in-depth re-evaluation from the
          following structural treatment control alternatives:

          •    Inline storage
          •    Offline storage
          •    Storage/sedimentation
          •    Swirl concentrator

          •    Screens
          •    Dissolved air flotation

          •    Treatment lagoons

Essential elements of the treatment systems approach include definition and
detailed evaluation of system effectiveness; design basis and background;
annual costs, including operation and maintenance; energy consumption;
achievement of stormwater quality goals and return benefits of  the system
(single or multipurpose); operation and maintenance difficulties including
expected usable life; and receiving water impacts.  The anlaysis
will also identify socio-economic impacts such as (a) effects on
land values and taxes, (b) housing and relocation,  (c)  general  public
acceptance and aesthetics, and (d) local community  facilities.  Applications
of value engineering techniques to optimize system  costs will also be
evaluated.  To the extent possible, this approach and analysis  will be
applied to the case studies in all three categories.


                                     13

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To identify as many potential candidate projects as possible, including those
local projects that may not be known nationally or have been uncovered
through past studies, over 100 survey questionnaires have been sent to
federal, local, and private agencies requesting nomination of any known
project for inclusion in this study.  The main emphasis of this study will be
centered around regionally encompassing projects or full-scale prototype
treatment 'facilities.

The work upon which this portion of the paper is based was performed pursuant
to Contract No. 68-03-2617 with the Environmental Protection Agency.

San Francisco Pilot Plant Project

As an integral element of San Francisco's 201 facilities planning effort to
control and treat combined sewer overflows from the entire city area and
treat dry-weather flows from the west side of the city, a pilot plant was
constructed to study and test the applicability, effectiveness, and costs of
various unit processes.  The pilot facilities consist of three process
systems:  (1) dry-weather treatment, (2) wet-weather treatment, and
(3) sludge processing.

The pilot plant program was developed before the enactment of the amendments
of PL 92-500, with the presumption of secondary levels of treatment for dry-
weather flows.

Scope and Objectives of the Program.  The proposed prototype secondary
facilities would treat dry-weather sewage to effluent quality levels required
for ocean discharge.  The secondary facilities would also be integrated with
the wet-weather facilities to treat wet-weather flows up to its peak
hydraulic capacity.  A wet-weather treatment plant would be operated in
parallel with the secondary plant on an intermittent basis, as required, to
accommodate wet-weather flows in excess of the peak hydraulic capacity of the
secondary plant.

The primary sites under consideration  for these facilities are limited in
area with possible restrictions on surface use, and the site's proximity to
high use public recreational areas create complications for an already very
difficult waste treatment problem.  In addition, wet-weather discharge
requirements have not been finalized pending the results of a cost-
effectiveness study of wet-weather treatment alternatives.  Other
uncertanties of the program include:

     •    During wet weather, the composition of the feed to the secondary
          plant will be continuously changing.  The response of secondary
          plant effluent quality to expected variations in pollutant load and
          chemical characteristics during storms is unknown.
     •    An attractive alternative for wet-weather operation is to pass as
          much wet-weather sewage through the secondary plant as is possible
          without serious degradation of effluent quality.  This minimizes
          the volume of wet-weather treatment necessary, minimizes the number
          of times per year the wet-weather treatment plant must be operated,
          and provides a better effluent quality for small storms.  The

                                     14

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          extent to which the secondary plant can be hydraulically forced
          before treatment breakdown is unknown.

     •    It is possible to provide a short-term upgrading of the secondary
          plant capacity by providing chemical addition to primary clarifiers
          and flexible use of secondary clarifiers.  The technical
          feasibility, cost effectiveness, and long-term effect of this on
          secondary plant operations are unknown.

     •    Secondary clarifier performance could be the limiting factor on
          hydraulically forcing the secondary plant.  Both the effect of the
          increased flow and the sudden reduction of biosludge age on the
          secondary clarification process cannot be forecast for the
          transient conditions expected during wet-weather operations.

The pilot plant program will seek to answer these important questions to
ensure the cost effectiveness of the proposed facility.  The program will
also include study of sewage sludge processing and disposal alternatives.
Emphasis will be on wet-weather sludges because of their unknown
characteristics and potential impacts on existing sludge disposal operations.

The pilot plant program will consist of evaluation and comparison of
individual unit processes under controlled conditions.  This will be done
primarily with synthetic wet-weather wastewater.  One or more treatment
process trains will also be demonstrated with emphasis on reliability,
effluent quality stability, and development of operating procedures for wet-
weather plant startup, shutdown, and interstorm conditions.

Process Systems.  The secondary treatment alternatives selected for pilot
plant evaluation include conventional activated sludge, oxygen activated
sludge, and rotating biological contactors; they are shown schematically in
Figure 2-2.

The wet-weather treatment units selected for evaluation are shown in Figure 2-
3.  The candidate processes include:

     •    Chemically assisted primary sedimentation using a variety of
          chemical additives
     •    Primary sedimentation without chemical addition

     •    High-rate gravity sedimentation using a lamella separator to reduce
          equipment volume

     •    Induced air floation which may offer capital and operating costs
          comparable to chemically assisted primary sedimentation with much
          lower space requirements than conventional dissolved air flotation
          processes.  However, their performance in stormwater service is
          unknown.

     •    Microscreens (23 micron mesh size) can provide high rate solids
          separation in a small space.
                                     15

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?AW WASTEWATER

WET-WEATHER
WASTEWATER
SYNTHESIZER

i
^

HEADWORD PRIMARY
*" CLARI Fl ER
1
PRIMARY SLUDGE
TO SLUDGE
PILOT PLANT






CONVENTIONAL
ACTIVATED
SLUDGE
t

OXYGEN
ACTIVATED
SLUDGE
—
-»
SECONDARY
CLARIFIER




SECONDARY
CLARIFIER
\

ROTATING
BIOLOGICAL
CONTACTORS
— ^*


SECONDARY
CLARIFIER
I









!-*• DISINFECTION "^




                                                 SECONDARY SLUDGE
                                                    TO SLUDGE
                                                    PILOT PLANT
Figure 2-2.  Preliminary Process Flow Diagram-
             Secondary  Pilot Plant.

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WET  WEATHER
SYNTHESIZER
                                                                          WET-WEATHER SLUDGE
                                                                              TO  SLUDGE
                                                                              PILOT PLANT
                        Figure  2-3.  Preliminary Process  Flow  Diagram-
                                    Wet-Weather  Pilot  Plant.

-------
     •    Swirl regulator/separators have the potential to concentrate the
          majority of the suspended solid material in a minority of the flow
          volume.  Their utility in this application will depend on whether
          the quality of the overflow is suitable for ocean discharge under
          wet-weather conditions.

     •    Disinfection will be evaluated for ability to control biological
          problems in an effluent of variable quality and flow.

A very preliminary process flow diagram for the sludge processing pilot plant
is presented in Figure 2-4.  The unit operations and processes actually
tested during the program will depend on the types of sludges produced by the
processes which are selected in the dry- and wet-weather evaluations.
Included in the sludge testing program will be evaluations of sludge storage
and transport.  The very restricted site conditions  may preclude any sludge
processing at the site itself.

A unique feature of the pilot plant will be the utilization of a wet-weather
synthesizer which will be capable of providing the various process trains
with simulated storm flow hydrographs and transient  pollutant loads.  This
facility is being provided to enable pilot plant operation and evaluation
during non-rain periods.  The synthesizer will be operated by mixing various
quantities of raw sewage, potable water, solids, brine, and oil-simulating
mass emission curves resulting from monitoring at the headworks of the
existing treatment plants and from citywide computer simulation runs.

Photographs of the pilot plant facility are shown in Figure 2-5.

The dry weather portion of the pilot plant, first operational in November
1977, is currently under> evaluation.  The wet-weather facilities are nearing
completion, and testing is expected to commence in January 1978.
                                     18

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SECONDARY
SLUDGE
SLUDGE FROM
WET-WEATHER

PLANT
DRY-WEATHER
PLANT
                       Figure 2-4.  Preliminary  Process Flow Diagram-
                                       Sludge  Pilot Plant.

-------
                                                 (a)

                                                  (c)
Figure 2-5.  San Francisco pilot plant facilities.  (a) Overall view of
pilot plant-RBC enclosure and high-rate lamella settler.  (b) RBC unit
under construction.  (c) Overhead view of lamella settler.

                                    20

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REFERENCES

1.   Heaney, J.P., et al.  Storm Water Management Model:  Level  I -
     Preliminary Screening Procedures.  USEPA Report No. EPA-600/2-76^275.
     NTIS No. PB 259 916.  October 1976.

2.   Amy, G., et al.  Water Quality Management Planning for Urban Runoff.
     USEPA Report No. EPA-440/9-75-004.  NTIS No. PB 241 689.  December 1974.

3.   Areawide Assessment Procedures Manual, Volumes I, II and III.  USEPA
     Report No. EPA-600/9-76-014.  July 1976.

4.   Leiser, C.P.  Computer Management of a Combined Sewer System.  USEPA
     Report No. EPA-670/2-74-022.  NTIS No. PB 235 717.  July 1974.

5.   Sullivan, R.H., et al.  Nationwide Evaluation of Combined Sewer
     Overflows and Urban Stormwater Discharges, Volume I:  Executive Summary.
     USEPA Report No. EPA-600/2-77-064a.  September 1977.

6.   Metcalf & Eddy, Inc.  Report to National Commission on Water Quality on
     Assessment of Technologies and Costs for Publicly Owned Treatment Works
     Under Public Law 92-500, Volumes I, II, and III.   September 1975.

7.   Field, R., et al.  Urban Runoff Pollution Control Technology Overview.
     USEPA Report NO. EPA-600/2-77-047.  NTIS No. PB 264 452.  March 1977-
                                      21

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       SUMMARY CHARACTERIZATION OF URBAN WASTEWATER MANAGEMENT OPTIONS

                                     By

           Stephan J. Nix,   James P- Heaney,   and Kevin Smolenyak*
     Evaluation of control alternatives has become much more complex with the
advent of 208 planning in urban areas throughout the United States.  The
traditional problem of locating and sizing wastewater treatment facilities
for control of sewage has been expanded to include wet-weather pollution
control.  Unlike uniform wastewater flows, the wet-weather flows are inter-
mittent.  Furthermore, control options to be considered consist of, not only
the traditional structural approaches, e.g., storage-treatment devices, but
also non-structural alternatives, e.g., street sweeping.

     The problem facing the 208 analyst is to find the combination of all of
the above control options which achieves a specified pollutant control level
at minimum cost.  As part of earlier EPA sponsored studies, we have developed
procedures for performing these analyses [1, 2, 3].  The method can be ex-
pressed as a problem in production economics, i.e.,

                          minimize Z = f(X)

                          subject to g(Y,X) = 0	(1)

                                          Y = Y

     where_Z = cost of attaining a specified level of_pollutant control Y;
        f(X)^ = cost function for a vector of inputs, X;
           X = vector of inputs, X_= (Xl, x2, . .  . , xif . . . , xm);
           Y = vector of outputs, Y = (y^ y?. . . . , y^, . . . , y );
           Y = specified output level, Y - (^, y2> . . . , )K , .  . . , yn);

               and

      g(Y,X) = 0 is a production function expressing the maximum attainable
               level of_output which can be achieved with a given vector of
               inputs, X.

     This rather esoteric formulation can be translated into a relevant
engineering tool if appropriate cost data and knowledge of the production

* Graduate Research Assistant, Associate Professor, and Graduate Assistant,
 Dept. of Environmental Engineering Sciences, University of Florida,
 Gainesville, FL, 32611.


                                      22

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function are available.    While engineers are accustomed to deriving cost
functions, they are usually unfamiliar with notions of production functions.
Production functions define the locus of technically efficient combinations
of inputs and outputs.  In this presentation, a single output is used, i.e.,
the proportion of pollutant discharge which is controlled.  Inputs are two
control technologies, storage and treatment.  Linear objective functions are
used.  The same problem is solved graphically and analytically to show the
relative merits of each approach.  Data from earlier studies of Minneapolis
are used for the test application.

PRODUCTION FUNCTION FOR STORAGE- TREATMENT

     The STORM model was used in an earlier study to generate isoquants of
the percent pollutant control, y, as a function of treatment rate, x^, and
storage volume, X2 [1,4].  Results for Minneapolis are shown in Figure 3-1.
The isoquants are interpolations between the data points from the simulation
runs.  Each data point is the result of a one year simulation with a speci-
fied storage-treatment configuration.  These isoquants exhibit several rele-
vant properties:

               1) They slope downward and to the right because as one input
                  increases, it takes less of the other input to achieve the
                  same level of output.

               2) They are convex to the origin because of the decreasing
                  ability of one input to be substituted for another to
                  obtain a given level of output.  This is known as the
                  principle of diminishing marginal rate of substitution.

               3) The isoquants intersect the x^ axis indicating that it is
                  possible to use this input exclusively.

               4) The isoquants become asymptotic to the Xo axis at the
                  point where x-^ is being used continuously.  Further in-
                  creases in X2 are nonproductive because x^ is limitational,
Exponential Production Function

     In the earlier studies, the following functional form was used to
obtain the equation for a fixed value of y [1, 2]:
                                                                       (2)
     where x. = treatment rate at which the isoquant becomes asymptotic
                to the ordinate, inches/hour,
           x  = treatment rate at which isoquant intersects the abscissa,
                inches/hour, and
            k = constant, inch ^.

The lower limit on treatment, .X-, > can be found directly, as follows, for
8760 hours per year:
                                     23

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                                                  ISOQUANT OF v (PERCENT CONTROL)
                 0.002
  0.004            0.006
TREATMENT  RATE ,x,, tn/hr
0.006
0.010
Figure 3-1.   Storage-Treatment Isoquants  for Minneapolis, Minnesota

-------
                                                                        (3)
     where AR = annual runoff, inches/year, and
            a = coefficient.
By relating the parameters X-,, x-, - x_i > an^ ^ to t^e level of control,  y,  one
equation was developed for the entire  response surface.  The x  - _x_-,  and k
terms were found to be of the following general form:


                        X.. - x/i = be   , and .............   (4)

                              k = de~fy ..............  .   (5)

     where b, d, f, h = coefficients.

Substituting equations 3, 4, and 5 into equation 2 yields the production
function:

                                 , ,  hy -  (de   )x9                      ,,,
                        x  = ay + be y           *•  .........   (6)


This function, while statistically the most accurate, proved clumsy  to
manipulate, e.g., finding 3y/9x., the marginal productivity of each  input,
in the subsequent optimization.  Thus, another functional form was evaluated.

Cobb-Douglas Production Function

     While a wide variety of production functions are used by economists
[5, 6], this study examines only the so-called Cobb-Douglas production
function of one output and two inputs.  It has the following form:

                        y = Ax^l x2H2 ................  (7)

     where A = coefficient, and
      a , ot_ = input intensity parameters, a  < 1, a. < 1.


This function has several properties which make it convenient to use  [5],
e.g.,
     1) it is a homogeneous equation;

                     !<1 ->• decreasing returns to scale
                     =1 -> constant returns to scale
                     >1 ->• economies of scale;

     3) marginal products, 9y/9x., are positive but decreasing as x  increases;
        and
     4) the optimal solution for any value of y can be expressed directly  as
        a function of the inputs.
                                      25

-------
A potentially major limitation of this functional form is that it  is asymp-
totic to both input axes.  Thus, it does not permit using treatment, x-j ,
alone.  This problem can be avoided by defining another single-input produc-
tion function,
                        y =
     where A  = coefficient, and
                                                                        (8)
            a = input intensity parameter,
which is used in the optimization procedure as an alternative to equation  7.
Comparison of the results will tell the range over which each production
function is superior.  Another limitation of the Cobb-Douglas functional form
is that it loses accuracy as one approaches the X2 asymptote.  However,
experience to date indicates that the "elbow" of the function is the area  of
primary concern.

     In the earlier work, isoquants (using the exponential function) were
generated for San Francisco, Denver, Minneapolis, Atlanta, and Washington,
B.C. [1].  Table 3-1 shows the results of fitting two-input and single-input
production functions to this same data.  The sum of the a's is less than one,
indicating diseconomies of scale.  Lastly, a single predictive equation for
the entire Untied States was determined by using the data for the five cities
with annual runoff as the third input.  The result for the two- input model i^:
                        y = [967 (AR)-'] x'    x,0'390 .....  (9)

SYSTEM OPTIMIZATION

Graphical Method

     The question of what functional form is suited to a particular optimiza
tion problem is avoided by a graphical solution procedure.  The general opti
mization problem for storage-treatment is

                                         h       B2
                        minimize Z = c..x    + c^x-   .........  (10)

                        subject to g(y, x^, x2) = 0  .........  (11)

                                         x1, x2 >^ 0

     where Z = total annual control costs, $/acre,
      g.. , £„ = cost exponents,
          c^ = unit cost of x.. , annual $/inch/hour,
          c2 = unit cost of x2, annual $/inch, and
g(y, x.^, x^) = 0 is the production function derived by interpolation between
               data points.
                                      26

-------
Table 3-1. Parameters for Coob-Douelas  Production  Function for Five Cities
                                       Deveic ied
Cobb-Douglas Production Function Coefficients
Runoff, ?opuj.acic:i
City j= Inches /Year Density,
Persons/Acre
San Francisco 1 9.37 9.96
Beaver 2 5.59 9.11
Minneapolis 3 10.50 7.92
ro
--J Atlanta 4 16.18 8.24
Washington, B.C. 5 ±7.22 20.02
b
y
; A
489
584
464

660
570

Ql
0.198 0.410
0.252 0.334
0.215 0.333

0.246 0.490
0.255 0.394
y~ = Al X/
Al
566 0.65
449 0.56
410 0.56

431 0.69
440 0.63
          ^Source:  Heaney, J.P., et al. , Natior.wice  Evaluation  of  Combined  Se\;er Overrlows and Urban
                    Scoriwu-er Discharges:  Voloire  II:   Cost .Assessment  and  Impacts,  EPA-600/2-77-064,
                    March, 1977.

           Multiple correlation coefficients  >  .95

          "Correlation coefficients > 0.92

-------
The optimal combination of xn and x2 and the associated  cost,  Z*,  for any
value of y is found by locating the point of tangency  of the  isoquant (equa-
tion 11) and isocost (equation 10) lines giving  the  lowest  value of Z.
Exponential Production Function
     The formulation of the optimization model using the exponential produc-
tion function, i.e., equation 2, and assuming linear control  costs,  is [1,  2]:
                        minimize Z = G..X  + c~x-  ............  (12)
                                              -        -kx
                        subject to x  = 21-, + (XT  ~ 2ii )e
                                       X  -
Solving this constrained optimization problem yields
                        x2* = max  [ £ In -^  [k^ - x£) ,  0] .......  (14)

     where x~* = optimal amount of storage,  inches,
     and x * = x, +  (x. - Xl)e~kx2* ...................  (15)
          l    — 1     1   — 1
     where x * = optimal amount of treatment, inches/hour.
Note that x-,* is a function of x2* so it i-s  necessary  to  find  x2*  first.
Knowing x-^* and X2*, the optimal solution is
                        Z* = c^* + c2x2* ...............  (16)
     where Z* = total annual cost for optimal solution, $/acre.
     The procedure used to find Z* for any level of control was  to solve  the
optimization problem for several assumed values of y and  then  fit  a function
to the result.  The equation of best fit was:
                        Z* = meY   ...................   (17)
     where m, y  = coefficients.
This equation is unsuitable at low levels of y since Z* -»• m as y -> 0.
Cobb-Douglas Production Function
     The above problem is formulated below using a Cobb-Douglas  production
function and a nonlinear cost function with  economies  of  scale in  storage
and treatment.
                                        31       32
                       Minimize Z = c x    + c2x2   ..........   (18)
                                          al  a2
                       subject to y = Ax
                                          X2 -
                                      28

-------
     where 6, , 6  = cost exponents and  g^<  1 implies economies of scale  in
     the cost.
Solution of this problem yields:
                        X * =
                        xl
                                    62  C2
                                                                          (19)
Note that the optimal combination of x  and x_ is independent of y.
final solution is shown below:
                                                                     The
                   Z* = Ky
                                                                          (20)
     where K = cr
                   62al
c
Plcla2
62C2al
*1\
A'1
                                                 *2ei + alB2
Equations 19 and 20 are simplified if linear costs are assumed, i.e.,
6, = 8=1.
     The solution shown in equation 20 provides an excellent summary
characterization of whether overall economies of scale exist.  Most environ-
mental control facilities exhibit economies of scale in the cost of construc-
tion and operation and maintenance, i.e., 6^ < 1.  However, these savings
are offset by the diminishing marginal productivity of the inputs, i.e.,
a. < 1.  Thus, the general test for the effect of scale is whether

                                        ;>!->• diseconomies of scale
                                        = 1 -> constant returns to scale
                                        < 1 ->• economies of scale
Furthermore, it is relatively straightforward to do sensitivity analysis
using equation 20. Of course, the Cobb-Douglas production function is not as
accurate as equation 6.  Nevertheless, it may be preferable  to  use it in
certain cases.

Example Comparison

     With data for the developed portion of Minneapolis and linear costs
reported earlier, an optimal strategy will be derived using each of the
three methods [4, 2].  The developed area of Minneapolis encompasses 215,000
                                     29

-------
acres and has a population density of 7.92 persons  per  acre.   The runoff for
the year used to generate the data points with  STORM was  10.50 inches.  The
unit cost of secondary treatment, c-^, is $9810/ac-in/hr,  whereas the unit
cost of storage, CT, is $219/ac-in.  Secondary  treatment  is  assumed to have
a constant removal efficiency of 85 percent.  In all the  above equations, y
is assumed to be the pollutant control at a removal efficiency of 100 percent.
If another efficiency is desired, the following transformation is made:
                          =  IQOy'
                                                          (21)
     where y'
           n
actual percent pollutant control, Q <_y' <_r\  , and
removal efficiency, 0 <^ n ^_ 100.
     Application of the graphical method and the expansion  path  produced by
each method is shown in Figure 3-2.  The cost equation and  optimal  strategy
for various levels of pollutant (BOD) control are shown  in  Table 3-2.   The
results reveal discrepancies among the methods, but all  are reasonably close
to the answer provided by the graphical solution.  It is assumed that  the
graphical solution is the "best" answer since it is unaffected by the  error
introduced by fitting functions.  However, for preliminary  estimates,  each
method seems to provide results well within the accuracy required for  such
an analysis.
                     0.002
                                0.004        0.006
                              TREATMENT RATE ,x,. in/lw
                                                      0.008
                                                                 0.010
         Figure  3-2.   Application of  Graphical Method and Expansion Paths
                                      30

-------
Table 3-2.  Results of System Optimization, Minneapolis,  Minnesota (see text for units of .variables)
   Percent Pollutant (BOD)
Graphical Method
Exponential
Local Cobb-Douglas
National Cobb-Douglas
control , y ,
o i y' 1 85
10
25
50
75
V
.00028
.00052
.0011
.0023
X2*
.014
.036
.092
.235
?.*
6
13
31
74
Z* = 4
.00027
.00053
.00125
,00238
.65RU'U
.017
.032
.092
.254
jo^y
Z*
7
13
32
79
Z* = 0.
x *
.00009
.00043
.00170
.00358
,0331(y')J
V
.006
.034
.119
.251
L.OJZ
2*
2
12
43
90
Z* =
.00014
.00061
.00104
.00354
0.0907(y')
X2*
.011
.046
.138
.266
. . UU J
Z'x
4
16
48
93

-------
REFERENCES

1.  Heaney, J.P., Huber, W.C., Medina, M.A., Murphy, M.P., Nix, S.J., and
    Hasan, S.M. , "Nationwide Evaluation of Combined Sewer Overflows and
    Urban Stormwater Discharges, Volume II:  Cost Assessment," USEPA
    Report EPA-600/2-77-064, March 1977.

2.  Heaney, J.P., Huber, W.C.,  and Nix,  S.J.,  "Storm Water Management Model:
    Level I - - Preliminary Screening Procedures," USEPA Report EPA-600/2-
    76-275, October,  1976.

3.  Heaney, J.P.  and  Nix, S.J.,  "Storm Water Management Model: Level I - -
    Comparative Evaluation of Storage-Treatment and Other Management Prac-
    tices," USEPA Report EPA-600/2-77-083,  April,  1966.

4.  Hydrologic Engineering Center,  Corps of Engineers,  "Urban Storm Water
    Runoff:  STORM,"  Generalized Computer Program  723-58-L2520, May,  1975.

5.  Ferguson, C.E., The Neoclassical  Theory of  Production and Distribution,
    Cambridge University Press,  London,  1975.

6.  Heady, E.O. and Dillon,  J.L.,  Agricultural  Production Functions,  Iowa
    State University  Press,  Ames,  Iowa,  1961.
                                   32

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                    OVERVIEW  OF  NOVEMBER 1977  RELEASE  OF  SWMM

                                      by

     Wayne  C. Huber,  Stephan J.  Nix, W.  Alan  Peltz  and James P.  Heaney*


 CHRONOLOGY  AND  STATUS

     The most recent  previous SWMM  release prepared by the University  of
 Florida  (UF) was  in September 1976.  This version differed in minor ways
 (error cleanup, I/O improvements) from  the release  made  in the spring  of  1976.
 Since September 1976,  a  list of  errors  has been prepared and distributed, but
 the program on  the  tapes  given  to users  has remained  the September 1976 ver-
 sion.

     A November 1977  (NOV77)  release has been prepared.  As  usual, it  is
 available from  both UF and EPA  (through  Harry Torno).  The package includes
 a  tape with the Fortran  source  listing  and sample input data.  The Version II
 SWMM User's Manual  (1) will  still apply  as far as general input  data prepara-
 tion and formats  for  some blocks.   However, as for  the 1976  releases,  there is
 also included a documentation packet containing revised input formats  (mainly
 for Runoff  and  Storage/Treatment),  documentation for  WRE Transport (EXTRAN)
 and, as much as possible, documentation  of new procedures included in  the
 NOV77 release.  This  documentation  includes descriptions of  some procedures
 placed in the 1976  releases  as well  (e.g., infiltration routine  in Runoff).
 Hopefully,  it is  organized in a  better manner than  in the past such that users
 will be able to locate relevant  material more easily, (e.g.,  it  has a  table
 of contents).

