EPA-R2-73-283
August 1973 Environmental Protection Technology Series
Toxic Materials Analysis Of Street
Surface Contaminants
Office Of Research And Development
U.S. Environmental Protection Agency
Washington, D.C 20460
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EPA-R2-73-283
August 1973
TOXIC MATERIALS ANALYSIS
OF STREET SURFACE CONTAMINANTS
By
Robert E. Pitt and Gary Amy
Contract No. 14-12-921
Project 11034 FUJ
Project Officer
Francis J. Condon
Municipal Pollution Control Division
Environmental Protection Agency
Washington, D.C. 20460
Prepared for
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
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EPA Review Notice
This report has been reviewed by the Environmental Protec-
tion Agency and approved for publication. Approval does
not signify that the contents necessarily reflect the views
and policies of the Environmental Protection Agency, nor
does mention of trade names or commercial products consti-
tute endorsement or recommendation for use.
11
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ABSTRACT
Because of the large amounts of toxic materials (especially heavy
metals) found associated with street surface particulates during the
course of a previous study (Water Pollution Aspects of Street Sur-
face Contaminants), additional work has recently been completed which
defines the distribution and range of heavy metals on the nation's
city streets .
This project defined the breakdown of the particulates' compositions
by having mass spectographic analyses performed on various samples.
Using these results, the heavy metals which were determined to have
the greatest water pollution potential (As, Cd, Cr, Cu, Fe, Pb, Mn,
Hg, Ni, Sr, Ti, Zn and Zr) were analyzed in each of about 75 samples
collected nationwide in 10 cities in the previous study.
Other analyses conducted included: size affinities of the metals,
solubilities and toxicities of the road surface runoff mixture, and
certain organic analyses on selected samples. Additional sampling
was conducted on rural road, highway and airport surfaces and partic-
ulates were analyzed for the following common water pollution parame-
ters: BOD , COD and nutrients, plus selected heavy metals, for com-
o
parison with values representative of normal city streets •
This report was submitted in fulfillment of Contract 14-12-921 under
the sponsorship of the Water Quality Office, Environmental Protection
Agency.
111
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CONTENTS
Section
Page
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
Conclusions
Recommendations (noc included)
Introduction
Mass Spectographi- Analyses
Atomic Absorption Analyses of
Individual Lanf-Use Samples
Solubilities ind Toxicities of Heavy
Metals Assoc/ated With Road Surface
Runoff
Particle &ze Distribution - of Heavy
Metals As/ociated With Road Surface
Particulates
l Analyses on Highway, Rural
Road an/ Airport Surfaces
Organi Analysis
Acknowledgments
Appeidices
5
11
19
57
73
94
100
106
107
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ICURES
Page
1 Solubility Curves for Selected Heavy Metals 65
2 Particle Size Distribution of Oadmium 85
3 Particle Size Distribution of Ch-omium 86
4 Particle Size Distribution of Cop^r 87
5 Particle Size Distribution of Iron _ 88
6 Particle Size Distribution of Mangantse 89
7 Particle Size Distribution of Nickel 90
8 Particle Size Distribution of Lead 91
9 Particle Size Distribution of Strontium 92
10 Particle Size Distribution of Zinc 93
vi
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TABLES
No. Page
1 Elemental Composition of Street Surface Contaminants
as Determined by Mass Spectograph Techniques 12
2 Loading Values for "M" Designated Elements From
Table 1 1*.
3 Abundant Elements Found in Street Contaminant
Samples 15
4 Metals Chosen to be Analyzed in Further Detail 16
5 Elements Having Substantial (>10 times) Strength
Differences Between Different Land-Use Samples 16
6 Elements Having Substantial (>10 times) Loading
Differences Between Different Land-Use Samples 17
7 Concentration of Cadmium (mg/kg),
Distribution by Land Use 22
8 Concentration of Chromium (mg/kg),
Distribution by Land Use 23
9 Concentration of Copper (mg/kg),
Distribution by Land Use 24
10 Concentration of Iron (mg/kg),
Distribution by Land Use 25
11 Concentration of Manganese (mg/kg),
Distribution by Land Use 26
12 Concentration of Nickel (mg/kg),
Distribution by Land Use 27
13 Concentration of Lead (mg/kg),
Distribution by Land Use 28
14 Concentration of Strontium (mg/kg),
Distribution by Land Use 29
VI1
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TABLES (cont'd)
15 Concentration of Zinc (mg/kg) ,
Distribution by Land Use 30
2
16 Loading of Total Solids (lb/1,000 ft ),
Distribution by Land Use 31
2
17 Loading of Cadmium (lb/1,000 ft ),
Distribution by Land Use 32
2
:8 Loading of Chromium (lb/1,000 ft ),
Distribution by Land Use 33
2
19 Loading of Copper (lb/1,000 ft ),
Distribution by Land Use 34
2
20 Loading of Iron (lb/1,000 ft ),
Distribution by Land Use 35
2
21 Loading of Manganese (lb/1,000 ft ),
Distribution by Land Use 36
22 Loading of Nickel (lb/1,000 ft2),
Distribution by Land Use 37
23 Loading of Lead (lb/1,000 ft2),
Distribution by Land Use 38
2
24 Loading of Strontium (lb/1,000 ft ),
Distribution by Land Use 39
25 Loading of Zinc (lb/1,000 ft2),
Distribution by Land Use 40
26 Loading of Total Solids (Ib/curb mi),
Distribution by Land Use 41
27 Loading of Cadmium (Ib/curb mi),
Distribution by Land Use 42
28 Loading of Chromium (Ib/curb mi),
Distribution by Land Use 43
Vlll
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TABLES (cont'd)
No. Page
29 Loading of Copper (Ib/curb mi),
Distribution by Land Use 44
30 Loading of Iron (Ib/curb mi),
Distribution by Land Use 45
31 Loading of Manganese (Ib/curb mi),
Distribution by Land Use 46
32 Loading of Nickel (Ib/curb mi),
Distribution by Land Use 47
33 Loading of Lead (Ib/curb mi),
Distribution by Land Use 48
34 Loading of Strontium (Ib/curb mi)y
Distribution by Land Use 49
35 Loading of Zinc (Ib/curb mi),
Distribution by Land Use 50
36 Hypothetical City Parameters 53
37 Metal Loading From Road Surface Runoff Compared
to Normal Sanitary Sewage 53
38 Metal Loading From Road Surface Runoff Compared
to Normal Sanitary Sewage Flow 54
39 Effects of Heavy Metals on Biological Treatment
Processes 55
40 Removal Efficiencies in Sewage Treatment Processes 56
41 Heavy Metal Concentrations (as measured) and
Bioassay Results for Simulated Receiving Body of
Water 59
42 Heavy Metal Concentrations and Solubilities in
Simulated Receiving Body of Water (1-day sample) 61
ix
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TABLES (cont'd)
No. Page
43 Heavy Metal Concentrations and Solubilities in
Simulated Receiving Body of Water (5-day sample) 62
44 Heavy Metal Concentrations and Solubilities in
Simulated Receiving Body of Water (25-day sample) 63
45 Comparison of Standard Solubilities of Simple
Metallic Salts and Metallic Elements With Ranges
of Solubility Increases Found in Tests 67
46 Comparison of Maximum Concentrations of Heavy Metals
Found in Simulated Receiving Water Test With Values
That Have Been Shown to Have Effects on Aquatic
Organisms 71
47 Particle Size Distribution for Cadmium 75
48 Particle Size Distribution for Chromium 75
49 Particle Size Distribution for Copper 76
50 Particle Size Distribution for Iron 76
51 Particle Size Distribution for Manganese 77
52 Particle Size Distribution for Nickel 77
53 Particle Size Distribution for Lead 78
54 Particle Size Distribution for Strontium 78
55 Particle Size Distribution for Zinc 79
56 Percent of Heavy Metals in Various Particle
Size Ranges (Seattle) 79
57 Percent of Heavy Metals in Various Particle
Size Ranges (Tulsa) 80
58 Percent of Heavy Metals in Various Particle
Size Ranges (San Jose II) 81
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TABLES (cont'd)
No. Page
59 Percent of Heavy Metals in Various Particle
Size Ranges (Baltimore) 82
60 Percent of Heavy Metals in Various Particle
Size Ranges (Average of Four Cities) 83
61 Average Street Sweeper Removal Efficiency 84
62 Percent Heavy Metal Removal by Average Street
Sweeper 84
63 Comparison of Strengths (mg/kg) of Different
Paved Surface Particulates for Common Pollution
Parameters and Certain Heavy Metals 97
64 Comparison of Loadings of Different Types of
Roadways for Common Pollution Parameters and
Certain Heavy Metals 98
65 Organic Analysis of Selected Samples (mg/kg) 102
66 Organic Analysis of Selected Samples (Ib/curb mile) 103
2
67 Organic Analysis of Selected Samples (lb/1,000 ft ) 104
XI
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SECTION I
CONCLUSIONS
Possibly the most important metallic elements, from a water pollu-
tion standpoint, include: lead, zinc, copper, nickel, chromium,
strontium, titanium, and zirconium.
• Some differences in strength (mg/kg) and loading
(Ib/curb mile, kg/km) were found between different land
use samples. In most cases, the industrial samples
had the greatest strength and loading factors, while
the commercial sample showed the least. These dis-
similarities are most likely due to different activi-
ties (for strength) and to different public works
practices (for loadings) in each land use.
• Industrial and commercial land-use areas have the
greatest strengths (mg/kg) of heavy metals.
• Industrial land-use areas have the greatest loading
factors (Ib/curb mile, kg/km) of heavy metals.
• Cities with high particulate loadings have high
metal loadings.
• The range of values obtained within one land use or
one city is usually within a factor of ten, while
the land use and city averages are usually within a
factor of 2 to 4 for each metal.
When metals associated with street runoff are compared to the metal
content of sanitary sewage, most of the runoff metals are 100 to
1000 times greater than the sewage metals on a slug load (Ibs/hour,
kg/hr) basis, and from 10 to 100 times on a concentration (mg/1)
basis.
• The metal content of street runoff is usually not suffi-
cient to cause noticeable reductions in biological treat-
ment efficiency in plants handling combined sewage/storm
drain systems.
1
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The solubilities of heavy metals into a simulated receiving
water environment are low, most being less than 10% of the
available metal.
• Some metals showed decreases in concentration
through time after "discharged" to receiving
water, possibly being sorbed onto the street
surface particulates.
• The highest solubilities were found for larger
particle sizes ,(>246M) .
• Copper; cadmium, lead and zinc are soluble to
a sufficient degree to cause toxic effects to
certain aquatic organisms under selected con-
ditions (such as soft water).
Bioassay tests conducted in aerated, moderately hard water,
indicated no short-term (96-hr) toxic effects on stickle-
back.
• Immediate toxic effects of road surface runoff
are most likely due to extreme oxygen demand.
• The most dramatic toxic effects of metals most
likely occur when runoff is discharged into
quiescent water where it is allowed to accumu-
late to toxic concentrations.
In most cases, more than 50 percent of all the metals are
found in size ranges smaller than4'95/j.
• The overall removal rate by normal street sweep-
ing practices of heavy metals range from 38% for
cadmium to 56% for chromium, with an overall
average of 49% for all metals.
By comparing city street surface contaminants with those
found on rural roads and highways, one finds that the
city street particulates have greater pollution potential
on a strength (mg/kg) basis. The major difference is
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that the BOD strength of the city samples is an order
o
of magnitude greater than the other samples.
• The BOD /COD ratio is much less for rural road and
«D
highway samples than for city street samples,
possibly being caused by an increase in toxicity
of these samples, depressing the BOD values.
O
• On a loading basis (Ib/curb mile), the highway sur-
faces contribute a greater amount of pollutants
than any other type of surface tested. This is
due to the large amounts of particulates found
on the highway surfaces.
• The heavy metal content of airport surface par-
ticulates is quite similar to the metal content of
road surface particulates. This is probably due
to the similarity of paving material and the
large volume of gasoline-powered aircraft at
the airport that was sampled.
• About 2/3 of the five-day BOD values was found
to be exerted during the first day of discharge
of the road surface particulates into the re-
ceiving water. This, in conjunction with very
high BOD values, can cause serious oxygen de-
o
pletion problems in the receiving water near
the time of discharge.
Grease and oil were found to be the major organic constit-
uents of major land-use samples. The smaller size ranges
of particulates appeared to contain a greater percentage
of grease and oil than the larger size ranges, possibly
due to greater surface areas per unit weight.
• There does not appear to be any major differences
in organic strengths (mg/kg) of the different
land-use samples.
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• Samples were analyzed for common pesticides,
but the results indicated that the pesticides
were unstable during the storage period.
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SECTION III
INTRODUCTION
Background
Under the sponsorship of the Office of Research and Monitoring, U.S.
Environmental Protection Agency, URS Research Company has conducted
a program to determine the water pollution effects of street surface
contaminants. During the course of this study, numerous samples were
collected from a number of cities throughout the country, representing
a wide range of land-use areas. These samples were analyzed for conven-
tional water pollution parameters such as total and volatile solids,
coliform bacteria, biochemical oxygen demand, chemical oxygen demand,
kjeldahl nitrogen, soluble nitrates and phosphates. Other parameters
analyzed on selected samples included certain heavy metals and pesti-
cides. The results of these prior analyses are reported in Water Pollu-
tion Aspects of Street Surface Contaminants, EPA-R2-72-081. The amounts
of heavy metals and pesticides found on the road surfaces justified fur-
ther study to determine their distribution, solubilities and toxicities.
This report summarizes and analyzes the results of this effort to obtain
the specified additional information. The greatest usefulness of this
report will be in the wealth of data presented, enabling the reader to
apply these values in a more sophisticated data reduction effort than
was possible in this study. Conclusions are presented, but are neces-
sarily based on limited data analysis. To avoid redundancy, this report
will only comment on results that are specific to these additional toxic
materials analyses. For a complete description of all the test sites
and prior discussions of the theory and practice of municipal street
sweeping, the reader is referred to the previously mentioned report:
Water Pollution Aspects of Street Surface Contaminants. Because of the
nature of the toxic materials investigation, this report should be
treated as an addition to the Water Pollution Aspects of Street Surface
Contaminants report, a brief description of which follows:
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URS Research Company was awarded a contract by the Environmental Pro-
tection Agency (EPA) relating to the development and evaluation of
methods and techniques for reducing water pollution resulting from the
water runoff from urban streets and paved areas.
Materials which commonly reside on street surfaces have been found to
contribute substantially to urban pollution when washed into receiving
waters by storm runoff. The research program focused on the following:
* determining the amount and nature of such contaminants
and how their distribution varies with respect to
local factors
• establishing the importance of this source, relative
to other point and non-point sources
• evaluationg the effectiveness of conventional public
works practices in coping with this problem
• proposing potential means of achieving effective
control.
The first part of the project was concerned with problem definition;
i.e., answering the question, "What are the characteristics of street
surface contaminants in terms of potential water pollutants?" Answer-
ing this involved a sizable research effort directed toward:
• determining the constituents of street surface materials
and their sources
« measuring loading intensities of contaminants on streets
« identifying the significance of factors which affect
loading intensities
« defining mechanisms by which contaminants are transported
by rainfall runoff
• determining the effects of such contaminants as pollu-
tants in receiving waters.
The second major part of the project was concerned with answering the
question, "How effective are current public works practices in control-
ling this source of pollution?" This involved examining potential
-------
control techniques as to their effectiveness and operational character-
istics. Primary emphasis was directed toward evaluating conventional
street sweeping equipment and practices. Less emphasis was placed on
such systems as the newly introduced vacuum sweepers, conventional and
special water flushers, catch basins, and specially designed curb and
gutter systems.
The third major part of the study was concerned with answering the ques-
tion, "is street runoff actually a significant source of water pollu-
tion?" This involved comparing its pollutional effects to those attrib-
utable to other sources; primarily, treated municipal waste and storm
runoff in general. For ease of presentation, much of the discussion
centers around the pollutional effects of a hypothetical but rather typ-
ical city.
An important aspect of this study is that it provides a basis for evalu-
ating the significance of this source of water pollution relative to
other pollution sources. For this reason, the study was designed to in-
clude information for communities having a broad range of sizes, geo-
graphical locales, and public works practices. Information was devel-
oped for major land-use areas within the cities (such as residential,
commercial and industrial). A mobile rainmaking device was developed to
simulate rainfall conditions on selected city streets. Runoff was ana-
lyzed for the following pollutants: BOD, COD, total and volatile solids,
kjeldahl nitrogen, nitrates, phosphates, and a range of pesticides and
heavy metals.
In an attempt to correlate pollutant loads on receiving waters to dis-
charge from municipal treatment plants, average hourly discharge
loadings were compared. In general, street runoff was a greater
pollutant than sanitary sewage. Load ratios of street runoff to
treated municipal sewage effluents range from a low of about 5:1 for
BOD to a high of 1800:1 for lead. The only exception to these ratios
occurred in the case of total coliform bacteria where the sanitary
sewage contributed greater coliform numbers than did street runoff.
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Samples were also analyzed to determine the relation between particle
size distribution and specific pollutants. As an example, it was found
that approximately 77% of most of the pollutants were associated with
particles of 840M size and smaller. It was also significant to find
that many of the pollutants did not go into solution but continued to
be identified with particles in the effluent stream. Finally, calcu-
lations made to determine the relative efficiency of street sweepers in
controlling a street surface pollutant indicated a maximum removal range
between 15 and 79 percent of the selected contaminants studied.
Methodology
The analysis program was divided into the following phases:
• Mass spectrographic analyses to determine elemental com-
position of selected samples
• Selected heavy metal analyses of each sample to determine
distribution
• Simulated discharge of road surface contaminants to
receiving water to determine solubilities and toxicities
• Heavy metal distribution by particle size to determine
removal effectiveness of common street sweeping practices
• Heavy metal and common pollution parameter analyses of
grab samples from highway, rural road and airport surfaces
• Organic analyses of selected samples
Phase I - Mass Spectrographic Analyses
Mass spectrographic techniques were used to screen selected street sur-
face contaminant samples to determine their overall elemental compos-
ition. The results of this phase helped determine which heavy metals
should be analyzed in the subsequent phases.
The samples were combined into three major land-use categories for anal-
ysis. These composited samples were representative of residential,
industrial and commercial areas. These divisions were chosen because
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the previous study indicated that this means of dividing samples is the
only one which reflects consistent, significant differences. This is
largely due to the different activities within each land-use category
that contribute to road surface contamination, and to differences in pub-
lic works practices in each of the land-use categories.
Phase II - Atomic Absorption Analyses of Individual Land-Use Samples
The results from Phase I indicated which heavy metals were most abundant.
From this list, those metals having the greatest water pollution poten-
tial were selected for detailed investigations. Each sample collected
in the previous study was then analyzed for the selected metals. A dis-
tribution of each metal was then found by comparing metal loadings from
each land use in each city. Ranges of loadings for each metal that
could be expected for a specific land-use area were also determined.
Phase III - Solubilities and Toxicities of Heavy Metals Associated
With Road Surface Runoff
An overall sample was divided into two size categories (<246p, and >246n,)
which represent material effectively removed by street sweepers and mat-
erial usually not removed by street sweepers. These two samples, plus
an undivided control sample were added to dechlorinated tap water making
a solid concentration representative of normal storm water. These sam-
ples were aerated for a period of twenty-five days with water samples
withdrawn at one, five, and twenty-five day intervals, and analyzed for
dissolved heavy metal content and toxicities. The results from this
study phase were used to determine the solubilities of the various
metals and corresponding toxicities of the mixtures.
Phase IV - Particle Size Distribution Of Heavy Metals Associated
With Road Surface Particulates
Material was combined into samples from several cities representative
of geographical areas of the country. Metal analyses were then per-
formed on these samples after they were divided into several size
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ranges. These results enabled predictions to be made on the removal
effectiveness of the metals by current street sweeping methods.
Phase V - Additional Analyses on Highway, Rural Road and Airport
Surfaces
Additional sampling was conducted on rural roads, freeways and on air-
port grounds in northern California. Several highways were sampled
and the collected material was combined for analyses. The same pro-
cedure was used for the rural road and airport samples, except that
since only one airport was selected, several different locations on
the airport grounds were sampled. The pollution parameters analyzed
included: BOD , COD, kjeldahl nitrogen, nitrates, phosphates, plus
D
selected heavy metals.
Phase VI - Organic Analyses
Certain organic analyses were performed during the course of this study.
In conjunction with the Phase I mass spectrographs, organic analyses
were performed on the three major land-use samples. They were also
performed on the sized samples Phase III solubility tests. The analyses
performed included: tanins and lignins, carbohydrates, organic acids,
MBAS (methylene blue active substances), grease and oil, plus the
quantities of hydrocarbons and fatty matter in the grease and oil.
PCBs (polychlorinated biphenols) and certain pesticides were also ana-
lyzed.
