EPA-450/3-76-003
January 1976
DEVELOPMENT
OF A METHODOLOGY
AND EMISSION INVENTORY
FOR FUGITIVE DUST
FOR THE REGIONAL
AIR POLLUTION STUDY
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
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EPA-450/3-76-003
DEVELOPMENT
OF A METHODOLOGY
AND EMISSION INVENTORY
FOR FUGITIVE DUST
FOR THE REGIONAL
AIR POLLUTION STUDY
Dr. Chaltcu Cowherd ami Ms. Christine (Jiienlher
Midwest Research Iii.stitiiti*
125 Volker Boulc\ard
Kansas Cilj. Missouri1 61110
Contract .No. 68-02-20-1 0
EPA Project Officer: Charles C. .Masser
Prepared for
ENVIROMEiNTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 277 1 1
Januarv 1976
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This report is issued by the Environmental Protection Agency to report
technical data of interest to a limited number of readers. Copies are
available free of charge to Federal employees, current contractors and
grantees, and nonprofit organizations - as supplies permit - from the
Air Pollution Technical Information Center, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711; or, for a fee,
from the National Technical Information Service, 5285 Port Rdyal Road,
Springfield, Virginia 22161.
This report was furnished to the Environmental Protection Agency by
Midwest Research Institute, Kansas City, Missouri 64110, in fulfillment
of Contract No, 68-02-2040. The contents of this report are reproduced
herein as received from Midwest Research Institute. The opinions,
findings, and conclusions expressed are those of the author and not
necessarily those of the Environmental Protection Agency. Mention of
company or product names is not to be considered as an endorsement
by the Environmental Protection Agency.
Publication No. EPA-450/3-76-003
11
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ACKNOWLEDGEMENTS
This report was prepared for the Environmental Protection Agency's
Office of Air Quality Planning and Standards under EPA Contract No. 68-
02-2040. Mr. Charles Masser served as EPA Project Officer.
The program was conducted in MRI1s Physical Sciences Division under
the supervision of Dr. Larry J. Shannon, Assistant Director. Dr. Chatten
Cowherd, Jr., Project Leader for MRI, was assisted by Ms. Christine
Guenther, Mr. Daniel Nelson, and Mr. Kenneth Walker.
Approved for:
MIDWEST RESEARCH INSTITUTE
I
L. J.lJShannon, Assistant Director
Physical Sciences Division
March 22, 1976
iii
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CONTENTS
Page
List of Figures vii
List of Tables ix
Summary 1
Introduction ... ................... 3
Technical Approach 5
Unpaved Roads 9
Grid Source Extent 9
Emission Factor 12
Temporal Apportioning Factors 12
Agricultural Tilling 15
Grid Source Extent 15
Emission Factor 18
Temporal Apportioning Factors 19
Wind Erosion From Tilled Land 23
Grid Source Extent 23
Emission Factor 23
Temporal Apportioning Factors .26
Construction ..... 29
Grid Source Extent 29
Emission Factor 32
Temporal Apportioning Factors. ....... ....32
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CONTENTS (concluded)
Page
Aggregate Storage 35
Grid Source Extent 35
Emission Factor 37
Temporal Apportioning Factors 37
Unpaved Airstrips 41
Grid Source Extent 41
Emission Factor 44
Temporal Apportioning Factors. ...... 44
Data Tabulations and Calculated Results 47
Analysis of Results and Estimated Accuracies .... 53
References .......... ......... 57
Appendix A - Example Calculations (RAPS Grid No. 1) 61
Appendix B - Factors Affecting Atmospheric Transport of Fugitive
Dust 65
vi
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FIGURES
[2s. Title Page
1 Project Data Flow Diagram 6
2 Example of RAPS Grid System Overlay 7
3 Procedure for Determination of Annual Vehicle-Miles on Un-
paved roads. 10
4 Percentage of Total Daily, Weekly, and Annual Vehicle-Miles
on Unpaved Roads 13
5 Procedure for Determination of Annual Acres of Land Tilled . 16
6 Soil Silt Content (7o) for RAPS Grid System 20
7 Percentage of Total Daily, Weekly, and Annual Agricultural
Tilling 21
8 Procedure for Determination of Acreage of Exposed Agri-
cultural Land 24
9 Percentage of Total Daily, Weekly, and Annual Wind Erosion
from Agricultural Tilled Land 27
10 Procedure for Determination of Annual Acres of Construc-
tion 30
11 Percentage of Total Daily, Weekly, and Annual Construction
Activity 33
12 Procedure for Determination of Annual Tons of Aggregate
Storage 36
vii
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FIGURES (concluded)
Title Page
13 Percentage of Total Daily, Weekly, and Annual Aggregate
Storage Operations 39
14 Procedure for Determination of Annual LTO Cycles on Un-
paved Airstrips 42
15 Percentage of Total Daily, Weekly, and Annual LTO Cycles. . . 45
16 Simplified Flow Diagram of Calculation Procedure for Annual
Emissions by Grid ....... 50
17. Simplified Flow Diagram of Calculation Procedure for Hourly
Emissions by Grid ......... 51
18 Example Computer Output of Annual Emissions by Grid 52
B-l Roughness Heights for Various Surfaces 68
B-2 Relationship Between Particle Size and Drift Distance .... 72
vizi
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TABLES
No. Title Page
1 County Statistics for Unpaved Roads 11
2 County Breakdown of Harvested Acres by Crop and Equivalent
Tillings 17
3 Agricultural Operations by Crop 18
4 Seasonal Exposed Acreage by County 25
5 Construction Acreage by County 31
6 Annual Acres of Aggregate Stored by County . . . 38
7 Data on Unpaved (Turf) Airstrips by County 43
8 Example Coded Source Extent and Correction Factor Data ... 48
9 Hourly Adjustment Example Coded Factors 49
10 Summary of Annual Emissions by County 54
11 Estimated Errors for Tabulated Data 55
B-l Particle Drift Distances Calculated from Eq. (9b) 71
B-2 Distances to Point of Maximum Settling, xmax , Calculated
from Eq. (12) 74
ix
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SUMMARY
This report outlines the methodology that was used in developing
an hourly fugitive dust emissions inventory for the Metropolitan
St. Louis Air Quality Control Region as part of the Regional Air Pol-
lution Study (RAPS). The inventory encompassed the following source
categories: (a) unpaved roads, (b) agricultural land tilling, (c) wind
erosion of agricultural land, (d) construction sites, (e) aggregate
storage piles, and (f) unpaved airstrips.
For each of approximately 2,000 RAPS grid areas, data were compiled
on annual emissions of fugitive dust. This required, in addition to basic
emission factors adjusted for local climatic and surface conditions, an-
nual measures of source extent (vehicle-miles traveled on unpaved roads,
acres of land tilled, etc.) for each grid area. Finally, hourly apportioning
factors were derived to account for emissions variations by hour of the
day, day of the week, and season of the year.
Results presented in this report include temporal apportioning fac-
tors, county totals of annual source extent and annual emissions for
each source category. Fine particle emissions from fugitive dust sources
in the St. Louis area are found to comprise 39% of the total emissions
of suspended particulates.
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INTRODUCTION
Analysis of the physical relationships between air pollutant source
emissions and ambient air quality is essential to the rational develop-
ment and implementation of pollution abatement and control strategies.
These relationships are predictable through the use of mathematical mod-
els which simulate the processes of atmospheric transport, dispersion,
transformation, and removal of pollutant emissions.
The Environmental Protection Agency (EPA) is currently sponsoring
a comprehensive regional air pollution study (RAPS) in the St. Louis
Air Quality Control Region (AQCR 70). The primary purpose of the RAPS
program is the development and validation of improved air quality mod-
els. To accomplish this purpose, a major portion of the program effort
is being directed to the preparation of a comprehensive regional data
base.
Inputs required for model verification include an emissions in-
ventory, meteorological data (wind velocity and temperature) and air
quality data. The spatial and temporal resolution of the RAPS data base
will be far more precise than any previously compiled in an undertaking
of this type. This will permit verification of sophisticated models
which predict air quality distributions on a short term (hourly) basis.
Recently it has become evident that fugitive dust sources contri-
bute substantially to atmospheric concentrations of total suspended
particulates (TSP) in both urban and rural areas. Failure to incorporate
fugitive source emissions into model-based control strategies has re-
sulted in widespread overestimation of TSP reductions resulting from
the control of conventional point and area sources. Therefore, the need
to include fugitive dust sources in the RAPS emissions inventory is
evident.
This report presents the results of an investigative program di-
rected to (a) development of a methodology for reporting fugitive dust
-------
emissions in the RAPS region and (b) compilation of an hourly emissions
inventory of fugitive dust sources for the nearly 2,000 RAPS grid areas.
The following six categories of fugitive dust sources were addressed
in this study:
1.- Unpaved roads;
2. Agricultural land tilling;
3. Wind erosion of agricultural land;
4. Construction sites;
5. Aggregate storage piles; and
6. Unpaved airstrips.
Appendix B presents an assessment of factors affecting atmospheric
transport of fugitive dust.
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TECHNICAL APPROACH
Figure 1 traces the methodology that was developed to compile hourly
emissions of fugitive dust by grid. The key data elements in this scheme
are:
1. Appropriate annual measures of the extent of each source_type
within each grid area.
2. Emission factors adjusted to climatic conditions and surface
properties characteristic of the St. Louis area.
3. Temporal apportioning factors to account for emissions varia-
tions by hour of the day, day of the week, and season of the
year.
The basic emission factors and associated correction terms used in this
study, as shown in Figure 1, were developed by Midwest Research Institute
(MRI) under EPA Contract No. 68-02-0619.!/ These factors refer to dust
particles smaller than 30 \im in diameter, the approximate effective cut-
off diameter of a standard high-volume particulate sampler (based on a
particle density of 2 to 2.5 g/cnP).
The initial work objective was to prepare a base map of the RAPS
grid system which incorporated county outlines and river outlines.
United States Geological Survey (USGS) maps with a scale of 1:250,000
were used to locate the RAPS grid system based on Universal Transverse
Mercator (UTM) coordinates designated for Zone 15.
A reduction of the resulting overlay map is shown in Figure 2. The
overlay was photographically scaled to fit appropriate land use and
street maps of the St. Louis area. A computer-generated plot of the grid
system, supplied by the EPA project officer, was also reduced to the size
of the MRI overlay for comparative purposes.
