OF CHARACTERIZATION URBAN AND RURAL INHALABLE PARTICULATES Document No. 83/11
Illinois Department of Energy and Natural Resources
James R. Thompson, Governor Michael B. Witte, Director
ENR Doc. No. 83/11 SWS Contract Report 308 February, 1983
CHARACTERIZATION OF URBAN AND RURAL INHALABLE PARTICULATES
by
Donald F. Gatz 1) Susan T. Wiley Lih-Ching Chu
Project No. 10.093
James R. Thompson, Governor State of Illinois
Michael B. Witte, Director Department of Energy and Natural Resources
1)
Prepared under contract with the Illinois Department of Energy and Natural Resources as project number 10.093; from the State Water Survey Division, Champaign, Illinois.
NOTE
This report has been reviewed by the Department of Energy and Natural Resources and approved for publication by ENR.
Printed by the Authority of the State of Illinois. Date Printed: Quantity Printed: March, 1983 200
One of a series of research publications published since 1975. This series includes the following categories and are color coded as follows:
Prior to July, 1982 - Green - Blue - White - White - Buff - Buff - Cherry - Canary
After July, 1982 Green Blue Grey Olive Brown Orange Red Yellow
Air Quality Water Environmental Health Solid and Hazardous Waste Economic Impact Study Noise Management Energy Information Services
Illinois Department of Energy and Natural Resources Division of Policy and Planning 325 West Adams Street Springfield, Illinois 62706 [phone] ii
TABLE OF CONTENTS List of Figures List of Tables 1 EXECUTIVE SUMMARY 1.1 ISSUES 1.1.1 1.1.2 1.2 1.3 1.4 Impacts and control of fugitive dust sources of TSP Inhalable mass vs total particle mass concentration v vi 1
1 2 3 3 4
THE AUDIENCE FOR THESE RESULTS OBJECTIVES FINDINGS 1.4.1 1.4.2 1.4.3 Comparisons of Inhalable and Total Particle Mass Effects of Street Sweeping on Urban Aerosol Concentrations Sources of Urban Aerosols, and their Relative Importance
4 4 5 6 8 8 12 12 13 13 15 17 17 18 19 20 21 21 22 23 24 25
2 3
INTRODUCTION LITERATURE REVIEW 3.1 3.2 SOURCE IDENTIFICATION AND APPORTIONMENT WIND EROSION 3.2.1 3.2.2 Introduction and History Wind Erosion Processes 3.2.2.1 Natural Wind 3.2.2.2 Saltation 3.2.2.3 Surface Creep 3.2.2.4 Suspension 3.2.2.5 Effects on the Soil 3.2.3 Factors Affecting Wind Erosion 3.2.3.1 Soil Cloddiness 3.2.3.2 Surface Roughness 3.2.3.3 Wind Speed and Soil Moisture 3.2.3.4 Unsheltered Field Length 3.2.3.5 Vegetative Cover 3.2.4 Wind Erosion Equation 3.2.5 Summary
iii
Page
3.3 EMISSIONS FROM PAVED AND UNPAVED ROADS 3.4 COMPARISON OF AEROSOL MEASUREMENT METHODS 4 EXPERIMENTAL METHODS 4.1 SAMPLER LOCATIONS 4.2 SAMPLING METHODS 4.3 ANALYSIS METHODS 4.4 STREET SWEEPING OPERATIONS 4.5 SUPPORTING DATA 4.6 SOURCE APPORTIONMENT BY CHEMICAL ELEMENT BALANCE 5 RESULTS AND DISCUSSION 5.1 RURAL MEASUREMENTS 5.1.1 Comparison of Inhalable and Total Aerosols 5.2 URBAN HIVOL MEASUREMENTS 5.2.1 Data Summary 5.2.2 Effects of Street Sweeping 5.2.3 TSP Concentrations Upwind and Downwind of Mattis Avenue 5.2.4 TSP Concentrations in Commercial and Residential Areas with and without Sweeping 5.3 URBAN SOURCE APPORTIONMENT USING DICHOTOMOUS SAMPLER MEASUREMENTS 5.3.1 The Data 5.3.2 CEB on Mean Ambient Concentrations 5.3.3 CEBs on Individual Samples 6 SUMMARY AND CONCLUSIONS 7 ACKNOWLEDGEMENTS 8 REFERENCES _ 27 28 28 28 31 32 33 34 35 37 37 37 42 42 44 45 49 56 56 64 86 96 98 100
iv
LIST OF FIGURES
Number
Figure 1. Map of a portion of Champaign, Illinois, showing aerosol sampling sites, drainage basins, and sampling sites of the Water Survey's project in the Nationwide Urban Runoff Program. Comparison of rural particle mass concentrations measured by standard and size-selective high volume samplers. Illustration of data collected in 1981 for evaluation of effects of street sweeping on air quality. Comparison of TSP concentrations upwind and downwind of Mattis Avenue on days when winds were predominantly from the east or west. Comparison of TSP concentrations on the east and west sides of Mattis Avenue on days when winds were not predominantly from the east or west. Summary of source contributions to total aerosol mass. Frequency distributions of source contributions, based on CEB analyses of individual samples fine aerosol. Frequency distributions of source contributions, based on CEB analyses of individual samples coarse aerosol. Mean street dust contributions to total mass <15 m as a function of wind direction. Street dust contribution to total mass <15 m as a function of the fraction of time winds were from the westerly sector during sample collection.
Page
30
Figure 2.
38
Figure 3.
43
Figure 4.
46
Figure 5.
47
Figure 6.
77
Figure 7.
88
Figure 8.
89
Figure 9.
94
Figure 10.
95
V
LIST OF TABLES
Number
Table 1. Comparison of inhalable particle/total particle ratios in this study with literature values. Results of 2-way analysis of variance on upwind-downwind TSP differences across the test roadway. Results of 2-way analysis of variance of commercial-residential TSP differences. Summary of TSP concentrations and differences by season for three different sample sets. Summary of ambient fine particle elemental concentrations measured on 95 sampling days, Mattis Ave., Champaign, Illinois, June October, 1981. Summary of ambient coarse particle elemental concentrations measured on 95 sampling days, Matt-is Ave., Champaign, Illinois, June October, 1981. Source compositions (%), fine fraction, based on resuspended samples. Source compositions (%), coarse fraction, based on resuspended samples. Summary of CEB results on fine particles, Champaign dicbotomous filter data, based on mean observed element concentrations.
Page
40
Table 2.
51
Table 3.
52
Table 4.
54
Table 5.
57
Table 6.
58
Table 7.
60
Table 8.
61
Table 9.
65
Table 10. Chemical element balance for Champaign dichotomous fine particles. Table 11. Summary of CEB results on coarse particles, Champaign dichotomous filter data, based on mean observed element concentrations. Table 12. Chemical element balance for Champaign dichotomous coarse particles. Table 13. Summary of major sources and fit characteristics of individual elements in fine and coarse particle classes.
68
70
75
78
vi
1
1 EXECUTIVE SUMMARY
1.1
ISSUES
1.1.1
Impacts and Control of Fugitive Dust Sources of TSP
Provisions of the Clean Air Act enacted in 1977 revise their
require
states
to
State Implementation Plans (SIPs) for all areas that have National for Ambient Air Quality Standards (NAAQS). The
not attained Illinois SIP
Total Suspended Particulates (TSP) was conditionally Environmental One of Protection Agency (USEPA) with
approved by the certain minor
U.S.
deficiencies.
the reasons for the conditional documentation of the
approval of the Illinois TSP SIP was impacts and effects of various these
inadequate
controls on non-traditional fugitive sources include reentrained road
sources of TSP. dust, This
Examples of
wind erosion from agricultural lands, and unpaved road emissions. and similar and studies have been designed to correct those
deficiencies that purpose. Environmental
will be submitted to the USEPA as part of the SIP for
The results of these studies will be used by the Illinois Protection Agency (IEPA) to define further the estimated TSP non-attainment
impacts of non-traditional fugitive dust sources on areas throughout the state. They will also
be used to refine the
2
control
strategies
which
may
need
to
be
applied
to
various
non-traditional fugitive dust sources.
1.1.2
Inhalable Mass vs. Total Particle Mass Concentrations
The Clean Air Act, as most recently amended (1977), emphasizes
that
health aspects should be considered very strongly when assessing effects of air pollutants. volume (hivol) This has caused a concern does that the standard high
sampler
not provide a sample of particles in the effects. Thus, the
limited size range important for assessing health standard
hivols may soon need to be replaced with samplers that measure to be
concentrations of particles within the specific size range known capable are of reaching the lungs through inhalation. consideration. One is the
Several such devices virtual impactor
under
two-stage
(dichotomous sampler), which collects particles in two size ranges: less than about 2.5 m and include limit sizeselective the particles such as 2.5 to 15 m, aerodynamic diameter. Others
inlets collected
for the standard hivols. to those smaller than
These devices some cutoff
diameter,
15 m. In this report, we shall consider inhalable
particles to be those less than or equal to 15 m aerodynamic diameter.
3
1.2 THE AUDIENCE FOR THESE RESULTS
The results of this study should be of interest to pollution control officials that have responsibility must results for monitoring ambient pollutant strategies also be for of meeting interest and air to
concentrations and those that pollution standards. The
plan
should
atmospheric chemists interested in sources of urban aerosols relative
their
contributions to ambient concentrations of both total mass and
individual elements.
1.3
OBJECTIVES
The purposes of the study were:
1.
To compare concentrations of inhalable and total airborne mass in both urban and rural areas, To assess the effects of street sweeping on urban air quality, by comparing hivol and dichotomous sampler measurements in urban areas in the presence and absence of regular street sweeping, and To determine the sources of airborne particulate matter, and their relative contributions to TSP, in an urban area.
2.
3.
4
1.4
FINDINGS
1.4.1
Comparisons of Inhalable and Total Particle Mass
Concentrations of inhalable particles in a rural area,
as
measured
by a size-selective hivol sampler, averaged 75% of those of TSP measured in a standard hivol.
Mean inhalable mass concentrations in Champaign, as dichotomous sampler, were about 50% of
measured
by
a
the mean TSP concentrations
measured by a standard hivol.
Thus, IP-TSP Illinois
comparisons
in
urban
and
rural
areas
of
central
agree very closely with similar comparisons in the literature,
made in other parts of the country.
1.4.2
Effects of Street Sweeping on Urban Aerosol Concentrations
5
Dust from the test roadway increased TSP average of about 22 3 g/m with winds at 7 m downwind 68 be by an of that
within
degrees found
perpendicular to the roadway. routine aerosol weekly sweeping had near
Yet, no any the
evidence
could
effect roadway.
in reducing (or increasing) It appears that the
concentrations removal of
inefficient
street particles <125 m in diameter by street and
sweepers is crucial, since the particles of street dust that become remain airborne are in this size range.
1.4.3
Sources of Urban Aerosols, and their Relative Importance
Source apportionment calculations indicated that atmospheric sulfate accounted for more than 50% of the fine (<2.5 m) aerosol. Street dust 15
accounted for only 2% of the fine mass, but 66% of the coarse (2.5 m) mass, and by inference, about 50% of hivol TSP concentrations.
The influence of nearby Mattis measurements, but not in those
Ave
could
be
seen
in
the
hivol
from the dichotomous sampler, so the
major contribution of Mattis Ave must have been in particles larger than 15 m.
6
2 INTRODUCTION
Provisions of the Clean Air Act enacted in 1977 revise their
require
states
to
State Implementation Plans (SIPs) for all areas that have National for Ambient Air Quality Standards (NAAQS). The
not attained Illinois SIP
Total Suspended Particulates (TSP) was conditionally Environmental One of Protection Agency (USEPA) with
approved by the certain minor
U.S.
deficiencies.
the reasons for the conditional documentation of the
approval of the Illinois TSP SIP was impacts and effects of various these
inadequate
controls on non-traditional fugitive sources include reentrained road
sources of TSP. dust, This
Examples of
wind erosion from agricultural lands, and unpaved road emissions. and similar and studies have been designed to correct those
deficiencies that purpose. Environmental
will be submitted to the USEPA as part of the SIP for
The results of these studies will be used by the Illinois Protection Agency (IEPA) to define further the estimated TSP non-attainment
impacts of non-traditional fugitive dust sources on areas control throughout the state. may They need will to also be
be used to refine the applied to various
strategies
which
non-traditional fugitive dust sources.
The Clean Air Act, as most recently amended (1977), emphasizes
that
health aspects should be considered very strongly when assessing effects
7
of air pollutants.
This has caused a concern that the standard high
volume (hivol) sampler does not provide a sample of particles in the limited size range important for assessing health effects. Thus the
standard hivols may soon need to be replaced with samplers that measure concentrations of particles within the specific size range known to be capable of reaching the lungs through inhalation. are under consideration. One is the Several such devices virtual impactor
two-stage
(dichotomous sampler), which collects particles in two size ranges: less than about 2.5 m and 2.5 to 15 m, aerodynamic diameter. Others
include size-selective inlets for the standard hivols. limit the particles collected m. to those smaller than
These devices some cutoff
diameter, such as 15
In this report, we shall consider inhalable m aerodynamic diameter.
particles to be those less than or equal to 15
In October, 1980, the Illinois Institute of Natural Resources (now the Department of Energy and Natural Resources) initiated a 10-month
first phase study with the State Water Survey Division to characterize urban and November, rural 1981. particulate This is matter. A second phase was funded in
a final report of results on both phases
through 30 June 1982.
The purposes of the study were: 1. To compare concentrations of inhalable and total airborne mass in both urban and rural areas,
8
2. To assess the effects of street sweeping on urban air quality, by comparing hivol and dichotomous sampler measurements in urban areas in the presence and absence of regular street sweeping, and To determine the sources of airborne particulate matter, and their relative contributions to TSP, in an urban area.
3.
