MRI BS REPORT
Urban Fugitive Dust Test Needs
Final Report
For Air and Energy Environmental
Research Laboratory (MD-62)
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Attn: Charles Masser
EPA Contract No. 68-DO-0137
Assignment No. 8
MRI Project No. 9800-A(8)
September 30, 1991
MIDWEST RESEARCH INSTITUTE 425 Volker Boulevard, Kansas City, MO 64110-2299 (816) 753-7600
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MRIBS REPORT
Urban Fugitive Dust Test Needs
Final Report
For Air and Energy Environmental
Research Laboratory (MD-62)
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Attn: Charles Masser
EPA Contract No. 68-DO-0137
Assignment No. 8
MR! Project No. 9800-A(8)
September 30, 1991
MIDWEST RESEARCH INSTITUTE 425 Volker Boulevard, Kansas City, MO 64110-2299 (816) 753-7600
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DISCLAIMER
This document has not been released formally by the Office of Toxic
Substances, Office of Pesticides and Toxic Substances, U.S. Environmental
Protection Agency. It is being circulated for comments on its technical merit and
policy implications. The use of trade names for commercial products does not
constitute Agency endorsement or recommendation for use.
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PREFACE
This report was prepared for the Air and Energy Environmental Research
Laboratory (AEERL), U.S. Environmental Protection Agency (EPA), under EPA
Contract No. 68-DO-0137, Assignment No. 8, which is with the Field Studies
Branch of the Office of Toxic Substances. Mr. Charles Masser was the requester
of the work. The report was prepared by Mr. John S. Kinsey, Dr. Gregory
Muleski, Ms. Mary Ann Grelinger, and Dr. Chatten Cowherd, Jr.
Approved for:
MIDWEST RESEARCH INSTITUTE
Paul C. Constant
Program Manager
September 30, 1991
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CONTENTS
Preface iii
Executive Summary ES-1
1. Introduction 1
2. Urban Fugitive Dust Sources 3
2.1 Open dust sources 3
2.2 Process fugitive sources 5
3. Review of Current Emission Factors 7
3.1 Generic emission factors for open dust sources . 7
3.2 Uncontrolled emission factors for paved
and unpaved roads 9
3.3 Uncontrolled emission factors for material
storage and handling 16
3.4 Uncontrolled emission factors for agricultural
operations 22
3.5 Uncontrolled emission factors for construction
activities 23
3.6 Uncontrolled emission factors for miscellaneous
open dust sources 24
3.7 Uncontrolled emission factors for process
sources 26
4. Preparation of Emission Inventories 27
4.1 Emission inventory methodology 27
4.2 Spatial/temporal distribution of correction
parameters 31
5. Future Testing Needs 32
5.1 Improvements in existing emission factors 32
5.2 New emission factors 35
5.3 Control efficiency determination 39
6. Proposed Test Matrix 47
References 53
Appendices
A. Emission factors for process fugitive sources A-1
B. General measurement methods for fugitive dust
emissions B-1
C. Description of MRI dustiness test chamber C-1
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LIST OF FIGURES
Number Page
5-1 Watering control effectiveness for unpaved travel surfaces 42
5-2 Emissions quantification requirements for performance evaluation
of capture/collection system 45
LIST OF TABLES
Number Page
2-1 Generic categories of open dust sources 4
2-2 Open dust sources associated with 5
3-1 Correction parameters for AP-42 emission factor models 8
3-2 Selection of paved road emission factor 10
3-3 Typical silt content and loading values for paved roads at
industrial facilities 11
3-4 Summary of silt loadings (sL) for paved urban roadways 13
3-5 Paved urban roadway classification 13
3-6 Typical silt content values of surface material on industrial
and rural unpaved roads 15
3-7 Typical silt and moisture content values of materials at
various industries 18
3-8 Threshold friction velocitiesindustrial aggregates . 20
3-9 Threshold friction velocitiesArizona sites 21
3-10 Emissions increase (AB) by site traffic volume 25
3-11 Emissions increase (AB) by construction type 26
6-1 Tentative test matrix for urban fugitive dust sources 48
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EXECUTIVE SUMMARY
Regulatory plans for attainment of the National Ambient Air Quality
Standard for PM10 (i.e., particles < 10 /urn in aerodynamic diameter) require
reliable emission estimating methods. Significant research needs currently exist
with respect to emission factors for urban fugitive dust sources that impact
significantly on PM10 nonattainment areas. The bulk of the field test data
supporting currently available emission factors was obtained in the 10-year
period beginning in 1972.
This document reviews the reliability of current emission factors and
inventories and makes recommendations for cost-effective field testing programs
designed to address the most significant needs. Emphasis is placed on opened
dust sources as opposed to process sources of fugitive dust emissions. Recent
emission inventories of. urban dust sources show that road traffic , construction,
and in some cases agriculturally related sources are often the predominant
contributors to PM10 emissions in urban areas.
The reliability of currently available emission factors may be limited for
several reasons including:
Insufficient number of source tests
Limited range of tested parameter values
No directly applicable source tests, necessitating use of emission
factors for similar source(s)
No directly or indirectly applicable source tests, necessitating an
engineering estimation (e.g., "gap filling" approach) of emission
factors
Because of the substantial variability of fugitive dust emissions,
single-valued emission factors are generally inappropriate. Therefore, emission
factors for fugitive dust sources have been formulated as predictive equations
that can be adjusted to source-specific conditions. Correspondingly larger
bases of test data are needed to support the development of predictive emission
factor equations, as compared to single-valued emission factors.
In some cases, enlarging the test data base by combining the data for two
(or more) similar operations may produce a more reliable emission factor
equation representing both operations. This was found to be the case for
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ES-1
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materials handling emission factor created by combining the data bases
supporting the original "batch drop" and "continuous drop" equations. For
example, one recommendation in this report is that the data bases supporting
the urban and industrial paved road emission factors be explored in this manner.
Another significant source of uncertainty in the preparation of urban
emission inventories is the lack of correction parameter data. This is the case for
urban paved roads and construction activities. Although a relationship between
"background" paved road silt loading and average daily traffic has been
developed, for example, there are many localized conditions such as
construction site trackout that may elevate the silt loading by more than an order
of magnitude above background. Isolating such paved road segments is critical
not only to reliably estimating uncontrolled emissions, but also to developing
effective control strategies.
A number of testing needs are identified in this report for paved roads,
construction/demolition, and agriculture. The highest (i.e., A) priority testing
needs identified in the study include:
Expansion of paved road silt loading data base to develop
corrections with land use, etc.; and
Development of new emission factors for controlled and
uncontrolled mud/dirt carryout
Table 6-1 provides detailed information on these and other tests needed
to improve existing PM10 emission inventories in urban areas.
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SECTION 1
INTRODUCTION
As more local, state, and regional agencies are required to submit
regulatory plans for attainment of the National Ambient Air Quality Standard
(NAAQS) for PM10 (i.e., particles < 10 Mm in aerodynamic diameter), methods for
accurately determining the emissions from fugitive dust sources will be crucial.
Consequently, the U.S. Environmental Protection Agency (EPA) is evaluating the
methodologies used to inventory these sources in order to provide guidance to
other regulatory agencies. In addition, appropriate research and development
goals for enhancing existing fugitive dust emission estimation techniques need to
be identified and prioritized and new factors developed where necessary.
In this document, Midwest Research Institute (MRI) reviews the state-of-
the-art with respect to the estimation of fugitive PM10 emissions in urban areas.
Major urban fugitive sources are identified in Section 2, current fugitive emission
factors are reviewed in Section 3, and current methodologies used to inventory
these sources are discussed in Section 4. In these sections, the emphasis is
placed on open rather than process sources of fugitive emissions. Future
research needs are identified in Section 5 and a proposed test matrix is provided
in Section 6. The research needs and associated test matrix provided in
Sections 5 and 6 are based on those sources or control methods that MRI
believes to be of greatest importance in the preparation of urban emission
inventories and that exhibit the highest level of cost effectiveness. Finally,
references cited in the report are listed in Section 7.
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SECTION 2
URBAN FUGITIVE DUST SOURCES
Fugitive emissions refer to those air pollutants that: (a) enter the
atmosphere without first passing through a stack or duct designed to direct or
control their flow; or (b) leak from ducting systems. Sources of fugitive
particulate emissions may be separated into two broad categories: open dust
sources; and process sources. Of the two, open dust sources are emphasized
in this document.
2.1 OPEN DUST SOURCES
Open dust sources produce fugitive emissions of solid particles when the
forces of wind or machinery act on exposed materials. Open dust sources
include industrial emissions associated with the open transport, storage, and
transfer of raw, intermediate, and waste aggregate materials, and nonindustrial
sources such as unpaved roads and parking lots, paved streets and highways,
heavy construction activities, and agricultural tilling. Generic categories of open
dust sources are listed in Table 2-1 (Cowherd and Kinsey, 1986).
The partially enclosed storage and transfer of materials to or from a
process operation do not fit well into either of the two categories of fugitive
particulate emissions defined above. Examples are partially enclosed conveyor
transfer stations and front-end loaders operating within buildings. Nonetheless,
partially enclosed materials handling operations are usually classified as open
sources (Cowherd and Kinsey, 1986).
The various types of open dust sources listed in Table 2-1 can be found
either in an industrial facility or in the public sector. The mechanisms of dust
formation and thus the type of controls that can be applied in either case are
essentially the same. However, both the suitability and cost-effectiveness
associated with a specific control measure can change significantly when applied
in an industrial setting as compared to the same control used for public sector
sources. Therefore, the control strategies used by public agencies often differ
from those employed by industrial concerns.
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Table 2-1. GENERIC CATEGORIES OF OPEN DUST SOURCES
Unoaved Travel Surfaces
Roads
Parking lots and staging areas
Storage piles
Paved Travel Surfaces
Streets and highways
Parking lots and staging areas
Exposed Areas (wind erosion)
Storage piles
Bare ground areas
Materials Handling
Batch drop (dumping)
Continuous drop (conveyor, transfer, stacking)
Pushing (dozing, grading, scraping)
Agricultural tilling
A number of sources are perceived as single sources when in actuality
they are a series of different dust generating operations confined to the same
locality. Examples of this type of source include construction and demolition
activities, both of which involve dust generation by various material handling
operations as well as vehicular traffic. Table 2-2 lists the specific sources
associated with construction and demolition activities using the same general
notation indicated in Table 2-1.
One final source worthy of note is agricultural operations. Agricultural
tilling encompasses those activities associated with soil preparation and soil
maintenance. Crop harvesting activities also frequently involve fugitive dust
emissions. The emissions from these operations are generally significant but
usually are not controlled except by work practice modifications. Although
agricultural operations normally are not found in urban areas, transport of
agricultural emissions to urban areas may be significant in some cases.
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Table 2-2. OPEN DUST SOURCES ASSOCIATED WITH
CONSTRUCTION AND DEMOLITION
1.
Construction Sites
Vehicular traffic on unpaved surfaces
Storage piles
Mud/dirt carryout onto paved travel surfaces
Exposed areas
Batch drop operations
Pushing (earth moving)
Demolition Sites
Vehicular traffic on unpaved surfaces
Storage piles
Mud/dirt carryout onto paved travel surfaces
Exposed areas
Batch drop operations
Pushing (dozer operation)
Blasting
2.2 PROCESS FUGITIVE SOURCES
Process sources of fugitive emissions are those associated with industrial
operations that alter the chemical or physical characteristics of a feed material.
Examples are emissions from charging and tapping of metallurgical furnaces and
emissions from crushing of mineral aggregates. Such emissions normally occur
within buildings and, unless captured, are discharged to the atmosphere through
forced or natural draft ventilation systems. However, a process source of fugitive
emissions can also occur in the open atmosphere (e.g., scrap metal cutting).
Process sources of fugitive particulate emissions typically found in urban areas
are listed by industry in Appendix A (EPA, 1990).
Examples of industrial facilities that contain fugitive PM10 sources include
asphalt plants, cement plants, mineral processing facilities, concrete ready-mix
plants, power plants, quarries and rock products plants, slag processing plants,
steel mills, and grain transfer terminals.
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SECTION 3
REVIEW OF CURRENT EMISSION FACTORS
The reliability of currently available emission factors may be limited for a
variety of reasons including:
Insufficient number of source tests
Limited range of tested parameters
No directly applicable source tests, necessitating the use of
emission factors for a similar source(s)
No directly or indirectly applicable source tests, necessitating an
engineering estimation of emission factors
The following section provides a brief overview of emission factors
currently available for urban fugitive dust sources and their limitations. Although
open sources are emphasized, emission factors for both types of fugitive
sources are included.
3.1 GENERIC EMISSION FACTORS FOR OPEN DUST SOURCES
Emission factors have been published for a variety of open dust sources,
either in AP-42 or elsewhere. These sources include: paved and unpaved
roads, materials storage and handling, agricultural tilling, and a number of
miscellaneous sources related to construction and demolition activities.
Open source emission factors share many common features. For
example, models are formulated as empirical expressions that relate variations in
the emission factor (e) to differences in the physical properties (p) of the material
being disturbed and the mechanical energy (m) responsible for the generation of
paniculate according to the general form:
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e = Kpam
(3-1)
As empirical models, open dust source emission factors have adjustable
coefficients (K,a,b) that reflect relationships determined from actual open dust
source testing. For example, Table 3-1 provides the correction parameters used
in the emission factor models published in AP-42 for common open dust
sources.