The following sections of this report document, for each source
category, the methodology used to obtain annual grid source extent, cor-
rected emission factors, and temporal apportioning factors. Also pre-
sented are key computational results summarized by county, including
extent of fugitive dust sources, temporal apportioning factors, and
annual totals of fugitive dust emissions.
5
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For Each County
Annual for Each County
DATA
Miles of Unpaved Roads
Acres of Harvested Cropland
Construction Projects
NEDS Aggregate Listing
Number of Based Aircraft
COUNTY SOURCE EXTENT
Unpaved Roads (vehicle miles)-
Land Tilling (acres)
Wind Erosion (acres)
Construction (acres)
Aggregate Storage (tons)
Unpaved Airstrips (LTO cycles)
For Each Grid
Annual for Each Grid i
SPATIAL APPORTIONING
FACTORS
Land Use
Grid Area
GRID SOURCE EXTENT
Unpaved Roads (vehicle miles)
Land Tilling (acres)
Wind Erosion (acres)
Consfrucfion (acres)
Aggregate Storage (tons)
Unpaved Airstrips (LTO c/cles)
Annual for Each Grid
CORRECTION FACTORS
Number of Dry Days per Year (d)
Precipitation-Evaporation Index (PE)
Duration of Construction Activity (D^
Silt Content - Roads, Gravel (sg)
- Roads, Dirt (sd)
- Tilling (sr)
- Airstrips (sa)
Vehicle Speed - Roads (Sr)
- Airstrips (SQ)
Calculate : EMISSIONS
(tons/yr, Mtons/yr)
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
Hourly for Each Grid '
TEMPORAL APPORTIONING
FACTORS
Critical Wind Speed
Activity
-Work Cycle
-Traffic Cycle
Compute : EMISSIONS
(Ib/hr, kg/M
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
Compute: EMISSION FACTORS
*"'"
Unpaved Roads
- Grovel
d
365
- Dirt
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
EFd=0.49sdK
(30
EFt= l.Ut
(PE/50)2
EFW = 0.9
EFC=D
EF$ = 0.33
EFa = 0.49sa/Sa.
\30
d
365
Ib
vehicle mile
Ib
vehicle mile
Ib/acre
tons/acre
tons/acre
Ib/tons stored
S
Ib
LTO cycle
Figure 1. Project data flow diagram
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Figure 2. Example of RAPS grid system overlay
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UNPAVED ROADS
GRID SOURCE EXTENT
The measure of source extent for fugitive dust emissions from un-
paved roads is vehicle miles traveled (VMT). The basic equation for cal-
culation of annual VMT on unpaved roads in a specified grid area is given
by:
4
VMT = 365 £ (ADT.)m.
i = l
where ADT^ is average daily traffic on unpaved roads with surface type
i , and m. is the mileage of unpaved roads with surface type i within
the grid area. Road surface types considered in this study were: (a)
gravel/stone surfaced, (b) soil surfaced, (c) graded and drained, and
(d) unimproved. The procedure used to determine ADT. and m.j. for each
grid is depicted in Figure 3.
Traffic volume on unpaved roads within each grid was derived from
appropriate county maps. Traffic flow and road surface-type maps were
obtained from the Illinois Department of Transportation^-' for each of
the seven Illinois counties in the St. Louis AQCR. Highway maps, desig-
nating road surface type, were obtained from the Missouri State Highway
Commission?-' for the counties of Franklin, Jefferson, St. Charles, and
St. Louis in Missouri. Communications with officials of St. Louis City
and County-^' indicated that there are no unpaved roads in the city and
only a few municipal or private unpaved roads in the St. Louis County.
The RAPS grid system was scaled to each county map, and mileage
and average ADT for each of the four road surface types were manually
obtained for each grid. Table 1 presents a county summary of the mile-
age and ADT for each road type. As indicated, values for ADT on unpaved
roads in the Missouri counties were estimated based on reported ADT val-
ues for Illinois roads differentiated by road surface type.
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For Illinois Counties:
By County
Map of Roads
by Surface Type
By County
Map of ADT
on Each Road
By Grid
Miles of Road
Gravel or Stone
Soi I Surfaced
Graded & Drained
Unimproved
By Grid
Daily Vehicle
Miles on Unpaved
Roads
By Grid
Annual Vehicle
Miles on
Unpaved Roads
For Missouri Counties:
By County
Map of Roads
by Surface Type
By Road Type
Average ADT
(Based on
7-County
Illinois Data)
By Grid
Miles of Road
Gravel or Stone
Soil Surfaced
Graded & Drained
Unimproved
By Grid
Annual Vehicle
Miles on
Unpaved Roads
Figure 3. Procedure for determination of annual vehicle-miles on unpaved roads
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Table 1. COUNTY STATISTICS FOR UNPAVED ROADS
State
Illinois
Missouri
Spunci
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
Mao
Road
surface
1972
1972
1973
1973
1973
1973
1971
1973
1975
1971
1969
date
Traffic
1969
1968
1971
1973
1973
1972
1973
__
..
--
St. Louis City --
Gravel/
stone
351.1
504.7
20.0
234.2
284.5
209.0
339.5
606.1
260.7
282.7
0.0
0.0
Soil
surfaced
39.5
11. 0
285.0
0.0
13.7
42.2
24.0
0.0
0.0
0.0
0.0
0.0
Mlleaae
Graded and
drained
41.7
36.0
11.0
32.5
42.2
15.0
107.5
1.2
0.0
0.0
0.0
0.0
l/n.inproved
1.5
1.5
0.2
1.7
9.0
0.2
3.5
3.0
0.0
0.0
0.0
0.0
Total
433.8
553.2
316.2
268.4
449.5
266.5
474.5
610.3
260.7
282.7
0.0
0.0
Gravel/
stone
81
83
92
64
65
73
71^
71-
71-a'
--
Soil
surfaced
57
58
63
280
85
73*;
7ja
73*'
ADT
Graded and
drained
51
72
50
64
50
51
53*'
53*'
53*'
Annual VMT
(th.9U.sand.s)
Unimoroved
25
25
25
25
25
25
25
25
25
25
Qrave,!
10,321
15,226
675
5,548
9,429
13,500
9,079
16,563
6,882
6,967
0
0
Dirt
1,607
1,361
6,736
591
2,304
2,284
2,821
33
0
190
0
0
Average value based on Illinois counties in the St. Louis AQCR weighted by miles of each road surface type.
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EMISSION FACTOR
The emission factor for dust emissions from unpaved roads (pounds
per VMT) is given by:
where sr = silt content of road surface material, gravel (sg) and dirt
(sj) (percent), i.e., particles smaller than 75 p-m in diameter, Sr =
average vehicle speed (miles per hour), and d = number of dry days per
year, i.e., days with less than 0.01 in. of precipitation. Based on driver
interviews, the average vehicle speed on unpaved roads in the St. Louis
area was taken to be 30 mph. On the average, there are 250 dry days per
year in the RAPS study region.^.'
6/
The silt content of gravel roads was estimated to be 16%,"" and the
silt content of dirt roads (i.e., soil-surfaced, graded and drained, and
unimproved) was assumed to be the same as the soil silt content deter-
mined for agricultural sources (see Section Agricultural Tilling, Emis-
sion Factors). Composite road silt content by grid was found to vary from
10 to 70% with corresponding emission factors ranging from 3.36 to 23.5
Ib/vehicle mile.
TEMPORAL APPORTIONING FACTORS
Little data are available describing temporal variations in traffic
on unpaved roads. Figure 4 illustrates hourly, daily, and seasonal varia-
tions of VMT on unpaved roads for a farming area in California.-'-' These
data were assumed to approximate temporal variations in the St. Louis
area.
12
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TEMPORAL APPORTIONING FACTORS
Source Type: Unpaved Roads
A
s
s
X
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Mon Tue Wed Thu Fri
Day of Week
Sat
Sun
30
25
20
15
10
5
Winter
Spring Summer
Season of Year
Fall
Figure 4. Percentage of total daily, weekly, and annual
vehicle-miles on unpaved roads
13
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AGRICULTURAL TILLING
GRID SOURCE EXTENT
Dust emissions from agricultural tilling can be quantified in terms
of annual acres of cropland tilled. Data used for this determination (see
Figure 5) were:
1. Acreage of harvested cropland by grid, for five major crops (corn,
soybeans, wheat, milo and hay).
2. Number of yearly agricultural operations by crop, including til-
ling, planting, and harvesting.
The acres of harvested cropland for all farms on a county basis,
as presented in Table 2, were obtained from the 1969 Census of Agricul-
ture. The number of yearly agricultural operations for the five major
crops (see Table 3) were estimated by knowledgeable MRI personnel. This
information was used to determine the equivalent acres of land tilled
per year by county, based on the following equation:
Equivalent acres
of land tilled
annually by ...
county
'Number of equiv- Acres of har-"
alent tilling vested crop-
operations by land, by crop
crop, i i,
by county
Planting and harvesting operations were estimated to have half of the fugi-
tive dust potential of tilling, based on visual observations made by MRI
personnel.
Annual acreage of land tilled by grid was determined by spatial ap-
portioning of county totals on the basis of grid area and land use, ac-
cording to the following equation:
Annual acres of Annual acres of Agricultural acreage Fraction of
land tilled by = land tilled by X within grid X grid in county
grid county Agricultural acreage within county
15
-------
By County
Area of Each
Grid (Acres)
% of Grid
in County
By County
Acres in
Each Grid
By Grid
1970 Land
Use Maps
% Agricultural
Land in Grid
cr>
By Crop
Number of
Agricultural
Operations
Tilling
planting
Harvesting
By County
1969 Acres
of Each Crop
Harvested
By County
Number of
Equivalent
Til lings
per Year
By County
Annual
Acres of
Agricultural
Land
Tilled
i
By Grid
Annual
Land Tilled
Figure 5. Procedure for determination of annual acres of land tilled
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Table 2. COUNTY BREAKDOWN OF HARVESTED ACRES BY CROP AND EQUIVALENT TILLINGS
St.ate
Illinois
Missouri
County-
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson
St. Charles
St. Louis
Harvested
cropland
(acres)
122,755
170,718
224,634
114,343
148,136
214,750
204,371
74,974
27,506
108,909
35,460
b/
Percentage of acres by crot)
Corn (5)
32.9
39.7
32.0
36.9
32.9
31.5
28.3
32.7
18.0
37.9
29.1
Wheat (41
16.3
16.7
18.1
25.3
19.4
20.9
20.5
17.4
13.8
22.8
29.1
Soybean,? (4.)