This study was planned so as to utilize the regular street
sweeping
program being conducted in Champaign, Illinois, by a research group from the Water Survey's Surface Water Section and funded by EPA the Nationwide as part of
Urban Runoff Program (NURP); the Principal Investigator Michael L. Terstriep, Head of the Surface Water
of this study is Section.
3
LITERATURE REVIEW
3.1
SOURCE IDENTIFICATION AND APPORTIONMENT
Techniques have been developed in recent years whereby of
measurements
elemental concentrations of ambient aerosols may be analyzed in such sources may be of the aerosols may be identified. These
a way that the identifications
qualitative, i.e., simply listing the types of contributions quantitative, to the
sources present, without any indication of their total aerosol concentration, or they may be
giving
9
estimates of actual contributions by the various sources. factor analyses and cluster analyses yield Conventional source
qualitative
identifications, while chemical element balance (CEB) methods and target transformation factor analyses yield quantitative information. These
methods of source identification and apportionment have come to be known collectively as "receptor models," since they depend on data measured at receptors, rather than sources. Reviews of this field have been provided by Gordon (1980a,b).
Receptor models, both qualitative and "crustal dust," or a similarly-named
quantitative,
commonly
find
source, perhaps "soil dust" or Miller et al. (1972),
"limestone," to be a source of ambient aerosol. using a CEB
method, found that soil dust contributed about 10% of the Using a very similar technique, Gatz
aerosol in Pasadena, California.
(1975) found that soil dust accounted for about 18% of Chicago aerosols. Using aerosol composition METROMEX, Gatz (1978) data collected at St. a Louis in Project
qualitatively Dzubay the
identified (1980) St.
"soil and flyash" a CEB method on
source, using factor analysis. dichotomous sampler data from
used
Louis Regional Air Pollution
Study (RAPS) and found that "shale" and "limestone" accounted for 86% of the coarse (2.4 - 20 m) particles, 10% of the fine (<2.4 m) particles, and 43% of the total particle mass. Hopke et al. (1976) applied factor
analysis to aerosol composition data from the Boston area and identified a "crustal" source, perhaps combined to some extent with flyash. and Alpert
Hopke (1980) reanalyzed the Boston data using target transformation
10
factor analysis, and identified "soil" and "road dust" as major sources. Gaarenstroom et al. (1977) analyzed Tucson aerosol data using factor Kowalczyk et al.
analysis and found "soil" to be a significant source.
(1982) used a CEB method on a set of aerosol data from the Washington, D.C. area and found contributions of 25% from "soil" and about 3% from "limestone".
In a series of studies for the Department of Energy and Natural Resources involving microscopic examination of hivol filters, the
Illinois Institute of Technology Research Institute (IITRI) identified vehicle-raised "mineral particles" as the prime contributors to high TSP concentrations in Decatur and the Quad Cities (Arnold and Draftz, 1979), in Peoria (Arnold et al.. 1980), and in East Moline and Milan, Illinois (IITRI, 1982). In a similar study, Lynn et al. (1976) found that about
65% of the mass of particles on hivol filters from 14 U.S. cities consisted of minerals such as quartz, calcite, hematite, and feldspars, and attributed them to such sources as wind erosion, resuspension of soil, quarrying, cement manufacturing, iron and steel production, and fuel combustion (flyash).
Evans and Cooper (1980) compiled an inventory of particle emissions from "open sources," including paved and unpaved roads, tilling and wind erosion, piles. Estimates of construction, surface mining, agricultural and tailings In
emissions were made for each state in the U.S.
11
Illinois, unpaved roads accounted for 57%, wind tilling for 8% of such "open" emissions. erosion for 19%, and
In a subsequent paper, Evans the effects
and Cooper (1981) used this emission inventory to estimate of these various open sources on
3
TSP
concentrations. In Illinois,
unpaved roads accounted for 4.5 g/m , or about 6% of the statewide mean TSP concentration of 76.5 g/m .
3
Other
open
sources could not be to
detected as contributing to measured TSP concentrations, due in part the tendency for measurements
to be made in urban, rather than rural
areas.
The source apportionment techniques employed in above all require multielement
the
studies
cited
aerosol data as input.
Other studies (1978), for
reporting such data for major U.S. cities include Leaderer New York City, and Countess et al, (1980) for Denver.
In addition, composition,
Smith et al, (1981) have reviewed concentrations, chemical size distribution,
and health effects of inorganic inhalable particles
in Illinois.
The "crustal" dust identified in these various studies can come from wind erosion as or tilling or of agricultural soils, or from such man-made roads, parking lots, construction,
features
paved and
unpaved
demolition,
surface
mining.
Thus, we also review here some basic
information on soil erosion, and on vehicle-raised street dust.
12
3.2
WIND EROSION
3.2.1
Introduction and History
Wind erosion is caused by a stong turbulent wind blowing unprotected soil surface. Although generally believed by
across to be
an of may
consequence only in arid and semiarid areas, soil erosion occur wherever soil,
wind
vegetative, and climate conditions are conducive is loose,
(U.N.F.A.O., 1960). dry, and reasonably
These conditions exist when 1) the soil finely divided, is 4) absent the wind
2) the soil surface is somewhat or sparse, 3) the field is
smooth and vegetative cover sufficiently movement. large, and
is strong enough to start soil
Some soil from eroding fields becomes suspended and becomes part the atmospheric dust load.
of
Hagen and Woodruff (1973) estimated that
eroding lands of the Great Plains contributed 244 and 77 million tons of dust to the atmosphere in the 1950s and 1960s, respectively. for Soil dust long-range
is an important class of aerosol that has the potential transport (Gillette, 1974;
Clayton et al, 1972; Carlson and Prospero, Brock (1970) estimated that dust
1972; Rahn et al. 1979). raised by wind
Hidy and 9.3%
contributes
of the total production of aerosols
13
based on an estimate of Judson (1968), or 21% of the total production if source 1974). pollutes estimates are adjusted to known aerosol composition (Gillette,
Windblown soil dust obscures visibility (Patterson et al, 1976), the air, causes traffic hazards, fouls machinery, and impairs
human and animal health.
3.2.2
Wind Erosion Processes
3.2.2.1
Natural Wind
Movement of soil particles is caused by wind forces exerted the surface of the ground.
against
The molecules of air actually in contact (ideally) the airspeed increases
with a surface must be at rest, and logarithmically to
a constant value at some distance from the surface. The variation of wind speed
This region is called the "boundary layer." with height,
within the boundary layer determines the magnitude of the The surface
shear stress or drag force exerted on the ground surface. shear associated
with the decrease in wind speed near the surface is a Momentum decreases as the
vertical transfer of horizontal momentum. surface is approached.
14
The minimum wind speed required to start soil movement is called the threshold speed. the are This speed depends on the size and weight of the soil friction provided by neighboring particles. In
particles and general,
there
three types of pressures exerted on soil grains at wind (Chepil pressure and Woodruff, 1963;
the threshold of their movement by Chepil, 1959). Impact or
velocity
is a positive pressure This pressure
resulting from the impact of the fluid against the grain. causes the initial movement of
the grain and varies directly as the
square of the fluid velocity. the lee
The second type is a negative pressure on Its magnitude velocity.
side of the grain, known as viscosity pressure.
depends on the fluid's coefficient of viscosity, density, and The third type of pressure
is a negative pressure on the top of the effect. is Bernoulli's law states that
grain, caused hy the Bernoulli wherever the fluid velocity
increased, as at the top of the soil static, isotropic,
grain, the pressure is reduced. or internal pressure.
This is called the
This Bernoulli effect causes lift on the grain grain lying
(Chepil, 1959). on the ground
The impact or velocity pressure on a soil is
known as form drag, and the pressure due to viscous
shear in the fluid close to the surface of the soil grain is called skin friction drag. The sum of these two forces is the total drag (Chepil
and Woodruff, 1963).
Chepil (1959) analyzed the drag, lift, and gravity grains at the threshold of their movement by wind.
forces
on
soil
Equilibrium between shape,
these forces and the soil grains was influenced by the diameter,
15
and
density
of
the
grains;
the
angle
of repose of the grains with
respect to the mean drag level of the fluid; the closeness of packing of the top grains; and the impulses of fluid turbulence associated with 1963; Chepil, 1959;
drag and lift (Bagnold, 1965; Cepil and Woodruff, Lyles and Krauss, 1971).
Experimental results (Malina, 1941; Chepil, wind speed -30.5 required those cm. to initiate
1945a,b) indicated that the minimum movement of the most erodible
particles
about 0.1 mm in The practical
diameter -- is about 16 km/hr at a height of limit
under field conditions where a mixture of sizes of single-grained After soil
materials is present is about 21 km/hr at the same height. particles
start to move, they are carried by the wind in three types of and suspension, depending upon
movement -- saltation, surface creep,
their size in relation to the velocity and turbulence of the wind.
3.2.2.2
Saltation
This is the major mode of soil movement. movement takes place
Roughly
50-75%
of
soil
via saltation (Chepil, 1945c). a
Particles in the or jumping
size range from 0.1 to 0.5 mm diameter move in action called saltation.
bouncing
After the initial motion, soil grains begin to It
roll along the surface under the direct impact pressure of the wind. was observed that soil
grains jumped vertically off a smooth surface cm) (Free,
after rolling downwind for a short distance (about 2 - 3 0 1911; Bagnold, 1965; Chepil, 1945c).
Bagnold (1965) and Chepil (1945c)
16
explained that the causes of the vertical rise of particles in saltation is due to the spinning of the grains and the steep wind speed gradient The air speed at any point near the grain is made up
near the surface.
of two components, one due to the wind and the other due to the spinning of the grain. in the same On the upper side of the grain, these two components direction, whereas act
below the grain they act in opposite at the
directions. bottom.
Thus, the speed is greater at the top surface than to
According
the Bernoulli effect, the pressure is decreased is propelled upward. The
above and increased below the grain, so it Bernoulli effect,
as mentioned earlier, is further intensified via the Others (Lyles
steep wind speed gradient that exists near the surface. and Krauss, 1971;
Bisal and Nielsen, 1962) observed that as the fluid back
threshold was approached, some particles began to vibrate, or rock and forth.
Erosive particles vibrated with increasing intensity as wind
speed increased and then left the surface instantaneously as if ejected. Evidence supported the hypothesis that the particle-vibration frequency energy of the
is related to the frequency band containing the maximum turbulent motion. The saltating particles rise
almost vertically, times
rotating from 200 to 1000 revolutions per second, travel 10 to 15 their
height of rise, and return to the surface at forward and downwind Chepil and Woodruff,
angles of 6 to 12 degrees (Woodruff et al, 1977; 1963). When they strike the soil
surface, they either rebound and by
continue their movement in saltation, or impart most of their energy
striking other grains, causing these grains to rise upward or roll along the surface.
17
3.2.2.3
Surface Creep
The rolling or sliding of larger called creep. These
particles
along
the
surface
is
particles are too heavy to be lifted by the wind the particles in saltation,
and are moved primarily by the impact of rather than by the direct
impact force of the wind (Chepil, 1945c). 1.0 mm, according to their
They range in diameter from about 0.5 to density and the wind speed.
Winds of extremely high speed may move mm diameter are
particles larger than 1.0 mm, but particles about 0.1 the most erodible.
Bagnold (1965) observed that at low wind speeds the but and as the wind speed is
grains move in jerks, a few mm at a time, increased the distance moved increases
more appears
grains are set in to be creeping
motion, until in high winds the whole surface forward. 1945c). About
7 to 25% of soil movement occurs in this mode (Chepil,
3.2.2.4
Suspension
This is the third type of soil movement, and may be characterized as the floating of small particles in the airsteam. Movement of these fine impact of
particles, smaller than 0.1 mm, is usually initiated also by particles in saltation.
Fine dust particles generally must be kicked up
18
by larger saltating grains, since they either do not turbulent air layer or else they protude into the
exhibit enough mutual cohesion to wind. Once they enter the
prevent being picked up directly by turbulent air layers, they
the
can be lifted high into the atmosphere by
rising eddies, and they are often carried a great distance before coming back to the earth's surface. Although only 3 to 38% of the soil movement of fine
movement is via particles erosion.
suspension
(Chepil,
1945c),
this
is probably the most easily recognized characteristic of wind
3.2.2.5
Effects on the Soil
Once soil grains are loosened and soil movement begins, of the jumping particles in saltation abrades the
the
impact This down of as
surface. and wears numbers Such
abrasion breaks down clods, vegetative residues set and in
destroys living as
stable
crusts,
vegetation. erosion
Increasing downwind.
particles are
motion
moves
increasing soil flow is called soil avalanching (Chepil and Milne, 1941; Chepil, 1946a). an eroding The rate of soil flow is zero at the windward edge of
field and increases with distance downwind until it reaches The distance downwind at which The more shorter
the maximum that a given wind can carry. the
maximum rate of flow occurs varies with soil erodibility.
erodible the soil, the greater the rate of avalanching, and the the distance to maximum flow.
19
The most serious consequence of wind erosion is the it causes. the coarser Chepil,
sorting
action
An eroding wind removes the fine porous particles and leaves and denser Chepil, the particles 1946b). sorting behind (Moss, 1935; Daniel,1936;
1957;
Chepil (1957a) observed that the most process is the peak (predominant) than the peak
distinct feature in diameter of the
saltating
grains.
Fractions
larger
diameter tend to remain in the wind-eroded field; particles smaller than that diameter tend to be carried in suspension from sand far and through loamy the sand
atmosphere.
The drifted materials derived
fields were composed of principally discrete, nonporous grains having an
3
average bulk density of 2.37 g/cm .. Their peak diameter was mm.
about
0.4
The materials drifted from finer textured soils were predominantly degree of porosity and an average bulk
aggregates with a distinct density of 1.70 g/cm .
3
The peak diameter of this drifted material was Wind erosion sometimes virtually removes
about 0.6 mm (Chepil, 1957a). the surface soil (Chepil,
1946b, 1957a; Zingg, 1954; Chepil, 1957b). some fine-textured soils derived from
Very little sorting occurs on loess.