Table 3-1. CORRECTION PARAMETERS FOR AP-42
EMISSION FACTOR MODELS'
Source category
Unpaved roads
Paved roads
Exposed areas
Materials handling
Model parameter
Silt content of a surface material
Mean vehicle speed
Mean vehicle weight
Mean number of wheels
Number of wet days/year
Silt content of surface material
Total surface dust loading
Number of disturbances/year
Erosion potential
Mean wind speed
Material moisture content
Units of measure
Weight %
km/h (mph)
Mg or 1 06 g (tons)
Dimensionless
Dimensionless
Weight %
kg/km2 (Ib/mi2)
Dimensionless
g/m2
m/s (mph)
Weight %
From EPA. 1990.
The emission factor equations for open fugitive dust sources were
developed through field testing of representative sources for each source
category. Field samples were taken to determine emission quantities, surface
material properties (e.g., silt loadings), and appropriate meteorological
conditions. The data were then analyzed through linear regression for
correlation of emissions with the applicable correction parameters to formulate
each equation. Independent validation was not possible due to the limited data.
Instead, each equation was cross validated.
Application of the AP-42 equations is limited though, due to the small data
b:-^e used for each equation. The equations retain their quality ratings only if
applied within the ranges of parameters used to develop the equation. Use of
the equations for extrapolation outside the ranges specified will result in a lower
8
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quality rating (reduced by one level) of the estimated emissions. Also, to retain
the quality rating of the equations applied to specific sources, it is necessary that
reliable correction parameter values for the specific source in question be
determined. In the event that site specific values cannot be obtained, the
appropriate default (mean) values may be used, but the quality rating should be
reduced by at least one level. The following sections describe the emission
factor models for open dust sources in more detail.
3.2 UNCONTROLLED EMISSION FACTORS FOR PAVED AND UNPAVED
ROADS
Particulate emissions occur whenever a vehicle travels over a paved
surface, such as public and industrial roads and parking lots. These emissions
may originate from material previously deposited on the travel surface, or
resuspension of material from tires and undercarriages. In general, emissions
arise primarily from the surface material loading (measured as mass of material
per unit area), and that loading is in turn replenished by other sources (e.g.,
pavement wear, deposition of material from vehicles, deposition from other
nearby sources, carryout from surrounding unpaved areas, and litter). EPA's
Compilation of Air Pollutant Emission Factors (AP-42) indicates that the PM10
emission factors for paved roads may be written in the general form (EPA,
1990):
e = A
(3-2)
where A, B, and c are constants and:
e = PM10 emission factor, mass/vehicle/length of road segment
s = fractional surface silt content, weight %
L = total surface loading, mass/area
Surface silt content (s) is the fraction of material smaller than 200 mesh or 75
in physical diameter. The parameter L is the total dust loading, and the product
sL represents the mass of silt-size dust particles per unit area of the road
surface. As is the case for all predictive models in AP-42, the use of site-specific
values of sL is strongly recommended. The silt loading should be determined
using the sampling techniques and laboratory analysis procedures described in
Appendix D of AP-42.
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Selection of the appropriate emission factor model (i.e., the constants A,
B, and c) for a given road depends upon: the value of the silt loading (sL) and
the average weight of the vehicles traveling on the road. Table 3-2 describes the
selection process for paved road emission factors. It should be noted that for
purposes of preparing an emissions inventory, this equation would be applied to
each road segment in an industrial facility or urban area, as the case may be.
Ideally a road segment is characterized by a narrow range of silt loading.
Table 3-2. SELECTION OF PAVED ROAD EMISSION FACTOR
Silt loading (sL)
g/m2
sL < 2
sL < 2
sL>2d
2 < sL < 15
sL> 15"
oz/yd2
< 0.06
< 0.06
> 0.06
0.6 < sL <
0.44
> 0.44
Range weight (W)
Mg
W> 4
W < 4
W> 6
W < 6
W< 6
Ton
> 4.4
< 4.4
> 6.6
< 6.6
< 6.6
Applicable PM10 emission factor
g/VKT
220(sL/12)°-3b
2.28 (sL/0.5)°flC
220(sL/12)°3b
220(sL/12)°3b
93
Ib/VMT
0.78 (sL/O.SS)03"
0.0081 (sL/O.OIS)08'
0.78 (sL/0.35)03"
0.78 (sL/0.35)03"
0.33
VKT = Vehicle kilometers traveled, VMT = vehicle miles traveled.
b Commonly referred to as the "industrial" paved road model.
0 Commonly referred to as the "urban" paved road model.
d For heavily loaded surfaces [i.e., sL > ~ 300 to 400 g/m2 (9 to 12 oz/yd2)], it is
recommended that the resulting estimate be compared to that from the unpaved road
models and the smaller of the two values used.
The emission rate is determined by multiplying the emission factor of each
road segment by the total vehicle mileage accumulated on the segment over the
averaging time of interest. Totaling the individual emission rates for each road
segment will provide an uncontrolled emission rate for all paved roads.
The industrial paved road equation (Table 3-2) was developed for
medium and heavy duty vehicles traveling on dry roadways in 10 industrial sites
(Table 3-3). The equation (A-rated) was developed from samples ranging as
follows:
Silt Loading: 2 to 240 g/m2
Mean Vehicle Weight: 6 to 42 Mg
AP-42 also gives a default emission factor of 93 g/VKT with a C rating for use if
silt loadings are heavy.
10
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Table 3-3. TYPICAL SILT CONTENT AND LOADING VALUES FOR PAVED ROADS AT INDUSTRIAL FACILITIES'
Industry
Copper smelting
Iron and steel
production
Asphaltic concrete
Concrete batching
Sand and gravel
processing
No. of
sites
1
6
1
1
1
No. of
samples
3
20
3
3
3
Silt (wt. %)
Range
15.4-21.7
1.1 -35.7
2.6 - 4.6
5.2 - 6.0
6.4 - 7.9
Mean
19.0
12.5
3.3
5.5
7.1
No. of
travel
lanes
2
2
1
2
1
Total loading x 103
Range
12.9-19.5
45.8 - 69.2
0.006 - 4.77
0.020- 16.9
12.1 - 18.0
43.0 - 64.0
1.4- 1.8
5.0 - 6.4
2.8 - 5.5
9.9-19.4
Mean
15.9
55.4
0.495
1.75
14.9
52.8
1.7
5.9
3.8
13.3
Units"
kg/km
Ib/mi
kg/km
Ib/mi
kg/km
Ib/mi
kg/km
Ib/mi
kg/km
Ib/mi
Silt loading (g/m2)
Range
188-400
0.09 - 79
76-193
11 -12
53-95
Mean
292
12
120
12
70
From EPA, 1990.
b Multiply entries by 1,000 to obtain stated units.
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Although public roads tend to have lower surface loadings than industrial
roads, the fact that these roads have far greater traffic volumes may result in a
substantial contribution to the measured air quality in certain areas. In addition,
many public roads in industrial areas often are heavily loaded and traveled by
heavy vehicles. In that instance, better emission estimates would be obtained by
treating these roads as industrial roads. In an extreme case, a road or parking
lot may have such a high surface loading that the paved surface is essentially
covered and is easily mistaken for an unpaved road.
The emission factor for public paved roads is determined by the decision
rule discussed above. Tables 3-4 and 3-5 present summaries of silt loadings as
a function of roadway classification and the scheme used in AP-42 to classify
roadways, respectively (EPA, 1990).
The urban paved road equation (Table 3-2) was developed from a base of
44 emission tests and corresponding silt loadings obtained in five different cities
(Table 3-4). The test sites represent a broad range of Eastern and Midwestern
urban areas. However, no correlation with land use or road condition was
possible. The sampling locations are generally representative of many large
population centers, except those in the Southwest. It is also noted that early
spring loadings may be five or six times higher than the mean loading, due to
winter road sanding. Thus, the equation should be used with caution in the
Southwest and in areas with high winter sand application.
The range of silt loadings used in the urban paved road equation is 0.022
g/m2 to 2.11 g/m2 (Table 3-4). AP-42 recommends PM10 emission factors based
on default values (the mean loadings tested) if no site specific silt loadings are
available. These default values, which are typically used in many urban PM10
emission inventories in place of site-specific data, are as follows:
Local streets (< 500 ADT): 5.2 g/VKT
Collector streets (500-10,000 ADT): 3.7 g/VKT
Major streets (> 10,000 ADT): 1.8 g/VKT
Freeways (> 50,000 ADT): 0.21 g/VKT
12
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Table 3-4. SUMMARY OF SILT LOADINGS (sL) FOR
PAVED URBAN ROADWAYS*
City
Baltimore
Buffalo
Granite City,
IL
Kansas City
St. Louis
All
Local streets
Mg/m8)
1.42
1.41
1.41
n
2
5
7
Collector
streets
*0
(g/m2)
0.72
0.29
2.11
0.92
n
4
2
4
10
Major
streets/highways
Xgte/m2)
0.39
0.24
0.82
0.41
0.16
0.36
n
3
4
3
13
3
26
Freeways/
expressways
^(g/m2)
_
0.022
0.022
n
1
1
From EPA, 1990. Xg = geometric mean based on corresponding n sample size. Dash =
not available. To convert g/m2 to grains/ft2 multiply g/m2 by 1.4337.
Table 3-5. PAVED URBAN ROADWAY CLASSIFICATION*
Roadway category
Freeways/expressways
Major streets/highways
Collector streets
Local streets
Average daily traffic (vehicles)
> 50,000
> 10,000
500-10,000
< 500
No. lanes
.> 4
> 4
2b
2°
* From EPA, 1990.
b Road width > 32 ft.
0 Road width < 32 ft.
As is the case for paved roads, particulate emissions occur whenever a
vehicle travels over an unpaved surface. Unlike paved roads, however, the road
itself is the source of the emissions rather than any "surface loading." Within the
various categories of open dust sources (i.e., paved roads, storage piles, and
wind erosion), unpaved travel surfaces have historically accounted for the
greatest share of particulate emissions in industrial settings.
Unpaved travel surfaces are found in rural regions throughout the country,
in industrial settings, and in some limited cases, urban areas. Roads may be
unpaved because they experience only sporadic traffic and involve considerable
road length, making paving generally impractical. Some industrial roads are, by
MRK3TS\R9800.FNL
13
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their nature, not suitable for paving. For example, some roads may be traveled
by very heavy vehicles or subject to considerable spillage from haul trucks.
Other roads may have poorly constructed bases which make paving impractical.
Because of the additional maintenance costs associated with a paved road
under these service conditions, emissions from these roads are usually
controlled by regular applications of water and/or chemical dust suppressants.
In addition to roadways, many industries often contain unpaved travel
areas such as around stockpiles, and staging areas. These areas may often
account for a substantial fraction of traffic-generated emissions from industrial
facilities. In addition, these areas tend to be much more difficult to control than
stretches of roadway.
As was the case for paved roads, unpaved roads may be divided into the
two classes of public and industrial. However, for the purpose of estimating
emissions, there is no need to distinguish between the two, because the AP-42
emission factor equation takes source characteristics (such as average vehicle
weight and road surface texture) into consideration (EPA, 1990):
e , o.6i jL "fi (3-3)
where:
e = PM10 emission factor, kg/VKT
s = silt content of road surface material, percent
S = mean vehicle speed, km/h
W = mean vehicle weight, Mg
w = mean number of wheels (dimensionless)
p = number of days with > 0.254 mm (0.01 in.) of precipitation per year
The above equation is rated "A" in AP-42, if used within the range of
correction parameters tested. As is the case with all AP-42 emission factors, the
use of site-specific data is strongly encouraged.
Table 3-6 gives the measured silt values for unpaved roads in certain
industries and rural areas. The silt content of the road surface material should
be determined using the sampling techniques and laboratory analysis
procedures described in Appendix D of AP-42 (EPA, 1990).
14
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Table 3-6. TYPICAL SILT CONTENT VALUES OF SURFACE MATERIAL ON INDUSTRIAL AND RURAL UNPAVED ROADS'
Industry
Copper smelting
Iron and steel production
Sand and gravel processing
Stone quarrying and processing
Taconite mining and processing
Western surface coal mining
Rural roads
Road use or surface
material
Plant road
Plant road
Plant road
Plant road
Haul road
Service road
Access road
Haul road
Scraper road
Haul road
(freshly graded)
Gravel
Dirt
Crushed limestone
No. of sites
1
9
1
1
1
1
2
3
3
2
1
2
2
No. of test
samples
3
20
3
5
12
8
2
21
10
5
1
5
8
Silt (wt percent)
Range
15.9-19.1
4.0-16.0
4.1-6.0
10.5-15.6
3.7-9.7
2.4-7.1
4.9-5.3
2.8-18
7.2-25
18-29
NA
5.8-68
7.7-13
Mean
17.0
8.0
4.8
14.1
5.8
4.3
5.1
8.4
17
24
5.0
28.5
9.6
From EPA, 1990.
NA = Not applicable.
01
-------
The number of wet days (i.e., precipitation >. 0.254 mm or 0.01 in) per
year (p) for the geographical area of interest, can be determined from local
climatic data. Figure 11.2.1-1 of AP-42 gives the geographical distribution of the
mean annual number of wet days per year in the United States. Maps giving
similar data on a monthly basis are available from the U.S. Department of
Commerce National Climatic Center.
The emission rate is determined by multiplying the emission factor by the
length of the road segment and the vehicle passes per day. Adding the
emission rate from each segment will provide a total unpaved road emission rate.
