EPA-600/2-79-161
August 1979
DEMONSTRATION OF NONPOINT POLLUTION ABATEMENT
THROUGH IMPROVED STREET CLEANING PRACTICES
by
Robert Pitt
Woodward-Clyde Consultants
San Francisco, California 94111
Grant No. S-804432
Project Officers
Anthony N. Tafuri and Richard Field
Storm and Combined Sewer Section
Wastewater Research Division
Municipal Environmental Research Laboratory (Cincinnati)
Edison, New Jersey 08817
MUNICIPAL ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Municipal Environmental Research Lab-
oratory, U.S. Environmental Protection Agency and the City of San Jose Public
Works Department, and approved for publication. Approval does not signify that
the contents necessarily reflect the views and policies of the U.S. Environ-
mental Protection Agency and the City of San Jose, nor does mention of trade
names or commercial products constitute endorsement or recommendation for use.
11
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FOREWORD
The U.S. Environmental Protection Agency was created because of increas-
ing public and governmental 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 environ-
ment. The complexity of that environment and the interplay between its compo-
nents requires a concentrated and integrated attack on the problem.
Research and development is that necessary first step in problem solving,
and 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 munici-
pal and community sources; for the preservation and treatment of public drink-
ing water supplies; and to minimize the adverse economic, social, health, and
aesthetic effects wf pollution. This publication is one of the products of
that research and is a vital communications link between the researcher and
the user community.
A detailed evaluation of various street cleaning programs can be used by
those concerned with urban runoff control to estimate how adequately street
cleaning can help meet local control objectives. This report presents the re-
sults of many street cleaning tests conducted in San Jose, California. These
tests were influenced by normal conditions that can affect the effectiveness
of street cleaning programs, including street surface condition, nature of
street surface particulates, and parked cars. The effects of these variables
are quantified and can be used by planners in many parts of the country.
Other aspects of street cleaning and urban runoff were also studied and are
presented in this report. These include street surface contaminant accumu-
lation rates, runoff analyses, cost and effectiveness of alternative control
measures, decision analyses to select control measures, and roadside airborne
particulate concentrations.
Francis T. Mayo
Director
Municipal Environmental
Research Laboratory
iii
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ABSTRACT
This final report presents the results and conclusions from the EPA-spon-
sored demonstration study of nonpoint pollution abatement through improved street
cleaning practices. An important aspect of the study was the development of
sampling procedures to test street cleaning equipment performance in real-world
conditions. These sampling and experimental design procedures are described in
detail and can be used by others to directly determine both street surface con-
taminant accumulation rates and street cleaning performance using other equip-
ment in their own service areas.
The report describes accumulation rate characteristics of the various
pollutants associated with street dirt. The results of performance tests for
street cleaning equipment and the factors that are thought to affect this per-
formance are also presented. These data are used to draw conclusions about ele-
ments that must be considered in designing an effective street cleaning program.
The study of urban runoff yielded information on runoff flow charac-
teristics, concentrations and total mass yields of monitored pollutants in
the runoff, and street dirt removal capabilities and effects on deposition in
the sewerage for various kinds of storms. Estimated runoff control effective-
ness by various street cleaning programs are also given. These data are summa-
rized here, and urban runoff water quality is compared with recommended water
quality criteria and the quality of treated sanitary wastewater.
Cost and labor effectiveness of street cleaning, runoff treatment, and
combined runoff and wastewater treatment are also presented. In addition,
the results of a special study of airborne dust losses from street surfaces
are presented.
A comprehensive bibliography is also included for those who want further
information about street cleaning practices and urban runoff characteristics.
This is the first study in a series of projects being conducted in San
Jose, California, to evaluate the effects of urban runoff on a receiving water,
to determine the source areas of the problem pollutants, and to select the
most appropriate mixture of control measures.
This final report is submitted in fulfillment of Grant No. S-804432 by
the City of San Jose under the sponsorship of the U.S. Environmental Protec-
tion Agency. Woodward-Clyde Consultants participated in this study under a sub-
contract with the City of San Jose. This project began in September 1976 and
was completed in August 1978.
IV
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CONTENTS
Foreword i i i
Abstract i v
Fi gures vi i
Tab 1 es xi i i
Metric Conversion Table xix
Acknowledgment xx
1. Introduction 1
2. Conclusions 4
Sampling Techniques. 4
Street Cleaning Equipment Tests 4
Particulate Routing and Pollutant Mass Flow
Characteristics of Urban Runoff 8
Cost and Selection of Control Measures 11
Dust Losses from Street Surfaces to the Air 14
3. Street Cleaning Equipment Tests 15
Summary 15
Structure of the Study 16
Analyti cal Program 22
Concentrations of Street Surface Contaminants
as a Function of Particle Size 23
Determination of Accumulation Rates of Street
Surface Contaminants 26
General Description of Street Cleaning Equipment 33
San Jose Demonstration Study Results 45
Parking Interferences to Street Cleaning
Operations 62
4. Pollutant Mass Flow Characteristics of Urban Runoff 68
Summary 68
Structure of the Study \ [ \ 71
Analyti cal Program 72
Moni tored Rai ns 73
Runoff Sampling Program 75
Pollutant Removal Capabilities of Monitored Storms 81
Effectiveness of Street Cleaning in Improving
Urban Runoff Water Quality 83
Comparisons of Runoff Water Quality with
Recommended Receiving Water Quality Criteria 85
Comparisons of Runoff Water Quality with Sanitary
Wastewater Effluent Water Quality 88
Tracer Analysis of Sewerage Particulate Routing 88
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CONTENTS (continued)
5. Treatability of Nonpoint Pollutants by Street Cleaning 95
Summary 95
Structure of the Study 96
Street Cleaning Costs 96
Determination of Street Cleaning Program 105
6. Airborne Fugitive Particulate Losses from
Street Surfaces 112
Summary 112
Literature Review 112
Measured Roadside Dust Levels 117
Fugitive Parti cul ate Emission Rates 122
Street Cleaning Equipment Cab Particulate
Concentrati ons 131
References 133
Bibliography 138
Appendices
A. Street Surface Parti cul ate Sampling Procedures 153
B. Experimental Design 162
C. Selection and Description of Study Areas 167
D. Rainfall and Accumulation Rate History 181
E. Pollutant Strengths as a Function of Particle Size 201
F. Runoff Data 216
G. Alternative Urban Runoff Control Measures and the Use
of Decision Analysis 241
VI
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FIGURES
Number
Page
2-1 Annual amount removed as a function of the number
of passes per year •• ................. .... 7
2-2 Costs to remove a pound of street dirt as a function
of the number of passes per year • .............. . ]3
3-1 San Francisco Bay showing the general location of the
City of San Jose
3-2 Map showing the location of the three study areas 19
3-3 Sawtooth pattern associated with deposition and removal
of particulates ..' 20
3-4 Particle size distribution of "initial" loading samples .... 24
3-5 Total solids accumulation since last cleaned (all seasons
combined) 09
3-6 Particle ( 0.25 inch) size distribution before and after
sweeping tests ^Q
3-7 Effect of pattern on removal effectiveness 44
3-8 Effect of brush speed on removal effectiveness 44
3-9 Effect of forward speed on removal effectiveness 44
3-10 Total solids removal by particle size 55
3-11 Redistribution of total solids due to street cleaning -
Tropicana-Good Asphalt Test Area cc
3-12 Redistribution of total solids due to street cleaning -
Keyes-Oil and Screens Test Area r-7
3-13 Redistribution of total solids due to street cleaning -
Keyes-Good Asphalt Test Area r-,
b/
VII
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Number FIGURES (continued) Page
3-14 Loading distribution across the street . ............ 53
3-15 Parking lane total solids loading compared to full
street loading ............. . ........... 59
3-16 Effects of parking and street condition on solids
loading distribution ...................... /--i
3-17 Annual amount removed as a function of the number of
passes per year ........................
3-18 Effect of parked cars on street cleaner maneuverability .... 53
3-19 Effects of parking on urban street cleaning .......... 54
3-20 Effects of parking restrictions during street cleaning on
asphalt surfaced streets in good condition ........... 55
3-21 Effects of parking restrictions during street cleaning
on oil and screens surfaced streets .............. 55
4-1 BOD values as a function of incubation time ........... 75
4-2 Storm drainage in Keyes study area ............... QQ
4-3 Storm drainage from special catchbasin to outfall ........ Q-J
5-1 Costs to remove a pound of street dirt as a function of the
number of passes per year ................... -in*
5-2 Labor needs to remove a pound of street dirt .......... -\r\n
5-3 Relationship of objectives, operating conditions and
street cleaning equipment specifications ............ ing
5-4 Determination of allowable loading ............... 108
5-5 Days after significant rain to maximum street surface
loading ............................ 109
5-6 Maximum street surface loadings ................ 110
5-7 Portion of maximum loading values occurring versus the
number of cleaning cycles since last significant rain
and removal effectiveness
Ill
6-1 Particle resuspension rates caused by vehicle passage
for an asphalt road -•-,,,
A-l Street sampling trailer and major equipment components .-ir*
V l i 1
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FIGURES (continued)
Number Page
A-2 Sub-sample collection 155
A-3 Location of sub-sampling strips across a street 156
A-4 Disassembly of vacuum units for sample
transfer 159
A-5 Brushing some of the collected material from
the secondary coarse filter 159
A-6 Shaking the primary dacron filter in the vacuum 160
A-7 Collected material transferred from vacuum units into
a sample storage can 160
B-l Required number of sub-samples as a function of
allowable error and standard deviation 163
C-l San Francisco Bay Area showing the general location
of the Coyote Creek watershed 168
C-2 Coyote Creek watershed and study areas 169
C-3 Downtown buffer and test areas 170
C-4 Keyes street buffer and test areas 171
C-5 Tropicana good asphalt buffer and test areas 172
C-6 Downtown - good asphalt test area 173
C-7 Downtown - poor asphalt test area 173
C-8 Keyes - oil & screens test area 174
C-9 Keyes - good asphalt test area 174
C-10 Tropicana - good asphalt test area 175
C-ll Area map showing potential test site locations 178
D-l Rainfall history 182
D-2 Rainfall history (continued) 182
D-3 Rainfall history (continued) 183
D-4 Rainfall history (continued) 183
D-5 Rainfall history (concluded) % 184
ix
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Number
FIGURES (continued) Page
D-6 Total particulate loading and median particle size as a
function of time - Downtown-good asphalt test area 184
D-7 Total particulate loading and median particle size as a
function of time - Downtown-poor asphalt test area 185
D-8 Total particulate loading and median particle size as a
function of time - Keyes-good asphalt test area 186
D-9 Total particulate loading and median particle size as a
function of time - Keyes-good asphalt test (continued) 187
D-10 Total particulate loading and median particle size as a
function of time - Keyes-good asphalt test area (continued). . . 188
D-ll Total particulate loading and median particle size as a
function of time - Keyes-good asphalt test area (continued). . . 189
D-12 Total particulate loading and median particle size as a
function of time - Keyes-good asphalt test area (concluded). . . 190
D-13 Total particulate loading and median particle size as
a function of time - Keyes-oil and screens test area 191
D-14 Total particulate loading and median particle size as a
function of time - Keyes-oil and screens test area
(continued) 192
D-15 Total particulate loading and median particle size as a
function of time - Keyes-oil and screens test area
(continued) 193
D-16 Total particulate loading and median particle size as a
function of time - Keyes-oil and screens test area
(continued) 194
D-17 Total particulate loading and median particle size as a
function of time - Keyes-oil and screens test area
(concluded) '"5
D-18 Total particulate loading and median particle size as a
function of time - Tropicana-good asphalt test area 196
D-19 Total particulate loading and median particle size as a
function of time - Tropicana-good asphalt test area
(continued) • '97
D-20 Total particulate loading and median particle size as a
function of time - Tropicana-good asphalt test area
(continued) 198
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FIGURES (continued)
Number page
D-21 Total particulate loading and median particle size as a
function of time - Tropicana-good asphalt test area
(continued) 199
D-22 Total particulate loading and median particle size as a func-
tion of time - Tropicana - Good Asphaalt test area (concluded) . 200
E-l COD concentrations as a function of particle size (mg COD/kg
total solids) 12/13/76 through 5/15/77 average 201
E-2 Total orthophosphate concentrations as a function of
particle size (mg OP04/kg total solids) 12/13/76 through
5/15/77 average . 202
E-3 Kjeldahl nitrogen concentrations as a function of particle
size (mg KN/kg total solids) 12/13/76 through 5/15/77
average 203
E-4 Lead concentrations as a function of particle size
(mg Pb/kg total solids) 12/13/76 through 5/15/77 average .... 204
E-5 Zinc concentrations as a function of particle size
(mg Zn/kg total solids) 12/13/76 through 5/15/77 average .... 205
E-6 Chromium concentrations as a function of particle size
(mg Cr/kg total solids) 12/13/76 through 5/15/77 average .... 206
E-7 Copper concentrations as a function of particle size
(mg Cu/kg total solids) 12/13/76 through 5/15/77 average .... 207
E-8 Cadmium concentrations as a function of particle size
(mg Cd/kg total solids) 12/13/76 through 5/15/77 average .... 208
E-9 Mercury concentrations as a function of particle size -
all test areas combined (mg Hg/kg total solids)
12/13/76 through 5/15/76 average 209
E-10 Asbestos concentrations as a function of particle size -
all test areas combined (fibers/gram total solids)
12/13/76 through 5/15/77 average 210
F-l Runoff from Keyes Street study area during the rains of
March 15 and 16, 1977 216
F-2 Runoff from Keyes Street study area during the rains of
March 23, 1977 217
F-3 Runoff from Keyes Street study area during the rains of
March 24, 1977 217
xi
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FIGURES (continued)
Number Page
F-4 Runoff from Keyes Street study area during the rains of
April 30 and May 1, 1977 218
F-5 Runoff from Tropicana study area during the rains of
March 13, 1977 218
F-6 Runoff from Tropicana study area during the rains of
March 15 and 16, 1977 219
F-7 Runoff from Tropicana study area during the rains of
March 23, 1977 219
F-8 Runoff from Tropicana study area during the rains of
March 24, 1977 220
F-9 Runoff from Tropicana study area during the rains of
April 30 and May 1, 1977 221
G-l Example utility function for a water quality attribute 264
XII
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TABLES
Number
Page
0-1 Metric Conversion Table xix
2-1 Average Total Solids Accumulation Rate 5
2-2 Annual Average Accumulation Rates for Various Pollutants. . . 5
2-3 Median Particle Sizes of Street Surface Particulates 6
2-4 Average Removal Effectiveness for Street Cleaners 6
2-5 Average Total Solids Loading Distribution
Across the Street Q
2-6 Effects of Parked Cars on Cleaning Effectiveness 8
2-7 Observed Runoff Water Quality Concentrations 9
2-8 Recommended Beneficial Use Criteria Exceeded By Runoff. ... ]]
2-9 Comparison of Runoff Water Quality to Treated Secondary
Wastewater Effluent Water Quality 12
2-10 Costs to Remove Various Street Surface Contaminants by the
Street Cleaning Programs Tested 13
3-1 Street Cleaning Schedule for San Jose Study Areas 21
3-2 Average Nationwide Pollutant Strengths
Associated with Street Surface Particulates 25
3-3 Analysis of Possible Street Surface Contaminants 27
3-4 Annual Street Surface Pollutant Accumulations 30
3-5 Street Surface Pollutant Loadings for Various Times
Since Last Cleaned 3-1
3-6 Ratio of Pollutant Loading Values at Various Times
Since Last Cleaned to Residual Loading Values 3]
3-7 Pollutant Accumulation Rates for Different Periods
Since Last Cleaned op
X i i i
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TABLES (continued)
Number
3-8
3-9
3-10
3-11
3-12
3-13
3-14
3-15
3-16
3-17
3-18
3-19
3-20
3-21
3-22
3-23
3-24
3-25
Removal Efficiencies for Vacuumized Cleaner at
Different Initial Particulate Loadings and for
Mechanical Cleaner Efficiencies for Various
Median Particle Size for Various Street Surface
Removal Efficiencies from Cleaner Path for
Street Cleaner Performance During San Jose
Demonstration Project - Tropicana-Good Asphalt Test Area . . .
Street Cleaner Performance During San Jose
Demonstration Project - Keyes-Good Asphalt Test Area
Street Cleaner Performance During San Jose
Demonstration Project - Keyes-Oil and Screens Test Area . . .
Street Cleaner Performance During San Jose
Demonstration Project - Downtown Good and Poor
Street Cleaner Removal Effectiveness for Various
Street Cleaner Removal Effectiveness for Various
Street Cleaner Removal Effectiveness for Various
Street Cleaner Removal Effectiveness for Various
Total Solids Street Cleaner Removal Effectiveness
Page
36
36
37
39
39
42
43
46
47
48
48
50
51
52
53
54
60
57
XIV
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TABLES (continued)
Number
4-1
4-2
4-3
4-4
4-5
4-6
4-7
4-8
4-9
4-10
4-11
5-1
5-2
5-3
5-4
5-5
5-6
5-7
5-8
5-9
Rains During Field Activities
Oxygen Demand and Organic Characteristics of Runoff Samples
Runoff Pollutant Relative Strengths
Total Solids Street Surface Loading Removal by
Street Surface Pollutant Removals Compared with
Runoff Yields
Estimated Effectiveness of Various Street Cleaning
Runoff Water Quality Compared to Beneficial Use Criteria . . .
Comparison of Urban Runoff and Wastewater Treatment
Plant Effluent
Tracer Concentrations in Sewerage Compared to Catchbasin
Street Cleaning Program Costs (1973)
Street Cleaning Program Costs for Cities of Various
San Jose Annual Street Cleaning Effort (1976-1977)
Cost Effectiveness for San Jose Street Cleaning Operations,
Cost Effectiveness for San Jose Street Cleaning Operations,
Cost Effectiveness for San Jose Street Cleaning Operations,
Cost Effectiveness for San Jose Street Cleaning Operations,
Page
74
76
77
80
82
83
84
86
87
89
93
97
97
98
99
100
101
101
102
102
XV
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TABLES (continued)
Number
5-10
6-1
6-2
6-3
6-4
6-5
6-6
6-7
6-8
B-l
B-2
C-l
C-2
C-3
E-l
E-2
E-3
E-4
E-5
F-l
Cost Effectiveness for San Jose Street Cleaning Operations,
Near-Road Fugitive Particulate Concentration Increases ....
Fugitive Particulate Emission Factors for Street
Fugitive Particulate Emission Factors for Street
Fugitive Particulate Emission Factors for Street
Sampling Requirements for Various Study Area Groupings
(Initial Test Phase)
Chemical Concentrations by Particle Size
Chemical Concentrations by Particle Size
Chemical Concentrations by Particle Size
Chemical Concentrations by Particle Size
Asbestos and Mercury Concentrations by Particle Size -
Keyes Study Area Water Sample Data for March 15 and 16, 1977
Runoff
Page
103
115
120
121
123
125
127
129
132
164
165
176
179
180
211
212
213
214
215
222
XVI
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TABLES (continued)
Number Page
F-2 Keyes Study Area Water Sample Data for March 23, 1977
Runoff 222
F-3 Keyes Study Area Water Sample Data for March 24, 1977 Runoff. . . 223
F-4 Tropicana Study Area Water Sample Data for March 13, 1977
Runoff 223
F-5 Tropicana Study Area Water Sample Data for March 13 through
15, 1977 Runoff 223
F-6 Tropicana Study Area Water Sample Data for March 15 and 16,
1977 Runoff 224
F-7 Tropicana Study Area Water Sample Data for March 23, 1977
Runoff 224
F-8 Tropicana Study Area Water Sample Data for March 24, 1977
Runoff 225
F-9 Tropicana Study Area Water Sample Data for April 30 and May 1,
1977 Runoff 225
F-10 In Situ Dissolved Oxygen and Temperature Runoff
Measurements 226
F-ll Major Ions March 15 and 16, 1977, Runoff 227
F-12 Keyes Study Area Major Parameters for March 15 and 16, 1977
Runoff 228
F-13 Tropicana Study Area Major Parameters for March 15 and 16,1977
Runoff 229
F-14 Heavy Metals for March 15 and 16, 1977 Runoff 230
F-15 Tropicana Study Area Solids as a Function of Time for
March 15 and 16, 1977 Runoff 231
F-16 Major Ions for March 23 and 24, 1977, Runoff 232
F-17 Keyes Street Study Area Major Parameters for March 23 and
24, 1977 Runoff 233
F-18 Tropicana Study Area Major Parameters for March 23 and 24,
1977 Runoff 234
F-19 Heavy Metals for March 23 and 24, 1977 Runoff 235
xvii
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TABLES (continued)
Number Page
F-20 Tropicana Study Area Solids as a Function of Time for
March 23 and 24, 1977 Runoff 236
F-21 Tropicana Study Area Major Ions for April 30 and May 1, 1977
Runoff 238
F-22 Major Parameters for April 30 and May 1, 1977 Runoff 239
F-23 Tropicana Study Area Heavy Metals for April 30 and May 1, 1977
Runoff 240
G-l Potential Significant Urban Runoff Pollutant Sources 242
G-2 Control Measures Most Suitable for Controlling Pollutants
From Various Source Areas 243
G-3 Suitability of Control Measures for Controlling Common
Urban Runoff Pollutants 244
G-4 Estimated Control Measure Costs and Use Potentials for
Tropicana Study Area 245
G-5 Candidate Control Measure Priority Listing for Tropicana
Study Area 246
G-6 Ccst Estimates for Erosion Control Procedures 249
G-7 Estimated Construction Site Erosion Control Unit Benefits
(Ib controlled/acre/year) and costs ($/lb controlled) 250
G-8 Cost of Removals for Various Wet-Weather Flow Treatment
Systems 257
G-9 Estimated Unit Costs for Treating Urban Runoff 259
G-10 San Jose-Santa Clara Water Pollution Control Plant
Effluent Conditions 260
G-ll San Jose-Santa Clara Water Pollution Control Plant
Support Requirements (1975-76 data) 261
G-12 Decision Analysis Attributes, Measures, and Ranges 262
G-13 Definition of Alternatives 263
G-14 Estimated Attribute Levels for Each Alternative 263
G-15 Individual Attribute Utility Values for Each Alternative. . . . 268
G-16 Utility of Each Alternative 268
xv-ili
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ACKNOWLEDGMENTS
The Public Works Department of the City of San Jose was the grantee of
this project, with Woodward-Clyde Consultants (WCC) acting as consulting engi-
neers for the city.
Sincere gratitude goes to Mr. Anthony N. Tafuri and Mr. Richard Field,
both of the Storm and Combined Sewer Section (Edison, New Jersey) of the U.S.
EPA Municipal Environmental Research Laboratory, Cincinnati, Ohio, for their
valuable guidance and assistance during this project. Mr. James D. Sartor,
Vice President, was the project sponsor for Woodward-Clyde Consultants.
The San Jose Public Works Department Operations Division staff partici-
pated in this project under the direction of Mr. Richard Blackburn, project
manager and Chief Assistant Director of Public Works. Mr. David Pasquinelli
acted as project engineer for the city. Special thanks are extended to Mr. J.
Michael Sartor, who as project coordinator helped the city staff work effi-
ciently with the staff of WCC. Mr. Michael Sartor was assisted by Mr. William
Dotzler and Mr. Greg Rodriques. Mr. Thomas McGee, a private consultant, also
provided valuable field and laboratory assistance.
The San Jose Department of Public Works Street and Sewer Maintenance Divi-
sion staff participated in this project under the direction of Mr. Steven Sew-
ard, Superintendent. The Street Sanitation Section was of considerable help in
supplying needed street cleaning equipment and operators. The cooperation of
Mr. Stan Jacklich, Mr. Joe Padilla, and sweeper operators Messrs. Ernie Gomez,
Vince Lopez, and Carlos Vargas was greatly appreciated.
The staff of the Vehicle Maintenance Division of the San Jose Department
of Public Works was extremely helpful in determining street cleaner program
costs and in keeping the testing equipment in good operating condition. The
division is directed by Mr. Fred Wright, Superintendent. Special thanks are
also extended to Mr. Jim Albanese.
Two street cleaner manufacturing companies were vital to the success of
this project and must be acknowledged. Food Machinery Company (FMC) donated
the use of one of their street cleaners and an operator. The help of Mr. Pat-
rick Carroll, Mr. Bill Williams, and Mr. Clifford McNamara of FMC was apprec-
iated. Newark Brush Company, manufacturer, and GCS Inc., distributor, enabled
a different street cleaner to be used in the project. Thanks are extended to
Dr. John Horton of Newark Brush Company and Mr. Dick Moore and Mr. Don Loper of
GCS Inc.
XX
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SECTION 1
INTRODUCTION
Past research, notably that conducted for the U.S. Environmental Protec-
tion Agency (EPA), by the American Public Works Association (Sullivan 1969),
and by the URS Research Company (Sartor and Boyd 1972; Pitt and Amy 1973; Amy
et al* 1974), has clearly revealed the water pollution potential of street sur-
face contaminants. These projects present strong evidence relating contaminated
streets with the contamination 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
untreated nonpoint urban storm runoff with treated sanitary wastewater in their
potential effects on receiving waters. Reductions in runoff pollutants could
be accomplished by treating the runoff and/or reducing the quantities of pollu-
tants contaminating the runoff.
Although it is clear that pollutants in street dirt have a significant
effect on the quality of urban runoff and its effect on receiving water, there
are many questions that remain to be answered about the nature of this cause
and effect relationship. This project attempted to answer some of these ques-
tions and to develop more specific information that was needed in order to se-
lect effective control measures.
This study was designed to measure street cleaning equipment effective-
ness in removing pollutants from the street surface in a real-world situation.
It must be emphasized that the purpose of the project was not to compare spe-
cific types of equipment. Rather, it was to determine the range in capabilities
of current street cleaning equipment in order to gain information about the
general cost and effectiveness of street cleaning programs in removing street
surface pollutants.
The study also determined pollutant accumulation rates of street dirt in
test areas with different characteristics. Because the pollution characteris-
tics of street dirt are known to vary as a function of particle size (Sartor
and Boyd 1972; Pitt and Amy 1973), specific concentrations of various pollu-
tants in different particle size groups were examined. In addition, the effec-
tiveness of street cleaning equipment in removing different particle sizes from
the street, and bulk densities for various particle sizes were also examined.
These data demonstrate the potential quantity of pollutants that may be affec-
ted 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 sewerage or in a treatment process).
-------
Another area of concern is the transport of participates in sewerage sys-
tems and the associated mass balance relationships. In a combined sewerage
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 sanitary solids can settle out in
the sewerage, to be flushed out during the high flows of wet weather. This in-
creased concentration of solids can greatly add to the pollution load at the
beginning of a storm (Burgess and Niple, Ltd. 1969; Pisano and Queiriroz, 1977).
Storms with low runoff volumes may remove large quantities of road surface par-
ticulates and transport them to the sewerage system. These particutates may
settle out in the sewerage 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 and Queiriroz 1977). Some data are available on the buildup
and transport of these solids in combined and separated sanitary sewerage sys-
tems. Comparisons of the amounts of pollutants in the street dirt and in the
runoff from monitored storms provided information concerning deposition charac-
teristics in the sewerage and the relative quantity of pollutants in the runoff
originating 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 cleaner performance is available from earlier EPA-sponsored
studies, but these limited data are based on idealized strip test conditions.
Street cleaning performance data, which were used to make cost and labor effec-
tiveness comparisons with alternative control measures, were obtained from
tests in real-world conditions.
This study also examined resuspended street surface particulates. Esti-
mates of air pollutant emissions for EPA air quality regions, statewide areas,
and specific air basins are very important for continuing air quality control
planning. Most utility, industrial, and residential activities (including un-
paved roads) have received attention as particulate air pollutant sources. Re-
search by Roberts (1973), MWRI (Cowherd, jet al. 1977) and PEDCo (1977) indi-
cates 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. As part of an overall program to determine
the behavior of radioactive fallout, the Nuclear Regulatory Commission has fund-
ed continuing studies of particulate residence times in the atmosphere, air-
borne particulate deposition rates, and resuspension of settled particulates.
Some particle resuspension studies have included research of particle resus-
pension from asphalt streets caused by traffic. Their results and theories are
useful, but these studies consider only particles that have settled onto the
street surface from the atmosphere. This study examined losses from the total
-------
particulate loading on the street surface, including both losses washed into
the street through erosion, and tracked onto the street by vehicles.
It is expected that this study will have a two-fold benefit. First, the
data obtained will fill significant gaps in current knowledge about the role
of street dirt in causing water and air pollution, and to effect 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 characteristics and its effects on air and water
quality in their own cities.
-------
SECTION 2
CONCLUSIONS
The conclusions presented here summarize the information that has been
collected and analyzed as part of this current research. The effect these con-
clusions 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 or generically applicable
street cleaning guidelines. Rather, it indicates the type of information that
must be considered in designing effective control measures. For more detailed
information on results and a description of the analytical structure of the
study, the reader is referred to Sections 3 through 6.
SAMPLING TECHNIQUES
One important aspect of the study was the development of sampling tech-
niques that can be used to directly monitor changes in street surface loadings
for different test areas over a long period. These sampling procedures (see
Appendix A) can easily be used by a city's public works department to deter-
mine the specific loading conditions and street cleaning performance necessary.
The sampling equipment can be rented if it is not available within the depart-
ment. With these procedures, street surface loading conditions over a large
area can be sampled in a relatively short time. The experimental design proce-
dures (see Appendix B) can be used to determine the number of subsamples re-
quired for specific project objectives and study area conditions.
STREET CLEANING EQUIPMENT TESTS
The major element of the demonstration project was an evaluation of the
effectiveness of several types of street cleaning equipment currently available
under varying real-world conditions. This portion of the study investigated
accumulation rates of street dirt in the various test areas, the effect of par-
ticle size on pollution concentrations and equipment performance. The study
pointed out a number of elements that should be considered in designing an ef-
fective pollution abatement program.
One of these elements is the accumulation rate characteristics of street
dirt. Tables 2-1 and 2-2 summarize the observed accumulation rate conditions.
The study showed that accumulation rates vary widely in different test areas
depending on street surface conditions, land use, and activities within the
area. Street dirt loading was also found to increase more rapidly immediately
after street cleaning, and then level off somewhat after several days. This
loading pattern is expected to be due to wind and vehicle-caused turbulence
-------
TABLE 2-1. AVERAGE TOTAL SOLIDS ACCUMULATION RATE
Loading Immediately
After Cleaning
(Ib/curb-mile)
Accumulation Rate for Period
of Time Since Last Cleaned
(Ib/curb-mile/day)
Test Area
0+2 days
10 days 10 * 30 days
Keyes-good asphalt
Keyes-oil and screens
Tropicana-good asphalt
Downtown-good asphalt
Downtown-poor asphalt
290
1800
130
170
780
17
20
17
10
20
13
19
13
9
20
11
16
11
9
20
TABLE 2-2. ANNUAL AVERAGE ACCUMULATION RATES FOR VARIOUS POLLUTANTS*
(Ib/curb-mile/year)
Test Area
Keyes-Good Asphalt
Keyes-Oil and Screens
Tropicana-Good Asphalt
Downtown-Good Asphalt
Downtown-Poor Asphalt
Total
Solids
4000
5800
4000
3 TOO
7700
Ghemical
Oxygen
Demand
440
470
440
440
880
Kjeldahl
Ni trogen
8.4
8.6
8.4
6.2
18
Ortho-
Phosphates
0.62
0.37
0.62
0.47
1.1
Lead
20
7.3
20
20
15
Zinc
2.0
1.4
2.0
2.8
3.7
Chromium
1.5
2.0
1.5
1.8
3.5
Copper
2.5
2.9
2.5
3.5
7.3
Cadmium
0.009
0.008
0.009
0.01
0.02
*The overall annual average accumulation rate for mercury was 0.0015 1b/curb-mile/year,
and for asbestos was 3.7 x 10 fibers/curb-mile/year.
suspending the particles in the air, thus causing increased air pollution.
These characteristics should be considered in developing optimum street clean-
ing schedules.
Table 2-3 shows the median particle size of street surface particulates
(before street cleaning) for the five study areas. The areas with better qual-
ity street surfaces had more of the smaller sized particles present. The me-
dian particle size of street dirt was also found to increase with time between
cleaning and decrease with cleaning. Other tests also showed that street
cleaning equipment picks up larger particles more effectively than smaller
particles. As a result, the small particles tend to increase in abundance with
time. Most of the monitored pollutants showed increases in concentration as
particle size decreased. Thus, street cleaning equipment effectiveness at re-
moving pollutants in the smaller particle sizes must be considered. It is
important to note that street cleaning can remove important amounts of pollu-
tants: this is because they also occur in the larger particle sizes that com-
pose a greater amount of the total solids on the street than do the smaller
particle sizes. The analysis of particle size and pollution concentrations
-------
TABLE 2-3. MEDIAN PARTICLE SIZES OF STREET SURFACE PARTICULATES
Test Area
Median particle size (^)
(before street cleaning)
Keyes-good asphalt
Keyes-oil and screens
Tropicana-good asphalt
Downtown-good asphalt
Downtown-poor asphalt
200
330
150
155
230
makes it possible to assess removal capabilities for the various pollutants,
thus enabling design of control procedures to achieve specific pollutant re-
moval goals.
An important conclusion derived from the street cleaning equipment tests
showed that different test area conditions affected performance more than dif-
ferences in equipment type. Table 2-4 shows average street cleaning effective-
ness values for the different test areas. When the test area was held con-
stant, cleaning frequency and the number of passes affected performance more
than differences in equipment. Smoother (asphalt) streets were found to be
easier to keep clean than streets with oil and screens surfaces or those in
poor condition. The street surface loading values after cleaning were always
TABLE 2-4. AVERAGE REMOVAL EFFECTIVENESS FOR STREET CLEANERS
Total Solids
Test Area
Average
Loading
Before Percent
Cleaning Removal
Amount
Removed
Per Pass
(Ib/curb-
mile)
Amount Removed Per Pass (Ib/curb-mile)
Chemical
Oxygen Kjeldahl Ortho-
Demand Nitrogen Phosphates Lead Zinc Chromium Copper Cadmium
Ke yes-Good
Asphalt
Keyes-Oi1
& Screens
Tropicana-
Good
Asphalt
Downtown-
Good
Asphalt
Downtown-
Poor
Asphalt
400 33
2000
200 43
240 34
1400 40
130
170
100
83
540
16 0.28 0.018 0.81 0.079 0.051 0.081 0.00030
12 0.14 0.0089 0.15 0.066 0.071 0.13 0.00024
9.7 0.21 0.017 0.40 0.049 0.039 0.072 0.00027
11 0.16 0.012 0.49 0.072 0.047 0.093 0.0023
61 0.3 0.079 1.0 0.27 0.24 0.50 0.0015
-------
lower on the asphalt streets in good condition. These findings reinforce the
view that street cleaning programs should vary for different service area con-
ditions.
Results of the study showed that the pounds-per-curb-mile* unit is a much
more effective pollutant removal measurement than the percentage-of-initial-
loading-removed unit. Because of the wide variations in street dirt loadings
in different areas, the percentage of removal method cannot give a measurement
of the actual number of pounds of pollutants removed in a given time. Such in-
formation is required in order to make meaningful cost and labor effectiveness
estimates. Figure 2-1 relates the annual total solids removal with the street
cleaning frequency for different street surface conditions. Pollutant removal
per unit effort decreases with increasing numbers of passes per year.
UJ >.
QC^j
CO ~
50,000
40,000-
30.000-
20.000-
10.000-
Oil and screens surfaced
streets or asphalt streets
in poor condition
Asphalt streets in
good condition
10 100
NUMBER OF PASSES PER YEAR
1,000
Figure 2-1. Annual amount removed as a function of
the number of passes per year.
A model was also developed that describes the effects of parked cars on
street cleaning equipment performance, based on the distribution of partic-
ulates across the street for different parking conditions (Tables 2-5 and 2-6).
The need for parking controls was found to be dependent on street surface con-
dition and parking characteristics.
*See Metric Conversion Table 0-1.
-------
TABLE 2-5. AVERAGE TOTAL SOLIDS LOADING DISTRIBUTION ACROSS THE STREET
Test Area
Distance to Median
Loading Value (ft.)
Distance to 90% of
Loading Towards Curb (ft.)
Keyes-good asphalt
Keyes-oil and screens
Tropicana-good asphalt
6.5
1.5
1.0
14
6.7
3.8
TABLE 2-6. EFFECTS OF PARKED CARS ON CLEANING EFFECTIVENESS
Parking Regulations
Light
Percent Total Street Surface Solids Removal for the
Following Parking and Street Conditions
Smooth Streets
Moderate Extensive Extensive
Day/Night 24 hr.
Oil and Screened
Streets
Light Extensive
With parking prohi-
bition during street
cleaning* 48
No parking restric-
tions during street
cleaning** 36
44
20
28
23
15
43
15
13
*The street cleaner always operates next to the curb with 100% effective parking prohibitions.