This non-selective removal by wind is associated primarily with the atmosphere during past geologic
loess sorted and deposited from eras.
3.2.3
Factors Affecting Wind Erosion
20 Major factors that affect the amount of erosion from a are soil cloddiness, surface roughness, wind given field
speed, soil moisture,
unsheltered field length, and vegetative cover.
3.2.3.1
Soil Cloddiness
Soil clods diminish wind erosion because they are resist the forces of
large
enough
to
the wind and because they shelter other erosive Their firmness and stability
materials. depend
Clods form during tillage.
on soil mosture, compaction, organic matter, clay content, lime, Clods are broken down by weathering, tillage,
and microbial activity. implement and animal
traffic, and by abrasion (Woodruff et al, 1977).
The size and bulk density of clods, and the proportion of clods to total soil material affect the erodibility (ease of detachment and transport) From wind tunnel tests, Chepil of soils relatively free (1956) from determined relative
of a soil.
erodibilities
organic
residues as a of dry soil
function of apparent specific gravity and of aggregates in various sizes. nonerodible. 0.84 mm,
proportions
Clods larger than 0.84 mm in diameter were soil fraction greater than
Since then, the nonerodible
as determined by dry sieving, has been used as a simple index and Woodruff, 1963; Chepil,
of the wind erodibility of soils (Chepil 1957b).
21
3.2.3.2 Surface Roughness
In addition to clods and soil formed by tillage operations
aggregates, also
ridges
and
depressions
alter wind speed by absorbing and Chepil
deflecting part of the wind energy away from the erodible soil. and Milne (1941),
investigating the influence of surface roughness on soils, found that
intensity of drifting dune materials and cultivated the initial intensity of
drifting was always much less over a ridged Rough and surfaces also trap saltating
surface than a smooth particles. This
surface.
reduces
abrasion
the normal buildup of eroding
materials downwind.
While the general effect of surface roughness is to
reduce wind erosion, it also increases wind turbulence and exposes small areas to greater wind the forces. benefits Thus, excessive roughness may
substantially 1977).
reduce
(U.N.F.A.O., 1960; Woodruff et al,
3.2.3.3
Wind Speed and Soil Moisture
Wind speed and soil moisture both affect wind erosion. soil movement by wind varies directly as the cube
The rate
of
of wind speed inversely
(Bagnold, 1965; Chepil and Woodruff, 1963; Chepil, 1945c) and as the cube of average soil moisture (Chepil, 1956).
For convenience,
22 wind speed and soil moisture are considered together erosion climate factor (Chepil et al, 1962). as influenced as a local wind
It is an index of average by moisture content in
rate of soil movement by wind
surface soil particles and average wind speed.
3.2.3.4
Unsheltered Field Length
As mentioned earlier, rate of downwind across an eroding
soil
flow Erosive
increases winds
with
distance in of
field..
vary
highly
direction and seldom follow field boundaries. soil lost from a given
Therefore, the amount
field cannot be determined from the width or Thus, field length was initially considered
length of the field alone.
as the distance across a field in the prevailing wind-erosion direction. Also, if any barrier is present on the windward side of the distance it fully shelters field, the
the land from the wind must be subtracted
from the total distance across the field along the prevailing direction. However, direction. sometimes there is essentially no prevailing wind-erosion in the
Therefore, the preponderance of wind-erosion forces
prevailing wind-erosion direction is now used to assess equivalent field length (Skidmore and Woodruff, 1968; Skidmore, 1965).
23 3.2.3.5 Vegetative Cover
Good vegetative
cover
on
the
land
is
the
most
permanent
and
effective way to control wind erosion (Woodruff et al, 1977). dead vegetative matter protects the soil surface reducing wind speed from wind
Living or action by
and preventing much of the direct wind force from It also reduces rates of erosion by
reaching erodible soil particles. trapping soil
particles, which prevents the normal avalanching of soil Protection depends on Siddoway the amounts (Chepil, 1944;
material downwind. Chepil
et al, 1963;
et al, 1965), the size (Siddoway et al, (Chepil et al, In
1965; Woodruff and Siddoway, 1965), and the orientation 1955; Englehorn
et al, 1952; Skidmore et al, 1966) of the residue. residue stands
general, the finer the residue or the higher the the ground, the more it
above
slows the wind and the more it reduces wind with rows perpendicular to the wind
erosion. controlled
Standing
stubble
wind erosion much more effectively than did rows parallel to et al, 1952). Recent research (Lyles
the wind direction (Englehorn
et al, 1974) indicates that if residue is standing and is equidistantly spaced, much less residue is needed to control been reported previously. wind erosion than has
24
3.2.4 Wind Erosion Equation
Studies to understand factors influencing wind
the
wind
erosion and
process,
identify
major
erosion,
develop
wind erosion control 1961; The
methods led to the development of a wind erosion equation (Niles, Chepil and Woodruff, 1963; Woodruff and Siddoway, 1965).
relationship between average soil loss in tons per acre by wind
erosion
from a given field (E) and the five factors influencing wind erosion can be expressed as E = erodibility index, f(I', K' K', C, L', V), where I' is the soil
is the soil surface roughness factor, C' is the
climatic factor, L' is the unsheltered field length along the prevailing wind direction, and V is the equivalent quantity of vegetative cover.
The soil erodibility index is larger sieving. than 0.84 mm. in This
the
percentage
of
soil
aggregates
information the value
can best be obtained by dry is often estimated from
However,
practice
wind-erodibility
groups (Hayes, 1972), which are based on the soil type The surface roughness factor can The
or the predominant soil texture class.
be determined from the chart given by Skidmore and Woodruff (1968). climate factor can be obtained from maps given by the same
reference,
which indicate an approximate value for any location in the U.S. and the agricultural areas of Canada. be determined from 1) the The unsheltered field length angle of deviation of the factor can
prevailing
25
wind-erosion direction from right preponderance of wind erosion
angles forces
to
a
field
strip,
2)
the
in the prevailing wind-erosion and the field length.
direction, 3) the height of the wind
barrier,
Prevailing wind erosion directions, magnitudes, and the preponderence of wind erosion forces are available for 212 locations in the United States (Skidmore field. effects erosion. and Woodruff, 1968). quantity Other factors can be measured in the of vegetative cover expressed the
The of
equivalent
quantity, kind, and orientation of vegetative cover on wind V are available for most crops (Skidmore and
Values of
Woodruff, 1968; Siddoway et al, 1965; Woodruff and Siddoway, 1965; Craig and Tupelle, 1964). Conservation This equation has been used extensively by the Soil USDA, in designing control practices 1966). and Other
Service,
determining crop tolerance to wind erosion (Hayes, 1965, applications include: 1) predicting (Gillette
horizonal soil fluxes to compare et al, 1972), 2) estimating
with vertical aerosol fugitive
fluxes
dust emissions from agricultural and subdivision land (Wilson,
1975), and 3) estimating effects of wind erosion on productivity (Lyles, 1975).
Charts, tables, and other supplementary information needed to the wind erosion
solve
equation were given by Skidmore and Woodruff (1968). by the same
Examples of field applications of the equation were given authors and by Moldenhauer and Duncan (1969).
A computer program for
solving the equation is also available (Skidmore et al, 1970; Fisher and Skidmore, 1970).
26
3.2.5 Summary
This section summarizes basic information available on wind erosion. Wind erosion is caused by a strong turbulent wind blowing across an dry, and finely
unprotected soil surface that is smooth, bare, loose, divided. Wind erosion not only
sorts soil material, removing silt, sandblasts and destroys crops.
clay, and organic matter, but it also
The suspended dust also obscures visibility and pollutes the air.
Soil particles start to move They are carried by wind in
when three
wind
forces
overcome
gravity.
modes of movement -- saltation,
surface creep, and suspension. spinning, and jumping manner.
The saltating grains move in a bouncing, When they strike the soil surface, they dislodge other erodible particles.
break down clods and crusts, Movement in
and
suspension and in surface creep is a result of movement in Therefore, control of wind erosion depends on reduction or
saltation.
elimination of saltation.
Major factors affecting wind erosion are soil length,
cloddiness, surface roughness, wind speed, soil moisture, field and vegetative cover.
Studies of the influence of these factors on wind
erosion led to development of the wind erosion equation.
27
The general principles of wind erosion control include production of a greater fraction of clods larger than 0.84 mm, roughening the land
surface, establishing wind barriers or soil traps at intervals, reducing field length, and providing vegetative cover or residues.
3.3
EMISSIONS FROM PAVED AND UNPAVED ROADS
In contrast to wind erosion of soils, for which physical have
mechanisms
been discovered, resuspension of dust from paved and unpaved roads empirical methods the wide
has been investigated only to the point of obtaining of estimating * emissions. Evans and Cooper
(1980)
summarized
empirical formulas they used to estimate particle emissions from a variety of open sources.
The results of Cowherd et al, (1974) were used
to estimate emissions from unpaved roads.
Cowherd and his colleagues at the Midwest also provided formulas for estimating
Research
Institute paved
have roads
emissions
from
(Cowherd and Mann, 1976; Cowherd and Englehart, 1982).
Through analysis of TSP data from 20 sites over than
a
period
of
more
8 years, Newman et al, (1976) estimated the relative contributions
of wind and traffic to resuspension of urban street dust in Chicago.
28
3.4
COMPARISON OF AEROSOL MEASUREMENT METHODS
As new methods of sampling are adopted, it is important the new and old sampling methods in side-by-side operation. (1981) reviewed the recent literature
to
compare
Kolak and various
Visalli
comparing
size-selective samplers with the currently standard high-volume sampler. Samplers that exclude particles larger than about 15-20 m collect about 0.5 to 0.8 as much mass as the standard hivol sampler. only
Kolak and Countess
Visalli obtained very similar results in the Buffalo, NY area.
et al, (1980) compared the total mass collected by a dichotomous sampler with a 15 m cutoff with that collected by a standard hivol. a mean ratio of They found
0.44 with a standard deviation of 0.18 over a 40-day
period in November and December, 1978, at Denver.
4
EXPERIMENTAL METHODS
4.1
SAMPLER LOCATIONS
The rural samplers were located at the Water Survey's Bondville Road field site, about 10 km southwest field, of Champaign. by The site is a 3.0 of corn and
hectare (7.5 acre) grassy
surrounded
fields
29
soybeans. Precipitation and aerosol sampling are also carried out at scavenging for the
this site in support of our study of air pollution U.S. Department site of Energy both the (DOE).
This site is also a precipitation Multistate Atmospheric Power
sampling
for
DOE/EPA
Production Pollutants Study (MAP3S) network and the National Atmospheric Deposition Program (NADP).
A map of a portion of Champaign, showing the aerosol sampling within the NURP study basins, the is shown in Figure 1. South"
sites
Three hivol basin along
samplers were located in Mattis Avenue.
commercial
"Mattis
As shown in the figure, two samplers were located on the The dichotomous
west side of Mattis Avenue, and one on the east side. sampler Avenue. Champaign Figure 1. was located adjacent hivol this
to the hivol on the east side of Mattis sampler is was located the in a residential
An additional neighborhood;
designated
John Street site in
The hivol samplers on the east and west sides of Mattis Avenue all located 7.0
were
m ( 0.4 m) from the curb to minimize any effects of the various
distance from the street on the concentrations measured by samplers. curb. At the John
Street site the hivol was 17.6 m north of the
Figure 1.
Map of a portion of Champaign, Illinois, showing aerosol sampling sites, drainage basins, and sampling sites of the Water Survey's project in the Nationwide Urban Runoff Program. (Modified from Terstriep et al., 198l)
31
4.2
SAMPLING METHODS
All the aerosol samplers were installed so that their inlets were m above ground level. Thus,
2
they conformed to the standard height, must be placed. The
i.e., between 2 and 15 m, at which hivol inlets urban
hivol samplers were all positioned so that their inlets faced the It was important to be consistent on this detail since there is (Wedding et al., 1977; Ortiz, 1978) that hivol sampling
street. evidence
effectiveness varies with sampler orientation to the air flow.
The IEPA provided the standard hivol samplers prior to our use.
and
calibrated
them
Further, IEPA provided replacement pumps when needed. fiber and filters after for the hivol samplers,
The IEPA also provided glass weighed the filters before
exposure,
and calculated TSP
concentrations.
The urban hivol filters were changed at approximately 9:00 a.m. each Monday through Friday. This schedule provided five 24-hr duration
filters each week from each sampler. Monday
The first filter of the week began At the rural site, Friday mornings
morning and the last ended Saturday morning. Wednesday,
hivol filters were changed on Monday,
and
(about 10 a.m.); here also, the filters were exposed for 24 hr.
32
The dichotomous sampler collected a pair (fine and coarse) of duration filters each day of the week.
24-hr
The "fine" filter collected those
particles less than approximately 2.5 m, and the "coarse" filter between 2.5 and about 15 urn (aerodynamic diameter). between
The upper size 12 and 18 m
limit on the coarse particles is known to vary depending 1981).
on wind speed, for speeds less than 20 km/hr (Spengler et al, As et suggested al. , in the
Each sample began and ended at 9:00 a.m. manual for the sampler (Spengler
operations
1981), the use.
instrument was carefully checked for leaks and calibrated prior to Sampling in
Champaign began in June, 1981, and ended in October, 1981.
During normal operations, the dichotomous sampler was recalibrated every 2-4 weeks to insure accurate sample volume determinations.
4.3
ANALYSIS METHODS
As mentioned above, TSP measurements were made by standard methods.
the
IEPA,
using
The Teflon (TM) filters used in the dichotomous sampler were weighed before and after use to measure total aerosol mass. the balance used for these Unfortunately, measurements
sporadic difficulties with caused
the filter weights to be unreliable for the samples collected at site. Thus, total aerosol mass concentrations for the
the rural
33
dichotomous Champaign.
filters
are
available
only
for the samples collected in
Multielement analyses were dichotomous filters by NEA,
carried Inc.,
out
on
the
fine
and using of
coarse X-ray
Beaverton,
Oregon,
fluorescence methods.