The unpaved road equation was developed from 103 tests performed on
various plant, haul, and rural roads (Table 3-6). The equation was developed for
calculating average annual emissions. The ranges of parameters sampled are:
Silt content: 4.3 to 20%
Mean vehicle weight: 2.7 to 142 Mg
Mean vehicle speed: 21 to 64 km/hr
Mean no. of wheels: 4 to 13
3.3 UNCONTROLLED EMISSION FACTORS FOR MATERIAL STORAGE AND
HANDLING
Adding aggregate material to a storage pile or removing it usually involves
the dropping of material onto a receiving surface or receptacle. This can be
performed by one of two methods: by continuous feed, as exemplified by
conventional fixed stacker reclaimers; or by batch operations as would be
performed by mobile equipment (i.e., front-end loader, truck, or pan scraper).
The following equation is recommended in AP-42 for estimating emissions
from transfer operations (batch or continuous drop):
U
e = A-(0.0016) - (3-4)
where:
e = emission factor, kg/Mg
k = particle size multiplier (dimensionless) = 0.35 for PM10
16
MRI-OTS\R9800.FNL
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U = mean wind speed, m/s
M = material moisture content, percent
Equation (3-4) shows that, for the handling of aggregate materials,
emissions increase with the ambient wind speed and are reduced as the material
moisture content increases.
The equation was developed from 141 field samples of 12 different
aggregate materials in the five industries shown in Table 3-7. The ranges of
parameters used to develop the equation are:
Silt content: 0.44 to 19%
Moisture content: 0.25 to 4.8%
Wind speed: 0.6 to 6.7 m/s
To estimate the emissions from the loading of material in and out of
storage piles, one must first obtain a sample of the stockpiled material and a
record of historical wind data at the location. From the Local Climatological Data
(LCD) summaries, the mean wind speed can be obtained for the nearest
recording weather station. This wind speed should be adjusted from the
recording anemometer height to represent the wind speed at the average
storage pile height using the procedure described in Section 11.2.7 of AP-42.
Aggregate sampling and subsequent moisture analysis should then be
performed on the sample(s) as described in Appendix D of that document.
Although in some instances wind erosion of stockpiled material can be
a substantial source of fugitive PM10, it is usually considered the least significant
of the mechanisms responsible for dust emissions from storage piles. In most
industrial settings, fugitive emissions from vehicular traffic and materials handling
are far greater than the emissions from wind erosion. If vehicular traffic is
present in and around the storage pile area, those emissions are far greater than
emissions due to materials handling.
The AP-42 emission factor for wind erosion, as a summation over periods
between disturbances of the erodible surfaces, can be calculated as:
MFH-OTS\R9BOO.FNL
17
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Table 3-7. TYPICAL SILT AND MOISTURE CONTENT VALUES OF MATERIALS AT VARIOUS INDUSTRIES'
oo
Industry
Iron and steel
production
Stone quarrying and
processing
Taconite mining and
processing
Western surface coal
mining
Coal fired power
generation11
Material
Pellet ore
Lump ore
Coal
Slag
Flue dust
Coke breeze
Blended ore
Sinter
Limestone
Crushed
limestone
Pellets
Tailings
Coal
Overburden
Exposed
ground
Coal
Silt (weight %)
No. of test
samples
10
9
7
3
2
1
1
1
1
2
9
2
15
15
3
60
Range
1.4- 13
2.8- 19
2-7.7
3-7.3
14-23
1.3- 1.9
2.2 - 5.4
NA
3.4-16
3.8-15
5.1 -21
0.6 - 4.8
Mean
4.9
9.5
5
5.3
18.0
5.4
15.0
0.7
0.4
1.6
3.4
11.0
6.2
7.5
15.0
2.2
Moisture (weight %)
No. of test
samples
8
6
6
3
0
1
1
0
0
2
7
1
7
0
3
59
Range
0.64 - 3.5
1.6-8.1
2.8- 11
0.25 - 2.2
NA
NA
NA
0.3- 1.1
0.05 - 2.3
2.8 - 20
NA
0.8 - 6.4
2.7 - 7.4
Mean
2.1
5.4
4.8
0.92
NA
6.4
6.6
NA
NA
0.7
0.9
0.35
6.9
NA
3.4
4.5
From EPA, 1990.
h Values reflect "as received' conditions at a single power plant.
NA = not applicable. "
-------
e = k P (3'5)
where:
e = PM10 emission factor, g/m2
k = particle size multiplier = 0.5 for PM10
n = number of disturbances per year
Pj = erosion potential corresponding to the observed (or probable) fastest
mile of wind for the period between disturbances, g/m2
The erosion potential (P) can be calculated as:
P = 58 (u*-u;)2 + 25 (u*-ut) (3-6)
where:
u* = friction velocity (m/s)
u* = threshold friction velocity (m/s)
and u* > ut*
To estimate the particulate emissions from the wind erosion of storage
piles, one must first obtain a composite surface sample of the stockpiled
material. Aggregate sampling and determination of the threshold friction velocity
should be performed according to the procedures described in Appendix D and
Section 11.2.7 of AP-42, respectively. From the Local Climatological Data (LCD)
summaries, the fastest miles of wind can be obtained for the nearest recording
weather station. Each fastest mile of wind should then be adjusted to represent
the wind speed that impacts the storage pile surface and an equivalent friction
velocity determined as described in Section 11.2.7 of AP-42 (EPA, 1990).
Threshold friction velocities and the associated wind speeds (as measured
at 10 m) are presented in Tables 3-8 and 3-9 for typical soils and aggregate
materials, respectively (Cowherd et al., 1988). Estimates for emissions are
considered more accurate if site specific data are used.
MFU-OTB\R9800.FNL 1 9
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Table 3-8. THRESHOLD FRICTION VELOCITIES-INDUSTRIAL AGGREGATES
Material
Overburden"
Scoria (roadbed
material)"
Ground coal"
(surrounding coal pile)
Uncrusted coal pile*
Scraper tracks
on coal pile">b
Fine coal dust
on concrete padc
Threshold friction
velocity (m/s)
1.02
1.33
0.55
1.12
0.62
0.54
Roughness
height (cm)
0.3
0.3
0.01
0.3
0.06
0.2
Threshold wind velocity at
10m (m/s)
Z0 =
actual
21
27
16
23
15
11
Z0 = 0.5
cm
19
25
10
21
12
10
" Western surface coal mine.
" Lightly crusted.
c Eastern power plant.
From Cowherd et al., 1988.
20
MW-OTS\R9eoO.FNL
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Table 3-9. THRESHOLD FRICTION VELOCITIES-ARIZONA SITES
Location
Mesa - Agricultural site
Glendale - Construction site
Maricopa - Agricultural site
Yuma - Disturbed desert
Yuma - Agricultural site
Algodones - Dune flats
Yuma - Scrub desert
Santa Cruz River, Tucson
Tucson - Construction site
Ajo - Mine tailings
Hayden - Mine tailings
Salt River, Mesa
Casa Grande - Abandoned
agricultural land
Threshold friction
velocity (m/sec)
0.57
0.53
0.58
0.32
0.58
0.62
0.39
0.18
0.25
0.23
0.17
0.22
0.25
Roughness height
(cm)
0.0331
0.0301
0.1255
0.0731
0.0224
0.0166
0.0163
0.0204
0.0181
0.0176
0.0141
0.0100
0.0067
Threshold wind velocity
at 1 0 m (m/sec)
16
15
14
8
17
18
11
5
7
7
5
7
8
From Cowherd et al., 1988.
-------
The wind erosion equation for storage piles was formulated from field
testing using a portable wind tunnel. The equation applies only to dry, exposed
materials with limited erosion potential and is valid only for a time period longer
than the period between disturbances.
3.4 UNCONTROLLED EMISSION FACTORS FOR AGRICULTURAL
OPERATIONS
Fugitive dust from agricultural operations is suspected of contributing
significantly to the ambient particulate levels of many urban areas. Such
agricultural operations include plowing, disking, fertilizing, applying herbicides
and insecticides, bedding, flattening and firming beds, planting, cultivating, and
harvesting. These activities can be classified generically as soil preparation, soil
maintenance, and crop harvesting operations. Dust emissions are also
generated by wind erosion of bare or partially vegetated soil.
The mechanical tilling of agricultural land injects dust particles into the
atmosphere as the soil is loosened or turned under by plowing, disking,
harrowing, one-waying, and so on. AP-42 presents a predictive emission factor
equation (B-rated) for the estimation of PM10 emissions from agricultural tilling
(EPA, 1990):
e = k (5.38)(s)°-e (3-?)
where:
e = emission factor, kg/ha
k = particle size multiplier (dimensionless) = 0.21 for PM10
s = silt content (percent) of surface soil (default value of 18 percent)
The above equation is based on limited field testing information cited in AP-42.
Field measurements of mechanical tilling showed that emissions are not
dependent on implement type, speed below 8 to 10 km/hr, or soil moisture.
However, emissions do vary directly with silt content. The range for silt content
tested was 1.7 to 88 percent.
The technique currently used for predicting agricultural wind erosion is
based on variations of the Wind Erosion Equation developed as the result of
nearly 40 years of research by the U.S. Department of Agriculture to predict total
topsoil losses. This prediction system uses erosion loss estimates that are
22
MRK>TS\R9800.FNL
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integrated over large fields and long time scales to produce average annual
values.
The modified equation for agricultural wind erosion is of the form
(Cowherd et al., 1988):
e = kal KC LV (3-8)
where:
e = PM10 wind erosion losses of tilled fields, tons/acre/yr
k = 0.5, the estimated fraction of TSP* which is PM10
a = portion of total wind erosion losses that would be measured as
suspended particulate (estimated to be 0.025)
I = soil erodability, tons/acre/yr
K = surface roughness factor, dimensionless
C = climatic factor, dimensionless
L = unsheltered field width factor, dimensionless
V = vegetative cover factor, dimensionless
The calculation scheme used to derive the various parameters in Equation
3-8 is quite complex and thus will not be presented here. The reader is referred
to Section 7 of Cowherd et al. (1988) for further details.
3.5 UNCONTROLLED EMISSION FACTORS FOR CONSTRUCTION
ACTIVITIES
As stated in Section 2, construction sites consist of multiple open dust
sources which include vehicular traffic on unpaved surfaces, storage piles,
mud/dirt carryout onto paved roads, exposed areas, batch drop operations, and
earthmoving. However, only a single valued emission factor (not rated) is
provided in AP-42. The single valued AP-42 emission factor of 1.2 tons
TSP/acre/month for construction sites applies to operations with: (1) medium
activity level; (2) silt content about 30%; and (3) semiarid climates. Test data
were not sufficient to derive a dependence on correction factors.
In addition to the AP-42 single valued emission factor, PM10 emission
factors have been developed for site preparation activities at a road construction
project in Minnesota (Kinsey et al., 1983). The "gap filling" PM10 emission factors,
based on the level of vehicle activity (i.e., vehicle kilometers traveled or VKT), are
(Grelinger et al., 1988):
* TSP = Total suspended particulate matter « < 30
MFU-OTS\R9800.FNL 23
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Topsoil removal: 5.7 kg/VKT for pan scrapers
Earthmoving: 1.2 kg/VKT for pan scrapers
Truck haulage: 2.8 kg/VKT for haul trucks
The above factors have not been rated.
3.6 UNCONTROLLED EMISSION FACTORS FOR MISCELLANEOUS OPEN
DUST SOURCES
There are a number of PM10 emission factors for miscellaneous open
sources that have been developed especially for the preparation of State Imple-
mentation Plans as "gap filling" techniques. These gap filling emission factors are
generally broad engineering estimates based on extremely limited data or
extrapolation from another type of source. Therefore, particular caution must be
exercised in the use of these emission factors. The following information
describes gap filling emission factors for mechanical dismemberment at
demolition sites, demolition debris loading, pushing (bulldozing), and mud/dirt
carryout.
For demolition sites, the operations involved in demolishing and removing
structures from a site are:
Mechanical or explosive dismemberment
Debris loading
Onsite truck traffic
Pushing (dozing) operations
For mechanical dismemberment, the AP-42 materials handling emission
factor can be modified for waste tonnage related to structural floor space where
1 m2 of floor space represents 0.45 Mg of waste material (0.046 ton/ft2). The gap
filling emission factor related to structural floor space (using default parameters)
obtained was 0.00025 kg/m2 (Grelinger et al., 1988).
The emission factor for debris loading is based on two tests of the filling
of trucks with crushed limestone using a front-end loader, which is part of the
test basis for the AP-42 materials handling equation. The resulting emission
factor for debris loading is 0.0046 kg/m2 (Grelinger et al., 1988).
Finally, for pushing (bulldozer) operations at construction and
demolition sites, the AP-42 emission factor equation for overburden removal at
Western surface coal mines can be used. Although this equation actually relates
to particulate <15 ^mA, it would be expected that the PM10 emissions from such
operations would be generally comparable. The AP-42 dozer equation is:
24 MRI-OTS\R9800.FNL
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_ 0.45(s)1-5
W)1'4
(3-9)
where:
e = PM10 emission rate, kg/h
s = silt content of surface material, percent (default = 6.9 percent)
M = moisture content of surface material, percent (default = 7.9 percent)
and e = 0.45 kg/h (with default parameters).
The increase in emissions on paved roads due to mud/dirt carryout has
been estimated based on surface loading measurements at eight sites (Englehart
and Kinsey, 1983). Tables 3-10 and 3-11 provide these emission factors in terms
of g/vehicle pass which represents PM10 generated over and above the
"background" for the paved road tested. Table 3-10 expresses the emission
factors according to the volume of traffic entering and leaving the site whereas
Table 3-11 expresses the same data according to type of construction.