10
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SECTION IV
MASS SPECTROGRAPHIC ANALYSES
Objectives
To determine the overall elemental composition of street surface con-
taminants and compare the respective compositions for residential, in-
dustrial and commercial land-use areas.
Background
Before an orderly analytical plan could be devised to further determine
the heavy metal composition of the samples, initial screening tests by
mass spectrographic techniques were required. These tests resulted in
the complete breakdown of the samples to their elemental composition.
From these lists, heavy metals that are thought to have water pollu-
tion effects, at the detected concentrations, were chosen to be further
analyzed in each of the collected samples. These initial samples were
combined into major land-use combinations, prior to analyses, in order
to detect any major differences in elemental composition possibly
caused by different activities in each land-use area.
Methods of Analysis
The samples were combined into three major land-use composites by di-
viding the previously collected samples into residential, industrial
and commercial categories. These categorized samples were then inter-
nally mixed by combining identical weights of each sample. The three
samples were then shipped to a private laboratory which specializes
in mass spectrographic analyses. There the samples were screened and
all materials greater than 1/4 in. were removed. The remaining mat-
o
erial was ignited at 500 C, crushed, split to 1 gram samples and
ground to a <200 mesh (74 u) powder, then finally subjected to standard
mass spectrographic techniques. Because of the uniqueness of the samples,
several heavy metal values were verified using atomic absorption tech-
niques .
11
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Table 1
ELEMENTAL COMPOSITION OF STREET SURFACE CONTAMINANTS AS DETERMINED BY MASS SPECTROGRAPH TECHNIQUES
CO
ELEMENT
Aluminum
Ant i mony
Arseni c
Barium
Beryllium
Bismuth
Boron
Bromine
Cadmium
Calcium
Cerium
Cesium
Chlorine
Chromium
Cobalt
Copper
Dysprosium
Erbium
Europium
Fluorine
Gadolinium
Gallium
Germanium
Gold
Hafnium
Holmium
Indium
Iodine
Iridium
Iron
Lanthanum
Lead
Lithium
Lutetium
Magnesium
Manganese
Mercury
Molybdenum
SYMBOL
Al
Sb
As
Ba
Be
Bi
B
Br
Cd
Ca
Ce
Cs
Cl
Cr
Co
Cu
Dy
Er
Eu
F
Gd
Ga
Ge
Au
Hf
Ho
In
I
Ir
Fe
La
Pb
Li
Lu
Mg
Mn
Hg
Mo
RESIDENTIAL
(mg/kg)
V!
2
20
200
0.2
0.2
10
20
< 2
M
20
1
200
200
5
100
2
1
1
1
2
2
< 1
< 0.5
5
0.5
< 0.2
0.2
< 0.5
M
20
2,000
5
0.2
M
200
< 1
20
INDUSTRIAL
(mg/kg)
f
5
10
200
2
0.2
10
20
< 2
M
20
1
200
500
5
100
2
1
1
5
2
2
< 1
< 0.5
10
0.5
< 0.2
0.2
< 0.5
M
10
5,000
5
0.2
M
200
< 1
2O
COMMERCIAL
(mg/kg)
(,
2
20
200
0.2
0.2
10
50
< 2
a
20
1
200
100
5
100
2
1
0.5
0.5
2
2
< 1
< 0.5
2
0.5
< 0.2
0.2
< 0.5
M
10
5,000
5
0.2
M
200
< 1
5
RESIDENTIAL
(Ib/curb mi)
t:
.002
.024
.240
< .001
< .001
.012
.024
< .002
M
.024
.001
.24
.24
.006
.12
.002
.001
.001
.001
.002
.002
< .001
C .001
.006
< .001
< .001
< .001
< .001
M
.024
2.4
.006
C .001
M
.24
< . 001
.024
INDUSTRIAL
(Ib/curb mi)
M
.014
.028
. 56
.006
.001
.028
.056
< .006
M
.056
.003
.56
1.4
.014
.28
.006
.003
.003
.014
.006
.006
< .003
< .002
.028
.002
< .001
.001
t .002
M
.028
14
.014
.001
M
.56
.003
. O56
COMMERCIAL
(Ib/curb mi )
r,
.001
.006
.058
< .001
< .001
.003
.015
< .001
M
.006
< .001
.058
.029
.001
.029
.001
< .001
< .001
< .001
.001
.001
< .001
< .001
.001
< .001
< .001
< .001
< .001
M
.003
1.4
.001
< .001
M
.058
< .001
. 001
RESIDENTIAL
(10~3 lb'1000 ft )
r*
024
.24
2.4
.002
.002
.12
.24
.024
I1
.24
.012
2.4
2.4
.06
1.2
.024
.012
.012
.012
.024
.024
< .012
< .006
.06
.006
.002
.002
< .006
M
.24
24
.06
.002
N
2.4
< . 012
. 24
INDUSTRIAL
(10~3 lb'1000 ft2)
t,
. 14
. 2H
5.5
.055
.006
.28
.55
< .055
1
.55
.028
5.5
14
.13
2.8
.055
.028
.028
.14
.055
.055
< .028
< .014
.27
.014
< .006
.006
< .014
M
.27
140
.14
.006
M
5.5
< .028
.55
m'.lMEFK IAL
(10 1 b 1000 1 I )
f
007
066
.66
< .001
< .001
.033
. 17
< . 007
.066
.003
.66
.33
.017
.33
.007
.003
.002
.002
.007
.007
< .003
< .002
.007
.002
< .001
< .001
< .002
M
.033
17
.017
< .001
M
.66
< .003
. O17
-------
Table 1
ELEMENTAL COMPOSITION OF STREET SURFACE CONTAMINANTS AS DETERMINED BY MASS SPECTROGHAPH TECHNIQUES (continued)
ELEMENT
Neodymium
Nickel
Niobium
Osmium
Palladium
Phosphorus
Platinum
Potassium
Praseodymium
Rhenium
Rhodium
Rubidium
Ruthenium
Samarium
Scandium
Selenium
Silicon
Silver
Sodium
Strontium
Sulfer
Tantalum
Tellurium
Terbium
Thallium
Thorium
Thulium
Tin
Titanium
Tungsten
Uranium
Vanadium
Ytterbium
Yttrium
Zinc
Zirconium
SYMBOL
Nd
Ni
Nb
Os
Pd
P
Pt
K
Pr
Re
Rh
Rb
Ru
Sm
Sc
Se
Si
Ag
Na
Sr
S
Ta
Te
Tb
Tl
Th
Tm
Sn
Ti
W
U
V
Yb
Y
Zn
Zr
RESIDENTIAL
(ng/kg)
20
100
10
< 0.5
< 0.5
200
< 1
M
2
< 0.5
< 0.5
10
< 0.5
2
5
< 2
M
< 0.5
10,000
1,000
500
2
< 2
0.5
< 0.5
2
0.2
20
2,000
1
2
5
1
5
100
500
INDUSTRIAL
(mg/kg)
10
100
10
c 0.5
< 0.5
100
< 1
M
2
< 0.5
< 0.5
10
< 0.5
2
20
< 2
M
< 0.5
10,000
200
500
2
< 2
0.5
< 0.5
1
0.2
20
2,000
< 0.5
5
50
1
10
100
1,000
COMMERCIAL
(mg/kg)
10
50
10
< 0.5
< 0.5
100
< 1
M
2
< 0.5
< 0.5
10
< 0.5
2
5
< 2
M
< 0.5
10,000
100
500
1
< 2
0.5
< 0.5
1
0.2
20
2,000
1
0.5
50
1
10
100
200
RESIDENTIAL
(Ib/curb mi)
.024
.12
.012
< .001
< .001
.24
< .001
M
.002
< .001
< .001
.012
< .001
.002
.006
< .002
M
< .001
12
1.2
.60
.002
< .002
< .001
< .001
.002
< .001
.024
2.4
.001
.002
.006
.001
.006
.12
.60
INDUSTRIAL
(Ib/curb mi)
.028
.28
.028
< .002
< .002
.28
.003
M
.006
< .002
< .002
.028
< .002
.006
.056
< .006
M
< .002
28
.56
1.4
.006
< .006
.002
< .002
.003
.001
.056
5.6
< .002
.014
.14
.003
.028
.28
2.8
COMMERCIAL
(Ib/curb mi)
.003
.015
.003
< .001
C .001
.029
< .001
M
.001
< .001
< .001
.003
< .001
.001
.001
< .001
M
< .001
2.9
.029
.14
< .001
< .001
< .001
< .001
< .001
< .001
.006
.58
< .001
< .001
.015
< .001
.003
.029
.058
RESIDENTIAL
(10~3 lb/1000 ft2)
.24
1.2
.12
< .006
< .006
2.4
< .012
M
.024
< .006
< .006
.12
< .006
.024
.060
< .024
M
< .006
120
12
6.0
.024
< .024
.006
< .006
.024
.002
.24
24
.012
.024
.062
.012
.061
1.2
6.0
INDUSTRIAL
(10~3 lb/1000 ft2)
.22
2.8
.28
< .014
< .014
2.8
< .028
M
.055
< .014
< .014
.28
< .014
.055
.55
< .055
M
< .014
280
5.5
14
.055
< .055
.014
< .014
.028
.006
.55
55
.014
.14
1.4
.028
.28
2.0
28
COMMERCIAL
(10"3 lb/1000 ft2)
.033
.17
.033
< .002
< .002
.33
< .003
M
.007
< .002
< .002
.033
< .002
.007
.017
C .007
M
< .002
33
.33
1.7
.003
< .007
.002
< .002
.003
< .001
.066
6.6
.003
.002
.17
.003
.033
.33
.66
* M = major constituent.
-------
Results
Table 1 reports the results of the mass spectrographic analyses. The
values are reported for each of the three land-use samples—residential,
industrial and commercial—and for each of three units mg/kg, Ib/curb
mile and lb/1000 ft2 (in order to be consistent with the previous re-
port) . The mg/kg values represent the strengths of the samples, while
o
the Ibs/curb mile and lbs/1000 ft represent surface loadings of the
material.
The loadings are obviously greatly influenced by the amount of road sur-
face particulates found in a given area. When comparing the character-
istics of the particulate material for different land-use areas, the
mg/kg values should therefore be used. The surface loading values
should be used when rough estimates of the amount of material on the
streets is desired. Refer to a later section in this report for a dis-
cussion of the amounts of this material removed by normal street sweep-
ing practices.
The values designated by M in Table 1 refer to major components of the
street surface material. These elements make up greater than 1%
(10,000 ppm) of the material. The corresponding loading values for "M"
designations are shown in Table 2.
Table 2
LOADING VALUES FOR "M" DESIGNATED ELEMENTS FROM TABLE 1
(note that all values are "'greater than")
Residential
Industrial
Commercial
Ib/curb mile
> 12
> 28
> 2.9
10~3 lb/1000 ft2
> 120
> 280
> 29
14
-------
Table 3 summarizes the most abundant elements found in the samples.
From this list, heavy metals to be analyzed in each individual s.ample
were chosen.
Table 3
ABUNDANT ELEMENTS FOUND IN STREET CONTAMINANT SAMPLES
10,000 mg/kg
500-0.0,000 mg/kg 100-600 mg/kg
Aluminum
Calcium
Iron
Magnesium
Potassium
Silicon
Sodium
Lead
Sulfur
Titanium
Zirconium
Barium
Chlorine
Chromium
Copper
Manganese
Nickel
Phosphorus
Strontium
Zinc
Table 4 lists the metals chosen for further analysis. Most of the
elements occurring in concentrations greater than 10,000 mg/kg were
not analyzed because they are mostly naturally occurring. Cadmium,
arsenic and mercury were also chosen, not because of their abundance,
but because of their high toxic potential. The elements of intermed-
iate concentration, except sulfur, were found to be higher in concen-
tration than expected. The concentrations of these three elements—
lead, titanium and zirconium—were confirmed by independent methods
(atomic absorption).
15
-------
Table 4
METALS CHOSEN TO BE ANALYZED IN FURTHER DETAIL
Arsenic Iron Nickel
Cadmium Lead Strontium
Chromium Manganese Titanium
Copper Mercury Zinc
Zirconium
Tables 5 and 6 list the elements that were found to have substantial
(>10 times) differences in strengths (mg/kg) and loadings (Ibs/curb
mile) between the different land uses. It is seen that the strengths
of the industrial sample is greatest for all elements except strontium,
while the strengths of the commercial sample is least for all elements
except vanadium. These trends are most likely associated with activity
within land uses and not to public works practices. A difference in
frequency of cleaning or a difference in cleaning process cannot dra-
matically change the elemental strengths of the street surface particu-
lates, but will obviously affect the amounts of particulates on the
streets.
Table 5
ELEMENTS HAVING SUBSTANTIAL (>10 TIMES) STRENGTH
DIFFERENCES BETWEEN DIFFERENT LAND-USE SAMPLES
(mg/kg)
ELEMENT
Beryllium
Fluorine
Strontium
Uranium
Vanadium
RESIDENTIAL
0.2
1
1000
2
5
INDUSTRIAL
2
5
200
5
50
COMMERCIAL
0.2
0.5
100
0.5
50
16
-------
For all elements, the loading values (Ibs/curb mile) are least for the
commercial sample. All loading values, except for strontium, for the
industrial sample are greatest. These trends are most likely due to
differences in cleaning frequencies between the land uses. It is com-
mon practice for public works departments to clean commercial areas
every day, while some industrial areas are only cleaned once every sev-
eral weeks. The deviations in strengths of the samples also help to
amplify these loading differences.
Table 6
ELEMENTS HAVING SUBSTANTIAL (>10 times) LOADING
DIFFERENCES BETWEEN DIFFERENT LAND-USE SAMPLES
ELEMENT
Antimony
Barium
Chromium
Cobalt
Fluorine
Hofnium
Lead
Lithium
Molybdenium
Nickel
Scandium
Strontium
Sulfur
Uranium
Zirconium
RESIDENTIAL
0.002
0.240
0.240
0.006
0.001
0.006
2.4
0.006
0.024
0.12
0.006
1.2
0.60
0.002
0.60
LB/CURB MILE
INDUSTRIAL
0.014
0.56
1.4
0.014
0.014
0.028
14
0.014
0.056
0.28
0.056
0.56
1.4
0.014
2.8
COMMERCIAL
0.001
0.058
0.029
0.001
<0.001
0.001
1.4
0.001
0.001
0.015
0.001
0.029
0.14
<0.001
0.058
17
-------
SECTION V
ATOMIC ABSORPTION ANALYSES OF INDIVIDUAL LAND-USE SAMPLES
Objectives
To determine the distribution and range of heavy metal strengths and
loadings by analyzing each of the previously collected land-use samples.
Background
By utilizing the results from the previous phase, selected heavy metals
were chosen that have high water-pollution potential. These metals
were then analyzed in each of about 75 samples which were collected
nationwide in the previous study. A good indication of the range of
values that can be expected for a specific land use can be acquired by
examining the results. A geographical distribution of the metals can
also be studied by examining these data. These two objectives are
useful when attempting to apply the results of this study to a situ-
ation that was not tested, and to determine more accurately the extent
of heavy metal pollution resulting from road surface runoff.
Methods of Analysis
A sub-study was conducted to determine the best method to prepare the
solid samples prior to atomic absorption analysis. The variables in-
cluded: sample volume, grinding time (and therefore physical size),
digestion solution and digestion time. The samples were not prelim-
inarily ashed in order to keep volatile metal losses to a minimum.
The atomic absorption unit utilized in this study was a Perkin-Elmer
Model 306 with automatic burner controls. The hollow cathode lamps
were also of Perkin-Elmer manufacture. Multiple-element lamps were
used as much as possible to reduce the time required for analyses.
The individual samples were ground in a Pica ball mill for five minutes.
One gram of pulverized sample and several glass beads were added to a
19
-------
a reflex condenser apparatus, along with 20 ml of concentrated HCL and
20 ml of distilled water. This mixture was simmered for one hour and
then allowed to cool. The sample was then filtered through a 0.45u,
membrane filter to remove solid material which may clog the orifice
on the atomic absorption unit. The sample volume was then diluted to
50 ml with distilled water. The samples were analyzed for each metal
using the procedures recommended in the Perkin-Elmer "Procedures Manual
These component land uses are defined as follows:
Residential:
LOS low income/old neighborhood/single family residences
MNS medium income/new neighborhood/single family residences
MOS medium income/old neighborhood/single family residences
LOM low income/old neighborhood/multiple family residences
MOM medium income/old neighborhood/multiple family residences
Industrial:
LI light industry
MI medium industry
HI heavy industry
Commercial:
SC suburban shopping center
CBD central business district
The cities sampled include: San Jose, Phoenix, Bucyrus (Ohio),
Milwaukee, Baltimore, Tulsa, Atlanta and Seattle. San Jose and Phoenix
were sampled twice, once during the winter (first) and once during the
summer (second).
Refer to Appendix D for a more complete description of these land uses,
along with detailed descriptions of each individual test site. Param-
eters are recorded such as test date, location, street width, pavement
material and condition, gutter and curb material, area type adjacent
to parking strip (lawn, etc), sidewalks presence and material, area be-
yond sidewalks, traffic density, average traffic speed, minimum dis-
tance of traffic to curb, days since last major rain, days since last
cleaned, and cleaning method utilized.
20
-------
Results
The results of this phase are reported in Tables 7 through 35. The results
are shown for each test site, with numerical averages for each land use and
weighted averages for each city. The weighted averages are based on the
areas of each land use located within each city. Residential, industrial,
commercial and overall averages and ranges are also included. The categorical
land-use averages are determined by averaging the component land uses in the
following manner:
Industrial :
rir»TnmoYir» i al •
5
LI-HWI+HI
3
SC4CBD
The metals analyzed and reported include: cadmium, chromium, copper, iron,
manganese, nickel, lead, strontium and zinc.
Mercury and arsenic were analyzed, but their results are not reported.
Mercury values showed substantial reductions due to the storage time
to which the samples were subjected. Mercury values obtained when the
samples were fresh were between 10 and 300 mg/kg, and after 9 to 12
months' storage the values were between 1 and 20 mg/kg, with an over-
all average reduction in strength of about 50 fold. The arsenic values
were less than the detection limit of the apparatus, with all samples
being less than 50 mg/kg arsenic. (The sample preparation procedure di-
luted all samples 50 to 1; 50 grams of solution for one gram of solid.)