The analysis technique was capable
detecting
Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Cd, In, Sn, Sb, and Pb. purposes of correcting Laboratory blanks were measured for the
the measurements for filter impurities. samples to insure
Field the
blanks were analyzed with each batch' of filters were not contaminated
that
during handling or while stored in the Standards were analyzed with every
sampler before or after exposure. batch to insure accuracy.
4.4
STREET SWEEPING OPERATIONS
The street sweeper made single passes along both east and west curbs of the Mattis South basin once per week, normally on Tuesday morning, The sweeper used was a 1973 Elgin,
between 15 April and 26 May 1981. Model Pelican "S," a
three-wheeled mechanical sweeper with right and rotary broom. Its sweeping path
left side gutter brooms and a main
using one outside broom was 2.4 m (8 ft) wide.
34
Measurements of collection efficiency that the sweeper collected about 67% by Bender (1982) indicated
of the total dust mass on the maintained for all particle Other mass
street. sizes
This approximate efficiency was
down to 125 m, below which efficiency decreased markedly.
measurements by Bender (1982) indicated that 46% of the total dust was in particles greater than 1000 m in diameter.
In six collected
sweepings was 28.8
of
the
Mattis
South
basin,
the
median
mass
g/curb m (102 lb/curb m i ) .
The maximum collection 13.2 g/curb m (47
was 44.5 g/curb m (158 lb/curb mi) and the minimum lb/curb m i ) .
4.5
SUPPORTING DATA
Several kinds of data were also collected to aid of the air quality data. These include
in
interpretation data, traffic
meteorological
specifically winds and precipitation, as well as information on densities and the dates when the streets were swept.
Precipitation was measured at the raingage sites, as shown
Mattis
Avenue
and
John
Street
in Figure 1, and provided to us, along with Water Survey NURP
traffic counts and dates of street sweeping, by the
35
project
mentioned
earlier.
Winds
applicable
to the rural site were
measured at The University of Illinois Willard Airport, 8 km east of the rural site. Urban wind measurements were made at the Water Survey
Headquarters, about 4 km east of the Mattis Avenue sites.
4.6
SOURCE APPORTIONMENT BY CHEMICAL ELEMENT BALANCE
The method used was described by Dzubay et al, (1980), the weighting factor suggested by Watson (1979).
which
uses
Watson's weighting ambient
factor has the advantage that it includes uncertainties in both
elemental concentrations and the elemental composition of the sources.
The method assumes that the aerosol composition can be described by:
(1)
where F is the predicted concentration of element k (ng/m3 ), k Cjk Mj is the fractional abundance of element k in source j, 3 is the total mass concentration of source j (ng/m ), and the sum
is over all sources j.
36
The elemental abundances of any component j must be normalized so that
(2)
where the sum over k includes all elements that contribute significant mass to the aerosol.
The source concentrations Mj are obtained by linear least squares, 2 in which X is minimized in the expression
(3)
where 0 is the observed concentration of element k and o is Watson's k k weighting factor, given by
(4)
37
where a represents the uncertainties in ambient concentrations and b k jk the uncertainties in the source compositions.
The
solution
to
Equations
(3)
and
(4)
was
reached
after
three
iterations.
The first iteration calculated the weights c using M = 0. k j
The next two iterations calculated the weights using the values of M j from the previous iteration. The sum in Equation (3) included only
those elements k for which a fit was demanded.
The elements used in the
fit were varied in this work, and the variations are discussed later, with the results.
5
RESULTS AND DISCUSSION
5.1
RURAL MEASUREMENTS
5.1.1
Comparison of Inhalable and Total Aerosols
A
comparison
of
particle
mass
concentrations
measured
by
the
standard and size-selective hivols is shown in Figure 2. represents perfect agreement between the two samplers. the standard hivol commonly measures higher
The solid line
It is clear that than the
concentrations
38
Figure 2.
Comparison of rural particle mass concentrations measured by standard and size-selective high volume samplers.
39
size-selective aerodynamic hivol, which excludes particles larger than 15 m
diameter.
The dashed line in Figure 2 is the least squares 0.59(TSP) + 6.8. In as
linear regression line fitted to the data: IP = this equation, IP is the inhalable
particulate is
concentration, total
measured by the size-selective hivol, and TSP particulate concentration, as measured
the
suspended Thus, the
by the standard hivol.
3 when the standard hivol measures a TSP concentration of 100 g/m , size-selective 3 g/m .
hivol would measure, typically, a IP concentration of 66
Table 1 lists comparisons between TSP and IP from the literature well as from this study.
as
In the cases of the four previously published However, all the
results, IP was measured with dichotomous samplers. samplers used had upper cutoffs of
approximately 15 m aerodynamic rural phase
diameter, the same as the size-selective hivol used in the of this study, so comparisons are valid.
40 Table 1. Comparison of inhalable particle/total particle ratios in this study with literature values.
Site Reference
No. of N* IP/TSP IP/TSP
locations sites
Pace and Meyer, 1979 Spengler et al, 1980 Countess et al, 1980 Kolak and Visalli, 1981
urban urban urban urban rural
10 4 1 4 1 1 1
** **
0.5
- 0.7 0.59 - 0.66 0.44
16 17 21 98 95/ 236 0.58 - 0.64 0.80 0.74 0.50
This study
rural urban
0.76
* Approximate number of observations per site. ** Not known.
41
The table lists two measures of the IP/TSP ratio. The first,
IP/TSP, is the ratio of the mean values of IP and TSP, while the second, IP/TSP, is the mean of all the ratios computed separately for each of samples. (Mean values are indicated by bars over the quantity.) pair
The four studies of inhalable/total ratios in the range
ratios
in
urban
areas
found
0.4 to 0.7, whichever form of the ratio is used.
Our urban value of IP/TSP (0.50) falls within this range.
Kolak southwest
and
Visalli
(1981)
compared
results
at
one
rural
site
of Buffalo to four urban sites in the Buffalo area, and found Since these authors measured IP able by to deduce that in the the might the
higher IP/TSP values in the rural area. with a dichotomous sampler, they
were
urban-rural differences were measured fine particle
probably
caused
differences differences
concentrations.
These
represent real differences in airborne particle concentrations, but authors note that they could have sulfate been
caused by various sampling or a variation in
difficulties, such as artifact
formation
sampling efficiency with particulate loading.
Our rural measurements, based on 98 pairs of samples, gave an IP/TSP ratio slightly smaller that Kolak and Visalli's rural value, but higher areas. Our rural IP/TSP
than any of the literature values for urban
42
ratio
was
also
somewhat
higher
than the two published urban values. rural
Thus our results agree quite well with previous measurements in a area near Buffalo, New York. Kolak and Visalli used above
"standard" ground for
measurement techniques, but did not state the height their sampler inlets.
Since the standard height for sampler inlets is Buffalo
2-15 m, it is not possible to tell what height was used for the area measurements, or how it compares to our inlet height of 2 m.
5.2
URBAN EIVOL MEASUREMENTS
5.2.1
Data Summary
An illustration of a small portion of the against time, is given in Figure 3. along winds. with daily
available
data,
plotted
The data include TSF measurements amount, dates of street
at four sites, sweeping, and
rainfall
Wind directions are shown by direction category.
The westerly wind category (W in Figure 3) includes days when the hourly mean wind direction was from the 203 to 338 degree sector for at least Wind of direction was not
75% of the hours between 6 a.m. and midnight. considered because relevant a very in the small remaining fraction hours of
the sampling period occurs then.
only
the
traffic
Similarly,
the easterly wind category (E) includes days when the hourly
mean wind direction was from the 23 to 158 degree sector at least 75% of
43
Figure 3.
Illustration of data collected in 1981 for evaluation of effects of street sweeping on air quality.
44
the hours in the same time interval. Mean The wind "other" wind direction speed during sample
category (0) includes all other days.
collection and daily maximum 1-minute gust speed are also shown.
Figure 3 shows that during more. period. the period
rainfall
was
frequent
and
relatively
heavy or the
shown, with seven days having 1.27 cm (0.5 in.) during
Streets were swept at approximately weekly intervals
Figure 3 includes only a small fraction of the data collected, occurred throughout the
but it illustrates several relationships that sampling period.
One of these is the generally high correlation between
TSP observations from the several samplers, including the one located in a residential area. Generally, on days when TSP concentrations were were unusually high at all sites.
unusually high at one site, they There
were still differences in concentration between sites, of course. the residential (John)
A very obvious difference in Figure 3 was that site had consistently
lower concentrations than the commercial sites. on opposite sides of the commercial
Differences between collectors
roadway are also apparent in Figure 3.
5.2.2
Effects of Street Sweeping
45
5.2.3
TSP Concentrations Upwind and Downwind of Mattis Avenue
In assessing possible effects of street sweeping on air quality, was first
it
necessary to establish whether the test roadway was indeed a To examine this question, we divided the data
source of aerosols. according
to the wind direction categories mentioned earlier.
This was the
done to assess which samplers were upwind, and which downwind, when wind direction was either
easterly or westerly (i.e., having a large identify those
cross-wind component, relative to Mattis Avenue) and to days
when the wind (1) was from either north or south, or (2) blew from and from the west for
the east for a portion of the sampling period another portion of it,
so that it was difficult to assign upwind and
downwind directions.
Results for the east and west wind cases are shown
in Figure 4, and those for the "other" winds in Figure 5.
Figure 4 includes only measurements made winds, and shows a clear of
in
easterly
or
westerly
tendency for the downwind samplers to have these direction classes. The upwind
higher TSP values for both samplers
experienced higher TSF concentrations with westerly winds than 3 with easterly winds. For example, the upwind means were 71 ug/m for 26 3 days with west winds and 60 g/m for . 30 days with east winds, a For both east and
difference of 18%, relative to the east wind value. west winds, however, the downwind samplers had
higher TSF values,
46
Figure 4. Comparison of TSP concentrations upwind and downwind of Mattis Avenue on days when winds were predominantly from the east or west.
47
Figure 5. Comparison of TSP concentrations on the east and west sides of Mattis Avenue on days when winds were not predominantly from the east or west.
48
clearly showing the road to be before, the solid line in
a the
source figure The
of
airborne
particles.
As
represents perfect agreement dashed line is the least
between upwind and downwind samplers. squares regression line
for estimating the downwind concentration, D, Since the slope
3
from the upwind value, D: D = 0.94(U) + 21.7. regression indication attributed
of
the
line is close to 1.0, the intercept, 21.7 g/m gives a good of the extra airborne dust concentration that may be
to the roadway.
The slope near 1.0 shows that the added TSP constant for all
concentration caused by the roadway is approximately upwind concentrations.
To consider the possibility that the differences in mean upwind concentrations were the
TSP
result of differences in mean wind speed, the The mean value of the daily = 2.0 5.0
following wind speed data are presented. mean wind speed,
for 26 west wind samples, was 6.4 m/sec (S.D. was
m/sec), while the corresponding value for 30 east wind samples m/sec (S.D. = 2.1 m/sec).
Analogous means for the maximum gusts were wind samples and 18.2 the m/sec
24.0 m/sec (S.D. (S.D. = 5.4
= 8.6 m/sec) for west for east wind
m/sec)
samples.
Thus,
relative (32%)
differences in mean wind speed (28%) and mean maximum gust between easterly and westerly winds were
speed
somewhat greater than the speed differences
relative differences in TSP concentrations, but wind cannot be ruled out as a possible cause.
Another possible cause is that winds, since it
the site was closer to agricultural sources with west was located
near the western edge of the Champaign-Urbana metropolitan
49
area.
Beside differences in upwind TSP between east and west winds, examination of Figure 4 also
close
suggests systematic differences in the Although the
upwind/downwind differences between east and west winds.
3 overall upwind/downwind difference was 21.7 g/m , in 26 west wind cases 3 the upwind and downwind means were 73 and 82 g/m , respectively, 3 is a difference of only 9 g/m , or 11% of the downwind mean. which
Figure 5 shows the relationship between TSP east and west sides of
concentrations
on
the
Mattis Avenue for the "other" wind category. side of the street to have
There was no strong tendency for either higher values, as would be
expected in the absence of a strong wind
component perpendicular to the street.
5.2.4
TSP
Concentrations
in Commercial and Residential Areas with and
without Sweeping
The evidence presented thus far makes it clear that the indeed
street
was
the source of airborne particles, since the downwind sampler had had
higher concentrations in both east and west winds, but neither side predominantly higher concentrations with "other" winds.
Next we address
50
the question of whether street sweeping had any effect on strength of this source. This reducing the
is done by comparing downwind-upwind A
differences during periods with and without regular weekly sweeping. day was
considered to be in the "swept" period if the street was swept days, and in the "unswept"
on that day or any of the previous seven period otherwise.
A second comparison examines commercial-residential criteria) at
differences with and without weekly sweeping (by the same the commercial site. (The residential and thus site was was swept
not in one of the irregularly, at
experimental or control basins,
intervals of 2-4 weeks, during the sampling period.)