     A new  SWMM User's Manual is in preparation and should be ready by summer
 1978.  Most pending modifications will have been completed and documented at
 that time.

     The most significant differences between the NOV77 and  earlier releases
 are as follows.  More  details are presented subsequently.

     1.  Snowmelt is  included in the Runoff Block,  for both  single event and
         continuous simulation.

     2.  Continuous simulation capability has been  greatly enhanced in terms
         of I/O, documentation,  algorithms and useable output.   Continuous
         simulation uses only  the Runoff  and Storage/Treatment (S/T) Blocks.
*Dept. of Environmental Engineering Sciences, University of Florida,
 Gainesville, Florida  32611.

                                      33

-------
     3.  Sludge generation and simple solids handling routines are included in
         the S/T Block.  All S/T routines are working.

     4.  The latest cost data are in the S/T Block.  These will have some
         actual usefulness when running continuously, since 0 and M costs are
         computed on the basis of actual hours of operation.

     5.  Known bugs have been corrected.

     Different levels of urban stormwater analysis continue to be evaluated.
Two "Level I" reports were issued during 1977 (2,3).  A Level III Receiving
Model  (simplified, continuous) is nearing completion with a report scheduled
for winter 1978.  The Level IV SWMM report consists of the forthcoming Users
Manual, as discussed earlier.


RUNOFF

Continuous Simulation

     Although 1976 SWMM release  had the capability for continuous simulation,
it was poorly documented and there were bugs.  The NOV77 release hopefully
has few bugs and is understandable.  Using US Weather Service "Hourly Precipi-
tation Deck 488" or user-supplied precipitation input, the model will run for
an unlimited number of time steps.  Infiltration capacity, depression storage
and pollutant loads are regenerated during dry time steps.  Output is avail-
able on a time step, daily, monthly, annual and grand total basis.  The fifty
highest hourly precipitation, runoff and BOD loads are tabulated also.

     Preliminary comparisons have been made between continuous SWMM and STORM
(4).   For similar schematizations, STORM runs are cheaper and produce compar-
able output.  SWMM might be chosen over STORM if 1) gutter/pipe routing is
desired, 2) SWMM algorithms are preferred, or 3) the more flexible and real-
istic  SWMM S/T procedures are preferred.  STORM may be advantageous for rural
areas  since it allows the use of the SCS curve number technique for these
areas.

Snowmelt

     Following the earlier work of the Canadian SWMM study by Proctor and
Redfern and James F. MacLaren (5,6) snowmelt simulation has been added for
both single event and continuous simulation.  Most techniques are drawn from
Anderson's  (7) work for the US Weather Service (USWS).  For single event
simulation, temperature data are input for each time step.  For continuous
simulation, daily max-min temperatures from the USWS "WBAN Summary of the
Day, Deck 345" are converted to hourly values by sinusoidal interpolation.

     Each subcatchment is divided into the areas sketched in Figure 4-1.
Urban  snow removal practices may be simulated through  the "redistribution
fractions" input for each subcatchment, through alteration of the melt coef-
ficients and base temperatures for the three regions of each subcatchment,
and through the areal depletion curves used for continuous simulation.
                                      34

-------
                  Al =  IMPERVIOUS AREA WITH  DEPRESSION STORAGE
                  A2=  PERVIOUS  AREA
                  A3=  IMPERVIOUS AREA   WITH  ZERO  DEPRESSION  STORAGE
                  A4=  SNOW  COVERED  IMPERVIOUS AREA

              Al +A3=  NORMALLY  BARE
                       SFRAC (5)
oo
en
                                                     AMOUNT TRANSFERRED
                                                     IS FRACTION  OF  SNOW
                                                     ABOVE  WEPLOW INCHES
                                                     WATER  EQUIVALENT
          SFRAC (3)
          PERVIOUS IN
          LAST SUBCATCHMENT
* SFRAC (4)
 OUT OF SIMULATION
        Figure 4-1. Subcatchment schematization during simulation of snowmelt.  "Normally bare" areas
                  may represent streets, sidewalks, etc. that are kept clean  of snow.  Snow on these
                  areas is redistributed (e.g., plowed) according to the indicated fractions (i.e,
                  values of SFRAC).

-------
Anderson's (7) temperature-index and heat balance melt equations are used  for
melt computations during dry and rainy periods, respectively.  For continuous
simulation, the "cold content" of the pack is maintained in order to "ripen"
the snow before melting.  Routing of melt water through the snow pack is
performed as a simple reservoir routing procedure, as in the Canadian study
(5,6).

     The presence of a snow pack is assumed to have no effect on overland
flow processes beneath it.  Melt is treated in the same manner as rainfall.

Quality Routines

     As in the 1976 releases, the use of the dust and dirt etc. factors may
be avoided if desired by simply inputing loadings for each pollutant, in
Ib/ac, for each subcatchment.  Only one option will be available for calcula-
tion of SS washoff (that used for all other pollutants); thus, the "ISS"
parameter has been removed.  This follows a careful analysis of the two pro-
cedures by the University of Massachusetts (8).

     An availability factor has been included in the street sweeping equations
to reflect the fraction of total impervious area actually available to be
swept  (e.g., only the roadways).  This follows work of Heaney and Nix (3).
In general, the presence of snow is assumed to have no effect on pollutant
load regeneration or washoff.  However, there is no street sweeping if snow
is present on a subcatchment.  In addition, one user-supplied pollutant may
be simulated, to model chlorides, for example.  Although it is treated identi-
cally  to all other pollutants, as a user option, it may be regenerated only
when snow is present, thus avoiding high predicted chloride concentrations
during summer months.

Other

     Output from the NOV77 release should be more useful in terms of printed
explanations and user flexibility.  For instance,  inflows as well as outflows
from gutter/pipes may be printed out, and error messages contain somewhat
improved syntax.  The present size of the Runoff Block used in conjunction
with the Executive Block is about 85K words.   Some programming changes have
been made to enhance compatability with CDC compilers, although the NOV77
release has only been compiled on the UF Amdahl 470 (similar to the IBM 370/
165).

TRANSPORT AND WRE TRANSPORT

     No significant changes have been made to either of these routines.   Thus,
some problems remain as to the documentation and sample data for the latter.
UF has received a somewhat improved version of the WRE Transport Model (9)
and has incorporated it into the model.  However,  it is strongly recommended
that users encountering problems with the WRE Transport Model apply directly
to Water Resources Engineers for assistance.   Although they are unable to
provide free consulting, they are better equipped at the moment to solve the
problems.
                                      36

-------
S TORAGE/TREATMENT

     Modifications to the Storage/Treatment Block have been carried out in
two steps.  The first step was to correct the many bugs found throughout the
block.  Most corrections were concentrated in the subroutines TREAT and
STRAGE, the majority of them dealing with flow routing and pollutant removal
in storage units (sedimentation and external storage).  Other significant
changes involved the swirl concentrator  (flow routing and negative pollutant
removals) and the bleed-off from the sedimentation unit in the wet-weather
treatment string to the dry-weather plant during dry periods.  Another cor-
rection concerned the proper accounting of evaporation from storage units
operating under the assumption of plug flow.  Earlier versions, although
accounting for evaporation from the total storage volume, did not evaporate
water from the individual plugs in the unit.  Other errors, although numerous,
were not individually significant.  Most were unit conversion and minor I/O
errors.  As part of this phase, the Storage/Treatment Block was tested in the
continuous simulation mode and has worked successfully.

     The second step centered around modifying pollutant removal mechanisms
and adding a simplified sludge accounting procedure.  In earlier versions,
most of the treatment units employed a very simple (in some cases, a "black
box") removal mechanism.  In this version, the simplicity is retained but the
user is given more latitude with equation parameters in order to account for
the nature of the incoming sewage and local conditions.  Sludge generation is
performed by assuming that the suspended solids removed equals the sludge
generated. The dry weight equivalent is then retained for a user specified
detention time and released for disposal.  Although the sludge procedures are
greatly oversimplified, it is believed that the real value of this routine is
in showing the variations in a long term simulation.

     The cost functions in the original single event simulation were very
simple.  The major changes incorporated in this version include updated cost
functions for a wide variety of dry-weather treatment facilities.  More
importantly, the costs of wet-weather operation are determined based on the
operating hours as tabulated in the continuous simulation.


RECEIVING

     There have been no major changes made to this block.


USER ACCESS AND FEEDBACK

     As in the past, the program and documentation may be obtained in two
ways.  First, (and usually fastest), they will be provided free from Harry
Torno of EPA (address below) upon receipt of a magnetic tape.  Second, they
will be provided by UF (contacts below) upon receipt of an unused 9-track
magnetic tape or monetary equivalent ($15).  In addition, a nominal addition-
al charge (yet to be determined) is made to non-public agencies to cover the
cost of tape preparation and documentation duplication at UF.  Turn-around
time following a request is usually about two weeks.
                                      37

-------
     The primary EPA contact is thus,

               Mr.  Harry C.  Torno
               Staff Engineer
               Office of Research & Development (RD682)
               US Environmental Protection Agency
               Washington,  D.C.  20460                 Phone (202)426-0810

     The primary UF contacts are

               Dr.  Wayne C.  Huber or
               Mr.  W. Alan Peltz
               Dept. of Environmental  Engineering Sciences
               A.P. Black Hall
               University of Florida
               Gainesville,  Florida  32611             Phone (904)392-0846

     Feedback of users of program problems is heartily encouraged and should
be addressed to either of the UF contacts above.   In addition,  UF will provide
modest trouble-shooting and user assistance via phone or letter.

     All users are urged to "join" the SWMM Users Group.  This  is accomplished
simply by contacting Harry Torno with  a name and address.  He publishes
aperiodic newsletters with current information on SWMM and many other current
hydrologic analysis models and techniques.  All program corrections and up-
dates will be issued via his newsletter.  Also, Users Group Meetings are held
at approximately a semi-annual frequency at which presentations and group
discussions are held on all facets of  hydrologic modeling, including SWMM and
many other models.   These are currently being held in cooperation with the
Ontario Ministry for the Environment for service to users of models supported
by their office.  Their contact is

               Mr.  Donald Weatherbe
               Ontario Ministry for the Environment
               135 St. Clair Avenue, W. - 2nd Floor
               Toronto, Ontario  M4V 1P5               Phone (416)965-6194

     SWMM is just one of many available analysis techniques.  Users are
urged to stay in contact with the above groups for current information.

A CAVEAT TO USERS

     The University of Florida has maintained and updated SWMM as only a part
of contract and grant research sponsored by EPA.  As such, UF is not in the
"modeling business" and gladly performs these responsibilities in conjunction
with many other urban runoff analysis and evaluation efforts.

     As a result, the NOV77 SWMM release has not been thoroughly tested.
Almost all aspects of the program may be expected to execute properly, but
the many combinations of input parameters preclude adequate testing of all
features.  Hence, some bugs are to be expected.


                                      38

-------
     As a practical matter, UF depends upon users to perform much of the
actual testing of the model.  As much as possible, user feedback is then in-
corporated into future SWMM releases.

REFERENCES

1. Huber. W.C., Heaney, J.P., Medina, M.A., Peltz, W.A., Sheikh, H. and Smith,
   G.F., "Storm Water Management Model User's Manual, Version II", EPA-670/2-
   75-017, March 1975.

2. Heaney, J.P., Huber, W.C. and Nix, S.J., "Storm Water Management Model:
   Level I - Preliminary Screening Procedures", EPA-600/2-76-275, October 1976.

3. Heaney, J.P. and Nix, S.J., "Storm Water Management Model: Level I -
   Comparative Evaluation of Storage-Treatment and Other Management Practices",
   EPA-600/2-77-083, April 1977.

4. Hydrologic Engineering Center, "Storage, Treatment, Overflow, Runoff Model,
   STORM", User's Manual, Computer Program 723-58-L7520, Corps of Engineers,
   Davis, California, July 1976.

5. Proctor and Redfern, Ltd. and James F. MacLaren, Ltd., "Storm Water Manage-
   ment Model Study, Volume I, Final Report", Research Report No. 47, Canada-
   Ontario Agreement, Ontario Ministry for the Environment, Toronto, Sept.
   1976.

6. Proctor and Redfern, Ltd. and James F. MacLaren, Ltd., "Storm Water Manage-
   ment Model Study, Volume II, Final Report", Research Report No. 48, Canada-
   Ontario Agreement, Ontario Ministry of the Environment, Toronto, Sept.  1976.

7. Anderson, E.A., "National Weather Service River Forecast System - Snow
   Accumulation and Ablation Model", NOAA Technical Memo.  NWS HYDRO-17,
   Nov. 1973.

8. Jewell, T.K. and Adrian, D.D., "Modeling Pollutant Washoff from Urban
   Surfaces", Dept. of Civil Engineering, University of Massachusetts, Amherst,
   July 1977.

9. Sprenger, R. , Personal Communication, Templeton Engineering, Winnipeg,
   Manitoba, Sept. 1977.
                                      39

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            CASE STUDY:   BEST MANAGEMENT PRACTICE (BMP) SOLUTION
                        FOR A COMBINED SEWER PROBLEM

                                     by

                           DR. WILLIAM C. PISANO*

FOREWORD

     This paper suntmarizes the findings of a recent section 208 combined
sewer management study for portions of the sewerage system in the City of
Fitchburg, Massachusetts.  The results showed that sewerage system remedial
repairs and slight piping modifications were an order of magnitude less
expensive than the nominal BMP practices of sewer flushing, street sweeping
and catchbasin cleaning, and several orders of magnitude less than alternative
structural options.  An infiltration/inflow study is presently being conducted
in the remaining sewered areas within the City.  The general methodology and
problem solving orientation of the aforementioned 208 study was used as
guidance in the preparation of the scope of services for this new work.

BACKGROUND

     The Nashua River is located in the northwestern portion of the State of
Massachusetts and ultimately discharges into the Merrimack River.  There are
several old communities discharging industrial and domestic wastes into the
upper headwaters of the Nashua River.  The City of Fitchburg with a population
of 40,000 is one of these communities.  In recent years significant water
quality improvement has been achieved by treatment of pulp and paper plant
discharges.  Advanced forms of treatment are currently envisioned for the two
treatment plants in the City.  The financial base of the City will be
considerably taxed by these two treatment works.

     Sewers within the City are a complicated mix of separated, partially
separated and combined subsystems.  Older portions in the system are combined
but have been partially separated in some areas.  Sewers within more recent
portions of the City are separated but frequently were inter-connected into
the older combined sewered areas.  Complete intact and up-to-date sewerage
system maps together with accurate inventories of service type per subarea
and number and location of overflow points did not exist at the onset of
this work.
*
 Technical Director, Energy & Environmental Analysis, Inc. (EEA), 257 Vassar
Street, Cambridge, Massachusetts 02139.

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     The State of Massachusetts, Division of Water Pollution Control has been
monitoring the river water quality in the Fitchburg urban area over the last
several years and desires that the combined sewer overflows in this area be
eliminated.  The Montachusett Regional Planning Commission, Fitchburg,
Massachusetts was given a section 208 areawide wastewater management grant to
prepare as part of their overall mandate, a combined sewer management plan
that would be responsive to the limited funds the City has available for
abating this problem.  Energy and Environmental Analysis, Inc. (EEA), was
contracted to prepare the combined sewer subplan for a representative area
entailing 10-12 miles of sewer.  This area was to be selected after completion
of a general inventory of the sewers in the City.

PURPOSE OF PROJECT

     The ate of the prototype combined sewer management study was to study in
fine detail, the impact of potential structural and non-structural control
options for a representative portion of the Fitchburg system and then select
the least cost program for the area.  This study was designed to investigate
in detail, various mixes of structural and principally, non-structural
control options and to quantify the attendant pollutant reduction and program
flushing and maintenance programs, street cleaning, inflow control by down-
spout elimination and off-line storage tanks.  The principal motivation and
emphasis of the study from a methodological standpoint was to demonstrate the
feasibility and practicality of keying almost the entire effort and resources
on developing low cost, non-structural solutions for mitigating the number
and magnitude of combined sewer overflows from the prototype study area.

SUMMARY OF PROJECT

     Inventory and Mapping.  A considerable level of effort was expended to
develop the informational base needed to select the analysis area.  The
inventory phase indicated that the sewerage system in the City is mostly
separated with numerous small "slivers" of combined sewer streets.  There is
however, one 500 acre section in the northeasternly portion of the City
served by 10-12 miles of combined sewers with four well-defined overflow
points.  This area was deemed extremely suitable for the purposes of the
study and satisfied the contractual level of effort constraint.  Land use in
the study area includes a heavy commercial downtown area within a mixture of
dense single family and multi-family dwellings.  Topography in the area is
mostly hilly with a number of fairly flat cross-streets.

     The sewerage system in the study area consisted of three collection sub-
systems.  Sewer plan maps of the area and three special maps depicting each
complete collection subsystem in profile view were prepared.  These maps are
called "flow-line" profile maps and visually show the entire collection
system slope characteristics.  These maps were used in the development of
upstream collection system controls.  They were particularly useful during
the detailed physical surveys of the system and were used to rapidly
establish potential sites for upstream off-line storage tanks.

     Sewerage System Physical Surveys.  The next phase of the work involved
intensive physical surveys of the collection system manholes, regulators and


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other pertinent control points in the combined sewer study area.  Roughly
150 manholes were inspected and approximately 100 spot flow measurements in
the collection system were performed.  The regulators, overflow conduits and
other major control points within the system were repeatedly observed during
both dry and wet weather conditions over a nine month period.

     It was determined from the inspection program that overflow weir levels
at three of the four regulators could easily be raised a foot without any
adverse backwater effects.  Minor structural and piping modifications would
be required at one of these locations to permit the desired rise in weir
level elevation.  The overflow chamber at the fourth regulator merges
separate  storm and sanitary wastes from a 45 acre area with two combined
sewer trunk sewers and two wet weather overflow conduits from an upstream
relief point.  The 45 acre area was recently separated and is served by a
30" trunk storm drain and an 18" trunk sanitary sewer.  The chamber acts as
an effective mixing tank for all of the influent waste streams prior to
discharging through an 18" dry weather outlet and a 51" wet weather relief
outlet.  The net result of the junction chamber as it presently exists, is
to re-combine the separate  sanitary and storm drainage from the 45 acre
catchment area with the other combined sewer inputs.   The envisioned altera-
tion to the junction chamber is to install steel I-beams across the chamber
and extend the 30" drain directly across the chamber and install a side weir
for discharge into the 51" wet weather overflow conduit.  This recommendation
was determined only after repeated observations and careful scrutiny of out-
dated construction drawings because most of the inlet pipes in the chamber
are submerged.

     It was also determined during the intensive field surveys that there are
two storm drainage subsystems within the combined sewer study area that could
be rerouted to minimize the amount of stormwater  flow entering the sewerage
system. The first area is a 16 acre parcel served by separate  storm and
sanitary systems.  The total flow for this area is connected into the down-
stream combined sewer system.  All of the stormwater  currently flowing from
this area could be rerouted to tie into an existing trunk storm drain
discharging directly to the Nashua River.  The required piping would be
approximately 200' in length and the area through which the pipe would be
constructed is a vacant lot lying between the adjacent streets.  The second
area is a two acre parking lot whose drainage is then piped into a nearby
combined sewer.  If the storm drain connection was extended 20' beyond the
combined sewer connection, natural drainage would convey the parking lot
runoff down an enbankment to the river.  There are no buildings at that point
due to the proximity of the river and the steepness of the embankment, hence
it would require only the crudest of chutes and/or rip-rap to provide relief
from the system.

     Several corrections for illegal dry weather sewage discharges into
overflow conduits were also developed.  Although not part of the study effort
a number of other illegal dry weather sewage overflows were identified in
other areas of the City.  The on-going infiltration/inflow study will focus
on the correction of these problems.
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     Sewer Flushing Considerations.  Deposition of sewage solids during dry
weather in combined sewer systems has long been recognized as a major
contributor to "first-flush" phenomena occurring during wet weather runoff
periods.  The magnitude of these loadings during runoff periods has been
estimated to range up to 30 percent of the total daily dry weather sewage
loadings.

     Dry weather deposition is a potential problem in combined sewers in
that the pipes are oversized to convey both dry and wet weather flows. During
dry weather sewage flow is low, resulting in the deposition of sewage solids.
These deposited pollutant loads are scoured during wet weather periods and
may be carried to the Nashua River if overflows occur.  Flushing of these
materials during dry weather periods would help to mitigate these "first
flush" shock pollutant loadings.

     The 12 miles of combined sewer pipe in the prototype study area were
represented by 322 manhole-to-nanhole segments.  Physical characteristics of
each of the collection pipe segments such as segment length, diameter and
slope were tabulated, data processed and utilized in a computerized network
procedure for estimating daily dry weather pollutant deposition loadings per
segment.

     It is estimated that the total daily deposition of sewage solids within
this system is 71.6 Ib/day.  Roughly half of this loading can be attributed
to 51 out of the 322 segments, or 16 percent of the total.  All of these
segments are small upstream segments with diameters ranging from 8" to 15"
pipe.  The relative fraction of the daily overall deposition fraction of the
total system input is low, 4.1%, in comparison to results from other studies
where deposition rates were estimated to be as high as 30% with 10-15% being
average.  This low number is obviously accounted for by the severe topographic
relief in the City.

     The 51 segments identified as moderate/heavy deposition segments were
physically surveyed for feasibility and ease in flushing using manual means
and external flush water sources.  All segments were inspected for physical
suitability, that is, whether the manholes were excessively deep and/or
whether the manholes and channel bottoms could sustain flushing injection.
A segment was not eliminated if no channel bottom existed.  These costs are
nominal and were included in the flushing program costs.  The segments were
also reviewed with respect to traffic and congestion.  Finally, the segments
were evaluated for proximity to fire hydrants, as a source of flush waters.
An arbitrary criteria of 2000 feet was established as a cut-off distance for
either fire hose-connected flushing or filling a tanker and then moving to
the injection site.

     A total of 46 segments were deemed suitable for flushing by manual
means.  Roughly 33 Ibs/day would be removed by a flushing program for the 46
segments.  Overview details are given in Table 5-1.  Roughly half of the daily
dry weather deposition loading could be feasibly removed by a low level
labor-intensive flushing program.  It is interesting to note that the City
of Fitchburg Sewer Department routinely flushed troublesome sewer segments
on the average of once a week nearly a decade ago.  Limited public works
department funds have since curtailed this activity.

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Subsystem
1
2
3
Total
A
176
53
93
322
B
42.95
12.56
16.12
71.63
C
28
11
12
51
D
18.32
7.15
9.66
35.13
E
26
11
9
46
F
17.36
7.15
8.53
33.04
          Legend:  A - Number of manhole-to-manhole segments.
                   B - Estimated daily solids deposition load  (Ib/day).
                   C - Number of moderate/heavy deposition  segments.
                   D - Total load attributable to moderate/heavy
                       deposition segments (Ib/day).
                   E - Number of segments noted in C suitable  for
                       sewer flushing after field inspections.
                   F - Total deposition load removable by sewer flushing.

              Table 5-1.  SUMMARY OF SEWER FLUSHING POTENTIAL
     The manual method of flushing was recommended on the basis of
conclusions drawn from an operational study of sewer flushing practices.
EEA and Northeastern University (NU), Boston, have completed a massive
field-oriented sewer flushing study (EPA Research Grant No. R-804578)
aimed at assessing the pollutant removal effectiveness of various
methods of flushing small combined sewer collection system laterals.
The field results indicate that extremely high pollutant removal
efficiencies are possible over a single segment (85-95%).  Removal
efficiencies over several segments using a single flush point (600-800 feet)
result in favorable removals (75-85%) depending on the segment and the
pollutant.  The average flush water volumes for all experiments is 350
gallons.  All manual methods appear favorable.  The easiest and most feasible
method at this point in time appears to be injection by tanker and/or from
a near-by hydrant.  The state of the art with respect to operational
automated flushing methods, equipment, sensing interfaces, etc. has not been
demonstrated at this point in time.  It is for this reason that only manual
flushing methods are recommended.   Their effectiveness and ease in operation
has been adequately demonstrated by the EEA/NU research program.

     Street Sweeping Considerations.  Street sweeping is an extremely public
works oriented activity aimed at keeping streets visually clear, preventing the
filling and clogging of catchbasins and drainage lines,  and removing to some
degree, the accumulation of pollutants depositing on street surfaces during
dry weather between storm events.   The quantification of street sweeping
cleaning effectiveness depends upon a host of factors such as sweeping
frequency, the number of passes per sweeping, the type of equipment, the
condition of road surfaces, the existance of curbs, street surface density and
material,  and adequate and enforced off-street parking restriction ordinances.

     The City of Fitchburg streets, within the 208 combined sewer study area
were carefully surveyed for street sweeping feasibility.  Roughly 64% of
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all streets within the study area were inspected.  Four criteria were used
by the field crews:  1) existance of curbs; 2) condition of street surface;
3) street grade; and 4) degree of congestion and traffic.  If the first
three criteria were favorable for a given street (block to block) and the
traffic was reasonable, the street segment was deemed suitable.  If a
street was scored favorable for the first three criteria but the traffic
and/or congestion was heavy or parking was poor, the street was considered
unfavorable.  If any one of the three criteria were scored poorly for a
given street, it was considered unsuitable.
     The fraction of streets considered suitable for street sweeping within
the study area is 36.3 percent.  The percentage of streets termed unfavorably
is considerable and represents roughly a quarter of all streets.  It is very
likely that a portion of these streets would be suitable for flushing during
off-hours and/or during weekends.  The fraction of streets considered unsuit-
able is 44.8 percent and represents a sizeable area where surface and street
accumulations are in some sense not controllable.