21
-------
to
co
Table 7
Concentration of Cadmium (mg/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS MNS MOS LOM MOM LI MI HI SC CBD
3.5 4.5 3.5 3.4 2.2 5.0 2.6
4.0 8.8 5.5 6.0 11 1.7 2.0 6.6
4.2 0.60 1.4 2.3 6.3 1.6 2.3 3.9
3.0 2.6 1.6 4.7 4.0
6.1 5.5 8.8 5.2 8.2 8.8 6.8 3.7 25
6.0 5.4 2.0 3.7 4.0 3.1 4.9
0.0 0.0 1.5 0.4 6.4 0.0 5.3
0.95 1.3 2.4 2.8 0.0 9.3 1.6
1.1 0.0 0.30 0.8 3.1 0.3 6.4
0.0 1.3 3.4 1.6 1.4 1.5 2.3
2.5 3.3 2.8 3.6 3.1 4.4 3.2 4.7 3.7 6.5
AVERAGE RANGE
RESIDENTIAL 3.1 0 -> 8.8
INDUSTRIAL 4.1 0 -» 11
COMMERCIAL 5.1 0 -> 25
OVERALL 3.8 0 -* 25
WEIGHTED
AVERAGE
3.5
7.2
2.7
2.7
8.0
4.3
1.1
1.7
0.76
1.1
-------
to
w
Table 8
Concentration of Chromium (rag/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
325
203
130
132
295
182
186
185
233
208
MNS MOS LOM
295 325
215 159
153 141
138 178
290 120 210
245 75
127
150
111 165
250 239
192 183 188
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
300
238 208
125
215 760
306
162
24 138
193 188
254 239
175 288
AVERAGE
189
279
226
209
MI HI
285
256
179 128
335 159
290 345
194
275 585
74
310
244 304
RANGE
24 -* 325
74 -» 760
63 -» 430
24 -» 760
SC CBD
325 320
168 190
177 190
264 356
430 310
100 207
63 135
71
247 266
205 247
WEIGHTED
AVERAGE
304
211
147
180
273
245
220
112
141
243
-------
CO
Table 9
Concentration of Copper (mg/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
83
150
83
91
130
150
160
99
80
110
MNS MOS LOM
33 96
140 39
120 170
66 94
120 120 120
53 34
70
71
52 74
67 100
81 94 90
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
87
53 100
72
190 280
71
140
66 110
46 120
48 110
79 130
AVERAGE
91
120
170
120
MI HI
67
38
120 170
120 79
210 150
92
38 190
64
32
87 150
RANGE
33 -+ 190
32 -* 280
25 -> 810
25 -» 810
SC
80
25
120
210
96
30
66
99
63
88
WEIGHTED
AVERAGE
110 71
69 120
810 160
90
290 160
84 75
300 120
96 91
67
210 89
250
-------
to
Table 10
Concentration of Iron (mg/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS MNS MOS
27,000 21,000
23,000 21,000
15,000 18,000
13,000 15,000 22,000
24,000 15,000
48,000 26,000
24,000 13,000
20,000 17,000
21,000 11,000
27,000 23,000
24,000 18,000 20,000
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
23,000 24,000
17,000 23,000 20,000
14,000 15,000
19,000 18,000 31,000
11,000 17,000
16,000
1,400 15,000
20,000 25,000 24,000
37,000 59,000 27,000
20,000 24,000 22,000
AVERAGE
21,000
28,000
24,000
24,000
MI HI SC CBD
26,000 44,000 16,000
24,000 15,000 15,000
22,000 15,000 34,000 25,000
43,000 20,000
25,000 53,000 23,000 40,000
22,000 23,000 30,000
14,000 72,000 12,000 20,000
8,100 8,800 11,000
22,000 5,000
42,000 32,000
23,000 40,000 23,000 24,000
RANGE
1,400 -» 48,000
8,100 -» 72,000
5,000 -* 44,000
1,400 -» 72,000
WEIGHTED
AVERAGE
24,000
21,000
18,000
21,000
24,000
25,000
24,000
12,000
15,000
29,000
-------
to
Table 11
Concentration of Manganese (mg/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
450
320
280
420
560
210
430
700
430
420
MNS MOS LOM
350 450
680 280
250 230
370 490
430 150 290
470 230
280
520
450 370
460 490
420 370 330
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM
440
290
270
100
420
440
330
AVERAGE
370
590
400
440
LI
500
430
830
490
300
440
460
490
490
MI HI
600
330
270 310
620 470
680 1,600
450
240 1,100
180
400
420 870
RANGE
100 -» 700
180 -» 1,600
160 -» 770
100 - 1,600
sc
410
360
390
500
540
290
160
280
440
370
WEIGHTED
AVERAGE
470 460
380 540
300 280
470
770 480
500 460
280 350
250 340
490
430 460
420
-------
to
-J
Table 12
Concentration of Nickel (mg/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS MNS MOS LOM MOM LI
85 100 80 110
0 25 0 5.0 6.0
33 26 0 18
13 36 6.5
55 2.0 45 18 37
120 75 30 120
8.5 7.0 19
32 1.0 0 24
11 0 2.5 6.5 18
39 29 40 39 20
38 36 13 28 14 44
AVERAGE
RESIDENTIAL 26
INDUSTRIAL 37
COMMERCIAL 52
OVERALL 34
MI HI SC CBD
93 93 110
1.0 7.0 6.0
21 30 37 30
35 5.5
12 14 6.6 51
93 140 83
12 84 12 18
26 10 29
23 170
40 39
35 33 57 46
RANGE
0 -> 120
1.0 -> 120
6.0 -» 170
0 -+ 170
WEIGHTED
AVERAGE
96
15
22
17
31
87
19
9
11
32
-------
Table 13
Concentration of Lead (mg/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
bO
00 Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
2,400
1,200
790
350
5,700
280
1,100
340
1,700
1,500
MNS MOS LOM
2,100 2,000
970 3,700
970 580
430 1,600
1,000 730 1,700
3,900 600
480
970
220 2,000
2,500 3,000
1,200 1,600 1,900
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
2,000
3,600 2,500
470
1,500 10,000
2,700
740
230 1,100
2,900 2,100
2,600 1,100
1,900 2,800
AVERAGE
1,600
1,600
3,600
2,000
MI HI
3,500
1,200
660 360
780 260
1,800 310
1,500
1,400 940
65
1,700
1,400 470
RANGE
230 -» 5,700
65 -» 10,000
0 -» 10,000
0 -» 10,000
SC CBD WEIGHTED
AVERAGE
7,600 3,500 2,700
1,600 3,200 1,500
2,200 2,700 830
890
2,100 5,700 2,200
10,000 5,100 3,400
2,000 3,900 660
2,400 1,300 740
0.0 620
4,700 3,300 2,100
3,600 3,600
-------
to
Table 14
Concentration of Strontium (rag/kg),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
19
17
21
33
28
2.5
78
25
13
26
MNS MOS
5.0
13
76
33 41
23 4.5
9.0
4.0
110
12
9.0
32 18
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
17 0
13 12 15
24 20
24 6.0 34
21 13
5.5
5.5 93
23 15 15
8.0 10,000* 16
19 12 24
AVERAGE
21
22
18
21
MI HI
7.5
12
20 9
24 17
33 38
18
14 2.5
77
10
24 17
RANGE
2.5 -» 78
0 -» 93
0 -» 37
0 ^ 93
SC
10
11
7.0
33
13
5.0
38
25
0
16
WEIGHTED
AVERAGE
20 8.9
15 14
20 28
33
25 21
15 16
13 4.8
37 63
16
15 11
20
* Not included in average or range.
-------
Table 15
Concentration of Zinc (mg/kg),
Distribution bv Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
320
290
300
190
810
270
350
350
460
370
MNS MOS LOM
260 370
330 210
250 210
110 390
760 730 630
420 210
180
180
130 250
460 660
290 530 360
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM
490
210
490
220
290
410
350
AVERAGE
380
360
520
400
LI
350
230
780
340
320
360
360
480
400
MI HI
450
210
370 220
200 140
410 300
280
310 880
160
150
280 390
RANGE
110 -» 810
140 -» 880
190 -* 1,100
110 -» 1,100
SC CBD
410 600
720 335
320 650
510 1,000
380 510
320 1,100
190 420
400
390 500
400 640
WEIGHTED
AVERAGE
360
340
280
250
640
400
330
240
210
480
-------
Table 16
Loading of Total Solids (lb/1,000 ft ),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
6.31
5.80
12.36
26.9
6.93
8.55
1.8
21.91
8.77
11.03
MNS MOS LOM
2.17 8.6
1.36 14.69
3.47 9.15
6.49 30.69
13.77 23.78 14.21
2.48 6.27
4.46
11.77
6.02 18.7
4.13 6.77
5.77 19.53 11.19
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
12.0
2.37 3.43
65.56
4.46 16.39
92.4
.59 43.84
2.88 13.02
6.31 3.85
2.94 14.96
12.15 24.98
AVERAGE
11.93
27.62
3.31
14.64
MI HI
8.4
10.0
5.23 155.54
15.77 25.76
10.86 3.05
8.99
2.27 5.65
4.4
19.53
10.39 47.5
RANGE
1.36 -> 65.56
3.05 -* 43.84
0.59 -» 4.84
0.59 - 65.56
SC
3.49
4.84
2.66
.59
1.51
7.3
3.3
2.42
3.65
3.30
CBD
2.0
1.60
3.3
1.29
11.55
1.03
2.11
2.97
4.04
3.32
WEIGHTED
AVERAGE
6.88
4.88
35.13
21.01
11.99
46.81
6.31
5.67
13.17
7.87
-------
Table 17
Loading of Cadmium (lb/1,000 ft2),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
CO
to
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.00001
.00002
.00004
.00008
.00004
0
.000001
.00002
0
.000023
MNS MOS LOM
.000009 .00003
.00001 .00008
.000002 .00001
.00001 .00004
.00008 .0001 .0001
.00001 .00001
0
.00001
0 .000005
.000005 .00002
.000014 .000048 .000036
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM
.00001
.0001
.00002
.000006
.000005
.000004
.000024
AVERAGE
.000029
.00006
.000039
.000034
LI MI HI
.00004 .00001
.00003 .00001
.00003 .0002
.00007 .0001
.0001 .00009 .00002
.0003 .00003
.00006 .0000009.00003
.00003 0
.00001 .000005
.00002
.000073 .000027 .00008
RANGE
0 -> .0001
0 -> .0003
0 - .0005
0 -* .0005
WEIGHTED
SC CBD AVERAGE
.00001 .000005 .00002
.000009 .00001 .000035
.000006 .00001 .000094
.000056
.000002 .00003 .000095
.000004 .0005 .0002
0 .000005 .0000069
.00003 .000003 .0000096
.00001 .00001
.000005 .000009 .0000086
.0000084 .000071
-------
CO
w
Table 18
Loading of Chromium (lb/1,000 ft2),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.002
.0011
.0016
.0035
.002
.0015
.00033
.004
.002
.002
MNS MOS
.00064
. 00029
.00053
.00089 .0054
.0039 ,0028
.0006
.00053
.0017
.00066
.001
.001 .003
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
.0027 .0036
.0023 .00056 .00071
.0092 .0081
.0029 .00095 .012
.00047 .028
.0071
.000069 .0017
.003 .0012 .00072
.0016 .00074 .0035
.0031 .0019 .0071
AVERAGE
.0022
.0054
.00072
.0026
MI HI
.0023
.0025
.00093 .019
,0052 .004
.0031 .001
.0017
.00062 .0033
.00032
.006
,0025 .0068
RANGE
.000069 -» .0092
.00032 -» .028
.00015 -» .0011
.000069 -> .028
sc
,0011
.00081
.00047
. 00015
.00064
.00073
.0002
.00017
.0009
.00057
WEIGHTED
AVERAGE
.00064 .002
.0003 .001
.00062 .0051
.0037
.00045 .0032
.0035 .011
.00021 .0013
.00028 .00063
.0018
.001 .0019
.00087
-------
Table 19
2
Loading of Copper (lb/1,000 ft ),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.0005
.0008
.001
.002
.0009
.001
.0002
.002
.0007
.0010
MNS MOS LOM
.00007 .0008
.0001 .0005
.0004 .001
. 0004 . 002
.001 .002 .001
.0001 .0002
.0003
.0008
.0003 .001
. 0002 . 0006
.00038 .0014 .00072
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.001
.0001 .0003
.004
. 0008 . 004
.006
.006
.0001 .013
.0002 .0004
.0001 .001
.00088 .0032
AVERAGE
.00087
.0037
.00038
.00066
MI HI
.0005
.0003
.0006 .026
.001 .002
. 002 . 0004
.0008
.00008 .001
.0002
.0006
.00067 .0073
RANGE
.00007 -> .004
,00008 -» .026
.0001 -» .002
.00007 -> .026
SC CBD
.0002 .0002
.0001 .0001
. 0003 . 002
.0001 .0003
. 0001 . 0009
. 0002 . 0003
.0002 .0002
.0002
.0002 .0008
.00017 .0006
WEIGHTED
AVERAGE
.0004
.0005
.005
.001
.001
.003
.0007
.0005
.0008
.0007
-------
Table 20
Loading of Iron (lb/1,000 ft ),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
w San Jose II
Ol
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.170
.133
.185
.349
.332
.205
.036
.460
.236
.234
MNS MOS LOM
.045 .197
.028 .249
.062 .128
.097 .675
.330 .356 .269
.064 .068
.057
.200
.066 .374
.094 .250
.105 .375 .219
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.288
.054 .068
.983
.080 .508
1.57
.701
.040 .195
.157 .092
.173 .403
.247 .478
AVERAGE
.236
.525
.079
.285
MI HI
.218
.240
.115 2.33
.678 .515
.271 .161
.197
.031 .406
.035
.429
.246 .853
RANGE
.028 -> .983
.031 -» 2.33
.012 -> .346
.012 -* 2.33
SC
.153
.072
.090
.013
.034
.087
.029
.012
.153
.071
pRn WEIGHTED
C£UJ AVERAGE
.032 .165
. 024 . 102
.082 .632
.441
.051 .287
.346 1.17
.020 .151
.023 .068
.197
.129 .228
.088
-------
Table 21
2
Loading of Manganese (lb/1,000 ft ),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.0028
.0017
.0034
.011
.0038
.0017
.00077
.015
.0037
.0048
MNS MOS LOM
.00075 .0038
.00092 ,0041
.00086 .0021
.0024 .015
.0059 .0035 .0041
.0011 .0014
.0012
.0061
.0027 .0069
.0018 .0033
.0024 .0067 .0036
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM
.001
.019
.0012
.00028
.0026
.0012
.0042
AVERAGE
.0043
.010
.0044
.0054
LI MI HI
.006 .005
.0014 .0033
.0014 .048
.0097 .012
.013 .0073 .0048
.045 .004
.013 .00054 .0062
.0057 .00079
.0017 .0078
.0073
.011 .0049 .017
RANGE
.00028 -» .019
.00054 -» .048
.00027 -» .0021
.00027 -> .048
sc
.0014
.0017
.001
.00029
.00027
.0021
.00052
.00067
.0016
.0010
WEIGHTED
AVERAGE
.00094 .0031
.0006 .0026
.00099 .0098
.021
.00099 .0057
.0057 .021
.00028 .0022
.00052 .0019
.0064
.0017 .0036
.0078
-------
Table 22
Loading of Nickel (lb/1,000 ft ),
Distribution by Land Use
CO
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.00053
0
.0004
.00034
.00083
.000072
.000057
. 00024
.00034
.00031
MNS MOS LOM
.00021 .00068
.000034 0
.00009 0
.00023 .00019
.00075 .000047 .00063
.00018 .00018
.000031
.000011
0 .000046
.00011 .00027
.00017 .00011 .00025
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM
.000011
.0011
.00008
0
.000041
.00011
.00022
AVERAGE
.00021
.00072
.00016
.00029
LI MI HI
.0013 .00078
.00002 .00001
.0001 .0046
.00055 .00014
.0006 .00013 .000042
.011 .00083
.00083 .000027 .00047
.00031 .00011
.000069 .00044
.00029
.00055 .00033 .0013
RANGE
0 -» .00075
.00001 -» .011
.0000038 -» .00095
0 -» .011
SC
.00032
.000033
.000098
.0000038
.00021
.000087
.000033
.00041
.00014
.00014
WEIGHTED
AVERAGE
.00022 .00066
.0000096 .000073
.000099 .00077
.00035
.000065 .00037
.00095 .004
.000018 .00011
.000061 .000051
.00014
.00015 .00025
.00019
-------
Table 23
Loading of Lead (lb/1,000 ft ),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
o° Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.015
.006
.009
.009
.039
.002
.001
.007
.014
.016
MNS MOS
.004
.001
.003
.002 .049
.013 .017
.009
.002
.011
.001
.01
.006 .031
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
.017 .024
.054 .008 .008
.005 .03
.024 .006 .163
.003 .249
.032
0 .014
.037 .018 .008
.02 .007 .016
.021 .011 .069
AVERAGE
.017
.032
.010
.018
MI HI
.029
.012
.003 .055
.012 .006
.019 0
.013
.003 .005
0
.033
.013 .016
RANGE
0 -» .054
0 -> .249
0 -» .058
0 -» .249
SC
.026
.007
.005
.001
.015
.014
.007
0
.017
.010
WEIGHTED
AVERAGE
.007 .018
.005 .007
.008 .029
.018
.007 .026
.058 .159
.004 .004
.002 .004
.008
.013 .016
.011
-------
Table 24
Loading of Strontium (lb/1,000 ft ),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
to
50 Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.00011
.000098
.00025
.00088
.00019
.000021
.00014
.00054
.00011
.00025
MNS MOS LOM
.00001 .00014
.000017 .00019
.00026 .00021
.00021 .0012
.00031 .0001 .00034
.000022 .00013
.000017
.0012
.000072 .00043
.000037.000054
.00023 .00044 .00021
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
0
.000028 .000051
.0013
.000026 .00055
.0012
.00024
.000015 .0012
,000094 .000057
.029* .00023
.00029 .000028
AVERAGE
.0012
.00016
. 000049
.00093
MI HI SC CBD
.000063 .000034 .00004
.00012 .000053 .000024
.0001 .0013 .000018 .000066
.00037 .00043
.00035 .00011 .000019 .000032
.00016 .000019 .00017
.000031 .000014 .000036 .000013
.00033 .00012 .000078
.00019 .00006
0 . 00006
.000021 .00046 .000039 .00006
RANGE
.000010 -> .029
0 -> .0013
0 -» .00017
0 -» .029
WEIGHTED
AVERAGE
. 000061
.000068
.00098
. 00069
. 00025
.00074
. 00003
.00035
.00021
. 000086
* Not included in average or range.