Table 3-10. EMISSIONS INCREASE (A£) BY SITE TRAFFIC VOLUME*
Particle
size
fraction1"
<~30 pm
< 10 fim
< 2.5pm
Sites with > 25 vehicle/day
Mean, X
52
13
5.1
Standard
deviation, a
28
6.7
2.6
Range
15-80
4.4-20
1.7-7.8
Sites with < 25 vehicle/day
Mean, X
19
5.5
2.2
Standard
deviation, o
7.8
2.3
0.88
Range
14-28
4.2-8.1
1.6-3.2
A£ expressed in g/vehicle pass (Englehart & Kinsey, 1983).
b Aerodynamic diameter.
MRHDTS\R960aFNL
25
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Table 3-11. EMISSIONS INCREASE (A£) BY CONSTRUCTION TYPE'
Particle
size
fraction11
< -30 jim
<10^m
<2.5 nm
Commercial sites
Mean, X
65
16
6.3
Standard
deviation, a
39
9.3
3.6
Range
15-110
4.2-25
1.6-9.7
Residential sites
Mean, X
39
10
3.9
Standard
deviation, a
22
5.4
2.1
Range
10-72
2.8-19
1.1-7.3
' A£ expressed in g/vehicle pass.
b Aerodynamic diameter.
3.7 UNCONTROLLED EMISSION FACTORS FOR PROCESS SOURCES
Fugitive emissions can be generated by many different processes in a
wide variety of industrial facilities. These emissions are generally associated with
metallurgical operations such as furnace charging and tapping and also include
the dust generated by the processing of stone, wood, grain, and similar mate-
rials. The emissions produced by process sources can have different physical/
chemical characteristics, such as fine metal oxide particles formed by
condensation or solid particulate similar in composition to the parent material.
Emission factors for process sources are generally expressed as a single
value in terms of mass of pollutant per unit of source operation (e.g., feed or
product). Appendix A provides the available particulate emission factors
published in AP-42 for process fugitive sources (EPA, 1990). It should be noted
that sources other than those listed in Appendix A may also exist in urban areas,
but no particulate emission factors are currently available to estimate such
emissions. Moreover, in many cases, available emission factors do not address
the PM10 component.
Many of the emission factors shown in Appendix A have been assigned a
D or E rating. This indicates that either poor quality data or an engineering
estimate was used to derive the emission factor. It could be concluded,
therefore, that little real data exist for many types of process sources and that
only very limited testing has been conducted. However, because process
fugitives generally represent a relatively minor contribution to the total
uncontrolled emissions from the process, they may not substantially influence the
area-wide PM10 emissions inventory. Therefore, the remainder of this document
will be devoted strictly to open sources.
26
MH1-OTS\H9800.FNL
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SECTION 4
PREPARATION OF EMISSION INVENTORIES
This section discusses current emission inventory techniques associated
with urban sources of fugitive dust emissions. These techniques are limited to
the estimation and spatial distribution of emission factor parameters, and do not
address determination or apportionment of source extent. Also included in this
discussion are representative sources of data for the fugitive dust emission
factors necessary to compile an emission inventory.
Fugitive dust emissions are estimated using the applicable emission
factors (with their input parameters), combined with source extent and control
efficiencies. Emissions also must be temporally allocated and spatially
distributed over a grid for modeling purposes, and qualified by particle size.
Sections 3.1 to 3.6, presented previously, includes a compilation of AP-42 open
dust source emission factors.
Typically an emission inventory plan will be prepared and will include the
following activities:
Description of the purpose and type of inventory
Methods to gather source and subsidiary data
Approaches to estimating emissions
QA Plan
Final data presentation (including computer files and
documentation)
4.1 EMISSION INVENTORY METHODOLOGY
The open dust sources in each grid cell must be identified and coded
according to source type. Records of the local air pollution control agency are
often the starting point for obtaining this information, especially past emission
inventories, even though they may be incomplete. Occasionally, previously
determined emissions can be adjusted using statistically appropriate scaling
factors to produce current emission estimates.
The first step is to identify the study area boundaries and to grid a base
map of the study area. The grid cells may range from 0.5 x 0.5 km to 5 x 5 km,
MRI-OTS\R9800.FNL 27
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depending on the spatial resolution needed. Larger subareas of combined cells
may be defined within the inventory area for purposes of estimating emissions
that are relatively consistent across a wide region, such as from wind erosion of
fallow ground.
Sources such as landfills should be located by UTM or LATLONG
coordinates, and then placed into the proper cells. Sources such as
construction site activity, road traffic, and agricultural activity are less likely to
have locational data, and are usually apportioned across an entire city or county
using surrogate or activity data.
Emissions from open dust sources are estimated primarily by using the
factors presented in AP-42, Section 11, Miscellaneous Sources. This section
presents emission factors for paved and unpaved roads, aggregate handling,
wind erosion, and other fugitive dust sources. Other emission factors for PM10
from airport runways, burning, construction site preparation, and landfilling are
presented in the report "Gap Filling PM10 Emission Factors for Selected Open
Area Dust Sources" (Grelinger et al., 1988). Two additional EPA documents
published by Cowherd et al. (1988) and Cowherd and Kinsey (1986) are also
helpful in estimating emissions from fugitive dust sources.
Examples of several sources are discussed to demonstrate how fugitive
dust emission inventories are conducted. These include three of the highest
generators of PM10 emissions affecting many urban areas: road traffic,
construction activities, and agriculture-related sources.
4.1.1 Road Traffic
Vehicle traffic on unpaved and paved roads is often the major source of
particulate emissions in urban areas. The procedure to inventory these emis-
sions begins with an analysis of vehicle miles travelled. State Departments of
Transportation produce either hard copy or computer files of yearly traffic data
on a link by link (road segment) basis. Records for each link can contain
starting milepoint, length of subsection, ending milepoint, functional classification,
pavement width, lanes, and average daily traffic (ADT). The last value is used to
derive the source extent (VMT) which must be classified for paved and unpaved
road functional types. The VMT for each road class is then apportioned into the
gridded cells.
Silt loading is the only parameter in the paved road emission factor
equation presented in AP-42. Appropriate silt loading values can be obtained in
two different ways for emission inventory work. Silt loadings for different paved
road functional classifications can be obtained from the EPA report "Control of
Open Fugitive Dust Sources" (Cowherd et al., 1988). A second option is a
sampling program to collect representative silt loadings from paved roads in
28 MRI-OTSVRSeOO.FNL
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certain selected cells and apply these values to the remainder of the cells using
a Krieging technique associated with geo-statistics.
Emissions per VMT are then calculated for each road classification on a
per mile basis. The VMT in each cell associated with each functional class is
then multiplied by the emissions total.
The parameters of interest are silt contents of unpaved road surfaces,
vehicle speeds, daily precipitation events per year, and vehicle mean weights
and wheels. A series of default parameter values is most often used in emission
inventory work because of the substantial effort to classify road segments for
each of the above parameters. The single variable that can be easily obtained
and that will often be a single value for an entire region is the mean annual
number of days with rain. This information can be obtained from the National
Climatic Atlas (and is reprinted in Section 11 of AP-42). The other variables are
much more difficult to obtain for each road segment or for each grid cell but can
be estimated from composite samples and representative field traffic mixes.
Local government entities that have large computer resources, such as
GISs, can sometimes access road network files and develop data to assist with
describing unpaved road segments. In any case, the emission inventory effort
associated with unpaved roads is a particularly labor and computer intensive
effort because of the large number of parameters associated with the emission
factor equation.
4.1.2 Construction
One approach to estimating construction-related emissions in each cell of
the gridded inventory area is to determine the number of construction permits
issued per month by the governing district, estimate the disturbed acreage per
type of construction site, and apply the AP-42 emission factor of 1.2 tons/mo/
acre disturbed. In recent urban emissions inventories, MRI has found that
residential, commercial, and industrial construction activities are all seen to be
linearly related to population. Consequently, construction emissions from the
governing district can be apportioned across the applicable inventory cells using
population data.
The figures that can be used for source extent per construction site are:
Single family residences in high population area: 6/7 acre/mo
Single family residence in other area: 6/5 acre/mo
Multifamily housing (each unit) 3/10 acre/mo
Commercial building (per $ million valuation) 40.7 acre/mo
Industrial building (per $ million valuation) 44.0 acre/mo
Institutional building (per $ million valuation) 48.4 acre/mo
MRI-OTSNR9800.FNL
29
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The general approach to estimating road construction emissions is to use
the same AP-42 emission factor presented for building construction with the
additional assumptions that:
The construction of freeways, highways, and city/county roads
disturbs an area 100, 76, and 64 ft wide, respectively.
The average period of disturbance is 18 mo, regardless of the type
of road being constructed.
All road construction sites are watered to control dust (estimated
control efficiency of 75%).
For a specific construction site, the most reliable estimates can be
obtained by considering the separate contributions from a variety of unit
operations. These operations could include site preparation, materials handling,
vehicular traffic, mud/dirt trackout onto adjacent paved roads, etc. This
approach will produce a more realistic estimate of emissions, but has three
limitations:
It is very resource intensive.
It requires access to active construction sites.
The resulting data are not readily extrapolated over the study area.
4.1.3 Agricultural Wind Erosion
No procedure has been specifically developed for air quality purposes to
predict particulate emissions from wind erosion. Wind blown dust is often esti-
mated by using a modified version of the USDA wind erosion equation that pre-
dicts total soil loss. The source extent is the number of agricultural acres in each
grid cell. Because the USDA's primary interest is in the loss of farmland, their
equation considers all particles, regardless of size, rather than particle sizes of
interest to air pollution regulatory agencies. Consequently, the resulting
emissions are then multiplied by an estimated fraction to estimate particulate
matter of the appropriate size distribution.
4.1.4 Agricultural Tilling
Currently, the emission factor for tilling emissions consists of an empirical
relationship between emissions and soil silt content. A default silt content value
of 18% is listed in AP-42, Section 11.2.2, and may be used but it will reduce the
quality of the estimate by one level. Silt content values also can be measured in
30 MRl-OTS\R9800.FNL
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a sampling and analysis program, and would produce a considerably better
estimate of tilling emissions.
By utilizing the Soil Conservation Service's computerized soil maps with
soil horizons classified with size distribution data (but classified by a wet method
rather than the recommended dry silt content procedure used for dust predic-
tion), an emission inventory could be somewhat improved over using a default
sift content for an entire county or region.
4.2 SPATIAL/TEMPORAL DISTRIBUTION OF CORRECTION PARAMETERS
Fugitive dust emission factor equations require the following input
parameters:
Silt content of soil or aggregate
Silt loading on paved surfaces
Moisture content of the disturbed material
Traffic intensity and characterization
Wind speed and precipitation frequency
In many cases, available input parameter data lack the spatial resolution
for good emission estimates. Collection of surface dust samples is usually
desirable to address this need. Or, to improve emission estimates, appropriate
spatial surrogate data can be used to predict variation in local source conditions.
A generic procedure for this type of operation begins with spatial data bases that
contain physical features associated with fugitive dust emissions. These data
bases could be used to disaggregate the grossly estimated parameters, such as
silt loading that results from construction site carryout or salting/sanding for
apportionment to inventory grids. However, this procedure would require the
availability of correlations of emission factor parameters with such parameters as
population density, soil type, and road classification. Various commercial and
governmental data bases already are available to begin this work, such as U.S.
Census TIGER files and Department of Agriculture soil surveys. Many of the
relevant data bases can be imported into Geographic Information Systems
(GISs) for gridded analysis.
Other efforts associated with preparation of emission inventories include
the development of sampling strategies and analysis procedures for limited
resource scenarios (dollars, hours, data bases). Because many cities are finding
it very costly to gather sufficient data on the parent dust for good emission
estimates, efficient sampling methodologies are needed.
MRI-OTSVWaOO.FNL
31
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SECTION 5
FUTURE TESTING NEEDS
Over the past five to ten years, and especially since the 1987
promulgation of the PM10 NAAQS, it has become evident to many regulatory
agencies that nontraditional source control programs may be necessary to attain
the standards. That is, in addition to the more traditional sources of particulate
matter, attention must now be paid to other industries (e.g., MSW landfills and
road and building construction) as well as public sources such as public roads/
highways, unpaved driveways/parking lots, and landscaping operations. Recent
emission inventories of Lubbock, Tucson, Phoenix, and the Coachella Valley in
California show road traffic, construction, and agriculturally related sources are
often the predominant contributors to PM10 emissions in urban areas.
Because of the need to examine the newer, nontraditional sources (many
of the emission factor development studies were performed in the mid-1970s to
early 1980s), regulatory agencies have found themselves challenged in their
attempts to quantify the impacts associated with such sources. The following
discussion outlines research needs to either improve or develop emission factors
and improve control efficiency values for open dust sources.
5.1 IMPROVEMENTS IN EXISTING EMISSION FACTORS
As addressed in Section 3, many fugitive dust sources have unreliable
emission factors. Additional source measurements are required, therefore, to
improve the emission inventory estimates. The applicability of existing emission
factors to some of the newer sources of interest is often tenuousfor example,
using agricultural tilling emission factors to estimate the impact of lift construction
at landfills. For other sources, no factors are considered even potentially
applicable. For some emission sources studied in the past (such as urban
paved roads), the relatively few available tests make it difficult to determine
variability in the emissions.