     Inflow Correction Considerations.  Illegal downspout connections from
households and commercial/industrial buildings into sanitary sewerage systems
is a frequently occurring problem in the older communities throughout the
country.  Most of the original sewerage systems were combined and connection
of downspouts was acceptable.  Sewer separation brought about tighter
restrictions in building and plumbing codes forbidding the connection of
downspouts into the sewers.  It is not uncommon, however, to find residential
areas in separated areas with a blend of legal and illegal connections.
Assuming that half of the residences have illegal downspout connections in
separated areas results in a considerable stormwater  inflow problem.  A
separate  system would therefore act as a combined sewer during rainfall
events.

     The degree and extent of illegal downspout connections in the study area
was investigated in this study since the City of Fitchburg has an on-going
program to separate small pockets or "slivers" of combined sewered streets
in the future.  This information was also of use to the City in providing
baseline information for the on-going city-wide infiltration/inflow study.

     In this study the field crews selected four representative areas having
different land use characteristics within the study area for "wind shield"
inspections of illegal downspout connections.  The purpose of the surveys was
to primarily identify potential problems relegating the actual determination
of critical illegal downspout connections to the on-going infiltration/
inflow study.  The survey results indicate that the estimated number of
illegal connections per acre for the residential areas range from .3 to 1
depending on the relative land use density.  The survey also showed that
there are a number of commercial buildings near the river with flat roofs
with central roof drainage systems suspected of direct connection into the
sewer systems.  Revamping these drains in the commercial district could
significantly reduce the stormwater  inflow.
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     Off-Line Storage Potential Considerations.  A preliminary feasibility
study of storage potential at a number of sites throughout the combined sewer
study area was prepared for considering structural alternatives.  It was
assumed that storage could be developed in two ways:  small upstream modules
located along collection branches meant to capture highly concentrated up-
stream loads during storm periods and large downstream facilities for
capturing overflows from entire collections networks.

     Initially the special collection system "flow-line" profile maps were
used to determining potential upstream storage locations.  These drawings
were prepared by cataloging the entire network of collection systems, pipe
by pipe, from detailed analyses of City of Fitchburg sewer maps.  These
drawings present a pictorial view of the slopes for an entire collection
system.  The relative elevation of all pipes within any collection subsystem
can be easily ascertained.  The three collection systems in the study area
were visually scanned for locating subareas for potential upstream storage
module locations.  Such sites are ideally located at the end of collection
subareas near a trunk sewer.  A site near the intersection of the trunk
sewer having a large elevation drop, say, 10 feet, was then established. Such
a location is ideal for an upstream storage module.  The inlet is located at
a high point and the discharge outlet at a low point such that gravity
control devices can be used and piping kept to a minimum.  Downstream
locations near regulators were identified in the same manner.  A list of
potential sites was generated.

     The field inspection program determined the practical feasibility of
constructing storage modules.  Parking lots, vacant lots, adjacent parks,
wide median strips and other open areas were considered as prime choices.
There were other considerations investigated in the review of these areas
such as the disruptive effects on traffic/business during construction and
the mechanical practicality of gravity feed to a storage tank and drain by
gravity with minimal amount of piping.

     There are 13 possible locations for considering storage to capture wet
weather combined sewer flows.  The amount of storage that could be considered
in each of the three subsystems is ample and well beyond the requirements for
capturing portions and/or the entire overflow volumes for slight to moderate
level storm events.

     Combined Sewer Overflow Measurements.  During the spring of 1977 a
measurement program was initiated to monitor the three combined sewer overflow
points located within the first collection subsystem of the study area.
Discharges were monitored for three storm events for all three sites.
Pollutant characteristics were monitored at one location for the three storm
events.  The fourth control point is the complex junction chamber mentioned
earlier.  This location is the effective control point for the remaining two
subsystems.  This location was not monitored because of the complexity of
the chamber.  Two separate storm sewer catchment areas tributary to this
location were monitored for flow during this period.  This information was
used to establish reasonable parameter values of a combined sewer runoff
simulation model of the three subsystems within the study area.
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     One important observation noted from the analysis of the overflow
pollutographs was that the response times for "first-flush" to occur and pass
the monitoring sites were extremely rapid, on the order of 10 to 20 minutes
for the storms that were observed.  Analysis of the hydraulic profiles at
the three regulators indicated that raising the overflow weir levels would
permit much of the "first-flush" loadings to be transported to the treatment
plant headworks.  This observation somewhat reduced the technical attractive-
ness of sewer flushing and street sweeping since the treatment plant receiving
these wastes is presently underutilized.  The City was nevertheless concerned
over transporting and treating additional wet weather flows since advanced
forms of treatment would soon be required.  This position tempered the final
selection of recommended management alternatives in that a concerted effort
was made to maintain the present wet weather flows regime to the treatment
plant.  This balance was accomplished by approximating the additional
combined sewer overflow volumes captured by raising overflow weir elevations
at three regulators in one portion of the study area with the elimination of
several direct stormwater  drainage inputs tributary to the fourth regulator.

     Combined Sewer Runoff Simulation Model.   A combined sewer runoff/over-
flow simulation model was developed for each of the three subsystems within
the study area.  This model was used to determine the pollutant emission
reduction effectiveness of various alternative combined sewer control mixes.
Estimates of Total Suspended Solids, BOD, TKN and TP overflow loadings per
storm were computed by the model.   A synthetic unit hydrograph approach was
used to compute wet weather hydrographs.  These hydrographs were defined at
fine time intervals, smaller than the lag time of the peak flow of the
particular catchment area.  The stage discharge relationships developed for
the overflow conduits and downstream sewer conduits were approximated by
third order polynomials.  A Newton-Raphson procedure was used to balance head
and discharge levels in the computation of the overflow hydrographs at the
regulators.  Surface pollutant accumulations were computed in a similar
manner as in model STORM.  Algorithms were incorporated to keep track of the
succession of storm washoff, street sweeping and sewer flushing events along
time.  Control options included modification of the reg-ulator hydraulic
characteristics, elimination of storm drainage inputs, inflow reduction,
street sweeping, sewer flushing and storage.  Levels of control used in the
model for each of these options were determined from the feasibility analyses
previously described.

     Control Option Costs.  The four regulator modifications and the two
storm drain re-piping alterations totalled $26,500.  The downspout inflow
correction program including legal costs was estimated at $312,000.  This
cost assumes 40 connections costing $5000 each for commercial buildings and
70 household connections costing $600 each.

     Sewer flushing program costs for a daily operation are shown in Table 5-2.
Two alternative methods are considered for the 46 segments deemed suitable
for flushing.  The first alternative consists of utilizing automatic flushing
modules programmed to flush daily.  EEA has successfully fabricated, installed
and operated an inexpensive air-cylinder driven back-up and release gated
device in Dorchester as part of the R&D sewer flushing research study cited
earlier.  Further testing of such devices are necessary before recommendation


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Number of Segments:  46

Daily Flushing Program
Alternative 1 - Automatic Flushing Module Operation

   First Costs:
   - Site preparation (grout manhole, fix base,
     clean segment)
   - Fabricate & install air-operated module


   Annual Operational Costs (Total Program):
   - 3 men @ $15,000/yr.
   - Truck rental, gas, insurance
   - Equipment component replacement
     $300/yr/module
   - Sewer cleaning contingency
   - Water

   Cost/module/yr = $1,630
   Present value/segment = $18,745
   Total Present Value Cost (Segment)
   - First costs
   - O&M costs
Alternative 2:  Manual Flushing Mode

   First Costs:
   - 3 outfitted water tankers @ 18,000
     or $1100/segment

   Annual Operational Costs  (Total Program)
   - 6 men @ 15,000/yr.
   - Insurance, gas, maintenance
   - Water

   Cost/segment/yr. = $1938
   Present Value/segment = $22,287
   Total Present Value Cost/Segment
   - Site Preparation
   - First Costs
   - O&M
   Use  $25,000/segment
 Total Present Value Costs:
               TABLE 5-2.
                             $l,500/segment
                              7,500
                             $9,000/segment
                             45,000
                              8,000

                             14,700
                             10,000
                              2,300
                            $80,000
                              9,000
                             18.745
                            $27,745
                             54,000

                             90,000
                              3,000
                              2,000
                            $95,000
                              1,500
                              1,100
                             22,290
                            $24,880
                         $1,150,000
SEWER FLUSHING PROGRAM COSTS
      (Daily Operation)
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for general application can be made because no full scale network of
automated sewer flushing modules has ever been tried in practice.  Costs
for this type of operation are nevertheless presented for comparison with
a manual operation using water tankers.

     Initial costs including $1500 per segment for site preparation work
entailing repairing and in some cases, installing manhole tables, grouting
the flush manhole walls, mechanical cleaning and jetting of the flush segment
and other miscellaneous contingency items.  Fabrication and installation of
the devices is estimated at $7,500 each.  A three man maintenance crew is
assumed to ensure proper operation of the configuration of the 46 modules.
Equipment component replacement costs of $300 per module per year are also
assumed.  In addition, a contingency factor of $10,000 per year is included
to cover possible blockages, malfunctions etc.  The estimated annual budget
for this operation is $80,000:  The O&M cost per module per year is $1630.
The present value of O&M cost per module assuming a 20 year discount period
at 8 percent interest is $18,745.  Total present value cost per module is
$27,745.

     Alternative program costs consisting of manually flushing 46 segments
using three water-tankers and a six man labor force are shown in the second
half of Table 2.  Total present value costs for this alternative are
$24,880 per segment.  Present value sewer flushing program costs for the
study area is $1,226,000.

     Sewer flushing program costs for a manual operation with a weekly
frequency for flushing was also estimated.  It is assumed that a single crew
can complete the flushing circuit within a one week period.  The total present
value costs over a twenty year discount period for this option is $863,000.

     Street sweeping program costs were computed as follows.  Three sweepers
are assumed for the entire study area.  Single pass operation on a weekly
schedule was assumed.  An average cost of $35,000 per sweeper is used to
estimate the capital outlay.  This cost represents reasonable average of
current available technology.  O&M costs are estimated at $80,000 per year.
Total present value street sweeping costs for the area is $1,024,500.

     Off-line storage costs were computed using an average value of $3 per
gallon for capital costs.  Annual O&M costs were assumed to be 10 percent
of the first cost total.

     Assessment of Alternative Control Programs.  A total of 24 alternative
management plans for the study area were analyzed using the computer
simulation model for estimating the relative reduction of overflow volumes
and pollutant emissions.  These plans consisted of various control option
mixes in the areas tributory to the four regulators in the study area.  The
plans considered were developed by additive combinations of the various
control options.  Each option was not tested for its individual effect nor
were all possible combinations considered.

     The simulation analysis used as input 73 rainfall events occurring over
one calendar year that were measured at a  primary  gage  in  the


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study area.  The period chosen for the analysis represented an average year
in terms of storm duration, intensity and antecedent dry periods.  The
simulation results for each storm were then summed and average results were
reported  Under present conditions, the annual combined sewer overflow volumes
from the four control points in the study area is 166.9 x 105 cubic feet. The
estimated number of overflows for three of the four regulators range from
23 to 55 per year depending on the location.  Overflows occur 95 percent of
the time at the major junction chamber on the opposite side of the study area.
Roughly 43 x 10^ cubic feet of overflow at this chamber is attributable to the
direct storm drainage inputs.  For low intensity storms in which no overflows
occur, this storm drainage is conveyed to the treatment plant.  Annual
combined sewer emissions for all four overflow points is estimated at 10,530
Ib-BOD/year.

     Summary overflow and pollutant reduction results and present value costs
of four major alternative plans are shown in Table 5-3.  These results
capsulate  the analysis of the 24 alternative program mixes considered.  Plan
A consists of the regulator modification and storm drainage re-piping
programs.  Plan B adds the sewer flushing program (daily operation) and the
street sweeping program (weekly schedule) to the elements cited for plan A.
In plan C  the inflow control program is added to plan B.  Finally, in plan D
43,000 cubic feet of storage at 4 sites is added to the plan C control
elements.  The following information is provided for each plan:  the reduction
of combined sewer overflows per year, the percentage reduction of overflows
from base-line conditions, the annual emission of BOD loadings in the
combined sewer overflows, the percent reduction of BOD emissions from base-
line conditions and the total present value cost using a 20 year discount
period and an 8% interest rate.

Annual Combined Sewer
Overflow Volume Reduc-
ed (105 cf)
Percentage Overflow
Reduction from Present
Conditions
Annual Combined Sewer
BOD Loading Reduction
(Ib/year)
Percentage BOD Loading
Reduction from Present
Conditions
Present Value Costs
(20 yr, 8%)
Plan
A B
68.24 68.24
40.9 40.9
4624 5642
43.9 53.6
$26,500 $2,201,000 $2,
C D
78.24 92.97
46.9 55.7
6135 7031
58.3 66.8
513,000 $4,678,000
Plan A: Regulator Modification & Storm Drainage Revisions
Plan B: flan A + Sewer Flushing + Street Sweeping
Plan C: Plan B + Inflow Correction Plan D: Plan C + 43,000 cf Storage
TABLE 5-3. OVERVIEW OF OVERFLOW/POLLUTANT REDUCTIONS AND COSTS
OF FOUR ALTERNATIVE COMBINED SEWER MANAGEMENT PLANS
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     The results in Table 3 show that plan A is the superior cost-effective
alternative.  Reductions of 41 percent of the annual combined sewer overflow
volumes and 44 percent of the annual combined sewer BOD emissions are expected
Similar reductions for the other pollutants were noted for the four plans
considered.  Further overflow and pollutant loading reductions are possible
but are disproportionate to the estimated costs. Approximately 70% reduction
of combined sewer pollutant emissions can be achieved under Plan D, but the
present value costs are nearly 4.7 million dollars.

     Under plan A the number of overflows for three of the four regulators is
expected to reduce from 23 to 55 per year down to 5 to 14 per year.  Roughly
18 x 10  cubic feet of combined sewer overflow would be diverted from these
three overflow points to the downstream waste treatment plant instead of
discharge to the Nashua River.  The number of overflows would not be
significantly reduced at the fourth regulator, the major junction chamber.
The volumes of combined sewer overflows would, however, be considerably
reduced.  Approximately 50 x 105 cubic feet per year of storm drainage would
directly discharge into the overflow conduit instead of being mixed with sewage.
Furthermore 7 x 10-5 cubic feet per year of storm drainage occurring during
low level storms would not be discharged to the treatment plant.  The net
additional discharge to the treatment plant would be 11 x 10^ cubic feet
per year.

     The alternative combined sewer management plans were then assessed in
view of wet weather water quality impact analyses incorporating point sources
and major non-point sources in the area including storm drainage from the
balance of the City.  The storm drainage emissions from the rest of the City
are an order of magnitude greater than the combined sewered loadings.  A
"rough-cut" feasibility study for controlling storm drainage pollutant
loadings throughout the City showed that less than 10 percent of the loadings
could be realistically reduced and only at exhorbatant costs.  Control of
storm drainage pollutant loadings in the balance of the City was considered
infeasible.

     Water Quality impact analyses were performed using the steady-state
model of the Nashua River supplied by the Division of Water Pollution Control,
State of Massachusetts, (DWPC).  The physical parameters of the model
originally calibrated by DWPC for dry weather conditions required modification
to define time of travel, depth of flow and reaeration under wet weather mean
flow conditions.  The uncontrolled future  (1995) non-point source wet
weather loads for the entire 208 area were combined with the loadings from
the alternative dry weather control programs and were used in the DWPC
Nashua River model for assessing wet weather water quality impacts.  The
impact analysis indicated that on the average, there would be no dissolved
oxygen water quality problems during wet weather runoff.

     These results collectively defined the basis used to establish the level
of combined sewer overflow abatement in the study area ,  It was therefore
mutually agreed by DWPC and the 208 planning agency that the combined
sewer management plan  for the study area should  reduce  in a  cost-effective
manner, the combined sewer overflow levels and  pollutant emissions only  to a
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reasonable extent.   High levels of combined sewer abatement for the study
area are not warrented since the separated storm sewer loadings are extremely
large in comparison to the uncontrolled (present condition) combined sewer
loadings.  The combined sewer management control plan A, consisting of the
regulator modification and storm drainage re-piping programs, was therefore
deemed the preferred plan and was recommended in the final 208 report.

CONCLUSION

     Overall costs of various alternative control programs which were care-
fully developed from field feasibility studies together with their attendant
pollutant reductions were analyzed to determine the most favorable cost-
effective approach.  The preferred combined sewer management plan for the
study area is estimated to cost roughly $27,000 and will eliminate approxi-
mately half of the combined sewer overflows and pollutant loadings while only
slightly increasing the hydraulic load to the Fitchburg East Waste Water
Treatment Plant.

     The preferred plan consisted of minor repairs to four overflow
structures and several small alterations of storm sewer piping.  This plan is
far more economical than the nominal "Best Management Practices" of street
sweeping, sewer flushing, catchbasin cleaning, etc. and several orders of
magnitude cheaper than structurally oriented programs.

     This plan was developed on the a priori premise that significant control
of combined sewer overflows can in fact be accomplished by a purposeful effort
in restoring the condition of the existing system and jointly maximizing any
system control.  The work program for the combined sewer study included
atypical detailed field inspection surveys that are not performed in most
208 studies.  The final results, however, speak clearly to the point that
even before new and/or old technologies for combined sewer control can be
employed, a rational "first-principles" intensive effort at carefully under-
standing the complex details of collection systems must be accomplished. This
task is by no means simply, particularly for old complicated combined
sewerage systems like Fitchburg, but the results are both responsive to the
needs of the community and to our nation's resources, particularly the
federal commitment to spend large sums of money in the near future for
combined sewer overflow control.

ACKNOWLEDGMENTS

     Mr. Joseph Destefano, project manager of the 208 study for the
Montachusett Regional Planning Commission, encouraged from the onset of the
grant, the concept of the prototype combined sewer management study.
Mr. Garry Saxton, project manager for Anderson-Nichols, Inc.,(ANCO), Boston,
Massachusetts, supervised the initial combined sewer mapping effort performed
by ANCO and worked cooperatively with EEA throughout the entire study.
Mr. Celso Queiroz, environmental systems analysis (EEA), developed and
utilized the combined sewer simulation model in the assessment of alternatives.
Mr. Gerald Aronson, Senior Project Engineer (EEA) supervised the mapping and
field measurement program.  Dr. William Pisano was the project manager for
EEA and prepared the final report.


                                      52

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         STATISTICAL CHARACTERIZATION OF RUNOFF LOADING RATES
                AND COST FUNCTIONS OF CONTROL MEASURES
                                    by

              R. Berwick,  J.  Kiihner,  D.  Luecke,  M.  Shapiro*
INTRODUCTION

     An on-going Environmental Protection Agency (EPA) study (EPA Research
Grant No. R-805238) has been investigating the impact of new residential
development on stormwater quantity and quality.  The primary concern of
the study is the feasibility of estimating useful relationships between
precipitation and runoff given various on-site control measures and their
associated capital and operating costs.  If we look at a typical picture
of the stormwater runoff process (Figure 6-1) , we see

                FIGURE_6£L.  STORMWATER RUNOFF PROCESS

             Rain

         accumulation -- »- runoff  and  - ^  stormwater
            on land             washoff
               t                    t
         land uses,             control            control
           layout               measures           measures
th.'jt there are several components that determine the ultimate stormwater
quantity and quality.  Most of these remain to be usefully described so
that the impact of control measures can be easily discerned.

     We must start, then, at the beginning of the process with a better
characterization of accumulation rates on the land.  How do these rates
vary with different land uses, climates, street cleaning?  What is the
uncertainty associated with estimating residuals accumulation?  The first
section of the paper addresses these questions by reanalyzing a large
body of existing accumulation data.
*Meta  Systems Inc.  Cambridge,  Massachusetts 01238
                                     53

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      At the  other end of  the  runoff picture  is  the effect of the measures.
 Even if we could predict  exactly  the  quantity and quality of runoff from
 a given storm,  the selection  of structural  (porous pavement,  swales,  .  .  .)
 and nonstructural (vegetative cover)  controls is  guided by their unit costs.
 In many cases  cost estimates  are  as uncertain as  measures of stormwater
 control effectiveness;  this makes the selection of control alternatives
 doubly unsure.   The second part of the paper develops  regression models
 based on general runoff,  flow, and cost  relationships.   The resulting
 equations may  be used to  estimate control costs as a function of the  natural
 physical features of an area,  level of development, and precipitation.

ANALYSIS OF DATA FROM PREVIOUS STUDIES

     Two basic strategies  have been adopted in gathering data for use in
quantifying urban stormwater pollutant loadings.  One strategy has focused
on measuring the accumulation and composition of dust and dirt on street
surfaces.  Another has focused on "end-of-pipe"  or waterway measurements
of flow and concentration  during storm periods.   While  data from both types
of studies have been combined and analyzed (URS, 1974), it is unfortunate
that only a few ongoing studies (Pitt, 1974;  Woodward-Clyde Consultants,
1977? Envirex-DOT, 1977) are being performed in  which both types of
measurements are employed  simultaneously in the  same study area.  This
would provide a basis for  development of a mass  balance for quantitative
comparisons of the two measurement strategies.

     Street accumulation measurements have been  taken to provide bases for
evaluating street sweeper  effectiveness and for  calibrating the accumulation/
washoff  functions typically included in various  urban runoff models.  These
studies  are plagued by the difficulties involved in estimating the desired
deposition rates from street solids measurements,  in the presence of a
variety  of wet- and dry-weather deposition and removal  mechanisms.  Uniform
sampling and data reduction procedures for measuring street solids have
not been used.  Pollutant  loadings scoured from  off-street surfaces during
rainstorms and possible non-conservative behavior of some pollutants in
stormwater collection and transport systems are  both ignored when street
solids measurements are used to estimate loadings to urban waterways.

     Measurement of flow and concentration in collection systems or waterways
during storms provides a more direct basis for estimation of loadings.
Difficulties are associated with the temporal variability of flow and
concentration typical of urban storm events.   Such variability can cause
sampling, measurement, and data interpretation problems.  Possible seasonal
or longer term effects suggest that such studies should be carried out for
long periods to provide a  basis for estimating average  loadings.  Envirex,
Inc.  (for the Department of Transportation)  is conducting long-term,
simultaneous monitoring of this kind.

     In  general, however,  most investigations of urban pollutant loadings
from nonpoint sources are considered to have given disappointing results
to date.  For example, Singh concluded at the 1977 ASCE meeting:
                                     54

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           The main objective of this study was to determine
           the rates at which solids accumulate on street
           surfaces....  Initial efforts in fitting
           simple conceptual models and use of regression
           analysis were unsuccessful due to extreme scatter
           in data....

The misconception is, one can argue, to look at "extreme scatter in data"
as a nuisance rather than the interesting property of loading rates.  Why
is there such scatter? Or is there really such scatter?  Further, most
investigators have concentrated on developing regression models to describe
loading rates and have largely commented on their "failure."  For example,
Singh (1977), as well as Colston (1974), Whipple (1976), and Hammer (1976),
find no correlation between elapsed days of accumulation and pollutant con-
centrations;  Sutherland and McCuen  (1975)  claim the reverse.  In part, these
contradictions are due to sloppiness in the definition of "concentrations"
and lack of consideration of collinear effects between independent variables;
but there is also real uncertainty in the physical system being modeled.
Given the current lack of a thorough understanding of the accumulation/
washoff processes, any kind of straightforward regression technique is
doomed to failure.  Therefore, methods designed to discover something
about loading rates should be based on the following two premises:

     (1)  Exploratory, rather than predictive data analysis; and
     (2)  Stochastic, rather than deterministic, model building.

The exploratory process (partially) relieves us from the burden of having
a specific explanatory model of the data at hand;  the stochastic part is
similar — it throws everything we cannot specifically describe into a
lumped box of noise.  Actually, the two premises are not distinct; we use
either one to guide the other.

EXPLORATORY DATA ANALYSIS

     The commentary on loading rates (e.g., Sartor and Boyd, 1972; Hammer,
1976; URS, 1974)  invariably mentions the extreme range in urban loading
rates.   As a typical example, the URS data for residential loading rates
(Ibs/curb-mile-day) shows a range of 8-770, with a standard deviation of
195.  Indeed, for most categories of land uses, climates, and traffic
patterns, the coefficient of variation  (standard deviation/mean) is greater
than one, indicating large variability.   Does this mean that the situation
is so random as to be unusable?  More precisely; is this why regression
methods fail? This can be tested by using the URS data which are a summary
of all major studies on loading rates done before 1974. All raw data are
normalized to Ibs/curb-mile-day and we organize them in stem-and-leaf
displays.

     The basic purpose of such displays* is to arrange raw data into
roughly numerical order —  like a histogram — so that one can easily

*   The reference for this method and the two way table procedure presented
    below is  Tukey, J.W.,  Exploratory Data Analysis, Addison-Wesley, 1977.

                                     55

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answer questions like: what is the largest value?  the smallest? what does
the distribution of values look like?  Unlike a histogram, however, the
display retains some of the identity of the original data by using the
actual digits of the data values to construct the display.  This makes it
easier to see which data value is located where in the histogram.

     How then do we construct the display?  We first scan the list of the
original data (see Figure 6-2) to determine a suitable scale for the values;
in this case, units of tens seem appropriate.  Taking the first data value:
400 Ibs/curb/mile/day, we cut it to units of tens (the ones place is trun-
cated) .  Four hundred becomes 40 (tens).   Next, 40 is separated into two
parts:  the right-most digit 0, and the rest, 4.  The 0 part is called the
leaf  (because it hangs out from the left-hand-side); 4, the stem. We show the
separation of the stem from the leaf by drawing a vertical line between
them:  4**|o, the asterisks indicate that there are really two digits in
the leaf (400 really could become 4|00),  but we show only one of them — the
tens digit.  The display is built by writing down all the stems on the left
and the leaves on the right — see Figure 6-3 which shows the step-by-step
construction of the display.  At each step,  the number placed into the
display is marked with an arrow.  The next data value is 600;  it becomes
6**|o and step 2 of Figure 6-3 shows its placement on the display.  And
so it goes; value 390 becomes 3**|9 (step 3); 210 becomes 2**|l; 170 becomes
!**!?; 19 becomes 0**|l  32 goes on the same line as the latter and we have
0**|13.