Table 2 presents results of a two-way analysis of 1977) on downwind-upwind differences ( TSP1). and time as sources of variance. downwind-upwind
variance
(Brown,
It examines both sweeping
The upper part of the table gives mean
differences in TSP concentrations for swept and unswept 30 June). higher of
periods in both "spring" (up to 30 June) and "summer" (after There appear to be differences between seasons between
(with
concentrations in summer than in spring), but sweeping and not sweeping.
not
periods
The analysis of variance table in the lower no significant differences
part of the table confirms that there were
between periods of sweeping and no sweeping, but also indicates that the differences between seasons were significant at the 0.10 level, but at the 0.05 level. not
Table 3 presents results of a similar analysis of TSP differences ( TSP2). Again,
variance on
commercial-residential
differences between seasons appear large (with concentration differences
51
Table 2. Results of 2-way analysis of variance on upwind-downwind TSP differences (g/m3 ) across the test roadway. Soring Swept Unswept Number of cases Mean S.E.M. 12 12.9 4.2 11 12.8 5.2 Summer Swept Unswept 4 19.8 1.8 18 23.0 3.1
Source of variance Time Sweep ing Interaction Error
Sum of squares 603.4 20.7 23.4 8369.3
Degrees of freedom 1 1 1 41
Mean square 603.4 20.7 23.4 204.1
F 2.96 0.10 0.11
Tail probability 0.0931 0.7518 0.7368
52
Table 3. Results of 2-way analysis of variance of commercial-residential TSP differences (g/m3). Swept Number of cases Mean S.E.M. 27 22.0 3.0 Spring Unswept 19 19.6 2.0 Swept 8 7.2 3.5 Summer Unswept 36 11.0 1.7
Source of variance Time Sweeping Interaction Error
Sum of squares 2251.4 8.1 159.1 12166.8
Degrees of freedom 1 1 1 86
Mean square 2251.4 8.1 159.1 141.5
F 15.91 .06 1.12
Tail probability 0.0001 0.8116 0.2919
53
higher in spring than in summer in this case), but swept and unswept periods look small. differences between
The analysis of variance table in differences between swept
the lower part of Table 3 confirms that the and unswept
periods were not significant, but also shows that seasonal Interactions between
differences were significant at the 0.0001 level.
sweeping and time were not significant in either comparison.
The main question addressed by this paper Although the
has
now
been
answered.
test roadway was shown to be a source of TSF, no evidence that street sweeping had any effect, either
was found to indicate
beneficial or detrimental, on air quality at 7 m downwind.
Table 4 summarizes TSP concentrations and the discussed above. Mean TSP
two
TSF
differences
values are shown for three different data the samples used mean used in the downwind-upwind
sets: (1) all samples, (2) comparison, comparison. commercial and (3) the
samples
in the commercial-residential TSP concentrations sites do in the
It is apparent that the (Mattis) and residential
(John)
not vary much
between sample sets. 3 yg/m and a minimum
For example, the Mattis mean shows a maximum of 87 3 of 84 yg/m in the three data
,3
sets shown.
Similarly, at the John site the highest mean was 65 yg/m 3 62 yg/m .
and the lowest
Thus, regardless of the data set, mean TSP values were higher The Mattis site had higher
at both sites in spring than in summer. 3 concentrations by about 20 yg/m
3 in the spring and about 8 yg/m in the
54
Table 4. Summary of TSP concentrations and differences by season for three different sample sets. Spring Mean +- S.E. 53 52 23 23. 46 46 46 86 65 87 13 84 62 21 +++++++6 5 7 3 5 4 2
Sample set All samples Mattis John
Site
N
Summer Mean +- S.E. 62 +- 2 54 +- 2 65 22 63 55 10 +- 3 +- 3 +- 2 +- 2 +- 2
77 74 33 22 68 68 44
ATSP1**
Mattis Downwind-upwind difference Mattis John Mattis-John difference
ATSP2***
Includes days with at least one sampler from both sides of Mattis Avenue. Both sides weighted evenly. ** Includes days with measurements on both sides of Mattis Avenue, and east or west winds (as defined in the text). *** Includes days with measurements on both sides of Mattis Avenue and at John Street, with no restriction on wind direction.
*
55
summer.
TSP concentration differences, however, display behavior depending on the data set was at
different used. in
seasonal mean than mean in
,The spring The larger
commercial-residential difference (ATSP2) summer, as were TSP concentrations
higher both
sites. was
downwind-upwind difference (ATSP1 ), on the other hand,
the summer, when the Mattis site had lower concentrations.
The higher TSP concentrations
in
spring
are
consistent
with
an
agricultural soil dust source, which would be expected to be stronger in the spring from tilling operations, a lack of ground cover by crops, and generally stronger winds. It is reasonable for the Mattis site to have
higher TSP concentrations than the John St. site because it is closer to the sources with prevailing winds, and because the John St. site is more mature trees
located in a somewhat older part of town, where the may help to remove particles from the atmosphere.
The downwind-upwind differences were greatest concentrations in were lower at
summer,
when
TSP
both the John and Hattis sites. that roadway
Others
(e.g., Cowherd and Englehart, 1982) have found depend on both traffic volume
emissions We in
and roadway surface silt loading. greater traffic volumes
could find no evidence for
significantly
56
summer than in spring, but according to data provided by Bender (1982) , the mean and standard error of 11 spring measurements loadings were 108 5 g/curb-m, of total street
while the same values for 10 summer Thus, the spring/summer ratio of
measurements were 189 11 g/curb-m. loadings, 1.75, is very
close to that of ATSP1, 1.69, and it appears could well
that differences in street surface loadings between seasons have caused the observed seasonal differences in ATSF1.
5.3
URBAN SOURCE APPORTIONMENT USING DICH0T0M0US SAMPLER MEASUREMENTS
5.3.1
The Data
The fine and coarse aerosol samples were each analyzed ambient for
collected
near
Mattis
Avenue
about 25 elements. are
Results, in the form of a in Tables 5 and 6.
summary of Maximum,
concentrations,
given
minimum, and median concentrations are shown, along with means The percentage of filters for which the
and their standard errors. various
elements were present in less than detectable concentrations is of the confidence For we the may have in the
also shown as an indication measurements for the
various
elements.
purposes of these of the
tables, less than detectable values were assumed to be one-half detection limits.
Elements detected on 20% or less of the fine filters Additional elements detected
included Cl, Rb, Sr, Cd, In, Sn, and Sb.
57
Table 5.
Summary of ambient fine particle elemental concentrations measured on 95 sampling days, Mattis Ave., Champaign, Illinois, June - October, 1981. % of Concentration* . ng/m 3 filters Median Minimum Mean <D.L. Maximum 0 944 168 209 46 0 2308 283 366 98 0 287 106 92 20 2187 0 356 7938 2632 4.0 100 2.0 12 4.8 51 0 13 301 63.4 0 45 13 285 52.6 5.0 0.5 3 5.47 39 1.0 0.5 53 1.18 4 2.0 0.5 39 3.60 15 5.0 1.0 1 5.42 24 61 17 0 72.2 306 1.0 0.5 64 1.08 3 3.0 0.5 10 3.70 30 17 5.0 0 19.5 86 2.5 1.5 79 2.75 7 1.0 0.5 47 1.46 4 21 7.0 0 25.9 64 0.5 0.5 93 0.98 3 1.0 0.5 94 1.19 3 5.0 3.0 82 5.93 4.0 34 3.0 96 4.74 4.5 3.5 10 96 5.80 8.0 6.0 90 22 9.94 135 67 0 158 24 748 15000 0 17032 40000
Element Al Si P S Cl K Ca Ti V Cr Mn Fe Ni Cu Zn As Se Br Rb Sr Cd In Sn Sb Pb Total mass
*
S .E .M. 14.0 32.6 5.99 175 0.25 4.83 4.06 0.56 0.07 0.34 0.34 5.30 0.05 0.37 1.22 0.13 0.08 1.49 0.06 0.06 0.45 0.18 0.28 0.42 8.86 774
Measurements less than detection limits were taken as one-half detection limits.
58
Table 6.
Summary of ambient coarse particle elemental concentrations measured on 95 sampling days, Mattis Ave., Champaign, Illinois, June - October, 1981. % of filters <D.L. Maximum 0 1730 0 6661 0 80 0 909 0 340 0 462 0 5790 0 108 3 8 21 12 0 41 0 968 41 3 10 8 0 50 88 6 81 1 0 104 73 3 10 15 82 43 13 81 83 13 27 85 312 0
Concentration*, ng/m3 Median Minimum 574 98 2306 432 52 19 183 42 34 6.0 42 155 330 1197 38 8.0 3.0 1.0 4.0 0.5 12 3.0 373 85 1.0 0.5 2.0 0.5 3.0 17 1.0 1.5 1.0 0.5 12 4.0 1.0 0.5 3.0 0.5 3.5 1.5 5.0 2.5 5.0 2.5 7.5 3.5 45 19 18000 2000 Mean 619 2470 51.1 235 41.6 172 1339 42.2 2.76 4.40 12.8 408 1.19 2.50 18.0 1.77 0.82 15.1 1.18 3.05 5.06 5.21 5.62 9.38 58.3 19484 S.E.M. 33.2 141 1.51 16.9 4.31 8.18 82.4 2.31 0.15 0.33 0.65 18.5 0.05 0.14 0.84 0.10 0.02 1.26 0.06 0.18 0.51 0.23 0.26 0.47 4.07 1118
Element Al Si P S Cl K Ca Ti V Cr Mn Fe Ni Cu Zn As Se Br Rb Sr Cd In Sn Sb Pb Total mass
52000
* Measurements less than detection limits were taken as one-half detection limits.
59
on
50%
or
less of the filters were: V, Ni, and As.
Thus, 15 elements
were detected on more than half of the fine filters.
The elements As, Se, Cd, In, Sn, and Sb were detected
on
not
more
than 20% of the coarse filters, and in addition, Rb was detected on less than half of the coarse filters. This means that the remaining 18
elements were detected on at least half of the coarse particle filters.
As the earlier discussion composition, necessary considered to as well as the
of
the
method
shows,
data
on
source
ambient CEB
concentrations of the elements, are Compositions of six sources
apply
method.
likely
to contribute to the measured ambient concentrations fine and coarse particles,
are given in Tables 7 and 8, representing respectively.
The compositions of urban street dust, rural both limestone-covered and washed
soil,
and
dust
from
gravel-covered unpaved rural roads and the
were measured, the ammonium sulfate composition is well known, composition (1980). sources of
vehicle exhaust ("mobile") was taken from Dzubay et al, particle the emissions of the latter two
Both fine and coarse were assumed to
have
same respective compositions, but were measured for the other
separate fine and sources.
coarse
compositions
Table 7.
Source compositions (%), fine fraction, based on resuspended samples. Ammonium sulfate is based on known chemical composition, and "mobile" was taken from Dzubay (1980).
Element
Urban street dust Nx mean SEM
N
Rural soils mean SEM
N*
Limestone road dust mean SEM
Washed gravel road dust N value
Ammonium sulfate
Mobile
Al Si P S Cl K Ca Ti V Cr Mn Fe Ni Cu Zn As Se Br Rb Sr Cd In Sn Sb Pb Other
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
7.20 21.22 0.229 0.727 0.251 1.08 10.37 0.266 0.021 0.038 0.116 3.98 0.018 0.020 0.157 0.014 0.0027 0.034 0.009 0.022 0.022 0.038 0.037 0.050 0.681 53.40
0.322 0.806 0.006 0.070 0.009 0.035 0.840 0.005 0.003 0.002 0.005 0.175 0.002 0.003 0.013 0.004 0.0002 0.008 0.002 0.002 0.006 0.016 0.007 0.016 0.105
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
12.65 44.30 0.256 0.214 0.079 1.69 0.946 0.477 0.027 0.022 0.046 3.98 0.006 0.010 0.019 0.0029 0.0009 0.004 0.012 0.012 0.013 0.011 0.026 0.025 0.008 35.16
0.298 1.533 0.019 0.015 0.006 0.070 0.090 0.014 0.001 0.001 0.005 0.221 0.001 0.002 0.001 0.0006 0.0003 0.0009 0.001 0.002 0.002 0.001 0.005 0.003 0.001
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
4.72 12.74 0.132 0.694 0.330 0.738 22.65 0.177 0.011 0.011 0.113 2.08 0.002 0.009 0.012 0.0017 0.0012 0.003 0.006 0.033 0.0068 0.012 0.018 0.017 0.013 55.47
0.241 0.542 0.009 0.005 0.005 0.071 0.880 0.012 0.001 0.0007 0.008 0.175 0.0007 0.003 0.002 0.0002 0.0004 0.001 0.000 0.002 0.0003 0.003 0.005 0.0005 0.001
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
8.17 30.10 0.203 0.574 0.199 1.53 9.38 0.307 0.020 0.022 0.154 5.14 0.006 0.007 0.026 <0.005 <0.001 <0.002 0.009 0.016 <0.028 <0.016 <0.035 0.039 0.015 44.06
24.24
0.62 0.93
0.060 0.099 0.273 4.263
75.76
14.88 78.88
* Number of samples. ** Dzubay (1980).
Table 8.
Source compositions (%), coarse fraction, based on resuspended samples. Ammonium sulfate is based on known chemical composition, and "mobile" was taken from Dzubay (1980).
Element
Urban street dust N mean SEM
N*
Rural soils mean SEM
Al Si P S Cl K Ca Ti V Cr Mn Fe Ni Cu Zn As Se Br Rb Sr Cd In Sn Sb Pb Other
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5.21 20.57 0.138 0.513 0.226 1.03 12.28 0.299 0.014 0.023 0.103 3.38 0.009 0.015 0.102 0.007 0.0005 0.023 0.006 0.023 0.007 0.020 0.014 0.015 0.436 55.54
0.213 0.964 0.006 0.032 0.010 0.041 0.948 0.013 0.001 0.001 0.002 0.139 0.001 0.003 0.007 0.003 0.000 0.007 0.0023 0.002 0.0006 0.005 0.003 0.001 0.074
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
9.18 38.33 0.156 0.152 0.077 1.64 0.785 0.488 0.024 0.019 0.039 3.36 0.005 0.010 0.016 0.0013 0.0007 0.002 0.012 0.010 0.005 0.004 0.012 0.010 0.008 45.66
0.330 1.129 0.009 0.005 0.004 0.028 0.087 0.012 0.0005 0.002 0.003 0.245 0.001 0.002 0.001 0.0003 0.0001 0.0002 0.0004 0.0002 0.0011 0.0005 0.004 0.0001 0.002
N* 3 2.30 3 7.75 3 0.050 3 0.635 3 0.342 3 0.486 3 24.11 3 0.127 3 0.008 3 0.008 3 0.093 3 1.34 3 0.004 3 0.007 3 0.008 3 0.0012 3 0.0008 3 0.003 3 0.003 3 0.038 3 0.003 3 0.008 3 0.008 3 0.008 3 0.008 62.67
Limestone road dust mean SEM
Washed gravel road dust N* value
Ammonium sulfate
Mobile**
0.401 1.400 0.004 0.033 0.008 0.053 0.666 0.017 0.001 0.001 0.007 0.146 0.0003 0.002 0.0003 0.0007 0.0002 0.001 0.001 0.001 0.002 0.005 0.002 0.005 0.001
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
4.39 20.50 0.100 0.431 0.203 1.24 10.42 0.267 0.016 0.016 0.110 3.46 0.004 0.005 0.016 <0.002 0.000 0.002 0.007 0.016 <0.006 <0.004 <0.007 <0.009 0.009 58.77
24.24
0.62 0.93
0.060 0.099 0.273 4.263
75.76
14.88 78.88
* Number of samples. ** Dzubay (1980).