**The street cleaner operates along the curb, except when going around parked cars.
PARTICULATE ROUTING AND POLLUTANT MASS FLOW CHARACTERISTICS OF URBAN RUNOFF
This portion of the study examined overall urban runoff flow characteris-
tics for the study areas, sampled the runoff to determine pollution concentra-
tions, investigated the pollutant removal effects and deposition patterns in
the sewerage for various storms, and compared runoff water quality with recom-
mended water quality criteria and sanitary wastewater effluent. Table 2-7 sum-
marizes the observed runoff water quality during this study.
The urban runoff flows were measured so that pollutant mass yields could
be calculated from the concentration values monitored in the sampling program.
These estimates indicated the potential effect urban runoff may have on receiv-
-------
TABLE 2-7. OBSERVED RUNOFF WATER QUALITY CONCENTRATIONS
Parameter, Units*
Common Parameters and Major Ions
pH, pH units
Oxidation Reduction Potential, mV
Temperature, °C
Calcium
Magnesium
Sodium
Potassium
Bicarbonate
Carbonate
Sulfate
Chloride
Solids:
Total Solids
Total Dissolved Solids
Suspended Solids
Volatile Suspended Solids
Turbidity, NTU**
Specific Conductance, jamhos/cm
Oxygen and Oxygen Demanding Parameters
Dissolved Oxygen
Biochemical Oxygen (5-day)
Chemical Oxygen Demand
Nutrients:
Kjeldahl Nitrogen
Nitrate
Ort ho phosphate
Total Organic Carbon
Heavy Metals:
Lead
Zinc
Copper
Chromium
Cadium
Mercury
Number of
Analyses
88
39
11
5
5
5
5
5
5
5
5
20
20
20
10
88
88
.
11
13
13
13
5
13
5
11
11
11
11
11
11
Minimum
6.0
40
14
2.8
1.4
<0.002
1.5
<1
<0.001
6.3
3.9
110
22
15
5
4.8
20
5.4
17
53
2
0.3
0.2
19
0.10
0.06
0.01
0.005
< 0.002
< 0.0001
Maximum
7.6
150
17
19
6.2
0.04
3.5
150
0.005
27
18
450
376
845
200
130
660
13
30
520
25
1.5
18
290
1.5
0.55
0.09
0.04
0.006
0.0006
Average
6.7
120
16
13
4.0
0.01
2.7
54
0.019
18
12
310
150
240
38
49
160
8.0
24
200
7
0.7
2.4
110
0.4
0.18
0.03
0.02
< 0.002
< 0.0001
*mg/l unless otherwise noted
**Nephelometric turbidity units
-------
ing waters. The general hydrographic information from the study may also be
useful in verifying simple urban runoff models.
The runoff sampling program yielded several important conclusions. BOD
values were of particular interest because BOD can cause immediate and impor-
tant oxygen demands on receiving waters. Determining the actual rate of this
demand is important in determining the actual effect of BOD on receiving waters
and in designing effective control procedures. The study showed an unexpectd
increase by a factor of 2 or more (from about 30 mg/1 to about 100 mg/1) in BOD
values during the 10- to 20-day incubation period of the tests. Sanitary waste-
water BOD values typically increase by a factor of only about 0.5 during the
same time period. This apparent increase in BOD may be caused by inadequacies
in the standard BOD bottle test, or it may indicate that the long-term effects
of BOD from urban runoff on receiving waters may be more important than short-
term effects.
The relative strengths of pollutants in the runoff were compared with con-
centrations in the street dirt samples to determine the extent to which street
dirt was responsible for these pollutants. The study showed that monitored
heavy metal concentrations were much smaller in the runoff than in the street
dirt, and organics and nutrient concentrations were much larger. These data
indicate that street activity is probably responsible for most of the heavy
metal yields, while runoff and erosion from off-street areas during storms is
probably responsible for most of the organic and nutrient yields. Thus, if or-
ganics and nutrients must be significantly reduced in the runoff, street clean-
ing alone may not be sufficient.
The pollutant removal capabilities of various storms were studied because
of their effect on the loadings remaining on the streets after storms, and the
flow and deposition patterns of solids in the sewarage. The monitored storms
had a much smaller removal effect in the oil and screens test area than in the
test areas with asphalt streets. Interestingly, the first storm (which had a
much greater intensity than the other two storms monitored) showed smaller
relative removals, probably because larger amounts of eroded material were
washed onto the streets. The two less intense storms were capable of almost
completely removing street surface particulate material from the asphalt
streets without causing large amounts of erosion. Comparisons of the street
loading removal values with runoff yields measured at the outfall showed that
the two less intense storms deposited more material in the sewerage than did
the first storm, with its high runoff volume and flow velocity.
Frequent street cleaning on smooth asphalt streets (once or twice per day)
can remove up to 50 percent of the total solids and heavy metal yields of urban
runoff. Typical street cleaning programs (once or twice a month) remove less
than 5 percent of the total solids and heavy metals in the runoff. Organics and
nutrients in the runoff cannot be effectively controlled by intensive street
cleaning—typically much less than 10 percent removal, even for daily cleaning.
The comparison of runoff pollutant concentrations with recommended water
quality criteria (Table 2-8) showed that the heavy metals—cadmium chromium,
lead, copper, mercury, and zinc—as well as phosphates, BOD, suspended sol-
10
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TABLE 2-8. RECOMMENDED BENEFICIAL USE CRITERIA EXCEEDED BY RUNOFF
Beneficial Use Parameters Exceeding Recommended Criteria
Livestock lead*
Wildlife none
Aquatic life chromium, cadmium*, lead*, mercury*, biochemical
oxygen demand, turbidity, suspended solids
Marine life phosphates*, cadmium, copper, zinc
Recreation phosphates*
Public Fresh-
water Supply cadmium, lead*
Irrigation cadmium
*The maximum observed value was >10 times the minimum recommended criteria
ids, and turbidity exceeded some recommended water quality criteria. That
does not necessarily mean that a problem exists. However, a problem may 'arise
for these parameters and they should be investigated further in receiving
waters. The study showed that aquatic life beneficial uses can be adversely
affected by more pollutants than other beneficial uses.
Table 2-9 compares observed runoff water quality with treated secondary
sanitary wastewater effluent water quality. The concentrations of many pollu-
tants in the runoff samples were greater than in secondary treated sanitary
wastewater effluent. Annual yield comparisons showed that the yields for lead,
chromium and suspended solids were greater in the street surface portion of the
runoff than in the treated secondary effluent. Thus, urban runoff may cause
some greater short- and long-term receiving water pollution problems than the
treated sanitary wastewater effluent. Street cleaning and/or runoff treatment
may be a more effective control measure than further improvement in treated
sanitary wastewater effluent quality for some of the parameters.
COST AND SELECTION OF CONTROL MEASURES
This portion of the study assessed the cost and labor effectiveness of
various nonpoint pollution control measures: street cleaning, runoff treatment,
erosion control, and combined runoff and wastewater treatment.
San Jose's street cleaning costs for the study period (1976-1977) averaged
about $14 per curb-mile cleaned and required about 0.9 man-hours per curb-mile
cleaned. The cost and labor requirement analyses of street cleaning showed
several important factors. First, street cleaning is labor-intensive* in re-
*The majority (about 75 percent) of San Jose's street cleaning costs were for
labor.
11
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TABLE 2-9. COMPARISON OF RUNOFF WATER QUALITY TO TREATED SECONDARY WASTEWATER
EFFLUENT WATER QUALITY
Runoff parameters that exceed the corresponding treated secondary sanitary
wastewater effluent parameters for the following conditions:
Average Runoff
Concentrations
Peak Runoff Concentrations
Annual Runoff
Yield***
Biochemical oxygen demand
Chemical oxygen demand
Suspended solids
Total organic carbon
Turbidity
Lead*
Zinc
Ca dmi urn
Chromium
Biochemical oxygen demand
Chemical oxygen demand*
Suspended solids*
Total organic carbon
Turbidity
Lead**
Zinc
Cadmium*
Chromium
Copper
Suspended solids
Lead*
Chromium
* The runoff condition is >10 times the sanitary wastewater effluent condition.
** The runoff condition is >100 times the sanitary wastewater effluent condition.
*** The runoff annual yield only represented the street surface portion of the
total runoff.
lation to other control methods—a charcteristic that must be considered so-
cially beneficial. Second, maintenance costs composed about 30 percent of total
program costs in this study. The remaining 70 percent were for capital and
operational costs. Thus, equipment replacement for reducing costs would achieve
a maximum cost savings of much less than 30 percent. Other costs are constant
and would not vary significantly for different types of currently available
street cleaning equipment. Figure' 2-2 shows that the cost to remove a pound of
street dirt increases with increasing numbers of cleaning passes in a year. A
cost increase of about tenfold over typical street cleaning program costs may
be necessary to realize substantial improvements in urban runoff water quality
(greater than 25 percent removal of total solids and heavy metals). Increased
street cleaning costs would benefit areas not affected by other typical urban
runoff control measures such as air quality, public safety, and litter.
When all costs for the various control measures were considered, per unit
pollutant removal costs for street cleaning (Table 2-10) were found to be sig-
nificantly less than those for separate runoff treatment costs. The study in-
dicated that combined sewage and runoff treatment costs for the facility con-
sidered were somewhat less than for special runoff facilities. However, costs
of heavy metal runoff treatment could not be considered because of a lack of
12
-------
0 0.25 J
LJJ
0.20-
D
P 0.15-1
QC
UJ
Q.
0.10 -
O
Q 0.05 -
Asphalt streets in
good condition
Oil and screens surfaced
streets or asphalt streets
in poor condition
10 100
NUMBER OF PASSES PER YEAR
1,000
Figure 2-2. Costs to remove a pound of street dirt as a
function of the number of passes per year.
TABLE 2-10.
Parameters
Minimum
Maximum
Average5
Total Solids
Suspended Solids**
Chemical Oxygen Demand
Biochemical Oxygen Demand**
Orthophosphate
Kjeldahl Nitrogen
Lead
Zinc
Chromium
Copper
Cadmium
0.03
0.05
0.23
0.46
180
11
14
52
58
28
6100
0.17
0.33
1.4
2.9
1600
100
93
290
360
190
58,000
0.11
0.21
1.0
2.0
920
63
38
180
240
130
34,000
*These values are averaged for the different test areas.
**Estimates.
13
-------
data. Costs to remove heavy metals from runoff are expected to be much greater
than the street cleaning costs. It should be added that other control measures
affect only water quality, while street cleaning has multiple benefits and can
also improve air quality, aesthetic conditions, and public safety.
DUST LOSSES FROM STREET SURFACES TO THE AIR
This portion of the study investigated dust (fugitive particulate) concen-
tration increases and emissions from street surfaces. Various influencing fac-
tors such as traffic density, weather conditions, and street surface conditions
were also monitored. The loading of particulates on the street surface is be-
lieved to be an important factor in the level of these emissions, and improved
street cleaning may play an important role in their control. Downwind roadside
particulate concentrations were found to be about 10 percent greater than up-
wind concentrations (on a number basis). About 80 percent of the concentration
increases, by number, were associated with particles in the 0.5 to 1.0 micron
size range, but about 90 percent of the particle concentration increases, by
weight, were associated with particles greater than 10 microns. The study
showed that street surface particulate accumulation rates decrease with the
passage of time after street cleaning or a significant rain. It is thought that
this decrease is caused by particulate losses to the air. Differences between
initial street surface particulate accumulation rates and the lower rates ob-
served several days after street cleaning were used to estimate dust losses.
These calculations showed that about one week after street cleaning, approx-
imately 4 to 6 Ib/curb-mile per day of particulates were lost to the air. This
loss rate corresponds to an automobile use emission rate of about 0.66 to 18
grams per vehicle-mile. This rate increases for longer cleaning intervals and
varies widely for different conditions.
Dust levels in the cabs of street cleaning equipment were also investi-
gated with and without the use of the water spray. The study showed that, for a
state-of-the-art four-wheel mechanical street cleaner, the water spray was very
effective in controlling dust inside the cab and in the immediate vicinity of
the street cleaner. The spray, however, did not significantly reduce the total
high dust levels in the area immediately behind the street cleaner.
14
-------
SECTION 3
STREET CLEANING EQUIPMENT TESTS
SUMMARY
The objectives of the study of street cleaning equipment performance were:
• To determine the accumulation rate of street surface particulates
between each street cleaner test.
• To determine the characteristics of street dirt in relation to
particle size and concentrations of specific pollutants.
• To investigate various street cleaning practices under actual field
conditions (including various street surface conditions, residual
particulate loading, traffic density, parked car, and climatic con-
ditions) in order to determine the range of possible cleaning per-
formances offered by current types of street cleaning equipment.
Accumulation Rates
The accumulation rate characteristics of street surface contaminants must
be known in order (1) to understand the magnitude of the problem a street clean-
ing program must address , and (2) to determine the most effective control methods.
This study showed that the accumulation rates varied widely from test area to
test area. These variations are thought to be due to street surface conditions
and to land-use patterns and activities within the test area (e.g., vacant lots,
commercial development, pedestrian and automobile traffic, and parking). Such
variations should be considered in scheduling street cleaning programs for dif-
ferent types of areas.
The study also showed that the median particle size of street surface con-
taminants increased with time between street cleaning, then decreased with
cleaning. These data also show that street cleaning equipment picks up large
particles much more effectively than small particles. Thus the small particles,
which have higher concentrations of pollutants, tend to build up on the street
surface.
The loading was found to increase more rapidly immediately after the street
was cleaned; accumulating rates decreased as the number of days after street
cleaning increased, probably because wind and automobile-related "air turbulence
suspend the particles in the air. This should be considered in establishing
optimum street cleaning frequencies. It should be remembered that although
15
-------
longer periods between street cleaning may not result in similarly increased
loadings, they could cause greater road-side airborne particulate concentrations
(see Section 6).
Effects of Particle Size
Because street cleaning equipment performance varies with particle size,
analyses based on particle size groupings were necessary to determine street
cleaning performance for specific pollutants. Almost all of the monitored
pollutants showed increases in concentration as particle size decreased. Street
cleaning equipment was also found to be more effective at removing larger,
aesthetic-related particles than at removing smaller particles that have
generally higher pollutant concentrations. It is important to note, however,
that street cleaning equipment can remove important quantities of these pol-
lutants under many conditions. Typically, a much greater quantity of the total
solids on the street is of the larger particle sizes. Even though concen-
trations of the monitored pollutants are not as high in the larger particle
sizes, important amounts are found in them because of their greater quantity.
Assessemnts of removal capability for various pollutants can indicate what mix
of control measures should be used to achieve specific goals.
Equipment Performance
The equipment performance tests showed that the differences in test areas
affected the initial (before cleaning) and residual (after cleaning) loadings
much more than differences in equipment type. Furthermore, within any one test
area, the cleaning frequency and number of passes influenced before and after
loadings much more than differences in equipment type. It was found that smoother
streets (asphalt) can be maintained in a much cleaner condition than rougher-
surfaced (oil and screens) streets or streets in poor condition. Street cleaning
programs should, therefore, vary for different street surface conditions.
Because of the variability in initial loadings in different areas, it is
important to measure cleaning effectiveness on a pounds-removed-per-curb-mile
basis rather than on a percentage-of-initial-loading-removed basis. For example,
removing a small percentage of the initial loading in a dirty industrial area
could remove more pollutants than removing a high percentage of the initial
loading in a clean commercial area. The pounds-removed-per-curb-mile value is
necessary in designing a program to meet a goal of removing a certain number
of pounds of pollutant in a given time. This measurement also makes it possible
to compare the unit costs ($/lb* removed) and unit labor (man-hr/lb* removed)
requirements of street cleaning with these values for alternative control measures.
STRUCTURE OF THE STUDY
Several street cleaning programs using various types of equipment and
levels of effort were evaluated. This evaluation was the major element of
*See Metric Conversion Table 0-1.
16
-------
the demonstration project. The following types of street cleaning equipment
were studied under various operating conditions and cleaning frequencies:
• four-wheel mechanical street cleaner
• state-of-the-art mechanical four-wheel street cleaner
• vacuum-assisted street cleaner
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 dif-
ferent generic types and because they were readily 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
operating conditions. The scope and intent of this project was to demonstrate
the range of possible cleaning effectiveness of different types of street clean-
ing equipment under a variety of real-world operating conditions. The available
resources for the project required that the study be conducted in one city
with a limited selection of available equipment.
Street cleaning equipment performance is thought to be very sensitive to
operator and maintenance skill. The equipment must be adjusted adequately and
maintained and operated in a manner to optimize debris removal and minimize
costs. The operators and maintenance personnel used during these tests were
supplied by the manufacturers and by the city of San Jose's Public Works De-
partment. They were all well trained and skilled and operated the test equipment
in an optimum and recommended manner.
Eight potential study areas were considered within the city of San Jose.
Three were selected as being representative of the variety of conditions found
in San Jose and many other cities: the Tropicana study area, the Keyes Street
study area, and a Downtown study area. The selection criteria and more specific
information about the study areas are found in Appendix C.
Because of variable street surface conditions, the Downtown and Keyes
Street study areas were divided 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 - good asphalt street surface test area
Keyes Street - good asphalt street surface test area
Keyes Street - oil and screens street surface test area
Downtown - good asphalt street surface test area
Downtown - poor asphalt street surface test area
Figure 3-1 shows the San Francisco Bay Area and the general location of
the city of San Jose. Figure 3-2 shows the three study areas selected and their
location within the city of San Jose.
17
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10 15
miles
Figure 3-1. San Francisco Bay Area showing the general
location of the City of San Jose.
18
-------
Figure 3-2. Map showing the location of the three study areas.
19
-------
The cleaning frequencies used in this study ranged from two passes every
day to one pass every seven weeks. Each piece of equipment was evaluated
in the field during two different seven-week periods: once in the first and
once in the second phase (with the exception of the vacuum-assisted street
cleaner). The first two weeks of each seven weeks of equipment evaluation us-
ed daily cleaning. A single pass was made every weekday during the first week
and two passes were made each weekday during the other week. The last five
weeks of each test period used weekly cleaning intervals. Equipment was rota-
ted through the different testing areas at the end of each cleaning period.The
test schedule is shown in Table 3-1. One hundred sixty-three cleaning passes
were conducted, and about 20,000 samples were collected during the demonstra-
tion project in the test areas. This schedule allowed the different character-
istics and long-term seasonal differences in the test areas to be included in
the evaluation of the range of equipment effectiveness.
In addition to cleaning the specific test area, an adjacent buffer zone
up to three times the size of the test area was also cleaned in order to reduce
potential edge effects (tracking of particulates into the test areas from the
adjacent areas, which were usually significantly dirtier or cleaner).
The long-term and frequent sampling in the test areas made it possible
to directly measure accumulation rates of street surface contaminants. Street
surface samples were collected within a few hours before and after street cleaning
by the procedures described in Appendix A. The idealized loading pattern re-
sulting from sampling at these intervals, a sawtooth pattern depicting the
deposition and removal of street surface particulates, is illustrated in Figure
3-3. The accumulation rate can be determined by calculating the angle of the
slope between adjacent sampling periods. The two factors affecting the accumu-
lation rate are the deposition rate and the removal rate.* The deposition rate
Street
cleaned
Street
cleaned
Street
cleaned
Period of
street surface
sampling
Participate
loading
Actual load
Residual loading
Clean street
Time
Figure 3-3. Sawtooth pattern associated with deposition
and removal of particulates.
*Accumulation rate = deposition rate - removal rate.
20
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TABLE 3-1. STREET CLEANING SCHEDULE FOR SAN JOSE STUDY AREAS
5 -Day
Work Week
12/13 - 12/17/76
12/20 - 12/24
12/27 * 12/31
1/3 - 1/7/77
1/10 - 1/14
1/17 - 1/21
1/24 > 1/28
1/31 * 2/4
2/7 - 2/11
2/14 «• 2/18
2/21 + 2/25
2/28 > 3/4
3/7 - 3/11
3/14 + 3/18
3/21 + 3/25
3/28 - 4/1
4/4 * 4/8
4/11 + 4/15
4/18 > 4/22
4/25 + 4/29
5/2 •> 5/6
5/9 > 5/13
5/16 + 5/20
5/23 > 5/27
5/20 - 6/3
6/6 * 6/10
6/13 + 6/17
6/20 > 6/24
6/27 * 7/1
7/4 > 7/8
7/11 + 7/15
7/18 > 7/22
7/25 > 7/29
8/1 * 8/5
8/8 - 8/12
8/15 > 8/19
8/22 > 8/26
8/29 * 9/2
9/5 * 9/9
9/12 + 9/16
9/19 * 9/23
Equipment Type and Number of
Downtown Keyes
A- 5
A-l
A-l
A-l
A-l
B-10
B-l
B-l
B-l
B-l
B-l
C-10
A- 10
A-l
A-l
A-l
A-l
A-l
B-5
B-l
B-l
B-l
B-l
B-l
Passes per Week
Tropicana
A- 10
B-5
C-5
C-l
C-l
C-l
C-l
C-l
A- 5
A-l
A-l
A-l
A-l
A-l
B-10
B-l
B-l
B-l
B-l
B-l
Notes: A = 4-wheel mechanical street cleaner
B = state-of-the-art 4-wheel mechanical street cleaner
C = 4-wheel vacuum-assisted mechanical street cleaner
21
-------
is a function of the characteristics of the area, such as climate, land use,
traffic, and street surface conditions. Removal can occur by street cleaning
or naturally by winds or rains.
The data collected in these test areas were also used to identify the
range of performances that may be expected from currently available street clean-
ing equipment. Differences of removal values (Ib/curb-mile removed) instead of
percentage removals (percentage of initial loading removed) for the various
test conditions are used as a more meaningful measure of equipment performance.
ANALYTICAL PROGRAM
The design of the sampling program required decisions as to the method
of sample collection (see Appendix A) and the extent of sampling (see Appendix
B). Because the objectives of this project were unique, new procedures had
to be carefully developed so that the sampling program could yield sufficient
information. The following elements summarize the particulate sample analysis
program:
• Estimates of the volume of the hopper contents in the street cleaning
equipment were made after each test; the hopper contents were also
sampled and analyzed for particle size distributions.
• All samples (accumulation, hopper, across-the-street, driving lane,
and before and after tests) were sieved for particle size analyses
by using a 0.25-in. wire screen; Tyler screens numbered 10 (2000 y)
20 (850 y) 30 (600 y) 60 (250 y) 140 (106 y) and 325 (45 y); and
the pan.*
• The bulk density of each of the above sieved samples was determined.
• The loading (Ib/curb-mile) of each particle size was calculated for
accumulation and test samples; the percentage of sample in each size
was also calculated for accumulation, hopper, and test samples.
• The before and after test samples for each size, each test area, and
each equipment test phase were combined for the following analyses:**
Lead (Pb) Kjeldahl nitrogen
Zinc (Zn) Total orthophosphates (Ortho PO^)
Chromium (Cr) Mercury (Hg) (16 analyses only)
Copper (Cu) Asbestos (8 analyses only)
Cadmium (Cd)
Chemical oxygen
demand (COD)
*The pan collects all of the material passing through the finest screen.
**Approximately 8 sizes x 3 test areas x 5 equipment test phases = 120
samples.
22
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CONCENTRATIONS OF STREET SURFACE CONTAMINANTS AS A FUNCTION OF PARTICLE SIZE
Previous studies (Sartor and Bo yd 1972; Pitt and Amy 1973) have demon-
strated 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
sizes can vary significantly, and analyses of different particle sizes yield
more useful information than total sample analyses.
Each collected sample was divided into eight particle sizes:
(<45 y; 45 * 106 y; 106 -»• 250 y; 250 - 600 y;
600 * 850 u; 850 * 2000 y; 2000 - 6370 y; and >6370y).
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.
Tables E-l through E-5 of Appendix E present all the particle size pol-
lutant concentration data obtained during the project, while Figures E-l
through E-10 graphically summarize pollutant concentrations for the first test
phase. Figures are presented for chemical oxygen demand (COD), total ortho-
phosphates (Ortho P04), Kjeldahl nitrogen, lead (Pb), zinc (Zn), chromium (Cr),
copper (Cu), and cadmium (Cd) for each of the five test areas and for eight
particle sizes, plus a weighted average for most of the samples. The weighted
average is based on the total calculated loadings for each test area and pa-
rameter. Figures E-9 and E-10 present mercury and asbestos concentrations as
a function of particle size for all test areas combined.
The pollutant strengths are presented as milligrams of pollutant per kilo-
gram of total solids (equivalent toppm), except for asbestos, which is expressed
as fibers per gram of total solids. 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 the highest
concentrations with smaller particle sizes, while copper and chromium show the
lowest concentrations with the smallest particle size. The asbestos information
presented is 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 short-
est observed maximum fiber lengths.
Figure 3-4 shows the particle size distribution for each test area. This
figure is based on the "initial" loading samples (samples collected immediately
before the streets were cleaned) to minimize the effects of street cleaning on
the particle size distribution. The average median particle sizes ranged from
about 150 u to 400 y, with asphalt streets in good condition having the smallest
median particle sizes and the poor condition asphalt streets and oil and screens
surfaced streets having the largest particle sizes.
Only the oil and screens test area had significantly different pollutant
strengths associated with the different particle sizes than the other test areas.
The oil and screens pollutant concentrations are generally less (by about half)
than the concentrations from the other test areas. This reduction is due to
large quantities of street wear products "diluting" the pollutants originating
23
-------
Tropicana - Good Asphalt
Keyes - Good Asphalt
Keyes - Oil and Screens
Downtown - Good Asphalt
Downtown - Poor Asphalt
1000
I I T I I
10.000
PARTICLE SIZE
Figure 3-4. Particle size distribution of "initial" loading samples.
from other source areas (such as vehicle wear products and local erosion). None
of the different test periods had significantly different pollutant strengths.
The pollutant strengths observed were all within the range of strengths reported
in previous investigations, as shown on Table 3-2. This particle size information
was used to determine the accumulation rates and street cleaning equipment per-
formance for the different pollutants.
24
-------
TABLE 3-2. AVERAGE NATIONWIDE POLLUTANT STRENGTHS ASSOCIATED
WITH STREET SURFACE PARTICULATES
Parameter
(ppma except as noted)
BOD5 (b)
COD (b)
Ortho POA (b)
Total P04 (b)
N03 (b)
NH4 (b)
Kjeldahl N (b)
Cd (b)
Cr (b)
Cu (b)
Fe (b)
Pb (b)
Mn (b)
Ni (b)
Sr (b)
Zn (b)
Total coliforms
(no. /gram (d)
Fecal coliforms
(no. /gram) (d)
Asbestos (fibers/gram) (c)
Rubber (c)
p, p-DDD (d)
p, p-DDT (d)
Dieldrin (d)
Endrin (d)
Lindane (d)
Methoxychlor (d)
Methyl parathion (d)
PCBs (d)
Mean Minimum
Strength Strength
70,000e
140,000
1300
2900
800
2600
3000
3.4
210
100
22,000
1800
420
35
21
370
2.5xl06
1.7xl05
160,000
4600
0.082
0.075
0.028
0.00028
0.0022
0.50
0.0024
0.77
8500e
17,000
14
210
20
600
450
0
3
8
2200
0
100
0
0
21
1.2xl04
6.
0
500
0.
0.
0.
Maximum
Strength
270,000e
530,000
6700
5400
16,000
5400
13,000
25
760
290
72,000
10,000
1600
170
110
1100
8.6xl07
0 1.7xl07
770,000
11,000
0002 0.27
0004 0.38
003 0.074
0 0.0022
0 0.019
0 3.1
0 0.022
0.07 2.3
Standard Ratio of Standard
Deviation Deviation to Mean
80,000e
160,000
1400
f
2600
f
3100
3.6
110
100
11,000
2,000
220
38
21
210
8
8
180,000
2,600
0.080
0.12
0.028
0.00073
0.0063
1.1
0.0073
0.76
1.1
1.1
1.1
-
3.3
-
1.0
1.1
0.52
1.0
0.50
1.1
0.52
1.1
1.0
0.57
-
-
1.1
0.57
0.98
1.6
1.0
2.6
2.9
2.2
3.0
1.0
appm = microgram of pollutant per gram of total dry solids; the mean total solids (b) accumulation was 150
Ib/curb-mile/day, with a range of 3 to 2700 and a standard deviation of 370 Ib/curb-mile/day.
Amy, et_ _a_l. (1974) - a compilation of the results of many studies
°Shaheen (1975)
dSartor and Boyd (1972)
eBOD = 1/2 COD (see Colston, 1974)
Few samples (less than 10)
°Very large variance.
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 in the less abundant, smaller
particle sizes. Therefore, it may be difficult to meet objectives unless extra
effort is expended. However, street cleaning may remove important amounts of
these pollutants because they are also found in the more abundant larger particle
sizes. The effectiveness of street cleaning, therefore, depends on the specific
service area characteristics and program objectives.
25
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DETERMINATION OF ACCUMULATION RATES OF STREET SURFACE CONTAMINANTS
This portion of the study was aimed at determining specific accumulation
rates in the test areas. This information must be known before an effective
street cleaning program can be designed. The rainfall pattern during the time
of the study was examined and those periods in which rains had caused significant
natural removal of street surface contaminants were eliminated from analyses.
In order to determine accumulation rates of different pollutants, the samples
were analyzed on a particle size basis as described above. This procedure was
essential because different particle sizes have different concentrations of pol-
lutants. Equipment performance also varies with particle size, which affects
the overall amount of various pollutants that can be removed by street cleaning.
Sources of Street Surface Contaminants
Most of the street surface contaminants (by weight) are a function of the
local geological conditions, with added fractions resulting from motor vehicle
emissions and wear. For smooth streets in good repair, minor contributions
are made by wear of the street surfaces. The specific make-up of street surface
contaminants is a function of many site conditions and varies widely.
Table 3-3 presents chemical analyses for some possible street comtaminants.
Most of the materials listed are high in volatile solids. Brake linings contribute
extremely high concentrations of lead, chromium, copper, and nickel. Rubber
has high concentrations of lead and zinc. Asphalt pavement has a high concentration
of nickel. Cigarettes have high concentrations of lead, chromium, copper, nickel
and zinc (Shaheen 1975).
Usually, most street surface particulates are the products of erosion of
local soils. Nitrogen and phosphorus are contributed by local plants and soils
and are carried onto the street surface by rain, wind, and traffic. Potentially
adverse quantities of polychlorinated biphenyls (PCBs) have also been shown
to originate from local soils (Shaheen 1975).
Although a small percentage (by weight) of the street surface pollutants
results from wear and emissions from motor vehicles, the toxicity of these
contaminants increases their importance. Deposits of grease, petroleum, and
n-paraffin can result from spills or leaks of vehicle lubricants, antifreeze,
or hydraulic fluids. Phosphorus and zinc, used as oil additives, can also
be deposited from spills. Lead deposits can be deposited from spills or leaks,
or combustion of leaded fuels, and (along with zinc) from tire wear. Asbestos
can be deposited from wear of the clutch, brake linings, and tires. Copper,
nickel, and chromium can be deposited from wear of metal from platings; bearings,
and othermoving parts. Roadway abrasion is another source of street pollutants,
although studies showthatsuch contributions, for smooth streets in good repair,
are insignificant compared to contributions due to traffic activities and erosion
of local soil (Shaheen 1975).
Chlorides are deposited primarily from deicing compounds with some additional
chlorides resulting from roadway abrasion and local soils. Chloride accumulation
in regions with snow is probably traffic-dependent because of the application
of more deicing material on well-traveled streets.
26
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TABLE 3-3. ANALYSIS OF POSSIBLE STREET SURFACE CONTAMINANTS
Material
Gasoline
Lubricating Grease
Motor Oil
Transmission Fluid
Antifreeze
Undercoating
Asphalt Pavement
Concrete
Rubber
Diesel Fuel
Brake Linings
Brake Fluid
Cigarettes
Saltb
Cinders
Area Soilc
Material
Gasoline
Lubricating Grease
Motor Oil
Transmission Fluid
Antifreeze
Undercoating
Asphalt Pavement
Concrete
Rubber
Diesel Fuel
Brake Linings
Brake Fluid
Cigarettes
Salt
Cinders
Area Soil
Detection Limit
Tot. Vol.
Solids
Og/g)
1000
970
1000
1000
990
1000
64
71
990
1000
290
1000
860
75
0.0
—
Lead
(wg/g)
660
<2
9
8
6
120
100
450
1100
12
1100
7
490
2
<2
<2
2
BODa
Og/g)
150
140
140
100
38
90
1.2
1.4
27
80
17
26
85
-
-
—
Mercury
(ug/g)
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
<0.05
0.05
COD
(mg/g)
680
-
220
200
1100
310
86
64
2000
400
420
2400
780
-
59
-
Chromium
(vg/g)
15
<2
<2
<2
<2
<2
360
93
180
15
2200
19
71
2
<2
36
2
Grease
Og/g)
1.3
750
990
990
140
960
21
2.7
190
390
31
880
30
0
1.3
-
Petroleum n-Paraffins
Og/g)
1.3
670
940
940
70
180
15
1.3
100
310
8.3
33
21
0
1.2
Copper
(vg/g)
4
<1
3
<1
76
1
50
99
250
8
31,000
5
720
2
3
23
1
Nickel
(ug/g)
10
<1
17
21
16
480
1200
260
170
8
7500
31
190
9
4
25
1
Og/g)
1.3
570
850
880
6.1
120
9
1
56
210
7.6
19
2.7
0
1.2
Zinc
(ug/g)
10
160
1100
240
14
110
160
420
620
12
120
15
560
1
7
27
0.01
Source: Shaheen 1975
BOD determinations were made on
sewage organisms.
'pure" materials using a seed of unacclimated
Results are on a dry weight basis. Salt as received contained 3.7% water,
assayed 93.2% sodium chloride, and contained less than 0.005% cyanide.
Soils from the Washington, D.C. area contained a magnetic fraction of from
8.9% to 12.5%, less than 0.05 mg rubber per gram, less than 3 x 105 asbestos
fibers per gram, 50 to 100 mg/g volatile solids and 15 to 80 mg/g COD.
27
-------
Other categories of pollutant sources occur which are specific to a particular
area and on-going activities. For example, iron oxides are associated with
welding operations; strontium, used in the production of flares and ^reworks,
would probably be found on the streets in greater quantities around holiday
times or at the scenes of traffic accidents.
Appendix G and Section 4 discuss the relative contributions of the street
surf ace loadings to the total storm runoff yields. A current project (Source-Area
Contributions for Urban Runoff, Grant No. R805418) currently being conducted
in San Jose will result in additional information on this subject.
Long-Term Loading Variations
Figures D-l through D-5 of Appendix D present the rainfall history in
the study areas by time during the testing period.
The runoff monitoring program is discussed in Section 4 of this report.
During the testing phase of this study, significant rains occurred on a total
of 11 days, while measurable rains occurred on a total of 36 days.
A significant rain is one that is expected to remove a large portion of
the street surface contaminants present before the storm. However, these rains
can also add material to the street surface during the rain through erosion
of adjacent areas. A significant rain is defined as having a total rainfall
of about 0.2 in. or greater within about one day (irrespective of traffic con
ditions), or a peak instantaneous rainfall intensity of 0.5 in. per hour with
little or no traffic, or an average intensity of 0.1 in. per hour or greater
with moderate to heavy traffic. Rains and traffic conditions meeting one of
these sets of criteria are believed to be capable of imparting enough energy
to the street surface to loosen street surface contaminants and to supply enough
water to flush these contaminants along the street surface and gutters to storm
sewerage inlets. Enough water may not be available to carry the particulars
through the storm sewerage and out the outfall. This would result in deposition
of solids in the sewerage (see Section 4). Rainfall intensity and removal effec-
tiveness relationships were studied by Sartor and Boyd (1972) and discussed by
others (including Pitt and Field 1977).
Figures D-6 through D-22 of Appendix D present total street surface par-
ticulate loadings and median particle sizes as a function of time. These figures
show a sawtooth pattern similiar to that shown in Figure 3-3 for the total
solids loading conditions over much of the study period. Some unexplained
decreases in loadings are also periodically shown. It is thought that these
decreases in loadings may be caused by high winds. Significant rains in some
cases cause a decrease in street surface loadings, while they cause an increase
in others. Increases are thought to be caused by erosion. The median particle
size of street surface particulates also decreases with street cleaning and
increases with time until recleaned. The median particle size can decrease
either with removal of larger particles or with an increase in the quantities
of smaller particles. Decreases in median particle sizes were caused by the
removal of larger particle sizes during street cleaning operations. A more de-
tailed discussion of street cleaning performance as a function of particle size
is given later in this section.
28
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Accumulation Rates of Specific Pollutants
As described previously, all of the test and accumulation samples were
separated by particle size. Samples of each particle size category for each
test area and equipment type were then analyzed for the various pollutants.
Figure 3-5 shows computer assisted curves of total solids street loadings
as a function of time since last cleaned. All measured street surface loading
values (by particle size) and associated time periods since last cleaned were
grouped by test area and season, and computer analyzed to identify the best
fitting curves. Loading values that were affected by rains were eliminated
from the analyses. First, second and third order polynomial curves, with and
without logarithmic (natural) data transformations, were used. The data showed
considerable spread, with correlation coefficient (rz) values for the curves
used ranging from 0.35 to 0.9 (a correlation coefficient of 1.0 corresponds
to a "perfect fit" curve). Seasonal differences were not definitive because
of fewer resultant data points per curve and larger variations. Figure 3-5 is
highly influenced by the residual loading values, which are generally the
"cleanest" the streets can be, and are usually the loading values immediately
after street cleaning; however, streets after certain rains can be cleaner.