     When there are too many values to fit on one line, stems can be separated.
For example, we could have one stem for leaves 0, 1, 2, 3, and 4 and the
other for 5, 6,  7, 8, and 9.  If we were to do this for the data points
represented by 0** 138,
               we have
                        0**
13
    Figure 6-4 gives the completed stem-and-leaf display of the residential
loading rates.  In the spirit of exploratory analysis, we make two remarks
about the figure:

     1.  The data are skewed; this immediately makes the use of the
         standard deviation as a confidence-interval estimate (as was done
         in Singh, 1977),  or of regression analysis without data transfor-
         mation, suspicious.  Specifically the shape is suggestive of a log-
         normal distribution (see below).

     2.  If we calculate the median of the data (to avoid the skewness
         problem), it is 70.  URS reported a mean of 149 — revealing the
         skew of the data.   (As a comparison, the geometric mean — essentially
         a log transform of the data — gives a value of 84.)   Reporting
         only this arithmetic mean plus a standard deviation of 195, is
         misleading:  it says that about 67 percent of the data was between
         149 ± 195.  In fact, we can do much better than this:  if we cal-
         culate the hinges  (halfway between the median and each extreme
         value), we get values of 20 and 185.5 which tells us that about

                                      56

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       one-half of all data falls within this rangb.  In general, an
       analysis by medians is much less sensitive  (i.e., the analysis is
       robust)  to extreme fluctuations in data;  a simple arithmetic mean
       can be extremely misleading.  Notice also, that the spread (equals
       difference between hinge values),  is 165  close to the URS
       standard deviation of 195.

JFIGURE 6-2. ._JJAW_DATA:  RESIDENTIAL LOADING RATES URS LBS/CUKB-MILE/DAY
      400
      121
      019
      032
      070
      220
       77
      770
       33
       13
       39
600
148
020
035
092
372
238
950
11
69
45
390
081
096
024
2700
659
18
205
8
17
22
210
062
153
033
690
418
34
950
3
27
12
170
121
060
041
260
70
103
100
295
18

019
135
022
028
860
85
93
67
31
6

032
148



24
40
93
165
8

   Number of observations = 71
      FIGURE 6-3.   STEPS IN CONSTRUCTING STEM-AND-LEAF DISPLAY
Step 1 Step 2 Step 3 Step 10
6**
5
4**
3
2**
1
0**
6**
5
0-*- 4**
3
2**
1
0**
0^_ 6**
5
0 4**
3
2**
1
0**
0 6**
5
0 4**
9-«- 3
2**
1
0**
0

0
9
1
247
138-*-
    (value          (value          (value          (value
 placed = 400)    placed = 600)   placed = 390)   placed = 081)
                                   57

-------
                             Figure 6-4

                     Residential Loading Rates
                         (Ibs/curb-mile-day)
                             (URS, 1974)
                             27**
                         (Bucyrus,  Ohio)
      (Note:   stems
       separated into
       two pieces.)
Results:  upper hinge:  185
               median:   70
          lower hinge:   20
               spread:  165
10**

g**
•
8**
•
7**
•
6**
5**
4**
•
_**
2*1
.**
0**

55

6

7

95
0

01
97

69
12
75
24
Qf.
do
13
                                    8696797879696
                                    1312233234221342100311210G3421
Note:  median:
        hinge:
the data value half-way in from either end; half  the
data lies above, or below this data point.

upper (lower) data value  —.  75% (25%)  of data are
below this value.
       spread:  difference between upper and lower hinges,  i.e.,  contains
                about one-half of the data.
                                    58

-------
     Given the results of this simple analysis, one should be suspicious
about all of the reported summary data on loading rates.  We will, however,
postpone the analysis of other loading data for now because we want to pro-
ceed to investigate the log-normal transformation of the data of the re-
sidential areas.

     If we take logarithms of the loading rates, the resulting stem-and-leaf
display is more normal-looking (though still slightly skewed) (Figure 6-5).
Analysis of the data  divided into classes,  such as by climate (north-
east, southeast, southwest, northwest), traffic (<500 average daily traffic
volume  (ADT); 500-1500 ADT; 1500-5000 ADT; >5000 ADT), land use  (commercial,
light industrial) reveals the same general results:

     (1) The reported arithmetic means are skewed to the high side.
     (2) The data are close to log-normal.

     Now, what does a log-normal distribution suggest?  The standard
explanation is this:  suppose the increase in loading, Ax, is some multiple,
k, of the increase in a large number of other variables (z.), most of which
we don't know about, i.e., Ax = kz.x; and suppose the z.'s are random
variables.  Then the sum of the z's is the sum of these changes in x:

     2 = I 2  = I   J*  dx  =  1 log x
            i   k  x     x     k     x

The random variable z, being the sum of many random variables is by the
central limit theorem, normally distributed.  So x is log-normal — reason-
able behavior for loading rates.   Put another way, once we have a given
rate of load, say x , it seems likely that those factors that made it
high (say, a surrogate like employment density) are associated with a num-
ber of other factors that will only increment the loading.  Like many other
regional influences, effects are multiplicative.  (A further speculation:
loading rates are area effects — population, employment, etc. — funneled
into a more or less linear collection system (a street)  and, hence, increase
multiplicatively.)   It would seem that the loading data are not so inexplica-
bly "extreme" as stated.  Rather, it follows from what one would expect if
there were many random variables contributing to their ultimate values.

     The kind of distribution we have described indicates that we must
model a lot of what is going on in the loading process as the combined
effect of many factors — a black box.  For the process we do know, how-
ever, other methods can be used.   One approach is to dissaggregate the data
into categories — as in the URS study.  As mentioned in URS, the scatter
of the data results in a weak relationship between categories such as land
use, climate and the loading rate.  Also, the URS study used t-tests to try
to separate categories;  but, as we have seen, without first transforming the
skewed data, this approach is weak.

     Another approach is to use medians instead of a least squares analysis.
With medians we can explore the data in order to describe observed differences
                                      59

-------
         Figure  6-5
Log   (Residential Loading Rates)
     (Ibs/curb-mile-day)
          (URS, 1974)
      loading rate x 100
34*
32*
30*

28*

26*

24*-:

22*

20*

18*

16*

14*

12*

10*

8*

6*
4*
3


388
429
8
02
97
17
2481
32
7378
8810
186377
55934
98
105

















1142329
593
04884
88636
1
48
00

8

8
Results : log s
upper hinge: 2.
median : 1 ,
lower hinge: 1.










ca
27
79
43
                                  original

                                    (187.5)
                                      (70)
                                      (27)
              60

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between the categories used by URS.  Medians are insensitive to extreme
fluctuations in the data.  To perform the analysis we disaggregate the
data into a model of the form:

     LOADING = common value             + land use effect
               (over all categories)    + traffic effect
                                        + climate effect
                                        + residual

where the residual is a normally distributed source of error.  This will
enable us to place confidence limits on the estimated loading for a given
area.  We are in effect making the categories into "dummy variables," using
the standard regression parlance, but we are NOT using least squares.* We
present, as an illustration of the technique, "two-way" tables in log scale;
the first is median loading rate by climate and land use, the second, median
loading rate by traffic volume and land use.t

     The model is:

     LOADING = common value + row effect + column effect + residual
     where residual = value left in a cell.
We begin by looking at data in Table 6-1.


*  One problem is that there are scant data for some categories — for example,
   northwest region with traffic less than 500/day.  No matter; a resistant
   analysis proceeds with the data available, using medians to estimate the
   missing data.  However, it is a good idea to keep this in mind when
   attempting to predict  loadings.

t  The classic statistical approach to fitting a linear regression model to
   observed data is to use a "least squares" criterion for the fit.  If we
   displayed the data in categories  (as in Tables 6-1 and -2), we could follow
   this technique by subtracting out the row and column means for each cate-
   gory, and arriving at a formula like

      fit = grand mean + row effect  + column effect + residual

   It can be formally shown that the linear decomposition in a model of this
   kind is equivalent to a least squares analysis.

   However, least squares is ineffective with highly variable data, because
   (as a glance at the least squares derivation shows) points farther away
   from the grand mean  (X) contribute more to the placement of the least
   squares^ line than points closer to the mean  (the exact factor is
   3f(X.-X~)/3X where X. is the particular observation and X the mean).  But
   why should "untypical" points contribute more to where the line goes?
   Shouldn't it be rather the "typical" points that determine the line?  In
   the face of these arguments, it has been suggested to subtract out medians
   instead of means, thus providing an inherently "typical"  value, not one
   influenced by extreme values.  This gives us Tables 4 and 5 as presented
   in the text.

                                      61

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TABLE

6-1. TRANSFORMED
Climatic Region
Land Use Northeast Southeast
residential
commercial
light industry
industry
2.13
2.01
2.13
2.71
1.70
1.61
2.02
1.64
LOADING DATA

Northwest
1.59
1.20
1.77
1.59


Southwest
1.43
1.61
1.93
1.87

Row
Median
1.65
1.61
1.98
1.76
The first step in applying the technique is to find the effect of being
in a particular land use — for example, across all climates.  For  resi-
dential land uses only, what is the typical loading?  For this row the
typical value, as represented by the median, is (1.70 + 1.59)/2 = 1.65.
Similarly, the commercial, light industry, and industry row medians are
1.61, 1.98, and 1.76 respectively.  Proceeding, we subtract the row
medians from each of the data values to obtain a new table.
TABLE 6-2.
LOADING DATA MINUS ROW MEDIAN
Climatic Region
Land Use Northeast
residential
commercial
light industry
industry
column medians


0.
0.
0.
0.
0.


48
40
15
95
440


Southeast
0.05
0
0.04
-0.12
0.020


Northwest
-0.
-0.
-0.
-0.
-0.


06
41
21
17
190


Row
Southwest Median
-0

-0
0
-0


.22
0
.05
.11
.025


1.65
1.61
1.98
1.76
1.705
Overall
Value
We now find the climate fit, just as with land uses, only this time we
naturally look down each column.  For a particular climate, what is the
typical value?  (This time we are not using the original data values,
but those with land use effects subtracted out.)  In this way we find
the values labelled "column medians" in Table 6-2.  Notice that we also
find the median of all land use effects themselves by looking down the
column labelled "Row Medians."  This is the "median of the row medians"
the overall loading common to all land uses and climates.  Again, we
subtract the column medians from the respective table values above them
(and subtract the overall effect from each of the row medians).  The
results are presented in Table 6-3.
                                   62

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	^TABLE 6-3.  LOADING DATA WITH ROW AND COLUMN MEDIANS  SUBTRACTED
                  Climatic Region                                Row   Row
Land Use     Northeast    Southeast    Northwest     Southwest    Fit   Median
residential
commercial
light industry
industry
Column fit


0
-0
-0
0
0


.040
.040
.290
.150
.440


0.
-0.
0.
-0.
0.


030
020
020
140
020


0.
-0.
-0.
0.
-0.


130
220
020
020
190


-0.
0.
-0.
0.
-0.


195
025
025
135
025


-0.055
-0.095
0.275
0.055
1.705
Common
Value
0.
-0.
-0.
0.
-0.


035
030
023
078
003


     If we had used means as typical values, we would now be  done.  However,
since we are using medians, the process  is not quite complete.   If we now
calculate row medians, we see they are not quite  zero; this indicates that
we should go through the whole process once more, but this time  "polishing"
the fits by subtracting only the new, small row medians.  Then we must do the
same for the column medians.  We iterate, moving  from polishing  rows to  col-
umns, until the medians for both are relatively  (within  round-off error)
close to zero.  We are left  (in this case, after  four steps)  with row effects
along the right-hand side, column effects along the bottom, and  the common,
overall value in  the lower right-hand corner  (Table 6-4).  What  numbers  are
left in the cells?  Those not accounted  for by climate,  land  use, or over-
all value — the  residuals.  The results for land use and traffic density
appear in Table 6-5.

     The "common  value'1 is an overall measure of  the load.  The  "effects'1
are what they say they are — in the case of land use and region, the effect
of a particular value of land use or climatic region.  For example in Table
4, being in the northeast adds 0.446 to  the common value of 1,76 being
residential adds  -.05.  The URS "overall" value is 156 — too high, compared
to the model above  (anti-log 1.76 = 57.5).  Notice that  this  model is linear
in a log scale, hence actually multiplicative.

     The basic results are these:

     1.  Differences in land use do not  account for much of the  observed
differences in loadings; the effects are quite small, on the  order of the
residuals.  An exception is light industrial, with a +0.34 value; however,
there were very few observations for this land use.

     2.  There is a good sized positive  effect for the northeast region
(0.446), and negative effect for the northwest; the other two regional
eifects contribute little. While the northwest effect is based on just
a few samples, the sizeable northeast effect is probably due  to  associated
"infrastructure"  — old housing, density, etc.*   The URS study concludes
that loadings are lowest in commercial areas of the northwest — true by
*  Compare this with  Hammer's  1976  discussion of his regression results.

                                      63

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     TABLE 6-4.  LOG   LOADING MEDIAN LBS/CURB-MILE/DAY  URS  (1974)
Original Data

Land Use
Residential
Commercial
Light Industry
Industrial
Region


Northeast
2.
2.
2.
2.
13
01
13
71


Southeast
1.
1.
2.
1.
70
61
02
64


Northwest
1.
1.
1.
1.
59
20
77
59


Southwest
1.
1.
1.
1.
43
61
93
87
Row

Median
1
1
1
1
.65
.61
.98
.76
The Two-Way Fit: Loading =
               Common Value+
            Land Use (row)  Effect + Region  (column)
            Effect + Residual


Residential
Commercial
Light Industry
Industrial
Column Effect




0.01
-0.01
-0.26
0.435
0.446




-0.01
0.01
0.04
-0.22
0.03




0.12
-0.16
0.03
-0.03
-0.16




-0.26
-0.03
-0.03
0.03
-0.05


Row
Effect
-0.05
-0.02
0.34
0.02
1.76
(common
value)
     TABLE 6-5.  LOG   MEDIAN LOADING LBS/CURB-MILE/DAY  URS  (1974)
Original Data
                    Traffic Density (ADT)
Climate
<500
500-5000
5000-15000
>15000/day
Northeast
Southeast
Southwest
Northwest
2.55
1.76
1.15
absent
2.08
1.83
1.72
1.34
2.16
1.66
1.41
1.62
2.32
1.23
1.55
1.20









The Two-Way Fit
Northeast
Southeast
Southwest
Northwest
Column Effect


0.
0.
-0.
215
000
425
absent
0.


045


-0.
0.
0.
-0.
0.


298
027
102
028
022


-0.
0.
-0.
0.
-0.


042
033
032
428
042


0
0
0
-0
-0


.060
.455
.050
.050
.060
Row
Effect
0.623
0.138
-0.137
-0.167
1.623
(common
value)
                                      64

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our table; but what is important from our analysis is that the residuals
for commercial northwest areas is -0.16 — as large as the northwest
effect itself.  That means there is more going on in this cell than we
can explain with a "2-way" table.

     3.  There are large residuals for residential southwest  (-0.26),
northeast light industrial and industrial (-0.275, 0.435) — and south-
east industrial (-0.22) — as large as any land use or climate effect:
These all suggest details unaccounted for by the gross categories of
land use and climatic region, particularly for the northeast.  Are these
local disturbances?

     4.  The simple model does a better job than the URS study: the
residential northeast mean given by the URS t-test method is 291, whereas
the reported value is 197.  That is an error of 48 percent.  Our method
predicts a median value of 143  compared to an actual value of 135 — an
error of 6 percent.  Of course, in an area with large residuals — the
northeast -- neither method can be expected to do well.

     5.  Examination of residuals in a stem-and-leaf display  (Figure
6) indicates that the median is 0, as it should be.  The residuals look
about normal with some high and low values as noted.  A diagnostic plot
of residuals versus (row effect x column effect)/common value  (Figure
7) shows that the residuals are well-scattered as they should be.

     For Table 6-5, the following points are of interest:

     1.  There are fewer data points with simultaneous traffic-climate
measures; than with land-use/climate (Table 6-4).  In fact, the number of
data values limits our extension of the method  (and, of course, a "dummy-
variable" regression technique) to a 3- or 4-way table.  There are too
few observations to fill a multiplying number of tables entries.  For
example, Table 6-5 has four levels of traffic and four levels of climate
effects, which makes 4 x 4 = 16 cells.   For a 4-way table, with an additional
four levels of "land uses" and four levels of "landscaping beyond the side-
walk," we have 4x4x4x4= 256 cells.  But we have only 200 data points,
not enough to fill all these cells.

     2. We see that there is little or no effect from increased traffic
density.  Instead, the climatic effect dominates:  the northeast is
highly positive (as in Table 6-4), the northwest negative.  This result
agrees with the URS study:  "loadings are lowest in areas with greater
traffic,...possibly due to...[wind erosion]," but not as strongly as they
would suggest.  Our table indicates the lower loading is due mostly to
climatic region, not traffic; URS considered only one category at a time.

     3.  The residuals are displayed in Figure 6-6.  The median is 0, ex-
pected for "random" residuals,  and the distribution looks fairly normal.
The diagnostic plot of residuals shows well scattered points; the linear
model is doing as well as can be expected.
                                     65

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            Figure  6-6
Residuals from Two-Way Fit of Land Use
      versus Climatic Region
          residuals x 100
4
3
2
1
+0
-0
-1
-2
-3
-4
dia
3


2
113433
4-133
6
762


n = 0.0
                  66

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     RESIDUAL* 100
         	T
     40
     30
     20
     10
      0
    -10
    -20
    -30 —
1	T
                                                 "1	1	T
        XX

   x     xxx
    X   X
                       X
                        X
      -4-202468
                     DIAGNOSTIC VALUE x 1,000

Figure 6-7.  RESIDUALS  FROM  2-WAY FIT, LAND  USE  vs.  CLIMATIC  REGION
                                67

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RESIDUALS x 1QQ	         	
      	1	1	1         I         T
                                 X
  41—
                                  X
                           x   x
                                 X

                            x    x .
  -1
 -2
  -3
  -4
                          X
                          X
                                       I
           -20      -10       0        10       20
                     DIAGNOSTIC VALUE x 1,000
 Figure 6-8.  RESIDUALS  FROM 2'WAY FIT, CLIMATE vs  TRAFFIC
                            68

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     Based on the URS data of population density, impervious cover, land-
scaping beyond the sidewalk, and so forth, we could prepare other tables
to delineate differences in loading rates.  As we have seen, however, we
cannot push limited data to cover too many categories.  There is always some
residual variation that grows larger and larger as the number of observations
in each n-way cell becomes smaller and smaller.  Some authors have gone further
and claimed (Hammer, 1976) that the observed loadings in receiving waters
might not reflect any areal buildup but rather are related to a more local,
micro-scale erosion.  If true, such an effect would render any more aggregate
view  (the usual one) generally useless.
NON-LINEAR ACCUMULATION RATES

     Let us now turn from data analysis to modeling.  The usual procedure
to model the buildup of material on streets has been to imagine the observed
loading as an equilibrium between accumulation and removal processes.  An
easy simplifying assumption is to make accumulation and the removal by
washoff linear.  As Shaheen(1975) and others have noted, this isn't true.
Further, the rates probably aren't smooth, but rather, discontinuous.*  The
real picture is more like a sequence of independent events much like the
arrival times and departures at a queue.

     The simplest kind of rate model for deposition/removal is:


     ^- = Kl - K2 x L
     dt
where L = Load; t = time; and Kl and K2 are two constants.  This integrates
to
      j     dL
        K2 x L - Kl    ~ J dt

      ln(L - K1/K2) = -K2 x t + constant

      L = K1/K2(1 - exp(-K2 x t) ) (initial conditions: t=0, L=0)  (3)

where t is the time since the last washoff event  (storm or street cleaning) .

     If the basic rate equation is generalized we obtain:
      dL/dt = f (t) - f (L) + random component.                      (4)

Suppose the deposition is non-linear, say a/(l+bt) , so that possible
deposition diminishes with time.  There is no simple solution to this
   Certainly they are as we decrease the observed time interval.  For
   example, at a large time scale, rain events themselves are discontinuous
   happenings that change the accumulation of material.
                                      69

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differential equation  (there is an integral equation  solution),  but a
series solution expanded to two terms gives:

     L = -a/(l+bt) + ac/b  (ln(l+bt) exp -  (ct))
                   + cjl+bt) gxp - (ct)
                   + c  (1+bt)  exp -  (ct)
                   + (a/b+c+c /4).                                 (5)

     The load approaches its asymptotic value quickly for a  selection
of a, b, and c, corresponding to the "standard" differential equation
(more linear accumulation rate),  (3).  Figure 6-9 shows a graph  with some
typical a, b, c values — the asymptotic limits are just fictional.   It
is quickly seen that this more general equation can cover the  simpler case,
equation  (3), as well as the empirically found curves of Sutherland and
McCuen  (1975), Figure 6-10:

     load (industrial)  = 1388(1 - exp(-.19t)
     load (commercial)  =  500(1 - exp(-.535t))
     load (residential)= 1089t(1+1.3t)).

     The Sutherland and McCuen model added dummy variables for traffic
and street condition.  We have incorporated these through the table  categor-
ies and added the stochastic component.
                                     70

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                                                           = 60 (Sutherland &
                                                          b=.l   McCuen
                                                          c=1   "residential"
  012345678
                                   t, TIME (DAYS)

Figure 6-9. ACCUMULATION  LOAD vs  TIME; 'COMPLEX' DIFFERENTIAL  EQUATION

-------
ro
                     1200 r—
                     1000H
                      800H-
              TOTAL
              SOLIDS
               LBS.   600
               PER
             CURB-MILE
                      400H
                      200
                                             3456
                                                 DAYS  OF ACCUMULATION
8
                      Figure 6-10.  SUTHERLAND  AND McCUEN  EMPIRICAL CURVES

-------
ESTIMATING COSTS OF ON-SITE CONTROL MEASURES

DEVELOPING A BASELINE

     We have begun analyzing methods for estimating the capital cost require-
ments of stormwater control measures.  Ideally, we would like to develop
functional relationships between costs and the factors which are available at
the preliminary planning level.  These factors typically include general site
characteristics, such as slope, size, and intensity of development, and
meteorological information such as the design storm.  At this level of analy-
sis engineering design data, such as the size of drainage pipes and the maxi-
mum discharge, would not be available.  Thus cost estimates at this level
will, of necessity, be crude relative to estimates made later in the design
process.  But if properly developed, such preliminary cost estimating rela-
tions would be useful in the evaluation of area-wide stormwater management
strategies and in the comparison among alternative combinations of measures.

     Our analysis has initially focused upon the evaluation of traditional
drainage system costs.  This choice is based upon two considerations:

     1.  In most areas conventional storm sewer systems represent the
         current practice against which options such as on~site storage
         will inevitably be compared.  Thus it makes sense to treat the
         conventional systems as a baseline for cost evaluation purposes.

     2.  Since conventional storm sewer systems are widely applied there
         is more knowledge and data on costs for this system than for
         other measures.  Cost estimating approaches can be tested against
         a conventional system before extending them to measures for which
         cost data are limited or unavailable.
PREVIOUS STUDIES

     In order to establish cost functions of the type desired it is neces-
sary to establish an appropriate data base and to evaluate functional forms
in light of the data base.   In each  instance two paths might be followed.
For developing a data base it is possible to use actual or bid costs of a
suitable sample of projects, or to develop a series of synthetic costs, cost
estimates of hypothetical projects designed from scratch.  Each approach has
its own strengths and weaknesses.  Utilizing actual project data has the
advantage of encompassing, in a suitable sample, the range of conditions and
                                     73

-------
factors found in practice.  On the other hand, great care must be taken to
insure that reported costs actually incorporate the full range of appropriate
project expenses and no others.  In addition, there may be a number of pro-
ject-specific factors which influence costs and add to uriexplainable variance
in the data set, thereby reducing the precision of estimated cost functions*
A data set developed by synthetic costing techniques eliminates this type of
random variation in the cost data.  But in the process the assumptions and
design philosophy of a single individual or group becomes imbedded in the
data set.  This may bias the data in ways which are not predictable a priori.
In addition, synthetic cost studies may often miss cost areas which are
important in real applications.  For example, procedures developed in a
recent cost study of sewage treatment plants predicts costs that are substan-
tially lower than those found in practice (EPA, 1976).

     Given any particular data set, cost functions may be developed by empi-
rically fitting the data to arbitrary functional forms so as to produce a
best fit by some criteria.  Usually the techniques of linear or non-linear
regression analysis are employed for this purpose.  Alternatively, a particu-
lar functional form might be specified based upon the physical relationships
of the variables in the system.

     It is possible, and often desirable, to combine the approaches discussed
above.  But in the two papers which have been reviewed here, separate paths
are taken to estimate stormwater drainage costs.  Grigg and O'Hearn (1976)
developed a cost function based upon a simplified model of the hydraulics of
runoff and estimated the parameters of the model from synthetic costs devel-
oped for a single drainage area.  Rawls and Knapp (1972) developed a data
base of actual projects and fit a variety of linear and non-linear models to
develop estimating equations.

     The Rawls and Knapp data base consisted of 70 projects from 23 areas
located across the United States.   The project data obtained included the
design storm frequency (T), in years; average slope  (S) ; in percent; runoff
coefficient (C) ; number of inlets and manholes  (I); smallest pipe diameter
 (DB), in inches; largest diameter(0), in inches;  outlet capacity (Q), in ft3/
sec; total length of drains  (L ), in feet; total drainage area (A), in acres;
developed area (A ), in acres; and total cost, in 1963 dollars (C ) .  The
individual variables most highly correlated with the total costs were the
total and developed drainage area, maximum pipe size, outlet capacity, length
of drains, and number of inlets and manholes.  For all of these variables
 |r| > 0.5.  Noticeably absent from the data set were data on the magnitude of
the design storm or soil characteristics, although these are, to some extent,
reflected in the other variables.