39.6
29.1
38.7
27.1
29.6
38.6
38.1
3.9
7.0
25.5
22.2
Milo (5)
0.4
0.4
0.9
0.1
0.1
0.2
0.5
0.9
1.8
0.2
0.5
H.aY (2.5)
8.6
12.0
8.0
6.9
13.4
5.0
9.4
39.8
52.6
10.7
10.5
Other (0)
2.2
2.1
2.3
3.7
4.6
3.8
3.2
5.3
6.8
2.9
8.6
Equivalent
tillings
oer year
4.1
4.1
4.1
4.1
4.0
4.1
4.0
3.5
3.1
4.1
3.8
aj St. Louis City not included; harvested cropland (acres) = 0.
_b/ Numbers in parentheses are equivalent tillings per year for each crop.
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Table 3. AGRICULTURAL OPERATIONS BY CROP
Number of equivalent tillings per year'
Crop
Corn
Wheat
Soybeans
Milo
Hay
Primary
tilling
1
1
1
1
1/2
(F)
(Su)
(W, Sp)
(F, W,
Sp)
(Su)
Secondary
tilling
3 (Sp)
2 (Su, F)
2 (Sp)
3 (Sp)
1 (Su, F)
Planting
1/2 (Sp)
1/2
1/2
1/2
1/2
(F)
(Sp)
(Sp)
(F)
Harvesting Total
1/2 (F) 5
1/2
1/2
1/2
1/2
(Su) 4
(F) 4
(F) 5
(Su) 2.5
aj Season of operation is abbreviated by W = winter, Sp = spring,
Su = summer, and F = fall.
Agricultural acreage within each grid and within each county was
determined by analysis of land use maps supplied by the East-West Gateway
Coordinating Council.9»lw xhe area of a grid lying within a particular
county was determined from the base map of the RAPS grid system (Figure
2). Results for grids which cross county lines were summed.
EMISSION FACTOR
The emission factor for dust emissions from agricultural tilling
operations (pounds per acre tilled) is given by:
EFt =1.1
(PE/50)'
where st = silt content of soil (percent), i.e., particles between 2
and 50 pm in diameter, and PE = Thornthwaite* s Precipitation- Evaporation
Soil silt content for each grid was determined from an analysis of
soils maps, obtained from Soil Conservation Service offices for the coun-
ties of Bond, Clinton, Madison, St. Clair, and Washington in Illinoisi?/
18
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and St. Charles County in Missouri.' A map of the soils of the North
Central United Statesi^t' was used for the remaining counties and to pro-
vide data comparisons.
The soil classification system for each map was converted to soil
families (the second most specific classification of soils, indicating
the soil texture), and a soil texture trianglei^/ was used to estimate
silt content for each family designation. Areas of uniform soil family
were superimposed on a grid map (see Figure 6) and appropriate silt con-
tent values were assigned to each grid.
A map of the PE-index by state climatic division, generated in an
earlier MRI study^i' indicates a PE-index of 93 for both state climatic
divisions which comprise the Metropolitan St. Louis AQCR.
TEMPORAL APPORTIONING FACTORS
Agricultural land tilling, planting, and harvesting follow a regu-
lar yearly cycle dependent on the type of crop. Within these yearly cy-
cles, agricultural operations are performed mainly during the hours from
dawn to dusk and uniformly through the week, with only a slight reduction
on Sundays. The temporal apportioning factors derived for agricultural
operations are shown in Figure 7.
Based on seasonal performance of primary and secondary tilling, plant-
ing, cultivation, and harvesting for the main crops in the St. Louis AQCR,
as determined by MRI personnel (see Table 3), seasonal apportioning factors
were determined for each county, taking into account the respective crop
mixes. Separate average seasonal factors were calculated for Missouri and
Illinois to reflect wide differences in types of crops in the two states.
19
-------
FRANKLIN ' j
40 :-: '
i WASHINGTON '
70
Figure 6. Soil silt content (7.) for RAPS Grid system
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TEMPORAL APPORTIONING FACTORS
Source Type: Agricultural Tilling
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Mon Tue Wed Thu Fri
Day of Week
Sat Sun
/
f
//
//
//
r
/\
/ . V
/ '^ \
/ / ^'A
/ / N
/
\
V- ^
\N^- "
\ ^
-^-'
Illinois
Missouri
50
45
40
35
30
25
20
15
10
Winter
Spring Summer
Season of Year
Fall
Figure 7. Percentage of total daily, weekly, and annual
agricultural tilling
21
-------
WIND EROSION FROM TILLED LAND
GRID SOURCE EXTENT
The measure of source extent for wind erosion from tilled agricul-
tural land is average exposed (unvegetated) acreage. Agricultural land
is assumed to remain vulnerable to wind erosion from the time of primary
tilling to about 1 month after planting. The procedure used to determine
average area of erodible agricultural land within each grid is depicted
in Figure 8.
Annual average exposed acreage for each county was determined from
seasonal values (see Table 4) which were calculated from the acreage
planted in each crop and the corresponding months of exposure. Erodible
acreage for each grid was determined by apportioning county totals on
the basis of the proportion of county agricultural acreage which lies
within the grid.
EMISSION FACTOR
An emission factor for wind erosion from agriculturally tilled land
was derived from data on atmospheric loadings of suspended dust measured
by Gillette^/ during dust storms in West Texas. The threshold rate of
wind erosion was adjusted to apply to values of soil silt content and
climatic factor which are representative of the St. Louis area.
The threshold value for the St. Louis area was calculated to be:
3.5 tons/acre/year. Based on meteorological data for 3-hr time incre-
ments, winds in the St. Louis region exceed 12 mph approximately 26% of
the time.-=-£' Thus, the annual average emission factor for wind erosion
becomes:
3.5 tons/acre x 0.26 = 0.9 tons/acre
23
-------
By County
Area of Each
Grid (Acres)
% of Grid
In County
1970 Land
Use Maps
By County
Acres in
"" Each Grid
By Grid
% Agricultural
"" Land in Grid
By County
Acres of
Agricultural
Land in
Each Grid
By Crop
Time Period
of Crop
Til lings
By County
1969 Acres
of Each Crop
Harvested
By County
Annual
Exposed Acres
of Agricultural
Tilled Land
By Grid
Annual
Exposed Acres
of Agricultural
Tilled Land
Figure 8. Procedure for determination of acreage of
exposed agricultural land
24
-------
Table 4. SEASONAL EXPOSED ACREAGE BY COUNTY
State
Illinois
Missouri
_ /
a/
Countv~
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson
St. Charles
St. Louis
Average acres exposed
Winter
58,292
86,609
104,702
53,762
65,426
97,786
86,719
26,462
6,166
51,505
13,384
Spring
77,427
97,376
138,675
60,500
78,736
131,115
119,832
19,943
5,626
56,288
15,168
Summer
18,474
27,109
37,021
25,744
27,379
39,798
38,340
15,061
5,154
22,584
9,269
Fall
42,190
67,420
79,008
49,070
54,667
77,749
69,983
27,822
7,216
45,657
14,402
Average
49,096
69,628
89,851
47,269
56,552
86,612
78,718
22,322
6,040
44,008
13,055
a) St. Louis City not included; harvested cropland (acres) = 0.
-------
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors for wind erosion are shown in Figure
9. Seasonal apportioning factors were scaled to the product of (a) sea-
sonal values of exposed acreage by state and (b) the seasonal climatic
factorM' for the St. Louis AQCR. Hourly factors were proportioned to
the probabilities that the wind speed will exceed 12 mph, the threshold
value for the onset of wind erosion.
26
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Wind Erosion from Tilled Land
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Mon Tue Wed Thu Frl
Day of Week
Sat
Sun
Winter
Spring Summer
Season of Year
Fall
Figure 9. Percentage of total daily, weekly, and annual wind erosion
from agricultural tilled land
27
-------
CONSTRUCTION
GRID SOURCE EXTENT
Fugitive dust emissions from construction activities are directly
related to the land area being worked, over a specific time period. Fig-
ure 10 presents the methodology used to determine annual acres of con-
struction within each grid area. Construction activity considered in this
study was confined to the Source Industrial Classification (SIC) Major
Group 15 (Building ConstructionGeneral Contractors and Operative
Builders) and Group 16 (Construction Other than Building Construction-
General Contractors).
Detailed 1974 data for major building construction sites in the
Missouri counties except Franklin were obtained from the East-West
Gateway Coordinating Council.«=?-' These data included: county, location,
census tract, description of activity, project name, size in acres (or
square feet), and stage of development. All sites were located by grid
and construction acreage was totaled by county. It was evident that the
building construction centered around St. Louis County.
A detailed listing of road construction projects in the St. Louis
area was also obtained from the East-West Gateway Coordinating Council.
For the Missouri counties except Franklin, road construction projects
differentiated by type and mileage were assigned to the proper grid areas.
Estimates of contruction acreage per mile of road construction, for each
type of project, were used to convert mileage to acreage within each grid.
Road construction acreage totals for St. Charles and St. Louis Counties,
which amounted to less than 10% of building construction acreage, were
disregarded.
Table 5 gives construction acreage by county. Construction acreage
totals for Jefferson, St. Charles and St. Louis counties are slightly
larger than the estimates reported earlier by MRI, which were based
on state construction receipts,^-**' and county construction employment;=='
this is apparently due to increased area development. However, the
St. Louis City construction acreage was smaller than the previously reported
29
-------
CONSTRUCTION
GRID SOURCE EXTENT
Fugitive dust emissions from construction activities are directly
related to the land area being worked, over a specific time period. Fig-
ure 10 presents the methodology used to determine annual acres of con-
struction within each grid area. Construction activity considered in this
study was confined to the Source Industrial Classification (SIC) Major
Group 15 (Building ConstructionGeneral Contractors and Operative
Builders) and Group 16 (Construction Other than Building Construction
General Contractors).