62
Samples of dry-sieved (<53 m) source materials were resuspended a
in
laboratory chamber and collected on separate filters in a dichotomous that used for the ambient sampling. The
sampler closely matching resuspensions, Inc., which also ambient samples. sample
collections, and analyses were performed by NEA, the X-ray fluorescence analyses on the
performed
The measured compositions shown in Tables 7 and 8 based on the
are
mean
values
number of samples shown for each of the various sources. For elements
Only a single sample of washed gravel dust was analyzed. not detected in this
single sample, values of one-half the detection This applies only to six
limit were used for the source composition. elements
in the fine fraction, and five in the coarse fraction, none of The standard errors given
which was used in the least squares fitting. in
the tables were used as the uncertainties on the source compositions Similarly, the standard errors of
needed as input data in Equation (4). the mean ambient
concentrations from Tables 5 and 6 were also used in
Equation (4).
The variables that may be manipulated in search
of
a
satisfactory
CEB solution include 1) the number and nature of the sources considered, and 2) the elements used in the fit. In general, it appears that the
fewer elements used, the better the fit.
One must insure, however, that or two more, elements
there are at least as many, and preferably one
63
than
sources,
and
that
all
significant
sources of the elements are
included among the sources considered. CEB solution, poor results were
In the search for a satisfactory indicated by 1) any negative
contributions to the aerosol mass, 2) total source contributions of more than 100% (both of of which of are physically and impossible), and 3) large observed concentrations
deviations
ratios
predicted
(larger/smaller) from 1.0.
The CEB method may be used in a number of ways to contributions from a set of ambient data.
apportion
source
The usual method is to apply the mean concentrations of
the technique to a data set consisting of each
element. Alternatively, the technique can be applied separately to and the results presented in the form of
each individual sample, histograms showing the
distributions
of percent contributions of the set
various sources identified. of mean concentrations.
In this work, we first analyzed a data Once the main sources
were identified, we same
applied the technique to each individual sample, assuming that the set of sources
identified for the mean was active for each individual
sample also. This was was somewhat of a simplification, since it ignored sources that may have been important mean on some individual days, but However, the
contributed only negligibly to the results
concentrations.
serve to show the kind of sample-to-sample variability that can
occur in source contributions to total aerosol mass from day to day.
64
5.3.
CEB on Mean Ambient Concentrations
Results of a series of attempts to fit the mean fine are given in Table 9.
particle
data
Six trials were attempted before a satisfactory fit and
solution was reached.
The first trial used 13 elements in the
all six potential sources:
1. 2. 3. 4.
urban street dust ("street" in the table), local rural soil ("soil"), limestone unpaved road dust ("limestone"), dust "from unpaved roads covered with washed glacially-derived gravel ("washed gravel"), ammonium sulfate ("sulfate"), and vehicle exhaust ("mobile").
5. 6.
Compositions Table 7.
of fine particles derived from these sources were given in
Trial 1 was obviously
less
than
satisfactory
because
calculated
contributions of three of the six sources considered were negative.
Trial 2 used the same six sources, but a elements: Al, Si, S, K, Ca,
shorter
list
of
fitting
Fe, Br, and Fb.
Results were somewhat negative,
better, because only two of the calculated contributions were
Table 9.
Summary of CEB results on fine particles, Champaign dichotomous filter data, based on mean observed element concentrations. Ratio of predicted and observed values of elements in fit (larger/smaller) median maximum 1.23 4.66 (Ca) 1.12 3.15 (K) 1.22 2.74 (K) 1.06 1.40 (Ti) 1.67 2.47 (Ca) 1.00 1.01 (Fe)
Elements Trial used in fit* Long list 1 Short list 2 Short list 3 Al,S,Ca,Ti,Fe,Pb 4 Al,S,Ca,Ti,Fe,Pb 5 Al,S,Ca,Fe,Pb 6
Contribution of sources considered, % Lime- Washed SulStreet Soil stone gravel fate Mobile [phone] 25.9 2.6 4.2 -5.8 -11.7 55.3 2.3 5.3 5.4 55.5 2.1 6.3 5.3 55.5 6.5 5.2 55.4 1.9 7.3 5.3 55.5
Total mass accounted for. % 68.0 70.5 68.5 69.2 67.1 70.0
*Long l i s t = Al, S i , S, Cl, K, Ca, T i , Cr, Mn, Fe, Zn, Br, and Pb. Short l i s t = A l , S i , S, K, Ca, Fe, Br, and Pb.
66
and
the median and maximum ratios of predicted and observed values were results were still unsatisfactory because of the
smaller, but the
negative contributions calculated for limestone and washed gravel.
Trial 3 kept the same short list of fitting elements, but eliminated limestone and washed gravel as potential sources, as suggested by the This time there were no negative
results of the first two trials.
contributions, but the median and maximum ratios were still unacceptably high. ratios Close examination of the results showed that the Si, were the largest; that K, and Br
is, predicted concentrations of these the largest
elements, based, on a least squares fit of the data, showed deviations from observed concentrations.
Thus, in Trial 4, the four sources were kept the same as in Trial 3, but fit. trial, Si, K, and Br were removed from the list of elements used in the
The calculated contributions were little changed from the previous but the ratios were considerably improved, the median being 1.06 This suggested we wanted removing to see Ti from the
and the maximum 1.40 (for T i ) . elements in the fit, but
first
the results of
eliminating another potential source, namely soil.
Thus, Trial 5 was carried out with the same soil was not considered as a potential source.
fitting
elements,
but
The results in Table 9
67
show that elimination of negative contributtions
soil
as
a
source the
was
not
desirable.
No
occurred,
but
median and maximum ratios
increased dramatically from the results of Trial 4.
Having learned that soil was a necessary carry out another trial
source,
we
proceeded
to
without Ti as a fitting element, in hopes of The results of Trial 6, shown in
improving on the previous results. Table 9, were excellent.
No negative contributions appeared, the total
contributions of all the sources considered did not exceed 100%, and the maximum ratio of observed and predicted values was 1.01. This best fit indicates that
of the observed concentrations to the potential sources urban street dust contributed 1.9%
of the total fine particle mass, The
rural soils 7.3%, ammonium sulfate 55.5%, and vehicle exhaust 5.3%. remaining 30% was not
explained, but is likely to consist largely of which are not measured in our
water, nitrates, and organic matter, analyses.
The detailed results of Trial 6 on the fine particle means are given in Table 10. The upper part of the table gives the results for the lower part gives the
elements used in the least squares fit, and the results for the remaining elements. The
table shows the calculated each source, and the
("predicted") contribution of each element from
sums of all contributions ("predicted" total concentrations), as well as the corresponding observed concentrations. The rightmost column gives
68
Table 10. Chemical element balance for Champaign dichotomous fine particles. Contributions from sources (ng/m3 ) Street Soil Sulfate Mobile [phone] Total concentration (ng/m3 ) Observed* Predicted 208 2632 53 73 158 209 2632 53 72 158 +- 14 +- 175 +- 4 +- 5 +- 9 Median Larger** Smaller 1.00 1.00 1.00 1.01 1.00 = 1.00
Element
Elements used in fitting Al 27 181 S 3 3 Ca 13 39 Fe 57 15 Pb 3 0
Remaining elements Si 80 P 1 Cl 1 K 4 Ti 1 V 0 Cr 0 Mn 0 Ni 0 Cu 0 Zn 1 As 0 Se 0 Br 0 Rb 0 Sr 0 Cd 0 In 0 Sn 0 Sb 0 201 Other Total % Contrib 377 1.9
632 4 1 24 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 501 1427 7.3
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8188 10808 55.5
0 0 10 0 0 0 0 1 0 0 3 0 0 44 0 0 0 0 0 0 822 1042 5.3
712 5 12 28 8 0 0 2 0 0 4 0 0 45 0 0 0 0 1 1 9712 13473 70.0
366 106 5 63 5 1 4 5 1 4 19 3 1 26 1 1 6 5 6 10 19480
++++++++++++++-
++++ ++-
33 6 0 5 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0
1.94 21.3 2.40 2.25 1.60
---
2.50
---
4.75
---
1.73
-----
6.00 10.00
* Uncertainty is the standard error of the mean. **
Ratio of predicted and observed values (larger/smaller).
69
the ratio of the larger of the predicted and observed concentrations to For the elements used in the fit, the results in Table agreement between the predicted remaining and 10
the smaller. show
excellent
observed a
concentrations: i.e., ratios near 1.00. few were overpredicted, i.e.,
Of the
elements,
P/0 > 0, but most were underpredicted,
i.e., 0/P > 0. since many of
Dnderprediction was expected for many of these elements, them are emitted by sources not considered in this
analysis.
Continuing with results of the CEB analysis of mean we discuss next the various trials in the
concentrations,
analysis of the coarse
particles; these results are summarized in Table 11.
Decisions regarding specific sources to consider in the various trials on the
CEB
coarse particle means were guided by the principle that difficult to resolve reliably
sources having similar composition are (Gordon, 1980).
Since we perceived that urban vehicle-raised dust would of
likely be an important aerosol source, and that other likely sources aerosol, such as
vehicle exhaust and soil dust, were also known to be
sources of the street dust (Hopke et al, 1980), so that the compositions of the street dust might be quite similar to several other sources. source, for example, could be The
Thus, it was not clear whether a soil considered at the same time as a
street dust source, or not.
following discussion illustrates this concern.
Table 11.
Summary of CEB results on coarse particles, Champaign dichotomous filter data, based on mean observed element concentrations. Ratio of predicted and observed values of elements in fit (larger/smaller) median maximum 1.12 1.36 (Br) 1.11 1.67 (Br) 1.16 1.35 (Fe) 1.04 1.13 (Br) 1.08 5.00 (Br) 1.01 1.08 (Ca) 1.02 1.14 (Ti) 1.02 1.03 (Ca,Fe) 1.00 1.06 (Ca) 1.02 1.07 (Al)
Elements used in fit* Trial 1 Long list 2 Long list 3 Long list 4 Long list 5 Long list 6 Short list 7 Short list + Ti 8 Short list 9 Short list 10 Short list + Ti
Contribution of sources considered, % Lime- Washed SulStreet Soil stone gravel fate Mobile 4.3 53.2 12.7 6.2 1.2 4.1 74.0 0.9 4.5 2.3 39.7 32.5 5.2 4.3 6.6 29.7 2.2 4.1 75.9 4.2 69.2 0.3 4.2 72.2 0.2 4.3 66.0 0.4 2.9 4.3 53.0 0.7 8.2 4.3 50.2 0.8 7.2 6.5
Total mass accounted for. % 77.6 79.0 79.0 48.0 80.0 73.7 76.6 73.6 66.2 69.0
Long list = Al, Si, S,K, Ca,Fe, Br, and Pb. Short list = Al, S, Ca, Fe, Pb.
71
Results of ten successive CEB trials are given first five trials used
in
Table
11.
The
Al, Si, S, K, Ca, Fe, Br, and Pb in the least in
squares fit, and the last five used only Al, S, Ca, Fe, and Pb, and two cases, also Ti.
The table refers to these element groups as the not
"long" and "short" lists, respectively, but note that the lists are the same as those given
the same labels in the analysis of the fine
particles (Table 9 ) .
Trial 1 considered five limestone dust, sulfate,
sources: and
urban
street
dust,
soil
dust,
vehicle
exhaust.
The calculated mass
contributions were all positive, and together accounted for 77.6% of the total observed mass. The median ratio of predicted in and observed the fit was
concentrations (larger/smaller) for the elements used 1.12, and the maximum ratio was 1.36 for Br.
This was a reasonably good number
fit for the first trial, but of course it was necessary to try a
of other combinations of sources to see which would give the best fit.
Trial 2 attempted a fit using only street sources, and the same fitting elements for
dust, as in and
soil,
and
mobile
the previous trial. the median ratio
Slightly more mass was accounted decreased 1.67. slightly, but
overall,
the maximum ratio (still for Br) increased to
72
Trials 3 and 4 were carried out to see source could effectively be replaced
if
the
urban
street
dust
with a combination of soil and and limestone sources along
unpaved road sources. with well. overall sulfate and
Trial 3 used soil
mobile,
and Trial 4 added a washed gravel source as maintained the 79.0%
The results in Table 11 show that Trial 3 mass explained
in the previous trial, and achieved median and achieved
maximum ratios of 1.16 and 1.35, respectively, whereas Trial 4 ratios even closer to 1.0, but
accounted for only 48% of the total
aerosol mass.
Trial 5, the last with the long list of fitting elements, similar
was
very
to Trial 2, but was an attempt to see whether the mobile source as street dust was included. Trial 5
could be eliminated as long accounted
for 80% of the total mass, the highest of all the trials, and jumped
achieved a respectable mean ratio of 1.08, but its maximum ratio to 5.00, for Br.
Trial 6, the first to use the short list of essentially a repeat of Trial
fitting
elements,
was
2 with different fitting elements. It of
accounted for 73.7% of the total mass, and obtained excellent ratios 1.01 and 1.08. Trial 7
was essentially a repeat of Trial 6 with Ti
added to the fitting elements. Its results were very similar, but Ti was responsible for the maximum ratio of 1.14.