     Using their entire data set, Rawls and Knapp estimated several cost
models by nonlinear techniques.  Their principal models incorporated engi-
neering design variables such as pipe size, maximum discharge, and number of
manholes and inlets as cost determinants.   Thus while their fits, as measured
by R2, were good  (typically on the order of .9), these models are not appro-
priate for the preliminary planning level that we have in mind.
                                     74

-------
     Rawls and Knapp also analyzed simpler linear cost functions individually
for subsets of data from three separate states and found that they could
explain a substantial proportion of the variation in costs in California and
Texas.  This result indicates that regional effects were important and might
be related to the omission of rainfall and soil factors from the data base.

     Grigg and O'Hearn developed a functional form for cost estimation from
an idealized model of a single storm sewer draining a small basin.  Their
model combined the rational formula for runoff, Manning's formula for pipe-
flow, a rainfall intensity/duration formula and an empirical formula for pipe
costs to arrive at a formula relating collection costs to drainage area,
design storm frequency, runoff coefficient, slope, storm duration and a cor-
rection factor for the extensiveness of the collection network.

     The authors did not estimate the function for different areas or topo^
graphic and rainfall conditions, but considered only a single site with
different degrees of impervious area and different design storm frequencies.
Costs for drainage systems were synthesized and fit to a function of the
design frequency for each level of impervious area.  While the approach
yielded good fits for the synthesized costs of the particular area under con-
sideration, the adequacy of their general model for estimating costs for
different areas, slopes, and characteristic rainfalls was not really evalu-
ated.
REANALYSIS OF RAWLS AND KNAPP DATA

     We have used a generalization of the approach developed by Grigg and
O'Hearn to reevaluate the Rawls and Knapp data.  This reanalysis has focused
on developing a cost relationship suitable for preliminary analysis when no
detailed engineering data are available.  The model is outlined below:

     Rational Formula:       Q = CIA,                               (6)

     General Flow Formula:   Q = aiD  S  ,                          (7)

                                    b2
     Cost Per Foot:          C = biD                                (8)

     Pipe Length:            LT= CiA°2                              (9)

     Combining these expressions yields:

                 c-c.b.-'.

     where C  = installed cost of pipe  in  /foot, and  other
     variables are as defined previously.
                                      75

-------
     This last expression can be multiplied by a factor  (1 + E) to account
for additional costs (engineering, etc.).  Finally a factor pan be introduced
to account for the degree of development in the drainage basin served.  We
shall use the term (A/A )°3  which is the ratio of area served (A) to area
developed (A ).   The parameter c 3 is assumed to be less than zero since the
larger the ratio the less extensive would be the pipe network required to
serve a given area.  The expression for total costs becomes:


     CT = (H
                  t
which can be simplified to:

     _    ,  ~da _ds  du  ds /1\ \ut,                                n?\
     C  = di C   I   A   S   /—I                                   (13)
where the d . ' s represent the appropriate combinations of parameters and are
to be estimated from the data.

     Examination of the derivation of the total cost equation leads to the
following predictions  based on the equation forms and generally recognized
properties of the individual coefficients.

     da = da

     d2, da, d4> 0,

and

     ds, d6 < 0.

Moreover, if Manning's equation is the appropriate flow equation, and pipe,
costs follow the Grigg and O'Hearn cost formula (bz = 1.663) , then

     ,     ,    b2    1.663    co
     d2 = ds = —  = 0 ,--,  = -62
               az    2.67
and
                  T rr->
        _      _ -1.663      _
     ds - -a^-- -$^r (-5) - -.31

     In order to estimate the total cost relationship  (13) from the Rawls and
Knapp data, it was necessary to include the rainfall intensity, I.  This
value was approximated by including the 15-minute storm for the appropriate
location and design period  (Yarnell, 1935).  The intensity, I, could be esti-
mated for only 67 of the 70 original projects; thus three projects were
dropped from the sample.  The total cost equation was estimated by taking the
logarithms of each variable and using linear regression.  The resulting esti-
mates are presented in Table 6-6.
                                     76

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                TABLE 6-6.  ESTIMATES OF COEFFICIENTS
Coefficient dj
Estimate 2,591
t Value
Significance level
da
.764
(1.86)
.07
d3
.530
(1.48)
.15
cU
.696
(8.00)
<.001
ds
-.134
(2.00)
.05
d6
-.356
(1.86)
.07
  (two tail)
                     R2 = .529
     It may be seen that, while not all the coefficients are highly signifi-
cant, all signs are in the direction predicted; moreover, the numerical
values of d ,  d ,  and d  seem reasonably close to those predicted by Manning's
equation and the Grigg and O'Hearn cost function, given the sampling errors
involved.  A restricted regression was also run with da = da = .62 and ds =
.31.  The results of this regression are reported in Table 6-7.
                TABLE 6-7.  RESTRICTED REGRESSION RESULTS
d i da d ;
Estimate 2,073 .62 .62
t value
Significance level
di+ ds
.681 -.31
(7.84)
<.001
d6
-.313
(-1.69)
.1
  (two tail)
                     R2 = .491
     The reduction in R2 going from the first to the second regression was
not statistically significant at a 5 percent level; thus the predicted values
for da, da and ds cannot be rejected.

     The R2 given in the two regressions is not directly comparable to the
Rawls and Knapp values because it is computed on the log of the dependent
variable.  A comparable measure can be obtained by using actual and predicted
cost values after a retransformation to the original form of the data.  The
value corresponding to Table 6 is R2 = .65 which is still much lower than the
Rawls and Knapp results.  But given the exclusion of design information from
the analysis, the results appear to be quite encouraging.
                                      77

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 CONCLUSIONS


     At the end of studies of stormwater runoff, one often hears the call for
"more data."  It would appear to be the duty of every investigator to point
out the need for additional information.  We find it hard to argue with the
observation or, if you will, plea; however, a careful reexamination of data
we have on hand can reveal much about the runoff process.  The beginning of
stormwater runoff is in the residuals accumulation, and here we have found
that loading rates can be characterized as log-normally distributed (influ-
enced by multiplicative factors) and non-linear over time.  Using resilient
statistical techniques we can separate these factors into components like
traffic density, climate, and land use, and we can gage the relative impor-
tance of each.   The results could improve modeling efforts like SWMM in two
ways:  (1) by giving better initial estimates of loads; and (2) by reproducing
more closely observed accumulation changes over time.  On the cost side, we
have shown how to develop a general predictive model for the cost of conven-
tional sewer systems, given little or no specific engineering design informa-
tion, but utilizing more of the readily available site and precipitation data.

     To make these analyses more useful in the context of stormwater manage-
ment, we must account for the washoff and transport of pollutants and the
impact of control measures.  We are proceeding by putting our improved accumu-
lation equation and data estimates into a simulation model that deals with the
uncertainty of some hydrologic  (washoff) events.
 REFERENCES


 1.    APWA.  Water Pollution Aspects of Urban Runoff.  FWPCA,  January  1969.

 2.    AVCO Systems Corporation.  Storm Water Pollution from Urban  Land Acti-
      vity.  Tulsa Study  2; 11034FKL, July  1970.

 3.    Bradford, W.   Urban Stormwater Pollutant Loadings  — A Statistical
      Summary  Through  1972.  Journal of the Water Pollution Control  Federa-
      tion,  April 1977.

 4.    Colston, N.  Characterization and Treatment of Urban Land Runoff.  EPA-
      670/2-74-096,  December 1974.

 5.    Grigg, Neil S. and  John  P. O'Hearn.   Development of Storm Drainage Cost
      Functions.  Journal of the Hydraulics Division, ASCE, Vol.  102,  No. HY4,
      April 1976.  pp.  515-526,

 6.    Hammer,  T. R.  Planning  Methodologies for Analysis of Land Use/Water
      Quality  Relationships.   U.S. EPA Contract No. 68-01-3551, October 1976.
                                      78

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7.    McElroy,  A.  D., et al.  Loading Functions for Assessment of Water Pollu-
     tion from Non-Point Sources.  Midwest Research Institute, EPA-600/2-
     76-151, May 1976.

8.    Rawls,  Walter J. and John w. Knapp.  Methods for Predicting Urban
     Drainage Costs.  Journal of the Hydraulics Division, ASCE, Vol. 98,
     No.  HY3,  September 1972.  pp. 1575-1585.

9.    Sartor, J. D. and G. B. Boyd.  Water Pollution Aspects of Street Sur-
     face Contaminants.  EPA-R2-72-081, November 1972.

10.  Shaheen,  D.G.  Contributions of Urban Roadway Usage to Water Pollution.
     EPA-600/2-75-004, March 1975.

11.  Singh,  R.  Statistical Coefficient of Variation of Pollutant Loading
     Rates.   Presented at ASCE Meeting, Dallas, Texas, April 1977.

12.  Sutherland,  R. and R. McCuen.  A Mathematical Model for Estimating
     Pollution Loadings in Runoff from Urban Streets.  In C. A. Brebbia,
     Mathematical Models for Urban Problems, Pentech Press, London, 1976.

13.  Tukey,  J. W.  Exploratory Data Analysis.  Addison-Wesley, 1977.

14.  URS  Research Co.  Water Pollution Aspects of Street Surface Contaminants.
     EPA-R2-72-081, November 1972.

15.  URS  Research Co.  Water Quality Management Planning for Urban Runoff,
     A Manual.  EPA-400/9-75-004, December 1974.


16.  U.S. Environmental Protection Agency, Office of Water Program Operations,
     Municipal Construction Division.  An Analysis of Construction Cost Expe-
     rience  for Wastewater Treatment Plants.  EPA-430/9-76-002, February 1976.

17.  Whipple,  W., J. Hunter, and S. Yu.  Characterization of Urban Runoff —
     New  Jersey.   OWRT Project C-5341, June 1976.

18.  Woodward-Clyde Consultants.  Demonstration of Non-Point Pollution
     Abatement Through Improved Street Cleaning Practices.  First Technical
     Report  for U.S. EPA, New Jersey, April 1977.

19.  Yarnell,  David Q.  Rainfall Intensity-Frequency Data.  U.S. Department
     of Agriculture, Washington, D.C., August 1935.
                                     79

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            INTERIM PROGRESS REPORT ON CHARACTERIZATION OF SOLIDS
          BEHAVIOR IN, AND VARIABILITY TESTING OF SELECTED CONTROL
                    TECHNIQUES FOR COMBINED SEWER SYSTEMS

                                     by

                           DR. WILLIAM C. PISANO*

FOREWORD

     The objectives, scope of work and progress to date of the R&D sewer
flushing study currently being conducted in Boston, Massachusetts will be
presented.  The field experiments were performed on sewer segments in
Dorchester which is a community of Boston.  The project was initiated in
the summer of 1976 under a U.S. Environmental Protection Agency research
grant (EPA No. R-804578).   The grantee for this project is Northeastern
University, Boston.**  Energy and Environmental Analysis, Inc., Cambridge,
Massachusetts is the prime subcontractor for this work.

BACKGROUND

     Solids deposition in sewer lines has always been a plague to effective
maintenance.  Recently the significance of such loads as a major contribution
to first-flush pollution has been recognized.  Studies in Buffalo, NY have
shown that 20 to 30 percent of the annual domestic wastewater solids settle
in the combined sewer system and eventually are discharged during storms. As
a result, a large residual sanitary pollution load over and above that
normally carried is discharged over a relatively short interval of time, often
resulting in what is known as a "first-flush" phenomenon.  This can produce
shock loadings detrimental to receiving water life.

     Another manifestation of first-flush, in addition to the scouring of
materials already deposited in the lines, is the first flush of loose solid
particles on the urban ground surface that are transported into the sewerage
system.   These particulates may settle out in the system and be available for
flushing during periods of large flows.  The main purpose of the project is
to evaluate the effectiveness of various sewer flushing techniques in con-
trolling the first-flush pollution problem.

     In 1966 a research effort was made by the FMC Corporation through the
 Technical Director, Energy and Environmental Analysis, Inc., 257 Vassar
Street, Cambridge, Massachusetts 02139
**Drs. Frederic Blanc and James O'Shaughnessy are the project managers for
Northeastern University.

                                     80

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U.S.  EPA, Storm and Combined Sewer Research Program to demonstrate the feasi-
bility of reducing pollution from combined sewer overflows by means of periodic
flushing during dry weather.  The first phase included a study of the
overall flushing concept, small-scale hydraulic modeling, and design and
development of cost estimates for constructing test equipment.  The second
phase was a large-scale flushing evaluation of a 1600 foot, 12 in. and 18 in.
diameter above-the-ground test facility at FMC.  This phase allowed for
adjustments to slope with holding tanks at three points along the test sewers
for the flushing experiments.  The Phase II 1972 final report* recommended that
further studies be made for flushing larger sizes of pipe, of wave sequencing
and of solids build-up over long time periods.  The current R&D project
addresses these recommendations as well as evaluation of various types of
flushing devices and methods not looked at during the predecessor FMC work.

CONCEPTUAL VIEWS OF PROGRAM

     The solids control demonstration/research program has been developed to
address many of the issues relating to the feasibility, cost-effectiveness,
and ease of application of upstream solids control program as an integral
part of overall combined sewer management.  Basically, there are five funda-
mental issues that must be answered before widespread acceptance of upstream
solids control may be considered.  The issues include:  1) what are the best
flushing methods to use for a given situation; 2) what is the expected
pollutant removal efficiency associated with the various methods; 3)  what are
the costs associated with such programs; 4) how do you screen large systems
to identify problem pipes with respect to deposition and; 5) what are the
effects of stormwater runoff on such a strategy as applied to combined sewer
systems.
PROGRAM OBJECTIVES

     1.  Test the feasibility of applying various solids control techniques
         as a method of deposition control in combined and sanitary sewer
         lines on test segments in the Boston sewerage system.

     2.  Carefully monitor deposition rates on a number of test segments.

     3.  Monitor pollutant removal including solids, organics, nutrients and
         heavy metals associated with the various flushing techniques.

     4.  Assess solids characteristics (particle distribution) of both the
         flushed and remaining materials as well as analyze (grit/organic)
         ratios.
     5.  Recommend most favorable solids control techniques for operational
         testing by both automated and manual means.

     6.  Develop, test and evaluate an automated flushing module in a field
         operational testing program.
     7.  Develop, test and evaluate manual sewer flushing techniques utilizing
         specially equipped water tankers in a field operational testing program.
*FMC Corporation "A Flushing System for Combined Sewer Cleansing" EPA Water
Pollution Control Research Series Report No. 11020 DNO 03/72, NTIS No. PB
210 858,  March 1972.
                                      81

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     8.   Assess the operational feasibility and performance of flushing both
         long and short upstream collection segments and/or networks.

     9.   Refine existing deposition model and flushing criteria.

    10.   Compare solids control by flushing versus selected structural options
         as combined sewer overflow pollution abatement technique using "desk-
         top" procedures.
    11.   Develop user guidelines for solids control program as an integral
         part of combined  sewer management schemes.

SCOPE

     This project is functionally divided into four major phases.  The first
three phases are intensive field engineering investigations while the fourth
phase is relegated to data reduction and desk-top analytical efforts.  All of
the first three phases of  work are completed.

     In the first phase of field work four test segments on different streets
in the Dorchester sewerage system were field flushed over an extended
period using different flushing methods.  External sources of fresh water,
as well as sewage were used.   The experiments were aimed at quantifying the
effectiveness of flushing  deposition accumulations from a single pipe segment
on a routine basis as well as roughly estimating deposition characteristics
within collection system laterials.

     The second phase of field work was concerned with the problem of flushing
a long flat stretch of combined sewer laterial.  The street contains five
manholes and is roughly 1000 feet in length.  Flushes were injected into the
upper most manhole and pollutant levels in the flush wave passing three down-
stream manholes were monitored.  Work was divided into two subphases.
Initially, pollutant removals over the three segments were determined for
different flushing conditions established in the first manhole.  These
results provided insights  into flushing effectiveness over three segments of
pipe.  Next, settleability tests were performed on samples taken from flushes
conducted in a similar manner for the purpose of crudely extrapolating how
far beyond the flushing monitoring manholes would the materials be carried.

     In the final phase of field operation, an automatic sewer flushing
module was designed, fabricated, installed and operated on a single segment
for an extended period.  The purpose of this work was to begin to develop
operational experience using automated flushing equipment.  The state of
the art with respect to operational automated flushing methods, equipment,
sensing interfaces, etc. has not been fully demonstrated at this point in time.
The  effort in this study is viewed as a pilot prototype investigation.

     In the fourth phase,  various predictive deposition loading and flushing
criteria will be generated from the large data base developed during the
field programs.  These refined formalisms will allow for scanning of large-
scale sewer systems to identify problem pipes with respect to deposition.
The refined tools will allow for comparative analysis of upstream solids
control vs selected structural options to compare program efficiencies.  The

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tools will also allow for rough assessment of first flush phenomena as relat-
ed to combined sewer systems to better clarify the questions over the use of
upstreams "first-flush" collection devices.

PHASE I  OVERVIEW-SINGLE SEGMENT FLUSHING

     After a careful review and inspection program, four streets were
selected for the flushing experiments.  These streets are all in Dorchester
characterized by high density, 3-story multi-family dwellings.  Two of the
test segments located on Port Norfolk and Walnut Street are served by flat
combined sewer laterals of 12" and 15" circular pipe, respectively.  Total
tributary population down to and including the test segments are 94 and 71
people, respectively.  The other two test segments on Shepton and Templeton
Streets, are serviced by separate sewer laterals of 12" and 15" circular pipe,
respectively.  There are downspouts on both streets connected to the sanitary
sewer.  Although these two segments are separated, considerable stormwater
inflow occurs during storm events.  Total tributary population for these two
streets are 230 and 221, respectively.  The characteristics of the four
segments are given in Table 7-1.

     The flushing program in this phase is concerned only with the effects of
flushing a single manhole to manhole segment.  Three different methods of
manual flushing were performed.  The first method consisted of backing up
the upper end of the flushing manhole with an inflatible rubber stopper with
quick release.  The other two methods were gravity and pressurized dump
discharge into the flush manhole with the upper end of the flush manhole
blocked off.  These flushes were performed using a specially designed water
tanker equipped with two 1000 gallon tanks mounted on a steel I-beam skid.
The tanker was equipped with a pneumatic system to pressurize the tanks to
30 psi.  The operation under gravity conditions provided a controlled flush
release of 35 to 50 cubic feet at a rate of 0.25 to 0.50 cfs.  Under pressur-
ized conditions the same volumetric range of flush was accomplished at a
rate of 0.5 to 1.25 cfs.  All flush volumes were measured by a water meter
supplied by the City of Boston.  The meter was repeatedly calibrated to
ensure accurate monitoring of the delivered flush volumes.

     A total of 87 separate flushing experiments were performed during the
period of August 30, 1976 through November 12, 1976.  Roughly 20 flushes on
a 3-4 day basis were accomplished for each of the four test segments.  The
method of flushing was rotated per street so that all methods were applied
over the test period.  The segments were mechanically cleaned by the City of
Boston at the onset of work and then three weeks later by a professional
sewer cleaning contractor using high pressure water jets.  A thin (l"-2")
layer of sand and gravel remained after the cleaning operations.

     The sequence of pertinent operations during a given flushing experiment
is the following.  After the safety equipment was set-up and the segment lamped,
several liquid background samples at five minute intervals and depth of flow
were taken.  Next, the,upper end of the flush manhole was blocked-off (in most
cases) and sediment samples over a prescribed unit length was taken in both the
flush and downstream sampling manholes.  The flushing experiment was then con-
ducted either with backed-up sewage or from injection from the flush-truck.


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Characteristics
Pipe Shape & Size
Service Type

Length of Flush
Segment
Plan Pipe Slope
Contributing
Population
General Sediment
Appearance
Dry Weather Flow
Appearance
Street Surface
Appearance
Port Norfolk
12" circ.
Combined

247'
.0049
94
Fresh sanitary
depositions &
fine sand
Slight Meander-
ing movement
Good surface w/
considerable
surface trash
Shepton
12" circ.
Separated w/
clear water
inflow
226'
.0035
230
Fresh sanitary
depositions
Slight- good
movement
Good surface,
clean
Templeton
15" circ.
Separated w/
clear water
inflow
187'
.0032
221
Septic sani-
tary deposits
Sluggish
Poor surface
dirty
Walnut
15"circ.
Combined

136'
.0048
71
Septic
sanitary
deposits &
sandy^ravel
Sluggish
Good sur-
face, clean
        TABLE 7-1.  DESCRIPTION OF FLUSHING SEGMENT CHARACTERISTICS

Dye was injected in the wave and at the instant of arrival, one-liter
aliquots were taken with a specially designed hand scoup for obtaining a
reasonable cross-sectional sample of the solids within the flush wave at the
downstream sampling manhole.  The device specifically excluded bed load materials.
Eight grab samples of the flush wave were taken at 10-second intervals.  Once the
flush wave was noted at the downstream, then an additional 10 samples were taken
at 20-second intervals.  Wave heights were taken at each interval of time which
were later used to determine the instantaneous flow rate for computing mass
pollutants removed by the flushing experiment.
     Development of the stage/discharge relationships for computing discharge
rates during the flushing periods was an extremely difficult task.  The
flush waves are characterized by non-steady state flow conditions with
rapid velocities at and during peak discharge conditions and low velocities
thereafter.  Initially, Manning's equation with plan and profile map pipe
slope was used to estimate flow rates.  Comparison of computed flush volumes
with truck delivery meters volumes was extremely poor, characterized by
large unaccountable biases and nearly zero correlation between truck and
computed volumes.  Next, the flush truck with hydrant input was repetively
used to deliver sustained known rates of flow at several flow depths.
Steady state discharge rating curves were established by a least squares
curve fitting of Manning's equation to the field data.  The computed versus
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the measured metered volumes on the average showed little bias for Walnut and
Shepton Streets while nearly a 45 percent error for the other two streets.  The
computed versus measured volume correlation coefficients for Walnut and Shepton
were less than 0.3 and the variances were extremely large.  Mathematical pro-
gramming optimization techniques were finally used to determine the parameters
of a non-steady state (histeresis) model while minimizing the variance between
computed and measured flush volumes per street.  Four different approaches were
investigated before the final procedure was adopted.  Special dye injection
field experiments using fluorometric methods were conducted to determine in-situ
non-steady flow conditions for the purpose of establishing and verifying the
flow model parameters.  The flow model reproduced with remarkable consistancy
flush wave volumes and flow rates conducted during the dye injection experi-
ments.  The optimized flow model(s) were then used to compute flow conditions
for the 87 flushing experiments.  The correlation coefficients between computed
and measured volumes ranged from 0.75 to 0.99.  Bias is nearly zero for all
cases.  This procedure was also used to establish the stage/discharge relation-
ships for experiments conducted in the next two phases of work.

     Average pollutant removal results for the Phase I flushing experiments are
given in Table 7-2.  These results are final and supercede all previously re-
ported findings.  Part A of Table 7-2 presents the average quantities in kilo-
grams of pollutants transported from each of the test segments together with the
average for all four streets.  The average deposited raw loadings do not vary
considerably from street to street for a given pollutant.  Average deposited
pollutant loadings normalized for the number of antecedent days between flush-
ing events, in kilograms/day, are given in Part B of Table 7-2.  The results
again are remarkedly consistent from street to street.  Coefficients of vari-
ations for all pollutants and for all streets range from 0.5 up to 1.0, in-
dicating farily consistent deposition accumulation and flushing removal charac-
tistics.  Part C of Table 7-2 presents average pollutant removal characteristics
normalized by both the number of antecedent days between flushing periods and
the contributing population down to the monitoring manhole, in grams/capita/day.
Population estimates are accurate since they reflect last year's census tally.
The average normalized pollutant removals for the two combined sewered seg-
ments exceeded by at least a factor of two the normalized removals from the
two separate sewered segments.  Heavy metal results are completed but are not
presented in this tabulation.  Analysis of heavy metals (Ca, Cr, Cu, Pb, Ni, Zu
and Hg) of flow-composited flush wave samples, settled for one hour, indicated
that nearly all metals were contained in the settled fractions.  The average
measured mass of metals from the two combined sewer flush experiments were
twice the level that they were in the two separate sewer flush experiments.
The concentrations of heavy metals, during wet weather were approximately four
to five times the level noted for flushes characterized by the dry weather con-
ditions.  Flushes made at these locations considerably reduced the amount of
heavy metals available for transport during rainfall events.

     The Phase I experiments showed that all flush methods provided about the
same degree of removal.   The best method, as expected, is an external source
high volume/high rate flush.  Average flush volume during this experimentation
period was 350 gallons.amounting to two to five percent of the dry-weather flow.
The periodic flushing removed the domestic sewage deposits that accumulated
between flushing events  and maintained levels of grit, rock, and debris that
remained after the professional sewer cleaning operation.