32 MRI-OTS\R9800.FNL
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5.1.1 Paved Roads
Although additional field testing is needed to improve existing emission
factors, valuable information can be obtained in a very cost-efficient manner by
reexamining existing data bases. For example, in keeping with EPA's general
guidance, only uncontrolled emissions tests were used in the development of
AP-42 Sections 11.2.5 (Urban Paved Roads) and 11.2.6 (Industrial Paved Roads).
Inclusion of controlled tests in the emission factor data base would result in a
100 to 200% increase in the size of the PM10 data base.
Selection of the appropriate emission factor is not a question of ownership
(public vs. private) but rather a question of surface loading and traffic
characteristics. Public roads in industrial neighborhoods often have heavy
surface loadings and are traveled by heavy vehicles. In that case, the source is
better characterized as "industrial" rather than "nonindustrial" in terms of
particulate emissions. Conversely, some roads within industrial plants have been
found to exhibit emission characteristics that are far more "urban" than "industrial"
in nature. Reexamination of the differences between industrial and urban paved
road tests may also reduce the uncertainty in emission inventories noted in
various EPA documents.
Unlike many fugitive dust controls, the primary control measures available
for paved roads seek to reduce silt loading, the independent variable in the
emission factor equation. Additional paved road surface sampling is required to
enlarge the small existing data base of silt-loadings associated with this major
source. Paved road silt loading data should be collected to encompass spatial
and temporal variations, climatology, land-use patterns, and elevated silt loadings
created by operations such as sanding and salting. This data base could then
be coupled with traditional urban planning data to allow better prediction of silt
loading, without the need for extensive sampling by local agencies.
5.1.2 Agriculture
Fugitive dust emissions from agriculture-related sources are suspected of
contributing significantly to ambient air particulate levels, even in some major
urban areas. These particulate emissions are generated primarily from two
activities: (1) wind erosion of open fields and (2) tilling operations. For the most
part, uncontrolled emission estimates for these sources are based on data from
the 1940s through the mid-1970s, and do not consider modern machinery and
agricultural practices. Moreover, the emission measurements generally do not
incorporate state-of-the-art techniques for obtaining particle size distribution.
For example, wind erosion emissions of bare or partially vegetated
agricultural land currently are estimated based on studies reported in 1965 and
MRK3TS\R9800.FNL
33
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1968. This prediction system evaluates soil loss of total participate by integrating
over large fields and long-time scales to produce average annual values. The
same equation is used to estimate emissions from: (1) a single field, (2) a
regional area such as a valley or county, or (3) an entire State.
Almost all PM10 emissions resulting from agricultural operations are
estimated with D-rated factors, the lowest rating usually given to measured
emission factors. PM10 emissions must now be estimated cautiously by drawing
on existing emission factors and particle size distributions for related sources.
Estimates listed in an interim report, "Gap Filling PM10 Emission Factors for
Selected Open Area Dust Sources" are still being used by local and State
agencies, even though this report was intended only for short-term usage
(Grelinger et al., 1988). The agricultural sources treated in the gap-filling report
include:
Agricultural tilling
Agricultural harvesting of cotton
Agricultural harvesting of grain
Cattle feedlots
New studies, therefore, are needed to better predict and understand
particulate emissions from agricultural operations. These studies include:
Investigation and integration of new size selective emission data
into EPA recommended emission factors for wind erosion from bare
and partially tilled ground and from revised tilling practices and
environments
Improvement of methods to predict high ambient PM10 episodes in
urban vicinities, using satellite technology to show soil condition
(moisture content, bare surface exposure, etc.) and 48-96 hr
meteorological forecasting of high winds. Short-term control
measures, such as limited irrigation or specialized plowing, might
also be studied under such situations.
5.1.3 Other Improvements
Other emission factor data bases that should be reexamined with respect
to application to other sources include:
Examination of the agricultural tilling and Section 8.24 overburden
bulldozing factors to determine applicability to MSW landfilling
operations. Because of recent interest in inventorying landfill
34 MRI-OTS\R9800.FNL
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emissions, the need for an emission factor for lift construction has
become evident.
Review of the above data bases, as well as tests of pan scrapers,
to assess applicability to construction site preparation (i.e., earth-
moving) activities. Construction sites have recently come under air
regulatory scrutiny. Although the majority of earth-moving
emissions are expected to be due to travel (of pan scrapers or haul
trucks), the dust-emitting activities often occur on bare earth rather
than on'Well-defined and well-constructed roads. Consequently,
there are doubts that the unpaved road equation can adequately
predict emissions from this source.
Determination of the applicability of wind erosion data bases to
material losses from vehicles in transit. Several agencies have
expressed interest in transit-related losses, but at present it is not
known whether these emissions are limited largely to the first few
minutes during travel (the so-called "limited potential") or whether
emissions continue over the entire duration of the trip. It is likely
that some additional field observations and measurements will be
necessary to answer these questions.
Finally, a standardized emission inventory procedure (and applicable
computer tools) should be developed for State and local agencies. Currently,
there are few consistent methods being used nationwide. Part of this effort
should be to develop the necessary correlations between surrogate data and the
main parameters associated with fugitive dust emissions. These mathematical
relationships would allow silt content and silt-loading values to be more reliably
ascribed to gridded cells of the inventory area, where no measurements are
available.
5.2 NEW EMISSION FACTORS
For other sources of current regulatory interest, no potentially applicable
emission factors have been identified. For those sources, suitable field tests
represent the only viable means to obtain applicable factors. Furthermore, even
for factors that are available, additional testing provides a means to better
characterize emission sources and their variability.
This section briefly describes field-testing needs for new fugitive dust
emission factors. The source list is not intended to be exhaustive; virtually every
EPA regional office could list at least one or two other sources of interest within
its jurisdiction. The sources listed represent those which MRI believes to be the
MR1-OTS\H8800.FNL
35
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most important (on the national scale) to provide the greatest immediate
improvements in urban PM10 inventorying practices.
5.2.1 Salting and Sanding
The application of antiskid materials (i.e., deicers and abrasives) to paved
roads produces a temporary, but substantial, increase in surface silt loading and
thus PM10 emissions. Increases in silt loading are a direct function of the quantity
and quality of the material(s) applied. For example, an abrasive which is both
properly sized and highly durable will tend to produce less silt when exposed to
traffic, which will reduce the potential for PM10 emissions.
In a recent study (EPA-450/3-90-007), MRI developed criteria for the
selection of antiskid materials for ice and snow control (Kinsey et al., 1991).
These criteria were based on an extensive literature review and subsequent
experimental program conducted in MRI's cold room. In the study, different
material samples were exposed to the same simulated traffic conditions, and
those with the lowest silt generation potential were identified. The selection
criteria were based on the characteristics of those materials that exhibited the
lowest silt generation and thus the lowest potential for PM10 emissions. No direct
information was (or could be) developed in the study with regard to the actual
PM10 emissions related to the use of antiskid materials, the changes in surface
silt loading resulting from such application, or the degree of control that could be
achieved by compliance with the material selection criteria.
Although several field studies have attempted to quantify the adverse air
quality impact due to the application of antiskid materials, the results of these
studies are, at best, inconclusive. Additional work is needed to improve the
current state of knowledge with regard to the PM10 emissions associated with the
use of deicers and abrasives for ice and snow control. The recommended
studies are described briefly below.
As suggested by Kinsey et al. (1991), additional study of surface-loading
dynamics is needed to better understand the PM10 impacts associated with
various antiskid program scenarios. This could be accomplished by a well-
designed and implemented surface-sampling program performed before, during,
and after individual storm events.
Also, the fate and transport of salts (and other deicers) applied for skid
control should be studied to determine an applicable emission factor based on
field testing. A mass balance approach would be useful for this purpose,
combining surface and air sampling to determine the PM10 emissions associated
with deicing salts.
36 MRMDTS\R9800.FNL
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5.2.2 Mud/Dirt Carryout
Carryout of mud and dirt from unpaved areas onto paved public roads
often accounts for a substantial fraction of paved road silt loadings in many
areas. Examples of such unpaved travel areas include:
Residences (i.e., driveways)
Industrial operations
Parking lots
Construction sites
Road shoulders
Once material has been deposited on the paved surface, the material is subject
to entrainment by passing vehicles.
Quantification of control efficiencies for preventive measures is essentially
impossible using the standard before/after measurement approach. Further-
more, tracking of material onto a paved road results in substantial spatial
variation in loading about the access point. This variation complicates the
modeling of emission reductions as well as their estimation, although these
difficulties become less important as the number of unpaved areas in an area
and their access points become larger.
Future testing is needed to adequately characterize mud/dirt carryout.
Past effects have been based on an assumed spatial functional form for road
loading and application of the urban paved road equation (Englehart and Kinsey,
1983). MRI recommends a field program be developed that combines extensive
(in terms of space and time) sampling of deposited material together with
intensive source testing of emissions from vehicle travel for the areas affected by
trackout. Unlike many important nonindustrial PM10 sources, trackout emissions
represent a source that could be reasonably and successfully simulated under
controlled conditions.
Consequently, it is recommended that a comprehensive field study be
conducted to (a) determine the amount of trackout as a function of source
parameters (e.g., number of vehicles entering/leaving an unpaved area, unpaved
surface material properties, spatial/temporal distribution of the additional surface
loading); (b) characterize the emission strength of the tracked material with air
sampling; and, (c) determine the effectiveness of control measures applied to
this source.
MRI-OTS\R9600.FNL
37
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5.2.3 Construction and Demolition Activities
Construction and demolition activities can be broken into generic
operations such as truck travel over paved and unpaved surfaces or handling of
bulk aggregate materials, as suggested by recent EPA documents. Vehicle
movements are reasonably easy to obtain by knowing the net weight of trucks
and how much material is to be moved.
What is not readily available at this time, however, is guidance on the
design of effective control programs for construction sites. It is generally
acknowledged that the most cost-effective control programs for nonindustrial
sources are necessarily the same as for industrial operations. However, the
temporary nature of construction site activities rules out some controls commonly
found in industry. For example, because construction often occurs over short
periods of time, chemical dust suppressants are often prohibitively expensive.
Thus, water is often used to control dust at construction sites, even though
watering often compounds any mud/dirt trackout problems present at the site.
A secondary source is created by demolition activities. Mechanical
dismemberment and debris loading of buildings emit PM10 directly to the
atmosphere and enhance emissions from paved roads due to deposited
material. Further field work should address this secondary source in that an
accounting of secondary effects may be more important than direct emissions
from the site.
5.2.4 Abrasive Blasting
Abrasives from building restoration projects contribute not only direct-
airborne emissions but also represent a secondary impact from paved roads.
Because abrasive blasting often occurs in highly urbanized areas with high traffic
volumes, passing traffic pulverizes and reentrains the deposited material. It is
recommended that a field program be undertaken to address not only direct
emissions but also to determine the secondary impact from paved roads.
Based on a recent assessment of outdoor blasting (Kinsey, 1989),
particulate emission factors presented in the literature are of poor quality. In
addition, control data for abrasive blasting operations are limited at best. New
field tests are necessary to develop emission factors for both wet and dry
blasting systems typical'of current industry practice. Different types of abrasives
also should be evaluated in the experimental program.
38 MHI-OTS\R9800.FNL
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5.2.5 Agriculture
Agricultural tilling operations have changed recently due to the two Food
Securities Acts (FSAs). These Acts mandate conservation tilling that leaves crop
residues on the soil surface. Modern agricultural tilling practices, such as use of
coulters and zone eliminators, may decrease fugitive dust emissions, but test
measurements are not available to confirm this and to establish the basis for
revision of the AP-42 emission factors. In addition, available data are insufficient
for estimating efficiencies associated with measures to control agricultural wind
erosion.
In addition, the following new studies are recommended for agricultural
sources:
Characterization of reasonable and cost-effective emission control
practices for wind erosion. Well-designed experimental programs
should be expanded to develop reliable control efficiency
algorithms.
Characterization of the ambient PM10 for agricultural chemical
constituents, including fertilizers, pesticides, and herbicides. While
major studies have examined water runoff from fields to which
agricultural chemicals have been applied, almost no work has been
done to characterize particulate emissions contaminated with such
chemicals.
Advancement to a clearer understanding of the "limited potential"
and "unlimited potential" erosion processes using a portable wind
tunnel to test a variety of agricultural surfaces. The current
distinction is based on an estimated threshold friction velocity of
50 cm/sec, and should be clarified. [Recent analysis of "unlimited
potential" desert surfaces shows that time duration of a wind storm
also affects the erosion process and in effect creates a "limited
potential" surface.]
5.3 CONTROL EFFICIENCY DETERMINATION
In addition to uncontrolled emission factors, measured values for control
efficiency also are required to develop fugitive emission inventories for urban
areas. These values are needed, not only to determine the impact of historical
control strategies, but also to predict future emission reductions.
At present, the only open source control efficiencies published in AP-42
are for unpaved roads and heavy construction activities. Therefore, a number of
MHI-OTS\FB600.FNL
39
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guidance documents have been published by EPA that summarize available
information for open source controls (Cowherd and Kinsey, 1986; Cowherd et al.,
1988). For several of these control techniques, however, the data base is
extremely limited and in need of improvement. The following subsections outline
those control techniques that are in need of additional field testing.