73
Thus, Ti was dropped from the list of fitting elements in At this point it
Trial
8.
seemed quite obvious that street dust, sulfate, and a good fit to the ambient
mobile sources were all required to give observations, and
it only remained to determine whether any better fit more of the soil and road dust
could be achieved by adding one or sources.
Trial 8 added soil, and.obtained excellent results, accounting and
for 73.6% of the total mass, with median and maximum ratios of 1.01 1.03, respectively.
Trial 9 added washed gravel road dust, rather than obtained satisfactory results, although
soil,
and
also
the total mass accounted for Trial 10 included both soil
decreased slightly, from 73.6% to 66.2%. and washed gravel.
It accounted for 69% of the total mass, and yielded (Ti was added
median and maximum ratios of 1.02 and 1.07, respectively. to the list of
fitting elements so that there would be more elements
than sources.)
Other combinations of sources combination of urban
were
tried,
but
all
involved
the
street dust and limestone dust, and results gave
negative contributions for one or more of the sources.
Thus, Trials 8, 9, and 10 all gave reasonable fits to mean coarse particle concentrations.
the
observed
Quite likely the reason for the
74
several possible combinations of sources is that soil and washed
gravel
have rather similar compositions, so that it is difficult to distinguish between them. fraction of Based on slightly better overall results, in terms of the the mass accounted for, and the ratios of predicted and to show
observed concentrations, we chose Trial 8 as the one for which detailed results of the CEB on the coarse means.
These detailed results it clear that
are given in Table 12, but the results in Table 11 make where the soil source is listed in Table 12,
it represents some
combination of soil and washed gravel road dust.
Table 11 shows that whenever street various sources considered, This of is
dust
was
included
among
the
it contributed the majority of the coarse in Table 12, which gives the
particle mass. detailed
illustrated 8.
results
Trial
Street dust accounted for 66.0% of the and mobile sources
total coarse particle mass, whereas soil, sulfate, contribute less than 8% among them.
This heavy contribution from street elements, Even
dust is also reflected in the contributions to the individual except,
of course, for S, which came mostly from sulfate aerosol. tracer for vehicle
Pb, which is generally acknowledged as a
exhaust,
came predominantly from the street source for the coarse particles.
Again, the ratios indicate that an excellent fit to the ambient mean coarse concentrations was achieved using the five elements indicated. particles, the remaining elements were
As was the case for the fine
75
Table 12. Chemical element balance for Champaign dichotomous coarse particles.
Contributions from sources (ng/m 3 ) Street Soil Sulfate Mobile Total concentration (ng/m3) Observed* Predicted Larger** Smaller
Element
Elements used in fitting Al 585 46 S 58 1 1380 4 Ca 380 Fe 17 0 Pb 49 Remaining elements Si 2311 P 16 Cl 25 116 K Ti 34 2 V 3 Cr 12 Mn 1 Ni 2 Cu 11 Zn 1 As 0 Se 3 Br 1 Rb 3 Sr 1 Cd 2 In 2 Sn 2 Sb 6240 Other Total % Contrib 11236 66.0
0 177 0 0 0
0 0 0 0 9
631 235 1384 397 58
620 235 1339 408 58
+- 33 +- 17 +- 82 +- 19 +- 4 Median
+- 141 +2 +4 +8 +2 +0 +0 +1 +0 +0 +1 +0 +0 +1 +0 +0 +1 +0 +0 +0
1.02 1.00 1.03 1.03 1.00 = 1.02 1.01 3.18 1.62 1.39 1.17 1.50 1.33 1.08 1.00 1.50 1.50 2.00
--
192 1 0 8 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 229 501 2.9
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 552 728 4.3
0 0 1 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 49 62 0.4
2503 16 26 124 36 2 3 12 1 2 12 1 0 5 1 3 1 2 2 2 7070 12640 73.6
2470 51 42 172 42 3 4 13 1 3 18 2 1 15 1 3 5 5 6 9 17030
3.00 1.00 1.00 5.00 2.50 3.00 4.50
Uncertainty is the standard error of the mean. ** Ratio of predicted and observed values (larger/smaller).
76
quite consistently underpredicted.
A graphical illustration of the overall results coarse particles is given in Figure 6.
on
both
fine
and
This illustrates the overall dust
dominance of sulfate as the major fine particle source, and street
as the major coarse particle source in Champaign in the summer and fall.
It is instructive to examine individual elements
the
observed
concentrations
of
the
in relation to the concentrations predicted in the potential
various trials, i.e., when considering various combination of sources.
In this way, insights were provided regarding the necessity to sources, and possibilities of missing sources or
include specific
questionable source compositions were suggested.
Table 13 provides a summary elements
of
the
major
sources
of
individual
in both fine and coarse particles.
It also summarizes the fit predicted and observed
achieved for the element--that is, whether the concentrations agreed
closely, the predicted concentration was greater was
than that observed (overprediction), or the predicted concentration less than that observed (underprediction).
77
Figure 6.
Summary of source contributions to total aerosol mass.
78
Table 13.
Summary of major sources and fit characteristics of individual elements in fine and coarse particle classes.
Fine Element Al Main source Soil Fit Excellent if neither Si nor Ti in fit. Otherwise, underpredicted: 0/P = 1.1-1.2 (with soil in fit). Overpredicted. If used in fit, P/0 = 1.5; otherwise, P/0 = 2.0 Excellent Underpredicted. 0/P = 2.5-2.7 (if not in fit). Good Main source Street
Coarse Fit Good if Si not in fit. Otherwise, overpredicted. P/0 = 1.1-1.2.
Si
Soil
Street
Overpredicted. If used in fit, P/0 = 1.08; otherwise, P/0 = 1.02. Excellent Underpredicted. 0/P = 1.4 (if not in fit). Slightly overpredicted. Underpredicted. 0/P - 1.2.
S K
Sulfate Soil
Sulfate Street
Ca
Street
Street
Ti
Soil
Overpredicted. P/0 - 1 . 6 .
Street
Mn
Soil, mobile Soil Mobile
Underpredicted. 0/P - 2.5. Good Substantially underpredicted. 0/P - 4.8. Overpredicted. P/0 = 1.7. Good
Street
Good
Fe Zn
Street Street
Good Underpredicted. 0/P = 1.5.
Br
Mobile
Street, mobile Street
Underpredicted. 0/P = 3.0. Good
Pb
Mobile
79
Aluminum primarily dust. from
Based on Tables 7 soil
and
9,
Al
in
fine
particles
came
dust, whereas coarse particle Al came from street closely related to
Success in predicting Al concentrations was
which elements were used in the fit.
Fine Al was predicted very well if If either fine Al or was
neither Si nor Ti was among the elements used in the fit. both of those elements were used in fitting, then
underpredicted.
Similarly, coarse Al was predicted well if Si was not fit, but was slightly overpredicted if it was.
used
in
the
Silicon the soil,
Table 10 shows that fine Si also came with some contribution from
predominantly
from
street dust.
When used as a If
fitting element, fine Si was overpredicted by a factor of about 1.5.
not among the fitting elements, fine Si was overpredicted by a factor of about 2.0.
Coarse Si came mostly from street dust, as Tables 9 and 10 indicate. Again, coarse Si was overpredicted, with the amount the of fit the or
overprediction varying according to whether Si was used in not.
80
In fine particles, Al underprediction occurred with Si because Si was higher in the fit
in abundance with respect to Al (Si/Al = 3.50, main source (soil) than it was in the
data from Table 7) in
the
atmosphere (Si/Al = 1.75, data from Table 5 ) .
Thus, the best fit to both elements was a contribution that made the
compromise
on
a
source
predicted Si higher than observed, and Al
less than observed.
Thus, if the fine Al abundances in the sources are correct, then the fine Si abundances in one or more of the sources must be too high, since the prediction based actually observed. on On the the source composition gave more Si than
other hand, if the fine Si abundances are are
correct, then either the fine Al abundances are too low, or else we overlooking a significant source of Al.
In the coarse particles, Al was overpredicted with because Si/Al was slightly
Si
in
the
fit
smaller (3.95) in the main source (street
dust) than it was in the atmospheric coarse particles (3.99).
These sources,
considerations in terms of
emphasize the
that
proper of their
characterization aerosol, is
of very
composition
81
important. That is, we must know and use the element abundances in the
dust that blows off the soil or the street, not simply the abundances in bulk soil or street dust. were resuspended before To generate samples of aerosol, bulk being sampled and analyzed. samples
Thus, we hope we thought to
have measured the proper material. occur both in
However, Si in soils is
relatively large particles as quartz, and in relatively would occur
small particles as both quartz and clay minerals, while Al primarily in clay minerals in Illinois soils.
In the environment the clay minerals are found agglomerated into, or attached to, relatively large particles, but during saltation it is the
relatively small clay-sized particles that become airborne and are small enough to remain airborne for long times (hours).
Thus,
it
is Si
likely in
that of
nature
discriminates
against
the
large-particle
favor
the small-particle Al and Si, through after they enter
gravitational deposition of the large particles soon the atmosphere. This
is essentially the argument Rahn (1976) used to ratios question fine lower than those of bulk crustal
explain atmospheric Si/Al materials. The crucial
is whether the resuspension process and coarse source materials is
used to prepare the samples of
adequate to simulate the natural resuspension processes.
82
Sulfur Ammonium sulfate was the dominant source of S in both fine
and coarse particles, and the fit was always exact.
Potassium soil, coarse and K
As indicated in Table 13, fine K came
mainly
from
the
street were
dust was the main source of coarse K. by sizable amounts,
Both fine and although the
underpredicted
underprediction
was somewhat less if K was used in the fit.
It appears we
that there must be additional sources of both fine and coarse K that have not identified.
Calcium dust,
Both fine and coarse Ca soils also
came
mainly
from
urban
street
although
made an important contribution to fine Ca.
Predicted and observed concentrations agreed closely for fine Ca, unless soil dust was not among the sources, in which case Ca was overpredicted Thus, it appears that soil, with its relatively low
by a factor of 2.5. abundance
of Ca, is necessary for an overall good fit of the fine data.
Coarse Ca was consistently overpredicted, but only slightly, unless soil was omitted as a source, in which case the overprediction was larger.
Titanium contributed
Soil dust was the main source of fine Ti, and street dust most of the coarse Ti. Fine Ti was overpredicted by a of
factor of 1.6, while coarse Ti was underpredicted, with an 0/P ratio 1.2.
83
Manganese vehicles, but Fine Mn was contributed sources about equally by soil and
additional
are needed to fully account for the The coarse
observed fine Mn, since the CEB yielded an underprediction. Mn fit was good, with street dust the main source.
Iron
Soil dust was the major source of fine Fe,
and
street
dust
the major source of coarse Fe, and predicted and observed concentrations were in close agreement for both size classes. fine particle Omitting soil from the
sources and street dust from the coarse-particle sources for these
resulted in sizable underpredictions, and confirmed the need sources to explain the respective observations.
Zinc but
A vehicular source was the major one identified for fine
Zn,
the substantial underprediction indicates that there must have been Coarse Zn was also underpredicted, although not as much as the
others.
fine Zn, and street dust was the major source.
Bromine and Lead the ultimate source
We consider these two of both of them in
elements
together,
since
Champaign is exhaust from
vehicles burning leaded fuels. since a significant
Street dust also contains both elements,
fraction of tailpipe exhaust is deposited on road
surfaces, but Br is greatly depleted in the street dust, relative to Pb. The Pb/Br ratio assumed for both fine and coarse vehicle exhaust (Dzubay
84
et al, 1980) was 3.49, while the ratios
measured
in
fine
and
coarse
street dust were 20.0 and 19.0, respectively.
Fine particle Br was overpredicted by a factor of was used in the fit or not.
1.7,
whether
Br
Since Br compounds are relatively volatile, aging,
the overprediction suggests losses of Br from the aerosol during
so that the observed concentration of Br would be disproportionately low relative to Pb.
Fine Pb came overwhelmingly from vehicle exhaust, and was well.
fit
very
Additional insights into the behavior of Br and Pb came from the CEB results on the mean ambient composition of the coarse particles. fit, the results depended on When which
both Br and Pb were used in the sources were considered.
If both mobile and street dust sources were For Br the
considered, Br was underpredicted and Pb was overpredicted. 0/P ratio was 1.36 and 1.67 in two such trials.
For Pb in the same
trials, the P/0 ratio was 1.22 and 1.34.
In the case where only the street dust source was fit, Pb was slightly
included
in
the
underpredicted (0/P = 1.04), but Br was greatly
85
underpredicted (0/P = 5.00), indicating that the street
Pb
abundance
in
the
dust was quite reasonable in relation to the other elements used dust to
in the fit, but that there was far too little Br in the street account for what was in the atmosphere.
On the other hand, if only the mobile source fit,
was
included
in
the
the Pb was predicted exactly (P/O = 1.00), and the Br was slightly
overpredicted (P/0 = 1.13).
If only Pb was used in the fit, and sources of co'arse particles were
both
street the
dust
and
mobile
considered,
Pb was predicted
exactly, but the Br was seriously underpredicted (0/P = 3.00, 3.75 , and 3.00 in three trials). These results reflect the dominance of the if
relatively Br-deficient street dust as a coarse particle source, but they are valid, they do not explain all the observed coarse Br.
Remaining Elements concentrations time. that
Many of less
the than
remaining
elements
occurred
in
were
detectable more than half of the therefore be considered very
Results for these elements
must
tentative.
The poorly-detected elements in the fine fraction (Table 5) The remaining elements
include Cl, V, Ni, As, Rb, Cd, In, Sn, and Sb. not on this
list were consistently underpredicted, an indication that this
their sources are not well known, or at least not accounted for in
86
analysis.
Poorly-detected elements in the coarse fraction As, Se, Rb, Cd, In, Sn, and Sb.
(Table
6)
include
Of the coarse elements not on this or
list, there were several for which predicted values were within 10%, 1 3 ng/m , of the observed value.