-------
Table 25
Loading of Zinc (lb/1,000 ft"),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.002
.001
.003
.005
.005
.002
.0006
.007
.004
.0032
MNS MOS LOM
.0005 .003
.0004 .003
.0008 .001
.0007 .011
.010 .017 .008
.001 .001
.0008
.002
.0007 .004
.001 .004
.0018 .0096 .0034
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.004
.001 .0007
,013
.002 .012
.031
,,014
.001 .004
.001 .001
,001 .007
.0031 .0092
AVERAGE
.0042
.0070
.0013
.0045
MI HI
.003
.002
.001 .034
.003 .003
. 004 . 0009
.002
.0007 .004
.0007
.002
.0020 .010
RANGE
.0004 -» .017
.0007 -* .034
.003 -» .005
.0004 -* .034
SC CBD
.001 .001
.003 .0005
.0008 .002
.0003 .001
.0005 .005
.002 .001
.0006 .0008
.0009
.001 .002
.0011 ,0016
WEIGHTED
AVERAGE
.002
.001
.009
.005
.007
.018
.002
.001
.002
.003
-------
Table 26
Loading of Total Solids (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
835
768
719
1,850
624
587
115
1,620
463
842
MNS MOS LOM
288 1,138
180 1,940
275 557
410 1,940
1,240 1,380 1,280
197 465
329 31
621
384 1,090
263 536
436 1,194 - 880
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
1,740
314 454
6,940
495 1,300
12,200
3,710
152 1,100
500 264
141 711
1,424 2,685
AVERAGE
895
2,384
281
1,188
MI HI
1,112
1,330
414 12,300
997 1,630
860 242
1,050
168 298
280
1,140
817 3,617
RANGE
31 -» 6,940
168 -> 12,300
25 -» 1,220
31 -. 12,300
SC
463
640
210
63
161
425
25
179
193
262
CBD
265
212
261
68
1,220
60
179
204
193
296
WEIGHTED
AVERAGE
911
646
2,700
1,375
1,030
6,000
433
325
910
455
-------
Table 27
Loading of Cadmium (Ib/curbmi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.0029
.0031
.0030
.0056
.0037
0
.0001
.0018
0
.0022
MNS MOS LOM
.0013 .0040
.0016 .0107
.0002 .0008
.0011 .0031
.0076 .0076 .0113
.0011 .0009
0
.0008
0 .0003
.0003 .0018
.0015 .0037 .0043
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.0059
.0019 .0050
.0160
.0026 .0107
.0450
.0056
.0004 .0031
.0004 .0008
.0002 .0010
.0036 .0096
AVERAGE
.0031
.0066
.0011
.0037
MI HI
.0024
.0023
.0026 .0197
.0047 .0065
.0076 .0016
.0042
.0001 .0019
0
.0003
.0027 .0074
RANGE
0 -» .0160
0 -» .0450
0 -» .0060
0 - .0450
SC CBD
.0023 .0007
.0013 .0014
.0005 .0010
.0002 .0017
.0005 .0060
0 .0003
.0002 .0003
.0011
.0003 .0004
.0007 .0015
WEIGHTED
AVERAGE
.0032
.0047
.0073
.0037
.0082
.0258
.0005
.0006
.0007
.0005
-------
co
Table 28
Loading of Chromium (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.271
.156
.093
.244
.184
.107
.021
.300
.108
.165
MNS MOS LOM
.085 .370
.039 .308
.042 .079
.057 .345
.360 .166 .269
.048 .035
.042
.093
.043 .180
.066 .128
.090 .192 .196
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.522
.075 .094
.868
.106 .988
3.733
.601
.004 .152
.097 .050
.036 .170
.198 .789
AVERAGE
.168
.509
.069
.231
MI HI
.317
.340
.074 1.574
.334 .259
.249 .083
.204
.046 .174
.021
.353
.215 .523
RANGE
.004 -> .868
.021 -» 3.733
.002 -> .378
.002 -» 3.733
SC
.150
.108
.037
.017
.069
.043
.002
.013
.048
.054
CBD WEIGHTED
AVERAGE
.085 .277
.040 .136
.050 .397
.248
.024 .281
.378 1.470
.012 .095
.024 .036
.128
.051 .111
.083
-------
Table 29
Loading of Copper (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.069
.115
.060
.168
.081
.088
.018
.160
.037
.088
MNS MOS
.010
.025
.033
.027 .182
.149 .166
.010
.023
.044
.020
.018
.038 .122
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
.109 .151
.076 .017 .045
.095 .500
.154 .094 .364
.016 .866
.519
.010 .121
.081 .023 .032
.054 .007 .078
.084 .109 .272
AVERAGE
.088
.307
.037
.129
MI HI
.075
.051
.050 2.091
.120 .129
.181 .036
.097
.006 .057
.018
.036
.070 .578
RANGE
.007 -» .500
.006 -> 2.091
.002 -> .211
.002 -» 2.091
sc
.037
.016
.025
.013
.015
.013
.002
.018
.012
.017
WEIGHTED
CBD AVERAGE
.029 .065
.015 .078
.211 .432
.124
.020 .165
.102 .450
.018 .052
.017 .030
.061
.041 .040
.057
-------
Table 30
Loading of Iron (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS MNS MOS LOM
22.55 6.05 26.17
17.66 3.78 32.98
10.79 4.95 7.80
24.05 6.15 42.68
29.76 20.70 24.32
29.95 5.12 5.12
14.09 4.28
2.30 10.56
34.02 4.22 21.80
12.50 6.05 19.83
18.66 8.32 23.14 19.72
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
41.76
7.22 9.08
104.1
8.91 40.30
207.4
59.36
0.21 16.50
12.50 6.34
8.32 19.20
23.54 49.99
AVERAGE
20.24
44.56
7.06
24.44
MI HI
28.91
31.94
9.52 184.5
42.87 32.60
21.50 12.83
23.1
2.35 21.46
2.27
25.08
20.84 62.85
RANGE
0.21 -» 104.1
2.27 -* 207.4
0.22 -» 36.60
0.21 -» 207.4
sc CBD HE™
20.37 4.24 21.86
9.60 3.18 13.57
7.14 6.53 48.60
28.88
1.45 2.72 24.72
3.70 36.60 150.00
5.10 1.20 10.39
0.22 1.97 3.90
0.90 13.65
8.11 6.18 13.20
6.29 7.83
-------
Table 31
Loading of Manganese (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.376
.246
.201
.777
.349
.123
.049
1.134
.199
.384
MNS MOS LOM
.101 .512
.122 .543
.069 .128
.152 .951
.533 .207 .371
.093 .107
.092
.323
.173 .403
.121 .263
.184 .426 .332
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.870
.138 .195
2.013
.134 1.079
5.978
1.113
.015 .484
.210 .121
.062 .348
.429 1.274
AVERAGE
.351
.993
.117
.468
MI HI
.667
.439
.112 3.813
.618 .766
.585 .387
.473
.040 .328
.050
.456
.382 1.324
RANGE
.015 -> 2.013
.040 -» 5.978
.004 -> .610
.004 -» 5.978
sc
.190
.230
.082
.032
.087
.123
.004
.050
.085
.098
WEIGHTED
AVERAGE
.125 .419
.081 .349
.078 .756
.646
.052 .494
.610 2.760
.017 .152
.045 .111
.446
.083 .209
.136
-------
Table 32
Loading of Nickel (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.0710
0
.0237
.0241
.0749
.0050
.0037
.0178
.0181
.0265
MNS MOS LOM
.0288 .0910
.0045 0
.0072 0
.0148 .0126
.0682 .0028 .0576
.0148 .0140
.0023
.0006
0 .0027
.0076 .0214
.0157 .0077 .0267
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM LI
.1914
.0016 .0027
.1249
.0089 .0481
1.464
.0705
0 .0264
.0033 .0048
.0055 .0142
.0240 .2278
AVERAGE
.0201
.1206
.0166
.0400
MI HI
.1034
.0013
.0087 .3690
.0349 .0090
.0103 .0034
.0977
.0020 .0250
.0073
.0262
.0324 .1016
RANGE
0 -> .1249
.0013 -> 1.464
.0003 -» .1013
0 -» 1.464
SC CBD
.0431 .0292
.0045 .0013
.0078 .0078
.0004 .0035
.0225 .1013
.0051 .0011
.0003 .0052
.0304
.0077 .0075
.0135 .0196
WEIGHTED
AVERAGE
.0875
.0097
.0594
.0234
.0319
.5220
.0082
.0029
.0100
.0146
-------
00
Table 33
Loading of Lead (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS MNS MOS
2.00 0.60
0.92 0.17
0.57 0.27
0.65 0.18 3.10
1.24 1.01
3.56 0.77
0.16 0.16
0.13 0.60
0.55 0.08
0.79 0.66
1.04 0.45 1.59
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
2.28 3.48
7.18 1.13 1.14
0.32 3.26
2.18 0.74 13.00
0.28 32.94
2.75
0.03 1.21
2.18 1.45 0.55
1.61 0.37 0.78
2.29 1.16 6.97
AVERAGE
1.31
3.19
1.10
1.66
MI HI SC CBD
3.89 3.52 0.93
1.60 1.02 0.68
0.27 4.43 0.46 0.70
0.78 0.42
1.55 0.08 0.13 0.39
1.58 1.61 6.22
0.24 0.28 0.85 0.23
0.02 0.06 0.23
1.94 0
0.91 0.64
1.32 1.30 0.95 1.25
RANGE
0.03 -» 7.18
0.02 -» 32.94
0 -* 6.22
0 -> 32.94
WEIGHTED
AVERAGE
2.46
0.97
2.24
1.22
2.27
20.40
0.29
0.24
0.56
0.96
-------
Table 34
Loading of Strontium (Ib/curb mi),
Distribution by Land Use
CD
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.0159
.0131
.0151
.0611
.0175
.0015
.0090
.0405
.0060
.0200
MNS MOS LOM
.0014 .0193
.0023 .0252
.0209 .0134
.0135 .0795
.0285 .0062 .0307
.0018 .0098
.0013
.0683
.0046 .0251
.0024 .0043
.0158 .0294 .0183
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
MOM
.0038
.1388
.0030
.0008
.0075
1.410*
.0308
AVERAGE
.0209
.0320
.0042
.0223
LI MI HI
0 .0083
.0058 .0160
.0083 .1107
.0239 .0277
.0442 .0284 .0092
.1586 .0189
.0204 .0027 .0007
.1023 .0216
.0040 .0114
.0114
.0433 .0155 .0371
RANGE
.0008 -» .1388
0 -» .1586
0 -» .0183
0 -* .1586
SC CBD
.0046 .0053
.0070 .0032
.0015 .0052
.0021 .0017
.0021 .0183
.0021 .0008
.0010 .0066
.0045
0 . 0029
.0028 .0055
WEIGHTED
AVERAGE
.0081
.0090
.0756
.0454
.0216
.0960
.0021
.0205
.0146
.0050
* Not included in average or range.
-------
Table 35
Loading of Zinc (Ib/curb mi),
Distribution by Land Use
San Jose I
Phoenix I
Milwaukee
Bucyrus
Baltimore
San Jose II
01
o
Atlanta
Tulsa
Phoenix II
Seattle
NUMERICAL
AVERAGE
LOS
.267
.223
.216
.352
.505
.158
.040
.567
.213
.282
MNS MOS
.075
.059
.069
.045 .757
.942 1.007
.083
.059
.112
.050
.121
.166 .628
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
OVERALL
LOM MOM LI
.421 .609
.407 .154 .104
.117 1.457
.806 .243 1.014
.098 4.148
1.187
.033 .396
.273 .145 .095
.372 .058 .341
.356 .348 .987
AVERAGE
.356
.677
.144
.409
MI HI
.500
.279
.153 2.706
.199 .228
.353 .073
.294
.052 .262
.045
.171
.227 .817
RANGE
.033 -» 1.457
.045 -» 4.148
.005 -* .622
.005 -» 4.148
SC
,190
.461
.067
.032
.061
.136
,005
.072
.075
.122
WEIGHTED
CBD AVERAGE
.159 .328
.071 .220
.170 .756
.344
.068 .659
.622 2.400
.066 .143
.075 .078
.191
.097 .218
.166
-------
The results are reported as mg/kg of metal (strength), pounds per 1000
square feet of street surface (loading) and pounds per curb mile
(loading). Only the first and last units will be discussed here as
both loading units lead to approximately similar conclusions.
From examining Tables 7 through 15, one finds some trends. The in-
dustrial and commercial land uses continually have the most metals on
a strength (mg/kg) basis, while the residential land uses usually have
the least. This is the same conclusion that was made in the first
phase of analyses. On a geographical basis, no clear trends can be
established.
Table 14 does demonstrate an interesting anomaly under Seattle MOM.
It is seen that a tremendous amount of strontium is associated with
this one test site. No other sample even comes to within 1/100 of
this value. Because it is associated with only this test site, this
must be the result of an accidental spill on the roadway. (The
sample was further analyzed and was found to be homogeneous.)
2
Tables 16 through 25 (lbs/1000 ft ) show similar trends with Tables 26
through 35 because they all represent the loading of the particulates as
the most important defining parameter.
Tables 26 through 35 show more substantial trends based on loading
factors. These trends are most likely the result of definite parti-
culate loading patterns as shown on Table 26. The highest loading
values for all metals are almost exclusively associated with indus-
trial land use areas, while the lowest values are found in residen-
tial and commercial areas. On a geographical basis, it is seen that
San Jose, Baltimore and Milwaukee have the highest loading factors,
while Tulsa, Seattle and Atlanta have the lowest factors. All these
metal loading trends are similar to particulate loading trends as
shown in Table 26.
The range of values for each metal in each city and land use is sig-
nificant. Except for occasional zero values, the ranges of metals
51
-------
are usually limited to less than a factor of ten, with averages about a
factor of two to four for mg/kg values. In order to predict amounts of
metals on roadways for a specific city, a loading factor is more useful.
Because of the added variable of particulate loadings, metal loadings have
to be more variable. For loading factors, the range of values often exceeds
a factor of 100 within one city or land-use area. The averaged values are
much better, with the worst ranges not much greater than a factor of ten,
and usually within several fold.
A time element is not included in the results because the sampling pro-
gram was designed to sample test areas at random, without any regard to
when the streets were last cleaned. It can be expected that these
amounts of metals will be found whenever a sample is taken. Available
city records for the test areas indicated that all areas were cleaned,
on the average; about five days prior to the sampling. It can be ex-
pected that these amounts of metals will be washed off the streets dur-
ing the first hour of a rainstorm of moderate intensity, having a peak
intensity of at least 0.5 in/hr (1.27 cm/hr) .
These amounts of metals can cause significant problems during certain
conditions. To help put these metal loadings in perspective, the fol-
lowing discussion compares heavy metal content of road surface runoff
to sanitary sewage for a hypothetical city. The metal loadings used
are the overall averaged values. Metal contents of sanitary sewage are
from Richmond, California sewage treatment plant records for spring
1972 and San Jose-Santa Clara sewage treatment plant records for Jan-
uary 1970. Table 36 defines the hypothetical city parameters, while
Tables 37 and 38 compare the metal content of road surface runoff to
sanitary sewage (Ibs/hr[kg/hr] and mg/1).
52
-------
Table 36
HYPOTHETICAL CITY PARAMETERS
Population:
Total land area:
Land-use distribution:
Residential
Commercial
Industrial
Total street lengths:
Sanitary sewage flow:
100,000 people
14,000 acres
75%
5%
20%
400 curb miles
12 MOD
Table 37
METAL LOADING FROM ROAD SURFACE RUNOFF
COMPARED TO NORMAL SANITARY SEWAGE*
METAL
Lead
Cadmium
Nickel
Copper
Zinc
Iron
Manganese
Chromium
ROAD RUNOFF
(Ib/hr)
600
1.2
10
36
140
7,900
150
80
SANITARY
SEWAGE
(Ib/hr)
0.13
0.0032
0.042
0.17
0.84
54
9.7
12
rvrio- RUNOFF
RATI°- SANITARY
4,600
380
240
210
170
150
15
6.7
* "Hypothetical City" with 0.1 in. rain, lasting for
one hour.
53
-------
Table 38
METAL LOADING FROM ROAD SURFACE RUNOFF
COMPARED TO NORMAL SANITARY SEWAGE FLOW.
METAL
Pb
Cd
Ni
Cu
Zn
Fe
Mn
Cr
ROAD RUNOFF
(mg/1)
6.
0.
0.
0.
1.
83
1.
0.
2
012
10
37
4
6
80
SANITARY
SEWAGE
(mg/1)
0
0
0
0
0
13
2
2
.03
.00075
.01
.04
.20
.3
.8
RUNOFF
SEWAGE
210
16
10
9
7
6
0.7
0.3
(from 0.1 in. rain)
It can be seen that during the peak discharge period, runoff contributes a
substantially greater portion of metals to a receiving body than a normal
sewage treatment plant. If the storm water is collected in a combined system,
this metal content can then possibly affect the biological treatment
systems. Table 39 summarizes metal concentrations necessary to cause
reductions in biological treatment systems. It can be seen that the
necessary dosages required are not supplied by storm water runoff.
54
-------
Table 39
EFFECTS OF HEAVY METALS ON BIOLOGICAL TREATMENT PROCESSES
Cr
Cu
Ni
Zn
5->10%
REDUCTION
IN AEROBIC
TREATMENT
EFFICIENCY
10 mg/1
1
1-.2.5
5-^10
4-HR SLUG
DOSE, CAUSING
REDUCTION IN
COD REMOVAL
>500 mg/1
75
50-^200
160
HIGHEST ALLOWABLE
DOSE FOR
SATISFACTORY
ANAEROBIC
SLUDGE DIGESTION
>50 mg/1
5
>10
10
Interaction of Heavy Metals and Biological Sewage
Treatment Processes," Environmental Health Series,
Water Supply and Pollution Control, USPHS, May 1965.
Table 40 lists the removal efficiencies of various removal techniques
used in sewage treatment plants for some of the heavy metals studied
in this report. There exists removal techniques to abate almost any
heavy metal problem, especially for amounts introduced by road runoff.
In most combined systems, the hydraulic capacity of the treatment plant
is not sufficient to treat the total flow during periods of high runoff
Instead, most of the flow is diverted through overflows without treat-
ment.
55
-------
Table 40
REMOVAL EFFICIENCIES IN SEWAGE TREATMENT PROCESSES*
METAL
As
Cd
Cr+3
Cr+6
Cu
U1
Fe
Hg
Mn
Ni
Pb
Ti
Zn
LINE
REVERSE COAGULATION
OSMOSIS AND
RE-CARBONATION
<10%
50 _ 95
72% 99+
29 11
86 _ 99+
40 _. 99+
<10
45 _ 96
90 -4 99+
90+
90
90 +
SAND CARBON PRIMARY SECONDARY
FILTRATION ABSORPTION SEWAGE SEWAGE
TREATMENT TREATMENT
95% 99% "most"
77 70%
3 97 44 _ 50
60 20 _ 85
30 _ 60
r t .IT
most
76 60 _ 95
* Argo, David G. and Gulp, Gordon L, "Heavy Metals Removal in Wastewater Treatment
Processes" Two part series. Water and Sewage Works (August and September 1972).
-------
SECTION VI
SOLUBILITIES AND TOXICITIES OF HEAVY METALS
ASSOCIATED WITH ROAD SURFACE RUNOFF
Objective
To determine the extent to which heavy metals are in solution in a normal
receiving water environment. To determine the toxicity of the road surface
particulate mixture in a receiving water environment to a specific aquatic
organism (stickleback).
Background
The solution concentrations of heavy metals are important when attempting to
determine the toxicity of metals originating from road surfaces. Toxic limits
(TLm) reported in the literature are almost exclusively concerned with soluble
heavy metal forms. However it would not be realistic to assume that all the
metals in road surface runoff are completely soluble in receiving waters without
definitive laboratory testing.
Because of the problems of synergism and antagonism associated with heterogenous
mixtures such as road surface particulates, it is advisable to measure the
effects of the toxicity directly in laboratory toxicity tests.
Methods of Analysis
A quantity of particulate matter from a composite sample combined from nation-
wide samples was divided into two size ranges. These two size ranges represented
material which is effectively removed by street sweeping practices (>246/u) and
material usually remaining after sweeping (<246jy). These two sized samples
plus an undivided control sample were added to water at a concentration
representative of a moderate rain (0.04 inches for one hour). The mixtures
were aerated and mixed for 25 days at a temperature of 20°C. After 1, 5 and
25 days, samples were withdrawn and bioassays conducted. Filtered samples,
without digestion attempts, were analyzed for heavy metals.
57
-------
Results
Table 41 shows the results of the laboratory analyses. In addition
to the sample analyses, the dilution water heavy metal concentrations were
determined to establish a background value. In order to measure the total
available heavy metal concentrations, heavy metal analyses were also made
on the three dry samples (composite, < 246p. and > 246/z) . By subtracting
the heavy metal concentrations of the dilution water from the heavy metal
concentrations in the liquid samples, the actual change in heavy metal
content of the water column due to the presence of the road surface particulates
can be determined. Since the samples were allowed to settle, and then filtered
prior to analysis, any additional amounts of heavy metals in the sample, over
the initial concentration of the dilution (receiving) water, can be assumed
to be due to an amount of the heavy metals associated with particulate
fractions becoming soluble. Table 41 lists the results of the analyses,
showing the soluble metal concentrations of the dilution water, along with
the soluble metal concentrations of the mixture after one, five and twenty-
five days of mixing for each of the three samples. Also shown in Table 41
are the results of the bioassays conducted on the mixtures. Tables 42-44
present the results, after correction for the dilution water concentrations
for periods of one, five, and twenty-five days of mixing. Values for the
percent of available metal in solution are also given. These values were
determined by comparing the actual concentrations (after correcting for
dilution water values) to theoretical concentrations which would exist if
all the available heavy metals (as determined in the analysis of the dry
particulates) were in solution. These values are appropriately expressed as
percentages. It is seen that several values for metal solubility and
increased concentrations are not given. This is because the sample mixture
actually had a lower concentration of these metals than the dilution water,
as shown in Table 41. These decreases represent a loss of soluble metals
in the water column. This may result from a number of processes. The
soluble metal in the dilution water may have undergone an ion-exchange process,
become sorbed on the roadsurface particulates, or the metal may have become
58
-------
Table 41
to
HEAVY METAL CONCENTRATIONS (AS MEASURED) AND
BIOASSAY RESULTS FOR SIMULATED RECEIVING BODY OP WATER
Arsenic (As)
Cadmium (Cd)
Copper (Cu)
Chromium (Cr)
Iron (Fe)
Mercury (Hg)
Manganese (Mn)
Nickel (Ni)
Lead (Pb)
Strontium (Sr)
Titanium (Ti)
Zinc (Zn)
Zirconium (Zr)
DILUTION WATER
(mg/1)
0.0002
ND
ND (<0.001)
0.002
0.07
0.0005
0.01
ND (<0.02)
ND (<0.02)
ND (<0.01)
ND (<0.1)
0.70
ND (<1.0)
COMPOSITE
(mg/1)
0.0001
0.03
ND (<.002)
0.09
0.0005
0.025
ND
0.04
0.011
ND
0.04
ND
1-DAY
<246U
(rag/1)
0.00002
0.03
ND
0.06
0.0005
0.02
ND
0.03
0.011
ND
0.02
ND
>246U
(mg/1)
0.01
0.04
0.002
0.12
0.0003
0.02
ND
0.03
0.02
ND
0.63
ND
COMPOSITE
(mg/1)
0.007
0.00006
0.15
ND
0.10
0.0004
0.02
0.03
0.04
0.09
ND
0.07
ND
5 -DAY
<246U
(mg/1)
0.000
0.00003
0.005
0.006
0.09
0.0002
0.04
ND
0.025
0.10
ND
0.08
ND
>246(a
(mg/1)
0.003
0.002
0.007
ND
0.05
0.0003
0.02
ND
0.04
0.06
ND
0.47
ND
% survival after
96 hours exposure
100%
100%
100%
100%
100%
100%
100%
-------
Table 41
HEAVY METAL CONCENTRATIONS (AS MEASURED) AND
BIOASSAY RESULTS FOR SIMULATED RECEIVING BODY OF WATER (continued)
en
o
Arsenic (As)
Cadmium (Cd)
Copper (Cu)
Chromium (Cr)
Iron (Fe)
Mercury (Hg)
Manganese (Mn)
Nickel (Ni)
Lead (Pb)
Strontium (Sr)
Titanium (Ti)
Zinc (Zn)
Zirconium (Zr)
% survival after
96 hours exposure
DILUTION WATER
(mg/1)
0.002
ND
ND (246|j
(mg/1)
0.002
<0.001
0.120
0.003
0.06
0.0002
0.04
0.01
ND
0.20
ND
0.17
ND
100%
-------
BIOASSAY
(% survival)
TABLE 42
HEAVY METAL CONCENTRATIONS AND SOLUBILITIES
IN SIMULATED RECEIVING BODY OF WATER
1-DAY SAMPLE
Arsenic (As)
Cadmium (Cd)
Copper (Cu)
Chromium (Cr)
Iron (Fe)
Mercury (Hg)
Manganese (Mn)
Nickel (Ni)
Lead (Pb)
Strontium (Sr)
Titanium Ti)
Zinc (Zn)
Zirconium (Zr)
OVERALL
COMPOSITE
PERCENT OF
CONC. AVAILABLE METAL
mg/1 IN SOLUTION
—
0.0001
0.03
—
0.02
—
0.015
—
0.02
—
—
—
—
—
0.13%
2.7
—
0.01
—
0.38
—
0.37
—
—
—
—
< 246
CONC.