Detailed 1974 data for major building construction sites in the
Missouri counties except Franklin were obtained from the East-West
Gateway Coordinating Council.=£' These data included: county, location,
census tract, description of activity, project name, size in acres (or
square feet), and stage of development. All sites were located by grid
and construction acreage was totaled by county. It was evident that the
building construction centered around St. Louis County.
A detailed listing of road construction projects in the St. Louis .
area was also obtained from the East-West Gateway Coordinating Council.
For the Missouri counties except Franklin, road construction projects
differentiated by type and mileage were assigned to the proper grid areas.
Estimates of contruction acreage per mile of road construction, for each
type of project, were used to convert mileage to acreage within each grid.
Road construction acreage totals for St. Charles and St. Louis Counties,
which amounted to less than 10% of building construction acreage, were
disregarded.
Table 5 gives construction acreage by county. Construction acreage
totals for Jefferson, St. Charles and St. Louis counties are slightly
larger than the estimates reported earlier by MRI, which were based
on state construction receiptsy2i' and county construction employment»=='
this is apparently due to increased area development. However, the
St. Louis City construction acreage was smaller than the previously reported
29
-------
For Missouri CounHes (except Franklin)
1974 Construction
Projects
Residential
Commercial
Highways
By Project
Acres of
Construction
By Project
Grid Location
of Construction
Projects
By Grid
Annual Acres
of Construction
For Illinois Counties and Franklin County, Missouri
By County
1972 Acres
of Construction
By County
Land Area
By Grid
Land Area
By Grid
Annual Acres
of Construction
Figure 10. Procedure for determination of annual acres of construction
30
-------
Table 5. CONSTRUCTION ACREAGE BY COUNTY
Construction acreage
State County Building Road Total
Illinois Bond 143
Clinton 254
Madison 1,640
Monroe 151
Randolph 333
St. Glair 1,760
Washington 87
Missouri Franklin 435
Jefferson 989 204 1,193
St. Charles 1,088 al 1,088
St. Louis City 234 62 296
St. Louis 4,999 a/ 4,999
a/ Road construction acres less than 107. of total.
31
-------
value, which was based on the assumption that construction employees
residing in the city worked only within the city.
For the remaining counties, i.e., Franklin County in Missouri and
all of the Illinois counties, MRI estimates of total construction acre-
age^' (buildings plus roads) were apportioned to grids within a county
on the basis of grid area.
EMISSION FACTOR
County-wide emission factors for dust emissions from construction
activities were determined by multiplying a previously determined emis-
sion rate factor (1 ton/acre/month)i' by an average duration of construc-
tion within the county, weighted by the relative proportion of acreage
differentiated by project type and the average duration for each project
type. MRI estimates of the average duration of construction^/ are:
6 months for residential buildings,
11 months for nonresidential buildings, and
18 months for nonbuilding construction.
The emission factor for construction can thus be written as follows:
EFc = D tons/acre
where D = weighted average duration of construction within a given
county.
The value of D for St. Louis City and the Missouri counties of
Jefferson, St. Charles, and St. Louis was determined to be 9.1 months,
and the value for the remaining counties was estimated to be equal to
12 months .£/
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors for determining construction emissions
by hour of the day, day of the week, and season of the year were derived
from analysis of the work cycle of construction activity (see Figure 11).
Construction activity reaches its peak level during June and July and
is lowest during December through February. Weekday activity is relatively
uniform with some reduction on weekends. The hourly factor distribution
has mid-morning and mid-afternoon peaks.
32
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Construction
2
i
01 2345678 91011121314151617181920212223
Hour of Day
20
15
10
5
Man Tue Wed Thu Fri
Day of Week
Sat
Sun
35
30
25
20
15
10
5
I
^***^~
^^^-"^
^-^-
^-^^^-^^^
"*- _
Winter Spring Summer Fall
Season of Year
'igure 11. Percentage of total daily, weekly, and
annual construction activity
33
-------
AGGREGATE STORAGE
GRID SOURCE EXTENT
The amount of fugitive dust emissions from aggregate storage piles
is proportional to the quantity of aggregate storedyi' i.e., the tonnage
put through the storage cycle. Figure 12 illustrates the methodology for
determining the quantity of aggregate stored annually within each grid.
The following Source Classification Codes of the National Emissions
Data System (NEDS) were identified as industrial producers and users of
mineral aggregate:
SCC ID
I II III IV
3 05 All All
23/
A NEDS point source listing (August 25, 1975) for the above codes was
obtained for the St. Louis AQCR.
Aggregate storage data from the NEDS listing were analyzed and the
grid numbers for aggregate user and producer industries were determined
from the respective UTM coordinates. Only industries with open aggregate
storage were considered in this study. Producers are stone quarries and
sand/gravel processors, and users are cement manufacturing (wet and dry),
and concrete batching. Asphalt batching plants in the St. Louis area nor-
mally store aggregate in enclosed areas.
The methodology employed to determine the amount of aggregate mate-
rial stored on-site by a producer or user industry and the average period
of storage is presented below.
Stone quarries - The amount of aggregate material stored annually
is specified in the NEDS output. An estimated 3-month storage period is
assumed from previous experience with the stone quarry industry.
35
-------
CO
1975 NEDS
Listing of SCC
Codes 03-05-
(Industrial
Process -
Mineral Products)
Contacts with
Industries
By County
Aggregate Storage
Users
Producers
Methodology
for Determining
Annual Tons of
Material Stored
By Grid
Aggregate Storage
Producers
E
>y Grid
Annual Tons
of Aggregate
Storage
Figure 12. Procedure for determination of annual tons of aggregate storage
-------
Sand and gravel - The amount of aggregate material stored in an an-
nual period is taken to be 50% of the tonnage processed. An estimated
3-month inventory period is assumed from previous experience with the
sand and gravel industry.
Cement manufacturing - The following equation for calculating the
amount of aggregate stored by this user industry was determined from tele-
phone contacts with area plants and a literature survey:
A j r, 4. j j i i tons aggregate
Aggregate stored Cement produced x 1.2
65 5 * tons cement
(tons) (tons)
The NEDS output designates tons of cement produced from wet and dry pro-
cess facilities. On the average, aggregate material used in cement manu-
facturing is stored for 1 week.
Concrete batching - Cubic yards of concrete produced by each batch-
ing plant is specified in the NEDS listing. Based on contacts with this
user industry, the following conversion factors were obtained: (a) 1
cu yd of concrete is equivalent to 2 tons, and (b) approximately 75% of
each ton of concrete produced is comprised of aggregate material taken
from open storage. The average aggregate storage period for this user
industry is 1 week.
Table 6 summarizes by county the quantity of aggregate stored an-
nually for each of the above user and producer industries.
EMISSION FACTOR
The emission factor for dust emissions from aggregate placed in open
storage for a period of 3 months is:
EFg = 0.33 Ib/ton placed in storage
which includes emission contributions from wind erosion (33%), movement
of traffic among the storage piles (40%), and loading and unloading op-
erations (27%).' The corresponding emission factor for a 1-week storage
cycle is 0.22 Ib/ton placed in storage.
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors (see Figure 13) were determined sep-
arately for the emission contributions from storage pile activity and
from wind erosion. The factors for storage pile activity were derived
on the basis of the information from industrial personnel and NEDS data.
37
-------
Table 6. ANNUAL ACRES OF AGGREGATE STORED BY COUNTY
U)
00
Aggregate storage (tons/year)
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Franklin
Jefferson .
St. Charles
St. Louis City
St. Louis
Sand/
gravel
0
0
0
0
0
0
0
0
12,950
0
0
189,500
Stone
quarry
0
20,000
74,000
46,800
275,000
1,900,000
100,000
0
8,000
220,000
0
64,000
Cement
manufacturing
0
0
0
0
0
0
0
0
1,232,700
0
0
1,709,600
Concrete
batching
0
11,300
0
0
0
0
0
37,950
54,000
125,550
0
0
Total
0
31,300
74,000
46,800
275,000
1,900,000
100,000
37,950
1,307,650
345,550
0
1,163,100
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Aggregate Storage
6
/f
i
>
it
V
/
f-
/
/
*^~
^*
^~
[-
~*
'
k»
fc««l
.
\
S!
i
V
X
"^«
v
v^
«
^^
01 2345678 91011121314151617181920212223
Hour of Day
Wind
Erosion
~- Activity
Weighted
Average
20
15
10
5
Wind
Erosion
Activity
Weighted
Average
Mon Tue Wed Thu Fri
Day of Week
Sat
Sun
35
30
25
20
15
10
5
Winter
Spring Summer
Season of Year
Fall
Figure 13. Percentage of total daily, weekly, and annual
aggregate storage operations
Climatic
Factor
Activity
Weighted
Average
39
-------
For aggregate producers (stone quarries and sand/gravel processors),
approximately 75% of the industry operates year-round and the remaining
25% operate 9 months during the year. Production rates are at peak level
during June and July, and are lowest during December through February.
For most of the year, the operating schedule is 6 days/week and 15 hr/day.
For aggregate users (cement manufacturing and concrete batching),
approximately 60% of the industry in the St. Louis AQGR operate year-
round, and the remaining 40% operate 9 to 10 months during the year.
Production rates change seasonally with demand for concrete for local
construction projects. The operating schedule is normally 6 days/week
and 16 hr/day. Spring and summer are peak seasons, and activities de-
cline during the winter months (December through February).
Seasonal and hourly apportioning factors for wind erosion from stock-
piles were based on observed variations in governing climatic conditions.
Seasonal factors were scaled to values of the climatic factor for wind
erosion,.L§/ and hourly factors were proportioned to the probability that
the wind speed will exceed 12 mph, the threshold value for the onset of
wind erosion.
40
-------
UNPAVED AIRSTRIPS
GRID SOURCE EXTENT
The landing/takeoff (LTD) cycle is the designated measure of source
extent for fugitive emissions from unpaved airstrips. Figure 14 illus-
trates the procedure used to determine LTO cycles on unpaved airstrips
by grid.
9/ /
Airport data were extracted from an "Airport Services'1 computer
tape obtained by MR! from the Federal Aviation Administration (FAA) under
EPA Contract No. 68-02-1437. Data on this tape include the following in-
formation for each airport: site number, city, state, airport name,
county code, latitude, longitude, airport type, number of total based
aircraft, number of multi-engine based aircraft, runway pavement type,
runway length, population served, ownership type, and usage type. A com-
puter program was written to list all Missouri and Illinois airports and
to output required data onto standard computer cards.