2500-
2000-
1500-
1000-
500
„.. Keyes oil and screens
—***
•*
• — Downtown poor asphalt (winter only)
Keyes - good asphalt
•» -—
Tropicana - good asphalt
Downtown - good asphalt (winter only)
10
20
30 40 50 60 70
DAYS SINCE LAST CLEANED
Figure 3-5. Total solids accumulation since last cleaned (all seasons combined).
29
-------
The resulting loadings were quite different for each test area. The accumulation
rates for the different test areas were much more similar than the loading
values. The good condition "asphalt" test areas had the smallest loading values
at any one time, while the oil and screens test area and poor condition asphalt
test area had the largest loadings. No radical leveling off of the loadings
occurred, although the rate of loading gains decreased with time. Table 3-4
presents calculated annual average accumulation rates for the various pollutants
and for each test area.
TABLE 3-4. ANNUAL STREET SURFACE POLLUTANT ACCUMULATIONS
(lb/curb-mile/year)
Study Area Total Chemical
Solids Oxygen Kjeldahl Ortho-
Demand Nitrogen Phosphates Lead Zinc Chromium Copper Cadmium
Keyes and 4000 440 8.4 0.62
Tropicana -
good asphalt
Keyes-oil and 5800 470 6.6 0.37
screens
Downtown-good 3300 440 6.2 0.47
asphalt
Downtown-poor 7700 880 18 1.1
asphalt
20 2.0 1.5 2.5 0.009
7.3 1.4 2.0 2.9 0.008
20 2.8 1.8 3.5 0.01
15 3.7 3.5 7.3 0.02
Table 3-5 shows calculated street surface pollutant loadings for the dif-
ferent test areas and for different times since last cleaned. Table 3-6 compares
the loading values at any time with the initial loading values. The Tropicana
test area is seen to change in relative loading values much more than for the
other test areas. The oil and screens test area had smaller relative increases
in street surface loadings with time. Changes in cleaning frequencies would,
therefore, not affect street loadings in the oil and screens test area as much
as for the other test areas.
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 3-7,
which presents the accumulation rates expressed as pounds of pollutant per curb-
mile per day for each of the five test areas. The values are divided into
several accumulation time periods: 0 to 2.0, 2.1 to 4.0, 4.1 to 10.0, 10.1
to 20.0, 20.1 to 30.0, 30.1 to 45.0, 45.1 to 60.0 and 60.1 to 75.0 days. Accum-
ulation rates measured over a period of time near to the street cleaning date
were greater than accumulation rates measured over an accumulation period further
from the day of street cleaning. This would be portrayed with a sawtooth pattern
of accumulation in which loading values tend to level off with time. Differences
in accumulation rates were found between the different test areas, but the range
in average accumulation rates only varied by about 2 to 1 in most cases.
30
-------
TABLE 3-5. STREET SURFACE POLLUTANT LOADINGS FOR VARIOUS TIMES
SINCE LAST CLEANED (Ib/curb-mile)
Study Area
and Days
Since Last
Cleaned
Keyes-good as
0 days
2
4
10
20
30
45
60
75
Keyes-oll and
0
2
4
10
20
30
45
Total
Solids
iphalt
290
320
350
430
550
650
790
900
980
screens
1800
1800
1900
2000
2100
2300
2400
Chemical
Oxygen
Demand
32
36
39
48
61
72
87
100
110
120
120
130
130
150
160
170
Kjeldahl
Nitrogen
0.62
0.69
0.74
0.91
1.2
1.4
1.7
1.9
2.1
2.0
2.0
2.1
2.2
2.4
2.5
2.7
Ortho-
Phosphates
0.044
0.049
0.053
0.065
0.083
0.099
0.13
0.15
0.16
0.11
0.11
0.12
0.12
0.13
0.14
0.15
Lead
2.0
2.2
2.3
2.7
3.2
3.7
4.5
5.1
5.4
3.0
3.1
3.1
3.2
3.4
3.6
3.8
Zinc
0.20
0.22
0.23
0.27
0.33
0.38
0.46
0.52
0.38
0.51
0.52
0.53
0.55
0.59
0.63
0.66
Chromium
0.12
0.13
0.14
0.17
0.21
0.25
0.31
0.35
0.38
0.68
0.69
0.71
0.74
0.80
0.85
0.90
Copper
0.17
0.19
0.21
0.26
0.33
0.40
0.49
0.56
0.61
0.83
0.85
0.87
0.92
1.0
1.1
1.2
Cadmium
0.00076
0.00083
0.00089
0.0011
0.0013
0.0016
0.0020
0.0023
0.0025
0.0028
0.0029
0.0029
0.0031
0.0033
0.0035
0.0037
Troplcana-good asphalt
0
2
4
10
20
30
45
60
75
Downtown-good
0
2
4
10
20
30
Downtown-poor
0
2
4
10
20
30
130
160
190
270
390
490
630
740
820
asphalt
170
190
210
260
350
440
asphalt
780
820
860
990
1200
1400
13
17
20
29
42
53
68
81
89
23
25
28
35
47
59
89
94
99
110
140
160
0.28
0.35
0.40
0.57
0.81
1.0
1.3
1.6
1.7
0.32
0.35
0.39
0.49
0.66
0.83
1.8
1.9
2.0
2.3
2.8
3.3
0.024
0.029
0.033
0.045
0.063
0.079
0.11
0.13
0.14
0.025
0.028
0.030
0.038
0.051
0.064
0.11
0.12
0.12
0.14
0.17
0.20
0.50
0.66
0.79
1.2
1.7
2.2
3.0
3.6
3.9
1.0
1.1
1.2
1.5
2.1
2.6
1.5
1.6
1.7
1.9
2.3
2.7
0.12
0.14
0.15
0.19
0.25
0.30
0.38
0.44
0.48
0.15
0.17
0.18
0.23
0.31
0.38
0.37
0.39
0.41
0.47
0.57
0.67
0.044
0.056
0.066
0.094
0.14
0.18
0.23
0.27
0.30
0.094
0.10
0.11
0.14
0.19
0.24
0.35
0.37
0.39
0.45
0.54
0.64
0.078
0.098
0.12
0.17
0.24
0.31
0.39
0.47
0.52
0.18
0.20
0.22
0.28
0.37
0.47
0.74
0.78
0.82
0.94
1.1
1.3
0.00038
0.00045
0.00051
0.00068
0.00096
0.0012
0.0016
0.0019
0.0021
0.0051
0.0056
0.0062
0.0078
0.011
0.013
0.0021
0.0022
0.0023
0.0027
0.0032
0.0038
TABLE 3-6.
RATIO OF POLLUTANT LOADING VALUES AT VARIOUS TIMES SINCE
LAST CLEANED TO RESIDUAL LOADING VALUES
Study Area
Days Since Last Cleaned
10 20 30 45
60 75
Keyes-good asphalt
Keyes-oll and screens
Troplcana-good asphalt
Downtown-good asphalt
Downtown-poor asphalt
1.0
1.0
1.0
1.0
1.0
1.1
1.0
1.3
1.1
1.1
1.2
1.0
1.5
1.2
1.1
1.5
1.1
2.1
1.5
1.3
1.9
1.2
3.0
2.1
1.5
2.2
1.3
3.8
2.6
1.8
2.7 3.1 3.4
1.4
4.8 5.7 6.3
-
_
31
-------
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32
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The median particle sizes of the accumulating solids for the asphalt test
areas all were about the same (250 to350p), while the particle sizes associated
with the accumulating solids in the oil and screens test area were much larger
(about 1000P). In addition, these particle sizes do not change with accumulation
time for the asphalt streets, but appear to increase with time for oil and
screens surfaced streets. The larger sizes for the oil and screens accumulating
solids are caused by wear of the surfacing material itself (which is comprised
of small-sized gravel). The sizes of the accumulating solids on the asphalt
streets are generally smaller than the sizes of the total street dirt loadings
(indicating a build-up of the finer particle sizes on the asphalt streets),
while the sizes of the accumulating solids on the oil and screens surfaced
streets are larger than the sizes of the total street dirt loadings.
It is interesting to note that the overall pollutant accumulation rates
in the oil and screens test area are about the same or slightly smaller than
for any of the other test areas, 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 (see Section 4). The smoother asphalt
streets in the Tropicana and Downtown-good asphalt test areas had accumulation
rates that were about equal and had generally larger increases in street surface
loadings with time. The Downtown-poor asphalt street surface test area had the
largest accumulation rates of any of the test areas. These large rates are
thought to be caused by the poor condition of the streets and the character
of the area, which cause a greater 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, and is discussed in later sections.
GENERAL DESCRIPTION OF STREET CLEANING EQUIPMENT
Motorized street cleaners are designed to loosen dirt and debris from the
street surface, transport it onto a moving conveyor, and deposit it temporarily
in a storage hopper. The most common design (mechanical street cleaner) uses
a rotating gutter broom to remove the particles from the gutter area and place
them in the path of a large cylindrical broom which rotates to carry the material
onto a conveyor belt and into the hopper. This type of street cleaner uses
a water spray to control dust. This street cleaner is available in several
forms, including self-dumping street cleaners and three- or four-wheel street
cleaners. Three-wheel street cleaners are generally more maneuverable, but four-
wheel street cleaners usually travel at higher road speeds when not cleaning.
Vacuum assisted mechanical street cleaners have been in use in Europe for many
years and in limited use in this country for some time. Vacuum assisted street
cleaners use gutter and main pickup brooms for loosening and moving street
dirt and debris into the path of a vacuum intake, which places the debris in
the hopper. The vacuum system also replaces the conveyor system. All material
picked up by the vacuum nozzle is saturated with water on entry and passed
into a vacuum chamber where the water-laden dust and dirt settle out.
33
-------
Another type of street cleaner uses a regenerative air system. Using
recycled air, these street cleaners "blast" the dirt and debris from the road
surface into the hopper. Air is then vented through a dust separation system.
Some small, industrial-type vacuum street cleaners do not use main pickup
brooms, but use the vacuum system to directly clean the street. These small
street cleaners are most useful for cleaning parking lots, although they are also
used to clean factory floors and sidewalks. They are of limited use on city
streets.
When the hopper of a street cleaner is filled, the material may be taken
by the street cleaner to a storage or disposal site. More commonly, it is simply
dropped in a convenient place along the street cleaning route (preferably an
inconspicuous side street or vacant lot). The dirt and debris are later collected
by truck crews, usually with a front-end loader. The majority of street cleaners
dump their hoppers from the bottom, however, some manufacturers make street
cleaners with a hopper that swings up on arms and can dump directly into a
truck or debris box. This eliminates the need for a separate pickup crew and
decreases the chances of storage-pile losses.
The operating speed of most street cleaners falls in the range of 4 to
8 mph.* This is a normal speed for street cleaning operations in residential and
commercial areas where a street cleaner must maneuver around cars blocking access
to the curb. Several manufacturers offer four-wheel street cleaners that can
travel at speeds up to 50 mph when not cleaning. Auxiliary engines or special
power-takeoff transmissions provide additional speed and power to brooms and
elevators. They allow the operator to vary the cleaner speed as required for
street conditions (traffic, debris types, loading, etc.) while maintaining an
effective broom rotational speed.
Street flushing, as typically conducted, merely displaces dirt and debris
from the street surface to the gutter. Flushers do not remove potential pollutants
from the air and water environments. The volume of water used is usually in-
sufficient to transport the accumulated litter to the nearest drain. If the
water volume were sufficient to transport the material to the drain (several
thousand gallons per curb-mile*), it would probably be deposited in the catchbasin
or the sewerage. If the debris did reach the receiving water in separated
sewerage systems, the debris would probably cause a more severe water pollution
problem than if they were washed off the streets during a rain storm, when
larger receiving water flows occur for dilution. Adequate flushing in combined
sewerage systems could move the street surface pollutants into the sewerage
and toward the treatment facility. Most public works agencies use flushers
for aesthetic purposes or for quickly moving material out of travel lanes. A
street flusher consists of a water supply tank mounted on a truck or trailer,
a gasoline engine drive pump or power takeoff for supplying pressure, and three
or more nozzles for spraying the water in several directions. The large noz-
zles on the flusher are individually controlled. They are usually placed so
that one is pointed across the path of^ the flusher, and one on each side is
pointed toward the gutter. This arrangement makes it possible to flush an
*See Metric Conversion Table 0-1.
34
-------
entire street in one pass and provides flexibility in operation. The capacity
of the water carried on typical street flushers varies from 800 to 3500 gallons.*
The nozzle pressure of the water is usually between 30 and 55 psi.* The volume
of water delivered must be proportional to the speed of the vehicle and the
pumps must be capable of supplying sufficient water at suitable pressures.
Machine street cleaning may be assisted by manual cleaning in areas that
machines cannot reach, although machine cleaning accounts for the majority of
street cleaning activities in most communities. Manual cleaning is primarily
used to clean those streets where cars prevent the effective use of mechanical
equipment. It is most often used in business districts where the emphasis is
on keeping litter under control. Manual methods are also useful in supporting
mechanical operations. A manual crew can follow a street cleaner and clean
out catchbasin inlets, sweep up missed debris, and assist in transferring debris
from the street cleaner to trucks.
Typical Street Cleaning Programs and Operating Conditions
Information from two APWA questionnaires—one sent to more than 400 cities
in 1973 and a follow-up questionnaire sent to more than 200 cities in 1975,
concerning street cleaning operations in a recent project (APWA 1973 and 1975)
—can be used to define current cleaning programs. Other data sources (Scott
1970; Laird and Scott 1971; Mainstem 1 973; APWA 1945) can also be used to describe
typical street cleaning programs. The results of these surveys are presented
in the following discussion. These survey results should not be considered
a goal for any cleaning program, but only an indication of the norm. Part of
Section 5 discusses procedures for the determination of a street cleaning program.
Because of varying objectives and conditions, some cities will need much more
intensive street cleaning programs than other cities.
General City Characteristics
Table 3-8 presents the areas and the total street miles for cities with
various population ranges (APWA 1973). Obviously, as the population increases,
the size of the city increases. About 0.5 square miles* and about 3 street-
miles* are required for each 1000 people. These values may be substantially
larger for small cities (those with much fewer than 10,000 people).
Table 3-9 shows the street grades for cities throughout the country (APWA
1973). Most streets are flat with grades of less than 2 percent; however,
some cities only have flat grades on one percent of their streets. Of the cities
that responded, only 11 percent of the streets had grades greater than 6 percent;
but 50 percent of all of the streets of some cities had 6 percent grades.
Street cleaning equipment must be more powerful if the street grades are steeper.
The specific routes may be selected on a topographic basis to minimize the
number of street cleaners with large horsepower engines.
*See Metric Conversion Table 0-1.
35
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TABLE 3-8. AREA AND STREET MILES FOR NATIONWIDE CITIES
Overall
Source: APWA 1973
2
Area (mi )
Street miles
Population
Range
<10,000
10,000 *
25,000 +
50,000 +
100,000 +
250,000 +
500,000 +
>1, 000, 000
25,000
50,000
100,000
250,000
500,000
1,000,000
Average
5.6
13
15
34
47
110
420
220
Range
2
3 +
1 -^
3 +
8 +
21 +
46 +
52 +
* 11
73
120
550
120
520
3500
460
Average
51
120
130
220
440
830
1900
2600
Range
25
30 *
4 +
12 +
18 *
270 +
860 +
—
+ 74
600
1600
1400
1300
1600
4400
— ~
47
3500
310
4400
TABLE 3-9. STREET TOPOGRAPHY CONDITIONS FOR NATIONWIDE CITIES
Grade Range
Percent of Streets in Grade Range
0+2% grade
2+6% grade
>6% grade
Average
57
33
10
1.0 > 100
1.0 * 100
0.5 * 50
Source: APWA 1973
General Street Cleaning Program Characteristics
Table 3-10 shows the numbers of street cleaners that were operating in
1969 and 1970 based on street-miles and population groups (Scott 1970; Laird
and Scott 1971). About 20 cleaners were used for every 1000 street-miles. The
average street was cleaned about once every month, assuming an average cleaner
usage of about 25 curb-miles per day with some of the equipment not operating
because of repairs.
36
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TABLE 3-10. NUMBER OF STREET CLEANERS FOR NATIONWIDE CITIES
Cleaners per 1000 Average Number of
street miles3 (Cleaners per
City
Population
<25,000
25,000 - 50,000
50,000 -> 100,000
100,000 + 250,000
250,000 * 500,000
500,000 or more
Average
32
18
21
15
18
14
100,000 peopleb)
Range
6. 9 -
6.3 >
6.7 -»
3.0 *
4.4 *
2.6 *
220
40
78
43
87
28
9.6
5.4
5.8
4.2
3.7
2.7
Sources: ^Laird and Scott 1971
bScott 1970
From 3 to 10 cleaners were available for every 100,000 people. Based
on these values, 7200 street cleaners were available in the U.S. in 1970 (Scott
1970). Only about 35 percent of the cities had parking regulations to enhance
the street cleaning efforts (Scott 1970).
One of the major complaints about street cleaning operations concerns interim
storage of collected materials on streets. An average of 6 hours interim storage
was reported by the cities responding and the storage duration ranged from 5
minutes to 3 days (APWA 1973).
Operator training and operator performance are assumed to be directly related,
but only 43 percent of the cities that responded had a formal operator training
program. The average initial training period was 54 hours per operator with
subsequent training of about 30 hours per operator per year (APWA 1975).
Many cities with severe winter snow conditions do not conduct street cleaning
operations all year long. Most of the cities (56 percent) conducted their
street cleaning operations the whole year, but three percent cleaned streets
during only 3 or 4 months of the year (APWA 1975).
Public works departments removed, on the average, about 260 pounds per
person per year from the streets in 1973 (APWA 1975). Since street refuse
has a bulk density of about 1 ton per cubic yard*, this would be equal to
about 25 million cubic yards or 25 million tons* of material per year for a
*See Metric Conversion Table 0-1.
37
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city of 100,000 people. Therefore the ultimate disposal of this material is
an important aspect of a complete street cleaning program.
Cleaning Equipment
Ninety-six percent of the estimated 7200 street cleaners operating in the
U.S. in 1969 and 1970 were manufactured by one of three companies (Scott 1970).
This percentage is thought to have decreased since 1970, because of the rise
in the number of equipment manufacturers. Eighty-seven percent of the cleaners
were gasoline operated (Scott 1970).
Sixty-six percent of all streets were cleaned by mechanical cleaners. Twenty-
five percent were cleaned by vacuum assisted mechanical cleaners or by regen-
erative air street cleaners. The remaining streets were cleaned by flushers
only, or by a combination of equipment types (APWA 1973).
The reported operating speeds of mechanical and vacuum cleaners averaged
about 6 mph (they ranged from 2 to 25 mph). Flushers operated at a somewhat
faster speed, averaging 8 mph (APWA 1973). A faster street cleaner speed usually
results in less efficient removal of street dirt, but the relationship of speed
to removal efficiency for flushers is not known. Manufacturers usually recommend
an operating speed of 5 mph for mechanical and vacuum cleaners and 15 mph for
flushers. It is thought that cities operate their flushers at speeds slower
than recommended by the manufacturers because of public safety considerations.
The most common street cleaner hopper sizes were 3 and 4 cubic ya'rds,
with only 4 percent either smaller than 2.5 cubic yards or equal to or larger
than 5 cubic yards (Scott 1970). The average reported volume of debris picked
up during one machine's shift was about 15 cubic yards (APWA 1973). Therefore,
about four or five loads were dumped during each shift.
General Street Cleaning Equipment Performance
All street cleaning equipment currently used can efficiently remove litter
(larger than 0.25 in.) from the street cleaner path. The following general
discussion concerns the removal of smaller particles (less than 0.25 in.) as
measured in several previously conducted controlled tests. Information presented
later in this section about the San Jose test results concerns all particle
sizes. Most of the equipment used in these tests was in good maintenance
and operated under recommended conditions although some were quite different
than those currently available. Departures from recommended operating conditions
may result in lower or higher removal rates.
Past test results have shown direct relationships between cleaning efficiency,
particle size, and street surface particulate loading. Tables 3-11 and 3-12
show the cleaning effectiveness of vacuumized street cleaners (Clark and Cobbin
1963) and mechanical street cleaners (Sartor and Boyd 1972) for various particle
sizes and total particulate loading conditions. These values were determined
by examining data that were collected under several hundred controlled and in-situ
tests. Actual cleaning efficiency may vary substantially from these values
because of site-specific variables. It was found that street surface loading
strongly influences the removal efficiency. Results from this San Jose demonstration
38
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TABLE 3-11. REMOVAL EFFICIENCIES FOR VACUUMIZED STREET CLEANER AT DIFFERENT
INITIAL PARTICULATE LOADINGS AND FOR VARIOUS EQUIPMENT PASSES (%)*
20 -»•
Size
Street Surface Loading and Number of Passes
200/curb-mi 200 •»• 1,000 Ib/curb-mi 1,000 + 10,000 lb/
curb-mi
Range
1 pass 2
44+74y 3
74+177y 50
177-300y 50
300+500y 60
75CKl,OOOy 50
Source: Clark and
*From cleaner path
6
75
75
84
75
Cobbin
(0 to 8
3123 12
9 20 36 49 70 91
88 60 84 94 75 94
88 60 84 94 80 96
94 65 88 96 70 91
88 60 84 94 70 91
1963
ft. from curb), not total street loading.
3
97
99
99
94
97
TABLE 3-12. MECHANICAL STREET CLEANER EFFICIENCIES FOR VARIOUS
EQUIPMENT PASSES (%)
Size Range
<43
43 104
104 •»• 246u
246 * 840y
840 -" 2000y
2000y *• 6370y
1 pass
15
20
50
60
65
80
180 * 1800 Ib/curb-mile
2 passes
28
36
75
84
88
96
3 passes
39
49
88
94
96
99
Source: Sartor and Boyd 1972
39
-------
study also showed strong influences resulting from street surface conditions.
Without exception, higher loadings resulted in better removal percentages. In
a nationwide study (Sartor and Boyd 1972) , city-averaged street surface particulate
loadings ranged from about 300 to 6000 Ib/curb-mile, with an average of 1500
Ib/curb-mile. Therefore, it is expected that identical equipment will perform
differently in different cities and different sections of cities because of
differences in loadings.
Calculations were also made to show the effects of multiple passes by
the same equipment (see results in Tables 3-11 and 3-12). With multiple passes,
larger particles (and litter) are removed more effectively than smaller particles,
thus changing the particle size distribution. Figure 3-6 compares street surface
particle-size distributions before and after a single pass with mechanical street
cleaners (averaging results from four tests in separate cities, from Sartor
and Boyd 1972). Before cleaning, the median dust and dirt particle size (smaller
than 0.25 in.) is seen to be about 300 V and the median particle size after
cleaning is reduced to about 100 V. This modification in particle size distri-
bution and its effects on street cleaning efficiency can change the removal
rates for the various pollutants.
Data concerning flushers, regenerative air cleaners, and combinations of
equipment are scarce. Limited testing from in situ tests has demonstrated overall
< 5
Si
WUJ
-------
particle removal rates of 30 percent for a single pass of a conventional flusher
and 80 percent for a mechanical street cleaner followed by a flusher (Sartor
and Boyd 1972). Conventional flusher operations do not remove the various pol-
lutants from the street, they only move the particles to the curb. If sufficient
water was used to flush the particulates to the storm drainage system, the
pollutants would be discharged to the receiving waters, possibly during low
flow conditions. Large fractions of some pollutants can only be removed by
wet processes (Sartor and Boyd 1972; Pitt and Amy 1973). Pollutants with more
than 20 percent in the flushed fraction included: N02> N03, PO^, fecal coliform
bacteria, fecal strep bacteria, chloride, Kjeldahl N, and BOD. Therefore, in
order to remove more than 80 percent of these pollutants from the cleaner path,
it is expected that some type of effective wetting/flushing must be used. No
data are available concerning removal rates as a function of particle size for
flushers, manual cleaning, or regenerative air cleaner units.
When the size distributions for pollutants existing on the street are known,
it is possible to estimate their removal rates. Many of the pollutants have
greater concentrations associated with the smaller particle sizes. Table 3-13
lists the mass-weighted median particle sizes for various street surface pol-
lutants as measured during two previous EPA sponsored research projects (Sartor
and Boyd 1972; Pitt and Amy 1973). These small particle sizes are not as
efficiently removed by typical street cleaning equipment as are larger particle
sizes.
Table 3-14 shows calculated removal efficiencies of various street clean-
ing programs for various pollutants. Phosphates are the most difficult to re-
move by any of the listed programs; lead and iron are the easiest to remove.
The total solids (smaller than 0.25 in.) are removed at efficiencies ranging
from 40 percent to 50 percent .under normal conditions; but a mechanical street
cleaner followed by a flusher may remove about 80 percent of the solids of
the material in the street cleaner path.
If the equipment is not operated under recommended conditions, the removal
rates are expected to change. As an example, the following conclusions are
based on data from the Newark Brush Co. (Horton 1968). This study related
broom type, broom strike, brush speed, and vehicle speed to total solids removal
for mechanical street cleaners:
• Sweeping pattern (a measure of the pressure against the street surface)
and broom speed are critical factors in removing road debris.
• A worn broom sweeps all types of debris better than a new one.
• Crimped wire and fiber brooms were more efficient than plastic or
plastic-wire mixtures.
• The sweeping pattern contributes greatly to cleaning efficiency; small
patterns leave uncleaned streaks in depressions on irregular road sur-
faces (Figure 3-7).
• At faster travelling speeds, proportionally higher broom rotation speeds
should be employed (Figures 3-8 and 3-9).
41
-------
These tests were conducted with a single-engine street cleaner. Except for the
several gear ratios, higher broom speeds resulted from higher engine speeds.
These higher forward speeds may decrease cleaning effectiveness by reducing broom-
pavement contact. Thus, it is desirable to have an auxiliary speed control
to maintain a constant optimum broom speed. To maintain a high cleaning efficiency,
the data in Figures 3-7, 3-8 and 3-9 support a preference for a street cleaner
speed of about 4 mph with a fast broom rotational speed at high pressure. For
the ranges shown, brush speed and pattern are more important than forward speed.
TABLE 3-13 MEDIAN PARTICLE SIZE FOR VARIOUS STREET SURFACE CONTAMINANTS
Approximate Median
Parameter Particle Size (u)
Total Solids 220
BOD5 120
COD 42
nf.
PO
Kjeldahl - N 120
All Pesticides Combined 140
Cd 61
Sr 160
Cu 120
Ni 230
Cr 220
Zn 190
Mn 290
Pb 200
Fe 320
Sources: Sartor and Boyd, 1972
Pitt and Amy, 1974
42
-------
TABLE 3-14. REMOVAL EFFICIENCIES FROM CLEANER PATH FOR VARIOUS
STREET CLEANING PROGRAMS* (%)
Sreet Cleaning
Program and
Street Surface Total
Loading Conditions Solids
Vacuum Street Cleaner
1 pass; 20 - 200 31
Ib/curb mile
total solids
2 passes 45
3 passes 53
Vacuum Street Cleaner
1 pass; 200 - 1,000 37
Ib/curb mile
total solids
2 passes 51
3 passes 58
Vacuum Street Cleaner
1 pass; 1000 * 10,000 48
Ib/curb mile
total solids
2 passes 60
3 passes 63
Mechanical Street Cleaner
1 pass; 180 - 1800 54
Ib/curb mile
total solids
2 passes 75
3 passes 85
Flusher 30
Mechanical Street Cleaner
followed by a flusher 80
Pesti-
BOD5 COD KN PO^ cides Cd Sr Cu Ni Cr Zn Mn Pb Fe
24 16 26 8 33 23 27 30 37 34 34 37 40 40
35 22 37 12 50 34 35 45 54 53 52 56 59 59
41 27 ,45 14 59 40 48 52 63 60 59 65 70 68
29 21 31 12 40 30 34 36 43 42 41 45 49 59
42 29 46 17 59 43 48 49 59 60 59 63 68 68
47 35 51 20 67 50 53 59 68 66 67 70 76 75
38 33 43 20 57 45 44 49 55 53 55 58 62 63
50 42 54 25 72 57 55 63 70 68 69 72 79 77
52 44 57 26 75 60 58 66 73 72 73 76 83 82
40 31 40 20 40 28 40 38 45 44 43 47 44 49
58 48 58 35 60 45 59 58 65 64 64 64 65 71
69 59 69 46 72 57 70 69 76 75 75 79 77 82
(a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a) (a)
(b) (b) (b) (b) (b) (b) (b) (b) (b) (b) (b) (b) (b) (b)
(a) 15 > 40 percent estimated
(b) 35 * 100 percent estimated
*These removal values assume all the pollutants would lie within the cleaner path (0 to 8 ft. from the curb)
Sources: Calculated from Clark and Cobbin 1963; Sartor and Boyd 1972; and Pitt and Amy 1976.
43
-------
Wire broom
irregular surface
678
PATTERN (inches)
9 10
Source: Morton, 1968
Figure 3-7. Effect of pattern* on removal effectiveness.
*The pattern is a measure of pressure applied between the main pick-up broom
and the street surface. It is measured as the tangential length of main pick-up
broom in contact with the street surface.
UJ
z
LLJ
>
o
LLJ
UJ
rr
100
u.
LLJ
O
S
LU
QC
80-
60-
40-
20
Wire
broom
100 200 300 400
BRUSH SPEED (rpm)
Source: Morton, 1968
Figure 3-8. Effect of brush speed on
removal effectiveness.
100
UJ
> 80 H
o
60-
40-
20
Wire
broom
34567
FOREWARD SPEED (mph)
Source: Morton, 1968
Figure 3-9. Effect of foreward speed on
removal effectiveness.
44
-------
SAN JOSE DEMONSTRATION STUDY RESULTS
The design of an effective street cleaning program requires not only a
determination of accumulation rates but also an assessment of specific street
cleaning equipment performance in the actual conditions encountered. Service
goals*, another factor affecting the design of street cleaning programs, will
be discussed in Section 5. The aim of this study was to determine a range of
street cleaning equipment effectiveness for various types of equipment and clean-
ing schedules.
Tables 3-15 throtfgh 3-18 present the street cleaning equipment perform-
ance data. Twenty-six different test conditions are identified representing
different test areas, equipment types, number of passes, and 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 "after street cleaning" heading, the
residual street surface loading values (lb/curb-mile) are shown; these are gen-
erally 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 charac-
teristics (total loading value and particle size distribution), and various
other environmental factors. Street cleaning program variables that most affect
street cleaning performance include the number of passes the equipment makes
and the street cleaning interval. The most important measure of cleaning effec-
tiveness 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 (this
occurred in the tests conducted in the oil and screens area).
Student "t" statistical tests were conducted with the data shown in Tables
3-15 to 3-18 to determine important similarities and differences in street clean-
ing equipment performance under the various test conditions. These statistical
tests showed that initial loading values in any one test area varied depend-
ing on the street cleaning program (number of passes and cleaning intervals).
The differences in the initial loading values in various test areas were con-
rolled 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.
*Service goals consider effects on water quality, air quality, public safety,
aesthetics, and public relations.
45
-------
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When the residual loading values were statistically examined, the find-
ings 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 with the state-of-the-art mechanical four-wheel
machine 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
the excessive mechanical action of the broom on the "weak" oil and screens
street surface. During a single pass, any extra material loosened from the
street surface was removed along with some of the initial dust and dirt on
the street.
The selection of the type of street cleaning equipment is less important
than the characteristics of the area to be cleaned. In most cases, the street
cleaning interval and number of passes were more important than the specific
type of equipment used. Other considerations, 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 demonstration project in which one type of street cleaning
equipment may perform differently from 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 conditions. In all cases, the hopper
median particle sizes were much larger than the median particle sizes on the
street surface before street cleaning. The street surface median particle size
also decreased with street cleaning. There was 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.
Some differences in hopper content median particle sizes were found due to clean-
ing frequencies, but no differences were found due to equipment type.
Tables 3-19 through 3-22 summarize the loading and removal rates for the
various pollutants in each test area for all street cleaning programs combined.
The percentage removal values for the total solids pollutants are nearly the
same as for the other pollutants; however, the removal rates, expressed on a
Ib/curb-mile removed basis, vary greatly. These Ib/curb-mile removed values
may be used to estimate the quantity of pollutants that are removed over a
large area and long time period.
Table 3-23 and Figure 3-10 present removal rate information for street
surface particulates by particle size for the three study areas and for all
street cleaning programs combined. The larger particle sizes are shown to
have had the largest removal efficiencies (as high as 55 percent), while the
smallest particle sizes had the smallest removal efficiencies. However, the
49
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53
-------
TABLE 3-23. TOTAL SOLIDS STREET CLEANER REMOVAL EFFECTIVENESS
BY PARTICLE SIZE
Study Area and
Particle Size
Range
<»)
Tropicana-Good
Asphalt
>6370
2000 * 6370
850 * 2000
600 * 850
250 * 600
106 - 50
45 * 106
<45
all sizes
Keyes-Good
Asphalt
>6370
2000 * 6370
850 - 2000
600 * 850
250 * 600
106 * 250
45 * 106
<45
all sizes
Keyes-Oil
and Screens
>6370
2000 * 6370
850 * 2000
600 * 850
250 * 600
106 * 250
45 * 106
<45
all sizes
Downtown-Good
Asphalt
>6370
2000 - 6370
850 * 2000
600 - 850
250 * 600
106 * 250
45 + 106
<45
all sizes
Downtown-Poor
Asphalt
>6370
2000 + 6370
850 * 2000
600 * 850
250 * 600
106 * 250
45 * 106
<45
all sizes
Total Solids Initial Loading
(Ib/curb-mile)
Mean
15
15
21
15
42
50
51
16
220
18
38
54
28
85
83
76
21
400
73
270
270
160
480
380
270
63
2000
14
19
25
14
48
56
57
9.8
240
89
170
180
85
270
270
230
58
1400
Min.
9.5
10
13
8.2
19
22
24
7.0
120
6.0
10
16
9.2
39
45
34
13
170
13
77
170
100
320
280
160
40
1200
Max.
36
24
42
42
81
80
70
24
350
27
58
87
44
120
100
100
34
550
120
450
350
200
600
540
380
140
2700
Total Solids Removal
(Z)
Mean
50
46
47
53
46
41
40
19
43
54
39
35
35
31
26
23
8.3
31
36
24
6.0
4.0
3.3
4.0
3.1
-12
8.1
53
42
39
38
36
33
22
41
34
38
51
42
41
42
39
33
28
40
Min.
9
28
22
41
14
6
21
-54
13
- 8
13
8
12
14
11
-12
-44
14
20
- 5
-16
-10
-16
-20
-30
-47
- 6
Max.
75
68
74
79
63
58
54
64
60
69
5
5
5
4
4
5
48
47
58
47
23
20
18
25
25
24
22
*Not enough samples were collected to obtain meaningful loading ranges.
54
-------
40-
Q 32-
LL O
O 5
Uj LU
Otr
< LU
t- N
s55
O w
So
24 -
16 -
8 -
0 -
8 -
16
Keyes
Oil and Screens
Tropicana -
Good Asphalt
PARTICLE SIZES
PARTICLE SIZES
PARTICLE SIZES
Downtown -
Good Asphalt
« t =
Isggt 1
Downtown -
Poor Asphalt
PARTICLE SIZES
PARTICLE SIZES
Figure 3-10. Total solids removal by particle size.
55
-------
variabilities for specific values were quite large, with data ranges of about
3 to 1 not uncommon.
Figures 3-11, 3-12, and 3-13 show how the street surface material is re-
distributed across the street by the street cleaning equipment. Figure 3-11
for the Tropicana area (smooth streets in good repair with little parking) shows
an 81 percent removal of the solids loading in the first 12 in. from the curb
while the rest of the street area had increases in solids loadings. These loading
increases are due to partial redistribution of the high solids loadings from
the curb area out into the street due to broom action and turbulence. Figure
3-12 presents the loading redistribution of the solids during street cleaning
of an oil and screens surfaced street. The high loadings next to the curb
were reduced by 36 percent and some of the loadings were increased in other
areas of the street. The oil and screens streets had much higher unit area
loadings in the center of the street as compared with the asphalt streets.
The Keyes-good asphalt test results (Figure 3-13) were similar to the Tropicana
test results.
0025
0.020
0.015 -
0.010
0.005 -
81%)
(80%)
53%)
60%)
50%)
^*"
10
DISTANCE FROM CURB (feet)
15
Figure 3-11. Redistribution of total solids due to street cleaning
(Tropicana - Good Asphalt Test Area - averaged for
all equipment types - the overall removal effectiveness
was about 40%).