mg/1
—
0.0002
0.03
—
—
—
0.01
—
0.01
—
—
—
—
(j, COMPOSITE
PERCENT OF
AVAILABLE METAL
IN SOLUTION
—
0.2 %
4.3
—
—
0.31
—
0.23
—
—
—
—
>
CONC.
mg/1
—
0.01
0.04
—
0.05
—
0.01
—
0.01
0.01
—
—
—
246 (j, COMPOSITE
PERCENT OF
AVAILABLE METAL
IN SOLUTION
—
14 %
12
—
0.02
—
0.53
—
0.77
1.7
—
—
—
100%
100%
100%
-------
TABLE 43
cr>
to
BIOASSAY
(% survival)
HEAVY METAL CONCENTRATIONS AND SOLUBILITIES
IN SIMULATED RECEIVING BODY OF WATER
OVERALL COMPOSITE
5 -DAY SAMPLE
Arsenic (As)
Cadmium (Cd)
Copper (Cu)
Chromium (Cr)
Iron (Fe)
Mercury (Hg)
Manganese (Mn)
Nickel (Ni)
Lead (Pb)
Strontium (Sr)
Titanium (Ti)
Zinc (Zn)
Zirconium (Zr)
CONC.
mg/1
0.007
0.00006
0.014
—
0.03
—
0.01
0.03
0.04
0.09
—
—
—
PERCENT OF
AVAILABLE METAL
IN SOLUTION
—
0.08%
1.3
—
0.01
—
0.25
1.0
0.74
10
—
—
—
< 246
CONC.
mg/1
—
0.00003
0.004
0.004
0.02
—
0.03
—
0.025
0.10
—
—
—
U, COMPOSITE
PERCENT OF
AVAILABLE METAL
IN SOLUTION
—
0.03%
0.58
0.29
0.01
—
0.94
—
0.58
10
—
—
—
> 246
CONC.
mg/1
0.003
0.002
0.006
—
—
—
0.01
—
0.04
0.06
—
—
—
|j, COMPOSITE
PERCENT OF
AVAILABLE METAL
IN SOLUTION
—
2.9 %
1.8
—
—
—
0.53
—
3.1
9
—
—
—
100%
100%
100%
-------
TABLE 44
01
HEAVY METAL CONCENTRATIONS AND SOLUBILITIES
IN SIMULATED RECEIVING BODY OF WATER
OVERALL COMPOSITE <
25-DAY SAMPLE
Arsenic (As)
Cadmium (Cd)
Copper (Cu)
Chromium (Cr)
Iron (Fe)
Mercury (Hg)
Manganese (Mn)
Nickel (Ni)
Lead (Pb)
Strontium (Sr)
Titanium (Ti)
Zinc (Zn)
Zirconium (Zr)
CONC.
mg/1
0.005
< 0.001
0.011
0.003
0.03
—
0.03
0.01
0.04
0.40
—
0.10
— ,
PERCENT OF
AVAILABLE METAL
IN SOLUTION
--
< 1.3 %
0.91
0.17
0.01
—
0.75
2.9
0.74
50
—
2.5
CONC.
mg/1
__
< 0.001
0.016
—
0.05
—
0.03
0.01
—
0.26
0.15
—
246 [i, COMPOSITE
PERCENT OF
AVAILABLE METAL
IN SOLUTION
--
< 1.0 %
2.2
—
0.03
—
0.94
7.1
26
—
7.9
—
>
CONC.
mg/1
0.002
< 0.001
0.12
0.001
—
—
0.03
0.01
—
0.20
—
0.17
—
246 |o, COMPOSITE
PERCENT OF
AVAILABLE METAL
IN SOLUTION
--
< 1.4 %
36
0.10
—
__
1.6
5.0
34
—
8.0
—
BIOASSAY
(% survival)
100%
100%
100%
-------
volatilized due to the aerating and mixing action (especially for mercury),
and the metal may have precipitated. Causes of precipitation are usually
due to either a pH change or the solubility of salts from a solid which
form a precipitate with the metal. A pH change could have been caused by
the increase in dissolved oxygen due to aeration causing a striping of the
CO in the water, which in turn would lower the pH value. Another possible
£t
cause for a pH change would be the naturally low buffer capacity of the
dilution water, along with increasing concentrations of soluble salts forming
alkaline or acidic ions in the mixture. The solubilities of the metals are
low, most being less than 10% but the extreme being as high as 50%.
Figure 1 relates solubilities with time. Because only three time frames
were analyzed (1, 5 and 25 days), some of the figures necessarily show inflection
points at these times. In reality, it is not known what occurred between
these dates, but it is assumed that the trends were continued. A comparison
of curves of the same metal (e.g. Cu) show some disparities where the trend
of the composite sample was different from the trends of both the sized
samples. These disparities are difficult to explain, but one probably due
to the heteregeneous character of the samples.
About half of the metals showed a decrease in solution concentration
with time. These decreases were caused by a loss of metal from the soluble
state at a faster rate than the solubility of the metals from the solid
state, as shown by a continuously decreasing curve. As stated previously,
the loss of metals could be caused by ion-exchange, sorption, volatility,
precipitation, or a combination of all four. If the curve shows an
increasing trend it can be assumed that the metal is solubilizing at a faster
rate than the soluble form is being lost. Combinations of these two curve
types would reflect a situation where the rates of loss and gain are not
constant because of some other factor. It is reasonable to expect that the
solubilities would reach an equilibrium after sufficient time. As an
example, the cadmium curves are steadily decreasing for all size ranges,
even though the solubilities are widely different. Strontium and manganese
are steadily increasing. The copper and iron curves are combinations of
64
-------
_
J?
c
Overall Composite
CADMIUM
0.15
0.1
0.05
0
COPPER
3
<246/j. Composite
> 246p. Composite
IRON
0.01
I I
0.2
0.15
0.1
0.05
0
0.01
I' I
30
20
10
0
0.02
I I
Fig. 1. Solubility Curves for Selected Heavy Metals
65
-------
these two types, either reaching a maximum or a minimum value at an
intermediate point.
Table 45 compares standard solubility data of simple metallic salts and
metallic elements with the concentration increases of the heavy metals in the
laboratory tests. In all cases, the elemental form of the metals are
insoluble, except for strontium, which decomposes in water. For any one
element the solubilities of the different salt forms are highly variable.
It is therefore not possible to compare the test solubilities with this
data to determine the salt form in which the metal exists. Whenever any
soluble forms exist, the solubilities from the tests are much lower than the
literature values. This is reasonable, considering the length of time the
street surface particulate material resides on the streets before being
removed. During this time, leaching action caused by normal atmospheric
moisture probably removes some fraction of the more soluble forms of the
metals.
For all metals, it is found that the maximum solubilities occur in
association with the larger particle size fraction. This phenomena may be
attributed to several factors. The first of these may reflect the relative
distribution of surface energy. Particles in the smaller size fraction have
a significantly large total surface area than found in the larger sizes.
Surface attraction and adherance can be expected to be greater with the finer
distribution permitting greater quantities of metal salts to be available for
solution in the larger fraction.
The second and probably most significant explanation lies with the
mineralogical composition associated with the two fractions. The larger
particles tend to be relatively fresh rock fragments or monomineralic grains,
most commonly quartz and feldspar. Chemical or physical processes of metal
salt accumulation can be expected to be low for such grains.
The mechanical abrasion processes such as found on street surfaces tend
to grind the larger particles into smaller sizes having greater surface areas.
66
-------
Table 45
COMPARISON OF STANDARD SOLUBILITIES OF SIMPLE METALLIC SALTS AND METALLIC
ELEMENTS WITH RANGES OF SOLUBILITY INCREASES FOUND IN TESTS
Compound
Arsenic (As)
ASClg
AS2°5
AS2°3
AsOCl
As2S3
Cadmium (Cd)
CdCl2
Cd(OH)2
Cd(NO3)2
CdO
CdS04
CdS
CdS03
Copper (Cu)
Cu2C03
Cu(OH)2
Cu40
Cu2SOx
CuSO4
Cu0S
Cold Water Solubilities Range Found in Tests
g/100 ml mg/1 mg/1 increase
Insoluble - 0.003 - 0.007
Decomposes -
150 1.5 x 106
3.7 3.7 x 104
1.2 1.2 x 104
0.00005 0.5
Insoluble - < 0.001 -> 0.01
140 1.4 x 106
0.00026 2.6
109 1.1 x 106
Insoluble -
75.5 7.6 x 105
0.00013 1.3
Slightly soluble
Insoluble - 0.004 -* 0.12
Insoluble
70.6 7.1 x 105
Insoluble
Insoluble
Decomposes -
14.3 1.4 x 105
1 x 10~14 1 x 10-10
67
-------
Table 45 (Cont'd)
COMPARISON OF STANDARD SOLUBILITIES OF SIMPLE METALLIC SALTS AND METALLIC
ELEMENTS WITH RANGES OF SOLUBILITY INCREASES FOUND IN TESTS
Compound
Cold Water Solubilities
g/100 ml mg/1
Range Found in Tests
mg/1 increase
Chromium (Cr)
CrCl2
Cr(OH)2
CrO2, CrO,
CrS
Manganese (Mn)
MnC03
Mn(OH)0
Mn2°7
MnSO.
Insoluble
Very soluble
Decomposes
Insoluble
0.001 -> 0.004
Iron (Fe)
FeCl3
Fe(OH)2
FeS2
Mercury (Hg)
Hg2C03
Hg(C103)2
Hg2Cl2
HgCl2
Hg2(N02)2
Hg20
HgS04
Hg2S
Insoluble
74.4
0.00015
0.00049
Insoluble
0.0000045
25
0.0002
6.9
Decomposes
Insoluble
Decomposes
Insoluble
-
7.4 x 105
1.5
4.9
-
0.045
2.5 x 105
2
6.9 x 104
-
-
-
-
Decomposes
0.0065
0.0002
Insoluble
Very soluble
52
65
2
0.02 -» 0.05
ND
o.Ol -» 0.03
5.2 x 105
68
-------
Table 45 (Cont'd)
COMPARISON OF STANDARD SOLUBILITIES OF SIMPLE METALLIC SALTS AND METALLIC
ELEMENTS WITH RANGES OF SOLUBILITY INCREASES FOUND IN TESTS
Compound
Cold Water Solubilities
g/100 ml mg/1
Range Found in Tests
mg/1 increase
Nickel (Ni)
NiC03
NiCl2
NiO
NiS04
NiS
Insoluble
0.0093
64.2
Insoluble
29.3
0.00036
0.01 -» 0.03
93
6.4 x 105
2.9 x 105
3.6
69
-------
The greater surface area exposes more of the material to the weathering-
decomposition process. Weathering of many mineral species such as feldspars
is extremely rapid under such circumstances. Various clay minerals are the
end result of this action. The sorptive properties of many clay minerals,
with open lattice structures, has been well documented, thus providing a
mechanism for metal salt tie-up in the finer size ranges. This size range
also contains a larger amount of organic material on a surface area basis.
Organic material take-up of metal salts is also thought to be a significant
process.
Other factors which may plan a role in this process include biologic
action such as particle-surface bacterial assimilation. This process is
again related to the increased available surface area associated with the
finer particles. It also appears that most of the solubilities of the
composite samples have a random distribution between the solubilities of the
large and small sized samples, as expected. This probably results from the
overall heterogeneity of the samples which make accurate predictions of
causes and effects difficult.
Table 46 compares maximum values of heavy metals measured in the
simulated receiving water environment with values in the literature that
have been shown to be harmful. Maximum arsenic concentrations found are
about 1/10 the USPHS drinking water standard and about 1/500 of concentrations
shown to have no effect on the "self purification" of streams. The maximum
copper values are within the range that can be toxic to aquatic organisms,
depending on the water's chemistry, but are about 1/8 the USPHS drinking
water standard. The values of cadmium are less than 1/3 the values required
to be toxic to certain aquatic organisms, but are about equal to the USPHS
drinking water standard.
Lead values are less than 1/2 the concentration that has been shown to
be "very toxic" in soft water, and less than 3/4 the USPHS drinking water
standard. Maximum zinc concentrations are within the range that has been
shown to be lethal for certain aquatic organisms in soft water, but are about
1/30 the USPHS drinking water standard.
70
-------
Table 46
COMPARISON OF MAXIMUM CONCENTRATIONS OF HEAVY METALS
FOUND IN SIMULATED RECEIVING WATER TEST
WITH VALUES THAT HAVE BEEN SHOWN TO HAVE EFFECTS
ON AQUATIC ORGANISMS*
(not intended to be a complete list)
HEAVY METAL AND
MAXIMUM VALUE
INCREASES IN
SIMULATED
RECEIVING WATER
CONCENTRATION
(mg/1)
NOTES
Arsenic
(0.003 mg/1)
Copper
(0.12 mg/1)
Cadmium
(0.01 mg/1)
Lead
(0.04 mg/1)
Zinc
(0.63 mg/1)
3
2
0
3
20
. 4
0.05
0.015 - 3.0
1 .0
5
0.037
0.03+0.15 mg/lZn
0.01
0.05
0.1 _
5.0
.1 .0
No harm to certain aquatic insects
No interference with "self-
purification" of streams
USPHS drinking water standard
Toxic to variety of aquatic
organisms, depending on water
chemistry
USPHS drinking water standard
Toxic to Daphnia
No effect on fathead minnows for
exposure to complete generation
Mortal to salmon fry
USPHS drinking water standard
"Very toxic" in soft water
Found in drinking water in Germany,
1933 and the Netherlands, 1953 for
short period of time after water
was in pipes for 24 hours
USPHS drinking water standard
Toxic to aquatic organisms in
soft water
USPHS drinking water standard
Impact of Various Metals on the Aquatic Environment, EPA,
Water Quality Office, Tech. Report No. 2, 1971
71
-------
The bioassay tests conducted with the simulated receiving water showed
100% survival of stickleback for 96-hour exposure in all instances. The
receiving water used for the tests was dechlorinated tap water, having
moderate hardness (about 50 mg/1 CaCO ). If the receiving water was soft
o
water, more like normal river water into which the runoff usually is
discharged, the copper, lead and zinc concentrations as shown in Table 45
should be sufficient to cause mortality of certain more sensitive aquatic
organisms.
The lethal effects of the mixture are enhanced by the extremely high
oxygen demand of the road surface particulates. Because the test solutions
were continuously aerated, the dissolved oxygen in the samples did not reach
critically low levels because the oxygen demand was met. In all cases, low
dissolved oxygen is synergistic to other lethal mechanisms. The immediate
toxic effects of road surface runoff that have been reported are most likely
due to this high oxygen demand. The metals have their most probable toxic
effect when road surface runoff is discharged into a quiescent body of water
whe re the metals can be accumulated in the bottom muds and benthic organisms
until lethal limits are reached.
72
-------
SECTION VII
PARTICLE SIZE DISTRIBUTION OF HEAVY METALS
ASSOCIATED WITH ROAD SURFACE PARTICULATES
Objective
To measure the heavy metal content of selected city samples which have
been divided into size categories, to determine the removal efficiencies
of these metals by normal street sweeping practices.
Background
During the course of the previous study, Water Pollution Aspects of
Street Surface Contaminents, studies were made on the removal effective-
ness of normal street sweeping practices. It was determined that the
most important parameter which affects particulate removal was particle
size (assuming dry conditions). Removal effectivenesses for different
particulate sizes were determined. By analyzing the heavy metal con-
tent in specific size ranges to determine the percent of total heavy
metal associated with each size range, and by applying the results
from the previous study,, heavy metal removal rates can be determined.
Methods of Analysis
Composite samples of four cities distributed in different parts of the
country were divided into four size ranges. The cities tested included
Tulsa, Seattle, San Jose II and Baltimore, and the size ranges were
<104/Lt, 104-^246/n, 246-^495/u. and >495,u. These 16 samples were analyzed
for heavy metals after undergoing sample preparation procedures de-
scribed elsewhere in this report. .
Results
The direct results of this phase are reported in Tables 47 through 55.
These tables report the metal concentrations as rig/kg for each sample.
These values were combined with particulate loading values for each
73
-------
city and size range and were recalculated as percentages of the metal
found in each size range sample. These values are shown in Tables 56
through 60. These values are also shown in bar graph form in Figures
2 through 10.
By examining the bar graphs, trends can be established which determine
in what size ranges the metals are most abundant. Cadmium is only
found in two cities, and in both cases it is found only in the size
ranges less than 495|j,. In most cases, more than 50% of the total
metals are found in size ranges smaller than 495u,. The exceptions are
all for Tulsa, where strontium, manganese, iron and chromium are mostly
(55->75%) associated with size ranges greater than 495jj,.
Table 61 lists removal rates of particulates for specific size ranges.
Most of the material is not removed unless it is greater than 246^.
Table 62 shows the theoretical removal rates for each of the samples.
The overall removal rate, averaged for all metals is 49%. The values
for each metal range from 38% for cadmium to 56% for chromium, while
the individual rates range from 17% for strontium in San Jose II to
69% for chromium in Baltimore. Therefore, barely more than one-half of
the heavy metals found on the streets remain after the streets have
been cleaned by normal street sweeping practices.
74
-------
TABLE 47
PARTICLE SIZE DISTRIBUTION FOR CADMIUM
(rag/kg)
Tulsa
Seattle
San Jose II
Baltimore
< 104
u
0
0
9
8
104
to
246
U
0
0
6
8
246
to
495
u
0
0
5
0
> 495
U
0
0
0
0
TABLE 48
PARTICLE SIZE DISTRIBUTION FOR CHROMIUM
(mg/kg)
< 104
U
104
to
246
a
246
to
495
u
> 495
u
Tulsa 220 75 105 85
Seattle 400 220 200 215
San Jose II 700 750 450 220
Baltimore 1,100 650 250 700
75
-------
TABLE 49
PARTICLE SIZE DISTRIBUTION FOR COPPER
(mg/kg)
Tulsa
Seattle
San Jose
Baltimore
Tulsa
Seattle
San Jose
Baltimore
104
to
< 104 246
M- M-
137 1,500
228 75
II 137 111
500 200
TABLE 50
PARTICLE SIZE DISTRIBUTION
(mg/kg)
104
to
< 104 246
|j, |J,
18,000 66,000
0 32,000
II 35,000 33,000
65,000 35,000
246
to
495
M-
182
69
46
200
FOR IRON
246
to
495
M"
83,000
29,000
30,000
26,000
> 495
M-
160
50
50
100
> 495
M"
72 , 000
32,000
29 , 000
37,000
76
-------
TABLE 51
PARTICLE SIZE DISTRIBUTION FOR MANGANESE
(mg/kg)
Tulsa
Seattle
San Jose II
Baltimore
PARTICLE
Tulsa
Seattle
San Jose II
Baltimore
< 104
M-
303
540
450
890
SIZE
< 104
H
0
15
80
100
104
to
246
M-
170
350
370
600
TABLE 52
DISTRIBUTION
(mg/kg)
104
to
246
M-
0
0
100
30
246
to
495
M-
260
300
330
650
FOR NICKEL
246
to
495
H
0
30
70
30
> 495
M-
280
380
340
380
> 495
M-
0
5
40
55
77
-------
TABLE 53
PARTICLE SIZE DISTRIBUTION FOR LEAD
(mg/kg)
Tulsa
Seattle
San Jose II
Baltimore
PARTICLE
Tulsa
Seattle
San Jose II
Baltimore
104
to
< 104 246
M, (J,
1,100 3,200
5,000 4,000
7,000 7,500
2,450 1,700
TABLE 54
SIZE DISTRIBUTION FOR
(mg/kg)
104
to
< 104 246
M- (0,
280 55
130 80
50 0
50 55
246
to
495
M-
6,100
1,900
6,000
1,300
STRONTIUM
246
to
495
M-
100
150
0
50
> 495
M-
1,500
950
1,500
750
> 495
M>
150
170
0
50
78
-------
TABLE 55
PARTICLE SIZE DISTRIBUTION FOR ZINC
(mg/kg)
Tulsa
Seattle
San Jose II
Baltimore
< 104
M-
500 1,
600
600
800
104
to
246
M-
000
400
500
500
246
to
495
V-
1,400
300
200
400
> 495
M-
600
300
100
500
TABLE 56
SEATTLE
Zinc
Copper
Lead
Iron
Cadmium
Chromium
Manganese
Nickel
Strontium
PERCENT OF HEAVY
VARIOUS PARTICLE
< 104
M-
24%
38
5
4
—
24
21
20
14
METALS IN
SIZE RANGES
104
to
246
M-
26%
21
27
24
—
22
22
0
14
246
to
495
M-
17%
18
46
27
—
18
17
60
23
> 495
M"
33%
23
22
45
—
36
40
20
49
79
-------
TABLE 57
PERCENT OF HEAVY METALS IN
VARIOUS PARTICLE SIZE RANGES
TULSA
Zinc
Copper
Lead
Iron
Cadmium
Chromium
Manganese
Nickel
Strontium
< 104
M1
2%
2
2
1
—
9
4
—
8
104
to
246
M-
13%
53
13
10
—
9
7
—
4
246
to
495
M*
36%
11
48
24
—
22
20
—
15
> 495
V
49%
34
37
65
—
60
69
—
73
80
-------
TABLE 58
PERCENT OF HEAVY METALS IN
VARIOUS PARTICLE SIZE RANGES
SAN JOSE II
Zinc
Copper
Lead
Iron
Cadmium
Chromium
Manganese
Nickel
Strontium
< 104
M-
34%
29
25
18
39
25
19
21
100
104
to
246
M-
37%
31
35
22
35
35
22
33
0
246
to
495
fJ-
14%
11
25
19
26
19
18
21
0
> 495
M-
15%
29
15
41
0
21
41
25
0
81
-------
TABLE 59
PERCENT OF HEAVY METALS IN
VARIOUS PARTICLE SIZE RANGES
BALTIMORE
Zinc
Copper
Lead
Iron
Cadmium
Chromium
Manganese
Nickel
Strontium
< 104
H-
22%
34
24
23
32
23
21
29
14
104
to
246
V>
27%
28
36
26
68
29
30
17
31
246
to
495
H
16%
20
20
14
0
8
24
12
20
> 495
M"
35%
18
20
37
0
40
25
42
35
82
-------
TABLE 60
PERCENT OF HEAVY METALS IN
VARIOUS PARTICLE SIZE RANGES
AVERAGE OF FOUR
CITIES: TULSA,
BALTIMORE, SAN
JOSE II, SEATTLE
Zinc
Copper
Lead
Iron
Cadmium
Chromium
Manganese
Nickel
Strontium
< 104
M-
20%
26
14
11
36
20
16
23
34
104
to
246
M-
26%
33
28
21
52
24
20
17
12
246
to
495
M-
21%
15
35
21
12
17
20
31
15
> 495
M-
33%
26
23
47
0
39
44
29
39
83
-------
Table 61
AVERAGE STREET SWEEPER REMOVAL EFFICIENCY
PARTICLE SIZE
< 104 p,
104 to 246 jo,
246 to 495 (j,
> 495 (j,
PERCENT REMOVAL
17%
48
55
67
Table 62
PERCENT HEAVY METAL REMOVAL BY AVERAGE STREET SWEEPER
PERCENT REMOVED BY CITY
METAL
Zinc
Copper
Lead
Iron
Cadmium
Chromium
Manganese
Nickel
Strontium
TULSA
59%
55
58
62
—
58
61
—
61
SEATTLE
48%
42
54
57
—
49
50
50
55
SAN JOSE II
41%
45
45
52
38
46
51
48
17
BALTIMORE
49%
42
46
49
38
69
48
48
52
AVERAGE
49%
46
51
55
38
56
53
49
46
84
-------
SEATTLE
TULSA
80
60
40
No Cadmium Detected
No Cadmium Detected
20
0
SAN JOSE II
BALTIMORE
80
a>
O)
I
0)
/
o
o
?