Nine airports within the St. Louis AQCR were designated as Pavement
Type 5 (dirt or gravel runways). However, seven of these airports did
not have any based aircraft and the remaining two were helicopter bases.
Airstrips with Pavement Type 4 (turf runways) numbered 43, of which,
25 turf airstrips (excluding heliports) listed based aircraft. Grid num-
bers for each of these 25 airstrips (see Table 7) were determined from
latitude and longitude indicated on the FAA tape.
Regional FAA officials estimated the number of operations per based
aircraft at small airport facilities to be in the range of 400 to 800
operations per year with a typical value being 500, i.e., 250 LTO cycles
per yea.r.2.' The total number of LTO cycles on unpaved airstrips in each
grid was calculated by multiplying 250 LTO cycles per year times the total
number of aircraft based at unpaved airstrips within each grid.
41
-------
By Airport
1974 Based
Aircraft
By Airport
1974 Pavement
Type
Annual LTO
Cycles per
Based Aircraft
(Estimated =250)
By County
By Grid
Number of
Based Aircraft
on Unpaved
Airstrips
Number of
Based Aircraft
on Unpaved
Airstrips
By Grid
Annual LTO
Cycles on
Unpaved
Airstrips
Figure 14. Procedure for determination of annual LTO cycles on
unpaved airstrips
42
-------
Table 7. DATA ON UNPAVED (TURF) AIRSTRIPS BY COUNTY
State
Illinois
Missouri
County
Bond
Clinton
Madison
Monroe
Randolph
St. Clair
Washington
Franklin
Jefferson
St. Charles
St. Louis
St. Louis City
No. of turf
airstrips with
based aircraft
2
3
3
4
3
7
1
0
1
1
0
0
Grid location
1,764
1,739, 1,761, 1,784
1,595, 1,641, 1,710
951, 990, 1,057, 2,273
1,579, 1,582, 1,633
1,456, 1,484, 1,586,
1,617 (2), 1,639,
2,341
1,842
_
185
166
-
-
LTO cycles/
Year
500
2,500
5,250
12,000
750
8,750
250
0
3,000
1,250
0
0
-------
EMISSION FACTOR
The emission factor for unpaved airstrips, in units of pounds of
dust per landing/takeoff cycle, was derived by analogy to the equation
for unpaved roads,.2/ doubled to include propeller-generated wind ero-
sion. The expression for dirt airstrips is given by:
EFa = 2[0.49 sa
where s is the silt content (percent) of dirt airstrips (equivalent
to the agricultural soil silt content), Sa is the average aircraft
ground speed (mph), d is the number of dry days per year, and (1)
mile is the approximate length of runway used for an LTO cycled.' in-
cluding taxiing. Regional FAA officials*-' estimated Sa to be 40 mphj
and, on the average, there are 250 dry days per year in the St. Louis
area.J/
During the months of July through October, turf airstrips will ap-
proximate dirt airstrips due to dry weather conditions and higher volume
of traffic. It was estimated that the emission factor for turf airstrips
should be one-half the factor for dirt airstrips to account for the ef-
fect of grass cover in reducing wind erosion. The emission factor for
turf airstrips ranged from 4.5 to 31 Ib/LTO cycle for agricultural silt
contents ranging from 10 to 70%.
TEMPORAL APPORTIONING FACTORS
Temporal apportioning factors were derived from the following infor-
mation (see Figure 15):
1. Air traffic, i.e., landings and takeoffs, occurs primarily be-
tween the hours of dawn to dusk.
2. Approximately 50% of the air traffic occurs on weekends and holi-
days.
3. Approximately 70% of the air traffic occurs between the months
of April through October.
44
-------
TEMPORAL APPORTIONING FACTORS
Source Type: Unpaved Airstrips
__ Saturday,
Sunday
Monday
through
Friday
01 2345678 91011121314151617181920212223
Hour of Day
30
25
20
15
10
5
35
30
25
20
15
10
5
Winter
Spring Summer
Season of Year
Mon Tue Wed Thu Fri Sat Sun
Day of Week
Fall
Figure 15. Percentage of total daily, weekly, and annual LTO cycles
45
-------
DATA TABULATIONS AND CALCULATED RESULTS
Tables 8 and 9 illustrate example data tabulations prepared for this
project. Table 8 gives data on (a) annual extent of fugitive dust sources
and (b) agricultural soil silt content, for the first 35 grids in the
RAPS study region. Table 9 presents the hourly adjustment factors for
a Sunday in the winter season. A complete set of example calculations is
detailed in Appendix A.
The preceding data were used as input for two computer programs:
1. Program 1, which calculates the annual emissions of fugitive
dust for each source category, by grid, and
2. Program 2, which calculates hourly emissions of fugitive dust
within a specified grid, for any hour of the year, through
multiplication of the annual emissions total by the particu-
lar hourly adjustment factor.
Simplified logic diagrams of these programs are presented in Figures 16
and 17. Both programs were written in Fortran IV to provide compatibility
with most computer systems. Example output for the annual emissions com-
puter program is illustrated in Figure 18.
47
-------
Table 8. EXAMPLE CODED SOURCE EXTENT AND CORRECTION FACTOR DATA
Source extent
Coordinates
Grid
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
19
20
21
22
25
26
27
28
29
32
33
34
35
37
38
39
40
43
44
(UTM
E
640
640
640
640
645
645
645
645
645
650
650
650
650
650
650
655
660
660
660
660
665
670
670
670
670
670
670
670
670
671
671
672
672
673
673
Zone 15)
-JL
4,235
4,245
4,265
4,280
4,230
4,255
4,260
4,275
4,280
4,230
4,235
4,245
4,255
4,265
4,275
4,230
4,235
4,245
4,255
4,265
4,230
4,230
4,235
4,245
4,250
4,260
4,265
4,268
4,269
4,268
4,269
4,268
4,269
4,265
4,266
Size
(tan)
10
10
10
5
5
5
5
5
5
5
10
10
10
10
5
5
10
10
10
10
5
5
10
5
5
5
3
1
1
1
1
1
1
1
1
Unpaved
(102 veh.
Gravel
8,827
10,690
8,260
1,296
907
1,490
2,203
1,101
2,389
583
10,496
7,710
8,876
6,673
405
713
7,580
5,377
7,256
5,759
1,684
1,500
6,738
1,555
1,745
130
0
0
0
0
0
0
0
0
0
roads
mi.)
Dirt
0
0
0
0
242
0
0
0
0
0
0
0
0
0
0
0
0
0
0
101
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Land tilling
(acres)
11,103
11,102
11,102
2,774
2,776
2,776
2,776
2,776
2,774
2,776
11,105
11,105
11,105
11,448
2,774
2,776
11,105
11,105
11,105
11,102
2,776
2,776
11,105
2,776
2,776
2,776
999
110
110
110
110
110
110
110
110
Wind
erosion
(acres)
944
945
945
236
236
236
236
236
236
236
944
944
944
944
236
236
944
944
944
945
236
236
944
236
236
236
85
9
9
9
9
9
9
9
9
Construction
(10"1 acres)
147
153
156
23
46
46
46
46
34
46
184
184
184
178
32
46
184
184
184
156
46
46
184
46
46
46
17
2
2
2
2
2
2
2
2
Aggregate storage
(tons)
0
0
0
0
0
0
0
0
43,050
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Dirt airstrips
(LTO cvcles)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Correction
factor
Silt
content (7.)