56
-------
0 025 T—
~ 0015
0 010 -
(36%)
/ (-7%)
116%)
140%)
"^~~——— Initial loading distribution
— — — — —— Residual loading distribution
(80%) Values m parenthesis are the
percentage removals
(16%)
10
DISTANCE FROM CURB (feet)
15
20
Figure 3-12. Redistribution of total solids due to street cleaning
(Keyes Oil and Screens Test Area - averaged for all
equipment types - the overall removal effectiveness
was about 12%).
z
0
0.025
0.020 -
0.015 -
0.010 -
0.006 1 I
0.001 -
J
•—'
(58%)
Initial loading distribution
Residual loading distribution
(80%) Values in parenthesis are the
percentage removals
(53%)
(38%)
10 15
DISTANCE FROM CURB (feet)
20
Figure 3-13. Redistribution of total solids due to street cleaning
(Keyes Good Asphalt Test Area - averaged for all
equipment types the overall effectiveness was
about 26%).
57
-------
Figure 3-14 and Table 3-24 present information relating to the distribu-
tion of total solids loading across the street for the different test areas
The street cleaner can only remove the material from the street that lies in
its path. With an 8-ft.* path, only about 60 percent of the total solids
can be affected by street cleaning in the oil and screen test area, while greater
than 90 percent of total solids loading can be affected in the Keyes-good asphalt
and Tropicana-good asphalt test areas. This loading can be further modified
by parked cars, as discussed later. Figure 3-15 shows the percentage of solids,
on a size basis, that are within the normal street cleaning paths (0 to 8 ft.
from the curb). A greater percentage of larger particles than finer particles
were found in the oil and screens test area near the curb, possibly indicating
better transport of the larger material towards the curb. The size distribution
across the street in the Tropicana-good asphalt test area was about even, and
no clear trends were evident from the Keyes-good asphalt data. These participate
distributions can be radically changed if debris is swept from the sidewalks
onto the curb, or if leaves are piled on the street from landscaped areas.
Q
31
o G
00 £
I- <
-J <
< I
t- I-
LL D
O 0
O II
QC CJ
100 -
80 -
60 -
40 -
20 -
Keyes Oil and Screens
Keyes Good Asphalt*
Ttopicana Good Asphalt
The variation in loading distributions for
those good asphalt test areas are due to
different parking density conditions.
20
DISTANCE FROM CURB (feet)
Figure 3-14. Loading distribution across the street.
*See Metric Conversion Table 0-1.
58
-------
au-
o
z
§5
O 3
-1 E 60-
LU 2
LU >+-
DC ~
h- *
CO H-
— 1 00
_j • — •
DLU 40 H
UJ
U~ ^^
LL <
0 -I
LU (D
O Z
^ —
£ or 20-
LU ^
CJ a~
or z
LU
0_
0
^Mf
^
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<2 tf>
<0 0 (g
t CN 5
§ s *
CN 2 |£
8
CO
00s
O LO ^
^^l,,^
o oor~^
«N A|
t ^1
S SI
ml
l_^
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—
in Jr.
^
V 0
PARTICLE SIZES
(J
z
<
o
LU o
LU »-
"• z
LL <
O -I
LU O
O Z
100
80
40-
00-
£? 20
o_
Tropicana -
Good Asphalt
< -e
o g
H- 6
LU O
UJ >t
or ^
-I 00
_l —
H) UJ
"- Z
LL <
LU O
O Z
< 3
80-
60-
40-
Keyes -
Good Asphalt
PARTICLE SIZES
PARTICLE SIZES
Figure 3-15. Parking lane total solids loading compared to full street
loading (average of 7 to 9 tests for each study area).
59
-------
TABLE 3-24. LOADING DISTRIBUTION ACROSS THE STREET
Percentage of Total Street Loading
from Curb to Given Distrance (%)
Distance from Curb (ft.)
0.5
1
2
5
8
10
20
Keyes-Oil
and Screens
Test Area
3
5
12
36
62
75
100
Keyes-Good
Asphalt
Test Area*
22
38
58
84
93
96
100
Tropicana-
Good Asphalt
Test Area*
23
48
73
95
98
97
100
Distance to median
(50%) loading value
Distance to 90% of
total loading
6.5 ft
14 ft
1.5 ft
6.7 ft
1.0 ft
3.8 ft
*The variations in loading distributions for those good asphalt test areas
are due to different parking density conditions.
Figure 3-16 presents an idealized distribution of the total solids on the
street surface for smooth asphalt streets and oil and screens surfaced streets
for different parking conditions. This figure shows a more even distribution
of solids loadings on the oil and screened streets than on the smooth street
surfaces. About 50 percent of the solids on oil and screened streets were
within about 7 ft. of the curb for light or no parking conditions, while 50
percent of the solids on the smoother asphalt streets were within 1 ft. of
the curb for similar parking conditions. Parked cars also affected the load-
ing distribution much more radically on the smoother streets than on the rougher
streets. Parked cars blocked some of the airborne street particulates that
were suspended in the air by wind or by vehicle induced turbulence. The parked
cars acted as barriers and caused the particulates to resettle on the street
further from the curb area. With no parking, the curb itself acted as a
barrier, with much of the material possibly being transported by winds across
the curbs and onto adjacent areas.
Figure 3-17 is an idealized curve (based on a computer analysis of the
San Jose data) reflecting the total amount of street surface materials that
may be removed in a year for different street surface conditions as a func-
tion of the number of passes per year. This figure is a semi-log plot and
60
-------
/ Smooth Asphalt Streets
Parking Conditions
Extensive long term
Extensive short term
— • — • Moderate
Light or none
Traffic next to curb
2 4 6 8 10 12 14 16 18 20
DISTANCE FROM CURB (feet)
100
Oil and Screens
Surfaced Streets
Extensive
Light or none
Traffic next to curb
6 8 10 12 14
DISTANCE FROM CURB (feet)
Figure 3-16. Effects of parking and street condition
on solids loading distribution.
61
-------
Q
UJ
>
O~
5 co
•^ 0)
UJ >
tr^
to -
50,000-
40.000-
30,000-
20,000-
10,000-
Oil and screens surfaced
streets or asphalt streets
in poor condition
AsphaHt streets in
good condition
10 100
NUMBER OF PASSES PER YEAR
1 OOO
Figure 3-17. Annual amount removed as a function of
the number of passes per year.
demonstrates decreased per mile removal quantities per equipment pass as the
number of passes per year increases. The unit effort and costs increase by 10
times between 10 and 100 passes per year, but the actual amount removed only
increases by a factor of about 4.
PARKING INTERFERENCES TO STREET CLEANING OPERATIONS
Vehicles parked along a street cleaning route reduce the length of curb
that may be cleaned. Since most of the street surface pollutants are found
close to the curb on smooth streets with little parking, parked vehicles can
drastically reduce the cleaning effectiveness of normal cleaning programs on
these streets. The following discussion attempts to quantify this relationship.
Field work associated with this demonstration project has shown that street
cleaners can be partially effective when cleaning around cars. Extensively parked
cars block the migration of particulates toward the curb, resulting in higher
"middle-of-the-street" loading values than for streets with little or no parking.
Figure 3-18 (from Levis 1974) illustrates several possible configurations
for two cars: two closely parked cars, two parked cars with little space between
62
-------
SITUATION 1
Curb
I I I I
"^- Path of sweeper
SITUATION 2
Curb
Path of sweeper
SITUATIONS
Curb
Path of sweeper
SITUATION 4
Curb
Path of sweeper
Rear clearance
Blocked by car
Front clearance
Source: from Levis 1974
Figure 3-18. Effect of parked cars on street cleaner maneuverability
63
-------
them, two parked cars with enough space between them for the street cleaner
to just get back to the curb and leave again, and two parked cars quite a
distance from each other. The length of curb not cleaned because of parked
cars may be determined geometrically by knowing the turning radius of a street
cleaner and the parking layout along the street. As shown on Figure 3-19, the
percentage of curb length occupied by parked vehicles is close to the percentage
of parking spaces occupied, but is usually smaller due to parking restrictions
such as driveways and fire hydrants. As the number of parked cars increases,
the percentage of curb left uncleaned increases proportionally. The turning radius
has a small effect (less than 5 percent) on the percentage of curb left uncleaned.
100-
Q
LLJ
z
o
z
D
t-
00
tr
D
O
80-
60-
40
LU
CJ
cr
20-
Street cleaner with a
turning radius of 25 ft.
Street cleaner with a
turning radius of 7 ft.
60
80
100
PERCENTAGE OF CURB OCCUPIED
Figure 3-19. Effects of parking on urban street cleaning.
64
-------
Figures 3-20 and 3-21 demonstrate the effect of parking controls on street
cleaning effectiveness for two different street surface conditions and var-
ious parking conditions (based on Table 3-25). If a smooth street has extensive
on-street parking 24 hours a day (such as in a high-density residential neighbor-
hood), most of the street surface particulates would not be within the 8 ft.
strip next to the curb that is usually cleaned by street cleaning equipment.
Figure 3-20 shows that if the percentage of curb length occupied by parked
cars exceeds about 80 percent for extensive 24-hour parking conditions, it would
be best if the parked cars remained and the street cleaner swept around the
cars (in the 8 to 16 ft. strip from the curb). Of course, all of the cars
should be removed periodically to allow the street cleaner to operate next
to the curb to remove litter caught under the cars. In an area with extensive
daytime parking only (such as in downtown commercial areas), the parked cars
should remain parked during cleaning (daytime cleaning) if the percentage of
curb length occupied exceeds about 95 percent. The oil and screens surfaced
streets are less critical to parked cars because of the naturally flatter dis-
tribution of solids across the street. Parking controls would be effective on
those streets if the typical parking conditions involved less than about 95
percent curb length occupancy. Under most conditions, removal of parked cars
during street cleaning operations can significantly improve the street cleaning
effectiveness. Local monitoring of "across-the-street" loadings for various
parking conditions should be conducted for other cities to determine their spe-
cific relationship.
Parking regulations maybe necessary to improve street cleaning operations.
"No Parking" signs indicating the days and hours of cleaning operations and
illegal parking should be installed. The signs should be placed every 250
feet, or more frequently if objects such as trees block them from view. Compliance
with parking regulations usually requires parking patrolers who will ticket il-
legally parked cars ahead of the street cleaner. This results in an additional
labor cost, but the revenue from parking fines can be used to offset the program's
expenditures. Street cleaning and parking restrictions should be scheduled
on alternate sides of the street on consecutive days to lessen the problem
of finding parking spaces in high density residential areas. «
65
-------
Maximum
>
O
cc
CO
Q
O
CO
O
10
20 30 40 50 60 70 80
CURB LENGTH OCCUPIED BY PARKED CARS (%)
00
Figure 3-20. Effects of parking restrictions during street cleaning
on asphalt surfaced streets in good condition.
20-»
Oil and screens surfaced streets
With parking restrictions (100% effective)
No parking restrictions
1 1
20 30 40 50 60 70
CURB LENGTH OCCUPIED BY PARKED CARS (%)
10
90 100
Figure 3-21. Effects of parking restrictions during street
cleaning on oil and screens surfaced streets.
66
-------
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67
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For the tracer studies, fluorescent particles were placed in a specially
constructed catchbasin. These different colored particles were used to inves-
tigate flushing of catchbasin contents from different depths to the sewerage.
Resulting concentrations of fluorescent particles in the sewerage and from dif-
ferent depths in the catchbasin were periodically checked. Catchbasin cores
were taken with a carbon dioxide freezing core sampler in order to minimize
sample disturbance. The tracer study was confined to a single portion of the
storm drainage system in the Keyes Street study area. Samples were periodically
collected from eight internal sampling locations and at the outfall.
Automatic water samplers and flow meters were installed near the outfalls
in the storm sewerage systems draining the Keyes Street and Tropicana study areas.
These devices collected runoff samples during storms. The analytical programs
are listed in the following subsection.
ANALYTICAL PROGRAM
The collected runoff samples were analyzed individually and in selected
composites. The more important parameters were investigated at different times
during a rain to see how flow and concentrations change as the rain progresses.
Other parameters were analyzed only once during each monitored rain. Three storms
with several separate peaks each were continuously monitored in each of
the two study areas. The following list describes the general analytical scheme
used for the runoff analyses:
• Periodic in situ analyses:
dissolved oxygen
temperature
• Individual samples (as many as one analysis per hour for each rain
monitored):
specific conductance
PH
oxidation-reduction potential (ORP)
turbidity
• Up to three analyses per monitored rain:
total solids (TS) . settleable solids
suspended solids (SS) lead (Pb)
total dissolved solids (TDS) zinc (Zn) «
chemical oxygen demand (COD) chromium (Cr)
5-day biochemical oxygen copper (Cu)
demand (BODc) cadmium (Cd)
Kjeldahl nitrogen (TKN)
total orthophosphates (OPO)
72
-------
Runoff Sampling Program
The BOD values were of particular interest in the runoff sample analysis
program. A high BOD rate is thought to be one of the most important character-
istics of urban runoff because of the immediate and significant oxygen demand
it can make on certain receiving waters. This demand may cause an immediate
and/or long-term depletion of oxygen in the receiving waters.
BOD values obtained in the incubation period from 0 to 10 days were about
what was expected; the largest rate of BOD increase in this first 10 days of
incubation usually occurred on the first day, with the 1-day BOD values being
about 20 mg/1. This value remained relatively constant until about the fifth
day, when it gradually rose to the 10-day value. The most unusual aspect of
the BOD rate of change occurred in the incubation period from 10 to 20 days,
when the BOD values increased by a factor of 2 or more. The initial oxygen
demand is rapid and may have possible deleterious effects on certain receiving
waters close to the time of discharge. As the material settles out, however,
it apparently can exert a much larger, longterm oxygen demand.
These apparent BOD characteristics may be due to the standard BOD bottle
test in which a standard sewage seed material was used and the runoff sample
was diluted. Urban runoff has a relatively high heavy metal and low nutrient
content, which can decrease the bacteria activity in the closed bottle after
the wastes that are easily assimilated have been consumed. A long period of
time is then necessary to reestablish an acclimatized bacteria population that
will more completely stabilize the runoff. Ammonia oxygen demand can also result
in long-term oxygen depletion. From this current study it is not possible to
determine whether the potential long-term problem actually exists, or whether
the testing procedure is faulty.
The study also compared the relative strengths* of pollutants in the runoff
with the relative strengths of pollutants in the street dirt to compare the
pollutant contributions from the street surface with the other watershed areas.
This information helped identify those pollutants that may be most effectively
controlled by street cleaning. The study showed that for lead, chromium, and
copper, relative concentrations in the runoff were all much smaller than for
those measured in the street dirt. The relative concentrations for COD, Kjeldahl
nitrogen, and orthophosphates were much greater for the runoff samples than
for the street dirt samples. These data indicate that the major sources for
organics and nutrients are from areas other than the streets, while the major
sources for heavy metals are associated with street activity. Organic and nu-
trient material wash onto the streets and into the storm drains during runoff
and are diluted by the street dirt, which has lower concentrations of these
materials. Conversely, these erosion materials tend to be low in heavy metals,
and thus dilute the heavy metal concentrations of the street dirt. Therefore,
if it is important to significantly reduce organic and nutrient discharges in
the runoff, street cleaning may not be an appropriate control measure.
*Relative strength is measured as mg of pollutant per kg of total solids.
69
-------
TABLE 4-1. RAINS DURING FIELD ACTIVITIES*
Date
11/11/76**
11/12
11/13
11/14**
12/29**
12/30**
1/1/77
1/2**
1/3**
1/5
1/12
1/21
2/6
2/8
2/20
2/21
2/22
2/23
2/28
3/9
3/12
3/13
3/15**
3/16**
3/23
3/24**
4/8
4/25
4/30
5/1
5/6
5/7**
5/8**
5/9
5/11**
5/18
5/23
5/26
7/2
9/19**
10/27
10/28
10/29
11/5**
11/21**
11/22
12/5
12/14
12/15
12/16
12/17**
Total
Total
(in.)
0.35
0.09
0.07
0.29
0.34
0.37
0.04
0.24
0.20
0.08
0.07
0.01
0.01
0.08
0.03
0.13
0.02
0.13
0.06
0.08
0.01
0.11
0.91
0.25
0.02
0.19
0.03
0.02
0.06
0.18
0.01
0.28
0.28
0.01
0.20
0.09
0.07
0.01
0.14
0.58
0.18
0.01
0.01
0.51
0.28
0.10
0.01
0.06
0.06
0.11
0.73
8.20
Hours of Rain
8
4
3
5
3
9
3
6
9
2
2
1
1
4
1
3
2
6
2
1
1
2
15
5
2
5
2
1
3
6
1
2
4
1
6
4
2
1
5
5
5
1
1
3
6
1
1
2
2
4
13
Ave rage
Intensity
(in./hr)
0.04
0.02
0.02
0.06
0.11
0.04
0.01
0.04
0.02
0.04
0.04
0.01
0.01
0.02
0.03
0.04
0.01
0.02
0.03
0.08
0.01
0.06
0.06
0.05
0.01
0.04
0.02
0.02
0.02
0.03
0.01
0.14
0.07
0.01
0.03
0.02
0.04
0.01
0.03
0.12
0.04
0.01
0.01
0.17
0.05
0.01
0.01
0.03
0.03
0.03
0.06
Peak
Intensi ty
(in./hr)
0.10
0.04
0.04
0.11
0.18
0.11
0.02
0.09
0.05
0.06
0.06
0.01
0.01
0.03
0.03
0.10
0.01
0.06
0.04
0.08
0.01
0.08
0.13
0.12
0.01
0.08
0.02
0.02
0.04
0.08
0.01
0.19
0.09
0.01
0.08
0.03
0.05
0.01
0.10
0.33
0.07
0.01
0.01
0.25
0.20
0.01
0.01
0.05
0.05
0.05
0.12
* The period of study was characterized by low rainfall quantities.
The number of rains were slightly fewer (about 75%) but the total
rainfall quantity was substantially reduced (about 50%).
**Significant rains. See Section 3, discussion of accumulation
rates, for definition and importance of these rains.
74
-------
the receiving water. Monitoring the receiving water directly would give more
accurate results, but runoff comparisons can give a gross indication of potential
problems. Once again, identifying the problem pollutants and their source areas
help in the selection of the most effective control measures.
Recommended water quality criteria are designed to protect the beneficial
uses of the water with a reasonable amount of safety. If a monitored concen-
tration exceeds these criteria, it does not mean that a problem exists, but
only that a problem may occur. Additional monitoring and research should then
be conducted to define the relationships between the water quality and the po-
tential impairment of the beneficial uses for the specific receiving water.
The study showed that the heavy metals—cadmium, chromium, lead, mercury,
and zinc—along with phosphates, BOD, suspended solids, and turbidity exceeded
various recommended criteria during the monitored storms. Aquatic life use
may be adversely affected by more pollutants than other beneficial uses.
Comparison of Urban Runoff With Sanitary Wastewater Effluent
This study compared the monitored quality of urban runoff with treated
sanitary wastewater effluent. The latter is usually treated extensively, while
urban runoff usually gets little or no treatment.
Water quality comparisons of urban runoff with average secondary sewage
effluent showed that most of the nutrients, heavy metals, solids and oxygen-
demanding materials had greater concentrations in the runoff. Thus urban runoff
may have more important short-term effects on receiving waters than treated
secondary effluent.
Annual yields of pollutants (lb/yr*) are a measure of potential long-term
problems. Lead, chromium and suspended solids had greater annual yields in the
street surface portion of the runoff than in the treated secondary effluent.
Therefore, urban runoff may also cause greater long-term receiving water problems
because of these heavy metal and solids yields. It follows that improvements
in the sanitary sewage effluent may not be as cost-effective at removing these
pollutants from the receiving water as some removal of the street surface pol-
lutants by street cleaning.
STRUCTURE OF THE STUDY
Tracer studies and actual runoff sampling studies were conducted to investi-
gate the solids routing and pollutant mass flow characteristics of urban runoff.
These studies cannot yield data applicable to all situations because of limited
sampling. A methodology that can be used to investigate and validate the antic-
ipated processes was developed. These techniques can be reviewed and possibly
adapted for larger-scale investigations and investigations of combined sewerage
systems.
*See Metric Conversion Table 0-1.
71
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TABLE 4-2. MAJOR ION COMPOSITIONS OF RUNOFF SAMPLES (%)
Keyes Street
Study Area
Cations
Ca++
K+
X, ++
Mg
4-
Na
Zn++
Pb"1"1"
Total
An ions
HC03~
co3=
so4"
ci-
P04=
N03
Total
Major water
tvue
3/15 and
16/77
35.9%
10.3
30.8
23.1
<2.6
<2.6
100.1
42.6
0.2
21.3
18.0
16.4
1.6
100.1
Ca and Mg-
HCOo
3/23 and
24/77
53.7%
4.5
18.1
22.6
0.6
0.6
100.1
77.9
0.1
11.2
10.2
0.3
0.3
100.0
Ca-HC03
Tropicana Study Area
3/15 and
16/77
34.2%
3.3
21.1
41.5
<0. 7
<0.7
100.1
45.2
<0.1
23.7
24.4
5.2
1.5
100.0
Na and Ca-
HCOo
3/23 and
24/77
29.8%
3.6
20.2
46.4
<0.4
<0.4
100.0
50.0
0.1
27.0
21.6
1.0
0.5
100.2
Na-HC03
4/30 and
5/1/77
34.2%
4.0
17.4
43.6
0.4
0.4
100.0
<0.8
<0.8
44.8
40.0
15.2
<0.8
100.2
Na and Ca-
SOA and Cl
76
-------
• Up to ten two-hour composite analyses per monitored rain:
total solids
suspended solids
total dissolved solids
• One flow-weighted composite analysis per monitored rain:
mercury (Hg), sulfates ^
calcium (Ca ) bicarbonates (HCO^)
potassium (K+)^ carbonates (C03)
magnesium (Mg ) nitrates (N03)
sodium (Na+) _ BOD "k" rate
chlorides (Cl )
MONITORED RAINS
In 1977, twelve rain periods were monitored and analyzed in the two in-
strumented study areas. Many samples were obtained from these rains and were
generally analyzed as described above. These rain periods are summarized in the
following list:
• Keyes study area:
1700 March 15 through 0900 March 16 (1.16 in.)
1200 March 23 through 1300 March 23 (0.01 in.)
1000 March 24 through 1700 March 24 (0.19 in.)
1700 April 30 through 2200 April 30 (0.06 in.)
0200 May 1 through 1500 May 1 (0.18 in.)
• Tropicana study area:
1600 March 12 through 1100 March 13 (0.01 in.)
0900 March 15 through 1300 March 16 (1.16 in.)
1100 March 23 through 1700 March 23 (0.01 in.)
1900 March 23 through 0100 March 24 (0.01 in.)
1000 March 24 through 0000 March 25 (0.19 in.)
1700 April 30 through 2200 April 30 (0.06 in.)
0200 May 1 through 1500 May 1 (0.18 in.)
Table 4-1 lists the precipitation record for San Jose during the period
of study. These data are from the recording rain gauge station operated by
San Jose State University, 0.5 and 2 miles from the study areas. A total of
8.20 in. of rain fell from November 1976 through December 1977, as compared
with a long-term average for that period of 16.53 in. It rained on 51 days,
slightly fewer than normal. The runoff monitoring was started in March to
enable the previous year's accumulation of sewerage solids to be flushed from
the lines and to allow sufficient time for field installation and testing of
the automatic sampling equipment.
73
-------
Figure 4-1 presents BOD values as a function of incubation time. Selected
composite samples representative of each storm were incubated and BOD values
were measured at increments of approximately 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
values. The largest rate of BOD increase in this first 10 days occurred usually
on the first day, with 1-day BOD values of about 20 mg/1 (for 2 of the 3 samples).
This value remained relatively constant until about the fifth day when it grad-
ually 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 2 or more. Typical sanitary wastes would have
to BOD2Q increases of much less than a factor of 2. These results show that
the initial oxygen demand is rapid and may have possible deleterious effects
on certain receiving waters close to the time of discharge (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. (These time factors are all dependent on water temper-
ature and other physical and chemical characteristics of the receiving water.)
120
100-
80-
60-
40-
20 H
••••©•••• Tropicana storm of March 15, 16, 1977
• Tropicana storm of March 23, 24, 1977
—*— Tropicana storm of Api il 30, May 1, 1977
I ' ' ' ' I
5 10
DAYS OF INCUBATION
15
i
20
Figure 4-1. BOD values as a function of incubation time.
78
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RUNOFF SAMPLING PROGRAM
Appendix F presents the laboratory and field data for the runoff samples
that were collected. This appendix lists concentrations of major ions, major
parameters, heavy metals, and solids for each of the monitored rains (see Tables
F-ll through F-23). Figures F-l through F-9 of Appendix F are hydrographs of
the monitored rains showing the recorded sewerage flows, precipitation data,
and the water sampling periods. Several of these rains had multiple precipitation
peaks with distinct runoff peaks. A lag period of 1 to 6 hours occurred between
the beginning of the precipitation and the start of measurable flow. The most
common lag period was about 1 hour. The flows also continued for 3 to 8
hours after the precipitation stopped in the study areas. In almost all cases,
peak recorded flows occurred 1 to 2 hours after the peak precipitation. The
Tropicana study area, being about twice the size of the Keyes Street study
area, had significantly greater peak flows. The largest peak flow recorded in
the Tropicana study area was about 19 cubic feet per second (cfs)*. The other
peak flows in the Tropicana study area ranged from 1 to about 7 cfs. Flows
in the Keyes Street study area were much less, with a maximum recorded peak
flow of about 4 cfs. The other peak flows were all less than 1 cfs. In most
cases, a precipitation total of 0.01 in. caused a measurable flow at the outfalls.
All of the rains up to March 30 were sampled hourly, while the rains since
then were sampled on a flow-weighted basis.
Tables F-l through F-10 of Appendix F present the 'water sample informa-
tion. These tables show the water sample code numbers corresponding to the
coded callouts on Figures F-l through F-9. Also shown on these tables are the
date and time that the samples were taken and the average flow for that sample
period. The total flow represented by that sample, along with pH, ORP, specific
conductance, and turbidity values are also shown. Appendix F also presents
these data and the chemical constituents on a per unit time basis. As can be
expected, the concentrations of most of the pollutants decreased with time.
Table 4-2 presents the major ion compositions for the runoff samples. It
is interesting to note that the two study areas had slightly different major
water types. The Keyes Street study area had a calcium and magnesium-bicarbonate
or a calcium-bicarbonate major water type, and the Tropicana study area had a
sodium and calcium-bicarbonate, a sodium-bicarbonate, or a sodium and calcium-
sulfate and chloride water type. It is not known why sodium, sulfate, and chloride
were more prevalent in the Tropicana study area.
Table 4-3 summarizes the oxygen demand and organic characteristics of the
runoff samples. It presents the BOD5> COD, TOG,** and some VSS*** data for
selected samples. It is interesting to note that the COD concentrations are
about 3 to 10 times greater than the BOD5 values, and the TOG concentrations
are as much as 10 times the BODc concentrations. For a normal sanitary waste
having low toxicity and sufficient nutrients, the COD values should only be
slightly greater than the BOD^ values.
* See Metric Conversion Table 0-1.
** Total organic carbon.
***Volatile suspended solids.
75
-------
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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 concentra-
tions of heavy metals and low concentrations of nutrients, the seed bacteria
may require a longer time for acclimatization than normal. The initial oxygen
demand could be caused by the relatively easily assimilated organics being con-
sumed by the standard seed bacteria before significant bacteria dieoffs occur
from heavy metal toxicity. A lag period of several days could then be required
for the surviving seed bacteria to become acclimated and reestablished so as to
assimilate the remaining organics. Ammonia oxygen demand may also cause long-
term oxygen depletion with about one-fourth of the observed 10 to 20 day increase
possibly caused by ammonia oxidation. Colston (1974) has developed an alternative
BOD procedure for urban runoff based on measurements of COD with time. His
procedure uses an aerated and mixed sample, with typical receiving waters for
dilution. Colston has found that typical urban runoff BOD^ values are about
one-half the corresponding COD values.
Table 4-4 presents the runoff pollutant strengths expressed as milligrams
of pollutant per kilogram of total solids (or ppm) averaged over the durations
of the monitored rains. There are no clear differences (because of limited
data) in the pollutant concentrations between the different storms or study
areas. In most cases, the range of pollutant strengths for all of the storms
combined was less than a factor of 10 to 1, and in several cases even less
than 3 to 1. When these runoff pollutant strengths are compared with the street
surface contaminant pollutant strengths, notable differences are found. It is
interesting to note that the relative concentrations in the runoff for COD,
Kjeldahl nitrogen, and orthophosphates are much greater than the relative con-
centrations 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. 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. A difference in the particle
size makeup of the runoff solids and the street dirt may explain some of these
differences. It was expected that other causes would be important, such as
additioinal organic and nutrient material washing onto the streets and into the
storm drains from the surrounding areas because of erosion during rains. Lower
concentrations of heavy metals in the soil erosion products could also cause the
runoff heavy metal relative concentrations to be much smaller. If the erosion
products have lower concentrations of heavy metals, the resultant runoff concen-
trations of heavy metals would be diluted when compared to the higher concen-
trations in the street dirt. Therefore, much of the organic and nutrient material
in urban runoff may originate, not from the street surface or from automobile
activity, but from the surrounding areas during erosion. Similarly most of the
heavy metals in urban runoff are expected to be associated 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 sur-
face loading data in an attempt to determine a loading model as'a function of
various influencing characteristics (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 significant differences in loading pre-
79
-------
TABLE 4-5. TOTAL SOLIDS STREET SURFACE LOADING REMOVALS BY RAIN STORMS
Particle Size
and Storm
Date (w)
3/15 and 16/77
storm
>6370 M
2000 - 6370
850 - 2000
600 * 850
250 * 600
106 * 250
45 * 106
<45
Total
Avg; peak in-
tensity
Duration; total
rain
Days since last
swept; number
of passes
3/23 and 24/77
storm
>6370 M
2000 * 6370
850 * 2000
600 - 850
250 * 600
106 * 250
45 * 106
<45
Total
Avg; peak in-
tensity
Duration; total
rain
Days since last
swept; number
of passes
4/30 and 5/1/77
storm
>6370 w
200 - 6370
850 * 2000
600 - 850
250 * 600
106 * 600
45 - 106
<45
Total
Avg; peak in-
tensity
Duration; total
rain
Days since last
swept; number
of passes
Oil and
Before
Storm
Loading
(Ib/curb-
mile)
100
200
210
140
470
350
210
71
1900
92
350
290
190
700
520
310
110
2600
130
470
100
100
320
280
170
66
1600
Keyes -
Screens Test Area
Loading
Decrease
During Storm %
(Ib/curb- Differ-
mile) ence
45 44
2 1
-90 -42
-21 -15
-14 - 3
52 15
88 42
54 76
116 6
0.06: 0.13 in./hr
20 hrs; 1.16 in.
2 days; 1 pass
-33 -36
-99 -28
-216 -74
- 51 -27
- 14 - 2
Before
Storm
Keyes - Good
Asphalt Test Area
Loading
Decrease
Loading During Storm %
(Ib/curb- (Ib/curb- Differ-
inile)
16
18
25
16
64
72
62
12
290
18
50
75
44
150
109 21 i 160
95 31
81 74
-128 - 5
0.03; 0.08 in./hr
140
30
660
7 hrs; 0.21 in.
5 days; 1 pass
15 12
-145 -31
-343 -340
-65 -62
-124 -39
54 19
46 27
21 32
-541 -33
0.03; 0.08 in./hr
9 hrs; 0.25 in.
22 days; 2 passes
41
66
73
48
140
130
110
13
630
mile) ence
2.4
-4.8
-4.9
0.8
10.8
15.8
16.7
-7.7
29.0
0.06; 0.13 in/hr
20 hrs; 1.16 in.
2 days; 1 pass
15.7
27.0
32.1
26.1
92.9
125
115
0.6
434
0.03; 0.08 in./hr
7 hrs; 0.21 in.
5 days; 1 pass
51.7
55.2
53.0
45.2
153
155
136
- 3.8
645
0.03; 0.08 in./hr
9 hrs; 0.25 in.
22 days; 2 passes
17
-26
-20
5
17
22
27
-66
10
85
54
43
60
63
77
85
2
66
127
84
73
94
107
119
121
-30
103
Before
Storm
Tropicana - Good
Asphalt Test Area
Loading
Decrease
Loading During Storm
(Ib/curb- (Ib/curb-
mile)
18
5.
26
15
42
45
41
10
220
26
31
52
39
100
100
81
20
460
25
20
27
19
57
65
54
9
280
mile)
10.5
9 5.9
10.6
9.8
27.8
29.7
23.2
- 2.2
115
0.06; 0.13 in/hr
20 hrs; 1.16 in.
11 days; 1 pass
18.6
15.6
29.9
30.1
79.6
78.2
56.2
- 6.5
302
0.03; 0.08 in./hr
7. hrs; 0.21 in.
21 days; 1 pass
28.0
18.4
23.8
18.4
56.0
65.3
49.9
-3.1
257
0.03; 0.08 in./hr
9 hrs; 0.25 in.
4 days; 1 pass
Differ-
ence
60
30
41
65
66
66
57
-23
53
7
50
57
77
79
74
69
-32
66
112
93
88
96
98
100
92
-34
93
82
-------
dictions 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
Tables 4-5 and 4-6 present the total solids and various street surface
pollutant loading changes that occurred for each of the rain storms. Table
4-5 values were calculated from street surface loadings before and after the
rain storms. Table 4-6 compares these values with actual stormwater runoff
yields. A negative value in Table 4-5 signifies an increase in loading on
the street surface during the storm. It is interesting to note that the rains
had a much smaller effect on removing materials from the oil and screens streets
as compared with the asphalt streets. 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 sewerage system. The Keyes-good asphalt and Tropicana-good asphalt
test areas, both with relatively smooth asphalt streets, showed larger 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 surrounding 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 23-24 storm and for the April 30-May 1 storm 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 4-6 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 sur-
face, are compared to the total pollutant yields of the storms. The observed
ratios between street surface loading differences of the pollutants as mea-
sured on the street and the runoff yield as measured by analyzing runoff vary.
Values smaller than 1 possibly signify that more of that pollutant originated
in the surrounding areas and storm sewerage than on the street surface. Values
greater than 1 possibly indicate that most of the material that originated
from street surfaces accumulated in the storm sewerage.
These ratios appear to vary as a function of the rainstorm characteristics,
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 4-6 had many values greater than 1. Again,
the initial storm was of much greater intensity 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, however, 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 on 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
81
-------
streets would wash off during a rain and contribute to the pollution of urban
runoff. Table 4-7 shows the estimated effectivenesses of various street clean-
ing programs (cleaning intervals) in controlling total urban runoff pollutant
yields.
The estimates shown in Table 4-7 are based on too few runoff measurements
(as discussed previously in this section) to be more quantitative. A runoff
monitoring program designed to yield this specific information would require
sampling many storms over a relatively long period of time. Nevertheless,
several interesting observations were noted during this data analysis. It was
found that very little difference in. runoff water quality would be evident be-
tween cleaning programs operating twice every workday (520 passes a year) and
once every workday (260 passes a year). A similar conclusion was found for
cleaning programs of little intensity: cleaning once a month and once every
three months would yield similar runoff quality conditions. As expected, the
heavy metals may be controlled much more effectively (up to about 50 percent of
this runoff yield could be removed for very intensive cleaning efforts) than the
other pollutants. Total solids may also be controlled to a reasonably high value
(up to about 40 percent). Organics and nutrients, which originate mostly from
non-street areas within the watershed, would only be reduced by less than 10
percent. Removal effectiveness decreases by about a factor of three when reduc-
ing the cleaning effort from one or two passes every weekday to one pass every
week. The removal effectivenesses are reduced by more than a factor of ten when
reducing the effort from weekday cleaning to monthly (or less) cleaning.