-o
o
o
c
0)
o
60
40
20
•* -o
O ^-
r- CN
ON
S
•o
^o 10 u->
^t ON ON
CN ^t TJ-
0 0 ^
-i- •+-
'st O
Particle size range
in Microns
Fig. 3. Particle Size Distribution of Cadmium,
85
-------
SEATTLE
TULSA
80
60
40
SAN JOSE II
BALTIMORE
80
0)
O)
I
n
u
o
-a
0>
u
o
c
O
O ^T O °>
s
Particle size range
in Microns
Fig. 3. Particle Size Distribution of Chromium,
86
-------
SEATTLE
80-
60
TULSA
I
40-
SAN JOSE
80-
60
I
BALTIMORE
_c
u
D
"D
CD
U
o
c
0)
o
40-
20-
0
I
10
— CN
CS
Particle size range
in Microns
O ~"t C> O>
^00-^
s ^
— CN
Fig. 4. Particle Size Distribution of Copper.
87
-------
SEATTLE
TULSA
80
60
SAN JOSE II
BALTIMORE
80
I
60
_c
o
o
£.
o
°u
o
c
0)
o
£
40
20
0
t
"^ O if) if)
2 S °t °t
" O 0 ^
— CN
^ -o
2 S
Particle size range
in Microns
Fig. 5. Particle Size Distribution of Iron.
88
-------
0)
O)
I
0)
to
_c
(J
D
V
SEATTLE TULSA
80
60
SAN JOSE II BALTIMORE
80
•^- S3 IT) U"> rf S3 V)
-------
SEATTLE
TULSA
80
60
No Nickel Detected
40
20
0
SAN JOSE II
BALTIMORE
80
CD
I
0)
N
O
D
UJ
"0
0)
O
O
c
0)
O
60
40
20
I
"O "O
^f ON
CN ^J-
O O
•+- H—
S •<)
•— CN
vQ "O
-^J- ON
CN ^1-
o o
-4— -I—
^f """O
— CN
Particle Size Range
in Microns
Fig. 7. Particle Size Distribution of Nickel
90
-------
SEATTLE
80
60
TULSA
O)
I
-------
O)
I
o
o
£
jo
'o
o
8
80
60
SEATTLE
TULSA
40
20
SAN JOSE II
BALTIMORE
100
60
40
20
0
O "*
— CN
~ 0
10
O»
TJ-
0
>o
ON
Particle Size Range
in Microns
Fig. 9. Particle Size Distribution of Strontium,
92
-------
u
0
SEATTLE TULSA
60
40
20
I
I «
40
U on
O 20
§
1
SAN JOSE II BALTIMORE
80
tt ±U
v O O A VOOA
Particle Size Range
in Microns
Fig. 10. Particle Size Distribution of Zinc.
93
-------
SECTION VIII
ADDITIONAL ANALYSES ON HIGHWAY,
RURAL ROAD AND AIRPORT SURFACES
Objective
To compute concentrations of common water pollution parameters and heavy
metals of road surface particulates collected from a series of rural
road, highway and airport surfaces. To compare these values with those
obtained from analyzing samples collected previously from a variety of
city streets.
Background
There exists little information concerning the water pollution aspects
of rural road and highway particulates. Because these types of road-
ways make up a significant portion of the streets in most of the coun-
try, this type of information is extremely valuable in order to assess
the total pollution potential from road surface runoff. Airports
account for large areas of paved surfaces in comparatively small areas.
Consequently, airport runoff can cause serious problems if the large
volume of runoff generated has a high pollution potential.
Method of Analysis
A modest sampling effort was conducted to gather particulates from
rural road, highways, and airport surfaces in the San Francisco Bay
Area. Five rural roads were sampled and the collected particulates
were combined for analysis. The same procedure was used for the high-
way sample. Test sites were chosen that represent as many different
types of roadways and surrounding areas as possible.
All the airport sampling was conducted at the San Jose Municipal Airport
Several areas were sampled at the airport, including runway and taxiway
surfaces, soil on the side of the runway, and soil on the side of the
taxiway.
94
-------
The following list describes the roadway sampling areas:
Rural road sampling locations:
a) "Highway" 9, six miles west of Saratoga, California;
2 lanes, moderate auto traffic, 35-^50 mph.
b) Skyline Boulevard, near Alpine Road, 5 miles west of
Los Altos Hills, California; 2 lanes, light to moderate
auto traffic, 35-^50 mph.
c) Skyline Boulevard, near Highway 84, three miles west of
Woodside, California; 2 lanes, light to moderate auto
traffic, 35-^50 mph.
d) Tunitas Creek Road, near Kings Mountain Park; 2 lanes,
light auto traffic, 30 mph.
e) Labitos Creek Cutoff, near Highway 1, five miles south
of Half Moon Bay, California; 2 lanes, very light auto
traffic, 30 mph.
Highway sampling locations:
a) Highway 92, San Mateo, California; 4 lanes, heavy auto
and truck traffic, 60 mph.
b) Caliada Road, near Pulgas Water Temple; 2 lanes, very
heavy auto traffic, 40 mph.
c) Highway 280, near Palo Alto; 6 lanes, moderate auto
traffic, 70 mph.
d) Highway 85, near Los Altos; 4 lanes, very heavy auto
and truck traffic, 60 mph.
e) Highway 101, near San Jose; 6 lanes, very heavy auto
and truck traffic, 60 mph.
All the sampled areas experienced heavy rainfall about five days prior
to sampling. The roadway samples were analyzed for BOD , COD, phos-
D
phates, nitrates, kjeldahl nitrogen, and selected metals. The airport
samples were only analyzed for heavy metal content. The heavy metal
analyses were conducted as described elsewhere in this report, and the
other tests were conducted as per "Standard Methods."
95
-------
Results
The results are presented in Tables 63 and 64. There are major differ-
ences in strengths of pollutants between the different samples as shown
in Table 63.
• the city street sample has the highest values of BOD5,
COD, NO-, N, Cr, Fe, Pb and Zn;
O
• the rural road sample has highest PO=, and Mn strengths;
• the highway sample has highest Cd concentrations;
• the runway side sample has highest Cu and Ni strengths;
• the taxiway side sample has highest Fe and Sr concentrations.
The major differences are the higher street surface values; the BOD
values are an order of magnitude greater than for the other samples.
The BOD /COD ratio is much less for the rural road and highway samples,
o
possibly caused by increased toxicity of these samples depressing the
BOD values. Lead and zinc city street values are about 4 times the
5
highway values and 6 to 30 times the rural road values. This is
probably caused by the inefficiency of heavy stop-and-go traffic on the
city streets, and lower vehicle volumes on rural roads.
The airport values are surprisingly similar to the road surface values,
reflecting similar pavement composition and heavy gasoline powered gen-
eral aviation use. (San Jose Municipal Airport has one of the heaviest
general aviation traffic loads in the country.) The soil to the side
of the paved airport surfaces has metallic compositions similar to the
paved surfaces' particulates. Very little particulate material
3 2
(<0.1 lb/10 ft ) was found on the runways, reflecting high ground tur-
bulence caused when the large aircraft take off or land. The taxiways
3 2
did show larger amounts of surface particulates (about 1 lb/10 ft ) but
not as much as roadway surfaces. The airport surfaces are only swept
when the particulate material poses a safety hazard to the aircraft.
Table 64 compares the loading of different street surfaces. This table
reflects particulate loadings on the surfaces. The highway surfaces
96
-------
Table 63
COMPARISON OF STRENGTHS (mg/kg) OF DIFFERENT
PAVED SURFACE PARTICULATES FOR COMMON POLLUTION PARAMETERS
AND CERTAIN HEAVY METALS
PARAMETERS CITY
(mg/kg) STREET
BOD
D
COD
P0f
NO-
N
Cd
Cr
Cu
Fe
Mn
Ni
Pb
Sr
Zn
17,000
73,000
980
460
1,900
3.8
209
120
24,000
440
34
2,000
21
400
RURAL
ROAD
1,500
49,000
1,900
140
500
0
215
39
23,000
860
105
65
50
70
HIGHWAY
2,300
46,000
203
35
650
9
185
40
21,000
370
105
490
50
190
AIRPORT
TAXIWAY
AND
RUNWAY
-
-
6
125
18
21,000
310
85
110
0
75
SIDE
OF
RUNWAY
AIRPORT
-
-
7
100
214
22,000
220
140
75
80
175
SIDE
OF
TAXIWAY
AIRPORT
;
-
0
155
54
26,000
560
85
190
95
98
97
-------
Table 64
COMPARISON OF LOADINGS OF DIFFERENT TYPES
OF ROADWAYS FOR COMMON POLLUTION PARAMETERS
AND CERTAIN HEAVY METALS
POUNDS PER CURB MILE
PARAMETER
BOD
o
COD
P0=
NO-
N
Cd
Cr
Cu
Fe
Mn
Ni
Pb
Sr
Zn
CITY STREET
18
95
1
0
2
0
0
0
24
0
0
1
0
0
.1
.043
.4
.0037
.231
.129
.4
.468
.040
.66
.022
.409
RURAL ROAD
2.
77
3.
0.
0.
0
0.
0.
36
1.
0.
0.
0.
0.
4
0
22
79
34
06
35
16
10
078
11
HIGHWAY
15
299
1.
0.
4.
0.
1.
0.
136
2.
0.
3.
0.
1.
32
23
22
058
20
26
39
68
17
32
24
98
-------
therefore have the greatest loadings for most of the parameters. The
only exceptions are that the city street surfaces have slightly
greater BOD loadings, and the rural road sample showed greater phos-
JD
phate loadings. This would be caused by the greater number of cars
on the freeways and the infrequency of freeway sweeping. The freeway
loading results are perhaps too low because only the curbs were sampled;
material in the traffic lanes was not collected or analyzed.
BOD rate experiments were conducted on the rural road and highway
samples. Normal rate constants could not be computed because the
results are actually expressed as milligrams oxygen consumed per kilo-
gram solid sample, and not as milligram oxygen consumed per liter of
waste. It was shown that about 2/3 of the 5-day BOD was exerted during
the first day of discharge . This substantiates the immediate toxic
effects caused by fast oxygen depletion of the receiving water.
99
-------
SECTION IX
ORGANIC ANALYSIS
Objective
The objective of this particular phase of the study was to investigate
the concentrations of organic material found in street surface contam-
inants .
Background
Organic material may be found on street surfaces in a variety of
forms:
• Cellulose from paper, wood, bark, leaves and grasses
0 Tannins from tree bark and vegetation
• Lignins from wood fibers
• Grease and oil from automobile drippings
• Hydrocarbons from automobile exhaust emissions
• Carbohydrates from food-type litter
• Bird and animal droppings
Both the composition and amount of organic material found on street
surfaces are important. Large amounts of organic material can exert
a high BOD in receiving waters which may reduce the level of dissolved
oxygen below that required to maintain aquatic or marine life. Cer-
tain organic substances such as lignins are very resistant to biolog-
ical oxidation. The grease and oil characteristics of organic material
can seriously impair the aesthetic value of receiving waters by creat-
ing taste and odor problems. Due to its poor solubility, grease and
oil can complicate the transportation or storm water runoff by fouling
the surfaces of the storm drains.
Methods of Analysis
Both land-use and particle-size composites were prepared for organic anal-
ysis by using the original samples collected for the initial study,
Water Pollution Aspects of Street Surface Contaminants.
100
-------
These composite samples were analyzed for tannins and lignins, carbo-
hydrates, organic acids, MBAS (methylene blue active substances), grease
and oil, PCBs (polychlorinated biphenols) and various pesticides.
MBAS is a measure of anionic-type surface active materials, or detergents.
The relative amounts of hydrocarbons and fatty matter in the grease
were also determined. The methods of analysis were those described in
Standard Methods for the Examination of Water and Wastewater.
Results
The results of the organic analysis are shown in Tables 65, 66, and 67.
Significant amounts of carbohydrates were detected in the samples.
Tannins, lignins, and MBAS were detected in moderate amounts, while
organic acids were below the detection limits.
Grease and oil were the major organic constituents found in the samples.
The smaller particle-size composite (<246(j,) appeared to contain a
greater percentage of grease and oil than the larger particle-size com-
posite (<246|Jj) . Also, except for the residential composite, there was
a greater amount of hydrocarbons than fatty matter detected in the grease.
There does not appear to be any great difference in the organic strengths
(mg/kg) of the material collected from different land-use areas, except
for the proportions of hydrocarbons and fatty matter in the grease and
oil. Industrial and commercial samples appear to contain mostly (>90%)
hydrocarbons in the grease and oil, while the residential sample con-
tains about 70% fatty matter in the grease and oil. The street load-
2
ings (Ib/curb mile and lb/1000 ft ) are highly influenced by the amount
of street surface particulates and, therefore, one finds that the in-
dustrial areas contain the greatest amounts of all organic materials,
except fatty matter, per unit surface area or length.
The samples were tested for the presence of PCBs and various pesticides.
However, the results were significantly lower than the results obtained
in the initial study, Water Pollution Aspects of Street Surface Contam-
inants. This discrepancy is probably due to the instability of these
101
-------
to
Table 65
ORGANIC ANALYSIS OF SELECTED SAMPLES
LOADING INTENSITIES (mg/kg)
ITEM
Tanins and lignins
Carbohydrates
Organic acids
MBAS
Grease and oil
Hydrocarbon in
grease
Fatty matter in
grease
OVERALL
COMPOSITE
65
490
*
36
11,025
10,259
766
< 246 p,
COMPOSITE
120
1,000
—
57
14,551
13,802
749
> 246 (a,
COMPOSITE
115
480
—
23
10,052
9,288
764
RESIDENTIAL
COMPOSITE
105
1,270
—
49
15,526
4,677
10,849
INDUSTRIAL
COMPOSITE
150
1,100
—
33
11,699
11,236
463
COMMERCIAL
COMPOSITE
113
740
—
38
16,882
15,097
1,785
* Below detection limit.
-------
o
CO
Table 66
ORGANIC ANALYSIS OF SELECTED SAMPLES
ITEM
Tanins and lignins
Carbohydrates
Organic acids
MBAS
Grease and oil
Hydrocarbon in
grease
Fatty matter in
grease
OVERALL
COMPOSITE
.098
.735
*
.054
16.5
15.4
1.10
LOADING
INTENSITIES
< 246 (a, > 246 p,
COMPOSITE COMPOSITE
.078
.651
—
.037
9.47
8.99
.480
.098
.408
—
.020
8.53
7.89
.640
(Ib/curb mile)
RESIDENTIAL
COMPOSITE
.126
1.52
—
.059
18.6
5.60
13.0
INDUSTRIAL
COMPOSITE
.420
3.08
—
.092
32.8
31.5
1.30
COMMERCIAL
COMPOSITE
.038
.215
—
.011
4.90
4.38
0.52
* Below detection limit.
-------
Table 67
ORGANIC ANALYSIS OF SELECTED SAMPLES
ITEM
Tanins and lignins
Carbohydrates
Organic acids
MBAS
Grease and oil
Hydrocarbon in
grease
Fatty matter in
grease
OVERALL
COMPOSITE
9.5 x 10~4
-3
7.1 x 10
*
— —
5.2 x 10~4
.161
.150
.011
LOADING INTENSITIES
< 246 ^ > 246 |x
COMPOSITE COMPOSITE
7.6 x 10~4 9.5 x 10~4
-3 -3
6.3 x 10 3.9 x 10
— — — —
3.6 x 10~4 1.9 x 10~4
.092 .083
.087 .076
-3 -3
4.7 x 10 6.3 x 10
(lb/1,000 it2)
RESIDENTIAL
COMPOSITE
1.2 x 10~3
.015
— —
5.8 x 10~4
.185
.055
.129
INDUSTRIAL
COMPOSITE
4.1 x 10~3
.030
— —
9.1 x 10~4
.323
.310
,012
COMMERCIAL
COMPOSITE
3.7 x 10~4
-3
2.4 x 10
— —
1.2 x 10~4
.055
,049
-3
5.9 x 10
* Below detection limit.
-------
Materials since the original samples were stored for about 9 months
between the initial study and the current study.
The most significant point to evolve from the organic analysis is the
amount of grease and oil found on street surfaces. As much as 32.8 Ibs/
curb mi. of grease and oil was detected. This large amount of grease
and oil could have an adverse effect upon a receiving body of water
by exerting a high BOD and creating taste and odor problems.
D
A greater percentage of grease and oil was found in the smaller particle-
size composite than in the larger particle-size composite. This is
possibly due to sorption of grease and oil by clay and silt particles.
Industrial samples appear to contain greater surface loadings of
organics, except fatty matter, than other land-use categories, reflect-
ing greater particulate loadings in the industrial areas. The strengths
(mg/kg) of the organic content by land use does not seem to vary signif-
icantly, except that the residential areas contain a greater portion of
fatty matter (in grease and oil) than the other land-use categories.
105
-------
SECTION X
ACKNOWLEDGMENTS
This report summarizes research conducted uy uno Research Company
for the Water Quality Office, Environmental Protection Agency,
under Contract No. 14-12-921. The Project Officer was Francis J.
Condon. The work was performed under the direction of Dr. Franklin
J. Agardy, Executive Vice President and Director of the Environmental
Systems Division. Robert Pitt served as Project Manager. The lab-
oratory analyses were performed with the help of Gary Amy and
Charles Brennen.
106
-------
SECTION XI
APPENDICES
Page No.