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A. Raw Data
(kg)
Port Norfolk
Walnut
Shepton
Templeton
Average
B. Normalized for
Antecedent Days
(kg /day)
Port Norfolk
Walnut
Shepton
Templeton
Average
C. Normalized for
Antecedent Days
& Tributary
Population
(grams/capita/day)
Port Norfolk
Walnut
Shepton
Templeton
Average
TSS


5.46
4.24
4.56
5.56
4.96



1.590
1.234
1.270
1.550
1.411





16.91
17.38
5.52
7.01
11.705
VSS


3.18
2.94
3.92
4.15
3.55



.887
.838
1.110
1.150
.996





9.44
11.80
4.83
5.20
7.818
*
Results of 2-3 flushing experiments;
POLLUTANT
BOD COD


1.37
2.75
1.64
2.56
2.08



.396
.709
.480
.753
.585





4.21
9.99
2.09
3.41
4.925


3.93
5.81
6.50*
3.35*
4.90



1.28
1.63
1.79*
1.17*
1.468





13.62
22.96
7.78*
5.29*
12.413
otherwise 18-21
TKN


.142
.214
.143*
.094*
.0148



.041
.057
.041*
.031*
.0425





.436
.800
.178*
.140*
.389
NH3


.020
.083
.037*
.010*
.0375



.006
.024
.011*
.003*
.044





.063
.338
.048*
.014*
.116
P


.026
.052
.039*
.019*
.034



.007
.014
.011*
.006*
.0095





.075
.197
.048*
.027*
.0087
experiments.
           TABLE 7-2.  AVERAGE POLLUTANT.REMOVAL.CHARACTERISTICS:
                          PHASE I  FLUSH  EXPERIMENTS '

PHASE II  OVERVIEW

     A:   Serial Flushing - Pollutant Removals

     The purpose of these experiments were to ascertain the pollutant removal
effectiveness over three consecutive combined sewered segments on Port
Norfolk Street (875') by flushing  the uppermost manhole using similar flush
volumes/rates used in Phase I.   The other two test segments on Port Norfolk
Street have similar physical characteristics as the Phase I segment which
were reported in Table 1.

     The experimentation period began in December 1976 and extended through
March, 1976, entailing two replicate sets of three flushing rate/volume
                                     86

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experiments.  Each  experiment  consisted  of  three  flushes  conducted  within  a
short period of  each  other.  The  first two  flushes  on  a given  day were  the
same while the final  flush was a  maximal volume/rate flush  meant to remove
any remaining pollutant  load in the  segments.   Different  combinations of
flush volume (35  to 75 cubic feet) and delivery rate  (.3  cfs to 1 cfs)  were
considered.  The  backup  and release  method  of  flushing was  not considered  in
this phase since  there was no  appreciable contributary population at the
flushing manhole.   Three crews sampled the  flush  wave  passing  the downstream
manholes.  Samples  were  taken  at  the same frequency as in Phase I.  Appreci-
able flush waves  were also visually  noted at the  end of the street  roughly
1000 feet downstream.

     Results of  these experiments are summarized  in Table 7-3, showing  the
average percentage  per flush of the  total load removed for  each of  the  three
segments downstream of the flush  injection  manhole.  These  averages were
computed using the  loads computed per manhole  for the  six sets of flushing
experiments.  The results indicate that  most of the loads for  all three
segments were removed during the  first flush.   For example, 81 percent  of  the
volatile suspended  solids load removed from the first  flush.   The second and
third flush removed an additional 19 percent of the total.  No appreciable
gain is achieved  by repeated flushing.   Furthermore the experiments indicate
that a single flush at the upper  end of  the street was reasonably effective
in removing most  of the  deposited load along the  875 feet stretch of 12"
combined sewer lateral.

First Sampling Manhole
Flush 1
Flush 2
Flush 3
Second Sampling Manhole
Flush 1
Flush 2
Flush 3
Third Sampling Manhole
Flush 1
Flush 2
Flush 3
TSS

76
12
12

72
14
13

67
18
15
VSS

81
10
9

79
12
9

72
14
13
COD

70
20
10

73
15
12

63
31
6
BOD

88
6
6

63
31
6

-
-
—
      TABLE 7-3.  AVERAGE PERCENTAGES OF POLLUTANT LOADS REMOVED PER
                        FLUSH FOR EACH PIPE SEGMENT*

Visual inspections in the second and third downstream manholes after the
flushing experiments indicated that little organic sediments remained after
the flushing experiments on any flushing day.   The amount of  fixed  sand and
gravel depositions remained at constant levels  during  this  phase of work.
 Average percentages computed from six sets of flushing experiments.
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     B.  Serial Flushing - Settleability Analyses
     The purpose of these experiments on Port Norfolk Street was to roughly
assess the transport of flushed pollutants beyond the test segments (875 feet)
using information derived from settleability tests.  Special equipment was
fabricated to obtain "undisturbed" samples of flush waves at the three down-
stream manholes on Port Norfolk Street.  Samples were taken from each manhole
and then flow-composited for settleability experiments.  External source flush
volumes were injected into the uppermost manhole on Port Norfolk Street using
similar volumes/rates as used in Phase II-A.  Special settling column equip-
ment and procedures were established in order to perform settleability tests
of the flush waves.  A special yoke-frame installation was devised to permit
axial and transverse mixing of the column before settleability experimentation.
This ad hoc procedure was necessary since the settling velocities for a con-
siderable portion of the composited flush wave solids were extremely rapid.
Gentle mixing using air agitation was initially performed but resulted in
solids bulking since the solids content of the flush samples were typically
on the order of 6000 to 9000 mg/£.
     Six different flushing experiments were performed in the period of April,
1977 through August, 1977 in which settleability tests were performed for
samples taken at each of the three sampling manholes.  Solids, organics,
nutrients and heavy metals (copper, zinc, nickel, cadmium, lead and chromium)
were analyzed for 18 samples per settleability test.  Initially, samples were
withdrawn at 10 minute intervals for the first half hour and 30 minutes there-
after.  This schedule was then changed to 5 minute intervals since over half
of the solids removal occurred in the first half hour.
     The experiment showed definite shifts in suspended solids/settling velo-
city distribution from the first to the third downstream sampling manholes
indicating that heavier grit fractions would quickly resettle leaving the
lighter solid fractions in suspension.  About 20 to 30 percent of suspended
solids would remain in suspension after 30 minutes of settling time.  The
fractions of volatile solids relative to the suspended solids increased both
with settling time during the experiment and with a given manhole and with
each progressive downstream manhole.  Distribution of COD and BOD versus the
settling time showed the similar characteristics as the suspended solids
settling behavior.  About half of the initial BOD levels would reamin in sus-
pension after 30 minutes of settling.  Heavy metals removal characteristics
resemble the characteristics of suspended solids removal curve.
PHASE III  OVERVIEW-OPERATION OF AN AUTOMATIC FLUSHING DEVICE

     The purpose of this period of testing was to determine the feasibility
and pollutant removal effectiveness of a simple automated sewer flushing module.
An air on oil hydraulic gated device triggered by an automatic time-clock was
fabricated and installed in a manhole on Shepton Street.  This device backed-up
sewage for a prescribed time period, allowing the field crew to be on-site dur-
ing actual flushing operation to collect flush wave samples.  The manhole had to
be plastered to minimize infiltration through the manhole walls and table.  The
device was constructed to permit overflow over the gate during high flow conditions

     The device was installed and operated from August 12, 1977 till the end of
September, 1977.  The device worked reasonably well and was unaffected by fre-
quent and large rainstormes.  Samples were taken from seven flushes over this
period.  The pollutant removal rates are comparable with the Phase I results.

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PHASE IV  OVERVIEW-ANALYSIS/USER MANUALS

     An interim manual entitled, "procedures for Estimating Dry Weather
Pollutant Deposition in Sewerage Systems  (EPA-600/2-77-120) has been prepared
to provide 208/201 planners with procedures for roughly gaging the magnitude
of dry weather deposition in combined sewer systems.  This document provides
a simplified methodology for providing first-cut assessments of the total
amounts of solids (Ib/day) that deposit in a sewerage collection system; and
the extent of the collection system over which the deposition takes place.
A complex distribution-parameter dry weather sewage deposition model was
roughly calibrated using field data and then was applied to 75 separate and
combined sewer collection systems in eastern Massachusetts to generate
estimates of solids deposited daily per system (Ib/day). These estimated loads
were then regressed with selected variables representing the physical
characteristics of these collection systems including total pipe length,
service area, average collection system pipe slope, average pipe diameter
and other more complicated variables representing various points on the
lower end of the collection system pipe slope cumulative density function.

     Four alternative predictive single term power functions were developed
from the regression analysis.  The degree of fit of the non-linear functions
to the data set were remarkably high.  The R^ values of the alternative models
ranged from 0.85 for the simpliest approach requiring little external data
analysis and preparation, up to 0.95 for the most complex model requiring
substantial external engineering and data reduction analyses.

     The simpliest of the alternative models is given in Table 7-4 along with
two other empirical relationships derived from the analysis of the data for
the 75 collection systems.

     The Phase I field results were used  to regress other pollutants such as
BOD, COD, TKN, Total Phosphorous, NH3 and VSS with suspended solids, all
with high values of R2, extending therefore the use of the predictive equat-
ions for total solids deposited to the estimation of other pollutants.

     Extensive statistical analyses of sewerage system pipe slopes in this
effort revealed that collection system pipe slopes can be represented by an
exponential probability model.  Analysis  of the distribution of loads
deposited versus cumulative pipe length led to the development of generalized
curves as a function of collection system mean slope for estimating the
total fraction of collection system pipe  footage over which a given percent-
age of the total loads deposit.  These findings can be combined to locate
segments associated with the required fractions.

ACKNOWLEDGMENTS

     This project was funded in part by an R&D grant from the U.S. Environ-
mental Protection Agency and was directed by the Storm and Combined Sewer
Section, Municipal Environmental Research Laboratory.  Mr. Richard Field was
project officer for the USEPA and Mr. Richard P. Traver, Staff Engineer, of
the Storm and Combined Sewer Section provided valuable technical insights
over the course of the project.

                                     89

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     The Division of Water Pollution Control of the State of Massachusetts
contributed supporting funds to this project.  The Public Works Department
of the City of Boston provided metering equipment for the flush tanker and
mechancial pre-cleaning of the test segments.  Mr. Gerald Aronson and
Celso Queiroz of Energy & Environmental Analysis, Inc. supervised the field
experiments and the data processing/computational efforts, respectively.
     A.  Daily Deposition Loads

         TS = 0.076 L1-063 (I)'0-4375 q--51 (R2 = 0.845)
         where  TS = Collection system solids deposition loadings
                     (Ibs/day),
                 L = total length of collection system piping (feet),
                 S = average collection system pipe slope (ft/ft)
                     and,
                 q = daily per capita waste discharge rate (gpcd).
     B.  Pipe Length and Service Area

         1)  L - 168.95 A0'928   (R2 = 0>m)
         2)  L = 239.41 A°'928
         where  L = Collection system pipe length (feet) and
                A = service area (acres).
         and    1 = Low population density (10-20 people/acre)
                2 = moderate/high population density
                    (30-60 people/acre).
     C.  Average Pipe Slope and Ground Slope

         S = 0.348  (Sn)°'818 (R2 = 0.96)
                     (j
         where  S = Average collection system pipe slope and
               S  = average ground slope.
     TABLE  7-4.  PARTIAL SUMMARY OF PREDICTIVE PROCEDURES FOR ESTIMATING
       DAILY DRY WEATHER SEWAGE COLLECTION SYSTEM DEPOSITION LOADINGS
                                     90

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     THE POTENTIAL OF STREET CLEANING IN REDUCING NONPOINT POLLUTION

                              Robert Pitt*
ABSTRACT

     This paper briefly describes the conclusions available at this time
from a study of nonpoint pollution abatement through improved street
cleaning practices.  An improtant aspect of the study was development
of sampling procedures to test street cleaning equipment performance
in real-world conditions.
     The  paper summarizes accumulation rate characteristics of street
dirt in the area studied.  The results of performance tests for street
cleaning equipment and the factors that are thought to affect this perfor-
mance are also summarized.  These data are used to draw preliminary con-
clusions about elements that must be considered in designing an effec-
tive street cleaning program.

     The study of urban runoff yielded information on overall flow charac-
teristics, concentrations and total mass yields of monitored pollutants
in the runoff, and street dirt removal capabilities and effects on deposi-
tion in the sewer system for various kinds of storms.  These data are
summarized, and urban runoff water quality is compared with recommended
water quality criteria and the quality of sanitary wastewater effluent.

     Costs and labor effectiveness of street cleaning, runoff treatment,
and combined runoff and wastewater treatment are also compared, and pre-
liminary results from a study of airborne dust losses from street surfaces
are summarized.

     This paper is based on research conducted under a U.S. Environmental
Protection Agency grant to the city of San Jose (EPA Demonstration Grant
No.  S-804432).  Woodward-Clyde Consultants participated in this study
under subcontract with the city.  The project began in September 1976
and will be completed in March 1978.
*Environmental Engineer, Woodward-Clyde Consultants, Three Embarcadero
 Center, San Francisco, CA  94111.
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BACKGROUND

     Past research, notably that conducted for the U.S. Environmental
Protection Agency (EPA), by the American Public Works Association
(Sullivan 1969), and by URS Research Company (Sartor and Boyd 1972;
Pitt and Amy 1973; Amy et al. 1974), has clearly revealed the water
pollution potential of street surface contaminants.  These projects pre-
sent strong evidence relating contaminated streets with the contamina-
tion of receiving waters.  A paper presented at the American Water Works
Association annual conference in Boston in 1974 (Pitt and Field 1974)
using data from these reports compared the relative importance of un-
treated nonpoint urban storm runoff with treated sanitary wastewater in
their effect on receiving water.  In this example it was found that
pollutants in stormwater runoff must be reduced in order to significantly
reduce the total pollutant load to a receiving water.  These reductions
in runoff pollutants could be accomplished by treating the runoff and/or
by reducing the quantities of pollutants contaminating the runoff.

     Although it is clear that pollutants in street dirt have a signi-
ficant effect on the quality of urban runoff and its effect on receiving
water, there are many questions yet to be answered about the nature of
this cause and effect relationship.  The report on which this paper is
based attempts to answer some of these questions.  More specific infor-
mation is needed in order to develop effective control procedures.

     The study was designed to measure street cleaning equipment effec-
tiveness in removing pollution from the street surface in a real-world
situation.  It must be emphasized that the purpose of the project was
not to compare specific types of equipment but to determine the range
of capabilities for current street cleaning equipment to gain
information about the cost and effectiveness of street cleaning programs
for removing street surface pollutants.

     The study also determined accumulation rates of street dirt in test
areas with different characteristics.  The pollution characteristics of
street dirt are known to vary as a function of particle size (Sartor and
Boyd 1972; Pitt and Amy 1973).  This study examined specific concentra-
tions of various pollutants in different particle size groups.  It also
examined the effectiveness of street cleaning equipment in removing dif-
ferent particle sizes from the street, as well as settling velocities
and specific gravities for various particle sizes.  These data demon-
strate the potential quantity of pollutants that may be affected by
street cleaning, the relationship of the pollutants to street dirt par-
ticle size, and the way various particle sizes may settle out in a water
column (in the sewer system or in a treatment process).

     Another area of concern is the transport of particulates in sewer
systems and the associated mass balance relationships.  In a combined
sewer    system, the sanitary sewage flow velocities are much less
during dry weather than during wet weather when the additional urban
storm runoff adds to the flow volumes.  During dry weather, primary  sani-
tary solids can settle out in the sewer system, to be flushed out  during


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the high flows of wet weather.  This increased concentration of solids
can greatly add to the pollution load at the beginning of a storm
(Burgess & Niple, Ltd. 1969).  Storms with low runoff volumes may
remove large quantities of road surface particulates and transport them
to the sewer    system.  These particulates may settle out in the sewer
system and be available for flushing during periods of larger flows.
Stormwater management techniques utilizing in-line storage can also
cause large quantities of solids to build up in the system (Lager and
Smith 1974; Pisano  1977).  Some data are available on the buildup and
transport of these solids in separate  sanitary sewer    systems.  This
study obtains particulate routing data for a separate  stormwater
system through tracer studies.  Comparisons of the amounts of
pollutants in the street dirt and in the runoff from monitored storms
also provided information concerning deposition characteristics in the
sewer, system and the relative quantity of pollutants in the runoff origi-
nating in land-use areas other than the street surface.

     Metcalf and Eddy (Lager and Smith 1974), in a study conducted for
the EPA, summarized the technology available for the treatment and
management of urban runoff and costs and effectiveness of treatment.
Unfortunately, comparable data for street cleaning programs have not
been available.  Some information on typical street sweeper performance
is available from the earlier EPA-sponsored studies, but these limited
data are based on idealized strip test conditions.  This study obtained
street cleaning performance data from tests in real-world conditions.
These data were then used to make cost and labor effectiveness comparisons
with some alternative control measures.

     The study also examined the effect of street surface particulates
on air pollution.  Estimates of air pollutant emissions for EPA air
quality regions, statewide areas, and specific air basins are very im-
portant for continuing air quality control planning.  Most utility,
industrial, and residential activities (including unpaved roads) have
received attention as particulate air pollutant sources.  Research by
Roberts (1973) and Cowherd et al. (1977) indicates that paved roads
should also be considered as important particulate air pollutant
sources.  Dust from the atmosphere, soil from erosion, and vehicular
deposits on paved street surfaces can be disturbed by wind and traffic,
causing particulate emissions.  Street cleaning may be an effective
means of removing these particulates before they can be blown into
the air.

     Very little quantitative information about particulate emissions
from paved street surfaces is available, although some work has been
done on related subjects.  As part of an overall program to determine
the behavior of radioactive fallout, the Atomic Energy Commission (now
the Nuclear Regulatory Commission) has funded continuing studies of
particulate residence times in the atmosphere, airborne particulate
deposition rates, and resuspension of settled particulates.  The particle
resuspension studies have included research into resuspension from as-
phalt streets caused by traffic.  Their results and theories are useful,
but these studies consider only particles that have settled onto the


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street surface from the atmosphere.   This study examines losses from the
total particulate loading on the street surface, including that washed
into the street through erosion and  that tracked or deposited onto the
street by vehicles.

     It is expected that the study will have a two-fold benefit.  First,
the data obtained will fill in significant gaps in current knowledge
about the role street dirt plays in  causing water and air pollution and
its control.  Second, the carefully  developed experimental design and
sampling procedures for various portions of the study can be used by
others wishing to obtain specific information about street dirt charac-
teristics, its effects on air and water quality in their own cities, and
its control.

     The information presented here  summarizes the data that have been
collected and analyzed thus far.  This information is subject to change
as further data are gathered and analyzed.  The effect this information
may have on a specific city's street cleaning program is expected to
vary widely, depending on conditions in that city.  For this reason, the
study does not yield a set of specific, how-to instructions.  Rather, it
indicates the type of information that must be considered in designing
effective control measures.

DESCRIPTION OF THE STUDY AREAS

     Eight potential study areas were considered within the city of San
Jose.  Three were selected as being  representative of the variety of con-
ditions found in San Jose and many other cities:  the Tropicana study
area, the Keyes Street study area, and a downtown study area.

     The downtown and Keyes Street study areas lent themselves to divi-
sion into two test areas, while the  Tropicana study area was best treated
as a single test area.  Thus a total of five test areas were used in the
initial field activities:

          Tropicana test area
          Keyes Street - asphalt street surface test area
          Keyes Street - oil and screens street surface test area
          downtown - good street surface test area
          downtown - poor street surface test area

     Figure 8-1 shows the San Francisco Bay ARea and the general location
of the city of San Jose.  Figure 8-2 shows the three study areas selected
and their location within the city of San Jose.

     These areas were selected because they represent the variety of con-
ditions found in San Jose and in many other cities.  The combined down-
town study area covers about 100 acres and has 7.0 curb-miles and 25 storm
drain inlets.  Its major land uses are commercial and industrial, with
some older single- and multiple-family residential areas, much roadside
vegetation, and many vacant lots (previously cleared for redevelopment).
                                    94

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                                                          0    5    10    15
                                                                    I	I
                                                                miles
Figure 8-1.  San Francisco Bay Area showing the general
              location of the City of San Jose.
                             95

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Figure  8-2.  Map showing the location of the three study areas.
                               96

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There was some construction in the area, and several streets have heavy
traffic.  The stormwater from this area is discharged directly into the
Guadalupe River.  The downtown commercial part of the study area is
normally swept daily, and the remainder of the area is normally swept
once every five weeks.

     The combined Keyes Street study area covers about 92 acres and has
5.4 curb miles and 17 storm drain inlets.  Its major land use is residen-
tial, composed of older (early 1900) single-family homes, with some strip
commercial use.  The study area is adjacent to several schools and playing
fields.  This area has few vacant lots, many roadside trees, and no con-
struction.  Several streets have heavy traffic, but most have light
traffic.  The stormwater is discharged directly into Coyote Creek.  The
area is normally swept every five weeks.

     The Tropicana study area covers about 195 acres, and has 12.7 curb-
miles and 55 storm drain inlets.  The most common land use is residential,
composed of low-income, single-family homes built around 1960.  The area
includes a portion of a large shopping center and is adjacent to three
schools.  There are few vacant lots, some roadside trees, and no construc-
tion activities in the area.  Again, some streets have heavy traffic, but
most carry light traffic.  The stormwater is eventually discharged into
Silver Creek (a tributary to Coyote Creek).  This area is also normally
swept every five weeks.

SAMPLING TECHNIQUES

     One important aspect of the study is the development of sampling
techniques that can be used to monitor accurately the changes in street
surface loading for different test areas over a long period.  These
sampling procedures can be easily used by a public works department to
determine the specific loading conditions and street cleaning performance
for its city.  The equipment can be rented if it is not available within
the department.  With these procedures, street surface loading conditions
over a large area can be sampled in a relatively short time.  The experi-
mental design procedures can be used to determine the number of subsamples
required for specific project objectives and study area conditions.
STREET CLEANING EQUIPMENT TESTS

Accumulation Rates

     The accumulation rate characteristics of street surface contaminants
must be known in order to understand the magnitude of the problem a street
cleaning program must address and to determine the most effective control
methods.  This study showed that the accumulation rates varied widely in
the different test areas.  These variations are thought to be due to street
surface conditions and to land-use patterns and activities within the test
area such as the presence of vacant lots, commercial development, pedes-
trian and automobile traffic, and parking.  Such variations should be con-
sidered in scheduling street cleaning programs for different types of areas,

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     Samples of each particle size category for each test area and equip-
ment type were analyzed for various pollutants.  Calculations were made
to average the slopes (the change of street surface particulate loadings
as a function of time) of each particle size to determine accumulation
rates of each pollutant for each test area and equipment test phase.
These calculated pollutant accumulation rates are shown in Table 8-1.
This table presents the accumulation rates expressed as pounds of pollu-
tant per curb-mile per day for each of the five test areas.  The Tropi-
cana, Keyes - asphalt, and downtown - good street surface test area
values are divided into several accumulation time periods.  The Keyes -
oil and screens and the downtown - good street surface test area accumu-
lation rates are only shown for a combined time period.  Initially, accu-
mulation rates were, calculated for different effective accumulation
periods for all study areas.*

     Statistical tests were then conducted to determine which of these
accumulation period values were important when compared with the value
obtained from combining all the data together for each study area.  It
was expected that the accumulation rate measured over a short period of
effective accumulation (near to the street cleaning date) would be
greater than an accumulation rate measured over a longer period of effec-
tive accumulation.  However, there was significant scatter in the data,
and only a few subcategories of accumulation periods were found to be
important.  In most cases, the accumulation rates derived from the
shorter accumulation periods are smaller.  That would be portrayed with
a sawtooth pattern of accumulation in which loading values level off with
time.  This effect is thought to be caused by wind and automobile-related
air turbulence suspending the particles in the air.  This pattern should
be considered in establishing optimum street cleaning frequencies.
However, it should be remembered that although longer periods between
street cleaning may not result in significantly increased loadings, they
could cause increased roadside airborne particulate concentrations.  More
important, significant differences in accumulation rates were found
between the different test areas.  These analyses will be repeated when
all the data are available.

     It is interesting to note that the overall pollutant accumulation
rates in the oil and screens test area were smaller than for any of the
other test areas, and yet the oil and screens test area always had the
greatest street surface loadings observed.  Because of the increased
surface roughness and generally larger particle sizes in the oil and
screens test area, a large quantity of loose material could stay on the
street surface and not be removed significantly by rainfall.  The
smoother asphalt streets in the Tropicana and downtown - good test areas
had accumulation rates that were about equal and reflect a large increase
in street surface loadings with time (large accumulation rates).  The
smoother streets also allowed a more uniformly effective removal of
street surface contaminants by the street cleaning equipment.  The down-
town - poor street surface test area had the largest accumulation rates

*These periods were 0 to 2.0, 2.1 to 4.0, 4.1 to 9.0, 9.1 to 15.0, and
 greater than 15.0 days and for all time periods combined.


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TABLE 8-1.  ACCUMULATION RATES (Ibs/curb-mile day)
Study Area
Tropicana
Keyes - oil
and screens
Keyes - asphalt
Downtown — good
street surface
Downtown - poor
street surface
Study Area
Tropicana
Keyes - oil
and screens
Keyes - asphalt
Downtown - good
street surface
Downtown - poor
street surface
Effective
Accumulation
Period (days)
0*2.0
4.1*9.0
All combined
All combined
2.1*4.0
4.1*9.0
All combined
All combined
0*2.0
All combined
Effective
Accumulation
Period (days)
0*2.0
4.1*9.0
All combined
All combined
2.1*4.0
4.1*9.0
All combined
All combined
0*2.0
All combined
Total
Solids
86.3
6.0
54.1
11.3
58.6
18.6
73.9
58.6
729
312
Lead
0.37
0.017
0.22
0.068
0.29
0.075
0.29
0.36
1.4
0.60
COD
8.1
0.34
4.92
2.92
7.8
2.6
9.54
8.19
80.1
34.9
Zinc
0.040
0.0024
0.024
0.010
0.037
0.010
0.040
0.052
0.36
0.15
Kjeldahl
Nitrogen
0.17
0.0078
0.10
0.046
0.099
0.042
0.12
0.11
1.7
0.72
Chromium
0.043
0.0047
0.026
<0.001
0.029
0.0098
0.038
0.033
0.33
0.14
Ortho-
phosphates
0.015
0.00075
0.0093
0.0030
0.011
0.0032
0.012
0.0087
0.11
0.046






Copper Cadmium
0.092 0
0.010 0
0.057 0
<0.001 0
0.048 0
0.017 0
0.066 0
0.064 0
0.66 0
0.29 0
.00021
.000011
.00013
.00004
.00017
.00005
.00018
.0018
.0019
.00082
                           99

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of any of the test areas.  These largest rates are thought to be caused
by the poor condition of the streets and the character of the area, which
cause a larger erosion of the street surface and accumulation of material
from outside the street environment.  Street cleaning performance is
closely related to the accumulation rates and the initial contaminant
loading values on the streets before street cleaning.