5.3.1 Road Dust Controls
Although temporary in nature, the watering of unpaved roads is one of
the most universally applied controls used throughout the country. The control
efficiency of unpaved road watering depends on several factors including:
(a) the amount of water applied per unit area of road surface area; (b) the time
between applications; (c) traffic characteristics throughout that period; and
(d) prevailing meteorological conditions during the period of interest. While
several studies have estimated or investigated watering efficiencies, few have
specified all the above factors.
Two different techniques have been used to predict the efficiency of road
watering. In the first approach, an empirical model for the performance of
watering was developed from 14 tests performed in four states during five
different summer and fall months. The model for average control efficiency is
(Cowherd and Kinsey, 1986):
C = 100 - °'8 P d * (5-1)
where:
C = average control efficiency, percent
p = potential average hourly daytime evaporation rate, mm/h
d = average hourly daytime traffic rate, vehicles/h
i = application intensity, L/m2
t = time between applications, h
The potential average hourly evaporation rate (p) in the above equation can be
determined using the procedure outlined by Cowherd et al. (1988) with the other
parameters determined from on-site observations.
40 MRI-OTS\R9800.FNL
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The range of values used to derive the above expression was limited to
the following:
Average potential evaporation rate: 0.042 to 0.26 mm/h
Average traffic: 23 to 98 vehicles/h
Application intensity: 0.2 to 1.9 L/m*
Time between applications: 1.8 to 4.5 hours
As an alternate method, Figure 5-1 can also be used. This figure is
adapted from 11 field tests conducted of heavy vehicle travel over a coal pile
(Cowherd et al., 1988). Measured control efficiencies did not correlate well with
either time or vehicle passes after application. However, this is believed to be
due to reduced evening evaporation when the tests were conducted. Also,
surface samples taken throughout the test period showed a strong correlation of
moisture with control efficiency.
As can be seen from the above discussion, additional field data are
needed to improve the prediction of watering control efficiency. This study
should address both heavy and light duty vehicles traveling over road surfaces
with varying silt contents and traffic volumes. Also, other meteorological factors
such as insolation, wind speed, relative humidity, etc., should be characterized to
improve model prediction. Finally, a control decay history is also necessary for
watering, as was done for chemical treatments, to determine the average control
efficiency for the period between applications (Cowherd et al., 1988).
As stated in Section 5.2.2 above, the increase in paved road surface silt
loading due to mud/dirt carryout is a substantial contributor to urban PM10
problems. At present no efficiency data are available for carryout controls.
Potential control techniques to be evaluated for mud/dirt carryout include:
cleaning of vehicle tires and underbodies before entering a paved road, paving
or chemically treating access to site exit, or semicontinuously cleaning of the
paved road at the site access point.
Several approaches could be used to assess the efficacy of mud/dirt
carryout controls. The most cost-efficient method would be to conduct extensive
sampling of paved road loadings at "controlled" and "uncontrolled" construction
sites coupled with intensive emissions testing. The data generated in this
manner would give a good relative indication of control efficiency and would also
be useful for the determination of baseline emissions prior to the implementation
of control strategies for a particular urban area.
MFU
-------
100
0
95
2468
Surface Moisture Content %
Figure 5-1. Watering control effectiveness for unpaved travel surfaces.
42
MRI-OTS\R9800.FNL
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5.3.2 Controls for Materials Handling
Although the emissions from materials handling are generally minor
compared to other open dust sources such as roads, additional control
efficiency data are needed for wet suppression, full and partial enclosure, and
capture and collection. Each will be discussed below.
Wet suppression refers to the application of water, a water solution of
chemical agent, or micron-size foam to the surface of the dust-generating
material to prevent (or suppress) the liberation of fine dust. The efficiency of
control is dependent on a number of factors including: the type of material
handled, liquid application rate (L of water per Mg of material handled), droplet
size, water distribution, and point(s) of application.
All of the available data for wet suppression systems (including both water
sprays and foam) were originally published by Cowherd and Kinsey (1986). The
control efficiency values presented in that document were derived from
personnel sampling of dust concentrations during the handling of coal or sand.
Little information was available in the way of control application parameters.
To determine the control efficiency of wet suppression, a well-designed
and implemented field study is needed. This study should address a wide range
of material characteristics, as well as the key operational parameters listed
above. To limit influences of outside sources, the test location should be isolated
to the extent possible. Material samples should also be collected for moisture
content to determine emission rate for various levels of water application.
A second control technique needing additional data involves the use of
full or partial enclosures (including porous wind fences). Enclosures are a low
maintenance control alternative used in many materials handling and processing
applications to reduce the incident wind speed thus limiting the escape of
airborne particles to the atmosphere. Essentially no control efficiency data are
available for the use of enclosures. This lack of data is due to the fact that the
fugitive emissions must be determined either before and after installation of an
enclosure on the same source or with and without an enclosure on two
essentially identical sources.
To quantify the control efficiency of exposure profiling, air sampling could
be conducted before and after the installation of a temporary enclosure(s)
around a typical source(s). Wind speed and particle flux measurements could
be made to determine the controlled and uncontrolled emission rate as a
function of wind speed reduction. A source which is relatively small and located
near ground level would be required to make sampling possible. Also, different
types of operations handling a range of materials should be evaluated to provide
a suitable predictive model of control efficiency.
MHMDTS\R9800.FNU
43
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The control efficiency of capture and collection is a direct function of
both the capture efficiency and the collection efficiency of the system as shown
in Figure 5-2. Hood capture efficiency is dependent on a number of key design
and operational parameters including: airflow rate, hood-to-source distance,
capture velocity, hood cross-sectional area, material induced airflow, etc.
Cowherd and Kinsey (1986) found only limited data for capture efficiency
of hood systems applied to metallurgical operations. These data were obtained
indirectly using a tracer gas (SF6). Otherwise, no other data was available in the
open literature.
Additional data is definitely needed to characterize the control efficiency of
capture and collection systems that are used on many large material handling
systems (e.g., coal fired power plants). To meet this objective, a mass balance
approach should be used to determine the m4, m2, and m3 emissions shown in
Figure 5-2. Sampling of m2 and m3 emissions can be performed using standard
stack sampling techniques, whereas the m4 emissions would be determined
downwind of the source by exposure profiling. Again, a range of hood design
and operational parameters should be tested along with a variety of material
characteristics.
44 MRI-OTS\R9800.FNL
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Not /
Captured /"rfi- * Collection
. Device
Captured \
^^^
Capture
Device
Uncontrolled
\
/
Control Efficiency (%) -
where m-j = rh2 + rh4
Captured
but not
Collected
x 100
Figure 5-2. Emissions quantification requirements for performance
evaluation of capture/collection system.
MRMDTS\B9800.FNL
45
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SECTION 6
PROPOSED TEST MATRIX
Based on the data gaps identified in Section 5 above, a tentative test
matrix has been prepared for planning purposes. This matrix (Table 6-1) outlines
the individual test series to be conducted, the types of measurements to be
made, and the number of locations, sources, and sampling runs to be per-
formed. The recommended geographic location(s) of the tests to be performed
are also provided, as applicable.
As shown in Table 6-1, the proposed testing has been categorized in two
ways: by test objective; and by test priority. Test objective indicates whether the
series is intended: (a) to improve existing uncontrolled emission factors; (b) to
develop new uncontrolled emission factors; or (c) to improve control efficiency
estimates. Test priority is expressed as a relative ranking (A = highest) of all test
series within the matrix indicating the overall need for implementation.
The priority ratings assigned in Table 6-1 were based on the relative cost-
effectiveness of each test series to obtain the most useful information for the
least cost. For example, testing which involves only surface material sampling
and analysis was generally given a higher rating than more costly source
sampling, when a test-based emission factor is already available. Other factors
influencing test priority included the expected typical impact of the individual
sources (or control methods) on the total PM10 emissions inventory as well as the
extent and reliability of available data.
Table 6-1 also specifies the measurement method to be used in each test
series. Since the reader may not be familiar with some of these techniques,
Appendix B outlines general measurement methods for fugitive particulate
emissions and Appendix C provides similar information on use of the MRI
dustiness test chamber.
Finally, several'factors must be noted with regard to the information
provided in Table 6-1 which affects its future use. First, the testing needs
identified in Section 5 are preliminary in nature. Therefore, by necessity, the
testing proposed in Table 6-1 reflects a "first cut" analysis and should be used
accordingly.
MRI-OTS\FB600.FNL
47
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Table 6-1. TENTATIVE TEST MATRIX FOR URBAN FUGITIVE DUST SOURCES"
Test
objective
Improvements to
current AP-42
emission (actors
Develop new test-
based emission
factors
Test
priority
(A = highest)
A
B
B
B
C
A
Description of test series
to be conducted
Expand silt loading data
for paved roads and
develop correlations with
land use, etc.
Collect additional source
test data for "low till"
farming practices to
update current PM10
emission factor and
quantify associated air
toxics (e.g., pesticides).
Collect additional source
test data for pan
scrapers and truck
haulage in construction
sites.
Reevaluate existing
paved road data base to
combine data from
"industrial" and "urban"
roads as well as
"controlled" testing.
Update current emission
factor model for
agricultural wind erosion.
New emission factor for
mud/dirt earn/out onto
paved urban streets.
Type of
measurements to
be made
Surface sampling
Source testing
coupled with
surface sampling
Source testing
coupled with
surface sampling
N/A
N/A
Extensive surface
sampling coupled
with intensive
source testing
General test
method(s)b
Vacuuming
Exposure profiling,
grab sampling,
and selected
chemical analyses
of collected
paniculate
Exposure profiling
and grab sampling
N/A
N/A
Vacuuming of
paved road(s) near
access point(s)
and exposure
profiling of paved
road(s)
No. of test
locations0
10
3
3
N/A
N/A
4
No. of sources
per location
10
2
2
N/A
N/A
1 access point
x 3 points in
time
No. of test
runs per
source
1
3
3
N/A
N/A
3
Test locations and other
factors Influencing sampling
Sampling In one city per
EPA Region. May delete
certain Reglon(s) If adequate
data already exist One
sample composite/road
evaluated.
Tests conducted at 1
California site, 1 Texas site,
and 1 Iowa site. Two types
of implements or operations
tested.
Tests conducted at 1
Southwestern site, 1 Middle
States site, and 1
Southeastern site.
Data analysis only
No work proposed at
present track USDA
research.
All testing conducted locally
at 2 construction sites and 2
Industrial facilities at 3
different times during
construction project or
spring, summer, and fall for
Industrial sites.
-------
Table 6-1 (Continued)
Test
objective
Develop new test-
based emission
(actors
Test
priority
(A=hlghest)
B
B
C
C
C
Description of test series
to be conducted
Evaluation of surface
loading dynamics for
application of antiskid
abrasives.
New emission factor for
application of delcing
chemicals.
New emission factor for
handling of soil in active
construction sites.
New emission factor for
debris loading at
demolition sites.
New emission factor for
abrasive blasting.
Type of
measurements to
be made
Extensive surface
sampling before,
during, and after
selected storm
events
Extensive surface
sampling
combined with
Intensive source
testing
Emission sampling
of end loaders and
shovels plus
material grab
sampling
Emission testing of
typical building
debris
Emission testing
coupled with
determination of
heavy metals
General test
method(s)b
Vacuuming
Vacuuming,
exposure profiling,
and selected
chemical analyses
of collected
participate
Exposure profiling,
grab sampling,
and "dustiness"
testing of soils
from other
geographic areas
Exposure profiling
N/A
No. of test
locations"
4
1
1
1
N/A
No. of sources
per location*1
1 road
segment x 3
points in time
1
2
1
N/A
No. of test
runs per
source
3
3
3
3
N/A
Test locations and other
factors Influencing sampling
Testing conducted at 1
Northeastern site, 1 Middle
States site, 1 Northern Plains
site, and 1 Western
Intermountain site. Samples
collected 3 different times
during winter months.
Source testing of 1 local site
once/season and surface
sampling 6 times during
winter.
Source testing of 1 local site
and analysis of soil samples
form 9 other cities for
"dustiness Index."
Source testing at 1 local site
for end loaders only.
No work proposed at
present track Federal
Highway study.
CO
-------
en
o
Table 6-1 (Continued)
Tesl
objective
Improvements In
control efficiency
estimation
Test
priority
(A=highest)
A
B
C
C
C
Description of test series
to be conducted
Mud/dirt carryout
controls
Watering of unpaved
roads.
Wet suppression of
materials handling
Passive enclosures
Capture/collection
systems
Type of
measurements to
be made
Extensive surface
sampling coupled
with Intensive
source testing
Emission testing to
determine control
decay history
Emission testing
and material
sampling
Wind velocity
measurements
coupled with
source testing
Concurrent
emissions testing
at 3 locations
General test
method(s)b
Vacuuming of
paved road(s) near
access polnt(s)
plus exposure
profiling of paved
road (s)
Exposure profiling
and surface grab
sampling
Exposure profiling
or quasi-slack
sampling coupled
with grab sampling
and HjO analyses
Warm wire
anemometry and
exposure profiling
EPA Method 5
upstream and
downstream of
dust collector plus
exposure profiling
of uncaptured
emissions
No. of test
locations0
2
2
1
1
1
No. of sources
per location
3 emission
levels x 3
points In time
2 roads x 3
application
rates
2 materials x 2
handling
devices
2 materials x 3
emission
levels
2
No. of test
runs per
source
3
Multiple
3
3
3 tests x3
points
Test locations and other
factors Influencing sampling
All testing conducted locally
at 1 construction site and 1
Industrial facility. Testing for
uncontrolled emissions plus
paving of access point and
semlcontlnuous cleaning of
paved street
Test locations = 1 local site
plus 1 Western site.