Table 4-7. ESTIMATED EFFECTIVENESS OF VARIOUS STREET CLEANING PROGRAMS IN
CONTROLLING URBAN RUNOFF*
Cleaning Interval
One to Two One to Three
Passes Per One Pass Passes Every
Parameter Weekday Per Week Three Months
Total Solids A C C
COD C C D
KN C C D
Ortho PO^ C D D
Pb A C C
Zn A C C
Cr A C C
Cu A C C
Cd "B C C
*A = greater than 40% effective
B = 20 to 40% effectiveness
C = 1 to 20% effectiveness
D = less than 1% effective
84
-------
TABLE 4-6. STREET SURFACE POLLUTANT REMOVALS COMPARED WITH RUNOFF YIELDS
Kcyes Street Study Area
Oil and Screens
Param-
eter
Total
solids
COD
KN
OrthoPO,
Pb
Zn
Cr
Cu
Cd
Total
solids
COD
KN
OrthoPO^
Pb
Zn
Cr
Cu
Cd
Total
solids
COD
KN
OrthoPO,
4
Pb
Zn
Cr
Cu
Cd
Ib/curb-
mile dif-
ference
120
24
0.33
0.23
0.40
0.067
-0.0084
-0.014
0.00031
-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
total
Ib differ-
ence in 2. 2
curb-mile
260
53
0.73
0.051
0.88
0.15
-0.018
-0.031
0.001
-290
19
0.46
0.035
1.0
0.081
-0.31
0.7
0.0001
-1200
- 44
-0.53
0.040
-0.17
-0.2
-0.77
-1.4
-0.002
Asphalt
Ib/curb-
mile dif-
ference
29
3.0
5.2
0.0049
0.19
0.022
0.014
0.024
0.0001
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
total
Ib differ-
ence in 2. 7
curb-mile
MARCH
78
8.1
14
0.013
0.51
0.059
0.038
0.065
0.0001
MARCH
1200
160
2.6
0.21
5.4
0.70
0.59
1
0.003
APRIL 30
1800
240
3.8
0.30
7
0.97
0.92
1.6
0.005
Total
Keyes
Area
Ib
differ-
ence
15-16, 1977
340
61
15
0.064
1.4
0.21
0.020
0.034
0.001
23-24, 1977
910
180
3.1
0.25
6.4
0.78
0.28
1.7
0.003
Runoff
yield
(Ib)
, STORM
942
859
51.8
21.1
1.75
0.71
0.065
0.13
0.026
, STORM
134
68
0.7
—
0.15
0.063
0.0059
0.0079
0.0008
Street
Surface
Differ-
ence to
Runof f
Yield
Ratio
Tropicana Study Area
Ib/curb-
mile dif-
erence
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
I
6.8 300
2.6
4.4
—
43
12
47
210
3.8
-27
0.57
0.053
1.3
0.14
0.16
0.34
0.0007
total
Ib
differ-
ence
in 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
Street
Surface
Differ-
ence to
Runoff Runoff
Yield Yield
(Ib) Ratio
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
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
9.15
0.2
0.003
11.6
—
—
0.13
—
—
—
—
1
52 260
—
—
2.0
—
—
—
—
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
flows in the sewerage were not capable of preventing the material from depositing
in the sewerage. The small number of data points available prevents a specific
model from being developed. The data demonstrate several relationships between
rainfall characteristics, street surface conditions, relative pollutant yields
from street surfaces and surrounding land-use areas, and pollutant deposition
in the sewerage system.
EFFECTIVENESS OF STREET CLEANING IN IMPROVING URBAN RUNOFF WATER QUALITY
Street cleaning can be effective in reducing the quantity of some pollutants
in urban runoff. Most of the material removed by a street cleaner on smooth
83
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pollutants from the street surface before rains can wash them into the receiving
waters. Sections discusses the relative unit costs for removing these pollutants
by street cleaning as compared with alternative runoff treatment and combined
wastewater treatment systems.
COMPARISONS OF RUNOFF WATER QUALITY WITH
SANITARY WASTEWATER EFFLUENT WATER QUALITY
Table 4-10 presents a comparison between secondary sanitary wastewater ef-
fluent and urban runoff for the study areas. The average and peak one-hour
runoff concentrations observed and average secondary sanitary wastewater ef-
fluent concentrations are shown along with the ratios between them. The sani-
tary wastewater treatment facility is a modern, advanced secondary treatment
plant serving the study areas. The short-term effects of urban runoff on a
receiving water occur (by definition) during and immediately following a runoff
event: short-term effects are associated with instantaneous concentrations.
A comparison between the urban runoff average concentrations and the sanitary
wastewater treatment plant effluent average concentrations shows that the con-
centrations of lead, suspended solids, COD, cadmium, TOG, turbidity, zinc,
chromium, and BOD^ are all higher in the runoff than in the sanitary wastewater
effluent. Copper and Kjeldahl nitrogen, in addition to the previously listed
parameters, have greater runoff peak concentrations than the wastewater 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 4-10 shows the annual sanitary wastewater treat-
ment plant effluent yield expressed as tons per year (derived from monthly
average concentrations and effluent quantities), and the calculated annual
street surface portion of the urban runoff yield expressed in tons per year
for a similar service area. On an annual basis, the total orthophosphates and
Kjeldahl nitrogen associated with the street dirt are less than 2 percent of
the total sanitary wastewater treatment plant effluent plus urban street surface
runoff yield. Total solids, cadmium and mercury contribute from 1 to 10 percent
of this total, while chemical oxygen demand, biochemical oxygen demand, copper,
and zinc contribute from 10 to 50 percent of this total. Suspended solids,
chromium and lead street surface runoff contributes more than 50 percent of the
total.
These data show that for a receiving water getting both secondary treated
sanitary wastewater and untreated urban runoff, additional improvements in the
sanitary wastewater effluent may not be as cost-effective as some street clean-
ing (except for nutrients). That is especially true for lead where more than
95 percent of this total wasteload is due to street surface runoff. If all of
the lead were removed from the sanitary wastewater effluent, this total annual
lead discharge would only decrease by less than 4 percent.
TRACER ANALYSIS OF SEWERAGE PARTICULATE ROUTING
A special catchbasin was constructed and partially filled with street sur-
face particulate simulant and fluorescent particle tracer material to monitor
-------
TABLE 4-10. COMPARISON OF URBAN RUNOFF AND WASTEWATER TREATMENT PLANT EFFLUENT
Runoff
Concentration
(mg/1 unless
otherwise stated)
STP3 Effluent
Concentration
(mg/1 unless
otherwise stated)
Ratio
of Av g .
Runoff
to STP
cone.
Ratio
of Peak
Runo f f
to Avg.
STP cone.
Street
Surface
Annual
Runoff5
(tons/yr)
Annual
STP
Effluent0
(tons/yr)
Ratio of
Street
Surface
Runoff
to STP
Annual
Yields
Peak
Parameter Avg (1-hr)
Ca++
K+
.< -H-
Mg
Na+
Cl~
soA"
HC03
NO.
BODr
5
COD
KN
OrthoPO^
Total solids
TDSe
Suspended
solids
Cd
Cr
Cu
Pb
Zn
Hg
Specific
conductance
( ymhos/cm)
Turbidity (NTU)
pH (pH units)
TOCf
13
2.7
4.0
15
12
18
54
0.7
24
200
6.7
2.4
350
150
240
0.01
0.02
0.03
0.4
0.18
< 0.0001
120
49
6.7
110
19
3.5
6.2
27
18
27
150
1.5
30
350
25
18
950
380
850
0.04
0.04
0.09
1.5
0.55
0.0006
660
130
7.6
290
Avg.
65
24
35
220
330
150
230
4.9
21
35d
24
19
1000
1000
26
0.002
0.016
0.081
0.0098
0.087
0.0019
1900
20
7.6
30
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
—
3.5
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
—
9.7
350
73
110
410
330
490
1500
19
480
950
17
1.2
9500
4100
4700
0.018
3.5
5.5
36
3.9
0.0032
—
—
—
3000
8000
3200
4700
30,000
45,000
20,000
32,000
660
2800
4700d
3200
2600
140,000
140,000
3500
0.27
2.2
11
1.3
12
0.26
—
—
—
4100
0.040
0.023
0.023
0.014
0.007
0.025
0.047
0.029
0.17
0.20
0.005
0.0005
0.07
0.029
1.3
0.07
1.6
0.5
28
0.33
0.01
—
—
—
0.73
Secondary sanitary wastewater treatment plant.
About 200 people correspond to 1 curb-mile (2880 curb-miles in San Jose/575,000 population).
Therefore a population of 850,000 corresponds to about 4250 curb-miles, with about 1100 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 sanitary wastewater treatment facility.
Estimated.
"Total dissolved solids.
'Total organic carbon.
the routing of particulates in a stormwater sewerage system. Figure 4-2 shows
the storm drainage system in the Keyes Street study area that was selected for
this portion of the study. The catchbasin was constructed at the south corner
of south 12th and Bestor Streets. Figure 4-3 presents the storm drainage system
details from this catchbasin to the outfall. The sewerage is all concrete pipe
ranging in size from 10 to 27 in. in diameter. The sewerage slopes range from
0.16 to 0.79 percent. A total of about 2700 feet of sewerage is between the
catchbasin and the last manhole before the outfall.. The outfall is located
several hundred feet northeast of the last manhole and is directly on Coyote
Creek.
89
-------
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27" C.P. (1)
0.17% (2) -
350 ft. (3)
27" C.P.
0.16%
358 ft.
24" C.P.
0.27%
347 ft.
12" C.P.
0.28%
350 ft.
LEGEND
Storm main
O
V
a
A
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\
/
\
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\
S. 12th Street
10" C.P.
0.75% """
336 ft.
+
S. 11th Street
10" C.P. „
£ 0.9%
350 ft.
*
S. 1 0th Street
15" C.P.
* 24" CP °'41% N
£ ^ ^'r- 361 ft
5 0.24%
623 ft.
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S. 9th Street 10" C.P.
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357ft.
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Storm lateral
Manhole
Standard inlet
Experimental catch basin
Storm sewerage flow direction
(1) C.P. refers to concrete pipe
(2) % values are sewage slopes
(3) Ft. values are sewage segment lengths
Figure 4-3. Storm drainage from special catchbasin to outfall.
91
-------
A special catchbasin was constructed following the recommendations pre-
sented by Lager and Smith (1976); this design is supposed to maximize solids
retention. The catchbasin is circular in shape and was formed from a section
of 39 in. inside diameter (48 in. OD) reinforced concrete pipe. The outlet is
a 10 in. inside diameter concrete pipe located 25 in. below the top of the
catchbasin and 40 in. above the bottom. These dimensions follow the idealized
proportions as presented by Lager and Smith. If the outlet diameter is noted
as dimension D, it should be located 2.5D below the top of the catchbasin and
4D from the bottom of the catchbasin. The overall height of the catchbasin
from the street surface to the bottom is therefore 6.5D while the inside diameter
is 4D.
A total of 500 Ib of street surface simulant was placed in the catch-
basin. The simulant was designed to have the same solids size distribution as
the street surface particulates measured in this test area (See Figure 3-4).
Types and amounts of simulants used included: 105 Ib of No. 2 clay, 260 Ib of
No. 20 fine sand, 30 Ib of No. 1 sand, 60 Ib of No. 3 sand and 45 Ib of pea
gravel (slightly less than 0.25 in. in diameter). The clay, sand and gravel
were well washed and sieved before mixing. 2.5 Ib of yellow fluorescent parti-
cles were mixed with the bottom half of the simulant, and 2.5 Ib of green fluo-
rescent particles were mixed with the top half of this simulant<.
Samples were collected five times from the catchbasin, downstream manhole
locations, and directly off of the outfall in the creek between September 1977
and January 1978. During this time, more than 10 days of rain occurred with
each day having rain volumes ranging from 0.01 in. to more than 0.75 in. Rains
on at least four of these days were capable of washing off significant quantities
of street surface particulates, irrespective of traffic conditions.
Core samples were taken from the catchbasin using a carbon dioxide (€02)
freezing core sampling apparatus. This unit consisted of a 0.5 in. rigid cop-
per pipe with a braised brass point that was driven into the catch-basin sedi-
ment. A 0.375 in. flexible copper tube was connected to a liquid C^ supply
(a C02 gas bottle with a syphon tube). Liquid C^ was then supplied to the
larger copper tube which froze the adjoining sample to the outer tube. The
CO 2 flowed for about 1 minute, allowing a sample thickness of about 0.25 to
0.5 in. to form. This frozen core was then withdrawn from the catchbasin and
the frozen sample was separated from the tube and analyzed as a function of
depth.
The samples were collected from the manhole access points by manually scrap-
ing sediment into sample collection bottles. Sewerage inspections were also
routinely conducted during this time period. These inspections documented the
amount (depth) of sediment in the main sewerage and in the adjacent laterals.
All of the laterals and mains were flushed out before the beginning of the tests.
Table 4-11 presents the results of this tracer study averaged for all
sampling periods. This table shows the relative tracer concentrations for the
green and yellow particles in various locations of the storm sewerage system
compared to the catchbasin tracer concentrations. As an example, the average
green fluorescent particle concentration in the catchbasin simulant was about
18,000 green fluorescent particles/gm of simulant. The average concentration of
92
-------
TABLE 4-11. TRACER CONCENTRATIONS IN SEWERAGE COMPARED TO CATCHBASIN TRACER
CONCENTRATIONS (ppm)
Manhole
Location
1
2
3
4
5
6
7
8
Outfall
Green Particles*
Average
350
290
57
300
95
120
57
67
120
Min.
52
0
29
52
52
0
29
29
110
Max.
520
680
81
900
160
320
110
130
130
Yellow Particles**
Average
390
270
150
900
240
660
98
220
73
Min.
150
0
0
0
0
0
0
0
0
Max.
680
830
270
3500
680
1500
150
410
150
* The green fluorescent particles were mixed with the top half of the simulant
in the catchbasin.
**The yellow fluorescent particles were mixed with the bottom half of the
simulant in the catchbasin.
the green fluorescent participates at manhole location number one averaged about
7.4 particles of fluorescent material/gm of sediment. Therefore, the relative
concentration of green fluorescent particles at this station was about 350 parts
per million when compared to the concentration in the catchbasin. The range of
relative concentrations varied widely for the different periods of sample col-
lection. No trends were evident in particle concentrations, except that none
were found on the first day when the material was installed. Three days later,
green and yellow fluorescent particles were found at practically all of the
manhole stations, even though no rain occurred. The sewerage system had a con-
tinuous dry weather flow due to many small leaks from the domestic water supply
system, from sidewalk and automobile washing, possible groundwater infiltration,
and irrigation. The relative concentrations for the different dates of sampling
did not significantly change with time. A general decrease in relative concen-
trations was noted, but the variations were quite large. No significant pattern
was noted in relative concentrations at any of the sampled manhole locations.
Yellow participates were not found at most of the manhole sampling locations
during some of the sampling periods. This was expected because the yellow
material was located at the very bottom of the catchbasin and would not be
93
-------
discharged into the sewerage system except with runoff-induced turbulence. The
overall depth of simulant in the catchbasin slightly decreased (by about 20
percent) during the four-month period of study. The only notable increase in
catchbasin sediment material was floating organic material.
Some of the simulant and tracer material was removed from the catchbasin
during periods having dry weather flows. Increases in fluorescent tracer rela-
tive concentrations at the various sampling locations were not significant, even
with several significant rains. Little stratification of flourescent particles
was noted relative to the simulant material in the catchbasin. The concentrations
of flourescent particles in the catchbasin did not significantly change with
time. This technique may be a useful procedure for monitoring catchbasin per-
formance and sediment releases in other studies.
94
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SECTION 5
TREATABILITY OF NONPOINT POLLUTANTS BY STREET CLEANING
SUMMARY
The objective of this portion of the study was to assess the cost and labor
effectiveness of various methods of street cleaning, runoff treatment, and com-
bined wastewater treatment systems in controlling nonpoint pollution. The re-
sults of the street surface contaminant and runoff monitoring tests (see Sections
3 and 4) were used to estimate the treatability of urban runoff and to estimate
costs of treatment. The basic information for street cleaning labor and costs
were derived from San Jose's street cleaning program (September 1976 through
August 1977) . San Jose street cleaning costsjwere about $14 per curb-mile cleaned,
and about one man-hour was required for each curb-milecleaned(1976-1977 dollars).
About 75 percent of the street cleaning costs were for labor, which makes
street cleaning a labor-intensive operation. This trait is desirable, because
if different control measures have equal cost effectiveness, it is socially bene-
ficial to choose the measure that employs the most people. Maintenance costs
were about 30 percent of the overall program costs. Other important costs in-
clude disposal costs, equipment depreciation, and operating expenses. Equipment
replacement to reduce costs could achieve a maximum cost savings of much less
than 30 percent (the total maintenance costs). The other costs are constant
and would not vary significantly for different types of currently available
street cleaning equipment.
A cost increase of about a factor of 10 over typical monthly or bimonthly
cleaning program costs may be necessary to obtain significant runoff control
for heavy metals and total solids. This cost increase may increase the runoff
control possible from street cleaning from less than 10 percent to more than
25 percent (for these parameters). Increased street cleaning would also decrease
fugitive dust emissions to the air, improve litter loadings, etc., which is not
possible with other control practices.
To obtain a comparison of street cleaning costs with costs of other treat-
ment systems, the unit costs for these other systems were calculated. If flow
equalization costs were included, the unit pollutant removal costs for street
cleaning were found to be significantly less than runoff treatment costs. Unit
costs for the combined sewage and runoff treatment considered in this study
were generally less than for special runoff treatment facilities. There are no
data to show the effectiveness or cost of treating heavy metals In the runoff
by a combined system. Such costs are expected to be much greater than street
cleaning costs. Runoff treatment—whether in special systems or combined runoff
and sanitary wastewater systems—requires much less labor than street cleaning.
95
-------
The downstream alternative control-treatment practices affect only water quali-
ty, while street cleaning can also benefit air quality, aesthetics, and public
safety.
STRUCTURE OF THE STUDY
Typical runoff water quality (see Section 4) was compared with information
from the literature to determine approximate costs and removal effectiveness of
various runoff treatment systems (based on Lager and Smith 1974). This informa-
tion is presented in Appendix G. Street cleaning cost estimates are based on
the City of San Jose's experience. The cost effectiveness of the various street
cleaning practices are shown in dollars per pound removed and reflect the various
real-world conditions encountered. These conditions include such factors as
parked cars, traffic, and street cleaning schedules. An estimate of the final
cost for disposal of the street surface debris is also shown.
The unit costs and unit labor requirements were compared with similar rates
calculated for alternative treatment systems and are presented in Appendix G.
These include a range of systems that have been specially designed and tested
for treating urban runoff, combined sanitary wastewater and urban runoff and the
San Jose-Santa Clara Waste Water Treatment Facility, which treats only sanitary
wastewater. Erosion control costs and benefits are also presented in Appendix
G. Finally, because there are multiple objectives* in the choice of pollution
control methods, a decision analysis framework is discussed in Appendix G that
considers trade-offs among these objectives.
STREET CLEANING COSTS
Average 1973 street cleaning program costs for about 400 cities surveyed
nationwide are shown in Table 5-1. These costs, as a function of material re-
moved, population, and percentage of the city's budget are shown in Table 5-2.
The typical removal costs are between $15 and $20 per ton or cubic yard removed
or a little more than one dollar per person per year. This is 1 percent of the
typical city budget (APWA 1975). These program costs generally do not include
all of the costs associated with normal street cleaning operations, and are
therefore low. Inflation also has significantly increased these costs during
the past five years.
A large portion of the typical street cleaning budget goes for equipment
maintenance.Table 5-3 shows the average maintenance costs ($/curb-mile cleaned)
from 14 nationwide cities (Mainstem 1973). The total maintenance cost in 1973
was about $1.65 per curb-mile cleaned. The greatest portion was spent for brooms
and brushes and major repairs. These costs have also increased substantially
since the survey was conducted.
*Improved air quality, aesthetics, public safety, recreation, water supply, and
public relations are other important objectives.
96
-------
TABLE 5-1. STREET CLEANING PROGRAM COSTS (1973)
Costs
$/ton of material
o
$/yd of material
$/person/year
% of city budget
Source: APWA 1975.
Median
18
16
1.2
1
10th Percentile
3.0
6.1
0.60
0.015
90th Percentile
80
47
3.0
9.4
TABLE 5-2. STREET CLEANING PROGRAM COSTS FOR CITIES OF VARIOUS POPULATIONS
1973 Street Cleaning Program Costs
(thousands of dollars)
City
Population
<10,000
10,000 > 25,000
25,000 > 50,000
50,000 -» 100,000
100,000 - 250,000
250,000 - 500,000
500,000 + 1,000,000
>1, 000, 000
Overall
Average
39
88
73
160
350
840
2000
4900
360
Range
9 -
7 -»
3 *•
15 -
82 -
40 -»
360 +
3000 +
3 >
90
530
490
680
1500
2500
6200
6800
6800
Source: APWA 1973.
97
-------
TABLE 5-3. MAINTENANCE COSTS ($/curb-mile cleaned for 1973)
Major repairs
Minor repairs
Preventive maintenance
and lubrication
Brooms and brushes
Chains and sprockets
Other mounted systems
Average
$ 0.40
0.28
0.13
0.41
0.15
0.28
Percentage
of Total Range
24% $0. 18 *
17 0.07 +
8 0.02 *
25 0.08 -»•
9 0.02 -»
17 0.15 ->
0.84
0.46
0.45
0.71
0.30
0.46
Total Maintenance Cost
$1.65
100%
$0.69 -»> 3.10
Source: Mainstem 1973.
The following list shows which equipment components the surveyed cities
thought were most subject to wear (APWA 1975):
• Brushes (49 percent )
• Conveyor and elevator drives (26 percent)
• Tires (8 percent)
• Elevator (8 percent)
• Flights (5 percent)
• Hydraulic system (3 percent)
• Transmission (1 percent)
Table 5-4 shows the average main broom life (in miles) for three broom
materials (Laird and Scott 1971). Synthetics offered the best service, followed
by steel and natural fibers. However, Horton (1968) explains broom life is not
the most important factor: removal effectiveness is the goal and removal effec-
tiveness has been shown to be a function of broom fiber, brush speed, pattern,
and forward speed (as shown in Section 3).
98
-------
TABLE 5-4. AVERAGE MAIN BROOM LIFE (curb-miles cleaned)
Synthetic Natural Steel
Average 1100 270 560
Minimum 120 150 100
Maximum 2500 750 2000
Source: Laird and Scott, 1971.
Fifty percent of the cleaning equipment was operated with a main broom
rotational speed of 1500 to 2000 rpm and a strike of 4 to 6 inches (Scott 1970).
Optimum broom adjustments and selection of fiber must be determined for each
city. These determinations will depend on the type and quantity of litter and
particulates to be removed, street type and condition, weather, etc.
Table 5-5 presents San Jose street cleaning costs by specific item and the
total costs for the year ending September 30, 1977. Labor accounts for about
75 percent of the total costs which makes street cleaning a relatively labor in-
tensive urban runoff control measure. Those categories that may be affected by
a significant change in street cleaning equipment (maintenance supplies and la-
bor) make up 35 percent of the total costs. A major change in equipment type
may slightly reduce those maintenance costs. The other street cleaning costs
would not vary appreciably for different types of street cleaning equipment.
Actual maintenance savings would have to be determined by a specific city's ex-
perience using different equipment types. Replacement of street cleaning equip-
ment before it would normally be replaced could significantly increase deprecia-
tion costs.
During this test year (1976-1977), the Public Works Department of San Jose
spent about $800,000 to clean 55,761 curb-miles. The unit cost was therefore
about $14 per curb-mile cleaned and the labor requirement was about 0.9 man-hours
per curb-mile. These costs appear high, but it must be realized that most other
evaluations of street cleaning costs (such as summarized in the previous discus-
sion) do not include all of the actual costs of the street cleaning program.
Most other street cleaning cost evaluations include only maintenance and opera-
tions supplies and operator labor expenses. Few other jurisdictions have all
the other cost information available. The usual practice is to use the odometer
mileage on the street cleaner as an indication of curb-miles cleaned. The odom-
eter mileage is about twice the curb-mileage cleaned because of travel from the
service yard to the cleaning route, travel to the landfill, etc. This mileage
factor could double the unit cost alone.
Tables 5-6 through 5-10 present the average unit costs and labor require-
ments to remove a pound of the various pollutants from the five test areas.
The unit costs for total solids range from about $0.025 to $0.17/lb removed for
99
-------
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-------
TABLE 5-6. COST EFFECTIVENESS FOR SAN JOSE STREET CLEANING
OPERATIONS, TROPICANA-GOOD ASPHALT TEST AREA
*Average
Removal
(Ib/curb-mile
cleaned)
Average
Unit Cost
($/lb removed)
Average
Unit Labor
(hr/lb removed)
Total Solids
Suspended Solids**
COD
BOD **
Ortho PO^
Kjeldahl Nitrogen
Lead
Zinc
Chromium
Copper
Cadmium
*Average removal values
**Estimate.
100
50
9.7
4.9
0.017
0.21
0.40
0.049
0.039
0.072
0.00027
from Table
0.14
0.28
1.4
2.9
820
67
35
290
360
190
50,000
3-19.
0.009
0.018
0.093
0.18
52
4.3
2.3
18
23
13
3300
TABLE 5-7. COST EFFECTIVENESS FOR SAN JOSE STREET CLEANING
OPERATIONS, KEYES-GOOD ASPHALT TEST AREA
*Average
Removal
(Ib/curb-mile
cleaned)
Average
Unit Cost
($/lb removed)
Average
Unit Labor
(hr/lb removed)
Total Solids
Suspended Solids**
COD
BODc**
Ortho PO^
Kjeldahl Nitrogen
Lead
Zinc
Chromium
Copper
Cadmium
130
65
16
8.0
0.018
0.28
0.81
0.079
0.051
0.081
0.0003
0.11
0.22
0.88
1.8
780
50
17
180
270
170
47,000
0.0069
0.014
0.056
0.11
50
3.2
1.1
11
18
11
3000
* Average removal values from Table 3-17.
**Estimate
101
-------
TABLE 5-8. COST EFFECTIVENESS FOR SAN JOSE STREET CLEANING
OPERATIONS, KEYES-OIL AND SCREENS TEST AREA
*Average
Removal
(Ib/curb-mile
cleaned)
Average
Unit Cost
($/lb removed)
Average
Unit Labor
(hr/lb removed)
Total Solids
Suspended Solids**
COD
BOD **
Ortho PO,
Kjeldahl Nitrogen
Lead
Zinc
Chronium
Copper
Cadmium
170
85
12
6
0.0089
0.14
0.15
0.066
0.071
0.13
0.00024
0.082
0.16
1.2
2.3
1600
100
93
210
200
110
58,000
0.0053
0.011
0.075
0.15
100
0.38
6
14
13
6.9
3800
*Average removal values from Table 3-18.
**Estimate.
TABLE 5-9. COST EFFECTIVENESS FOR SAN JOSE STREET CLEANING
OPERATIONS, DOWNTOWN-GOOD ASPHALT TEST AREA
*Average
Removal
(Ib/curb-mile
cleaned)
Average
Unit Cost
($/lb removal)
Average
Unit Labor
(hr/lb removed)
Total Solids
Suspended Solids**
COD
BOD **
Ortho PO^
Kjeldahl Nitrogen
Lead
Zinc
Chromium
Copper
Cadmium
83
43
11
5.5
0.012
0.6
0.49
0.072
0.047
0.093
0.0023
0.17
0.33
1.3
2.5
1200
88
29
190
300
150
6100
0.010
0.021
0.082
0.16
75
5.6
1.8
13
19
9.7
390
*Average removal values from Table 3-16.
**Estimate.
102
-------
TABLE 5-10. COST EFFECTIVENESS FOR SAN JOSE STREET CLEANING
OPERATIONS, DOWNTOWN-POOR ASPHALT TEST AREA
*Average
Removal
(Ib/curb-mile
cleaned)
Average
Unit Cost
($/lb removal)
Average
Unit Labor
(hr/lb removed)
Total Solids
Suspended Solids**
COD
BOD.**
Ortho PO^
Kjeldahl Nitrogen
Lead
Zinc
Ch romium
Copper
Ca dmium
540
270
61
31
0.079
1.3
1.0
0.27
0.24
0.50
0.0015
0.026
0.052
0.23
0.46
180
11
14
52
58
28
9300
0.0017
0.0033
0.015
0.030
11
0.69
0.90
3.3
3.8
1.8
600
*Average removal values from Table 3-16.
**Estimate.
average conditions encountered in the five areas. As expected, it costs much
more ($0.11 to $0.17) to remove a pound of solids from the asphalt streets in
good condition as compared to the poorer quality asphalt streets ($0.025/lb) and
the oil and screens surfaced streets ($0.08/lb). The same is generally true for
the other pollutants, except for the oil and screens test area. Street surface
particulates were abundant in the oil and screens test area, but the pollutant
concentrations were relatively low. This was because the major source of the
particulates in this test area was street surface wear material, which was rela-
tively "clean". The same was true for the unit labor requirements, where more
labor was generally needed to remove the same quantity of material from the
smooth asphalt streets as compared to the streets in poorer condition.
Figure 5-1 (based on computer analyses of the San Jose data) demonstrates
the increase in unit costs to remove a pound of total solids as the number of
cleaning passes increases in a year. A cost of $0.08/lb corresponds to about
20 or 30 passes per year, but it could be as low as $0.02 or $0.03/lb for two
passes per year, or as high as $0. 25/lb for 200 to 300 passes per year, depending
on street surface condition. These increasing costs reflect the decrease in
rate of return as the streets are cleaned more often. Frequent street cleaning
results in lower solids loadings on the street surfaces and pollutant removals
per pass, while the cost of operating the street cleaning equipment remains
practically the same (within about + 10 percent) per pass. Figure 5-2 is a
similar figure for unit labor requirements. Again, the unit requirements in-
creased dramatically with increasing passes per year.
103
-------
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Asphalt streets in
good condition
S Oil and screens surfaced
streets or asphalt streets
in poor condition
10
100
1,000
NUMBER OF PASSES PER YEAR
Figure 5-1. Costs to remove a pound of street dirt as a
function of the number of passes per year.
10 100
NUMBER OF PASSES PER YEAR
Figure 5-2. Labor needs to remove a pound of street dirt.
104
1000
-------
A street cleaning program effective in reducing substantial quantities of
pollutants (more than 25 percent removal of total solids and heavy metals from
the runoff) would require cleaning frequencies of about three passes per week
or more (preferably on separate days). A typical street cleaning program con-
ducted to control litter in residential neighborhoods uses about one to two
passes per month. This less frequent cleaning may remove only about 10 percent,
or less, of the total solids and heavy metals in the runoff. Therefore, an ex-
penditure increase of about ten times is necessary to obtain about four times
the pollutant removals from the runoff.
Any existing litter control street cleaning program removes the least
costly portion of the pollutants and additional cleaning becomes more costly.
This should be considered in evaluating the street cleaning program over a large
area. The extensive street cleaning effort usually expended in downtown areas
may best be reduced in order to increase the effort in "dirtier" areas receiving
little street cleaning. A much greater quantity of pollutants can then be re-
moved from the watershed for the same total program expenditures. Re-education
of the residents in the service area receiving reduced street cleaning would of
course be necessary. Adequate litter control may be effective in downtown areas
by using some manual litter pick-up effort to supplement reduced mechanical
street cleaner use.
Additional street cleaning effort also improves the other benefits of street
cleaning. These include reducing fugitive particulate (dust) emissions to the
air (see Section 6), improving public safety by controlling excessive dirt on
the roadway, reducing litter, reducing service area complaints, and decreasing
flooding caused by clogged sewerage and inlets. Alternative urban runoff control
procedures (see Appendix G) usually only benefit water quality.
As stated above, if the objective of a street cleaning program is to re-
move the most pollutants from the runoff, then an appropriate street cleaning
program could be simply designed by stressing those service areas with road
types that result in the largest unit removal rates (pounds removed per pass)
and keeping the number of passes a year for a specific area to a minimum. No
service objectives are this simple, and more complex program design techniques
are usually necessary. The following discussion describes a procedure to select
the level of effort necessary, considering local rainfall patterns. Appendix G
describes alternative control measures that can be used to meet water quality
objectives and a decision analysis procedure that may be used in selecting the
most appropriate combination of control measures. If one wants to optimize
the existing street cleaning program for current budget conditions or for future
budget reductions, the Appendix G discussions are not necessary. Appendix G can
be appropriately used when a regional stormwater management control plan (208
study) is to be designed and to estimate the costs of several control objective
levels ("needs" survey).
DETERMINATION OF STREET CLEANING PROGRAM
Figure 5-3 (Pitt, Ugelow and Sartor, 1976) is a flow diagram that shows
the relationships between a city's street cleaning objectives, operating con-
ditions, and the resulting equipment performance requirements. This figure
shows that an accumulation rate and an accumulation interval must be determined
105
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106
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before the residual loading can be estimated. This information can be obtained
utilizing the procedures used during this study. The objectives of the street
cleaning program must be defined in terms of allowable residual loadings; the
required cleaning effectiveness and cleaning frequency are then determined based
on these prescriptive specifications. The prescriptive specifications are com-
pared with the achievable specifications and possible equipment performance im-
provements can then be identified.
Street Cleaning Program Objectives
The determination of a city's prescriptive specifications for street clean-
ing equipment is based on that city's objectives and operating conditions. These
objectives are determined by environmental, safety, aesthetic, and public rela-
tions requirements. They are defined in the following paragraphs.
• Environmental Objectives. These objectives should ensure compliance
with applicable water, air, and noise regulations, criteria, and stan-
dards. These may include urban runoff load allocations (as determined
in Areawide Wastewater Management -208- Plans), ambient air quality
standards, vehicle emission standards, roadway fugitive dust emission
allocations (from an area's air quality compliance plans), and state
and local noise regulations.
• Aesthetic and Traffic Safety. The objectives relate directly to the
quantity and type of street surface materials. Traffic safety problems
may be caused by excessive accumulations of loose debris or oils in
the traffic lanes. Aesthetic problems are subjective and depend on
an individual's personal values.
• Public Relations Objectives. These objectives include other objec-
tives but are measured by service-area complaints. Reduction of these
complaints to an acceptable level requires meeting the program objec-
tives and convincing the public that the objectives are correct and
that they are being met.
All of these objectives can be measured in various units. Water quality
measures can be expressed as concentrations (milligrams per liter) or runoff
yields (tons per acre per year*); air quality measures can be expressed as
concentrations (parts per million or micrograms per cubic meter) or emission
factors (grams per second or tons per year); noise can be expressed as noise
levels (dB^); safety and aesthetic measures can be expressed as street surface
particulate loading (pounds per curb-mile); and public relations objectives can
be expressed as the number of complaints received per unit time. It is necessary
that all these objectives be expressed as a common unit that can be directly
affected by the street cleaning program. With the exception of noise level ob-
jectives and possibly public relations objectives, allowable street surface load-
ings (pounds per curb-mile) can be used as a common unit.
*See Metric Conversion Table 0-1.
107
-------
Determining Allowable Street Surface Loading
If an urban street surface runoff discharge allocation value is available,
the maximum allowable street surface loading can be estimated knowing the number
of curb-miles in the watershed. A street cleaning program capable of meeting
the allowable loading can be designed if the pollutant accumulation rate for
the study area and the performance characteristics of the street cleaning equip-
ment are known. Figure 5-4 graphically relates street surface runoff allocations
to allowable loadings. The allowable loading increases as the runoff allocation
increases and as the curb-miles in the drainage area decrease. It is possible
to obtain a desirable residual particulate loading by using equipment with low
removal efficiencies, but the cleaning interval would have to be short.
10,000 F
1,000 -
o
z
Q
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LU
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CO
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1 Er
0.1
1,000
10,000 100,000 1,000,000
Source: Pitt, Ugelow, and Sartor; 1976.
STREET SURFACE RUNOFF ALLOCATIONS (Ib/storm event)
Figure 5-4. Determination of allowable loading.
108
-------
Other important variables that affect street cleaning programs include site-
specific conditions (uncontrollable external operating conditions). These in-
clude the assimilative capacity of the receiving environments (water and air) ,
the street surface pollutant accumulation rates, and the frequency of rainfall
that washes off the street surface pollutants.
Street surface particulates tend to accumulate as described earlier (see
Section 3). A significant rain is capable of washing off most of the street
surface particulates, and the loading after a storm of this type would be very
low, in the absence of erosion products. The particulates would then increase
until removed by street cleaning, wind or automobile induced turbulance and/or
rain runoff. The following methodology was developed to help estimate the type
of street cleaning program that may be necessary to meet street surface loading
objectives. Several simplifications were made to keep this procedure uncompli-
cated; namely, constant accumulation rates and street cleaning effectiveness
values are assumed. It is known that accumulation rates decrease with time
(due to wind or traffic induced turbulance causing fugitive dust losses) and
that the percentage removals of street surface particulates decrease with lower
loading values. Therefore, this simple model assumes that particulate loadings
would increase linearly with time, in the absence of rain or street cleaning,
but would reach a maximum, constant .value, after repeated street cleanings.
90
20
30 40 50 60
STREET CLEANING EFFECTIVENESS
70
80
90
100
Figure 5-5. Days after significant rain to maximum
street surface loading.
109
-------
Figure 5-5 shows when maximum particulate loading values would occur on
streets as a function of street cleaning effectiveness and cleaning interval (in
the absence of rains). If a significant rain occurs before these time limits
are reached, then the maximum values would not be obtained. An increase in
street cleaning effort (more frequent street cleaning) or an increase in cleaning
effectiveness, substantially reduces the time required before the maximum loading
value occurs. Figure 5-6 shows the value of the maximum loadings for different
street cleaning programs as measured by effective days of accumulation (EDA).
As an example, if the EDA was shown to be 10 for a particular condition and the
average accumulation rate for the area was 15 Ib/curb-mile/day, the maximum
loading condition would be 150 Ib/curb-mile. Therefore, these two figures can
be used to estimate the street cleaning program necessary to meet a specific
maximum allowable street surface loading condition. If an allowable loading
goal of 300 Ib curb-mile existed along with an average accumulation rate of 15
Ib/curb-mile/day, then an EDA of 20 (300 Ib/curb-mile divided by 15 Ib/curb-
mile/day) is necessary. Examining Figure 5-6 shows that this goal can be met
using several alternative street cleaning programs, including one witha cleaning
interval of three days and a removal efficiency of about 20 percent, or one
with a cleaning interval of about once every two weeks and a removal efficiency
of about 80 percent. Both of these cleaning programs would result in a maximum
10
20
30 40 50 60 70
STREET CLEANING EFFECTIVENESS (%)
80
90
100
Figure 5-6. Maximum street surface loadings
(effective days of accumulation).