A. Bibliography 108
B. Major Components of Street Surface Pollution
Potential 114
C. Probable Chemical Compounds Associated with
Heavy Metals 120
Table C-l: Ionic Forms and Possible Chemical
Compounds for Several Heavy Metals . . . 120
D. Summary of Characteristics of Test Sites in
Selected Cities 121
Table D-l: Descriptions of Test Sites in San Jose
During First Test Series 122
Table D-2: Descriptions of Test Sites in Phoenix
During First Test Series 123
Table D-3: Descriptions of Test Sites in Milwaukee
During First Test Series 124
Table D-4: Descriptions of Test Sites in Bucyrus
During First Test Series 125
Table D-5: Descriptions of Test Sites in Baltimore
During First Test Series 126
Table D-6: Descriptions of Test Sites in San Jose
During Second Test Series 127
Table D-7: Descriptions of Test Sites in Atlanta
During First Test Series 128
Table D-8: Descriptions of Test Sites in Tulsa
During First Test Series 129
Table D-9: Descriptions of Test Sites in Phoenix
During Second Test Series 130
Table D-10: Descriptions of Test Sites in Seattle
During First Test Series 131
Table D-ll: Descriptions of Test Sites in Mercer
Island, Wash.; Decatur, Ga.; Owasso,
Okla.; and Scottsdale, Ariz. During
First Test Series 132
E Conversion to Metric Units 133
107
-------
APPENDIX A
BIBLIOGRAPHY
The publications included in this list relate to the analyzing and
significance of toxic materials, primarily heavy metals and pesti-
cides. This list is not intended to be inclusive.
Included in this list are several bibliographies that should
supply the reader with additional information.
One should also refer to the bibliography included in the related
report, Water Pollution Aspects of Street Surface Contaminants,
EPA-RZ-72-081.
108
-------
Altshuller, A.P.; Lonneman, William A.; Sutterfield, Frank D.; and Kopczynski,
Stanley L. "Hydrocarbon Composition of the Atmosphere of the Los Angeles
Basin - 1967." Environmental Science and Technology; Vol. 5, no. 10
(Oct. 1971), pp. 1009-1016.
Argo, David G., and Gulp, Gordon L. "Heavy Metals Removal in Wastewater
Treatment Processes: Part 2 - Pilot Plant Operation." Water and
Sewage Works (Sept. 1972), pp. 128-132.
Baker, Robert A., and Luh, Ming-Dean. "Mercury Analyses and Toxicity: A
Review." Industrial Wastes (May-June 1971), pp. 21-28.
Barrett, Bruce R. "Monitors Solve Fish-Kill Mystery." Civil Engineering-
ASCE (Jan. 1971), pp. 40-42.
Bazell, Robert J. "Lead Poisoning: Combating the Threat from the Air."
Science: Vol. 174 (Nov. 5, 1971), pp. 574-576.
Becacos-Kontos, T. "Pollution in Greek Waters." Marine Pollution Bulletin;
Vol. 2, no. 10 (Oct. 1971), pp. 158-160.
Bowman, Harry R.; Conway, John G.; and Asaro, Frank. "Atmospheric Lead and
Bromine Concentration in Berkeley, Calif. (1963-70)." Environmental
Science and Technology: Vol. 6, no. 6 (June 1972), pp. 558-560.
Breidenbach, A.W.; Lichtenberg, J.J.; Henke, C.F.; Smith, D.J.; Eichelberger,
J.W., Jr.; and Stierli, H. The Identification and Measurement of
Chlorinated Hydrocarbon Pesticides in Surface Waters. U.S. Dept. of
Interior, Federal Water Pollution Control Administration. Nov. 1966.
Brown, B. , and Ahsanullah, M. "Effect of Heavy Metals on Mortality and
Growth." Marine Pollution Bulletin; Vol. 2, no. 12 (Dec. 1971), pp.
182-187.
Cairns, John, Jr., et al. The Use of Fish Movement Patterns to Monitor
Zinc. Virginia Polytechnic Institute and State U., Blacksburg, Center
for Environmental Studies. Dec. 1971.
California Dept. of Public Health, Bureau of Sanitary Engineering. Mercury
Analyses of California Water Supplies. Informational Circular SE 70-4.
Oct. 14, 1970.
Campbell, Irene R., et al. Biological Aspects of Lead; An Annotated
Bibliography; Part II - Literature from 1950 Through 1964. Ohio:
U. of Cincinnati, Kettering Lab., May 1972.
109
-------
Cheremisinoff, Paul N. , and Habib, Yousuf H. "Cadmium, Chromium, Lead,
Mercury: A Plenary Account for Water Pollution: Part 1 - Occurrence,
Toxicity and Detection." Water and Sewage Works (July 1972), pp. 73-86.
Collinson, Charles, and Shimp, Neil F. Trace Elements in Bottom Sediments
from Upper Peoria Lake, Middle Illinois River A Pilot Project.
Illinois State Geological Survey, Environmental Geology Notes, No. 56.
Sept. 1972.
Dean, John G.; Bosqui, Frank L.; and Lanouette, Kenneth H. "Removing Heavy
Metals from Waste Water." Environmental Science and Technology: Vol.
6, no. 6 (June 1972), pp. 518-522.
Dunlap, Lloyd. "Mercury: Anatomy of a Pollution Problem." Chemical and
Engineering News (July 5, 1971), pp. 22-34.
Eshleman, Alan; and Siegel, S.M. "Metals in the Ecosystem; Leaded Grass and
Other Abominations." U. of Hawaii, Dept. of Botany. Rev. Dec. 1970.
Faber, Raymond A.; Risebrough, Robert W.; and Pratt, Helen M. "Organochlorines
and Mercury in Common Egrets and Great Blue Herons." Environmental
Pollution: Vol. 3 (1972), pp. 111-122.
Frost, Justin. "Earth, Air, Water." Environment: Vol. 11, no. 6 (July-
Aug. 1969), pp. 15-33.
Goerlitz, Donald F., and Brown, Eugene. Techniques of Water-Resources
Investigations of the U.S. Geological Survey; Chapt. A3 - Methods for
Analysis of Organic Substances in Water. U.S. Dept. of Interior,
Geological Survey. 1972.
Hsu, Deh Y., and Pipes, Wesley 0. "Modification of Techniques for Determina-
tion of Aluminum in Water by Atomic Absorption Spectrophotometry."
Environmental Science and Technology: Vol. 6, no. 7 (July 1972), pp.
645-647.
Interdepartmental Task Force on PCBs. Polychlorinated Biphenyls and the
Environment. NTIS No. COM-72-10419. May 1972.
Jeffries, D.J.; and French, M.C. "Lead Concentrations in Small Mammals
Trapped on Roadside Verges and Field Sites." Environmental Pollution:
Vol. 3 (1972), pp. 147-156.
John, Matt K.; Chuah, Hong H.; and VanLaerhoven, Cornells J. "Cadmium
Contamination of Soil and Its Uptake by Oats." Environmental Science
and Technology: Vol. 6, no. 6 (July 1972), pp. 555-557.
Johnson, Richard E.; Rossano, August T., Jr.; Sylvester, Robert 0. "Dustfall
as a Source of Water Quality Impairment." Proceedings, ASCE: Journal of
Sanitary Engineering Division: Vol. SA1 (Feb. 1966), pp. 245-267.
110
-------
Kahn, Herbert L. "Principles and Practices of Atomic Absorption." Reprint
from Advances in Chemistry Series, No. 73, Trace Inorganics in Water,
pp. 183-229. 1967.
Klein, David H. "Mercury and Other Metals in Urban Soils." Environmental
Science and Technology: Vol. 6, no. 6 (June 1972), pp. 560-562.
y and Goldberg, Edward D. "Mercury in the Marine Environment."
Environmental Science and Technology; Vol. 4, no. 9 (Sept. 1970), pp.
765-768.
Lanford, Charles E. "Trace Minerals Affect Stream Ecology." Oil and Gas
Journal; Vol. 16, no. 13 (Mar. 31, 1969), pp. 82-84.
Lehman, G.S., and Wilson, L.G. "Trace Element Removal from Sewage Effluent
by Soil Filtration." Water Resources Research: Vol. 7, no. 1 (Feb. 1971),
pp. 90-99.
Morrow, Norman L., and Brief, Richard S. "Elemental Composition of Suspended
Particulate Matter in Metropolitan New York." Environmental Science and
Technology: Vol. 5, no. 10 (Oct. 1971), pp. 786-789.
Palmer, J.S. Toxicity of 45 Organic Herbicides to Cattle, Sheep, and Chickens.
U.S. Dept. of Agriculture, Agricultural Research Service, Production
Research Kept. No. 137. Mar. 1972.
Perkin-Elmer. Technique and Applications of Atomic Absorption. Perkin-
Elmer Corp., 1971.
Perkin-Elmer Corp. Analytical Methods in Atomic Absorption Spectroscopy.
Peterson, Frank L., et al. Effect of Storm Runoff Disposal and Other Arti-
ficial Recharge to Hawaiian Ghyben-Herzberg Aquifers. U. of Hawaii,
Water Resources Research Center. Nov. 1971.
Portmann, J.E., and Wilson, K.W. The Toxicity of 140 Substances to the
Brown Shrimp and Other Marine Animals. 2nd ed. Ministry of Agriculture,
Fisheries and Food. Shellfish Information Leaflet, No. 22. Dec. 1971.
Purves, David. "Consequences of Trace-Element Contamination of Soils."
Environmental Pollution: Vol. 3 (1972), pp. 17-24.
Rothschild, E.; Buxbaum, J.; Mauss, E.; Paulson, G.; Emerman, S.; and Cantwell,
A.M. Childhood Lead Poisoning: An Analysis of Current State and
Municipal Programs. New York: N.Y. Scientists' Comte. for Public
Information, Inc., 1971.
Sartor, James D., and Boyd, Gail B. Water Pollution Aspects of Street Surface
Contaminants. EPA, Municipal Pollution Control Branch. 1972.
Ill
-------
Schmidt, Timothy T.; Risebrough, Robert W.; and Gress, Franklin. "Input of
Polychlorinated Biphenyls into California Coastal Waters from Urban
Sewage Outfalls." Bulletin of Environmental Contamination and Toxicology:
Vol. 6, no. 3 (1971), pp. 235-243.
Schneider, Robert F. The Impact of Various Heavy Metals on the Aquatic
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Feb. 17, 1971.
Schroeder, Henry A. "Metals in the Air." Environment: Vol. 13, no. 8
(Oct. 1971), pp. 18-32.
Sinha, Evelyn. Metals as Pollutants in Air and Water; An Annotated Biblio-
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Sodergren, A.; Svensson, B.; and Ulfstrand, S. "DDT and PCB in South Swedish
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Stiff, M.J. "The Chemical States of Copper in Polluted Fresh Water and a
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Taras, Michael J. ; Greenberg, Arnold E.; Hoak, R.D.; and Rand, M.C.; eds.
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112
-------
Methods for Chemical Analysis of Water and Wastes. 1971.
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Div. of Health Effects Research. Corrections and Additons to "Health
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"USGS Completes Nationwide Reconnaissance of Metals in Streams." Water and
Sewage Works (June 1971), pp. 174-175.
U.S. Tariff Commission. Synthetic Organic Chemicals; U.S. Production and
Sales of Pesticides and Related Productivity, 1970. Preliminary.
Sept. 1971.
Warnick, Stephen L., and Bell, Henry L. "The Acute Toxicity of Some Heavy
Metals to Different Species of Aquatic Insects." Journal of Water
Pollution Control Federation; Vol. 41, no. 2, part 1 (Feb. 1969),
pp. 280-284.
Wurster, Charles F. "Aldrin and Dieldrin." Environment: Vol. 13, no. 8
(Oct. 1971), pp. 33-45.
Yeager, David W.; Cholak, Jacob; and Henderson, Ethel W. "Determination
of Lead in Biological and Related Material by Atomic Absorption Spectro-
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(Oct. 1971), pp. 1020-1022.
113
-------
APPENDIX B
MAJOR COMPONENTS OF STREET
SURFACE POLLUTION POTENTIAL
This outline lists the theoretically possible components of street
surface pollution-causing material. It is based on theoretical
considerations and does not imply to be inclusive. It should be
helpful when determining probable sources of the measured street
surface constituents.
114
-------
Major Components of Street Pollution
A. Large sized/biologically insignificant
1. bulk cellulosic matter
a. tree limbs, twigs, leaves, shrubs
b. lumber
c. paper
d. cotton materials
e. rayon
f. cellophane
2. bulk metals and alloys of construction and containerization
a. steel
b. iron
c . aluminum
d. magnesium
e . copper and bronze
f. zinc
g • tin
3. fabric, packaging and construction plastics
4. natural processed animal fibers
B. Variable sized/biologically insignificant
1 . soil conditioners
2. basic soil constituents
3. inorganic dustfalls from air pollutants
C. Variable sized/biologically nutritive/water soluble
1. natural and compounded fertilizers
a. nitrogen compounds (ammonium, nitrate, urea, cyanates, etc.)
b. phosphates
c. potassium compounds
d. secondary growth elements (Ca, Mg, Fe, Cu, Zn, Mn, B,
Mo, S)
115
-------
2. de-icing compounds
a. sodium hexametaphosphate
b. urea
c. ammonium nitrate
d. potassium pyrophosphate
3. soluble air pollutants
a. sulfur oxides (as SO )
b. nitrogen oxides (as NO )
o
c . ash
4. phosphate based detergents
5. lawn and garden ash
D. Variable sized solids or solutions/biologically inhibiting/
water soluble
1 . de-icing compounds
a . sodium chloride
b . calcium chloride
c . ferric ferrocyanide
d . sodium ferrocyanide
e . sodium chromate
2. air pollutants
a. carbon monoxide
b. sulfides, sulfites
c . nitrites
d. ozone
3. anti-freeze compounds
a. diacetone alcohol
b. methanol
c. ethylene glycol
4. roadway hydrocarbons
a. some highly oxygenated bitumens
5. water base paint solutions
116
-------
E. Variable sized, immiscible or suspendable biologically inhibiting/
water insoluble
1. vehicular and roadway hydrocarbons
a . oils
b. greases
c. tetraethyl lead and decomposition products
d. bitumens
2. hydraulic fluids
a. propylene glycol diricinoleate
b. tri-N-butylamine
3. water insoluble air pollutants
a. hydrocarbons
4. pesticide/herbicide carriers
F. Variable sized solids or solutions/biologically toxic/water
soluble
1. common pesticides, herbicides, etc.
a. arsenic (acetoarsenites, arsenites, arsenates)
b. copper (arsenites, etc.)
c. lead (arsenites, etc.)
d. thallium compounds
e. chloropicrin
f . dinitro-o-cresol
g. furfural
h. malathion
i. nicotine
j. phenol
G. Variable sized solids, liquids or suspensions/biologically toxic/
water insoluble
1. common pesticides, herbicides, etc.
a. benzene hexachloride
b. chlordane
117
-------
c. dichlorodiphenyltrlchioroethane (DDT)
d . dichloroethylene
e. dichloroethyl ether
f. 2-4 dichlorophenoxyacetic acid (2,4-D)
g. dinitro-o-cresol
h. methoxychlor
i. parathion
j. tetramethylthiuram disulfide
k. toxaphene
L. trichloroethylene
m- dichlorobenzenes (ortho and paraT
n. pyrethrins
o. aldrin
p. dieldrin
q. organo-mercury compounds
Variable sized culture media/biologically active/water susoendable
life forms
L. animal excretions
a. fecal coliforms
b. fecal streptococci
c. biological nutrient source
I. human excretions
a. fecal coliforms
b. fecal streptococci
c. biological nutrient source
o
dead animals
a. fecal coliforms
b. non-fecal coliforms
c. fecal streptococci
d. biological nutrient source
I. vegetation
a. biological nutrient source
118
-------
5. food wastes
a. biological nutrient source
6. soil
a. biological nutrient source
119
-------
APPENDIX C
PROBABLE CHEMICAL COMPOUNDS
ASSOCIATED WITH HEAVY METALS
A study was originated in an attempt to determine in what ionic
forms were the heavy metals. In close to neutral pH conditions,
most metals are restricted to two ionic forms. Table C-l describes
these ionic forms and possible associated chemical compounds for
some of the metals.
TABLE C-l
IONIC FORMS AND POSSIBLE CHEMICAL
COMPOUNDS FOR SEVERAL HEAVY METALS
METAL IONIC PROBABLE COMPOUNDS
FORMS
Pb +2, +4 PbS, PbCO , PbSO PbCrO PbO,
o 4 4
Pb(OH)2, PbCl2, PbI2
Cu +1, +2 Cu S, Cu 0, Cu(OH) , CuCN, CuSO ,
£ £i £ Q
CuO, Cul
Zn +2 ZnS, ZnO, ZnSO Zn(OH)
Fe +2,+3 Fe2°3> Fe°»
Fe2(S04)3,
Fe(OH) , FeCO,
£ O
The analytical method attempted (extraction at different pH values
to obtain solubility constants) was not sophisticated enough to
allow a complete description of the ionic forms of the metals.
The pH values of all the street surface particulates was within
the range of 6.5 to 7.8.
120
-------
APPENDIX D
SUMMARY OF CHARACTERISTICS OF TEST SITES
IN SELECTED CITIES
GLOSSARY OF TERMS USED IN TABLES D-l through D-ll
(Self-explanatory terms omitted)
Street
Pavement:
Condition:
Volume of Water;
Parking Density:
Traffic:
Density:
Minimum distance from
curb (ft) :
Type of surfacing
Excellent - Very smooth surface, no
cracks, essentially new condition.
Good - Few cracks, near new condition.
Fair - Cracks, some pavement deteriora-
tion.
Poor - Many cracks, moderate to exten-
sive deterioration.
The amount of water utilized for
collecting street surface sample (in
gallons) .
Heavy - Parking mostly continuous.
Moderate - Around half of available areas
filled.
Light - Very few vehicles parked.
Predominantly automobile, trucks, or
mixed .
Heavy - > 10,000 AADT (annual average
daily traffic) .
Moderate - 500-10,000 AADT
Light - < 500 AADT
The distance between the curb and traffic
flow.
121
-------
Table D-l
DESCRIPTIONS OF TEST SITES IN SAN JOSE DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • pavement
• condition
• width (ft)
(crown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (Et2)
VOLUME OF WATER (gal)
PACKING BtNSITV
TRAFFIC • main types
of vehicles
• density
• average speed (mph)
• min. distance
f.om curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/ OLD
finale
•SJ-I-/
Bz/ftcfifY
f£>OB£f?M
/3.2S
/2-/1 -70
ASPHALT
6OOO
/8
COriCff£T£
CO»CX£T£.
G-PASS
COHCfi£T£
LAWN
&80
/8
tietfr
AUTO
L/6HT
/o
4
12
fi a..
SlvfPT
multi
SJ-I -2
£ W/iiMM
f/8 r-"
/3.25
/2-/4- 70
ASPHALT
fA/R
15
CONCRETE.
CONCRETE
GRASS
CCWCff£TE
LAWN
St>0
27
4/a#r
AL/TO
LIGHT
10
6
/J
n.a.
•Su/£pr
MED / NEV/
single
SJ-I -3
CAMUS <
LOMBARD
26.5
12 -/4 -70
ASPHALT
GOOD
/(*
CONCRETE
CONCRETE
CRASS
COHCf?£T£
//IM//V
(,00
27
MOO-
AUTO
LI6HT
10-15
4-
12
fl. A
Sk/fPT
MED /OLD
single
multi
light
SJ~-I-&
COMMERCIAL
<•'/ a.
stvtpr
heavy
CENTRAL
BUSINESS
DISTRICT
SJ--/-3
SAN FERNANDO
f'j"-
4 5
/2 -15-70
ASPHALT
PAIR
20
ASPHALT
CONCRETE
a/ffr
CONCRETE
PARK LOT
SOO
40
MOD.
AUTO
HUVY
JO -35
5 - (*
13
na
swtrr
SUBURBAN
SHOPPING
CENTER
SJ~- [ - /o
FACE (
AUZZR/A.S
4 5
12 -/S-70
ASPHALT
GOOD
20
CONCRi TE
CONCRETE
COUCP£T£
COHCRL TL
PARK LOT
8OO
40
LIGHT
AUTO
MOD.
20
5
/ 8
n.a.
SWEPT
-------
Table D-2
DESCRIPTIONS OF TEST SITES IN PHOENIX DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • pavement
• width (ft)
(crown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gal)
PARKING DENSITY
TRAFFIC • main types
of vehicles
• density
• average speed (mphj
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/
single
PI -1
/in f POLK
/8.5
/-/S-71
ASPHALT
FAIR
/8
CEMENT
C£M£NT
DIRT
CfMEHT
LAW
fOOO
48
LtSHT
JUTO
LIGHT
/S -20
(,'8
/2
8
SW£PT
OLD
mulri
PI -2
/?3i E.POLK
2.4,
/-/4 -71
ASPHALT
FAIR
12
CEMEN T
C£MfA/r
Ct«ENr
CEMENT
LAWN
/OOO
/?
MOD.
30
8
/2
/o
INDUSTRY
Pi -7
7r-*J/.
2.5
'-f6 -71
ASPHALT
EXCEL .