40
40
59
59
40
40
40
59
59
40
40
40
40
59
59
40
40
40
40
59
30
40
40
40
40
67
67
50
50
50
50
50
50
67
67
-------
Table 9. HOURLY ADJUSTMENT EXAMPLE CODED FACTORS
vD
Hourly acHustment factors (10 )
Number
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Time of
day
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Unpaved
roads
283
252
189
63
63
377
1,227'
2,139
1,667
1,919
1,919
1,887
1,699
1,887
2,328
2,265
2,517
2,863
1,919
1,510
1,070
692
440
283
Land tilling
Illinois
3
3
3
3
3
78
130
156
182
182
208
208
182
182
208
182
182
156
130
104
78
26
10
3
Missouri
1
1
1
1
1
39
65
78
91
91
104
104
91
91
104
91
91
78
65
52
39
13
5
1
Wind erosion
Illinois
1,667
1,667
1,667
1,620
1,667
1,667
1,667
1,805
1,944
2,083
2,129
2,222
2,268
2,268
2,315
2,315
2,222
2,083
1,991
1,944
1,805
1,805
1,759
1,713
Missouri
1,713
1,713
1,713
1,665
1,713
1,713
1,713
1,855
1,998
2,141
2,188
2,283
2,331
2,331
2,378
2,378
2,283
2,141
2,046
1,998
1,855
1,855
1,808
1,760
Construction
8
8
8
8
38
188
375
525
600
675
675
525
525
675
675
600
525
375
225
150
75
30
8
8
Aggregate
storage
311
311
311
311
311
851
851
867
884
900
900
916
916
916
933
933
916
900
884
884
425
311
311
311
Unpaved
airstrips
36
36
36
36
72
360
720
1,440
2,160
3,240
2,880
2,880
2,880
2,880
5,400
4,320
2,880
1,440
1,080
720
360
72
36
36
-------
For Each Grid:
INPUT: GRID DATA
Number
Coordinates
Width (km)
County
Agricultural Silt
Content, % (st,
, so)
INPUT: SOURCE EXTENT DATA
Unpaved Roads (vehicle miles)
- Gravel & Dirt
Land Tilling (acres)
Wind Erosion (acres)
Construction (acres)
Aggregate Storage (tons)
Unpaved Airstrips (LTO cycles)
INPUT: CORRECTION FACTOR CONSTANTS
Number of Dry Days Per Year (d)
Precipitation-Evaporation Index (PE)
Duration of Construction Activity (D)
Missouri, Illinois
Silt Content - Roads, Gravel (sg)
- Roads, Dirt (sj)
- Tilling (it)
Vehicle Speed - Roads (Sr)
- Airstrips (Sa)
For Each Grid:
COMPUTE: EMISSION FACTORS
Unpaved Roads - Gravel EFg = 0.49 sg (Sr/30) (d/365) Ib/vehicle mile
- Dirt EFd = 0.49 sd (Sr/30) (d/365) Ib/vehicle mile
Land Tilling EFt = 1.1 st/(PE/50)2 Ib/ocre
Wind Erosion EFw =0.9 tons/acre
Construction EFC = D tons/acre
Aggregate Storage EFS = 0.33 Ib/ton stored
Unpaved Airstrips EFa = 0.49sa (Sg/30) (d/365) Ib/LTO cycle
For Each Grid:
COMPUTE AND OUTPUT: ANNUAL EMISSION RATE (tons/year, M tons/year)
Unpaved Roads
Land Tilling
Wind Erosion from Agricultural Tilled Land
Construction
Aggregate Storage
Unpaved Airstrips
*Total
Figure 16. Simplified flow diagram of calculation procedure
for annual emissions by grid
50
-------
For Each Season, Day, and Hour:
INPUT: TEMPORAL APPORTIONING FACTORS
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
I
COMPUTE: HOURLY ADJUSTMENT FACTORS
Season Factor x Day Factor x Hour Factor
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
For Each Grid:
INPUT: ANNUAL EMISSION RATE (tons/year)
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Roads
For Specified Grids:
COMPUTE & OUTPUT:
Unpaved Roads
Land Tilling
Wind Erosion
Construction
Aggregate Storage
Unpaved Airstrips
HOURLY EMISSION RATE (Ib/hr, kg/hr)
Figure 17. Simplified flow diagram of calculation procedure
for hourly emissions by grid
51
-------
CALCULATED ANNUAL EMISSION HATES BY &f>W
NJ
.RIO
1
2
3
4
5
6
7
a
9
10
ll
12
13
14
15
16
19
20
21
22
25
26
27
28
29
32
33
34
35
37
38
39
40
43
44
45
46
47
48
49
i, nun
E
640
640
640
640
645
645
645
645
645
650
650
650
650
650
650
655
660
660
660
660
665
670
670
67 i 1>
670
670
670
670
670
671
671
672
672
673
673
673
673
673
674
674
U i«« 1C
N
4235
4245
4265
4280
4230
4255
42bO
4275
42BO
4230
4235
4245
4255
4265
4275
4230
4235
4245
4255
4265
4230
4230
4235
4245
4250
4260
4265
4268
4269
4268
4269
4268
4269
4265
4266
4267
4268
4269
4265
4266
- u« ;
Si/
10
10
10
5
5
5
5
5
5
5
10
10
10
10
5
5
10
10
10
10
5
5
10
5
5
5
3
1
1
1
u
C
M
a
a
a
8
8
8
8
a
u
a
8
a
8
8
8
8
8
a
a
3
8
6
8
8
B
8
a
8
8
8
B
B
B
8
8
a
8
8
8
-''uNPV. ROADS
2369.99
2870.19
2217.75
347.97
405.96
400.05
591.49
295.61
641.43
156.53
2818.10
2070.08
2383.15
1791.65
108.74
191.44
2035.18
1443.69
1948.19
1646.25
452.14
402.74
1809.11
417.51
468.52
34.90
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
EMISSION RATE (TONS/YPl
AQ. TILLING WIND EROSION CONSTRUCTION
AG. STORAGE UNP.AIRSTRIP
70.61
70.61
10*.15
36.04
17.65
17.65
17.65
26.04
26.04
17.65
70.62
70.62
70.62
104.15
26.04
17.65
70.62
70.62
70.62
10-.15
13.24
17.65
70.62
17.65
17.65
29.57
10.64
.87
.87
.87
.87
.87
.87
1.17
1.17
1.17
.87
.87
1.17
1.17
849.6
850.5
850.5
212.4
212.4
212.4
212.4
212.4
212.4
212.4
849.6
849.6
849.6
212.4
212.4
849.6
849.6
849.6
850.5
212.4
212.4
849.6
212.4
212.4
212.4
76.5
8.1
8.1
8.1
8.1
8.1
8.1
8.1
8.1
8.1
8.1
8.1
8.1
8.1
176.4
1*1.6
187.2
27.6
55.2
55.2
55.2
55.2
40.8
55.2
220.8
220.8
220.8
213.6
38.4
55.2
220.8
220.8
220.8
187.2
55.2
55.2
220.8
55.2
55.2
55.2
?0.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.o
2.4
2.4
2.4
2.4
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
'.103
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
o.oeo
0.000
o.eoo
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
TOTAL**
3467
3975
3360
614
691
685
877
589
928
442
3959
3211
3524
2959
386
477
3176
2585
3089
2788
733
688
2950
703
754
332
108
11
11
11
11
11
11
12
12
12
11
11
12
12
a/ Grid size (width) in kilometers.
b/ County which represents major portion of grid.
Figure 18. Example computer output of annual emissions by grid
-------
ANALYSIS OF RESULTS AND ESTIMATED ACCURACIES
Table 10 presents a county breakdown of annual fugitive dust emis-
sions in the Metropolitan St. Louis AQCR. This data represents all grids
which lie entirely or partially within a specific county. As indicated,
unpaved roads and wind erosion from agricultural tilled land account for
more than 80% of the total fugitive dust emissions for the St. Louis area.
The total quantity of particulate emissions smaller than 30 um in
diameter emitted by fugitive dust sources considered in this project is
1,145,000 tons/year. Assuming that 20% of the emissions (i.e., the por-
tion smaller than 5 um in size) will be transported to ambient air qual-
ity monitoring stations (see Appendix B), then 229,000 tons/year of fugi-
tive dust will have an impact on regional air quality and must be taken
into account in modeling the St. Louis AQCR. In comparison, total nonfugitive
emissions for the St. Louis AQCR are 355,000 tons/year;2^J thus, fugitive
emissions may be said to represent 39% of the total particulate pollutant
problem.
Table 11 presents estimates for possible error in the calculated
values corresponding to a 90% confidence level and were determined by a
progressive analysis of errors associated with each calculation step.
Composite ranges of error are presented for calculated source extent, cor-
rected emission factors, and hourly adjustment factors.
53
-------
Table 10. SUMMARY OF ANNUAL EMISSIONS BY COUNTY
Oi
Emission rate (tons/vear)
State County
Illinois Bond
Clinton
Madison
Monroe
Randolph
St. Glair
Washington
Subtotal
Missouri Franklin
Jefferson
St. Charles
St. Louis City
St. Louis
Subtotal
Total
Unpaved
roads
46,594
56,874
69,509
21,338
50,431
62,286
57,524
364,556
44,721
18,478
19,818
0
0
83,017
447,573
Agricultural
tilling
5,612
7,804
8,796
4,852
6,132
9,509
9,115
51,820
3,750
678
2,478
0
1,395
8,303
60,123
Wind
erosion
44,186
62,665
80,865
42,542
50,896
77,950
70,846
429,950
20,089
5,436
39,607
0
11,749
76,881
506,831
Construction
1,716
3,048
19,680
1,812
3,996
21,120
1,044
52,416
5,220
12,765
9,901
3,256
45,491
76,633
129,049
Aggregate
storaee
0
5.2
12.2
7.7
45.4
313.5
16.5
400.5
6.3
215.8
57.0
0
323.9
603.0
1,003.5
Unpaved
airstrips
7.8
39.1
70.4
174.6
10.9
133.0
3.9
439.7
0
33.6
9.8
0
0
43.4
483.1
Total
98,115
130,435
178,932
70,726
111,511
171,311
138,549
899,582
73,788
37,606
71,870
3,256
58,958
245,478
1,145,058
-------
Table 11. ESTIMATED ERRORS FOR TABULATED DATA
Estimated relative error
Source Source Corrected Hourly adjust-
category extent emission factor ment factor
Unpaved roads + 5% + 20% + 15%
Agricultural tilling + 15% + 30% + 20%
Wind erosion + 30% + 20% + 15%
Construction + 35% + 30% + 20%
Aggregate storage + 25% + 30% + 20%
Unpaved airstrips + 15% + 25% + 20%
55
-------
REFERENCES
1. Cowherd, C., Jr., K. Axetell, Jr., C. M. Guenther, and G. A. Jutze,
Development of Emission Factors for Fugitive Dust Sources, prepared
for the U.S. Environmental Protection Agency, Office of Air and
Waste Management, Office of Air Quality Planning and Standards,
Contract No. 68-02-0619, Publication No. EPA-450/3-74-037, June
1974.
2. Personal communication from Mr. John Godar, Head, Planning Depart-
ment, Illinois Department of Transportation, District 8, East
St. Louis, Illinois, September 1975.
3. Personal communication from Mr. Robert Barren, Mapping Department,
Missouri State Highway Commission, Jefferson City, Missouri,
November 4, 1975.
4. Personal communication from Mr. George Daykin, County Engineer,
St. Louis County, Clayton, Missouri, November 3, 1975.
5. Climatic Atlas of the United States, U.S. Department of Commerce,
Environmental Science Services Administration, Environmental Data
Service, U.S. Government Printing Office, Washington, D.C., June
1968.
6. Cowherd, C., Jr., C. Guenther, and D. Wallace, Emissions Inventory
of Agricultural Tilling, Unpaved Roads and Airstrips, and Construc-
tion Sites, EPA Publication No. EPA-450/3-74-085, November 1974.
7. Kennedy, N., J. H. Kell, and W. S. Homburgcr, Fundamentals of Traffic
Engineering, 8th edition, Institute of Transportation and Traffic
Engineering, University of California, Berkeley, California, 1973.
8. 1969 Census of Agriculture, County Summary, Table 2, U.S. Department
of Commerce, U.S. Government Printing Office, Washington, D.C.
57
-------
9. 1971-72 Existing Land Use Update and Analysis, Land Use Component
Technical Report, East-West Gateway Coordinating Council, June 1973.
10. "Generalized Existing Land Use 1970-St. Louis Metropolitan Area,"
East-West Gateway Coordinating Council, 1973.
11. Thornthwaite, C. W., "Climates of North America According to a New
Classification," Geograph. Rev., 2^:633-655 (1931).
12. Personal communication from J. Wiley Scott, Assistant State Soil
Scientist, Soil Conservation Service, Champaign, Illinois, July
28, 1975.