Concentrations of Street Surface Contaminants as a Function of Particle
Size

     Previous studies (Sartor and Boyd 1972; Pitt and Amy 1973) have
demonstrated the importance of chemical analyses of different particle
sizes instead of the total sample.  The chemical character of each size
is relatively constant (within a specific test area and time frame), but
the percentage composition of the different sizes can vary significantly.
Therefore, analyses of different particle sizes yield more useful infor-
mation than total sample analyses.

     Each collected sample was divided into eight particle sizes (<45u,
45* 106u, 106* 250u, 250* 600u , 600* 850u , 850*  2000n , 2000*
637On, and >6370u).  All of the samples collected in each test area
for each equipment type were combined for chemical analyses by particle
size.  These chemical analyses were used to calculate total pollutant
loadings for all of the samples collected.   Almost all of the parameters
for all of the test areas show higher concentrations with decreasing
particle size.  Mercury, cadmium, zinc, lead, Kjeldahl nitrogen, and
total orthophosphates show highest concentrations with smaller particle
sizes.  However, copper and chromium show the lowest concentrations
with the smallest particle size.

     These data indicate that a control measure (such as conventional
street cleaning methods) that is most effective in removing large particle
sizes may be unable to remove enough of those pollutants found largely in
the less abundant, smaller particle sizes to completely meet objectives
unless extra effort is expended. Street cleaning can remove important
amounts of these pollutants because they are also found in the more abun-
dant larger particle sizes. The effectiveness of street cleaning therefore
depends on the specific service area characteristics and program objectives.

     The asbestos information obtained was  subject to wide variation
because of the small number of fibers counted in each sample aliquot.
The lengths of the fibers observed ranged from 5 to 250 microns in
length.  Generally the smallest particle sizes had the shortest observed
maximum fiber lengths.

     No specific test area or test period had significantly different pol-
lutant strengths.*  The pollutant strengths observed during the first test
phase were all within the range of strengths reported in previous investi-
gations (Sartor and Boyd 1972; Pitt and Amy 1973).  This information was

*Pollutant strengths are expressed as mg pollutant per kg total solids,
 which is equivalent to ppm on a weight basis.
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used to determine the accumulation rates and  street cleaning equipment
performance for the different pollutants, based on particle size measurements.

Street Cleaning Equipment Performance

     The design of an effective  street cleaning program requires not only
an understanding of the service  goals,* but also a determination of accumu-
lation rates and an assessment of specific street cleaning equipment perfor-
mance in the actual conditions encountered.   For this study, several street
cleaning programs using various  types of equipment and levels of effort were
evaluated.  This evaluation was  the major element of the demonstration pro-
ject.  The following types of street cleaning equipment were studied under
various operating conditions and cleaning frequencies:

        • 4-wheel mechanical sweeper
        • state-of-the-art mechanical sweeper
        • vacuum-assisted sweeper

     The purpose of this project was not to compare these specific types
of street cleaning equipment, but to determine the range and capabilities
of street cleaning equipment in  general.  These specific pieces of street
cleaning equipment were selected for study because they represent three
different generic types and were available for testing.  It must be
stressed that the performance, as measured in these tests, may not be an
accurate indication of the ability of this equipment under other oper-
ating conditions.  The scope and intent of this project was to demon-
strate the range of possible cleaning effectiveness of different types
of street cleaning equipment under a variety of real-world operating
conditions.  The available resources for the project required that the
test be conducted in one city with a limited selection of the available
equipment.

     The cleaning frequencies used in this study ranged from two passes
every day to one pass every week.  Each piece of equipment was evaluated
in the field in two different seven-week periods:  once during the winter
and once during the summer phase (with the exception of the vacuum-
assisted sweeper).  The first two weeks of each seven weeks of equipment
evaluation used daily cleaning.  One week used a single pass every week-
day, and two passes were made each weekday during the other week.  The
last five weeks of each test period used weekly cleaning intervals.  The
equipment was rotated through the different testing areas at the end of
each cleaning period.  This schedule allowed the different characteris-
tics and long-term seasonal differences in the test areas to be included
in the evaluation of the range of equipment effectiveness.  In addition
to sweeping the specific test area, an adjacent buffer zone up to three
times the size of the test area  was also swept in order to reduce poten-
tial edge effects (tracking of particulates into the test areas from the
adjacent areas).
*Service goals consider effects  on water quality, air quality, public
 safety, aesthetics, and public  relations.  For a more complete discus-
 sion of this topic, see Pitt, Ugelow, and Sartor (1976).
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     The long-term and frequent sampling in the test areas made it
possible to directly measure accumulation rates of street surface contam-
inants, which are discussed earlier in this paper.  Street surface samples
were collected within a few hours before and after street cleaning.  The
data collected in these tests were also used to identify the range of per-
formances that may be expected from currently available street cleaning
equipment.  Differences of removal values (Ibs/curb-mile removed) instead
of percentage removals (percentage of initial loading removed) for the
various test conditions were used as a more meaningful measure of equip-
ment performances.

     Table 2 presents the initial test phase street cleaning equipment
performance data.  Fourteen different test conditions are identified
representing the different test areas, equipment types, number of passes,
and the approximate cleaning intervals.  The information presented for
each of the before and after test samples includes the median particle
size, the bulk density, and the street surface loading conditions.  Under
the column heading, the residual street surface loading values (Ibs/curb-
mile) are shown; these are generally the lowest street surface loading
values that occur under each of the test conditions.  Also shown is the
amount removed, the percentage of the before loading removed, and the_
hopper content median particle size.  The values shown are the mean (x)
plus or minus the standard deviation (a).

     Street cleaning performance depends on many conditions.  These
include the character of the street surface, the street surface initial
loading characteristics (total loading value and particle size distri-
bution),  and various environmental factors.  Equipment variables that
affect street cleaning performance include the specific type of street
cleaning  equipment and its subsystems  (types and adjustments of brooms,
etc.), the number of passes that it makes, and the street cleaning inter-
val.  The most important measure of cleaning effectiveness is pounds per
curb-mile removed for a specific program condition.  This removal value,
in conjunction with the unit curb-mile costs,  allows one to calculate  the
cost for  removing a pound of pollutant for a specific street cleaning
program.  The percentage of the before loading removed has often been
used as a measure of street cleaning equipment performance.  It is very
misleading, however, because it is not a measure of the magnitude of the
amount of material removed.  A street  cleaning program may have a very
low percentage removal value,  but a high total amount removed,  if the
initial  loading  is high.  That occurred  in  the tests conducted  in the  oil
and screens area.

     Student _t statistical  tests were  conducted with the data  shown  in
Table  8-2 to  determine improtant  similarities  and differences  in street
cleaning equipment performance under  the various  test conditions.   These
tests  showed  that  initial loading values in any one  test area  varied
depending on  the street  cleaning  program (number  of  passes  and cleaning
intervals).   However,  the differences  in the initial loading values
in various  test  areas were  controlled  by differences  in  test  area
conditions  (largely  street  surface  conditions  and  accumulation rates),
irrespective  of  the  type  of  equipment  being used  and  the  number of  passes.

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    TABLE  8-2.  INITIAL  TEST PHASE -  STREET  CLEANER PERFORMANCE  (with  mean + standard  deviation)
Before Street Cleaning
Study Area
Tropicana
Keyes -
* — ' oil and screens
O
oo
Keyes -
asphalt
Downtown -
good street surface
Downtown -
poor street surface
Equipment
Type*
A
A
B
C
B
C
B
A
B
C
A
B
C
C
Number
of
Passes
1
1
1
2
1
1
2
2
1
1
2
2
1
1
Approx.
Cleaning
Interval
Daily
Weekly
Daily
Daily
Weekly
Weekly
Daily
Daily
Weekly
Weekly
Daily
Daily
Daily
Daily
Median
Particle
Size (u)
965+1160
510+63
430+130
410+95
670+35
930+350
650+250
560+19
520+67
510+120
480+36
450+175
430+62
570+27
Bulk
Density
1.30+0.14
0.98±0.05
1.12+0.11
0.82+0.13
1.37+0.06
1.15+0.13
1.3+0.14
1.35+0.06
0.87+0.06
0.78+0.15
0.98+0.05
1.00+0.17
0.99+0.06
0.98+0.18
Street
Surface
Loading
(Ibs/
curb-mile)
115+38
164±65
350+274
328+93
2370+110
2200+102
1830+378
2654+797
381+29
459+57
401+122
173+61
243+32
1350+394
After Street Cleaning
Median
Particle
Size (u)
430+57
450±78
300+46
320+29
600+32
660+21
570+24
600+36
390+28
390+25
460+69
340+45
380+54
530+66
Bulk
Density
1.20+0.08
1.15+0.06
1.10±0.17
1.06+0.09
1.310.17
1.20+0.20
1.28+0.10
1.38+0.05
0.97+0.12
0.90+0.18
1.1+0.08
1.07+0.15
1.03+0.05
0.98+0.08
Street
Surface
Loading
(Ibs/
curb-mi le)
98+45
87+38
165164
132+56
18601104
2030+293
19301403
2208+375
294+67
295±73
258+81
142±16
160+15
808+189
Cleaning Effectiveness
Amount
Removed
(Ibs/
curb-mile)
17+22
77138
185+225
196+131
510144
171+258
-98+300
4451461
87+45
165+34
144+155
32+49
83+18
543+429
Percentage
of Before
Loading
Removed
13+25
47+11
53119
60+32
2212
8+12
-6117
17±11
23+14
36+10
36+27
19+22
34+3
40+24
Hopper
Contents
Median
Particle
Size (M)
3170+1410
5750+4380
20901850
3190+1030
455011100(1)
4460+2500(2)
594012390(3)
9401380(4)
4550+1100(1)
4460+2500(2)
940±38U(O
5520±274U(3)
2660+1200(5)
2660+12110(5)
*Equipment types are designated in the following way: A = 4-wheel vacuum-assisted mechanical sweeper;   B - state-of-the-art  4-wheel mechanical sweeper;
 C = 4-wheel mechanical sweeper.

Note:  Adjacent test areas at (1), (2), (3), (4), and (5) were swept with the same equipment and  the hoppers were not cleaned out between these tests;
      the study areas overlapped.  As a result, these pairs have the same hopper content median  particle sizes.

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     When the residual loading values were statistically examined,  the
findings were similar.  Differences in test area conditions were much
more important than differences in equipment type.  Similarly,  the  amount
removed under each of the test conditions was more a function of the
test area than the street cleaning program.  In many cases, two passes
with the same piece of equipment removed a larger quantity of material
from the street than a single pass (as expected).  An exception was
found in the tests in the oil and screens test area.  Here two passes
per day of the state-of-the-art mechanical sweeper actually resulted in
a higher residual loading on the street surface than before the test.
This result is thought to be due to the extra erosion caused by excessive
mechanical action of the broom on the weaker street surface.  During a
single pass, any extra material loosened from the street surface was
removed along with the initial dust and dirt on the street.

     The selection of the specific type of street cleaning equipment is
less important than the characteristics of the area to be swept.  Also,
in most cases, the street cleaning interval and number of passes were
more important than the specific type of equipment used.  Other consid-
erations, such as maneuverability, life-cycle costs, hopper capacity,
etc. , may be more important from an equipment selection viewpoint.  There
are, however, expected to be situations not studied as part of this dem-
onstration project in which one type of street cleaning equipment would
perform significantly differently from the others.

     The median particle size of the material collected in the equipment
hopper can reflect differences in equipment performance as a function of
particle size.  A larger median particle size of the hopper material
signifies that not as many smaller particles were removed from the  street.
Similarly, a smaller median particle size of the hopper material signifies
a relatively greater removal of small particle sizes under the same condi-
tions.  In all cases, the hopper median particle sizes are much larger than
the median particle sizes on the street surface before street cleaning,
and the street surface median particle size decreases with street cleaning.
Thus, there is a larger percentage of smaller particles on the street
after street cleaning than before, with the street cleaning equipment
being most effective in removing the larger particle sizes.  The vacuum-
assisted mechanical street sweeper had a smaller median hopper sample
particle size than the other types of test equipment in the Keyes test
areas.  However, this difference observed in the hopper did not signifi-
cantly change the median particle size of material found on the street.

PARTICULATE ROUTING AND POLLUTANT MASS FLOW CHARACTERISTICS OF URBAN
RUNOFF

Flow Characteristics

     The purpose of measuring the urban runoff flows was to calculate
pollutant mass yields* from the concentration values found in the sampling
program. Information as to these mass yields is important in determining
the effect these pollutants may have on receiving waters.  The general
*As measured in Ibs/hr or total Ibs.

                                    104

-------
hydrographic information  from the  study  may  also  be  useful  in  verifying
urban runoff models,  particularly  because  the  characteristics  of  the
study area resulted  in  a  relatively  quick  response of  runoff flow to
precipitation.

     The hydrographs  of the  monitored  flows  showed a lag  of 1  to  6 hours
between the beginning of  precipitation and the  start of measurable flow.
The most common  lag was about 1  hour.  The flows  also  continued for 3  to
8 hours after precipitation,  and peak  recorded  flows lagged peak  precip-
itation by 1 to  2 hours.   In most  cases, a precipitation  total of  0.01
inch caused a measurable  flow at the outfalls.  Concentrations of  some
parameters were  also  monitored at  different  times during  the period of
flow; as could be expected,  most decreased in  concentration with  lapse
of time.

Pollutant Concentrations

     The COD concentrations  measured in  the  runoff were about  three to
ten times greater than  the BOD5  values,  and  the TOC  concentrations
were ten times the BODr concentrations.    For  a normal waste, having
low toxicity and sufficient  nutrients, the COD values  should only  be
slightly greater than the BODr values.

     Figure 8-3  presents  BOD  values as a function of incubation time.
Selected composite samples representative  of each storm were incubated
for up to 20 days and BOD values were  measured at increments of approxi-
mately 1, 3, 5,  10, and 20 days.   The  relative BOD values shown in the
time interval from 0  to 10 days  are about  what was expected.  The  5-day
BOD values are about  two-thirds  the 10-day BOD value.  The largest rate
of BOD increase  in the  first  10  days occurred usually on the first day,
with 1-day BOD values of  about 20  mg/L (for  two of the three samples).
This value remained relatively constant until about  the fifth day when it
gradually rose to the 10-day  value.  The most unusual character of the
BOD value is shown in the period of time from 10 to  20 days when the BOD
values typically increased by a  factor of  two or more.  These results
show that the initial oxygen  demand is rapid and may have possible dele-
terious effects  on certain receiving waters  close to the time of dis-
charge (within the first  day).   However, as  the material settles out, it
can exert a much larger,  long-term oxygen  demand.  Therefore, the oxygen
depletion caused by urban runoff is important both immediately after
discharge and at periods  of  time longer  than 10 days after discharge.
The period after the  first several days and  before 10 days may not pose
as great a problem.   (These  time factors are all dependent on Water
temperature and  other physical and chemical  characteristics of the
receiving water.)

     This apparent long-term  increase  in oxygen demand may be caused
by some of the inherent problems in the  standard bottle BOD test when
analyzing toxic  and/or  low nutrient samples.  Because urban runoff has
relatively high  concentrations of  heavy metals and low concentrations
of nutrients, the seed  bacteria  require  a  longer  time  for acclimati-
zation than normal.   The  initial oxygen demand could be caused by  the
relatively easily assimilated organics being consumed by  the standard
seed bacteria before  significant bacteria  die-offs occur from heavy

                                   105

-------
120
100-
 80-
 60-
 40-
 20-
•••O"" Tropicana storm of March 13, 15, 16, 1977
  •  Tropicana storm of March 23, 24, 1977
—*— Tropicana storm of April 30, May 1, 1977
                               DAYS OF INCUBATION
         Figure 8-3,  BOD values as a function of incubation .time.
                                       106

-------
metal toxicity.  The lag period of several days could be required for
the surviving seed bacteria to become acclimated and reestablished so
as to assimilate the remaining organics.  Colston  (1974) has developed
an alternative BOD procedure for urban runoff based on measurements of
COD with time in an aerated and mixed sample, using typical receiving
waters for dilution.  This alternative procedure will be used in ana-
lyzing the BOD of the street surface particulates, and the results will
be included in the final report.

     When the runoff pollutant strengths (mg pollutant/kg total solids)
are compared with the street surface contaminant pollutant strengths,
notable differences are found.  The relative concentrations in the run-
off for COD, Kjeldahl nitrogen, and orthophosphates are much greater
than the relative concentrations observed in the street dirt (about 3 to
180 times greater in the runoff).

     Some of the zinc and cadmium relative concentrations were also
greater in the runoff than in the street dirt.  However, the relative
concentrations of lead, chromium, and copper in the runoff were all much
smaller than those measured on the street.  These  differences ranged
from about 2 to 20.  If the erosion products have  lower concentrations of
heavy metals, the resultant runoff concentrations  of heavy metals would
be diluted when compared to the higher concentrations in the street
dirt.  Therefore, it may be that much of the organic and nutrient material
in urban runoff originates not from the street surface or from auto-
mobile activity, but from the surrounding areas through erosion.  Simi-
larly, most of the heavy metals in urban runoff are expected to be asso-
ciated with street surfaces and automobile activity.  A similar conclusion
was also identified by Amy et al. (1974).  In that study, the authors
analyzed existing runoff and street surface loading data in an attempt to
determine a loading model as a function of various influencing character-
istics (such as geographical area, land use, traffic conditions, etc.).
They found that when the street surface loading data were compared with
the runoff data, the only major differences in loading predictions were
for nutrients.  In that case, the nutrient values  predicted for runoff
data were greater than for street loading data, reflecting the fact that
most of the nutrients originate in off-street areas.

Pollutant Removal Capabilities of Monitored Storms

     The monitored rains had a much smaller effect on removing materials
from the oil and screens test area as compared with the asphalt test areas.
It is thought that the increased roughness of the  street surface in the oil
and screens area trapped much of the erosion material from the surrounding
areas on the street and prevented it from reaching the storm sewer    system.
The Keyes - asphalt and Tropicana test areas, both having relatively smooth
asphalt streets, showed large removals of material.  The first storm showed
a smaller absolute removal as compared to the latter two storms, possibly
because of its increased intensity and larger erosion yields from surround-
ing areas that found their way onto the street during the rain.

     The runoff removals in both the Keyes - asphalt and Tropicana study
areas for the March 23 and 24 storm and for the April 30 to May 1 storm

                                     107

-------
were very similar.  These last two relatively small storms were  capable
of removing significant quantities of material from the street surface,
yet did not; cause large amounts of erosion products in the runoff.

     Table 8-3 summarizes the pollutant street surface loading changes
for the different rain storms on a curb-mile basis and also on a total
pounds basis for the two study areas.  These runoff yields, as measured
on the street surface, are compared to the total pollutant yields of the
storms.  The observed ratios between street surface loading differences
of the pollutants as measured on the street and the runoff yield as
measured by analyzing runoff vary.  Values smaller than 1 possibly sig-
nify that more of that pollutant originated in the surrounding areas
than on the street surface.  Values greater than 1 possibly indicate
that much of the material washed off from the street surface accumulated
in the storm sewer.

     These ratios appear to vary as a function of the rainstorm  charac-
teristics, the study area, and the specific pollutants.  The March 15
and 16 storm generally had ratios less than 1 for all of the pollutants
in both study areas, while the last two storms shown in Table 3  had many
values greater than 1.  Again the initial storm was of much greater in-
tensity and volume, possibly causing greater erosion in the surrounding
areas and increased sewerage velocities that would keep the particulate
material from settling in the storm drainage.  The last two storms, how-
ever, were of relatively small intensity and showed almost complete
removal of street surface contaminants from the street surface.  That is
probably due to the extra energy imparted to the street surface  materials
from automobile traffic and the sufficient rain available to wash the
loosened materials from the street surface to the storm drain inlet.
However, the smaller flows in the sewerage were not capable of preventing
the material from depositing in the sewerage.

Comparison of Runoff Water Quality with Recommended Receiving Water
Quality Criteria

     Table 8-4 compares the overall ranges and average concentrations of
the monitored runoff to the recommended water quality criteria for various
beneficial uses.  The water quality criteria values shown for these uses
are recommended maximum limits designed to protect the beneficial uses
with a reasonable amount of safety.  If a monitored concentration exceeds
these criteria, it does not mean that a problem exists but that  a problem
may occur and additional monitoring may be necessary to define the rela-
tionships between water quality and impairment of the beneficial uses
for the specific receiving water.

     The following list summarizes those parameters that exceeded the
recommended beneficial use criteria.
                                   108

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TABLE 8-3.  STREET SURFACE POLLUTANT REMOVALS COMPARED WITH RUNOFF YIELDS


Keyes
Oil and Screens


Param-
eter

Lbs/Curb-
Mlle Dif-
ference

Lbs Differ-
ence (2.2
curb-mile)
Street Study Area
Asphalt

Lbs/Curb-
Mile Dif-
ference

Lbs Differ
ence (2.7
curb-mile)
Total
Keyes
Area
- Lbs
Differ-
ence
MARCH 15-16, 1977,
Total
solids
COD
KN
OrthoP04
Pb
Zn
Cr
Cu
Cd

120
24
0.33
0.023
0.40
0.067
-0.0084
-0.014
0.00031

260
53
0.73
0.051
0.88
0.15
-0.018
-0.031
0.001

29
3.0
5.2
0.0049
0.19
0.022
0.014
0.024
0.0001

78
8.1
14
0.013
0.51
0.059
0.038
0.065
0.0001

340
61
15
0.064
1.4
0.21
0.020
0.034
0.001
MARCH 23-24, 1977
Total
solids
COD
KN
OrthoPo^
Pb
Zn
Cr
Cu
Cd

Total
solids
COD
KN
OrthoP04
Pb
Zn
Cr
Cu
Cd

-130
8.8
0.21
0.016
0.47
0.037
-0.14
-0.32
0.0001


-540
20
-0.24
-0.018
-0.075
-0.089
-0.35
-0.62
-0.0007

-290
19
0.46
0.035
1.0
0.081
-0.31
0.70
0.0001


-1200
44
-0.53
-0.040
-0.17
-0.20
-0.77
-1.4
-0.002

430
58
0.97
0.076
2.0
0.26
0.22
0.37
0.0012


650
88
1.4
0.11
2.6
0.36
0.34
0.59
0.0017

1200
160
2.6
0.21
5.4
0.70
0.59
1.0
0.003
APRIL 30

1800
240
3.8
0.30
7.0
0.97
0.92
1.6
0.005

910
180
3.1
0.25
6.4
0.78
0.28
1.7
0.003


Runoff
Yield
(Ibs)
STORM

942
859
51.8
21.1
1.75
0.71
0.065
0.13
0.026
, STORM

134
68
0.70
—
0.15
0.063
0.0059
0.0079
0.0008
Tropicana Study Area
Street
Surface
Differ-
ence to
Runoff
Yield
Ratio


Lbs/Curb-
Mile Dif-
ference


0.36
0.071
0.28
0.003
0.79
0.29
0.31
0.26
0.038

120
11
0.22
0.020
0.47
0.054
0.059
0.13
0.0003


6.8
2.6
4.4
—
43
12
47
210
3.8

300
27
0.57
'0.053
1.3
0.14
0.16
0.34
0.0007
Lbs
Differ-
ence
(11.1
curb-
mile)


1300
120
2.4
0.22
5.2
0.60
0.66
1.4
0.003


3300
300
6.3
0.59
14
1.6
1.8
3.8
0.008


Runoff
Yield
(Ibs)


8099
2267
90.2
65.8
6.5
2.9
0.4
0.45
0.055


1260
740
17
2.1
0.90
0.53
0.042
0.060
0.009
Street
Surface
Differ-
ence to
Runoff
Yield
Ratio


0.16
0.05
0.03
0.003
0.80
0.21
1.6
3.2
0.06


2.6
0.41
0.37
0.28
16
2.9
42
63
0.86
- MAY 1, 1977, STORM

600
200
3.3
0.26
6.8
0.77
0.15
0.20
0.003

11.6
—
—
0.13
—
—
—
—
~

52
—
—
2.0
—
—
—
—


260
24
0.49
0.045
1.1
0.12
0.13
0.28
0.0006

2900
270
5.4
0.50
12
1.3
1.4
3.1
0.007

1850
1250
72
29
3.2
1.3
0.1
0.23
0.009

1.6
0.21
0.076
0.017
3.8
1.0
14
14
0.74
                                    109

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TABLE 8-4.  RUNOFF  WATER QUALITY  COMPARED  TO BENEFICIAL USE CRITERIA
Beneficial Use Criteria
Parameter3
pH (pH
units)
Temp. (°C)


DO

Turbidity
(NTU)
TDS

SS
no3

P04

Cl
so4
Na
Cd


Cr

Cu

Pb

Hg

Zn
BOD5
Overall
Observed
Range
6.0*7.6

14*16.5


5.4*12.8


4.8*130
22*376

15*845
0.3*1.5

0.2*17.6

3.9*17.6
6.3*27
2.1*26.8
<0.002*
0.04

0.005*
0.04
0.01*0.09

0.10*1.5

0.0001*
0.00006
0.06*0.55
17*29.8
Overall
Observed
Average
6.7

16


8.0


49
150

240
0.7

2.4

12.1
18
15
0.01


0.02

0.03

0.4

<0.0001

0.18
24
Irrigation Livestock Wildlife
4.5*9.0 — 6.0*9.0
desired desired
Narrative — Maintain
natural
pattern
—


—
500*5000
mg/L max.
Narrative — —
Narrative 450 mg/L (in-
cluding N02)
—

— — —
__
Narrative
0.01*0.05 0.5 mg/L
mg/L max.