110
-------
street surface participate loading value of about 300 lb/curb-mile, which would
occur after about 40 dry days (from Figure 5-5). If it rained before 40 days,
the street surface runoff yield could be much less.
Figure 5-7 relates the percentage of maximum street surface loading that
would occur for cleaning programs of different cleaning effectivenesses and for
various periods of time since the last significant rain. In the example de-
scribed above, assume a rainfall interval of 20 days. This would correspond to
about 7 cleaning cycles for a 3-day cleaning interval (of 20 percent effective-
ness) and about 1.5 cleaning cycles for a 14-day cleaning interval (of 80 percent
effectiveness). The resultant maximum street surface particulate loadings would
therefore be about 230 Ib/curb-mile (75 percent of 300 Ib/curb-mile) and about
270 Ib/curb-mile (90 percent of 300 Ib/curb-mile) respectively, both obviously
below the 300 Ib/curb-mile goal. Therefore, a sufficient street cleaning program
could be less effective than determined by directly using Figures 5-5 and 5-6
if the rainfall interval is less than the indicated time to maximum loading.
A more cost effective street cleaning program may be estimated using a reitera-
tive technique. Again, it must be stressed that this procedure only results in
estimates and that it is very difficult to have high percentage removal values
when the street surface particulate loadings are low.
10
20
30 40 50 60 70
PERCENT OF MAXIMUM LOADING VALUES
80
90
100
Figure 5-7. Portion of maximum loading values occurring
versus the number of cleaning cycles since last
significant rain and removal effectiveness.
Ill
-------
SECTION 6
AIRBORNE FUGITIVE PARTICULATE LOSSES FROM STREET SURFACES
SUMMARY
The objectives of this portion of the study were: (1) to determine
roadside dust (fugitive particulate) concentration increases and emissions
from paved street surfaces caused by automobile induced turbulance and wind;
and (2) to measure particulate concentrations in the street cleaning equipment
cabs during street cleaning operations. Downwind roadside particulate con-
centrations were about 10 percent greater than upwind concentrations (on a
number basis). About 80 percent of the concentration increases, by number,
were associated with particles in the 0.5 to 1.0 y size range, but about 90
percent of the particle concentration increases, by weight, were associated
with particles >10 y. Fugitive emission factors were estimated for the five
test areas based on differences between initial street surface particulate
accumulation rates and the lower rates observed at later periods. The accu-
mulation rates decreased with time after street cleaning or a significant rain,
and this decrease is assumed to be caused by particulate losses to the air.
Calculations showed that the loss rate was about 4 to 6 Ib/curb-mile/day.
This rate corresponds to an automobile use emission rate of approximately 0. 66
to 18 g/veh-mi. The rate increases for larger cleaning intervals and varies
widely for different street and traffic conditions. Particulate concentrations
in and around the state-of-the-art four-wheel mechanical street cleaner were
measured with and without use of the water spray to assess the effectiveness
of the water spray in dust control. It was found that the water spray was
very effective in controlling dust inside the cab and the ambient concentra-
tions* in the vicinity of the equipment. An exception was the area immediately
behind the main pickup broom, where the water spray did not significantly
change the high total dust levels. The changes in particulate concentrations
(by number) were mostly for the smaller particles (<10 y); the larger particle
concentrations did not change significantly. The study did not assess the ef-
fect of the water spray on street dirt removal effectiveness.
LITERATURE REVIEW
Street cleaning can reduce airborne particulate emissions and particulate
concentrations in areas near roadways. Studies have shown the potential re-
lationships between clean streets and reduced emissions of resuspended par-
ticulates (notably Sehmel 1973; Stewart 1964; Mishima 1964; Roberts 1973;
*Background dust levels in the immediate vicinity.
112
-------
Cowherd, et al., 1977; and PEDCo, 1977). Each of these studies demonstrated
this benefit of street cleaning, but none were able to quantify the specific
relationships. The following discussion attempts to describe this relation-
ship and its potential impact on the design of street cleaning programs.
As early as 1915 (Goss), there was concern about roadways being signi-
ficant particulate emission sources. But until recently, there have not
been significant attempts to improve air quality related to that source.
Roberts (1973) has shown that paving a dirt road could reduce roadway par-
ticulate emissions by 75 percent and cleaning a "dirty" paved road could
reduce particulate emissions by more than 80 percent.
Reductions in auto traffic have caused noticeable reductions in road-
side particulate concentrations. During a three-day driving moratorium in
Sweden in 1969 (to change signs and roadways from left-hand-side-of-the-
road to right-hand-side-of-the-road driving), particulate concentrations
dropped substantially, even though point source emissions and meteorologi-
cal conditions remained about the same (Murphy 1975). Diurnal fluctuations
in suspended particulate concentrations in Chicago were found to correlate
well with carbon monoxide concentrations (a good indicator of traffic acti-
vity), even though most of the recognized particulate emissions were not
associated with automobile exhaust (Murphy 1975). As part of this Chicago
study, the collected airborne particulate material and the street surface
particulates were microscopically examined and found to be similar in nature
(mostly limestone and quartz by weight), indicating that the airborne particu-
lates could have been resuspended street surface particulates.
Emission factors for the resuspension of particulates from roadways
can be estimated from several sources. Roberts (1973) measured particulate
losses for paved and unpaved roads in the Duwamish Valley, Washington. He
estimated a particulate emission factor of 3.5 Ib/veh-mi at 10 inph for un-
paved roads; 0.8 Ib/veh-mi at 20 mph for "dusty" paved roads with no curbs;
and 0.15 Ib/veh-mi at 20 mph for "clean" paved roads with curbs that are
flushed weekly and swept every two weeks. These results demonstrate the
degree of emission reductions possible by paving and cleaning a road. Un-
fortunately, no information was given to quantify the particulate loadings
on the streets.
Sehmel (1973) conducted experiments to quantify the relationships
between street surface particulate loading, vehicle speed, and particulate
resuspensions by using zinc sulfide (ZnS), a particulate tracer. He also
measured the effective area of the resulting downwind plume. The values
obtained by Sehmel are only approximate order-of-magnitude estimates because
of the differences between the tracer material and actual street dirt
(including particle size, density, weathering, and distribution of material
on the street). The tracer compound, which has a specific gravity of about
6.5 and a particle size <20 y, was evenly spread over the test area at about
100 Ib/curb-mile. Figure 6-1 shows the observed relationship of vehicle
speed and resuspension fraction for a car driven adjacent to the tracer, a
car driven through the tracer, and a light three-quarter-ton truck driven
through the tracer. Because most of the street surface particulates on
smooth roads that have moderate to heavy traffic with little parking have
been shown to lie close to the curb, the drive-through test results may only
113
-------
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Using the resuspension values in Table 6-1, it is possible to estimate
the order of magnitude of the total U.S. airborne emissions from this source.
In 1972, it was estimated that 680 billion vehicle-miles were driven in the
United States (EPA 1973). Assuming a low street surface particulate loading
of about 100 Ib/curb-mile and a vehicle speed ranging from 25 to 50 mph, 0.1
Ib of particulates/veh-mi may be lost. This results in an estimated total
particulate (<20 y) nationwide emission loss for 1972 of 35 million tons for
this fugitive particulate source. This value is compared to an estimated total
of 29 million tons of particulate emmissions from all point sources combined
(transportation: 1 million tons; stationary fuel combustion: 8 million tons;
industrial processes: 12 million tons; solid waste disposal: 6 million tons;
miscellaneous: 2 million tons) (EPA 1973, 1974).
TABLE 6-1. PARTICULATE RESUSPENSION FROM AUTO TRAFFIC
Street surface particulate loading
Particulates Lost per Car Pass
(Ib/vehicle-mile)
Ib/curb-mile
o
grams/ft
Curb lane
(driven through)
Parking lane
(driven by)
100 ("clean" street) 0.5
1000 ("dirty" street) 5.0
1.0
10.0
0.1
1.0
Resulting roadside particulate concentrations may be estimated from re-
suspension factors for vehicular traffic as presented by Stewart (1964) and
summarized by Mishima (1964). The resuspension factor is defined as the ratio
of airborne concentration (weight/volume) to the surface concentrations
(weight/area). It is not an accurate value because of irregularities in plume
geometry and meteorological conditions, but it may be indicative of roadside
particulate concentrations. Values of the resuspension factor for vehicular
traffic usually range from 10 to 10 per meter. With a "clean" street sur-
face (particulate loading of 100 Ib/curb-mile), the resulting roadside air-
borne jparticulate concentration from auto traffic may vary from 0.5 to
50 yg/m . These added concentrations may cause significant local problems.
A recent study conducted by PEDCo-Environmental, Inc. of Kansas City,
Missouri for the EPA (August 1977) examined the control of reentrained dust
from paved streets. They conducted some limited tests to measure directly
the effects of several different street cleaning control measures on road-
side particulate concentrations. They also reviewed several previous studies
that examined the resuspension of road surface fugitive particulates and the
effectiveness of control measures including street paving, flushing and sweep-
ing. They found the reentrained portion of the traffic-related particulate
115
-------
emissions (by weight) is an order of magnitude greater than the direct emis-
sions accounted for by vehicle exhaust and tire wear. They also found that
particulate emissions from a street are directly proportional to the traffic
volume and that the suspended particulate concentrations near the streets are
associated with relatively large particle sizes. The median particle size
found (by weight) was about 15 y with about 22 percent occurring at particle
sizes greater than 30 y. These relatively large particle sizes resulted in
substantial particulate fallout near the roads. They found that about 15 per-
cent of the resuspended particulates fall out at 10 meters, 25 percent at 20
meters and 35 percent at 30 meters from the street (all percentages are ex-
pressed by weight).
PEDCo's measurements of the effects of control measures and their lit-
erature review results were inconclusive in relating street cleaning effects
on adjacent road-side particulate concentrations. Exceptions were noted in
those areas that had large street surface loadings (especially at construction
sites). Their inconclusive results were most likely caused by large vari-
ations in measured concentrations and the lack of experimental controls (the
studies were conducted over long periods of time without quantifying other
particulate sources). The number of actual samples was also small. However,
PEDCo reviewed a study conducted by the New Jersey State Bureau of Air Pol-
lution that examined roadside particulate concentrations near streets on days
with flushing compared with days of no flushing. This New Jersey study found
significant reductions in roadside concentrations on days with flushing.
Although many studies were inconclusive, some of them reported reductions of
up to 20 micrograms/m in near-road particulate concentrations with extensive
use of various kinds of street cleaning operations. Again, these reductions
were most noticeable in those study areas with higher street surface partic-
ulate loadings. Paving roads reduced roadside concentrations up to 35 micro-
grams /m.
The vehicle-related reentrainment emission factors measured by PEDCo
averaged about 4 g/veh-mi. The standard deviation was about 3 g/veh-mi with
35 sampling periods, while the range of measured emission rates ranged from
about 0.2 to 20 g/veh-mi. When the data was separated by land-use type (and
therefore street surface loading, traffic characteristics and traffic volume),
differences in emission factors were found. Roads with no curbs had emission
factors of about 5 g/veh-mi, while the emission factor was about 3 g/veh-mi
in park areas. Residential streets having some commercial developments had
emission rates of about 2 g/veh-mi, while a commercial and campus area had an
emission rate of about 4 g/veh-mi. PEDCo also calculated emission rates for
lead and found them to average about 0.07 g/veh-mi, with no apparent fallout
of particulate lead near the roadway.
The measured street surface loadings for the different study areas ex-
amined by PEDCo were relatively small, ranging from 46 to 335 Ib/curb-mile
with an average of about 170. These low loadings are common on street surfaces
that are well maintained and in good condition, but can be 10 times these
amounts for rough streets or streets in poor condition.
Midwest Research Institute (MWRI) of Kansas City, Missouri also conducted
a study for the EPA on quantification of dust entrainment from paved roads
116
-------
(Cowherd, jjt_ al., 1977). MWRI's study differed from the PEDCo study in that
they applied an artificial material to road surfaces in large quantities (1500
to 5700 Ib/curb-mile) and measured the resulting downwind concentrations using
standard high volume samplers. MWRI's study resulted in an emission factor of
about 0.03 Ib (14 g) per veh-mi, and found direct relationships of emission
factors with particle loading. The emission factors reported by MWRI are
about four times those reported by PEDCo, while the MWRI street surface loading
values were about 10 times the PEDCo values. MWRI also reported a wind erosion
threshold value of about 13 mph. At this wind speed or greater, significant
dust losses from the road surface can result, even in the absence of traffic.
As described in the following sections, roadside particulate concentra-
tions and particulate emission rates were calculated from field measurements
using two different procedures in this San Jose demonstration project. The
procedures used in this study attempt to overcome some of the shortcomings
of the procedures and calculation techniques reported so far. Most of these
previous studies developed emission factors using line source dispersion and
diffusion models applied very close to the emission sources. These models were
developed for source distances substantially greater than used in these studies.
Some of the earlier work utilizing tracer materials, where actual decreases in
in tracer material loadings on the streets were compared to airborne tracer
concentrations, may be more reliable.
This San Jose study utilized particle counters to directly measure road-
side particle concentrations as a function of particle size. This allowed many
more reliable data sets to be obtained and analyzed for a given period of
time than the use of high-volume samplers alone. These measured concentrations
were then analyzed by computer to determine resultant concentrations downwind
from the road. Expected important variables, as described, did not vary signifi-
cantly during the course of our studies. The emission rates to be presented
are all based on evaluations of long term (up to one year) studies of actual
accumulation rates on the road surfaces in three different study areas. In all
cases, the accumulation rates decreased with time, reflecting an increase in
airborne losses from the road surface after the streets were cleaned or a sig-
nificant rainfall. It was assumed that the deposition rates were constant and
the decreases in accumulation rates with time were mostly associated with air-
borne losses. The following portions of this section describe the results of
these San Jose studies.
MEASURED ROADSIDE DUST LEVELS
Several factors influencing fugitive particulate emissions were measured
for each test monitoring roadside dust levels. These factors included traffic
speed and density, meteorological conditions (wind speed, wind direction, hum-
idity, and atmospheric stability), and street surface conditions (pavement ma-
terial and condition and particulate loading). Statistical tests were con-
ducted to determine the importance of these variables.
Specific information collected in this study included the variables noted
above and airborne particulate concentrations related to these variables. The
following list describes these variables and the estimated importance of their
effect on the fugitive particulate emission rates:
117
-------
• Traffic density: high importance; changed slowly throughout the day
as a function of time.
• Wind speed: high importance; changed during the day as a function
of time, season, and general synoptic conditions.
• Pavement material: high importance; was constant for each monitoring
site (asphalt or oil and screens surfaced).
• Pavement condition: high importance; was constant for each monitor-
ing site.
• Particulate loading: high importance; gradually changed for each
test day.
• Traffic speed: medium importance; changed slightly with traffic den-
sity.
• Particulate size distribution: medium importance; was generally cons-
tant for each test site.
• Wind direction: low importance (can be accounted for); changed dur-
ing the day as a function of time, season, and general synoptic con-
ditions.
• Relative humidity: low importance; changed slowly during the day as
a function of time, season, and general synoptic conditions.
An experimental design phase was also conducted to maximize the sampling
program efficiency. The design of the sampling program and number of required
samples depended upon the variability of the above listed field conditions and
the desired accuracy of the results. As the field program progressed, modi-
fications were made to account for new conditions.
Particle size and concentrations were measured at three stations, one
upwind and two downwind from the source street. A particle counter and a
high-volume (hi-vol) sampler were located at each of the stations. Sampling
was performed simultaneously at each location. Data from the particle count-
ers were displayed in five particle size ranges (>0.5, >1, >2, >5 and >10 u)
and recorded about every four minutes.
Data from the upwind station was used to indicate background particle
concentrations. The downwind stations were located so that the results were
not affected by other sources. As reported by PEDCo (1977), the automobile
particulate emissions (exhuast and tire wear) are expected to be much less,
by weight, than the fugitive particulate emissions (<10%).
A mechanical weather station was also used to measure and record air temp-
erature, wind speed and direction continuously during each sampling period.
It was located so that wind data was not influenced by traffic or other nearby
obstacles. Relative humidity was also periodically monitored. Particulate
loadings on the street surface and particle size distributions during the test
118
-------
periods were also measured. Automatic car counters were also used to record
total traffic every 15 minutes during the tests.
An appropriate monitoring location was difficult to find because of the
need to eliminate particle count interferences and topographic effects on par-
ticulate dispersion. The monitoring locations required flat topography with
no trees or buildings, and with open spaces on both sides of the road several
hundred feet deep. The open spaces could not be susceptible to wind erosion
and had to be either grass (in good condition) or paved. Care was also taken
to eliminate small areas of denuded loose soil near the sampling points. Nearby
construction activities or other sources of particulate emissions eliminated
potential test locations. Several days of testing were initially conducted
along a busy asphalt surfaced street in a mixed commercial/residential area.
This location was eliminated because of building interferences and small
patches of denuded soil along a cross street. Another area considered was an
oil and screens surfaced street in a well maintained residential area, but the
traffic volume on the monitored street was too low to allow sufficient and
complete data utilization. The sampling site finally selected was located
on a street that had been oil and screens surfaced about one year before and
had moderate to heavy traffic. One side of the street was a regional shopping
center that had a fairly clean asphalt surfaced parking lot with minimal
traffic activity, while the other side of the street was an abandoned gas
station surrounded by asphalt.
The prevailing winds were usually perpendicular to the street. Time
periods with low winds or winds less than 45 degrees to the street were elim-
inated. A total of about nine hours of continuous monitoring was utilized
out of more than 40 hours of actual field monitoring. In all cases, the
sampling probe inlets of the particle counters were kept facing into the wind.
The particle counters and other equipment were operated from a 5000 watt gen-
erator which was located so that its exhaust would not interfere with the
data.
Most of the data selected for reduction was collected between 1 p.m. and
5 p.m. on three days, when the prevailing winds were consistently perpendic-
ular to the road and of moderate speed. Table 6-2 summarizes the conditions
during these periods of monitoring. The wind speeds during the selected period
of monitoring ranged from about 0.5 to 6 mph, with most of the wind speeds
ranging from 2 to 5 mph. Relative humidity values ranged from about 30 to 60
percent and the cloud cover ranged from 0 to 100 percent. The total street
surface particulate loadings during these periods of monitoring ranged from
about 900 to 2200 Ib/curb-mile. The monitored traffic density ranged from
about 400 to 900 vehicles per hour. The range of total particle counts per
cubic foot monitored during the selected period of data reduction were as
follows:
Size Range (u) Particle Counts
0.5 - 1 15,000 - 130,000
1-2 10,000 - 30,000
2-5 600 - 7,500
5-10 0-2,000
119
-------
TABLE 6-2. CONDITIONS DURING FUGITIVE PARTICULATE MONITORING
Street
Wind Traffic Relative Cloud Atmos- Surface
Speed (Vehicle/ Humidity Cover pheric Loading
(mph) hour) (%) (%) Stability (Ib/curb-mi)
Mean (x)
Standard dev. (a)
Ratio of a to x (a/x)
Min.
Max.
3.8
1.2
0.32
0.50
6.5
675
71
0. 11
444
864
42 30 Unstable 1420
660
0.5
29 0 Unstable 860
60 100 Unstable 2150
Most of the particles were found, by count, in the smallest size ranges. These
smallest ranges were also more statistically significant from a particle count-
ing technique viewpoint. The precision of the counts in the smallest size
ranges typically had percent errors of less than +10 percent for a 50 percent
probability value. This means that the data most likely occurred within the
values reported + 10 percent with a 50 percent certainty. With a 95 percent
certainty, the true values lie within the reported values + 20 percent. The
larger particle sizes, because of the smaller counts, had precisions which
were much less. In these cases, the percentage errors ranged up to 100 per-
cent for the short sampling periods. When the data was combined, the percent
of errors substantially decreased (to much less than 1 percent for the small
sizes and less than 10 percent for the larger sizes).
Table 6-3 summarizes the total airborne particulate populations measured
over 135 selected time periods on these three days. The mean particulate
populations measured (expressed as number per 0.01 ft^), the standard dev-
iation, relative standard deviation (standard deviation divided by mean), and
number of data points are shown for each particle size range for the upwind
control station and the near road downwind station. The probability that the
downwind (about 4 meters from the curb) populations were not equal to the up-
upwind control populations is also shown. The probability values are based
on a 95 percent confidence limit (the probability value shown can be wrong
1 out of 20 times). A probability value of 0.75 signifies that the means
(or variations) are not equal 75 percent of the time. About three-quarters
of all the measured particles by count (in the size range from 0.5 y to about
100 y), were in the range of 0.5 to 1 micron. Most of the remaining particles
were in the range from 1 to 2 microns. Less than 5 percent of the total
particles were in the range from 2 to 100 microns. The larger particles, how-
ever, made up most of the particle mass. Particulate concentrations for the
downwind station were generally greater than for the upwind control station.
These increases were due to automobile and roadway related emissions. Auto-
120
-------
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mobile exhaust and tire wear particulate emissions (by weight) have been pre-
viously reported to be less than about 10 percent of the fugitive roadway
particulate emissions (PEDCo 1977). Almost all of these particulate concen-
tration increases can be assumed to be caused by the fugitive roadway partic-
ulate emissions. The concentration increases were larger for the smaller size
ranges. The relative standard deviation values (a measure of variability)
increased for the larger particle sizes signifying less precise results for
those particle sizes. The measured variabilities of the downwind and upwind
sampling stations were also significantly different in almost all cases.
Table 6-4 summarizes the fugitive particulate concentration increases at
the near road downwind station over background conditions for specific parti-
cle size ranges. Again, the important concentration increases by number occur-
red in the two smallest size ranges, while most of the increases by weight
occurred in the largest size range. About 80 percent of the concentration
increases (by count) occurred in the 0.5 to 1 y size range and about 19 per-
cent of the total increases occurred in the 1 to 2 y size range. Less than 1
percent of the concentration increases occurred in size ranges greater than
2 y. However, about 90 percent of the concentration increases by weight were
in the largest size range. These concentration increases were 10 percent or
more of the total populations measured. Concentration increases from asphalt
surfaced roads can be expected to be about 50 percent greater than these values
because of expected increased fugitive particulate losses from asphalt surfaced
streets (see the next subsection).
Statistically significant concentration increases also occurred further
downwind from the street (about 30 to 50 meters), but the absolute differences
were quite small; typically less than 1 percent of the total population counts.
The following discussion presents measured fugitive particulate emission
rates based on monitored street surface accumulation values over a long period
of time. It is not possible to reasonably predict emission rates directly
from these concentration values because of the proximity of the monitoring
stations to the emission sources, and the undefined effects of automobile in-
duced turbulence on dispersion and diffusion of particulates. Suitable tracer
material could be used to relate these close-by concentrations with probable
fugitive losses.
FUGITIVE PARTICULATE EMISSION RATES
As previously stated, the street surface particulate accumulation rates
were greatest when the streets were relatively clean, shortly after street
cleaning. Particulate loading values then tended to level off with the passage
of time. It is assumed that the deposition rate was constant and that the
increasing difference between the deposition rate and the accumulation rate
was caused by fugitive particulate losses to the air. Therefore, if the effects
of rain and street cleaning operations are eliminated, it is possible to esti-
mate these dust losses from the accumulation rates, if one assumes that the
initial highest accumulation rate value approximates the constant deposition
rate.
122
-------
TABLE 6-4. NEAR-ROAD FUGITIVE PARTICULATE CONCENTRATION INCREASES
(number per 0.01 ft3)
0.
1.
2.
5.
>1
5 ->• 1.0 y
mean (x)
st. dev. (a)
a/x
0 -»• 2.0 y
mean
st. dev. (a)
a /x
0 •»• 5.0 y
mean (x)
st. dev. (a)
a/x
0 + 10 y
mean (x)
st. dev. (a)
a/x
0 y
mean (x)
st. dev. (a)
a/x
February 28,
1978*
227
197
0.87
44
44
1.0
1.9
11
5.8
0.29
2.1
7.2
-0.51
1.4
— —
March 15,
1978**
13
85
6.5
-3.7
41
~~~
1.5
8.9
5.9
-0.15
3.5
~~~
0.22
0.75
3.4
March 16,
1978***
7.2
124
17
18
39
2.2
-2.8
9.9
—
2.0
4.9
2.5
0.22
1.3
5.9
Total
mean (x)
st. dev. (a)
a/x
272
221
0.81
10
85
8.5
24
121
5.0
*37 value data sets were obtained on February 28, 1978.
**48 value data sets were obtained on March 15, 1978.
***50 value data sets were obtained on March 16, 1978.
123
-------
Monitored accumulation rates, as presented in Section 3, were compared
for various periods of accumulation after street cleaning. These accumu-
lation rates were highest closest to the day of street cleaning. It is assumed
that this highest accumulation rate value approximates the constant depo-
sition rate. The difference between this assumed deposition rate and subsequent
accumulation rates is due to fugitive particulate losses to the air. Other
phenomena, such as tracking of dirt by vehicles, is assumed to be constant,
with equal amounts of dirt being brought into the test areas as carried out.
These accumulation rate calculations did not include any periods of data af-
fected by rain events. Fugitive particulate losses from the street can be
caused by a combination of wind and traffic induced turbulence. As stated
previously (Cowherd, et al. , 1977), a wind speed threshold value of about 13
mph is required before wind erosion of particulates from the street surface
becomes important. Most of the winds during the study period had wind speeds
much less than this threshold value. Therefore, most of the particulate losses
from the streets in the study areas were from automobile induced turbulence.
Tables 6-5 through 6-7 present the calculated fugitive particulate emis-
sion factors for the three test areas and for several different pollutants.
These emission factors are expressed as lb/curb-mile/day and as g/veh-mi. In
almost all cases, the emission rates are seen to increase with time since
street cleaning. The emission rates are typically 3 to 4 times as great in
the period from 60-75 days as compared with 2-4 days after street cleaning for
the Keyes-good asphalt and Tropicana-good asphalt test areas. Losses in the
Keyes-oil and screens test area at 30-45 days were over 10 times the values
found in the period of time of 2-4 days after street cleaning. Therefore,
street cleaning frequencies can be very important in affecting fugitive par-
ticulate emission rates from road surfaces.
The Keyes-good asphalt test area and Tropicana-good asphalt test area
emission rates, on a curb-mile basis, are the same because their accumulation
rates were similar. However, there were major differences in traffic volume
in these two test areas resulting in the Tropicana area having substantially
greater emission rates expressed on a vehicle-mile basis. The Keyes-oil and
screens test area had little traffic and high particulate losses expressed by
vehicle-mile. The average particulate emission losses from these three test
areas ranged from 0.66 to 18 g/veh-mi* Information presented f rom Sehmel (1973)
leads to an estimate of about 45 g/veh mi. PEDCo (1977) reported values ranging
from 0.2 to 20 with an average of about 4 g/veh-mi, while MWRI (Cowherd, 1977)
values averaged about 13 g/veh-mi. The overall reported range is about 0.2 to
45 g/veh-mi, with typical values in the range of 2 to 5 g/veh-mi, Tables 6-5
through 6-7 also present values for some other pollutants. The emission losses
for lead ranged from about 0.003 to 0.02 g/veh-mi, where PEDCo (1977) reported
an average fugitive particulate lead emission rate of about 0.07 g/veh-mi.
These particulate losses can contribute a large portion of an area's
total particulate emissions. Street cleaning frequency can have a large effect
on fugitive particulate emission rates. This is expected to be due to both an
overall reduction in street surface particulate loadings and a modification in
the particle size distribution. The particulate emission rates from a typical
asphalt surfaced street can be reduced to about one-thirdif it is cleaned every
week instead of every 2 or 3 months. Therefore, street cleaning can have a
124
-------
TABLE 6-5 FUGITIVE PARTICIPATE EMISSION FACTORS FOR STREET SURFACE
LOSSES - KEYES-GOOD ASPHALT TEST AREA
Time After Street
Cleaning or Signif- Ib/Curb-
Parameter icant Rain (Days) Mile/day
Total Solids
Chemical Oxygen
Demand
Kjeldahl Nitrogen
Orthophosphate
Lead
2+4
4+10
10 + 20
20 + 30
30 + 45
45 + 60
60 + 75
Average
2+4
4+10
10 + 20
20 + 30
30 + 45
45 + 60
60 + 75
Average
2+4
4+10
10 + 20
20 + 30
30 + 45
45 + 60
60 + 75
Average
2+4
4+10
10 + 20
20 + 30
30 + 45
45 + 60
60 + 75
Average
2+4
4+10
10 + 20
4
4
5
7
8
9
12
6
0.4
0.4
0.6
0.8
0.9
1.1
1.4
0.7
0.006
0.006
0.010
0.012
0.015
0.017
0.023
0.011
0.0006
0.0006
0.0008
0.0010
0.0008
0.0013
0.0018
0.0009
0.015
0.015
0.026
Increase
Over
Grams/ Initial
Vehicle-Mile Rate
0.44
0.44
0.55
0.77
0.88
0.98
1.3
0.66
0.044
0.044
0.066
0.088
0.098
0.12
0.15
0.077
0.00066
0.00066
0.0011
0.0013
0.0016
0.0019
0.0025
0.0012
0.000066
0.000066
0.000088
0.00011
0.000088
0.00014
0.00020
0.000098
0.0016
0.0016
0.0028
_
1.0
1.3
1.8
2.0
2.3
3.0
-
_
1.0
1.5
2.0
2.3
2.8
3.5
-
_
1.0
1.7
2.0
2.5
2.8
3.8
-
_
1.0
1.3
1.7
1.3
2.2
3.0
-
_
1.0
1.7
(Continued)
125
-------
TABLE 6-5 (Concluded)
Time After Street
Cleaning or Signif- Ib/Curb-
Parameters icant Rain (Days) mile/day
Lead 20 + 30
30 + 45
45 + 60
60 > 75
Average
Zinc 2 + 4
4 * 10
10 > 20
20 + 30
30 + 45
45 - 60
60 * 75
average
Chromium 2 + 4
4 * 10
10 -» 20
20 + 30
30 + 45
45 > 60
60 -> 75
average
Copper 2 -» 4
4 * 10
10 > 20
20 > 30
30 > 45
45 * 60
60 > 75
average
Cadmium 2 -»• 4
4 + 10
10 + 20
20 + 30
30 * 45
45 * 60
60 * 75
average
0.028
0.033
0.040
0.055
0.026
0.0017
0.0017
0.0024
0.0030
0.0036
0.0044
0.0057
0.0028
0.0012
0.0012
0.0017
0.0021
0.0025
0.0030
0.0041
0.0020
0.0014
0.0014
0.0028
0.0033
0.0041
0.0050
0.0069
0.0031
0.000007
0.000007
0.000007
0.000007
0.000012
0.000016
0.000021
0.000010
Increase
Grams/ Over
Vehicle- Initial
mile Rate
0.0031
0.0036
0.0044
0.0060
0.0028
0.00019
0.00019
0.00026
0.00033
0.00039
0.00048
0.00062
0.00031
0.00013
0.00013
0.00019
0.00023
0.00027
0.00033
0.00045
0.00022
0.00015
0.00015
0.00030
0.00036
0.00045
0.00055
0.00076
0.00034
0.00000077
0.00000077
0.00000077
0.00000077
0.000013
0.000018
0.000023
0.000011
1.9
2.2
2.7
3.7
-
_
1.0
1.4
1.8
2.1
2.5
3.3
-
_
1.0
1.4
1.8
2.1
2.5
3.4
-
1.0
2.0
2.4
2.9
3.6
4.9
-
„
1.0
1.0
1.0
1.7
2.3
3.0 "
—
126
-------
TABLE 6-6.
Parameter
Total Solids
Chemical Oxygen
Demand
Kjeldahl Nitrogen
Orthophosphates
Lead
Time After
Street Clean-
ing or Signif-
icant Rain
(Days)
2+4
4+10
10 + 20
20 * 30
30 + 45
Average
2+4
4+10
10 + 20
20 + 30
30 + 45
Average
2+4
4+10
10 + 20
20 + 30
30 + 45
Average
2+4
4+10
10 + 20
20 + 30
30 + 45
Average
2+4
4+10
10 + 20
Increase
Over
Ib/Curb- Grams/ Initial
mile/day Vehicle-mile Rate
<1
3
4
5
10
4
<0. 1
0.1
0.2
0.3
0.7
0.2
<0.001
0.002
0.004
0.005
0.010
0.003
<0.0001
0. 0004
0.0004
0.0005
0.0008
0.0004
<0.001
0.003
0.006
<4.5
14
18
23
45
18
<0.45
0.45
0.91
1.4
3.2
0.9
<0.0045
0.0091
0.018
0.023
0.045
0.014
<0. 00045
0.0018
0.0018
0.0023
0.0036
0.0018
<0.0045
0.014
0.027
>3.1
>4.0
>5.1
>10
—
>1.0
>2.0
>3.1
>7.1
—
>2.0
>4.0
>5.1
>10
—
>4.0
>4.0
>5.1
>8.0
—
..,.
>3.1
>6.0
127
-------
TABLE 6-6. (Concluded)
Parameter
Lead
Zinc
Chromium
Copper
Cadmium
Time After
Street Clean-
ing or Signif-
icant Rain
(Days)
20 - 30
30 - 45
Average
2-4
4-10
10 - 20
20 - 30
30 - 45
Average
2-4
4-10
10 - 20
20 - 30
30 - 45
Average
2-4
4-10
10 - 20
20 - 30
30 - 45
Average
2-4
4-10
10 - 20
20 - 30
30 - 45
Average
Increase
Over
Ib/Curb- Grams/ Initial
mile/day Vehicle-mile Rate
0.006
0.012
0.004
<0.0001
0.0006
0.0010
0.0011
0.0023
0.0008
<0.0001
0.0009
0.0014
0.0018
0.0034
0.0012
<0.0001
0.0015
0.0020
0.0025
0.0047
0.0018
<0. 000001
<0. 000001
0.000002
0.000003
0.000010
0.000001
0.027 >6.0
0.054 >12
0.018
<0. 00045
0.0027 >6.0
0.0045 >10
0.0050 >11
0.010 >22
0.0036
<0. 00045
0.0041 >9.1
0.0064 >14
0.0082 >18
0.015 >33
0.0054
<0. 00045
0.0068 >15
0.0091 >20
0.011 >24
0.021 >47
0.0082
<0. 0000045
<0. 0000045
<0. 0000091 >2.0
0.000014 >3,0
0.000045 >10
0.0000045
128
-------
TABLE 6-7. FUGITIVE PARTICIPATE EMISSION FACTORS FOR STREET
SURFACE LOSSES - TROPICANA-GOOD ASPHALT TEST AREA
Parameter
Total Solids
Chemical
Oxygen Demand
Kjeldahl Nitrogen
Orthophosphates
Lead
Time After
Street
Cleaning or
Significant
Rain (Days)
2+4
4+10
10*20
2030
30*45
43*60
60*75
Average
2+4
4+10
10*20
20*30
30*45
45*60
60*75
Average
2+4
4+10
10*20
20*30
30+45
45+60
60+75
Average
2+4
4+10
10+20
20+30
30+45
45+60
60+75
Average
2+4
4+10
10*20
Ib/Curb-
Mile/day
4
4
5
7
8
9
12
6
0.4
0.4
0.6
0.8
0.9
1.1
1.4
0.7
0.006
0.006
0.010
0.012
0.015
0.017
0.023
0.011
0.0006
0.0006
0.0008
0.0010
0.0008
0.0013
0.0018
0.0009
0.015
0.015
0.026
Grams/
Vehicle-
Mile
1.7
1.7
2.1
2.9
3.3
3.7
5.0
2.5
0.17
0.17
0.25
0.33
0.37
0.45
0.58
0.29
0.0025
0.0025
0.0041
0.0050
0.0062
0.0070
0.0095
0.0045
0.00025
0.00025
0.00033
0.00041
0.00033
0.00054
0.00074
0.00037
0.0062
0.0062
0.011
Increase
Over
Initial
Rate
1.0
1.3
1.8
2.0
2.3
3.0
-
_
1.0
1.5
2.0
2.3
2.8
3.5
-
1.0
1.7
2.0
2.5
2.8
3.8
-
1.0
1.3
1.7
1.3
2.2
3.0
1.0
1.7
129
-------
TABLE 6-7. (Concluded)
Time After
Street
Cleaning or
Significant Ib/Curb-
Parameter Rain (Days) Mile/day
Lead 20*30
30*45
45-60
60*75
Average
Zinc 2*4
4*10
10*20
20*30
30*45
4 5-* 60
60*75
Average
Chromium 2*4
4*10
10*20
20*30
30*45
45-60
60*75
Average
Copper 2*4
4-10
10-20
20*30
30-45
45-60
60*75
Average
Cadmium 2-4
4-10
10-20
20*30
30-45
45-60
60*75
Average
0.028
0.033
0.040
0.055
0.026
0.0017
0.0017
0.0024
0.0030
0.0036
0.0044
0.0057
0.0028
0.0012
0.0012
0.0017
0.0021
0.0025
0.0030
0.0041
0.0020
0.0014
0.0014
0.0028
0.0033
0.0041
0.0050
0.0069
0.0031
0.000007
0.000007
0.000007
0.000007
0.000012
0.000016
0.000021
0.000010
Increase
Grams/ Over
Vehicle- Initial
Mile Rate
0.012
0.013
0.017
0.023
0.011
0.00070
0.00070
0.0010
0.0012
0.0015
0.0018
0.0024
0.0012
0.00050
0.00050
0.00070
0.00087
0.0010
0.0012
0.0017
0.00083
0.00058
0.00058
0.0012
0.0014
0.0017
0.0021
0.0028
0.0013
0.0000029
0.0000029
0.0000029
0.0000029
0.0000050
0.0000066
0.0000087
0.0000041
1.9
2.2
2.7
3.7
-
1.0
1.4
1.8
2.1
2.6
3.4
-
1.0
1.4
1.8
2.1
2.5
3.4
-
1.0
2.0
2.4
2.9
3.6
4.9
-
1.0
1.0
1.0
1.7
2.3
3.0
_
130
-------
beneficial air pollution effect in addition to the other environmental objec-
tives described in Section 5.