13. Personal communication from J. Vernon Martin, State Conservationist,
Soil Conservation Service, Columbia, Missouri, July 22, 1975.
14. "Major Soils of the North Central Region, U.S.A.," a map from Soils
of the North Central Region of the United States, North Central
Regional Publication No. 76, Bulletin 544, published by the Agri-
cultural Experimental Station, University of Wisconsin, in coop-
eration with the U.S. Department of Agriculture, June 1960.
15. "Guide for Textural Classification in Soil Families," supplement
to Soil Classification; A Comprehensive System, Seventh Approxi-
mation, Soil Survey Staff, Soil Conservation Service, U.S. Depart-
ment of Agriculture, p. 40, March 1967.
16. Gillette, D. A., "Production of Fine Dust by Wind Erosion of Soil:
Effect of Wind and Soil Texture," paper presented at the Atmos-
phere-Surface Exchange of Particulate and Gaseous Pollutants, 1974
Symposium, September 1974.
17. STAR program, six stability classes (day/night), seasonal and annual
listing, National Climatic Center, Asheville, North Carolina,
January 1970 - December 1974.
18. Personal communication from Neil Woodruff, U.S. Department of Agri-
culture, Agricultural Research Service, Kansas State University,
Manhattan, Kansas, January 10, 1974.
19. Personal communication from John Kinsey, East-West Gateway Coordi-
nating Council, September-November 1975.
20. 1974 Short-Range Improvement Program, East-West Gateway Coordinating
Council, St. Louis, Missouri, June 1974.
58
-------
21. 1972 Census of Construction Industries, Preliminary Report, U.S.
Department of Commerce, Bureau of the Census.
22. County and City Data Book 1972, a Statistical Abstract Supplement,
U.S. Department of Commerce, Bureau of the Census, U.S. Govern-
ment Printing Office, Washington, D.C. (1973).
23. Personal communication from Mr. Charles C. Masser, Project Officer,
U.So Environmental Protection Agency, Office of Air Quality Plan-
ning and Standards, September 15, 1975.
24. "Airport Services Tape," Federal Aviation Administration, Public
Information Center, AIS 230, Washington, D.C. 20591.
25. 1972 National Emissions Report, National Emissions Data System (NEDS)
of the Aerometric and Emissions Reporting System (AEROS), U.S.
Environmental Protection Agency, Publication No. EPA-450/2-74-012,
June 1974.
59
-------
APPENDIX A
EXAMPLE CALCULATIONS
(RAPS GRID NO. 1)
61
-------
GRID DATA
Number: 1
UTM Coordinates: E 640, N 4235
Size (length): 10 km
County: Franklin
State: Missouri
ANNUAL SOURCE EXTENT
Unpaved Roads: gravel = 8,827 x 10^ vehicle miles
soil = 0 vehicle miles
Agricultural Tilling: 11,104 acres
Wind Erosion: 944 acres
Construction: 147 x 10 acres
Aggregate Storage: 0 tons
Unpaved Airstrips: 0 LTD cycles
CORRECTION FACTORS
Number of Dry Days Per Year (d): 250 days
Precipitation-Evaporation Index (PE): 93
Duration of Construction Activity (D): 12 months
Silt Content: Unpaved roads, gravel (sr): 16%
Dirt (sr): 40%
Agricultural tilling (st): 40%
Unpaved airstrips (sa): 40%
Vehicle Speed: Unpaved roads (Sr): 30 mph
Unpaved airstrips (S&): 40 mph
ANNUAL EMISSION FACTORS
Unpaved Roads: EFr = 0.49 sr { )(' lb
r J- I or> I \ n C. c
Gravel: EFr = 0.49 (lb)
Dirt: EFV = (0.49) (40)1= 13>4
Land Tilling: EFt = }'l . ~
L (PE/50)^ acre
30/1365/ vehicle mile
7 \
= 5.37
lb
vehicle mile
lb
r 30 365 vehicle mile
EF . 12.72 -.
t (93/50) 2 acre
Wind Erosion: EFW =0.9 tons /acre
62
-------
Construction: EFC - D
acre
EF = 12 months x 1 ton/acre = 12 ton/acre
c month
Aggregate Storage: EFS =0.33
ton stored
t \ /
Unpaved Airstrips: EFa = 0.49 (sa) (^Jj--!-.' lb
30/\365/ LTD cycle
. 0.49 (40) \ - 17.9
LTO cycle
\ /\ /
ANNUAL EMISSIONS
Annual Emissions (tons) = Annual Source Extent x Annual Emission Factor
Unpaved Roads: gravel = (8,827 x 1Q2 veh. mile)(5.37 Ib/veh. mile)
2,000 Ib/ton
= 2,370 tons
dirt = 0 tons
Land Tilling: (11,10* acres)(12.72 Ib/acre) = 70-6 tons
2,000 Ib/ton
Wind Erosion: (944 acres)(0.9 tons/acre) = 850 tons
Construction: (147 x 10 acres)(12 tons/acre) = 176.4 tons
Aggregate Storage: (0 tons)(0.33 lb/ton)(l ton/2,000 lb) = 0 tons
Unpaved Airstrips: (0 LTO cycles)(l7.9 Ib/LTO cycles)(l ton/2,000 lb) = 0 tons
TEMPORAL APPORTIONING FACTORS
Temporal Apportioning Factor = (Seasonal Factor)(Day of the Week
Factor)(Hour of the Day Factor)
Example: (Winter Factor)(Sunday Factor)(Hour 0 Factor)
Unpaved Roads: (0.214)(0.147)(0.009) = 283 x 10"6
Agricultural Tilling: 1 x 10"6
Wind Erosion: 1,713 x 10"6
Construction: 8 x 10""
63
-------
Aggregate Storage: 311 x 10"°
Unpaved Airstrips: 36 x 10"^
HOURLY EMISSIONS
Hourly Emissions (tons) = Annual Emissions (tons) x Temporal
Apportioning Factor
Example: Winter, Sunday, Hour 0, Grid 1
Unpaved Roads: (2,370 tons)(283 x 10"6) - 0.671 tons
Agricultural Tilling: (70.6 tons)(l x 10~6) =70.6 x 10"6 tons
Wind Erosion: (850 tons)(l,7l3 x 10"6) = 1.46 tons
Construction: (176.4 tons)(8 x 10~6) = 1.41 x 10~3 tons
Aggregate Storage: (0 tons)(311 x 10"^) = 0 tons
Unpaved Airstrips: (0 tons)(36 x 10~6) = 0 tons
METRIC UNITS CONVERSION
Annual Emissions (Mtons) = Annual Emissions (tons) x 0.907185
(Mtons/ton)
Hourly Emissions (Mtons) = Hourly Emissions (tons) x 0.907185
(Mtons/ton)
64
-------
APPENDIX B
FACTORS AFFECTING ATMOSPHERIC TRANSPORT OF FUGITIVE DUST
65
-------
This appendix presents an assessment of factors which determine
the drift distances of fugitive dust particles in the atmosphere. Drift
distance is defined as the horizontal displacement from the point of
particulate injection to the point of particulate removal by ground-
level deposition.
Factors to be considered in this assessment may be grouped into
two categories:
1. Meteorological factors - properties of the atmosphere which
affect contaminant advection and turbulent diffusion over surfaces of
varying roughness scales.
2. Source factors - height of injection and particulate properties
which affect gravitational settling and vertical mixing.
This assessment does not treat atmospheric washout of particulate matter.
METEOROLOGICAL FACTORS
Fugitive dust particles are typically injected into the lower por-
tion of the "surface layer" region of the atmosphere which extends from
ground level to a height of about 100 m. In this region the profile of
the wind and its turbulence characteristics are strongly dependent on
surface roughness properties.
For neutral atmospheric stability, the vertical profile of mean
wind speed, u(z) , in the surface layer is described by a logarithmic
relationship:
U.jt.
k
where u* = friction velocity
k = von Karman's constant (0.4 for clear fluids)
ZQ = surface roughness height
Neutral stability occurs with wind speed exceeding 12 mph or with over-
cast conditions regardless of wind speed.
The friction velocity, u* , is related to the rate of momentum ex-
change at the surface:
66
-------
1/2
=
-------
High Rise Buildings-
(30+Floors )jy
Suburban
Medium Buildings-
(Institutional)-!/
E
u
o
N
X
O
LLJ
X
1/1
00
UJ
z
X
O
o
Suburban
Residential Owe 11 i ngs_L/_
Wheat Field JA
Plowed FieldJ/-
Zo (cm)
1000
Natural SnowJy-
800
600
-400
-200
100
-80.0-
-60.0-
h-40.0_
-20.0-
10.0
-8.0-
-6.0-
4.0
-2.0-
1.0
-0.8-
-0.6-
0.4
-0.2-
0.1
Urban Area _i/
Woodland Forest-?/
Grassland -=-/
Figure B-l. Roughness heights for various surfaces
68
-------
where V,. = terminal settling velocity (cm/sec)
s
o
p = density of particle (g/cnr3)
D = particle diameter (um)
Fugitive dust particles typically have a mineral composition with a
density of about 2.5 g/cm .
CALCULATION OF DRIFT DISTANCE
In the past, most analyses of the atmospheric disperison of par-
ticles with appreciable settling tendencies have focused on the dis-
tribution of settling rate, S(x) , expressed as:
S(x) - Vg C0(x) (6)
where C0 = the ground-level concentration of particulate with
settling velocity Vs
x = downwind distance from the source
Accordingly, an Eulerian approach to the problem has been taken.
However, analysis of particle drift with no net effect of atmospheric
turbulence, is most conveniently treated by a Lagrangian approach. This
is illustrated in the following section.
Case 1: Monodisperse particles, single injection height, negligible
turbulence effect.
Consider the case of a steady stream of monodisperse particles re-
leased from a continuous crosswind line source at height h . It is
assumed that each particle during its lifetime in the atmosphere is sub-
jected to a balanced set of vertical turbulent velocity fluctuations with
the result that the particle does not deviate appreciably from the tra-
jectory it would have in the absence of turbulence.