0.1*1.0 1.0 mg/L
mg/L max.
0.2*5.0 0.5 mg/L
mg/L max.
5.0*10.0 0.1 mg/L
mg/L max.
0.001 mg/L Narrative

25 mg/L
—
Aquatic Life
6.0*9.0
desired
Narrative


Usually 5.0
mg/L min.

Small change
Narrative

80 mg/L
—

—

—
—
—
0.004*0.03 mg/L
max. for soft*
hard water
0.03 mg/L

Narrative

0.03 mg/L

0.00005 mg/L

Narrative
10 mg/L
t
Marine
Life
6.5*8.5
desired
Narrative


6.0 mg/L
min.

—
—

—
—

0.0003
mg/L
—
—
—
0.01 mg/L


0.1 mg/L

0.05 mg/L

Narrative

0.1 mg/L

0.1 mg/L
Narrative

Freshwater
Public
Recreational Uses Supply
5.0*9.0 5.0*9.0
desired desired
86°F Narrative


— Narrative

4 f.t Narrative
(secchi)
Narrative

—
45 mg/L

0.3 mg/L for streams; Narrative
0.08 for lakes
250 mg/L
250 mg/L
Narrative
0.01 mg/L


0.05 mg/L

1 mg/L

0.05 mg/L

0.002 mg/L

5 mg/L
Narrative
Parameters are measured in mg/L




''Maximum limits unless stated as
              unless otherwise noted.




              desired range or minimum values.

-------
      Livestock :  Pb*               Marine life:  P04,* Cd, Cu, Zn
      Wildlife:  none                Recreational uses:  PO,*
      Aquatic life:  Cr, Cd,* Pb,*   Freshwater public supply:  Cd, Pb*
        Hg,* BOD5, turbidity,*       Irrigation:  Cd
        suspended solids*

The heavy metals - cadmium, chromium, lead, mercury, and zinc - along
with phosphates, BOD5> suspended solids, and  turbidity can exceed the
recommended criteria.  Drinking water standards are not presented because
the water would be treated before use and the freshwater public supply
criteria applies to the water source.  The high turbidity of the runoff
water is expected to exceed the narrative criterion for aquatic life.
Observed average and maximum suspended solids runoff concentrations ex-
ceeded the aquatic life criterion.  All of the runoff phosphate concen-
trations exceeded the recreation criterion by a large amount.  The
phosphate recreation criterion is designed to prevent eutrophication** in
receiving water.  Average and maximum cadmium concentrations exceeded the
irrigation, aquatic life, marine, and freshwater supply criteria.  Maxi-
mum copper and chromium concentrations in the runoff also exceeded the
aquatic life and marine criteria.  All of the lead concentrations in the
runoff exceeded the livestock, aquatic life, and freshwater supply cri-
teria by large amounts.  The maximum runoff mercury concentrations ex-
ceeded the aquatic life criterion by a large amount.  The average and
maximum zinc runoff concentrations exceeded the marine life criterion.
All of the observed BOD,- concentration values in the runoff exceeded
the aquatic life criterion.  As these data show, those parameters most
potentially responsible for water quality impairment are solids, cadmium,
lead, and mercury for aquatic life uses; orthophosphates for marine life;
orthophosphates for eutrophication (recreational use); and lead for
freshwater public suply.  Street cleaning operations can remove portions
of these pollutants from the source area before rains can wash them into
the receiving water.

Comparisons of Runoff Water Quality with Sanitary Wastewater Effluent
Water Quality

     Table 8-5 presents a comparison between  sanitary wastewater treatment
facility effluent and urban runoff for the study areas.  The average and
peak one-hour runoff concentrations observed and average sewage treatment
plant effluent concentrations are shown along with the ratios between them.
The sewage treatment facility is a modern, advanced secondary treatment plant
serving the study areas.  The short-term effects of urban runoff on a receiv-
ing water are most important (by definition) during and immediately follow-
ing a runoff event: short-term effects are associated with instantaneous con-
centrations.  A comparison between the urban runoff average concentrations
and the sewage treatment plant effluent average concentrations shows that
the concentrations of lead, suspended solids, COD, cadmium, TOC, turbidity,
zinc, chromium, and BOD5 are all higher in the runoff than in the sewage
plant effluent. Copper and Kjeldahl nitrogen, in addition to the previously
listed parameters, have greater runoff peak concentrations than the sewage
*Greater than ten times the recommended criterion.
**Excessive algae growth that may become a nuisance.

                                     Ill

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TABLE  8-5.   COMPARISON OF  URBAN RUNOFF AND WASTEWATER TREATMENT PLANT  EFFLUENT
Runoff
Concentration
Parameter
Ca++
K+
Mg^
Na+
Cl
so4"
HCO,
N03
BOD5
COD
KN
OrthoPO^
Total solids
TDSe
Suspended
solids
Cd
Cr
Cu
Pb
Zn
Hg
Specific
conductance
(limhos/cm)
Turbidity (NTU)
pH (pH units)
TOCf
Avg
(mg/1)
13
2.7
4.0
15
12.1
18
54
0.7
24
196
6.7
2.4
350
150

240
0.01
0.02
0.03
0.4
0.18
<0.0001


118
49
6.7
106
Peak
(1-hr)
(mg/1)
19
3.5
6.2
26.8
17.6
27
153
1.5
29.8
350
25
17.6
952
376

845
0.04
0.04
0.09
1.5
0.55
0.0006


660
130
7.6
290
Average
STPa Effluent
Concentration
(mg/1)
65
23.8
35
218
330
148
233
4.9
21
35d
23.9
19.2
1,040
1,010

26
0.002
0.016
0.081
0.0098
0.087
0.0019


1850
20
7.6
30
Ratio
of Avg
Runoff
to STP
0.20
0.11
0.11
0.07
0.04
0.12
0.23
0.14
1.1
5.6
0.28
0.13
0.34
0.15

9.2
5
1.3
0.37
41
2.1
<0.05


0.06
2.5
0.88
3.5
Ratio
of Peak
Runoff
to STP
0.29
0.15
0.18
0.12
0.05
0.18
0.66
0.31
1.4
10
1.1
0.92
0.92
0.37

32
20
2.5
1.1
150
6.3
0.32


0.36
6.5
1.0
9.7
Annual
Runoff b
(tons/yr)

—
—
—
—
—
—
—
—
8,900
160
12
74,000
—

—
0.41
34
69
230
42
—


—
—
—
~
Annual
STP
Effluent0
(tons/yr)
8,790
3,220
4,690
29,500
44,600
20,000
31,500
663
2,840
4,730d
3,230
2,600
141,000
137,000

3,520
0.27
2.2
11.0
1.3
11.8
0.26


—
—
—
4,060
Ratio of
Runoff
to STP
Annual
Yields

—
—
—
—
—
—
—
—
1.9
0.05
0.005
0.53
—

—
1.5
15
6.3
180.
3.6



—
—
—
~
   Sewage treatment  plant.

   About 200 people  correspond to one curb-mile (2,880 curb-miles in San Jose/575,000 population).
   Therefore a population of 850,000 corresponds to about  4,250 curb-miles,  with about 1,100 curb-miles of
   streets surfaced  with oil and screens.  These annual runoff values were calculated based on a  year of
   the appropriate accumulation rates and these mileage estimates.

  cAn estimated population of 850,000 is served by the sewage treatment facility.

  dEstimated.    eTotal dissolved solids.    fTotal organic carbon.
                                                112

-------
plant effluent average concentrations.   Therefore,  urban  runoff may have
more important short-term effects on  receiving waters  than  average treated
sanitary wastewater effluent.

     The annual yield for the different  sources  gives  a measure that
indicates the long-term problems.  Table 5  shows  the annual sewage
treatment plant effluent yield expressed as  tons  per year (derived from
monthly average concentrations and effluent  quantities) and a  calculated
annual urban runoff yield expressed in tons  per  year for  a  similar ser-
vice area.  On an annual basis, the total orthophosphates associated
with the street dirt are less than 1  percent of  the total sewage treat-
ment plant plus urban runoff yield.   The Kjeldahl nitrogen  contribution
from urban runoff is about  5 percent  of  the  total urban runoff plus
sewage treatment effluent.  The total solids contribution from urban
runoff is about 35 percent  of the total.  Lead,  chromium, copper, zinc,
COD, and cadmium all have contributions  from urban  runoff greater than
50 percent of the total (99, 94, 86,  78,  66, and 60 percent, respec-
t ively).

     These data show that for a receiving water  getting both secondary
treated sewage wastes and untreated urban runoff, additional improvements
in the sanitary sewage effluent may not  be as cost-effective as some
treatment of urban runoff (except for nutrients).   That is  especially
true for lead and chromium, where more than 90 percent of the total
wasteload is due to urban runoff.  As an example, if all  of the lead were
removed from the sanitary wastewater  effluent, the  total  annual lead dis-
charge would only decrease  by about 1 percent, because the  urban runoff
accounts for approximately  99 percent of  the total  long-term lead yield.

TREATABILITY OF NONPOINT POLLUTANTS AND  COST AND SELECTION  OF CONTROL
MEASURES
Street Cleaning Costs

     Table 8-6 presents San Jose street  cleaning costs for  the year ending
September 30, 1977.  Labor  accounts for  about 65 percent  of the total costs.
Those categories that may be affected by  a significant change in street
cleaning equipment (maintenance costs) make up 30 percent of the total
costs.  During this same period, the  Public Works Department of San Jose
swept 55,761 miles.  The unit cost was therefore about $16  per mile swept,
and the labor requirement was about 0.9 man-hour per mile.  Initially, these
costs appear high, but it must be realized that most other  evaluations of
street cleaning costs do not include  all  of the  actual costs of the street
cleaning program.  Most other street  cleaning cost  evaluations include only
maintenance and operation supplies and operator  labor expenses.  Few other
jurisdictions have all the  other cost information available.

     Table 8-7 presents the unit cost effectiveness for the street cleaning
operations based on preliminary information.  The unit costs (dollars per
pound removal for a pollutant) range  from 8<: per pound of total solids
removed to $8,000 per pound of cadmium.  The average unit labor needs are
also shown in this table and range from  30 seconds  per pound of total solids
removed to 450 hours per pound of cadmium.

                                    113

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          TABLE  8-6. SAN JOSE ANNUAL  STREET CLEANING  COSTS
It en
Maintenance supplies
Operation supplies3
Debris transfer and
disposal
Equipment depreciation
Laborb
Maintenance personnel
Supervisors
Total annual costs
Total annual curb-
miles swept
Unit effort
Cost ($)
93,000
29,000
171,000
31,000
326 000
176,000
80,000
$906,000
55,761 miles
$16/mile swept
Percentage
Total Cost
10
3
19
3
36
20
9
100%

0.9
of Labor
(person-days)
—
—
780
—
3,400
1,200
650
6,030 days

hour/mile swept
           aTires, fuel, and oil.

           These labor costs include administration, warehouse, secretary, and over-
           head costs.
TABLE  8-7-  PRELIMINARY ESTIMATES OF  COST EFFECTIVENESS FOR  SAN JOSE
                          STREET CLEANING OPERATIONS
Parameter
Total solids
Suspended solids
COD
OrthoPO^
Kjeldahl nitrogen
Lead
Zinc
Chromium
Copper
Cadmium
Average
Removal
(Ibs/curb-mile)
200
100
24
0.032
0.42
0.80
0.12
0.10
0.20
0.002
Average
Unit Cost
($/lb removed)
0.08
0.16
0.67
500
38
20
130
160
80
8,000
Average
Unit Labor
(hrs/lb removed)
0.005
0.01
0.04
28
2.1
1.1
7.5
9.0
4.5
450
                                    114

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Urban Runoff Treatment Costs

     Table 8-8 presents estimated costs for treating urban runoff and the
various runoff treatment operations and processes.  When flow equalization
(storage) and collection facility costs are excluded, the unit costs
are all significantly less than the unit costs for street cleaning
operations.   However, when flow equalization costs are included, the
unit costs for removal of a pound of the various pollutants are all
much larger than similar costs for street cleaning operations.  If col-
lection facilities are also necessary (such as collection trunklines),
these unit costs would be much greater.  The costs utilized in these
calculations include the annual operation and maintenance costs, depre-
ciation costs, and interest costs over the expected life of the project.
Estimated average cost and labor effectiveness values are also shown
in this table.  The operation and maintenance labor unit effectiveness
for these runoff control processes are all about one-half to one-hundredth
of the unit labor requirements for street cleaning operations.

Combined Sanitary Wastewater and Runoff Treatment Costs

     Table 8-9 presents cost information for the San Jose-Santa Clara
Water Pollution Control Plant.  Unit costs and unit labor requirements
are also shown.  It is assumed that these costs and labor requirements
would remain approximately the same if the facility began treating combined
urban runoff and sanitary wastewater.  These costs are for the most part
less than the unit costs for the special treatment facilities without
flow equalization and collection processes.  Unfortunately, there are no
adequate data to compare the unit removal costs and labor effectiveness
for treating heavy metals in the runoff systems.  It is expected that
these unit requirements for the important heavy metals (Pb, Zn, Cu) would
be much greater than requirements for street cleaning programs.

Decision Analysis

     The types of information summarized here can be used to recommend
a specific set of treatment and removal systems necessary to meet multiple
objectives.  The objectives to be considered may include runoff and receiv-
ing water quality, air quality (as measured by fugitive dust concentrations
alongside roadways), aesthetic consideration, public safety objectives
(reducing accumulations of loose debris in intersections), least cost,
maximum labor use, and public relations objectives (demonstrating to the
taxpayers how their tax dollars are being spent).  A decision analysis
procedure that considers these multiple objectives and the partial ful-
fillments of each alternative should be used in determining how much
of the total urban runoff control program should be addressed by street
cleaning.  A candidate procedure will be described in the final report.

AIRBORNE FUGITIVE PARTICULATE LOSSES FROM STREET SURFACES

     As stated previously, the street surface particulate accumulation
rate is greatest shortly after street cleaning when the streets are rela-
tively clean.  Particulate loading values then level off with the passage


                                    115

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            TABLE  8-8.   ESTIMATED COSTS FOR TREATING URBAN  RUNOFF
Unit Costs,
Equalization and
Process
Swirl concentrator
Sedimentation
Dissolved air flotation
Micro-straining
Filtration
Contact stabilization
Trickling filters
Rotating biological
contactors
Aerated lagoons
Physical-chemical
Average cost (S/lb
removed)
Suspended
Solids
0.003
0.036
0.032
0.004
0.026
0.04
0.07
0.04
0.03
0.12
0.04
EOD5
—
—
0.42
0.08
0.31
0.38
0.59
0.33
0.29
1.00
0.40
Excluding Flow Unit Costs, Including Flow
Collection ($/lb) Equalization, Excluding Collection
Suspended
COD N P04 Solids
No
2.00
0.06 4.00 2.00 1.00
1.00
0.03 — — 0.90
2.00 5.00 0.90
— — — 1.30
0.06 2.00 4.00 1.10
1.00
0.17 3.50 8.00 0.90
0.08 2.90 5.00 1.10
BOD5 COD N
flow equalization needed
—
14 2.00 130
23
17 1.50
9 — 48
11 —
9 1.70 60
8
8 1.30 27
12 1.60 66
($/lb)
-4

—
70
—
—
110
—
110
—
65
90
Estimated  labor (hrs/lb
removed)                0.007    0.70   0.01   0.30  0.50   0.007      0.70   0.01    0.30  0.50

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        TABLE  8-9.  SAN JOSE-SANTA CLARA WATER POLLUTION  CONTROL PLANT,
                         EFFLUENT  CONDITIONS  (1975-76 data)
Influent Effluent
Concentration Concentration
(mg/L, except (mg/L, except Percentage
Parameter as noted) as noted) of Removal
Flow
Total solids
Suspended solids
Settleable solids
Total dissolved
solids
Specific conductance
Turbidity
pH
Alkalinity (as HCO,)
Hardness (as CaCO^)
BODc
TOC
Oil and grease
Total phosphate (PO^)
Organic nitrogen
Ammonia (NHj)
Kjeldahl nitrogen
Nitrates (NO-j)
Nitrites (N02)
Total coliform
bacteria
Fecal coliform
bacteria
Sulfates (SO^)
Chlorides (Cl)
Silica (Si02)
Sodium (Na)
Potassium (K)
Calcium (Ca)
Magnesium (Mg)
Phenols
Cyanide (CN)
Fluoride (F)
Boron (B)
Arsenic (As)
Cadmium (Cd)
Chromium (Cr)
Copper (Cu)
Lead (Pb)
Mercury (Hg)
Nickel (Ni)
Silver (Ag)
Zinc (Zn)
89xl06
gal. /day*
—
610
24

—
—
—
—
312
—
395
—
73.0
42.6
26.8
28.0
54.8
1.5
1.3

—

—
105
—
36
215
18.4
59
37
195
0.06
2.0
—
—
—
—
—
—
—
—
—
— _
—
1040
26*
0.05

1010
1850 umhos/cm
20 JTU
7.6 pH units
233
289
21*
30
3.1*
19.2*
5.1*
18.8*
23.9*
4.9*
1.4*
108 organisms/
100 mL
8 organisms/
100 mL
148
330
31
218
23.8
65
35
2.9
0.06
1.3
0.9
0.0004*
0.002*
0.016*
0.081*
0.0098*
0.0019*
0.038*
0.002*
0.087*
—
—
93.8*
99.8

—
—
—
—
25
—
94.2*
—
96
55
81
33
56
—
—

—

—
—
—
14
—
—
—
6
99
—
35
—
—
—
—
—
—
—
—
—

Tons/
Year
Removed
—
—
53,300
3,390

—
—
—
—
10,500
—
46,100
—
10,100
3,180
2,940
1,250
4,110
—
—

—

—
—
—
680
— •
—
—
300
38,600
—
95
—
—
—
—
—
—
—
—
—

Tons/
Year
Effluent
—
141,000
3,520
6.8

137,000
—
—
—
31,500
39,100
2,840
4,060
419
2,600
690
2,540
3,230
663
189

—

—
20,000
44,600
4,190
29,500
3,220
8,790
4,690
390
8.1
176
122
0.05
0.27
2.2
11.0
1.3
0.26
5.1
0.27
11.8
$/Lb
Removed
—
—
0.01
0.65

—
—
—
—
0.21
—
0.05
—
0.22
0.69
0.75
1.76
0.52
—
—

—

—
—
—
3.22
—
—
—
7.34
0.06
—
23
—
—
—
—
—
—
—
—
—

Man-Hours/
Lb Removed
—
—
0.003
0.04

—
—
—
—
0.014
—
0.003
—
0.015
0.047
0.051
0.12
0.037
—
—

—

—
—
—
0.22
—
—
—
0.50
0.004
—
1.6
—
—
—
—
—
—
—
—
—

*These values are from routine analyses (several grab samples per month).   The remaining values are  from
 only a few data points (1 to 4) collected during the spring of 1977.
                                           117

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of time.  It is assumed that the deposition rate is constant and that the
increasing difference between the deposition rate and the accumulation
rate as time passes is caused by particulate losses to the air.  Therefore,
if the effects of rain and street cleaning operations are eliminated,
it is possible to estimate these dust losses from the accumulation rates.
It is assumed that the initial high accumulation rate value approximates
the constant deposition rate.  The Tropicana test area may lose an aver-
age of about 80 Ibs per curb-mile per day during days 4 through 9, or
about a week after street sweeping or a significant rain. About 20 per-
cent of the total particulates on Tropicana streets are smaller than 106M
in size and could therefore remain suspended.  Thus, in the Tropicana test
area, about 16 Ibs per curb-mile per day of particulates may be suspended
owing to winds and automobile traffic.  About 80 percent of the total
particulates are larger than 10&M and would not remain suspended because
of their large size.  Thus, the remaining 64 Ibs per curb-mile per day
would rapidly settle out to the ground near the street.

     The average automobile traffic density in the Tropicana area is about
1000 cars per day.  Since there are 2 curb-miles per street-mile, the 15
Ibs per curb-mile per day value would give an automobile use emission
factor of about 0.03 Ib per car-mile (lOg per car-km) if all of the losses
to the air were the result of automobile-caused turbulence. It is impor-
tant to note, however, that winds by themselves can cause significant
losses.

     This emission factor would increase if the street cleaning
interval or the interval between significant rains were longer.  It
would also vary for different street, land-use, traffic,  and wind
conditions.
                                    118

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REFERENCES

Amy, G., R. Pitt, R. Singh, W.L. Bradford, and M.B. LaGraff.  Water
     Quality Management Planning for Urban Runoff.  EPA 440/9-75-004.
     U.S. Environmental Protection Agency, Washington, D.C., December 1974.

Burgess and Niple, Ltd.  Stream Pollution and Abatement from Combined
     Sewer Overflows.  11024 FKN 11/69, U.S. Environmental Protection
     Agency, Washington, B.C., November 1969.

Colston, N.V.  Aspects of Storm and Combined Sewer Overflow Technology:
     An Assessment.  EPA-67012-74-040.  U.S. Environmental Protection
     Agency, Cincinnati, Ohio, 1974.

Cowherd, C., Jr., C.M. Maxwell, and D.W. Nelson.  Quantification of Dust
     Entrainment from Paved Roadways.  EPA-450/3-77-027.  U.S. Environmental
     Protection Agency, Research Triangle Park, North Carolina, July 1977.

Lager, J.A., and W.G. Smith.  Urban Stormwater Management and Technology:
     An Assessment.  EPA-67012-74-040.  U.S. Environmental Protection
     Agency, Cincinnati, Ohio, 1974.

Pisano, W.C., and C.S. Queiroz.  Procedures for Estimating Dry Weather
     Pollutant Deposition in Sewerage Systems.  EPA-600/2-77-120.
     U.S. Environmental Protection Agency, Cincinnati, Ohio, July 1977.

Pitt, R.E., and G. Amy.  Toxic Materials Analysis of Street Surface
     Contaminants.  EPA-R2-73-283.  U.S. Environmental Protection
     Agency, Washington, D.C., November 1973.

Pitt, R.E., and R. Field.  Water Quality Effects from Urban Runoff.
     Presented at the 1974 AWWA Conference, Boston, Massachusetts, 1974.

Pitt, R., J. Ugelow, and J. Sartor.  Systems Analysis of Street Cleaning
     Techniques.  American Public Works Association and National Science
     Foundation, March 1976.  Unpublished.

Roberts, J.W.  The Measurement, Cost and Control of Air Pollution from
     Unpaved Roads and Parking Lots in Seattle's Duwamish Valley.
     M.S. Thesis, University of Washington, 1973.

Sartor, J.D., and G.B. Boyd.  Water Pollution Aspects of Street Surface
     Contaminants.  EPA-R2-72-081.  U.S. Environmental Protection Agency,
     Washington, D.C., November 1972.

Sullivan, R.  (APWA.)  Water Pollution Aspects of Urban Runoff.  Federal
     Water Pollution Control Administration, SP-20-15, January 1969,
     p. 272.
                                    119

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                                  TECHNICAL REPORT DATA
                           (Please read Instructions on the reverse before completing)
1. REPORT NO.
 EPA-600/9-78-017
                                                          3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
 URBAN STORMWATER MANAGEMENT WORKSHOP  PROCEEDINGS
 Edison,  New Jersey, December  1,  1977
               5. REPORT DATE
                August 1978
(Issuing Date)
               6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
 Richard Field (Editor)
                                                          8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
 Storm and Combined Sewer Section
 Wastewater Research Division
 Municipal Environmental Research  Laboratory (Cincinnati)
 Edison, New Jersey    08817
               10. PROGRAM ELEMENT NO.
                 1BC611
               11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
 Municipal Environmental Research Laboratory—Gin.,OH
 Office of Research and Development
 U.S.  Environmental Protection  Agency
 Cincinnati, Ohio   45268
               13. TYPE OF REPORT AND PERIOD COVERED
                Workshop Proceedings
               14. SPONSORING AGENCY CODE

                EPA/600/14
15. SUPPLEMENTARY NOTES
 Workshop Proceedings based  on  EPA Project Nos. 68-03-2617,  R802411,  R805238, R804578,
 Editor:   Richard Field   (201)  321-6674  (FTS 340-6674)                        S804432
16. ABSTRACT
      The workshop on urban  stormwater management technology  was  held on December 1,
 1977 at the offices of the  USEPA in Edison, New Jersey.   The purpose of the workshop
 was to exchange and disseminate the most up-to-date- research results and technical
 information from projects sponsored under USEPA Urban Runoff Control Research Develop-
 ment and Demonstration Program.  The proceedings contained herein represent the
 contributions from participating lecturers and include  the following topics:
      a.  Urban stormwater management and technology manual  (update),
      b.  Comprehensive planning for control of urban storm runoff and combined sewer
          overflows,
      c.  Low cost-effective alternative and comparative analysis from 208 areawide
          assessment study on combined sewer overflow and urban stormwater pollution
          control,
      d.  Statistical characterization of runoff loading rates and cost functions of
          control measures,  ,
      e.  Dry weather pollutant deposition in sewerage systems and associated first
          flush combined  sewer overflow pollution control by  dry weather sewer flushing
      f.  Nonpoint pollution abatement through improved  street cleaning practices.
17.
                               KEY WORDS AND DOCUMENT ANALYSIS
                  DESCRIPTORS
 Water quality, Runoff,  Urbanization, Strean
 pollution, Sewers, Water pollution,
 Mathematical models,  Surface water runoff,
 Cost analysis, Drainage, Hydraulics, Waste
 treatment
                                             b.IDENTIFIERS/OPEN ENDED TERMS
   Best management practice
   Sewer flushing, Street
   sweeping, Nonstructural
   control, Urban hydrology
   Computer models, New re-
   sidential development,
   Water quality control
                            c.  COS AT I Field/Group
     13B
13. DISTRIBUTION STATEMENT
 RELEASE  TO PUBLIC
                                             19. SECURITY CLASS {ThisReport)
                                                UNCLASSIFIED
                                                                        21. NO. OF PAGES
                                 130
  20. SECURITY CLASS (This page)
    UNCLASSIFIED
                                                                        22. PRICE
EPA Form 2220-1 (9-73)
120
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