25
CEMENT
CEMENT
CEMENT
CEMENT
ASPHALT
PARKING LOT
/OOO
48
V. LIGHT
/IL/TO
HEAVY
40
8
/2
8
heavy
CENTRAL
BUSINESS
DISTRICT
PT-<]
3.8
/ - /7 - 71
ASPHALT
f~A IR
24
ASPHALT
CEMENT
CEMENT
CEMENT
BUILDINGS
/OOO
48
HEAVY
AUTO
HEAVY
25 -JO
C, - 8
/2
SUBURBAN
SHOPPING
CENTER
PI -10
J100N.33*A
J.8
/ - n -ii
ASPHALT
OOOD
15
CEMEN T
CEM EH T
CEMENT
CEMENT
ASPHtdT LOT
/OOO
48
LIGHT
AUTO
MOD.
^5 -Jo
6 -8
f2
-------
Table D-3
DESCRIPTIONS OF TEST SITES IN MILWAUKEE DURING FIRST TEST SERIES
CODE NUMBER
SUE LOCATION
PERCENT LAND USE
DATE
' STREET « pavement
• condition
• width (ft)
(crown to gutter)
GUTTER
CURS
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gal)
PARKING DENSITY
TRAFFIC • main types
of vehicles
• density
• aveiage speed (mph)
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/ OLE)
single mulli
Mi -1
ti'-fE LLOYD
4-28-71
ASPHALT
GOOD
IZ
ASPHALT
CONCRETE
D/RT
CONCRETE
GRASS
4-4O
/O
L/GHT
AUTO
LIGHT
/S-20
4
0
7
SWEPT
M i-2
3T-> f W.V/NE
4 -28-71
ASPHALT
POOR
10
CONCRETE
CONCRETE
D/P.T
CONCRETE
D/RT
46,0
8
No PARK.
AUTO
\
LIGHT
/5 -25
2-3
O
SIVEPT
MED/ NEW
single
Mi -3
23* (BRIDGES
/6.J
4 -21-71
CONCRETE
GOOD
l&
CONCRETE
CONCRETE
LAWN
CONCRETE
LAWN
bOO,
/J
LIGHT
AUTO
LIGHT
20-25
6 -8
0
7
SWEPT
MED/
single
'OLD
multi
Mi-5
LATHAM (S. I0r-"
/(, J
4 -28 -71
ASPHALT
fAlR
18
CONCRETE
CONCRETE
DIRT
CONCRETE
LAWN
80O
/5
HO PARK.
AUTO
L/ GHT
20.-25
(,
0
9
SH/E.PT
light
INDUSTRY
Mi -7
BECHERfALLIS
12.5
4 - ^8 - 71
A5PHAL T
FA/R
/(*
ASPHALT
CONCRETE
CONCRETE
CONCRETE.
BU/LD/NGS
GOO
8
NO PARK
MIXED
MOD
15-20
4-6,
O
8
heavy
GR£CNFl£LD
f' BARCKY
17.5
4 -21 - 71
ASPHALT
FAIR
ASPHALT
CONCRETE
DIRT
CONCRETE
D/RT
6,00
17
NO PARK.
TRUCK
HEAVY
/5 -20
4-6,
0
8
CENTRAL
BUSINESS
DISTRICT
/V7/-V
MASON f
BROADWAY
4,7
4 -27 -71
ASPHALT
fXCEL.
25
ASPHALT
CONCRETE
CONCRETE
CONCRETE
BUILDINGS
6,00
3
NO PARK.
AUTO
HEAVY
JO -35
8
O
1
SWEPT
SUBURBAN
SHOPPING
CENTER
Mi -10
27a( PAR NELL
4 7
4-27-71
CONCRETE.
FAIR
25
CONCRETE
CONCRETE
DIRT
CONCRETE
PARK iOT
COO
25
L/GHT
AUTO
MOD
Z5-3O
8
0
7
SWEPT
-------
Table D-4
DESCRIPTIONS OF TEST SITES IN BUCYRUS DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • pavement
• condition
• width (ft)
(ciown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gol)
PARKING DENSITY
TRAFFIC • main typej
of vehicles
• density
• average speed (mph)
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/ OLD
single
8u- 1
SCHABERT
/M0WETT
18
4 -JO -71
ASPHALT
POOR
/5
CONCRETE
CONCRETE
LAu/N
CONCRETE.
LAWN
£20
15
LIGHT
AUTO
L/GHT
/5 -20
3-5
2
na.
SMPT
mulli
MED/ NEW
single
BuL-3
VICTORIA
f MARTHA
18
4 -30 -71
ASPHALT
£XCEL.
/4
CONCRETE
CONCRETE
LAWN
None
LAWN
480
/4
LIGHT
AUTO
LISHT
/5-20
d>
2
* J.
SWf?T
MED /OLD
single
Bu-4
WALLACE t
£AST
36,
4-30-71
ASPHALT
EXCEL.
14
ASPHALT
CONCRETE
LAWN
C6HCRETE
LAWN
480
20
NO PARK.
AUTO
LIGHT
15-20
6-7
2
X d
SWEPT
multi
light
INDUSTRY
medium
Bu-7
Al/TO fHA YHE
12
4 -30 -71
ASPHALT
£XCEL.
/4
ASPHALT
CONCRETE:
LAWN
HONE-
LAWN
480
//
LIGHT
AUTO
L/GHT
20-25
4
2
na.
SlV£PT
heavy
Bu-8
SOUTHERN
f HARRIS
8
4-30-71
ASPHALT
POOR
/4
CONCRETE
CONCRETE
GRASS
NOHE
GRASS
480
(1
NO PARK.
AUTO
MOO.
25-30
4
2
n.a.
SWEPT
CENTRAL
BUSINESS
DISTRICT
Bu- 1
IV. U/ARKEHT
fSANOVSKY
8
4 -30 -71
ASPHALT
FAIR
/7
ASPHALT
CONCRETE
CONCRETE
CONCRETE
0U/LDINGS
aoo
/z
MOD.
AUTO
MOD.
20-25
6 -8
2
/?.
-------
Table D-5
DESCRIPTIONS OF TEST SITES IN BALTIMORE DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET . povemenl
• widlh (ft)
(crown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
II*? Of rtJTAWA (It2)
VOLUME OP WATER (90!)
PARKING DENSITY
TRAFFIC • moin types
of vehicles
• density
• overage speed (mph)
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW
single
/OLD
Ba. -Z
MIL TON f
LANVALC
28.2
3-4 -71
ASPHALT
GOOD
1C,
ASPHAL T
CONCRETE
CONCRETE
CONCRETE
BUILDINGS
&8Q
/3
HEAVY
AUTO
\
MOD.
25
8
26
/
SW. f 'FLUSH
MED/ NEW
single
Bd-3
SEKOlS f
PICKWICK
14.1
3-4- -71
CONCRETE:
GOOD
/6
CONCRETE
CONCRETE
LAWfJ
NONE
LAWfJ
-io
ATHOL /
£DMOMOSON
4-0
6-5 -71
ASPHALT
EXCEL.
ZO
ASPHALT
CONCRETE
DIRT
CONCRETE
SHRUBS
800
LIGHT
AUTO
MOD.
25 30
6 -8
^<•
4-
SW. f FLUSH
-------
Table D-6
DESCRIPTIONS OF TEST SITES IN SAN JOSE DURING SECOND TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • povement
• condition
• width (ft)
(crown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gal)
PARKING DENSITY
TRAFFIC • main types
of vehicles
• density
• average speed (mphj
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/
single
SJTf-l
BERKLEY
f'DOBERN
/3 25
& -15-71
ASPHALT
GOOD
18
CONCRETE
CONCRETE
GRASS
CONCRETE
LAWN
68O
18
LIGHT
AUTO
LIGHT
/O
4
3O
27
LIGHT
AUTO
LIGHT
/O
6
31
n.e
3W£PT
MED/ NEvV
single
SJn-3
CAMOS f
LOMBARD
2C.5
(> -15-71
ASPHALT
GOOD
/&
CONCRETE
CONCRETE
GRASS
CONCRETE
LAWN
6OO
27
MOD.
AUTO
LIGHT
/O -15
4
J9
Aft
SlvfPT
MED/
single
'OLD
mulli
light
SJII-(e
COMMERCIAL
f'lO-a
/I 0
6 -f 5 -71
ASPHAL f
FAIR
25
CONCRETE
CONCRETE
ASP HA L r
NONE
D/ RT
/OOO
JO
LIGHT
MIXED
MOD.
25
/O
•59
n.f.
Sw£pr
INDUSTRY
SJX-7
/O&J
Ml S3 /ON
11.0
&-I5-7I
ASPHALT
GOOD
24-
CONCRETE
CONCRETE.
PI RT
NONE
0UUD/H65
880
25
MOD.
MIKED
HEAVY
JO -40
&-8
•S?
n.a.
SWEPT
heavy
CENTRAL
BUSINESS
DISTRICT
SJZ-1
E.J^f
SAN FERNANDO
4.S
(a -15 -71
ASPHALT
EAI R
20
ASPHALT
CONCRETE
Dl RT
CONCRETE
PARK. LOT
800
40
MOD.
AUTO
HEAVY
JO -35
3 -C,
•5?
n a..
SIVZPT
SUBURBAN
SHOPPING
CENTER
•SJJT-/0
AUZEUAIS
f 'RACE
45
6-/5-7I
ASPHALT
GOOD
20
CONCRETE
COA/CRETC
CONCRETE
CONCRETE
PARK LOT
80O
40
LIGHT
AUTO
MOD.
20
5
51
fl A
SWEPT
-------
Table D-7
DESCRIPTIONS OF TEST SITES IN ATLANTA DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • povement
• condition
• width (fl)
(crown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gol)
PA.DKINO DENSITY
TRAFFIC • moin type!
of vehicle*
• density
• average speed (mph)
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW
single
At- 1
WALNUT!
THURMOND
fl.J
6 -22- 71
ASPHALT
GOOD
18
ASPHAi T
CONCRETE
GRASS
NONE:
GRASS
620
/6
LtGHT
AUTO
LIGHT
/O
4-
/*
SiV f FLUSH
/OLD
/)/ -2.
DREWS
CLARR/LLA
II. 3
&-22-7I
CONCRETE
GOOD
20
CONCRETE
CONCRETE:
GRASS
CONCRETE:
LA H//V
640
/J
i. IGHT
AUTO
LIGH'T
/5
6
2
/
SWr'Fit/SH
MED/ NEW
single
At-3
FCRNLEAFCi
fFERNLEAf Rd
/
-------
Table D-8
DESCRIPTIONS OF TEST SITES IN TULSA DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
' STREET • pavement
• condition
• width (ft)
(crown to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gal)
PARKING OENBiTY
TRAFFIC « main types
of vehicles
• density
• average speed (mph)
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/
single
Tu-l
£A TON f
CfffENWOOD
24 0
C, -28 - 71
ASPHALT
FOOR
ASPHALT
CONCRETE
GRASS
CONCRE T£
3U/LD/N6S
480
/&
M*f>.
AUTO
MOO.
/5
6
t< 3
OLD
multi
MED/ NEW
single
Tu-J
45™ t
GffADffJ
350
&-25-7I
CONCRETE
FAIR
/4
CONCRETE
CONCRETE
GRASS
NONC
4 OO,
17
*,e»r
AUTO
SIGHT
/5
3
7
na
siv.f'nusH
MED/
single
OLD
multi
ST. LOUIS
35.0
(-25-71
CONCRETE
FAIR
CONCRETE
CONCRETE
GRASS
CONCRETE
STOHFWALL
400
20
#». nr?K.
Auro
MOO.
20
J
71 3.
sw.f FLUSH
light
6 -25-71
GOOD
18
CONCRETE
CONCRETE
GRASS
NONE:
GRASS
64O
30
ttenr
TXVCK
t/GHT
20
e.
9
»<3.
INDUSTRY
To -7
CATIMER
rOWASSO
2.0
6-25 '-71
ASPHALT
FAIR
CONCffETE
CONCRETE
GRASS
NONE:
BUILDINGS
460
20
MO PAffK.
TRUCK
MOD.
2O
6
na.
SlVf FLUSH
heavy
CENTRAL
BUSINESS
DISTRICT
77,-^
BOSTON
.7
(,-25-71
ASPHALT
FAIR
20
ASPHALT
CONCRETE
CONC#ET£
CON CPE Tt~
PARK. S.OT
6,40
/
-------
Table D-9
DESCRIPTIONS OF TEST SITES IN PHOENIX DURING SECOND TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • pavement
• condition
. width (ft)
(crown to goiter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gal)
PARKING DENSITY
TRAFFIC • mainlypej
of vehicles
• density
• overage speed (mph)
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW
single
PU- 1
/8 5
6-24-71
ASPHALT
POOR
18
CONCRETE
CONCRETE
D/RT
CONCRETE
LAWN
S&O
12
MOD.
AUTO
LIGHT
/5
£
ft 3.
1 OLD
PH-Z
£. POLKS/I™
£-28-71
ASPHALT
IZ
CONCRETE
CONCRETE
CONCRETE
CONCRETE
LAWN
44-0
20
HEAVY
AUTO
MOD.
20
8
&0'
MED/ NEW
single
PH-J
6-28-71
ASPHALT
GOOD
/4
CONCRETE
CONCRETE
CONCRETE
CONCRETE
LA WN
4SO
18
LIGHT
AUTO
LIGHT
10
4-
60*
SWEPT
MED/
single
'OLD
c™£*i*
3.8
& -28-71
ASPHAL T
PAIR
CONCRETE
CONCRETE
GRASS
CONCRETE
LAWN
£00
18
HEAVY
AUTO
LIGHT
/5
6
fl.3
SWEPT
light
x^Lt
6-28-71
ASPHALT
GOOD
ZO
CONCRETE
CONCRETE
DIRT
NONE
£>/RT LOT
3 '2O
/7
MCD.
M/XED
MOD.
20
8
&0 +
na
SWEPT
INDUSTRY
PIT -7
2.5
6-28-71
ASPHALT
GOOD
25
CONCRETE
CONCRETE
ASPHALT
ASPHALT
PARK. LOT
440
A5
NO PARKINS
MIXED
HEAVr
4O -SO
8
SWEPT
heavy
CENTRAL
BUSINESS
DISTRICT
MONROE] I?
J.8
6-21-71
ASPHAL T
FA IR
24-
ASPHAL T
CONCRETE
CON C RE TE
CONCRETE
BUILDING
-52O
20
row A H/A Y
M/XED
HEAVY'
20
8
&0-
n 3.
SWEPT
SUBURBAN
SHOPPING
CENTER
PIT- 10
33** S GRAND
J.8
6-28-71
ASPHALT
GOOD
15
CONCRETE
CONCRETE
CONCRETE.
CONCRETE
PARK LOT
J60
24
LIGHT
AUTO
LIGHT
20
6
GO-
n 3
SWEPT
-------
Table D-10
DESCRIPTIONS OF TEST SITES IN SEATTLE DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
• STREET • pavement
• condition
• width (ft)
(crown to gutter)
GUTTER
CURS
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (ft2)
VOLUME OF WATER (gol)
BABKIN6 BEN5ITY
TRAFFIC • main type!
of vehicles
e density
• average speed (mph)
• min. distance
from curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW/
single
Se-/
/^"r FIR
30.0
7-8-71
ASPHALT
POOR
/2
ASPHALT
CONCRETE
CXASS
CONCRETE
LAWti
40O
/3
£t£#r
AUTO
L/CHT
/5
4
/Z
n<3
SW. j FLUSH
OLD
Se-2
21st f
YESLER
9.0
7-8-71
ASPHALT
GOOD
/&
ASPHALT
CONCRETE
CMC#£T£
CCA/CRETE
BU1LO/N65
&OO
/&
Hd Pdfftf.
AUTO
//^Ai/y
JO .
8
/2
nd
SW{ FLUSH
MED/ NEW
single
MED,
single
3e-4
/2 & f
£. THISTLE
35 O
7-7-71
COf a
Siv( FLUSH
INDUSTRY
medium
Se-3.
JW.ffLUSH
SUBURBAN
SHOPPING
CENTER
•Se-IO
/SO v f1
N.5r*
/.O
7-8-71
ASPHAL T
FA /ft
/Z
ASPHALT
COA/CftETE
C0HCfl£TE.
CONCRETE
BUILDINGS
4OO
/5
NO PARK
AUTO
H£A YY
JO
8
/2
Tl. <3
Swf FLUSH
-------
Table D-ll
DESCRIPTIONS OF TEST SITES IN MERCER ISLAND, WASH. ; DECATAUR, GA. ; OWASSO, OKLA. ;
AND SCOTTSDALE, ARIZ. DURING FIRST TEST SERIES
CODE NUMBER
SITE LOCATION
PERCENT LAND USE
DATE
STREET • povement
. condition
« width (ft)
(cro^n to gutter)
GUTTER
CURB
PARKING STRIP
SIDEWALK
AREA BEYOND SIDEWALK
SIZE OF TEST AREA (Ft2)
VOLUME Or WATER (gal)
PARKING DENSITY
TRAFFIC • main types
of vehicles
• density
• average speed (mph)
• min. distance
From curb (ft)
DAYS SINCE LAST RAIN
DAYS SINCE LAST CLEANED
CLEANING METHOD
LOW /OLD
single
multi
MED/ NEW
single
Mis -3
MERCER IS.
n.a
7-7-71
ASPHALT
GOOD
/(*
CONCRETE
CONCRETE:
GRASS
CRASS
6RA55
66,6
2/
MOD.
ALfTO
LIGHT
/5
&
/2
/? a.
•n.a.
MED /OLD
single
Df -4
U/MTEFt AV£
fLARK PL
n.a
£ -23-71
ASPHAL T
fAIR
/4
ASPHAL T
CONCRETE
GRASS
CONCRETE
LAWN
440
/8
MOD.
AuTO
L/6HT
/O
3
2
n.a.
n.a..
multi
Ous -4
W.3"~(
SEA MONT
n a
&-2&-7I
ASPHALT
FAIR
16
COfJCRET£
CONCRETE
GRASS
CONCRETE
LAWN
480
23
LIGHT
AUTO
LIGHT
ro
3
9
n.a
n a
light
Sc - 4-
£_ 74 -v/
ROOSEVEL T
n.d.
(,-23-71
ASPHAL T
GOOD
20
CONCRETE
CONCRETE
CONCRETE
CONCRETE
LAWN
H80
JO
LIGHT
AUTO
LIGHT
10
10
30'
n.a
fl A.
INDUSTRY
medium
heavy
CENTRAL
BUSINESS
DISTRICT
SUBURBAN
SHOPPING
CENTER
-------
APPENDIX E
CONVERSION TO METRIC UNITS
ENGLISH
UNIT
Ib/curb mi
lb/1000 ft2
Ib/hr
inch
foot
mile
mph
acre
ft2
gallon
CONVERSION
FACTOR
x 0.28
x 4.88
x .454
x 2.54
x .3
x 1.609
x 1.609
x 4.05 x 10~3 =
x 9.29 x 10~
x 3.79
METRIC
UNIT
kg/curb km
g/m
kg/hr
cm
meter
km
kph
km
2
m
liter
HU.S. GOVERNMENT PRINTING OFFICE: 1973- 546-309/58
133
-------
SELECTED WATER
RESOURCES ABSTRACTS
INPUT TRANSACTION FORM
/.' Report No.
w
Toxic Materials Analysis of Street Surface Contaminants
EPA-R2-73-283
Pitt Robert E., Amy Gary
9. orsanizs.ticn UPS Research Company
155 Bovet Road
San Mateo, California 94402
12. Sponsorie
Environmental Protection Agency report number,
EPA-R2-73-283, August 1973.
5 Report Bate ,'
6 "%>''<
8 i/'rfQrmh bOrgartiftitiaSi **•",(,
Report No. ,
r 1 (. 7 *" f
11034 FUJ
?/, Contract/Grant No.
''PeriodCovered '•'
Because of the large amounts of toxic materials (especially heavy metals)
found associated with street surface particulates during the course of a
previous study (Water Pollution Aspects of Street Surface Contaminants),
additional work has recently been completed which defines the distribution and
range of heavy metals on the Nation's city streets.
This project defined the breakdown of the particulates' compositions by
having mass spectographic analyses performed on various samples. Using
these results, the heavy metals which were determined to have the greatest
water pollution potential (As, Cd, Cr, Cu, Fe, Pb, Mn, Hg, Ni, Sr, Ti, Zn,
and Zr) were analyzed in each of about 75 samples collected nationwide in 10
cities in the previous study.
17a. Descriptors
Storm Runoff, Surface Runoff, Urban Runoff, Pollution (Water)
BOD, COD, solids, heavy metals
lib. Identifiers
Street cleaning, street surface contaminants
17c. CO WRR Field & Group
18. Availability 1$ Sectltity Class
f Repot f
"0 Se tntyCi 1
------- |