STREET CLEANING EQUIPMENT CAB PARTICULATE CONCENTRATIONS
Tests were conducted to determine the concentrations of particulates (dust
levels) inside the street cleaning equipment cabs and directly behind the
state-of-the-art four-wheel mechanical street cleaner, both with and without
using the water spray. Table 6-8 presents these data. The concentrations of
particulates in the cab were not significantly different from the ambient con-
centrations when the windows were rolled up, the air conditioner was on, and
the water spray was in use. When the water spray was not used, particulate
concentrations in front of the equipment and within the cab increased signif-
icantly. In fact, the concentrations within the cab with the windows rolled
up and with the air conditioner on (but without the water spray) were about
equal to the concentrations directly behind the street cleaner. However,use of
the water spray did not significantly change the high total particulate con-
centrations directly behind the street cleaner.
Most of these changes in particulate concentrations (by count) are in the
smaller particle sizes. Concentrations of the larger particle sizes (greater
than 10 microns) were not significantly affected by use of the water spray.
131
-------
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132
-------
REFERENCES
Section 1. Introduction
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. Envi-
ronmental 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 1979.
Cowherd, C., Jr., C.M. Maxwell and D.W. Nelson, Quantification of Dust
Entrainment from Paved Roadways: EPA-450/3-77-027, U.S. Environ-
mental Protection Agency, Research Triangle Park, North Carolina,
July 1977.
Lager, J.A., and W.G. Smith, Urban Stormwater Management and Technology
—An Assessment: EPA-670/2-74-040, U.S. Environmental Protection
Agency, Cincinnati, Ohio, 1974.
PEDCo-Environmental, Inc., Control of Re-entrained Dust from Paved Streets:
EPA-907/9-77-007, U.S. Environmental Protection Agency, Kansas Citv
Missouri, 1977.
Pisano, W.C., and C.S. Queriroz, 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., and G. Amy, Toxic Materials Analysis of Street Surface Contami-
nants: EPA-R2-73-283, U.S. Environmental Protection Agency, Washing-
ton, D.C., November 1973.
Pitt, R., and R. Field, Water Quality Effects from Urban Runoff: Pre-
sented at the 1974 AWWA Conference, Boston, Massachusetts, 1974. Pub-
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Roberts, J.W., The Measurement, Cost and Control of Air Pollution from Un-
paved 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.
133
-------
Sullivan, R. (APWA.), Water Pollution Aspects of Urban Runoff: Federal
Water Pollution Control Administration, SP-20-15, January 1969.
Section 3. Street Cleaning Equipment Tests
American Public Works Association, Street Cleaning Questionnaire: Chicago,
Illinois, 1973. Unpublished.
> Street Cleaning Questionnaire: Chicago, Illinois, 1975. Unpub-
lished.
, Current and Suggested Street Cleaning and Maintenance Practices in
American Cities, Bulletin No. 25: Chicago, Illinois, 1945
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. Envi-
ronmental Protection Agency, Washington, D.C., December 1974.
Clark, D.E., Jr., and W.C. Cobbins, Removal Effectiveness of Simulated Dry
Fallout from Paved Areas by Motorized and Vacuumized Street Sweepers:
U.S. Naval Radiological Defense Laboratory, USNRDL-TR-745, 1963.
Colston, N.V., Characterization and Treatment of Urban Land Runoff: EPA-
670/2-74-096, U.S. Environmental Protection Agency, Cincinnati, Ohio,
1974.
Horton, J.P., Broom Life Isn't the Most Important Cost: American City
July 1968.
Laird, C.W., and J. Scott, How Street Sweepers Perform Today: American
City, March 1971.
Levis, A.H., Urban Street Cleaning—The Study of Mechanized Street Sweeping:
Polytechnic Institute of New York, January 1974.
Mainstem, Inc., Special Street Cleaning Study: Princeton, New Jersey
1973. Unpublished.
Pitt, R., and R. Field, Water Quality Effects from Urban Runoff: Present-
ed at the 1974 AWWA Conference, Boston, Massachusetts, 1974. Published
in the Journal American Water Works Assn., 69(8), August 1977.
Sartor, J.D., and G.B. Boyd, Water Pollution Aspects of Street Surface
Contaminants: EPA-R2-081, U.S. Environmental Protection Agency,
Washington, D.C., November 1972.
Scott, J.B., The American City 1970 Survey of Street Cleaning Equipment:
Market Research Report No. Bl-1270, American City, December 1970.
Shaheen, D.G. (Biospherics), Contributions of Urban Roadway Usage to Water
Pollution: EPA-600/2-75-004, U.S. Environmental Protection Agency
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134
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Section 4. Pollutant Mass Flow Characteristics of Urban Runoff
Amy, G., R. Pitt, R. Singh, W.L. Bradford, and M.B. LaGraff, Water Quality
Management Planning for Urban Runnoff: EPA 440/9-75-004, U.S. Envi-
ronmental Protection Agency, Washington, B.C., December 1974.
Colston, N.V., Characterization and Treatment of Urban Land Runnoff: EPA-
670/2-74-096, U.S. Environmental Protection Agency, Cincinnati, Ohio,
1974. '
Lager, J.A., and W.G. Smith, Urban Stormwater Management and Technology—
An Assessment: EPA 670/2-74-040, U.S. Environmental Protection
Agency, Cincinnati, Ohio, 1974.
^ Catchbasin Technology Overview and Assessment: EPA Contract No.
68-03-0274, U.S. Environmental Protection Agency, Cincinnati, Ohio,
1976.
McKee, J., and H.W. Wolf, Water Quality Criteria, 2nd ed.: State Water
Quality Control Board, Sacramento, California, 1963.
U.S. Environmental Protection Agency, 1975 Interim Primary Drinking Water
Standards: Subchapter D, Part 141, Subpart A, undated.
» Proposed Criteria for Water Quality: Vol. 1, October 1973.
_, Water Quality Criteria, Environmental Studies Board: NAS-NAE
EPA-R3-73-033, March 1973.
Section 5. Treatability of Nonpoint Pollutants by Street Cleaning
American Public Works Association, Street Cleaning Questionnaire: Chicago
Illinois, 1973. Unpublished. '
, Street Cleaning Questionnaire: Chicago, Illinois, 1975. Unpub-
lished.
Horton, J.P., Broom Life Isn't the Most Important Cost: American Citv
July 1968.
Laird, C.W., and J. Scott, How Street Sweepers Perform Today: American
City, March 1971.
Mainstem, Inc., Special Street Cleaning Study: Princeton, New Jersey,
1973. Unpublished.
Pitt, R., J. Ugelow, and J. Sartor, Systems Analysis of Street Cleaning
Techniques: American Public Works Association and National Science
Foundation, March 1976. Unpublished.
135
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Sartor, J.D., and G.B. Boyd, Water Pollution Aspects of Street Surface
Contaminants: EPA-R2-72-081, U.S. Environmental Protection Agency,
Washington, B.C., November 1972.
Section 6. Airborne Fugitive Particulate Losses from Street Surfaces
Cowherd, C. , C.M. Maxwell, and D.W. Nelson, Quantification of Dust En-
trainment from Paved Roadways: EPA-450/3-77-027, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, July 1977.
Goss, W.F.M. , Chief Engineer, Smoke Abatement and Electrification of Rail-
way Terminals in Chicago: Report of the Chicago Association of Com-
merce, Committee of Investigation on Smoke Abatement and Electrifi-
cation of Railway Terminals, Chicago, Rand McNally and Company, 1915.
Mishima, J. , A Review of Research on Plutonium Releases During Overheat-
ings and Fires: HW-83668, Hanford Laboratories, General Electric Co.,
Richland, Washington, 1964.
Murphy, W. , Roadway Fugitive Particulate Losses: American Public Works
Association, 1975. Unpublished.
PEDCo-Environmental, Inc., Control of Re-entrained Dust from Paved Streets:
EPA-907/9-77-007, U.S. Environmental Protection Agency, Kansas City,
Missouri, 1977.
Roberts, J.W., The Measurement, Cost and Control of Air Pollution from Un-
paved Roads and Parking Lots in Seattle's Duwamish Valley: M.S. The-
sis, University of Washington, 1973.
Sehmel, G.A. , Particle Resuspension from Asphalt Road Caused by Car and
Truck Traffic: Atmospheric Environment, 7(3) :291-309, 1973.
Steward, J. , The Resuspension of Particulate Material from Surfaces, in:
Surface Contamination, edited by B.R. Fish, Oxford, England, Pergamon
Press, 1964.
U.S. Environmental Protection Agency, Investigation of Fugitive Dust —
Sources, Emissions and Control: Office of Air Quality Planning and
Standards, Contract No. 68-02-044, 1973.
_ > Monitoring and Air Quality Trends Report, 1973: EPA-450/174-077,
Office of Air Quality Planning and Standards, Research Triangle Park,
North Carolina, October 1974.
National Air Monitoring Program, Air Quality and Emissions
Trends, Annual Report, Vol I,: Office of Air Quality Planning and
Standards, Research Triangle Park, North Carolina, August 1973.
136
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Appendix B. Experimental Design
Cochran, W.G., Sampling Techniques, Second edition: John Wiley & Sons
New York, 1963.
Appendix G. Alternative Urban Runoff Control Measures and the Use
of Decision Analysis~""
Field, R., and D. Knowles, Urban Runnoff and Combined Sewer Overflow:
Journal of the Water Pollution Control Federation, 47(6), June 1975,
Keeney, R.L., and H. Raiffa, Decison Analysis with Multiple Conflicting
Objectives: John Wiley & Sons, New York, 1976.
Lager, J.A., and W.G. Smith, Urban Stormwater Management and Technology—
An Assessment: EPA-670/2-74-040, U.S. Environmental Protection
Agency, Cincinnati, Ohio, 1974.
Thronson, R. E., Comparative Costs of Erosion and Sediment Control Con-
struction Activities: EPA - 430/9-73-016, U.S. Environmental Pro-
tection Agency, July 1973, p. 205.
137
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BIBLIOGRAPHY
This bibliography contains a comprehensive listing of literature
sources relating to the many aspects of this demonstration project.
Many of the items listed were referenced in the text and are also in-
cluded by subject in this section for convenience. This bibliography
should enable one to obtain apropriate and up-do-date information on
several important topics relating to urban runoff and its control.
Urban Runnoff Characterization
Amy, G., R. Pitt, R. Singh, W.L. Bradford, and M.B. LaGraff, Water Qual-
ity Management Planning for Urban Runoff: EPA 440/9-75-004, U.S.
Environmental Protection Agency, Washington, D.C., December 1974.
AVCO Economic Systems Corporation, Storm Water Pollution from Urban Land
Activity: Federal Water Quality Administration, 11034 FKL 07/70, 1970.
Barkdoll, M.P., D.E. Overton, and R.P. Betson, Some Effects of Dustfall on
Urban Stormwater Quality: Journal WPCF, September, 1977.
Bryan, E.H., Quality of Stormwater Drainage from Urban Lands in North
Carolina: Rept. 37, Water Resources Research Institute, University of
North Carolina, Chapel Hill, 1970.
Chow, V.T., and B.C. Yen, Urban Stormwater Runoff—Determination of Vol-
umes and Flowrates: EPA-600/2-76-116, U.S. Environmental Protection
Agency, Cincinnati, Ohio, May 1970.
Colston, N.V., Characterization and Treatment of Urban Land Runoff: EPA-
670/2-74-096, U.S. Environmental Protection Agency, Cincinnati,
Ohio, 1974.
Cowen, W.F., K. Sirisinha, and G.F. Lee, Nitrogen Availability in Urban
Runoff: Journal WPCF, Vol. 48, No. 2, February 1976, p. 339.
Dahir, S.H., and W.E. Meyer, Bituminous Pavement Polishing—Final Report,
No. S66: PDH Project 67-11, 415-51 PDH Polishing 3430, Pennsylvania
Dept. of Transportation, Comm. of Pennsylvania., November 1974.
DiGiano, F.A., R.A. Coler, R.C. Dahiya, and B.B. Berger, Characterization
of Urban Runoff: Greenfield, Massachusetts, Phase II, Univ. of
Massachusetts, Amherst, Massachusetts., August 1976.
138
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Enviro Control, Inc., Total Urban Pollutant Loads-Sources and Abatement
Strategies: Report to Council on Environmental Quality, Washington
D.C., October 1973.
Farmer, J.G., and T.D.B. Lyon, Lead in Glasgow Street Dirt and Soil: The
Science of the Total Environment, § (1977) 89-93.
Field R. and P. Szeeley, Urban Runoff and Combined Sewer Overflow (anno-
tated bibliography): National Environmental Research Center, Cincin-
nati, Ohio. U.S. Environmental Protection Agency, Edison, New Jersey,
Journal of the Water Pollution Control Federation, June 1974.
Field, R., and D. Knowles, Urban Runoff and Combined Sewer Overflow. Jour-
nal of the Water Pollution Control Federation, 47(6), June 1975.
Field, R., V.P. Olivieri, E.M. Davis, J.E. Smith, and E.G. Tifft Jr.
Proceedings of Workshop-Microorganisms in Urban Stormwater: EPA-600/
2 76-244, U.S. Environmental Protection Agency, Cincinnati, Ohio,
November 1976.
Fruh E.G., Urban Effects on Quality of Streamflow: Effects of Watershed
""" ReSOUrCes> S™™1™ «o. 2, Austin, Texas,
Guy, H.P., and G.E. Furguson, Sediment in Small Reservoirs Due to Urban-
ization: Journal of the Hydraulics Division, American Society of
Civil Engineers, 1962.
Hann, C.T., and R.W. Devore, Proceedings International Symposium on Urban
July 24"27> 1978> univ- °f Kentucky
Hasan, Nationwide Evaluation of Combined Sewer Overflows and'urban "
77 n*J1SChar8eS* Volume IJ—Cost Assessment and Impacts: EPA-
//-U64, U.S. Environmental Protection Agency, Cincinnati, Ohio,
Ep60n/«77m nc Rainf^l-R"no«-Quality Data Base:
Ohio! July 197?!' Envir°™ental Protection Agency, Cincinnati,
Kluesener J.W. and G.F. Lee, Nutrient Loading from a Separate Storm Sewer
°n
Manning M.J.R.H. Sullivan, and T.M. Kipp, Nationwide Evaluation of
Combined Sewer Overflows and Urban Stormwater Discharges, Volume III -
Characterization of Discharges: EPA-600/2-77-064c, U.S. Environmen-
tal Protection Agency, Cincinnati, Ohio, August 1977.
139
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McElroy, F.T.R., III, C.F. Mattox, D.W. Hartman, and J.M. Bell, Sampling and
Analysis of Stormwater Runoff from Urban and Semi-Urban/Rural Water-
sheds: Technical Report No. 64, Purdue University Water Resources Re-
search Center, West Lafayette, Indiana, September 1976.
McPherson, M.B., Urban Runoff: American Society of Engineers, Urban Water
Resources Research Program, Technical Memorandum No. 18, 1972.
Meta Systems, Inc., Second Progress Report, New Residential Developments
and the Quantity and Quality of Runoff: Grant No. R805238010, for the
U.S. Environmental Protection Agency, Edison, New Jersey, February
1978.
Miller, R.B., The Chemical Composition of Rain Water at Taita, New Zea-
land, 1956-1958: New Zealand Journal of Science, 4:844, 1961.
Olivieri, V.P., C.W. Kruse, and K. Kawata, Microorganisms in Urban Storm-
water: EPA-600/2-77-087, July 1977.
Pitt, R., and G. Amy, Toxic Materials Analysis of Street Surface Contami-
nants: EPA-R2-73-283, U.S. Environmental Protection Agency, Washing-
ton, D.C., November 1973.
Pitt, R., and R. Field, Water Quality Effects from Urban Runoff: Present-
ed at the 1974 AWWA Conference, Boston, Massachusetts, 1974. Published
in the Journal American Water Works Assn., 69(8), August 1977.
Pope, W., N.J.D. Graham, R.J. Young, and R. Perry, Urban Runoff from a
Road Surface—A Water Quality Study: Prog. Wat. Tech. 1978, Vol. 10,
Nos. 5/6, Pergamon Press, Great Britain, p. 533-543.
Ragan, R.M., and A.J. Dietemann, Characterization of Urban Runoff in the
Maryland Suburbs of Washington, B.C.: Technical Report No. 38, Com-
pletion Report NJC 5341, 14-31-0001-4239, April 1974-April 1976,
Univ. of Maryland, College Park, Maryland, April 1976.
Rimer, A.E., J.A. Nissen, and D.E. Reynolds, Characterization and Impact
of Stormwater Runoff from Various Land Cover Types: Journal WPCF,
February 1978, p. 252.
Ryden, J.C., J.K. Syers, and R.F. Harris, Potential of an Eroding Urban
Soil for the Phosphorus Enrichment of Streams—1. Evaluation of Me-
thods: Journal of Environmental Quality, 1(4):430-434, 1972.
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.
Sheehan, D.G. (Biospherics), Contribution of Urban Roadway Usage to Water
Pollution: EPA-600/2-75-004, U.S. Environmental Protection Agency,
March 1975, p. 350.
140
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Shuler, L., and R.R. Hegmon, Road Dust as Related to Pavement Polishing:
Tecnhical Report S49, PennDOT Proj. 67-11, 415-51 PDH Polishing (3434),
Pennsylvania Department of Transportation, Comm. of Pennyslvania,
May 1972.
Soderlund, G., and H. Lehtinen, Comparison of Discharges from Urban Storm-
Water Runoff, Mixed Storm Overflow and Treated Sewage: Presented at
6th International Water Pollution Research Conference, San Francisco,
California, 1972.
Solomon, R.L., J.W. Hartford, and D.M. Meinkoth, Sources of Automotive
Leads Contamination of Surface Water: Journal WPCF, December 1977
p. 2502.
Sullivan, R. (APWA), Water Pollution Aspects of Urban Runoff: Federal
Water Pollution Control Administration, WP-20-15, January 1969.
Sullivan, R.H., and M.J. Manning, Nationwide Evaluation of Combined Sewer
Overflows and Urban Stormwater Discharages, Volume I Executive Sum-
mary: EPA-600/2-77-064a, U.S. Environmental Protection Agency, Cin-
cinnati, Ohio, September 1977.
Tatsumi, S., and H. Ishihara, The Polluted Materials in Street Tree Leaves
(Cairo juyobu ni fuchaku suru osen busshitsu no tsuite): J. Japan Soc.
Air Pollution (Taiki Osen Kenkyu), 6(1):156, 1971.
Terstriep, M.L., M. Voorhees, and G.H. Bender, Conventional Urbanization
and Its Effect on Storm Runoff: Contract 47-26-84-390, Illinois State
Water Survey for the Illinois Dept. of Transportation, Urbana, Illinois,
August 1976.
Tomlinson, R.D., B.N. Bebee, D.E. Spyridakis, S. Lazoff, R.R. Whitney, M.F.
Shepard, K.K. Chew, and R.M. Thorn, Fate and Effects of Sediments from
Combined Sewer and Storm Drain Overflows in Seattle Nearshore Waters:
Quarterly Progress Reports, U.S. Environmental Protection Agency
Edison, New Jersey, 1978.
U.S. Department of the Interior, The Impact of Urbanization of New England
Lakes, An Experiment in Regional Interdisciplinary Research to Assist
Lake Management Efforts, Volume I: Project C5342, Grant 14-31-001-
4240, Washington, D.C., September 1977.
U.S. Environmental Protection Agency, Areawide Assessment Procedures Man-
ual: EPA-600/9-76-014, Cincinnati, Ohio, July 1976.
Weibel, S.R., R.J. Anderson, and R.L. Woodward, Urban Land Runoff as a
Factor in Stream Pollution: Journal of the Water Pollution Control
Federation, 36(914), 1964.
Weibel, S.R. R.B. Weidner, and A.G. Christianson, Characterization, Treat-
ment, and Disposal of Urban Stormwater: Proceedings of the 3rd Inter-
national Water Pollution Research Conference, Munich, Germany, 1966
pp. 329-343. '
141
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Whipple, W., J.V. Hunter, and S.L. Yu, Unrecorded Pollution from Urban
Runoff: Journal of the Water Pollution Control Federation, 46(873)
1974.
Whipple, W., Jr., J.V. Hunter, and S.L. Yu, Effects of Storm Frequency on
Pollution from Urban Runoff: Journal WPCF, November 1977, p. 2243.
Whipple, W., Jr., B.B. Berger, C.D. Gates, R.M. Ragan, and C.W. Randall,
Characterization of Urban Runoff: Water Resources Research, Vol. 14,
No. 2, April 1978.
Widmer, H.M., Effects of an Urban Road System on Lead Content of an Urban
Water Supply Source: Univ. of Massachusetts, Amherst, Massachusett
September 1976.
Wildrick, J.T., K. Kuhn, and W.R. Kerns, Urban Water Runoff and Water
Quality Control: Virginia Water Resources Research Center, Blacks-
burg, Virginia, December 1976.
Wilkinson, R., The Quality of Rainfall Runoff Water from a Housing Estate:
Journal of the Institution of Public Health Engineers, April 1956.
Wolfson, J.B., Graphic Analysis of Roadway Runoff: Civil Engineering,
41(4):64-66, 1971.
Wullschleger, R.E., A.E. Zanoni, and C.A. Hansen, Methodology for the
Study of Urban Storm Generated Pollution and Control: EPA-600/2-76-
145, U.S. Environmental Protection Agency, Cincinnati, Ohio, August
1976. 6
Combined Sewer Overflow Characterization
Burgess and Niple, Ltd., Stream Pollution and Abatement from Combined Sew-
er Overflows: 11024 FKN 11/69, U.S. Environmental Protection Agency,
Washington, D.C., November 1969.
Burm, R.J., D.F. Krawczyk, and G.L. Harlow, Chemical and Physical Compar-
ison of Combined and Separate Sewer Discharges: Journal of the Water
Pollution Control Federation, 40(1):112-126, 1968.
Field, R., Combined Sewer Overflows: Civil Engineering, 43(2), February
Field, R., and A. Tafuri, Combined Sewer Overflow Seminar Papers: EPA-
670-2-73-077, U.S. Environmental Protection Agency, Cincinnati. Ohio.
November 1973.
Friedland, A.O, T.G. Shea and H.F. Ludwig, Characterization and Treatment
of Combined Sewer Overflows: EPA-670/2-75-054, U.S. Environmental
Protection Agency, Cincinnati, Ohio, 1975.
142
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Hayes, Seay, Mattern, and Mattern, Engineering Investigation of Sewer
Overflow Problems: 11024 DMS 05/70, U.S. Environmental Protection
Agency, Washington, D.C., May 1970.
Mytelka, A.I., L.P. Cagbostro, D.J. Deutsch, and C.A. Hayst, Combined Sew-
er Overflow Study for the Hudson River Conference: EPA-R2-73-152
U.S. Environmental Protection Agency, Washington, D.C., January 1973.
Pisano W.C., and C.S. Queriroz, Procedures for Estimating Dry Weather
Pollutant Deposition in Sewerage Systems: EPA-600/2-77-120 U S En-
vironmental Protection Agency, Cincinnati, Ohio, July 1977.'
Highway Runoff Characterization
Envirex Corp., On-going Study to Characterize Highway Runoff and Its Con-
No DOT FH n'qwV^. U'S' °epartment of Transportation, Contract
No. DOT-FH-11-9357, Washington, D.C., 1978.
?t*rCFn' I*?*? f *ighways on Surface Waterways: Metropolitan San-
itary District of Greater Chicago, 1972.
Sylvester, R.O., Character and Significance of Highway Runoff Waters-A
Preliminary Appraisal: PB-220-083, National Technical Information
Service, Springfield, Virginia, December 1972.
Sylvester R.O., and F.B. Dewalle, Character and Significance of Highway
Runoff Waters, A Preliminary Appraisal: Washington State Highway De-
partment Research Program Report 7.1, November 1972.
Rural and General Runoff Characterization
Benson R.D. The Quality of Surface Runoff from a Farmland Area in South
Dakota During 1969: M.S. Thesis, South Dakota State University
Brookings, South Dakota.
Campbell, F.R., and L.R. Webber, Contribution of Rain and Runoff to Lake
Eutrophication: Presented at the 5th International Water Pollution
Conference, July-August 1970.
Cooper, C.F., Nutrient Output from Managed Forests, in: Eutrophication:
Causes, Consequences, Correctives: National Academy of Sciences
Washington, D.C., 1969. eiu-eb,
Dornbush, J.N. J.R. Anderson, and L.L. Harms, Quantification of Pollu-
tants in Agricultural Runoff: U.S. Environmental Protection Agency
Publication No. 660-2-74-005, Washington, D.C., 1974. 7
Engelbrecht, R.C., and J.J. Morgan, Land Drainage as a Source of Phos-
phorus in Illinois Surface Waters, in: Algae and Metropolitan Wastes:
TR WMTT611' ^n^' Education and Welfare, Publication No. SEC-
TR-W61-3, Cincinnati, Ohio, 1961.
143
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Gearheart, R.A. , Agricultural Contribution to the Eutrophication Process
in Beaver Reservoir: Paper 69-708, presented at the American Society
of Agricultural Engineers Winter Meeting, 1969.
Holt, R.F., Runoff and Sediment as Nutrient Sources: Presented at 1969
Annual Meeting of the Minnesota Chapter of the Soil Conservation So-
ciety of America, Bull. No. 13, University of Minnesota, Minneapolis,
Huff, D.D., Studies of the Contributions of Nonpoint Terrestrial Sources
to Mineral Water Quality: Environmental Sciences Division, Publica-
tion No. 1046, Contract No. W-7405-eng-26, Energy Research and Devel-
opment Administration, May 1977.
Jaworski, N.A. , and L.J. Hetling, Relative Contributions of Nutrients to
the Potomac River Basin from Various Sources, in: Relationship of Ag
riculture to Soil and Water Pollution: Cornell University Conference
on Agricultural Waste Management, Ithaca, New York, 1970.
Johnston, W.R., _et al . , Nitrogen and Phosphorus in Tile Drainage Effluent:
Soil Science Society of America, Proceedings, 29:287, 1965.
Loehr, R.C., Agricultural Runoff —Characteristics and Control: Journal
of the Sanitary Engineering Division, American Society of Civil Engi-
neers, December 1972, pp. 909-923.
McCarl, T.A. , Quality and Quantity of Surface Runoff from a Cropland Area
in South Dakota During 1970: M.S. Thesis, South Dakota State Univer-
sity, Brookings, South Dakota, 1971.
McKee, P.W. , Sediment, Problems of the Potomac Estuary: Interstate Com-
mission of the Potomac River Basin, Washington, D.C., 1964, pp. 40-46.
Minshall, N.E., M.S. Nichols, and S.A. Witzel, Plant Nutrients in Base
Flow of Streams in Southwestern Wisconsin: Water Resources Research
American Geophysical Union, 5(3) : 706-713, 1969.
Stream Enrichment from Farm Operations: Journal of the Sanitary
Engineering Division, American Society of Civil Engineers, 96(SA2):
513-524, 1970.
North Carolina State University, Role of Animal Waste in Agricultural Land
Runoff: Department of Biology and Agricultural Engineering, Raleigh
North Carolina, EPA Report No. 13020 DGX 08171, 1971. '
Omernik, J.M., Nonpoint Source—Stream Nutrient Level Relationships A
Nationwide Study: EPA-600/3-77-105, U.S. Environmental Protection
Agency, Corvallis, Oregon, September 1977.
Sawyer, C.N., Fertilization of Lakes by Agricultural and Urban Drainage:
Journal of the New England Water Works Association, 61(109), 1974.
144
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Sonzogni, W.C., and G.F. Lee, Nutrient Sources for Lake Mendota: Report
of the Water Chemistry Program, M.S. Thesis, University of Wiscon-
sin, Madison, Wisconsin, 1972.
Southerland, E.V. , Agricultural and Forest Land Runoff in Upper South
River, Near Waynesboro, Virginia: M.S. Thesis, Virginia Polytechnic
Institute (also U.S. Environmental Protection Agency), September 1974,
Sylvester, R.O., Nutrient Content of Drainage Water from Forested, Urban
and Agricultural Areas, in: Algae and Metropolitan Wastes: Publi-
cation No. SEC-TR-W61-3, U.S. Department of Health, Education and Wel-
fare, Cincinnati, Ohio, 1961.
Taylor, A.W. W.M. Edwards, and E.G. Simpson, Nutrients in Streams Drain-
search 7(81)and Famland near Coshocton> Ohio: Water Resources Re-
Thomas, G.W., and J.D. Crutchfield, Nitrate-Nitrogen and Phosporus Con-
tents of Streams Draining Small Agricultural Watersheds in Kentucky:
Journal of Environmental Quality, 3(46), 1974.
Weidner R.B., A.G. Christianson, S.R. Weibel, and G.G. Robeck, Rural Run
off as a Factor in Stream Pollution: Journal of the Water Pollution
Control Federation, 41(377), 1969.
Deicing Effects on Urban Runoff
Cohn, M.M., and R.R. Fleming, Managing Snow Removal and Ice Control Pro-
grams: APWA-SR-42, APWA, Chicago, Illinois, 1974.
Field R, E.J. Struzeski, Jr., H.E. Masters, and A.N. Tafuri, Water Pollu
tion and Associated Effects from Street Salting: Journal of the En-
n&i™er±ng Division' No' EE2> April 1974, p. 459 and
, Cincinnati, Ohio, May 1973.
Hanes, R.E. L.W. Zelazny, K.G. Verghese, R.P. Bosshard, E.W. Carson, Jr.
R. E. Blaser, and D.D. Wolf, Effects of Deicing Salts on Plant Biota
and Soil-Experimental Phase: National Cooperative Highway Research
Program Report No. 170, Transportation Research Board, National Re-
search Council, Washington, D.C., 1976.
Vol*
Vol. 64, No. 5, May 1972, pp 290-295.
Lockwood, R.K., Snow Removal and Ice Control in Urban Areas: Research
Project No. 114, Volume 1, APWA, Chicago, Illinois, August 1975.
Murray, D.M. , and M.R. Eigerman, A Search-New Technology for Pavement
Snow and Ice Control: EPA-R2-72-125, U.S. Environmental Protection
Agency, Edison, New Jersey, December 1972.
145
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National Research Board, Minimizing Deicing Chemical Use: Synthesis of
Highway Practice, No. 24, Washington, D.C., 1974
Richardson, D.L., R.C. Terry, J.B. Metzger, and R.J. Carroll, Manual for
Deicing Chemicals, Application Practices: EPA-670/2-74-045, U.S. En-
vironmental Protection Agency, Cincinnati, Ohio, December 1974.
Richardson, D.L., Manual for Deicing Chemicals, Storage and Handling:
EPA-670/2-74-033, U.S. Environmental Protection Agency, Cincinnati,
Ohio, July 1974.
Struzeski, E., Environmental Impact of Highway Deicing: 11040 GKK, U.S.
Environmental Protection Agency, Edison, New Jersey, June 1971.
Urban Erosion and Its Control
Ateshian, K.H., Comparative Costs of Erosion and Sedimentation Control Me-
asures, Engineering-Science, Inc., Berkeley, California.
Becker, B.C., and T.R. Mills, Guidelines for Erosion and Sediment Control
Planning and Implementation: EPA-R2-72-015, U.S. Environmental Pro-
tection Agency, August 1972, p. 228.
Harrison, E.A., Erosion Control Methodology—A Bibliography With Abstracts:
National Technical Information Service, October 1973.
Holberger, R.L. and J.B. Truett, Sediment Yield from Construction Sites:
Mitre Corporation, McLean, Virginia.
Thronson, R.E., Comparative Costs of Erosion and Sediment Control Con-
struction Activities: EPA-430/9-73-016, U.S. Environmental Protection
Agency, July 1973.
Yorke, T.H. and W.J. Herb, Urban Area Sediment Yield—Effects of Con-
struction Site Conditions and Sediment Control Methods: U.S. Geolog-
ical Survey, College Park and Parkville, Maryland.
Young, K.K., Erosion Potential of Soils: Soil Survey Interpretations
Division, Soil Conservation Service, Washington, D.C.
Street Cleaning Effectiveness
Blair, L.H., and A.I. Schwartz, How Clean is Our City?: The Urban Insti-
tute, Washington, D.C., 1972, p. 67.
CH2M-Hill, Feasible Methods to Control Pollution from Urban Storm Water
Runoff, Champaign-Urbana: Second Interim Report, Illinois Environmen-
tal Protection Agency, May 1978.
146
-------
Clark, D.E., Jr., and W.C. Cobbin, Removal Effectiveness of Simulated Dry
Fallout from Paved Areas by Motorized and Vacuumized Street Sweepers:
U.S. Naval Radiological Defense Laboratory, USNRDL-TR-745, 1963.
- , Removal of Simulated Fallout from Pavement by Conventional Street
Flushers: U.S. Naval Radiological Defense Laboratory, USNRDL-TR-797
June 1964. '
- , Removal Effectiveness of Simulated Dry Fallout from Paved Areas by
Motorized and Vacuumized Street Sweepers: U.S. Radiological Defense
Laboratory, USNRDL-TR-746, August 8, 1968, p. 100.
Fleming, R.R., Street Cleaning Practice, Third Edition: American Public
Works Association, Chicago, Illinois, 1978.
Heaney, J.P., and R. Sullivan, Source Control of Urban Water Pollution-
Journal of the Water Pollution Control Federation, 43(4) :57l-579, 1971.
Hinkle, G.J. S. Cordes, J.M. Brown, E. Kauffman, M.J. Manning, and R. Pitt
Research on Equipment Technology Utilized by Local Government: Street*
Cleaning, Grant APR 74-20419 A01, National Science Foundation, Wash-
ington, D.C., April, 1977.
Horton, J.P. Broom Life Isn't the Most Important Cost: American City
July 1968. ' y*
Korbitz W.E., Urban Public Works Administration: pub. for Institute of
Training in Municipal Administration, International City Management
Association, 1976.
Laird, C.W., and J. Scott, How Street Sweepers Perform Today: American
City, March 1971.
Lee, H. J.D. Sartor, and W.H. Van Horn, Stoneman II Test of Reclamation
Performance, Vol. Ill, Performance Characteristics of Dry Decontam-
Levis, A.H., Urban Street Cleaning— Study of Mechanized Sweeping: U.S.
Environmental Protection Agency, Office of Research and Development,
Ongerth R. State-of-the-Art in Litter Collection: U.S. Environmental
Protection Agency, Office of Research and Development, No. 72P20855.
Pltt' R'' J' U8elow» and J. Sartor, Systems Analysis of Street Cleaning
Techniques: American Public Works Association and National Science
Foundation, Woodward-Clyde Consultants, San Francisco March 1976.
Scott J.B., The American City 1970 Survey of Street Cleaning Equipment-
Market Research Report No. Bl-1270, American City, December 1970.
147
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Sutherland, R.C., A Mathematical Model for Estimating Pollution Loadings
and Removals from Urban Streets: MS Thesis submitted to the Univer-
sity of Maryland, 1975.
Urban Runoff and CSO Structural Treatment
Agnew, R.W., C.A. Hansen, M.J. Clark, O.F. Nelson, and W.H. Richardson,
Biological Treatment of Combined Sewer Overflow at Kenosha, Wisconsin:
EPA-670/2-75-019, U.S. Environmental Protection Agency, Cincinnati,
Ohio, April 1975.
Benjes, H.H., Jr., Cost Estimating Manual—Combined Sewer Overflow Stor-
age and Treatment: EPA-600/2-76-286, U.S. Environmental Protection
Agency, Cincinnati, Ohio, December 1976.
Chandler, R.W., and W.R. Lewis, Control of Sewer Overflows by Polymer In-
jection: EPA-600/2-77-189, U.S. Environmental Protection Agency, Cin-
cinnati, Ohio, September 1977.
Giggey, M.D., and W.G. Smith, National Needs for Combined Sewer Overflow
Control: Journal of the Environmental Engineering Division, No. EE2,
April 1978, p. 351.