These were V, Cr, Mn, Ni, Cu (except The other elements not on
when the street source was omitted), and Sr.
the poorly-detected list were consistently underpredicted; these include F, Cl and Zn.
5.3.3
CEBs on Individual Samples
CEB analysis was applied to using
individual
fine
and
coarse
samples,
the same source compositions as were used for the analyses of the the
mean compositions, and using the same sets of sources identified in earlier analyses of the mean compositions.
That is, since street dust, the relevant
soil dust, sulfate, and mobile sources were identified as sources
for both fine and coarse particles from the CEB analyses of the valid
respective mean compositions, the same sources were assumed to be for the CEB analyses of the individual samples.
87
Results of the CEB analyses on individual fine filter data are shown in Figure 7 in the form of histograms of the frequency of occurrence of
the percent contributions of the four sources to the total fine aerosol. There is an overall qualitative agreement between these results and That is, the histograms
those based on the mean fine concentrations. show that sulfate accounted
for 50-60% of the fine mass in a typical contributed
case, while street dust, soil, and mobile sources typically less than 10%. This agrees
with the results obtained from the mean 55.5%; soil, Note
concentrations (Table 1 0 ) , in which sulfate accounted for 7.3%;
mobile, 5.3%; and street dust, 1.9% of the total fine mass.
that none of the CEB calculations on individual filter yielded contributions of any of the sources.
negative
Two samples indicated sulfate
contributions over 100%, however.
Results of the CEB analyses on the shown in Figure 8.
individual
coarse
filters
are
Again, there was good qualitative agreement with the that in street good figure
results based on the mean concentrations. The figure shows dust typically contributed 60 - 80% of the coarse
mass,
agreement with the figure of 66.0% obtained from the means. also shows typical contributions of less than
The
10% from soil dust, values of 5.9%,
sulfate, and mobile sources, and these agree with the 4.3%, and 0.4% obtained
by analysis of the mean concentrations. Note of the over individual 100%, filters yielded
also, however, that the several street dust
analyses
contributions
and frequent negative these negative
contributions of soil and mobile sources.
The cause of
88
Figure 7. Frequency distributions of source contributions, based on CEB analyses of individual samples fine aerosol.
89
Figure 8. Frequency distributions of source contributions, based on CEB analyses of individual samples coarse aerosol.
90
contributions is not clear, but it is possible that they may reflect the occasional presence of other sources not considered in the analyses, or
they may simply be random errors on true contributions very near zero.
One may ask how the indicate
results
of
the
source
apportionment,
which
that street dust contributed about 2% of the fine mass and 66% the hivol filter results,
of the coarse mass, agree or disagree with
which showed that Mattis Ave increased the downwind TSP concentration by about 22 g/m3 when the wind was blowing perpendicular to the roadway.
To
compare
these
results, in
we
first
must
express
the
source
apportionment
results
terms of the street dust contribution to the the fine and
total aerosol, rather than its separate contributions to coarse aerosol. Tables
5 and 6 give the mean concentrations of total
3
fine and coarse aerosol as 19.5 and
3
17.0 g/m ,
respectively.,
or
a
total
of 35.6g/m. Thus, the fine and coarse aerosol accounted for 53% the dichotomous
and 47%, respectively, of the total aerosol sampled by sampler.
Weighting the street dust contributions to the fine and coarse that street
aerosols by 53% and 47%, respectively, and summing, we see dust
contributed about 32% of the total mass sampled by the dichotomous 32%
sampler, i.e., the mass on particles smaller than 15 m diameter.
3 3
of
the
mean total mass (35.6 g/m ) is 11.7 g/m .
It is important to to the particle
note that the street dust source that contributed 32% mass less than
15 m may include. but is not necessarily limited to,
91 that from nearby Mattis Ave.
It is also important to be clear about the hivol results. all, it
First
of
is important to know the range of particle diameters collected Unfortunately, the evidence on this point is not Kolak and Visalli (1981) mentioned that about 100 m, but gave neither a
by the hivol samplers. as clear as one
would like. up to
hivols collect particles reference
nor an efficiency of collection for 100 urn particles.
Sierra wide
Instrument Co. product literature says that hivols collect "over a size range, up to 30-100 um."
Wedding et al., found in wind tunnel
tests that hivols collected particles of about 19 urn with 50% efficiency when the angle of incidence of the wind to the collector was 45 degrees. At 0 degrees, particles of efficiency. 11 m diameter were collected with 50%
For both wind angles, particles larger than those mentioned (but not zero) efficiencies. Thus, it
were collected with smaller appears likely that the
standard
hivol samplers collected particles dichotomous sampler
larger than 15 m with greater efficiency than the did.
Figure 4 and the discussion of it indicate that, overall both east
(i.e.,
in
and west winds), Mattis Ave added 21.7 ug/m3 to the downwind sampled by the hivols.
concentration of total mass in the size range Previous discussion
(p. 44) made clear, however, that in "west" winds, or about
i.e., those between 203 and 338 degrees, only about 9 g/m ,
92
11%
of
the
mean
hivol
TSP for such cases, was added to the downwind Ave, equally
3
concentration. The overall mean TSP for Mattis both east
weighting
and west sides of the street, was 71.1 g/m , almost exactly sampler, but the dichotomous sampler
double that of the dichotomous collected particles
having diameters in the range 0-15 m, whereas the diameter. Thus, it is
hivol collected numerous particles >15 m in clear that about half of
the mean hivol TSP concentration came from
particles >15 m in diameter.
Now, if 32% of the total mass between 0 and 15 ym was and 66% of
street
dust,
the coarse mass (2.5-15 m) was street dust, and we assume mass also holds
that the street dust contribution (66%) to the coarse for the >15 urn size range, then the weighted
average
percent (66%
contribution of steet dust to the hivol TSP would be (32% x 0.5) + x 0.5) = 49%. This should be true
on the average, over all wind contributed 11% of the
directions, but since we know that Mattis Ave mean total hivol mass
in west winds, we might expect that the street greater than 49% in
dust contribution to the hivol TSP would be even west winds.
Thus, in west winds, at least half of the hivol TSP mass steet dust, and Mattis Ave contributed only
should
be
11%, so other streets
farther upwind must have contributed the rest.
93 If this is so, the relative influence of Mattis Ave dust contribution to on the street
the 0-15 m size class (dichotomous data) should since a local source would be expected to
have been small
also,
contribute more mass in the larger particle sizes than the smaller ones. That is, the street dust contribution to the winds 0-15 m mass with west
should not have been noticeably different from that in other wind
directions, nor should it have been a strong function of the fraction of time during a given sample that the wind was from the westerly sector.
The data plotted in Figures 9 and 10 agree Figure 9 shows the
with
these
hypotheses.
street dust contribution to the 0-15 m mass as a It is true that the single value in 248 -
function of wind direction. 293 degree subsector and
was unusually large, but the mean values for the subsectors, real which had 22 and 8 samples,
southwest
northwest showed no
respectively,
differences from most other directions. large, but again,
The mean value for the east wind subsector was also based on only 3 samples.
Figure 10 shows the street dust contribution to the mass as a function of the
total
0-15
m
fraction of the time winds were from the The street dust contribution
westerly sector during sample collection. clearly
did not increase with an increasing fraction of westerly winds,
and may, on the contrary, have decreased.
94
Figure 9. Mean street dust contributions to total mass <15m as a function of wind direction. Radial bars are plus/minus 1 S.E.M. N = number of samples in each direction category.
95
Figure
10. Street dust contribution to total mass <15m as a function of the fraction of time winds were from the westerly sector during sample collection.
96
Thus, the two results are consistent. (mostly particles mass to Mattis Ave contributed 11%
with diameters larger than 15 m, apparently) to the in the westerly hivol winds, but the overall street dust
total hivol contribution
TSF was approximately 50%.
The Mattis Ave
contribution to the 0-15 m mass was not detectable, but overall, street dust accounted for 32% of the 0-15 ym mass.
6
SUMMARY AND CONCLUSIONS
Inhalable particle mass concentrations were on the the standard TSP in a rural agree area, well and with
average
75%
of
50% of the standard TSF in previous rural and urban
Champaign.
These values
measurements, respectively.
This study was carried out 1) inhalable
to
compare
mass
concentrations
of
particles (i.e., those with aerodynamic diameters of 15 m or
less) with TSP concentrations measured in standard hivol samplers, 2) to assess the effects of street sweeping on urban TSP concentrations, and
3) to determine the sources of airborne particulate matter in Champaign, and their relative contributions to total aerosol mass.
97
Comparison of 98 pairs of standard and size-selective (<15m) hivol filters collected in a rural agricultural area indicated that inhalable concentration, on the
particle mass was about 75% of the standard TSP average.
Comparison of mean total mass concentrations from 95 fine and in
coarse pairs of dichotomous samples and 236 hivol TSP concentrations an
urban area showed that urban IP concentrations averaged about 50% of
standard TSP concentrations. These results agree closely with previously published results of both urban and rural comparisons.
To
determine
the
effects
of . street
sweeping
on
urban
TSP
concentrations,
three standard hivol samplers were operated near a four was operated in a
lane commercial roadway, and an additional sampler residential 1981. area of Champaign, blowing Illinois, across the
between April and October, test roadway in either
When winds
were
direction,
concentrations at 7 m downwind from the curb exceeded upwind In a comparison of downwind-upwind TSP a period of regular weekly street discontinued,
by an average of about 22 g/m3 . concentration differences during
sweeping with those from another period when sweeping was analysis of variance was unable to detect the to
a significant effect of roadway. be Thus, for a
sweeping on the amount of TSP produced by commercial urban roadway clearly shown
a source of airborne have any effect in
particles, street sweeping could not be shown reducing the amount of dust produced.
to
98
Application of a chemical element balance model to apportion sources of fine and coarse particles collected in a dichotomous virtual impactor indicated that street dust accounted for about 2% of the fine 66% of the coarse mass, on the mass and
average, or about 32% of the total contribution to the coarse
particle mass <15 m. If the street dust particles also applies to
larger particles collected by the standard the
hivol samplers, street dust sources should account for about 50% of hivol mass.
Although street dust accounted for 32% of the mean mass of particles <15 m, the influence of nearby Mattis Ave on the composition of both Thus, it appears that the
fine and coarse particles was negligible. increases in
TSP observed in the hivol samplers downwind of Mattis Ave
were predominantly particles >15 m. On the other hand, 32% of the mass <15 m was contributed by street dust, mostly in the 2.5 - 15 m size
range, and apparently from streets more distant than Mattis Ave.
7
ACKNOWLEDGEMENTS
This research was Department of Energy
carried and
out
under
support
from
the
Illinois Mr.
Natural
Resources,
Project
10.093.
William P. Murphy was the Project Manager.
99
Sample collection was facilitated by the efforts of Bruce who devised a convenient Thanks
Komadina,
means of mounting the hivols and supervised are also due Randall who K. Stahlhut, with and for the Dr.
their installation. programming
assistance,
Eberhard
Brieschke,
helped Boastick,
installations, and Mr. Kenneth Porter, Mr. Lawrence and Mrs. Glenn Stout, who allowed us
to operate samplers on their
property. Mr. Bob
The Illinois EPA, through the assistance of Mr. Arden Ahnell, Hutton, and Mr. John Shrock, provided, calibrated, the and
maintained the standard hivols, and provided and We also thank Mr.
weighed
filters.
Michael Terstriep and Mr. Michael Bender for their
cooperation in supplying data.
100
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30777-101
REPORT DOCUMENTATION PAGE
4. Title and Subtitle
L REPORT NO.
2.
3. Recipient's Accession No.
83/11
5. Report Date
Characterization of Urban and Rural Inhalable Particulates
7. Author(s)
February 1983
6.
8. Performing Organization Rapt. No. 10. Project/Task/Work Unit No.
Donald F. Gatz, Susan T. Wiley, Lih-Ching Chu
9. Performing Organ Organization and Address
IL Department of Energy and Natural Resources State Water Survey Division 605 East Springfield Avenue P. 0. Box 5050, Station A Champaign, IL
12. Sponsoring Organization Name and Address
10.093
1 1 . Contract(C) or Grant(O) No.
(C)
(Q) 13. Typa of Raport t, Parlod Covered
I l l i n o i s Department of Energy and Natural Resources 325 W. Adams Street S p r i n g f i e l d , IL 62706
15. Supplementary Notes
14.
16. Abstract (Limit: 200 words)
The Clean A i r Act assessing effects the standard high p a r t i c l e s f o r use
emphasizes that health aspects should be considered very strongly when of a i r p o l l u t a n t s . This has caused a concern at the Federal level that volume ( h i v o l ) sampler does not provide a proper sample of airborne in assessing health e f f e c t s .
'
In order to be prepared f o r potential future Federal requirements in the area of TSP sampling, the IEPA needs- comparative measurements or airborne p a r t i c l e s using the standard hivol and the various samplers such as the two stage dichotomous sampler. This report compares concentrations of inhalable and t o t a l airborne mass in both urban and r u r a l areas, looks at the effects of s t r e e t sweeping on urban a i r q u a l i t y , by comparing hivol and dichotomous sampler measurements in urban areas in the presence and absence of regular s t r e e t sweeping and determines the sources if airborne p a r t i c u l a t e matter, and t h e i r r e l a t i v e contributions to TSP in an urban area.
17. Document Analysts a. Descriptors
Inhalable Particulates Total Suspended Particulates Urban Aerosols
b. Identifiers/Open-Ended Terms
Illinois
Dichotomous Sampler Hivol Sampler
C. COSATI Field/Group 18. Availability statemon:
No r e s t r i c t i o n on d i s t r i b u t i o n . Available at the IL depository l i b r a r i e s or from the National Technical Information Service, S p r i n o f i e l d . VA 22161
19. Security Class (This Report)
2 1 . No. of Pages
Unclassified
20. Security Class (This Page) 22. Price
108
OPTIONAL FORM 272 (4-77) (Formerly NTIS-35) Department of Commarca
Unclassified
(See ANSI-Z39.18)