The vertical position, z , of the particle as a function of time is
given by
zp(t) = h-Vgt (7)
69
-------
Substitution of Eq. (7) into Eq. (1) gives the following expression for
the horizontal speed of the particle:
(8)
The particle drift distance, x , is given by:
(9a)
where the upper limit of integration is the lifetime of the particle
in the atmosphere. Integration of Eq. (9a) yields
(9b)
To determine the effect of injection height and roughness height
on the drift distance of particles of given aerodynamic sizes, the wind
speed at z = 100 m was fixed at 6.9 m/s (15.4 mph) and friction velocities
were determined from Eq. (1). The results are shown in Table B-l for
injection heights of 1, 3 and 10 m and for roughness heights spanning
the range given in Table B-l. Figure B-2 shows the variations of xp
for h = 3 m, measured above ZQ .
As expected, for particles of a given size, drift distance increases
with injection height and decreases with roughness height. The latter
effect is a direct result of the decrease in wind velocity near the sur-
face caused by obstacles to the flow.
Case 2: Monodisperse particles, single injection height, turbulent
atmosphere.
The analysis presented under Case 1 assumed that all particles gen-
erated from a particular fugitive dust source were deposited at the same
point downwind (xp). Clearly, however, particles subjected to a pre-
ponderance of downward turbulent velocity fluctuations will settle from
the atmosphere at distances less than xp and particles propelled above
the trajectory defined above may drift far beyond Xp . In other words,
because of the random nature of turbulent velocities, Xp approximates
the distance at which half of the particles have deposited on the surface.
70
-------
Table B-l. PARTICLE DRIFT DISTANCES CALCULATED FROM EQ. (9b)
Injection Roughness
height,-/ height,
h zo
(m) (m)
I 0.01
0.05
0.10
0.50
3 0.01
0.05
0.10
0.50
1.00
10 0.01
0.05
0.10
0.50
1.00
Friction
velocity,
u*
(cm/sec)^
30.0
36.4
40.0
52.2
30.0
36.4
40.0
52.2
60.0
30.0
36.4
40.0
52.2
60.0
Drift distance, xp , by particle size
30 um
40.6 m
29.5
24.2
12.5
157.1 m
128.2
112.9
73.5
56.4
655 m
582
541
423
363
20 urn
91.2 m
66.4
54.4
28.1
353 m
288
254
165
127
1,474 m
1,309
1,216
952
816
10 um
366 m
266
218
113
1,418 m
1,157
1,019
663
509
5.92 km
5.25
4.88
3.82
3.28
5 um
1,460 m
1,060
871
450
5.66 km
4.62
4.07
2.65
2.03
23.6 km
21.0
19.5
15.3
13.1
1 um
36.6 km
26.7
21.8
11.3
141.8 km
115.7
101.9
66.3
50.9
592 km
525
488
382
328
a/ Injection height measured above roughness height.
-------
to
Injection Height (h) = 3m above z<
Natural Snow (zo= 0.1 cm)
Plowed Field (z0= 1 .0 cm)
Grassland (zo = 3.0 cm)
Suburban Residential
Dwelling (zo = 5.0 cm)
Suburban Medium
Building (zo= 70.0 cm)
103 104
DRIFT DISTANCE (meters)
105
Figure B-2. Relationship between particle size and drift distance
-------
The specific question addressed here has to do with the form of
the settling rate distribution. Recalling Eq. (6), this problem re-
duces to finding the distribution of ground-level concentration by
solving the appropriate transport equations and accompanying boundary
conditions.
The phenomena of quasi-steady advection and turbulent diffusion
from a continuous line source under the condition of uniform wind speed
is described by the following equation:
(10)
where C = particulate concentration
U = uniform speed of crosswind
p = turbulence parameter.
The uniform wind speed, U , is assumed to have the value given by the
Case 1 velocity profile at z = h. The quantity pUz becomes the coef-
ficient of eddy diffusivity.
case in point, it has been shown=i/ that the distribution of ground-level
Although Eq. (10) is not amenable to analytical solution for the
in point, it has been shown=i/ th.
concentration has the following form:
-h/px
Co(x) = A
where A = constant
The function given in Eq. (11), and hence the settling rate, reaches a
maximum at:
and then decays to zero as x>« . Values for xL.ax are given in
Table B-2 based on values of p determined by comparing the two forms
of the eddy diffusivity, yielding
p = ku*/U . (13)
73
-------
Table B-2. DISTANCES TO POINT OF MAXIMUM SETTLING,
CALCULATED FROM EQ. (12)
Injection Roughness
height, height,
h
(m)
1
3
10
zo
(m)
0.01
0.05
0.10
0.50
0.01
0.05
0.10
0.50
1.00
0.01
0.05
0.10
0.50
1.00
Turbulence
parameter,
P
0.0347
0.0534
0.0695
0.2308
0.0281
0.0391
0.0470
0.0893
0.1456
0.0232
0.0302
0.0347
0.0534
0.0695
Friction
u*
(cm/ sec)
30.0
36.4
40.0
52.2
30.0
36.4
40.0
52.2
60.0
30.0
36.4
40.0
52.2
60.0
Values of a and xmax (m) by particle size
30
a
0.564
0.465
0.423
0.324
0.564
0.465
0.423
0.324
0.282
0.564
0.465
0.423
0.324
0.282
urn
^a^
18.4
12.8
10.1
3.27
68.3
52.4
44.9
25.4
16.1
276
226
203
141
112
20 um
« "max
0.251 23.0
0.207 15.5
0.188 12.1
0.144 3.79
0.251 85.3
0.207 63.6
0.188 53.7
0.144 29.4
0.125 18.3
0.251 345
0.207 274
0.188 243
0.144 164
0.125 128
10
a
0.0625
0.0515
0.0469
0.0359
0.0625
0.0515
0.0469
0.0359
0.0312
0.0625
0.0515
0.0469
0.0359
0.0312
pm
Xmax
27.1
17.8
13.7
4.18
100.5
73.0
61.0
32.4
20.0
406
315
275
181
140
5 um
« xmax
0.0157 28.4
0.0129 18.5
0.0118 14.2
0.0090 4.29
0.0157 105.1
0.0129 75.7
0.0118 63.1
0.0090 33.3
0.0078 20.4
0.0157 424
0.0129 327
0.0118 285
0.0090 186
0.0078 143
1
or
0.00062
0.00052
0.00047
0.00036
0.00062
0.00052
0.00047
0.00036
0.00031
0.00062
0.00052
0.00047
0.00036
0.00031
um
Xmax
28.8
18.7
14.4
4.33
106.7
76.7
63.8
33.6
20.6
431
331
288
187
144
-------
The constant A in Eq. (11) may be evaluated by equating the emis-
sion rate E to the integrated settling rate.
ff<
CoVs dx = AVS /
-
o
E = / CoVQ dx - AVS / ei+g dx (14)
With the transformation y = b/x where b = h/p , the above equation
becomes
AV /"°
'£/
b y
dy
where T(.oi) is the gamma function.
Similarly it can be shown that the mass fraction K of particles
remaining suspended beyond some distance x is given by:
where the incomplete gamma function F(a,b/x) is defined as
-b/x
dy (17)
The above analysis assumes that particles of all sizes are uniformly
responsive to turbulent diffusion. More realistically, the time constant
of particle response to vertical velocity fluctions increases with in-
creasing aerodynamic particle size.
In studies of the vertical flux of particulates over an agricultural
field undergoing wind erosion, Gillette et al.' have characterized this
phenomena in terms of the ratio Va/u* . If settling velocity is small
compared to the root mean square velocity fluctuation, i.e., Vg/u* <0.1 ,
the particulate is dispersed as a gas. On the other hand for Vs/u* ~ 1 ,
settling effects begin to predominate. Clearly, in the latter case, the
settling distribution is more strongly focused around the distance xp .
75
-------
Case 3; Polydisperse particles, distributed injection height, tur-
bulent atmosphere.
This case is treated by separately analyzing the dispersion of
particles within narrow size ranges and injection height ranges and by
superimposing the results. The analytical techniques to be used are
those described above.
76
-------
REFERENCES TO
APPENDIX B
1. Lettau, H. H., "Physical and Meteorological Basis for Mathematical
Models of Urban Diffusion Processes," Chapter 2, Proceedings of
Symposium on Multiple-Source Urban Diffusion Models, U.S. Environ-
mental Protection Agency, Publication No. AP-86 (1970).
2. Davenport, A. G., "The Relationships of Wind Structure to Wind Load-
ing, in Wind Effects on Buildings and Structures," National Physi-
cal Laboratory, Symposium 16, Her Majesty's Stationery Office,
London (1965).
3. Deacon, E. L., "Vertical Diffusion in the Lowest Layers of the
Atmosphere," Quarterly J. Royal Meteorological Society, 75:89
(1949).
4. Gillette, D. A., and P. A. Goodwin, "Microscale Transport of Sand-
Sized Soil Aggregates Eroded by Wind," J. of Geophysical Research.
79(27):4080-4084, September 20, 1974.
5. Bosanquet, C. H., and J. L. Pearson, "The Spread of Smoke and Gases
from Chimneys," Trans. Faraday Soc.. 32:1249-1264 (1936).
6. Gillette, D. A., and I. H. Blifford, Jr., "The Influence of Wind
Velocity on Size Distribution of Aerosols Generated by the Wind
Erosion of Soils," J. Geophysical Research. 79(27):4068-4075,
September 20, 1974.
77
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-450/3-76-003
3. RECIPIENT'S ACCESSIOWNO.
4. TITLE AND SUBTITLE
Development of a Methodology and Emission Inventory
for Fugitive Dust for the Regional Air Pollution
Study
5. REPORT DATE
January. 1976
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Dr. Chatten Cowherd and Ms. Christine Guenther
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-2040
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final Report
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report outlines the methodology that was used in developing an hourly
fugitive dust emissions inventory for the Metropolitan St. Louis Air Quality
Control Region as part of the Regional Air Pollution Study (RAPS). The inventory
encompassed the following source categories: (a) unpaved roads, (b) agricultural
land tilling, (c) wind erosion of agricultural land, (d) construction sites,
(e) aggregate storage piles, and (f) unpaved airstrips.
Results presented in this report include temporal apportioning factors, county
totals of annual source extent and annual emissions for each source category.
7.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
Regional Air Pollution Study
Fugitive Dust Emissions
Emission Models
3. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
84
20. SECURITY CLASS (This page)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
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