Grizzard, T.J., and E.M. Jennelle, Will Wastewater Treament Stop Eutrophi-
cation of Impoundments?: Presented at 27th Annual Purdue, Indiana,
Waste Conference, Purdue University, 1972.
Gupta, M. K., D.G. Mason, M.J. Clark, T.L. Meinholz, C.A. Hansen, and A.
Geinopolos, Screening-Flotation Treatment of Combined Sewer Overflows,
Volume 1, Bench Scale and Pilot Plant Investigations: EPA-600/2-77-
069a, U.S. Environmental Protection Agency, Cincinnati, Ohio, August
1977.
Field, R., and E.J. Struzieksi, Jr., Management Control of Combined Sewer
Overflow: Journal of the Water Pollution Control Federation, 44(7),
July 1972.
Field, R., and J.A. Lager, Urban Runoff Pollution Control—the State of
the Art: Journal of the Environmental Engineering Division, American
Society of Civil Engineers, lOl(EEl), February 1975.
Field, R., A.N. Tafuri, and H.E. Masters, Urban Runoff Pollution Control
Technology Overview: EPA-600/2-77-047, U.S. Environmental Protection
Agency, Cincinnati, Ohio, September 1976.
Freeman, P.A., Evaluation of Fluidic Combined Sewer Regulators Under Mu-
nicipal Service Conditions: EPA-600/2-77-071, U.S. Environmental Pro-
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148
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Foster, W.S., and R. H. Sullivan, Sewer Infiltration and Inflow Control
Product Equipment Guide: EPA-600/2-77-017c, U.S. Environmental Pro-
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Heaney, J.P. and S.J. Nix, and M.P. Murphy, Storage-Treatment Mixes for
Stormwater Control: Journal of the Environmental Engineering Division,
No. EE4, August 1978, p. 581
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An Assessment: EPA-670/2-74-040. U.S. Environmental Protection
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Lager, J.A. , and W.G. Smith, Catchbasin Technology Overview and Assess-
ment: EPA Contract No. 68-03-0274, U.S. Environmental Protection
Agency, Cincinnati, Ohio, 1976.
Lager J.A., W.G. Smith, W.G. Lynard, R.M. Finn, and J. Finnemore, Urban
^n^o^^r ^naZement and Technology— Update and Users' Guide: EPA
600/8-77-014 U.S. Environmental Protection Agency, Washington, D.C.,
September 1977. '
Masters H. , Using Porous Pavement to Control Runoff: News of Environmen-
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--
June
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Sullivan R.H., J.E. Ure, and P. Zielinski, Field Prototype Demonstration
of the Swirl Degritter: EPA-600/2-77-185, U.S. Environmental Protec-
tion Agency, Cincinnati, Ohio, September 1977.
149
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Porous Pavements for Urban Runoff Control: Project No. 11034 BUY,
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U.S. Environmental Protection Agency, Proceedings Urban Stormwater Manage-
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rado, December 2-4, 1975: Contract 68-01-3565, Washington, D.C.,
January 1976.
Weibel, S.R., R.B. Weidner, and A.G. Christiansen, Characterization,
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Urban Hydrology
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605-637.
Chow, V.T., and B.C. Yen, Urban Stormwater Runoff—Determination of Vol-
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and Specific Curb Length for Forecasting Stormwater Quality and Quan-
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1972.
McPherson, M.B., and W.J. Schneider, Problems in Modeling Urban Watersheds:
Water Resources Research Vol. 10, No. 3, June 1974, pp 434-440.
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150
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Runoff: Civil
Yen, B.C., K.T. Chow, and A.O. Akan, Stormwater Runoff on Urban Areas of
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Airborne Fugitive Particulate Losses from Roadways"
Cadle S.H. and R.L. Williams, Gas and Particle Emissions from Automobile
I-IT-QO ^n Laboratorv anr? F-foi^ c*-,,^-,-^^. AJ_ Poll t-'
Cardina, J.A., Particle Size Determination of Tire-Tread Rubber in Atmos-
. Septem
Cowherd C., Jr., C.M. Maxwell, and D.W. Nelson, Quantisation of Dust
Entrainment from Paved Roadways: EPA-450/3-77-027, U.S. Environ-
' ReS6arch Trian§le par*, North Carolina,
Kelenffy S. and J. Morik, Some Results of the Investigation of Air Pol-
lution Caused by Road Traffic (A Kozlekedes Okozta Legszennyezodes
Vizsgalatanak Nehany Eredmenye) : Idojaras (Budapest), 7(4):277-231
j.y\) / • *
n v * Reentrained ^ ^om Paved Streets:
1977 ' Environmental Protection Agency, Kansas City,
Pierson W.R and W.W. Brachaczek, Airborne Particulate Debris from Rub-
ber Tires. Rubber Chemical Technology, 47(5), 1978, pp. 1275-1299.
Roberts, J.W., The Measurement, Cost and Control of Air Pollution from Un-
paved Roads and Parking Lots in Seattle's Duwamish Valley : Mfs The-
sis, University of Washington, 1973.
!piAJ;W" A*T' T°SSano> and H-A- Watters, Dirty Roads = Dirty Air:
APWA Reporter, November 1973. y
SehmelG-A. Particle Resuspension from an Asphalt Road Caused by Vehicu-
BNWL-1651 Parf '1 PaClf!C N°rthweSt Laboratories, Richland, Wash.
BNWL 1651, Part 1, in: annual report for 1971 to the USAED Division of
Biology and Medicine, 1972, pp. 146-147. Division of
, Tracer Particle Resuspension Caused by Wind Forces Upon an Asphalt
'
r1^*^, Battelle *~^^ iNortnwest Laboratories, Richland", Wash.
WWL-1651, Part 1 in: annual report for 1971 to USAEC Division of Bi-
ology and Medicine, 1972, pp., 136-138.
151
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> Particle Resuspension from Asphalt Road Caused by Car and Truck
Traffic: Atmospheric Environment, 7(3) :291-309, 1973.
> Resuspension of Tracer Particles by Wind: Battelle Pacific North
west Laboratories, Richland, Wash: BNWL-1850, Part 3, 1974, pp. 201
Steward, J., The Resuspension of Particulate Material from Surfaces, in:
Surface Contamination, edited by B.R. Fish: Oxford, England, Pergamon
Press, 1964.
Subramani, J.P, Particulate Air Pollution from Automobile Tire Tread Wear:
Disseration submitted to the Department of Civil Engineering, Univ. of
Cincinnati, 1971.
152
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APPENDIX A
STREET SURFACE PARTICULTATE SAMPLING PROCEDURES
The sampling procedures described in this appendix were specifically de-
veloped for this study. The objectives of the study were different from those
of past studies of street surface contaminants, so it was necessary to design
special sampling procedures. These procedures were intended to maximize the
accuracy and completeness of the information from the sampling program. The
procedures are described here in detail so that they can be used by public
works departments wishing to determine loading conditions, accumulation rates,
and street cleaning effectiveness for their own cities.
EQUIPMENT DESCRIPTION
and major ^iP-oent components. A
fi ~M trailer "»• «sed to carry the generator, tools,
fire extinguisher vacuum hose and wand, and two wet-dry vacuum units during
sample collection.* A truck with a suitable hitch and signal light connections
was used to pull the trailer. The truck also had warning lights, JnSS a
roof-top flasher unit. The truck operated with its headHghts and warning
°° § **" ""^ °f Samle collec"°<" The sampler and hosf
• ,h! trUCk ?nd the Street cleaner "sed to clean the test area were
equipped with radios (CB radios were adequate), so that the sampling te« Juld
to determine th"" f"" °Perat°r "^ ™cess*rV Experiments were conducted
to determin
to determine th xperments were conducted
to determine the most appropriate sampling vacuum and filter bag combination.
Two-horsepower (hp) industrial vacuum cleaners with one secondary filter and
and^d? fCT< I"'" ^ "ere SeleCted' ^ Vacuum unlts were heav^ duty
and made of stainless steel to reduce contamination of the samples. Two 2-hD
vacuums were used together by using a wye connector. This combination extended
so that ?, len8tV°f the K5 ln" vacuum hose to 35 ft. and increased the suction
so that it was adequate to remove all particles of interest from the street
surface
an adequate procedure ^-^^T^Z^^X^ ^l^^.
153
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Figure A-1. Street sampling trailer and major equipment components.
surface. A wand and a gobbler* attachment were also needed. The generator used
to power the vacuum units was of sufficient power to handle the electrical
current load drawn by the vacuum units—about 5000 watts for two 2-hp vacuums.
Finally a secure, protected garage was used to store the trailer and equipment
near the study areas when not in use.
*The gobbler attaches
about 6 in. across.
to the end of the wand and is triangular in shape and
154
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SAMPLING PROCEDURE
Because the street surfaces were more likely to be dry during daylight
hours (necessary for good sample collection), collection did not begin before
sunrise nor continue after sunset, unless additional personnel were available
for traffic control. Two people were required for sampling at all times—one
acting as the sampler, the other acting as the vacuum hose tender and traffic
controller. This lessened individual responsibility and enabled both persons
to be more aware of traffic conditions.
Before each day of sampling, the equipment was checked to make sure that
the generator's oil and gasoline levels were adequate, and that vacuum hose
wand, and gobbler were in good condition. A check was also made to ensure that
the vacuum units were clean, the electrical cords were securely attached to the
generator, and the trailer lights and warning lights were operable. The genera-
tor required about 3 to 5 minutes to warm up before the vacuum units were turned
on one at a time (about 5 to 10 seconds apart to prevent excessive current load-
ing on the generator). The amperage and voltage meters of the generator were
also periodically checked.
Figure A-2 illustrates the general samping procedure. Each subsample in-
cluded all of the street surface material that would be removed during a severe
rain (including loose materials and caked-on mud in the gutter and street ar-
eas). The location of the subsample strip was carefully selected to ensure
that it had no unusual loading conditions (e.g., a subsample was not collected
through the middle of a pile of leaves; rather, it was collected where the
Figure A-2. Sub-sample collection.
155
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leaves were lying on the street in their normal distribution pattern). When
possible, wet areas were avoided. If a sample was wet and the particles caked
around the intake nozzle, the caked mud from the gobbler was carefully scraped
into the vacuum hose while the vacuum units were running.
Subsamples were collected in a narrow strip about 6 in. wide (the width of
the gobbler) from one side of the street to the other (curb to curb) as shown
in Figure A-3. In heavily traveled streets where traffic was a problem, some
subsamples consisted of two separate one-half street strips (curb to crown).
Traffic was not stopped for subsample collection; the operators waited for a
suitable traffic break. On wide or busy roadways, a subsample was often col-
lected from two strips several feet apart, halfway into the street. On busy
roadways with no parking and good street surfaces, most particulates were found
within a few feet of the curb, and a good subsample could be collected by vacu-
uming two adjacent strips from the curb as far into the traffic lanes as possi-
ble. A sufficient break in traffic allowed a subsample to be collected halfway
across the street.
Subsamples taken in areas of heavy parking were collected between vehicles
along the curb, as necessary. The sampling line across the street did not have
to be a continuous line if a parked car blocked the most obvious and easiest
subsample strip. A subsample could be collected in shorter strips, provided
the combined length of the strip was representative of different distances from
the curb. Again, in all instances, each subsample was representative of the
overall curb-to-curb loading condition.
When sampling, the leading edge of the gobbler was slightly elevated above
the street surface (0.125 in.) to permit an adequate air flow and to collect
pebbles and large particles. The gobbler was lifted further to accept larger
material as necessary. If necessary, leaves in the subsample strip were manually
removed and placed in the sample storage container to prevent the hose from
clogging. If a noticeable decrease in sampling efficiency was observed, the
Curb
Curb
Figure A-3. Location of sub-sampling strips
across a street.
156
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vacuum hoses were cleaned immediately by disconnecting the hose lengths, cleaning
out the connectors (placing the debris into the sample storage container), and
reversing the air flows in the hoses (blowing them out by connecting the hose
to the vacuum exhaust and directing the dislodged debris into the vacuum inlet).
If any mud was caked on the street surface in the subsample strip, the sampler
loosened it by scraping a shoe along the subsample path (being certain that
street construction material was not removed from the subsample path unless it
was very loose). Scraping caked-on mud was done after an initial vacuum pass.
After scraping was completed, the strip was revacuumed. A rough street surface
was sampled most easily by pulling (not pushing) the wand and gobbler toward
the curb. Smooth and busy streets were usually sampled with a pushing action.
An important aspect of the sample collection was the speed at which the
gobbler was moved across the street. A very rapid movement significantly de-
creased the amount of material collected; too slow a movement required more
time than was necessary. The correct movement rate depended on the roughness of
the street and the amount of material on it. When sampling a street that had a
heavy loading of particulates , or a rough surface, the wand was pulled at a
velocity of less than 1 ft. per second. In areas of lower loading and smoother
streets, the wand was pushed at a velocity of 2 to 3 ft. per second. The best
indication of the correct collection speed was by examining how well the street
was visually being cleaned in the sampling strip and by listening to the col-
lected material rattle up the wand and through the vacuum hose. The objective
was to remove everything that was lying on the street that could be removed by
a significant rainstorm. It was quite common to leave a visually cleaner strip
on the street where the subsample was collected, even on streets that appeared
to be clean.
In all cases of subsample collection, the sampler and hose tender continu-
ously watched for oncoming vehicles. While working near the curb out of the
traffic lane (typically an area of high loadings) , the sampler visually monitored
the performance of the vacuum sampler. In the street, he constantly watched
traffic and monitored the collection process by listening to particles moving
up the wand. A large break in traffic was required to collect dust and dirt
from street cracks in the traffic lanes, because the sampler had to watch the
gobbler to make sure that all of the loose material in the cracks was removed.
The hose tender also always watched for traffic. In addition, he played
out the hose to the sampler as needed and kept the hose as straight as possible
to prevent kinking. If a kink developed, sampling stopped until the hose tender
straightened the hose.
When moving from one subsample location to another, the hose, wand, and
gobbler were securely placed in the trailer. The hose was placed away from the
generator s hot muffler to prevent hose damage. The generator and vacuum units
^ iS T/ /n the trail6r during the entire subsample collection period.
This helped dry damp samples and reduced the strain on the vacuum and generator
°
motors.
The length of time it took to collect the subsample varied with the number
of subsamples and the test area. For the first phase of this study, the test
areas required the following sampling effort:
157
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Test Area No. of Samples Sampling Period
Downtown - poor asphalt street surface 14 Q.5 hr.
Downtown - good asphalt street surface 35 i hr.
Keyes Street - oil and screens street surface 10 0.5-1 hr.
Keyes Street - good asphalt street surface 36 1 hr.
Tropicana - good asphalt street surface 16 0.5-1 hr.
In the oil and screens test area, the sampling procedure was slightly dif-
ferent because of the relatively large amount of pea gravel (screens) that was
removed from the street surface. The gobbler attachment was drawn across the
street more slowly (at a rate of about 3 seconds per ft.). Each subsample was
collected by a half pass (from the crown to the curb of the street) and contained
one-half of the normal sample. Two curb-to-curb passes were made for each
Tropicana subsample because of the relatively low particulate loadings in this
area. Several hundred grams of sample material were needed for the laboratory
tests. An after street cleaning subsample was not collected from exactly the
same location as the before street cleaning subsample (they were taken from the
same general area, but at least a few feet apart).
A field-data record sheet kept for each sample contained:
• Subsample numbers
• Dates and time of the collection period
• Any unusual conditions or sampling techniques.
Subsample numbers were crossed off as each subsample was collected. After-
cleaning, subsample numbers were marked if the street cleaner operated next to
the curb at that location. This differentiation enabled the effect of parked
cars on street cleaning performance to be analyzed.
SAMPLE TRANSFER
After all subsamples for a test area were collected, the hose and wye con-
nections were cleaned by disconnecting the hose lengths, reversing them, and
holding them in front of the vacuum intake. Leaves and rocks that may have
become caught were carefully removed and placed in the vacuum can, the generator
was then turned off. The vacuums were either emptied at the last station or at
a more convenient location.
To empty the vacuums, the top motor units were removed and placed out of
the way of traffic, as shown in Figure A-4. The vacuum units were then discon-
nected from the trailer and lifted out. The secondary, coarse vacuum filters
were removed from the vacuum can and were carefully brushed with a small whisk
broom into a large funnel placed in the storage can, as shown in Figure A-5.
The primary dacron filter bags were kept in the vacuum can and shaken carefully
to knock off most of the filtered material. (Figure A-6 shows how the hose in-
let was blocked with a leg or knee, and the primary filter bag was held onto the
vacuum drum with arms and chest). The dust inside the can was allowed to settle
for a few minutes, then the primary filter was removed and brushed carefully
into the sample can with the whisk broom. Any dirt from the top part of the bag
158
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where it was bent over the top of the vacuum was also carefully removed and
placed into the sample can.
Figure A-7 shows how the material was transferred from the vacuum units
into the sample can. After the filters were removed and cleaned, one person
picked up the vacuum can and poured it into the large funnel on top of the
sample can, while the other person carefully brushed the inside of the vacuum
can with a soft 3- to 4-in. paint brush to remove the collected sample. In
order to prevent excessive dust losses, the emptying and brushing was done in
areas protected from the wind. To prevent inhaling the sample dust, both the
sampler and the hose tender wore mouth and nose dust filters while removing the
samples from the vacuums.
To reassemble the vacuum cans, the primary dacron filter bag was inserted
into the top of the vacuum can with the filters's elastic edge bent over the
^top of the can. The secondary, coarse filter was placed into the can and
assembled on the trailer. The motor heads were then carefully replaced on the
vacuum cans, making sure that the filters were on correctly and the excessive
electrical cord was wrapped around the handles of the vacuum units. The vacuum
hoses and wand were attached so that the unit was ready for the next sample
collection. K
The storage cans were labled with the date, the test area's name, and an
indication of whether the sample was taken before or after the street cleaning
test or if it was an accumulation (or other type of) sample. Finally, the
lids of the sample cans were taped shut and transported to the laboratory for
logging-in and analysis or storage.
161
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APPENDIX B
EXPERIMENTAL DESIGN
The samples were collected from narrow strips the width of the street from
curb to curb, as described in Appendix A. The analytical procedure used to determine
the number of subsamples needed involved weighing individual subsamples in the
study area to calculate the standard deviation (a) and the mean (x) of the
street surface loading values. From these two values, the number of subsamples
necessary (N), depending on the allowable error (L), were determined. An allowable
error value of about 25 percent, or less, was used. The formula used (after
Cochran 1963) is
N = 402/L2.
With 95 percent confidence, it calculates the number of sub-samples necessary
to determine the true value for the loading within a range of + L. Figure
B-l relates the ratio of the standard deviation to the mean for various allowable
errors (as a percentage of the mean) to determine N.
As the a:x ratio increases, more samples are required for a specific allow-
able error. Similarly, as the allowable error decreases for a specific a:x
ratio, more samples are required. Therefore, with an allowable error of 25
percent, the required number of subsamples for a study area with a arxratio of
0.8 would be 36. For a test area with about 3 curb-miles, it then follows that
a subsample would be taken about every 450 feet.
The total amount of street surface particulate sample removed during each
test is insignificant when compared to the total street surface loadings in the
whole test area. (In the above example, the sample would be 0.1 percent of
the total street surface loadings for the area.)The number of sub-samples required
was evaluated for each test area at the beginning of both sampling phases.
DETERMINING THE NUMBER OF SUBSAMPLES REQUIRED
Initially, individual samples were taken at 49 locations in the three study
areas to determine the loading variability. Table B-l presents the calculated
loadings and the influential characteristic information for each sample. The
loadings averaged about 2700 Ib/curb-mile in the Downtown and Keyes Street areas,
and were found to vary greatly within these two areas. The Tropicana area loadings
were not as high, and averaged 310 Ib/curb-mile.
The analytical procedure previously described was used to determine the re-
quired number of subsamples in each test area with an allowable error of 25
162
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10.0
IX
o
<
LJJ
CO
O
I-
z
o
<
111
Q
Q
OC
Q
Z
CO
LL
O
ALLOWABLE ERROR AS A FRACTION OF THE MEAN, x
Figure B-1. Required number of sub-samples as a fuction of
allowable error and standard deviation.
163
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TABLE B-l. EXPERIMENTAL DESIGN SAMPLE INFORMATION(samples collected 11/29/76)
Study Area
and Station
Number Location
Downtown
1 N. 1st 9 Bassett
2 N. 1st 9 Julian
3 N. 1st 9 St. James
4 St. James between N. 1st & Market
5 Devine between N. 1st & Market
6 Julian between N. 1st & Market
7 Old Market
8 Market St. 9 Devine
9 Market 9 St. James
10a Market 9 Julian
lla Pleasant 9 Bassett
12a Bassett 9 Pleasant
13a Bassett between Pleasant & Terraine
14a Bassett 9 Terraine
15 Bassett 9 San Pedro
a
16 Terraine 9 Bassett
17 Terraine 9 Julian
18 San Pedro 9 Bassett
19 San Pedro 9 Julian
20a Julian 9 Pleasant
21a Julian between Sta. Teresa and Terraine
22 Julian 9 San Pedro
23 Devine between Sta. Teresa and Terraine
24 Devine between Market and N. 1st
25 St. James between Terraine & San Pedro
26 St. John between Pleasant & Santa
Teresa
27a St. John between Terraine & San Pedro
28 Pleasant between St. James & St. John
29 Terraine @ Devine
Keyes
la 12th St. No. of Martha
2 12th 9 Humboldt
3 llth 9 Keyes
4 10th 9 Bestor
5 No. end of 9th St.
6 9th 9 Keyes
7 9th 9 Humboldt
8 Martha 9 8th
9 Bestor 9 10th
10a Humboldt 9 8th
Tropicana
1 Cathay 9 Naples
2 Cathay 9 Seaview
3 Palmview Way
4 Loyola Dr.
5 Darwin Way
6 Bal Harbor Way 9 Everglade
7 Chiplay Dr.
8 Bermuda Way 9 Ocala
9 Orlando 9 Ocala
10 King Rd. 9 Biscayne
1 7
Land uses: Street types:
C: commercial A: asphalt
I: industrial O&S: oil and screens
V: vacant lots overlay
R: residential
Street Number
Land Use Type of Curbs
C
C
C
C
C
C
C
C
C
I
I
I
I
I
I
I
I
I
I
C/I
C/I
C/I
V
i/v
R/C
V
C
R
V
Rb
R
R
R
R
R
R
R
R
R
RC
R
R
R
R
R
R
R
R
R
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
"A
A
A
A
O&S
O&S
A
A
O&S
O&S
O&S
A
O&S
O&S
A
A
A
A
A
A
A
A
A
A
2
2
2
2
2
2
2
2
2
0
0
0
0
0
0
0
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
2
2
2
Street
Street Traffic Width
Condition Density^* (ft.)
G
G
G
G
G
G
G
G
G
P
P
P
P
P
P
P
P
P
P
G
G
G
P
G
G
G
G
P
P
O&S
O&S
G
G
O&S
O&S
O&S
G
O&S
O&S
G
G
G
G
G
G
G
G
G
G
Street condition:
G: good
P : poor
O&S: oil
and screens
M/H
M/H
M/H
M
M/L
M
L
H
H
L
L
L
L
L
L
L
L
L
L
M
M
M
L
L
M
M
M
L
L
L
L
H
H
L
M
L
L
L
L
L
L
L
L
L
L
L
L
L
H
^Traffic density
M: moderate
H: high
L: low
50
50
52
40
36
40
40
66
66
29
40
40
46
48
45
60
40
45
45
40
40
40
36
38
40
42
42
40
40
40
40
50
50
50
50
50
40
40
35
36
36
36
36
36
36
36
36
36
60
Dust/Dirt
Particulate
Loading (lb/
curb-mile)
630
47
105
220
680
262
640
396
175
3270
20,000
7600
7400
5500
4025
9850
3050
3240
2300
610
303
361
890
580
1740
151
700
1570
995
1733
3680
582
413
4380
3760
6380
262
2520
2860
220
180
145
640
174
407
424
366
320
233
Outside final study area.
There is a substantial amount of commercial land use in this study area along Keyes.
"There is some commercial land use in this study area.
164
-------
percent or less. This percentage was chosen to keep the precision and sampling
effort at reasonable levels. The data were then examined to determine if the
study areas should be divided into meaningful test area groups.
Table B-2 presents the results of grouping the data by influential para-
meters for each study area. It is interesting to note the similar groupings
for much of the data: downtown streets in poor condition were generally in
industrial areas, had low traffic, and had one or no curbs; the streets in
good condition were generally in commercial areas, had moderate to high traffic,
and had two curbs. The measured loading values within each of the different
but related groupings were also similar. The purpose of the exercise was to
identify a small number of meaningful test area groupings that required a reason-
able number of subsamples and to increase the usefulness of the test data.
Therefore, the Downtown study area was divided into two test areas: one with
good asphalt street surface conditions and the other with poor asphalt street
surface conditions. The Keyes Street study area was also divided into two test
areas: good asphalt street surface and oil and screens street surface. The
Tropicana study area was left undivided. There was reason to believe that the
street cleaning equipment would perform significantly differently in each test
area. This reasoning was based on the influencing external and uncontrollable
operating conditions of street surface type, condition, and initial particulate
loadings. Therefore the tests were started with five test areas, each with the
number of subsamples and curb-mile lengths as listed below:
Downtown study area:
• good asphalt street surface (3.0 curb-miles) - 35 subsamples
• poor asphalt street surface (1.5 curb-miles) - 14 subsamples
Keyes Street study area:
• good asphalt street surface (2.7 curb-miles) - 36 subsamples
• oil and screens street surface (2.2 curb-miles) - 10 subsamples
Tropicana study area:
• good asphalt street surface (11.1 curb-miles) - 16 subsamples
In addition, buffer zones (Downtown: 5.1 curb-miles; Keyes: 2. 7 curb-miles;
and Tropicana: 7. 0 curb-miles) were established around each study area to minimize
tracking of particulates. A total of 20.5 curb-miles was included in all five
test areas with about 15 curb^niles in the buffer zones. The downtown test areas
were eliminated after the initial six weeks of testing because of an unauthorized
plating discharge in the storm sewerage and excessive sampling requirements.
The second phase reevaluations resulted in slightly modifying the number
of subsamples to be collected in each test area, but the physical test area
divisions remained the same.
165
-------
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166
-------
APPENDIX C
SELECTION AND DESCRIPTION OF STUDY AREAS
Figure C-l shows the San Francisco Bay Area and the general location of
the Coyote Creek watershed. Figure C-2 is a more detailed map of the watershed
and shows the locations of the study areas. All of the study areas are located
within the urban area of San Jose, California. Figures C-3, C-4, and C-5
show detailed street maps of the three study areas and five test areas. Also
shown are the buffer zones established around each study area. The buffer zones
are cleaned at the same time and with the same number of passes as the test
areas in order to prevent excessive tracking or blowing of street dirt into
the test areas. Figures C-6 through C-10 are photographs of portions of the
test areas.
In the process of selecting study areas, information on several potential
study areas in the city of San Jose was collected. Eight areas that met many
of the criteria necessary to conduct the field program were identified. These
criteria included:
• Each study area must be at least 10 acres in size and have separ-
ated storm drainage and sanitary sewage systems.
• Each study area should have its own complete storm drain sewerage
system.
• The surface drainage of each study area should closely coincide with
the area drained by the stormwater sewerage system.
• The study areas should have little construction activity during the
time of study and a minimum amount of vacant land area.
• The study sites should represent a cross-section of land uses and
economic conditions in the city.
• The storm sewerage system must be well documented to show no cross
connections between sanitary sewage and any upstream drainage areas,
and should have no illegal discharges.
• The slope of the sewerage system should be small, with potential or
known solids accumulation problems in the sewerage.
• The study sites should have a variety of traffic conditions, and should
be located close to the City of San Jose Public Works Department main
service yard.
167
-------
SAN
FRANCISCO \ HAYWARD
BAY
COY OTE
CREEK
WA TE RSH E D
10 15
miles
Figure C-1. San Francisco Bay Area showing the general
location of the Coyote Creek watershed.
168
-------
T3
C
CD
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North Third Street
j L
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170
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171
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172
-------
Figure C-6. Downtown - good asphalt test area.
Figure C-7. Downtown - poor asphalt test area.
173
-------
Figure C-8. Keyes - oil and screens test area.
Figure C-9. Keyes - good asphalt test area.
174
-------
Figure C-10. Tropicana - good asphalt test area.
Table C-l presents the information collected for the eight potential study
areas; Figure Oil shows their locations. The areas selected for initial study
include the south Downtown area (site 2), the Keyes Street area (site 6), and
the Tropicana area (site 8). These were chosen because they represent the
variety of conditions found in San Jose and many other cities. As discussed
in Appendix B, the Downtown and Keyes Street areas were found to be better
represented by dividing each of them into two areas. Therefore, a total of
five test areas was used in the initial field activities. Some data were col-
lected from the five test areas, but most of the data are based on studies
conducted in the two Keyes Street test areas and in the Tropicana test area.
Other important study area characteristics that affect street cleaning oper-
ations include soil type (determines the erodability of adjacent land and the
175
-------
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Air qual
sampling
sites
177
-------
Figure C-11. Area map showing potential test site locations.
178
-------
chemical make-up of erosion products that can wash onto the streets during
major rains), topography, and gutter type. These characteristics were very sim-
ilar for all of the study areas: the topography was flat, and most of the
gutters were made of concrete with straight sides (very few rolled asphalt
gutters were present). Table B-l in Appendix B describes the gutter presence
within the selected test areas.
Table C-2 shows the land use and surface area compositions for the three
study areas selected. In the Downtown area, vacant spaces and rooftops make up
most of the area, while landscaped areas are most predominant in the Keyes and
and Tropicana Study areas. Street surfaces composed between 14 and 21 percent
of the three areas. Buildings greater than three stories tall only existed in
the Downtown area. The Downtown area was also significantly different in that
only 1 percent of the total area consisted of lawns or otherwise planted. The
Downtown area had few residential areas, but quite a bit of institutional areas
and vacant lots. About 1/3 of the Downtown area was commercial. Most of the
land use in the Keyes and Tropicana areas was residential.
Table C-3 presents the estimated annual average daily traffic conditions
for the test areas. The weighted average for all street segments in each test
area ranged from about 200 cars/day in the Keyes-oil and screens test area
to about 10,000 cars/day in the Downtown-good asphalt test area. Those street
segments having the most traffic also had the best street conditions.
TABLE C-2. STUDY AREA SURFACE AND LAND USE COMPOSITIONS (%)
Downtown
Keyes
Tropicana
Surface Area
Rooftops (<3 stories tall)
Rooftops (>3 stories tall)
Lawn/landscaped area
Vacant space
Sidewalks
Street
Parking lots
24
2
1
34
4
21
14
19
0
44
4
5
21
7
17
0
39
18
4
15
7
Land Use
Commercial
Residential
Industrial
Other (institutional, vacant
land, etc.)
Total Acreage
33
2
31
34
100 acres
11
86
0
92 acres
0
83
(some)
17
195 acres
179
-------
TABLE C-3. ESTIMATED DAILY TRAFFIC VOLUMES IN TEST AREAS
Weighted Estimated
Average Minimum
Daily Traffic* Daily Traffic**
Estimated
Maximum
Daily Traffic***
Downtown-overall
Downtown-good asphalt
street surfaces
Downtown-poor asphalt
street surfaces
Keyes-overall
Keyes-good asphalt streets
Keyes-oil and screens
surfaced streets
7700
10,000
2800
4600
8300
200
500
1500
500
50
200
50
25,000
25,000
7500
26,000
26,000
1000
Tropicana-good asphalt
street surfaces
2200
100
18,000
*Estimated based on some field measurements. Weighted by representative
street segment lengths.
**Minimum estimated daily traffic for any one street segment in test area,
***Maximum estimated daily traffic for any one street segment in test area,
180
-------
APPENDIX D
RAINFALL AND ACCUMULATION RATE HISTORY
Figures D-l through D-5 present the rainfall history in the study areas
as a function of time. These figures include a bar graph on each day that
rainfall occurred (>0.01 in.) along with values for the total rain, the hours
of rain, and the rainfall intensities.
Figures D-6 through D-22 present total street surface particulate loading
and median particle sizes as a function of time. The dates with significant
rains are also shown with a solid vertical line and the dates of street clean-
ing are designated with a code showing the type of street cleaning equipment
used and the number of passes made that day. The values of total particulate
loading and particle size for each day of sampling are connected by straight
lines. Solid lines signify a positive slope (an increase in median particle
size or an increase in total solids loading). Dashed and dotted lines show
a decrease in median particle size or total solids loading.
181
-------
Significant rains
Other rains
ONE-HOUR PEAK
INTENSITY (in/hr)
AVERAGE
INTENSITY (in/hr)
HOURS OF RAIN
TOTAL RAIN
(inches)
1.0
BAR GRAPH
SHOWING
TOTAL RAIN
DURING DAY 0.5-
(inches)
o o o o
00 ^ KjS
00 0 0
CT1 Gn CD — *
OOOOOO 0 <->
-2 22
S 2 2 2
to to w on K> _M 5JW
g g PPPP P 0 00 00
2 8 8wSw 8 § o^ bjo
I
- n ^PLn n n n I
30 1 5 10 15 20 25 i 5 10 15
JANUARY FEBRUARY MARCH
1977 1977 1977
I
1J , 1
20
Figure D-2. Rainfall history (continued).
182
-------
fcv:-x-:.vwi Significant rams
Other rains
ONE-HOUR PEAK
INTENSITY (in/hr)
AVERAGE
INTENSITY (in/hr)
HOURS OF RAIN
TOTAL RAIN
(inches)
1.0-
BAR GRAPH
SHOWING
TOTAL RAIN
DURING DAY 0.5-
(inches)
PP p o oo oooo o
°8 S 8 2g 2£g2 §
pp p p oo oooo o
o o o b b b b '-. b b b
-» * M N> KJ CO II.*vJ-» OJ
MW M - W« -.10*.-. 05
PP o OOOOOOOO
S£ 8 8 8£ 2gg2 g
nl
L ^-- , n , , , , J . j
1 /
25
MARCH
1977
30 1 5
APRIL
1977
10
15
20
25
301 5
MAY
1977
Figure D-3. Rainfall history (continued).
10
15
ONE-HOUR PEAK
INTENSITY (in/hr)
AVERAGE
INTENSITY (in/hr)
HOURS OF RAIN
TOTAL RAIN
(inches)
BAR GRAPH
SHOWING
TOTAL RAIN
DURING DAY
(inches)
t'vivivfl Significant rains I I Other rains
O o o
b b '—
w ii o
00 0
2b b
-. CO
M -> Ul
o o o
32 3:
n r, n
15 20 25 30 1 5 10 15 20 25 30 1 5 10 - 15 20
JUNE
1977
JULY
1977
AUGUST
1977
Figure D-4. Rainfall history (continued).
183
-------
ONE-HOUR PEAK
INTENSITY (m/hr)
AVERAGE
INTENSITY (m/hr)
HOURS OF RAIN
TOTAL RAIN
(inches)
1.0-
BAR GRAPH
SHOWING
TOTAL RAIN
DURING DAY 0-5-
(mches)
____ tsv*:gi Significant rains 1 1 Other rains
s
U) 00
vj -•
P 00
•o go
Ol
Ul -«
« si
i
1
1 n
25 30 1 5 10 15 20 25 30 1 5 10 15 20 25
AUGUST
1977
SEPTEMBER
1977
OCTOBER
1977
Figure D-5. Rainfall history (concluded).
F - 4-wheel mechanical street sweeper
V • Vacuum-assisted mechanical street sweeper
S - State-of-the-art mechanical street sweeper
1 - 1 pass
2 - 2 passes
Decreasing median particle size (^1)
- Increasing median particle size (^t)
Decreasing total dust and dirt loading
(Ib/curb-mile)
Increasing total dust and dirt loading
(Ib/curb-mile)
LOADING
(Ib/curb-mile)
Significant rains
2000
1500
1000-
500-
0 •
F-1
ALL F-1
^—' . EQUIPMENT TYPE
••••• AND NUMBER OF
PASSES
PARTICLE
SIZE (U)
6 10
DECEMBER
1976
15
20
25
30 1 5
JANUARY
1977
—t—
10
800
600
400
200
15
Figure D-6. Total paniculate loading and median particle size as a
function of time - Downtown - good asphalt test area.
184
-------
F - 4-wheel mechanical street sweeper
V - Vacuum-assisted mechanical street sweeper
S - State-of-the-art mechanical street sweeper
1 - 1 pass
2 2 passes
Decreasing median particle size (p.)
Increasing median particle size (fj.)
Decreasing total dust and dirt loading
(Ib/curb-mile)
Increasing total dust and dirt loading
(Ib/curb-mile)
LOADING
(Ib/curb-mile)
2500
2000
1500-
1000-
500-
Significant rains
PARTICLE
;IZE (JLI)
-1000
800
600
400
200
6 10
DECEMBER
1976
30 1 5
JANUARY
1977
Figure D-7. Total paniculate loading and median particle size as a
function of time - Downtown - poor asphalt test area.
185
-------
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