Testing at local site on
handling of crushed stone
and coal using stacker and
belt conveyor.
Testing at one Plains state
site for two different
aggregate materials.
Sampling of uncontrolled
emissions plus two levels of
wind speed reduction.
Same site and materials
used for enclosures above.
Matrix prepared from "first-cut" analysis of testing needs. Specific test plans (and associated budget) required for each test series.
b See Appendix B for description of general measurement methods and Appendix C for determination of "dustiness index."
0 Geographic locale In contiguous United States.
** Sources or emission points to be tested.
-------
Specific test plans must be prepared prior to any type of field activity to more
closely define the scope (and associated budget) of each test series, the precise
methodology to be used, and applicable Quality Control/ Quality Assurance
protocols. Also, good test design calls for controlled experimental conditions in
order to collect meaningful data. Thus, in every case possible, it is preferred to
use sites, sources, and controls which are under the direct supervision of the
testing organization rather than using the more traditional "take what you can
get" approach to source sampling.
MRI-OTS\R9800.FNL
51
-------
SECTION 7
REFERENCES
Cowherd, C. and J.S. Kinsey (1986). "Identification, Assessment, and Control of
Fugitive Particulate Emissions." EPA-600/8-86-023, U. S. Environmental
Protection Agency, Research Triangle Park, NC, May.
Cowherd, C. et al. (1988). "Control of Open Fugitive Dust Sources." EPA-450/3-
88-008, U. S. Environmental Protection Agency, Research Triangle Park, NC,
September.
Englehart, P.J. and J.S. Kinsey (1983). "Study of Construction Related Mud/Dirt
Carryout." Final Report, EPA Contract No. 68-02-3177, Work Assignment No. 21,
U. S. Environmental Protection Agency, Research Triangle Park, NC, July.
Grelinger, M.A. et al. (1988). "Gap Filling PM10 Emission Factors for Selected
Open Area Dust Sources." EPA-450/3-88-008, U. S. Environmental Protection
Agency, Research Triangle Park, NC, March.
Kinsey, J.S. et al. (1983). "Study of Construction Related Dust Control." Final
Report, Contract No. 32200-07976-01, Minnesota Pollution Control Agency,
Roseville, MN, April 19.
Kinsey, J.S. (1989). "Assessment of Outdoor Abrasive Blasting." Interim Report,
EPA Contract No. 68-02-4395, Work Assignment No. 29, U.S. Environmental
Protection Agency, Research Triangle Park, NC, August.
Kinsey, J.S. et al. (1991). "Guidance Document for Selecting Antiskid Materials
Applied to Ice- and Snow-Covered Roadways." EPA-450/3-90-007, U.S. Environ-
mental Protection Agency, Research Triangle Park, NC, July.
MRI-OTSXR9800.FNL
53
-------
U.S. Environmental Protection Agency (1980). 'Technical Procedures for
Developing AP-42 Emission Factors and Preparing AP-42 Sections." Office of Air
Quality Planning and Standards, Research Triangle Park, NC, April.
U.S. Environmental Protection Agency (1990). Compilation of Air Pollutant
Emission Factors, Volume I: Stationary and Area Sources. AP-42 (with
Supplements A-C), Fourth Edition, Office of Air Quality Planning and Standards,
Research Triangle Park, NC, September.
54 MRI-OTS\R9800.FNL
-------
APPENDIX A
EMISSION FACTORS FOR PROCESS FUGITIVE SOURCES
MRI-OTS\R9800.FNL
-------
APPENDIX A
EMISSION FACTORS FOR PROCESS FUGITIVE SOURCES
The emission factors currently published in AP-42 for process fugitive
sources typically found in urban areas are provided in Table A-1 (EPA, 1990).
The individual factors are provided by industry and source category in the order
presented in AP-42. Also shown is the rating of each factor according to
published criteria (EPA, 1980). For other comments on Table A-1, the reader is
referred to Sections 2.2 and 3.7 of the main text.
MRI-OTS\R9800.FNL
A-3
-------
Table A-1. AP-42 PARTICULATE EMISSION FACTORS FOR PROCESS FUGITIVE SOURCES
AP-42
Section
5.3
5.8
5.14
5.22
6.4
6.5
7.1
7.2
7.3
Industry category
Carbon black
Hydrofluoric acid
Printing ink
Lead alkvl manufacture
Grain elevators
Grain processing
Beer making
Primary aluminum production
Coke manufacturing
Primary copper smelters
Process source
Oil furnace process
Spar handling
Pigment mixing
Sludge pits
Drying
Cleaning
Drying
Spent grain drying
Bauxite grinding
Anode baking furnace
Prebake cell
Vertical Soderberg stud cell
Horizontal Soderberg stud cell
Coal crushing
Coal charging
Coke pushing
Roaster calcine discharge
Reverb furnace matte tapping
Reverb furnace slag tapping
Converter
Converter slag and copper blow
Anode furnace
Slag cleaning furnace
AP-42 Emission factor*
0.10 kg PM,JMq carbon black
3.0 kg TP/Mg fluorospar
1 kg TP/Mg pigment
0.6 kg Pb/Mg product0
0.4-0.6 kg TP/Mg grain handled
1.5 kg TP/Mg grain handled
0.15-3.6 kg TP/Mg grain received
2.5 kg TP/Mg grain handled
NA
NA
1.45 kg PM10/Mg aluminum produced
6.0 kg TP/Mg aluminum produced
1.55 kg PM10/Mg aluminum produced
0.055 kg TP/Mg coal charged
0.004 kg PM10/Mg coal charged
0.25 kg PM10/Mg coal charged
1 .3 kg TP/Mg ore concentrate
0.074 kg PM10/Mg ore concentrate
0.028 kg PM10/Mg ore concentrate
2.2 kg TP/Mg ore concentrate
2.1 kg PM10/Mg ore concentrate
0.25 kg TP/Mg ore concentrate
4 kg TP/Mg ore concentrate
Emission factor
rating"
C
E
E
B
B
B
D
D
C
A
D
D
E
D
B
D
D
B
D
B
B
-------
Table A-1 (Continued)
AP42
Section
Industry category
Process source
AP-42 Emission factor*
Emission factor
rating*
7.5
Iron and steel mills
Sinter machine windbox
Sinter discharge
Blast furnace casthouse
Hot metal desulfurization
BOF charging
BOF melting/refining
BOF tapping
Hot metal transfer
Electric arc furnace melting/refining
Electric arc furnace charging,
tapping & slagging
Open hearth furnace
melting/refining
Leaded steel teeming
Unleaded steel teeming
Scarfing
0.83 kg PM10/Mg sinter
0.016 kg PM10/Mg sinter
0.15 kg PM10/Mg metal
0.10 kg PM10/Mg metal
0.14 kg PM10/Mg metal
14.25 kg TP/Mg steel
0.21 kg PM10/Mg metal
0.028 kg TP/Mg steel
11.02kgPM10/Mgsteel
0.7 kg TP/Mg steel
8.76 kg PM10 metal
0.405 kg TP/Mg steel
0.035 kg TP/Mg steel
0.05 kg TP/Mg metal feed
D
C
C
E
E
B
E
B
D
C
A
A
B
7.6
Primary lead smelters
Ore mixing/pelletizing
Sinter machine leakage
Sinter return handling
Sinter discharge, crushing &
screening
Blast furnace pouring
Blast furnace slag cooling
Zinc fuming furnace vents
Dross kettle
Reverb furnace leaks
Silver retort building
Lead casting
1.13 kg TP/Mg lead produced
0.34 kg TP/Mg sinter processed
4.5 kg TP/Mg sinter processed
0.75 kg TP/Mg sinter processed
0.47 kg TP/Mg lead produced
0.24 kg TP/Mg lead produced
2.3 kg TP/Mg lead produced
0.24 kg TP/Mg lead produced
1.5 kg TP/Mg lead produced
0.9 kg TP/Mg lead produced
0.44 kg TP/Mg lead produced
E
D
E
E
D
E
E
D
D
E
E
>
cn
-------
>
CD
Table A-1 (Continued)
AP-42
Section
7.7
7.8
7.9
7.10
Industry category
Primary zinc smelters
Secondary aluminum
processing
Secondary copper
smelting/alloying
Gray iron foundries
Process source
Roasting
Sinter plant windbox
Sinter plant discharge/screens
Retort building
Casting
Crushing/screening/bailing
Shredding/classifying
Rotating barrel dross furnace
Dry milling
Reverb furnace chlorine
demagging
Reverb furnace refining
Sweating furnace
Crucible furnace
Furnace charging/tapping
Magnesium treatment
Inoculation
Pouring/cooling
Shakeout
Cleaning/finishing
Core making/baking
AP-42 Emission factor*
Nil
0.12-0.55 kg TP/Mg zinc slab
0.28-1.22 kg TP/Mg zinc slab
1-2 kg TP/Mg zinc slab
1 .26 kg TP/Mg zinc slab
NA
NA
NA
NA
266 kg PM10/Mg chlorine feed
1.3 kg PM10/Mg aluminum processed
7.25 kg TP/Mg aluminum processed
0.95 kg TP/Mg aluminum processed
NA
0.9 kg TP/Mg metal
1.5-2.5 kg TP/Mg metal
1.03 kg PM1(/Mg metal
1.12kg PM10/Mg metal
8.5 kg TP/Mg metal
0.6 kg TP/Mg metal
Emission factor
rating*.
E
E
E
E
D
D
C
C
E
D
D
E
D
D
-------
Table A-1 (Continued)
AP-42
Section
Industry category
Process source
AP-42 Emission factor"
Emission factor
rating"
7.11
Secondary lead processing
Battery breaking/crushing
Sweating furnace charging/tapping
Smelting furnace charging/tapping
Kettle refining
Casting
NA
0.8-1.8 kg TP/Mg material charged
4.3-12.1 kg TP/Mg material charged
0.001 kg TP/Mg material charged
0.001 kg TP/Mg material charged
E
E
E
E
7.12
Steel foundries
Charging/tapping
Backcharging
Alloying
Oxygen lancing
Slag removal
NA
NA
NA
NA
NA
7.13
Secondary zinc processing
Crushing/screening
Reverb sweating
Rotary sweating
Muffle sweating
Kettle (pot) sweating
Electric resistance sweating
Sodium carbonate leaching
Melting furnace
Alloying retort distillation
Retort and muffle distillation
Casting
Distillation/oxidation
Retort reduction
2.3 kg TP/Mg product
0.63 kg TP/Mg product
0.45 kg TP/Mg product
0.54 kg TP/Mg product
0.17 kg PM10/Mg product
0.25 kg TP/Mg scrap
NA
0.0025 kg TP/Mg product
NA
1.18 kg TP/Mg product
0.0075 kg TP/Mg product
NA
NA
E
E
E
E
E
E
E
E
-------
>
00
Table A-1 (Continued)
AP-42
Section
7.14
7.17
8.1
8.3
8.4
8.10
8.11
8.14
Industry category
Storage battery production
Miscellaneous lead products
Asphaltic concrete plants
Bricks and related clay
products
Calcium carbide
manufacturing
Concrete batching
Glass fiber manufacturing
Gypsum manufacturing
Process source
Grid casting
Paste mixing
Plate stacking/burning/assembly
Lead reclaim furnace charge/tap
Dry formation
Melting furnace
charqinq/tappinq/castinq
Hot side
conveying/classifying/mixing
(puqmill)
Raw material grinding/screening
Tap fume vents
Furnace room vents
Primary/secondary crushing
Mixer loading
Raw material mixing/weighing
Crushing/batch charging
Primary/secondary crushing and
screening
Plaster bagging
Pin mixer
AP-42 Emission factor*
NA
NA
NA
NA
NA
NA
NA
2.66 kg PM10/Mg brick produced
NA
13 kg TP/Mg of calcium carbide
NA
0.02 kq TP/Mg material processed
0.3 kg TP/Mg material processed
Nil
NA
NA
NA
Emission factor
raiingb
E
C
E
B
-------
Table A-1 (Continued)
AP-42
Section
8.15
8.19.1
8.19.2
8.23
Industry category
Lime manufacturing
Sand and gravel processing
Crushed stone processing
Metallic minerals processing
Process source
Primary crushing/screening
Secondary crushing/screening
Pulverizing/screening
Calciner
crushing/pulverizing/screens
Primary/secondary crushing
Primary/secondary crushing (dry)
Primary/secondary crushing (wet)
Tertiary crushing (dry)
Primary crushing
Secondary crushing (high moisture
ore)
Tertiary crushing
AP-42 Emission factor*
NA
NA
NA
NA
0.009 kg TSP/Mg material processed
0.0085 kg PM10/Mg feed
0.009 kg TSP/Mg feed
0.93 kg TSP/Mg feed
0.02-0.004 kg PM10/Mg primary feed
0.012 kg PM10/Mg primary feed
0.08-0.001 kg PM10/Mg primary feed
Emission factor
rating*
D
D
D
E
C
D
E
>
CD
-------
>
o
Table A-1 (Continued)
Ap-43
Section
Industry category
Process source
AP-42 Emisstof} faetbi^
mte$i0r> factor
10.3
Plywood veneer & layout
operations
Log debarking
Log sawing
Veneer lathing
Plywood cutting/sanding
0.012 kg TP/Mg logs processed
0.175 kg TP/Mg logs processed
NA
0.05 kg TP/m2 plywood produced
E
E
PM,0 = particles < 10 ^m in aerodynamic diameter (/jmA). 1 Mg = 10" gm.
TP = total paniculate matter regardless of size.
TSP = total suspended particulate matter particles < 30
Per EPA, 1980.
Assumed to be all PM10 emissions.
-------
APPENDIX B
GENERAL MEASUREMENT METHODS FOR FUGITIVE DUST EMISSIONS
MRI-OTS\R9800-FNL
B-1
-------
APPENDIX B
GENERAL MEASUREMENT METHODS FOR FUGITIVE DUST EMISSIONS
B.1 MASS EMISSION MEASUREMENT METHODS
Fugitive particulate emission rates and particle size distributions are
difficult to quantify because of the diffuse and variable nature of such sources
and the wide range of particle sizes involved (including particles which deposit
immediately adjacent to the source). Standard source testing methods, which
are designed for application to confined streams under essentially steady-state,
forced-flow conditions, are not suitable for measurement of fugitive emissions
unless the plume can be drawn into a forced-flow system.
For field measurement of fugitive mass emissions, four basic techniques
have evolved:
1. The quasi-stack method involves capturing the entire particulate
emissions stream with enclosures or hoods and applying
conventional source testing techniques to the confined flow.
2. The roof monitor method involves measurement of particulate
concentrations and airflows across well-defined building openings
such as roof monitors, ceiling vents, and windows, followed by
calculation of particulate mass flux exiting the building.
3. The upwind-downwind method involves measurement of upwind
and downwind particulate concentrations utilizing ground-based
samplers under known meteorological conditions, followed by
calculation of source strength (mass emission rate) with
atmospheric dispersion equations.
4. The exposure profiling method involves simultaneous, multipoint
measurements of particulate concentration and wind speed over
the effective cross section of the plume, followed by calculation of
net particulate mass flux through integration of the plume profiles.
5. The wind tunnel method involves the use of a portable open-
floored wind tunnel for in situ measurement of emissions from
representative surfaces under predetermined wind conditions.
Each of these methods is discussed below.
MRI-OTS\R9800.FNL
B-3
-------
B.1.1 Quasi-Stack Method
In practice, the quasi-stack method (Kolnsberg et al., 1976) converts a
fugitive emission source to a conventional ducted source. Because it is usually
impractical to enclose an open dust source or capture its entire emissions
plume, the quasi-stack method is generally limited in applicability to process
sources.
The quasi-stack method qualifies as a sound methodology only if the
enclosure or hood is capturing the entire emissions stream without affecting the
emission rate. In addition, an accepted sampling technique must be chosen to
quantify the emission rate, taking into consideration the special problems
associated with highly fluctuating emissions.
B.1.2 Roof-Monitor Method
The roof monitor method (Kenson and Bartlett, 1976) is similar to the
quasi-stack method in that it utilizes the building ventilation system to direct the
emissions stream to the sampling location. Usually this method is practical only
for high temperature processes which produce buoyant plumes.
The roof-monitor method qualifies as a sound methodology only if flows
and concentrations can be adequately characterized within building discharge
openings. Also, it must be shown that plume interference from other sources in
the same building is not occurring. Finally, as with the quasi-stack method, the
special problems associated with highly fluctuating emissions (and, in the case of
natural ventilation, fluctuating ambient winds) must be dealt with.
B.1.3 Upwind-Downwind Method
The basic procedure of the upwind-downwind method (Kolnsberg, 1976)
involves the measurement of particulate concentrations both upwind and
downwind of the pollutant source. The number of required upwind sampling
instruments depends on the isolability of the source operation of concern (i.e.,
the absence of interference from other sources upwind). Although at least five
downwind particulate samplers must be operated during a test, increasing the
number of downwind instruments improves the reliability in determining the
emission rate by providing better plume definition. In order to reasonably define
the plume emanating from a point source, instruments need to be located at a
minimum of two downwind distances and three crosswind distances. The same
sampling requirements pertain to line sources except that measurements at
multiple crosswind distances are not required.
B-4 MFU-OTSXFWeoo.FNL
-------
After the concentration(s) measured upwind are subtracted from the
downwind concentrations, the net downwind concentrations are input to
standard dispersion equations (normally of the Gaussian type). The dispersion
equations are used to back-calculate the source strength (i.e., particulate
emission rate) required to generate a pattern of downwind concentrations. A
number of meteorological parameters must be recorded concurrently for input to
these dispersion equations. The minimum parameters that must be recorded
on-site are wind direction and speed.
B.1.4 Exposure Profiling Method
In much the same manner as the quasi-stack and roof monitor methods,
exposure profiling (Cowherd et al., 1974) uses the same basic concept as
conventional (ducted) source testing. The difference is that in the case of
exposure profiling, the ambient wind directs the plume toward the sampling
array. The passage of airborne particulate matter immediately downwind of the
source is measured directly by means of simultaneous multipoint sampling of
particulate concentration and wind velocity over the effective cross section of the
fugitive emissions plume.
For measurement of nonbuoyant fugitive emissions, profiling sampling
heads are distributed over a vertical network positioned immediately downwind
(usually about 5 m) from the source. Particulate sampling heads should be
symmetrically distributed over the concentrated portion of the plume representing
about 90% of the total mass flux (exposure). A vertical line grid of at least three
samplers is sufficient for measurement of emissions from line or moving point
sources, while a two-dimensional array of at least five samplers is required for
quantification of fixed virtual point source emissions. At least one upwind
sampler must be operated to measure background concentration, and wind
speed on-site must be measured concurrently.
Unlike the upwind-downwind method, exposure profiling employs a mass-
balance calculation scheme rather than requiring indirect calculation through
application of a generalized atmospheric dispersion model. The mass of
airborne particulate matter emitted by the source is obtained by spatial
integration of distributed measurements of particulate flux, after subtraction of the
background contribution. The exposure is the point value of the flux
(concentration of airborne particulate accumulated over the measurement
period).
MRI-OTS\R9800.FNL
B-5
-------
B.1.5 Wind Tunnel Method
Finally, the wind tunnel method (Cuscino et al., 1983) employs a portable
pull-through wind tunnel with an open-floored test section placed directly over
the surface to be tested. Air is drawn through the tunnel at controlled velocities.
The exit air stream from the test section passes through a circular duct fitted with
a sampling probe at the downstream end. Air is drawn through the probe by a
high-volume sampling train. This technique enables precise study of the wind
erosion process with minimal interference from background sources.
B.2 PARTICLE SIZING
A number of different methods have been used for the determination of
particle size in fugitive dust studies. These methods include high-volume
impactors and cyclones; dichotomous samplers; modified inlets for high volume
air samplers; and optical and electron microscopy. Each is briefly described
below.
High-volume cascade impactors with glass fiber (or other) substrates may
be adapted for sizing of fugitive particulate emissions. A cyclone preseparator
(or similar device) is necessary to remove coarse particles which otherwise
would be subject to particle bounce within the impactor, causing fine particle
bias. The cyclone can also be operated without the cascade impactor if a single
size cut is desired. The sampling intake should be pointed into the wind and the
sampling velocity adjusted to the mean local wind speed by fitting the intake with
a nozzle of appropriate size.
The EPA version of the dichotomous sampler, which is virtually free of
particle bounce problems, can also be useful for quantification of PM10
concentrations. However, this device operates at a low flow rate, yielding only
relatively small quantities of sample in a 24-h period. Thus, an analytical balance
of high precision is required to determine mass concentrations below and above
the fine particulate (2.5 /imA) outpoint. This outpoint falls at the minimum in the
typical bimodal size distribution of atmospheric particulate.
Modifications to the standard high-volume sampler have also been
designed to capture particulate matter smaller than 10 ^mA. These units are
much less wind-sensitive than the dichotomous sampler but do not produce a
cutpoint at 2.5 jinnA.
Microscopy is another particle sizing technique used in fugitive dust
studies. Optical or light microscopes, transmission electron microscopes (TEMs),
and scanning electron microscopes (SEMs) have been used in particle sizing.
Optical microscopy has proven useful in determining particle sizes greater than
B-6 MRI-OTS\R9800.FNL
-------
0.25 p.m in diameter. An electron microscope is needed to size particles greater
than 0.001 /im in diameter.
Of the many microscopic techniques available for sizing particles by their
physical dimensions, the most common is the projected area technique. The
particle diameter is set equal to the diameter of an equivalent circle with the
same area as the projected area of the particle. A minimum of 300 particles is
usually required to determine a distribution of about nine size categories by this
method. Because such work is long and tedious when performed manually,
automation of the process is now common. Automatic image analysis for optical
microscopy and computer-controlled scanning electron microscopy (CCSEM)
are examples.
B.3 REFERENCES FOR APPENDIX B
Cowherd, C. Jr., et al. (1974). Development of Emission Factors for Fugitive
Dust Sources. EPA-450/3-74-037, U.S. Environmental Protection Agency,
Research Triangle Park, NC, June.
Cuscino, T., et al. (1983). Iron and Steel Plant Open Source Fugitive Emission
Control Evaluation. EPA-600/2-83-110, U.S. Environmental Protection Agency,
Research Triangle Park, NC, October.
Kenson, R. E., and P. T. Bartlett (1976). Technical Manual for the Measurement
of Fugitive Emissions: Roof Monitor Sampling Method for Industrial Fugitive
Emissions. EPA-600/2-76-089b, U.S. Environmental Protection Agency, Research
Triangle Park, NC, May.
Kolnsberg, H. J. (1976). Technical Manual for Measurement of Fugitive
Emissions: Upwind/Downwind Sampling Method for Industrial Emissions.
EPA-600/2-76-089a, U.S. Environmental Protection Agency, Research Triangle
Park, NC, April.
Kolnsberg, H. J., et al. (1976). Technical Manual for the Measurement of Fugitive
Emissions: Quasi-stack Sampling Method for Industrial Fugitive Emissions.
EPA-600/2-76-089C, U.S. Environmental Protection Agency, Research Triangle
Park, NC, May.
MRI-OTS\R9800.FNL
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APPENDIX C
DESCRIPTION OF MRI DUSTINESS TEST CHAMBER
MRI-OTS\R9800.FNL
C-1
-------
APPENDIX C
DESCRIPTION OF MRI DUSTINESS TEST CHAMBER
The MRI dustiness test chamber is a bench-scale device used to generate
and sample airborne particulate resulting from the pouring of bulk material in
the chamber. Because dustiness testing has been shown to produce unit
emissions comparable to large scale operations, it may be used to bracket
projected emissions from the field transfer of bulk materials.
C.1 Test Chamber Configuration
The basic design of the chamber includes the following features for the
sampling of total particulate (TP approximate particle diameter < 75 //m):
Test chamber dimensions of 20 cm x 20 cm x 50 cm high
Cup with 0.27 L capacity used to hold, then pour the sample
material 25 cm to the floor of the chamber
Vibratory apparatus attached to the cup for smooth pouring at a
cup rotation rate of 0.8 rpm
Chamber air exchange flowrate of 8.3 L/min with exhaust through
a filter cartridge mounted in the chamber lid
A 47-mm diameter, mixed cellulose ester filter (MCEF) with a pore
size of 0.8 um for particulate collection
Two side vents into the chamber, designed to direct make-up air
into the lower portion of the chamber
A Matheson No. 604 rotameter for control and monitoring of the
sampling flowrate
The chamber lid can be modified to accept four personal samplers (37-
mm filter cassettes plus 10-mm Dorr Oliver cyclone) with approximate cutpoints
of 3.5 pmA for the determination of respirable particulate (RP) matter. In this
alternate configuration, four personal samplers are mounted symmetrically in
the modified lid. Each 37-mm filter cassette is located on top the lid with the
10 mm nylon cyclone extending down inside the chamber. The flowrate of
each personal sampler is 2.07 L/min to give a total air exchange rate of 8.3
L/min (equivalent to dustiness tests for TP).
MRI 10/10/91
-------
Even though a 2.07 L/min flowrate is slightly above the recommended
1.85 L/min for a personal sampler, the ACGIH manual on "Air Sampling
Instruments" states that "For non-fibrous test aerosols, including mica and
silica, there was essentially no change in the mass collected on the filter for
flowrates between 1.3 and 2.65 L/min. As the flowrate increases, the aerosol
mass entering the cyclone increases proportionally, but so apparently does the
collection efficiency."
C.2 Test Conduct
Each dustiness test generates suspended mass that is captured on filters
mounted in the chamber lid. The following steps represent a typical test
scenario for sampling both total and respirable paniculate resulting from the
pouring of sample material in the chamber.
1. Record the masses of material to be poured in the chamber
2. Conduct triplicate tests for each material with:
- Standard lid and 47-mm filter sampler for TP
- Modified lid and four 37-mm personal samplers with cyclones
for RP
3. Gravimetrically analyze each filter and record tare and final weights
4. If required, analyze filter substrates for other components. For
example, analyze for asbestos fibers by transmission electron
microscopy (TEM) or for crystalline silica by NIOSH Method 7500.
In the latter instance, ash the four 37 mm filters from each test for
one Method 7500 analysis.
C.3 Test Calculations
The net weights of particulate matter resulting from the above tests are
used to calculate the dustiness index (or emission factor -- mg of suspended
particulate per kg of material poured). The PM35/TP fraction is obtained by
comparing the combined catch on the four 37-mm filters with the catch on the
single 47-mm filter. The PM10 fraction can be estimated using a log-normal
distribution with reference points at an approximate largest particle size of 75
and at 3.5 //m.
MRI 10/10/91
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