You are here: EPA Home Technology Transfer Network Clearinghouse for Inventories & Emissions Factors

Emissions Factors & AP 42 

Emissions Factors & AP 42, Compilation of Air Pollutant 

Emission Factors 

An emissions factor is a representative value that attempts to relate the quantity of a pollutant released to 

the atmosphere with an activity associated with the release of that pollutant. These factors are usually

expressed as the weight of pollutant divided by a unit weight, volume, distance, or duration of the activity 

emitting the pollutant (e.g., kilograms of particulate emitted per megagram of coal burned). Such factors 

facilitate estimation of emissions from various sources of air pollution. In most cases, these factors are 

simply averages of all available data of acceptable quality, and are generally assumed to be representative of 

long-term averages for all facilities in the source category (i.e., a population average).

The general equation for emissions estimation is: 

E = A x EF x (1-ER/100)

where:

E = emissions;

A = activity rate; 

EF = emission factor, and 

ER =overall emission reduction efficiency, % 

AP-42, Compilation of Air Pollutant Emission Factors, has been published since 1972 as the primary 

compilation of EPA's emission factor information. It contains emission factors and process information for 

more than 200 air pollution source categories. A source category is a specific industry sector or group of 

similar emitting sources. The emission factors have been developed and compiled from source test data, 

material balance studies, and engineering estimates. The Fifth Edition of AP-42 was published in January 

1995. Since then EPA has published supplements and updates to the fifteen chapters available in Volume I, 

Stationary Point and Area Sources. The latest emissions factors are available below on this website. Use 

the AP 42 Chapter webpage links below to access the document by chapter.

WebFIRE: Access to these emissions factors and other EPA reviewed stationary point and area source 

factors is also available from the WebFIRE application. WebFIRE provides fast and complete access to the

Agency’s air emissions factors information.

For information about emissions factors from highway vehicles and nonroad mobile sources, visit the Office 

of Transportation and Air Quality web site.

Proposed and final rules related to emissions factors

October 14, 2009 - The Emissions Factors Program Improvements - Advanced notice of proposed 

rulemaking is available on the www.regulations.gov website , and the EPA's OAR and Policy 

Guidance Recent Additons webpage (PDF 290K). The comment period ended December 14, 2009. 

AP 42, Fifth Edition

Compilation of Air Pollutant Emission Factors, 

Volume 1: Stationary Point and Area Sources

Technology Transfer Network 

Clearinghouse for Inventories & Emissions Factors

http://www.epa.gov/ttn/chief/ap42/index.html

Last updated on Wednesday, February 19, 2014

Emissions Factors & AP 42 | Clearinghouse for Emission Inventories and Emissions Factors | Technology ... Page 1 of 3

http://www.epa.gov/ttn/chief/ap42/index.html 7/9/2014
AP 42 FAQs

Answers to frequently asked questions about AP 42

Drafts

Draft Sections Under Review

AP 42 listing of supplements and updates.

Supplements A through F (1996 - 2000)

Updates 2001 to present

Older editions of AP-42, Volume 1 

This information is available for historical purposes only. For the most recent emissions factors, supported by 

the EPA, please see the table of contents below.

Emissions Factors Procedures

Procedures for the Development of Emissions Factors from Stationary Sources. This page includes both the 

existing procedures and the review draft for the proposed new procedures. 

Table of Contents, AP 42, Volume I, Fifth Edition

Cover page 

and Contents

Cover page, detailed Table of Contents, Publications in Series, Insertion 

Instructions, and Key Word Index (PDF 128K). This is current through the 

Fifth Edition, Supplement C of AP 42. For sections and chapters added 

after November 1997, see the chapter web pages below.

Introduction Introduction to AP 42, Volume I, Fifth Edition - January 1995 (PDF

40K)

Chapter 1 External Combustion Sources

Chapter 2 Solid Waste Disposal

Chapter 3 Stationary Internal Combustion Sources

Chapter 4 Evaporation Loss Sources

Chapter 5 Petroleum Industry

Chapter 6 Organic Chemical Process Industry

Chapter 7 Liquid Storage Tanks

Chapter 8 Inorganic Chemical Industry

Chapter 9 Food and Agricultural Industries

Chapter 10 Wood Products Industry

Chapter 11 Mineral Products Industry

Chapter 12 Metallurgical Industry

Chapter 13 Miscellaneous Sources

Chapter 14 Greenhouse Gas Biogenic Sources

Chapter 15 Ordnance Detonation

Appendix A Miscellaneous Data & Conversion Factors -- September 1985 (PDF

103K)

Appendix B.1 Particle Size Distribution Data and Sized Emission Factors for 

Selected Sources -- October 1986 (PDF 2M)

Emissions Factors & AP 42 | Clearinghouse for Emission Inventories and Emissions Factors | Technology ... Page 2 of 3

http://www.epa.gov/ttn/chief/ap42/index.html 7/9/2014
For current information on AP 42 updates and the activities of the Measurement Policy Group, you can 

subscribe to the CHIEF Listserv.

Appendix B.2 Generalized Particle Size Distributions -- September 1996 (PDF 137K)

Appendix C.1 Procedures for Sampling Surface/Bulk Dust Loading -- July 1993 

(PDF 65K)

Appendix C.2 Procedures for Laboratory Analysis of Surface/Bulk Dust Loading 

Samples -- July 1993 (PDF 42K)

Draft Sections Under Review 

After the comment period, can I use a draft section? (TXT 1K)

Chapter & 

Section

Description Comments Requested by:

Chapter 15 Ordnance Detonation December 7, 2009

Section 4.12 Manufacture of Rubber 

Products

February 2, 2009

Section 2.4 Municipal Solid Waste 

Landfills

May 5, 2009

Emissions Factors & AP 42 | Clearinghouse for Emission Inventories and Emissions Factors | Technology ... Page 3 of 3

http://www.epa.gov/ttn/chief/ap42/index.html 7/9/2014
You are here: EPA Home Technology Transfer Network Clearinghouse for Inventories & Emissions Factors

Emissions Factors & AP 42 Chapter 13: Miscellaneous Sources 

AP 42, Fifth Edition, Volume I

Chapter 13: Miscellaneous Sources

13.0 Introduction to Miscellaneous Sources

13.1 Wildfires and Prescribed Burning

Final Section - Supplement B, October 1996 (PDF 99K) 

Related Information

13.2 Introduction to Fugitive Dust Sources

Final Section - January 1995 (PDF 9K)

13.2.1 Paved Roads

Final Section - January 2011 (PDF 400K) 

Background Documentation - January 2011 (PDF 1.7M) 

Related Information

13.2.2 Unpaved Roads

Final Section - November 2006 (PDF 350K) 

Background Document for Revisions to Fine Fraction Ratios Used for AP-42 

Fugitive Dust Emission Factors - November 2006 (PDF 444K)

Background Documentation - September 1998 

Related Information

13.2.3 Heavy Construction Operations

Final Section - January 1995 (PDF 31K) 

Errata - Section corrected February 2010 

References to a "Material Handling Factor in section 13.2.2" in Table 13.2.3-1 

were updated to say "Material handling emission factor equation in Section 

13.2.4." The reference to section to 13.2.2 was an error. 

13.2.4 Aggregate Handling and Storage Piles

Final Section - November 2006 (PDF 395K) 

Background Document for Revisions to Fine Fraction Ratios Used for AP-42 

Fugitive Dust Emission Factors - November 2006 (PDF 444K)

Related Information

13.2.5 Industrial Wind Erosion

Final Section - November 2006 (PDF 170K) 

Background Document for Revisions to Fine Fraction Ratios Used for AP-42 

Fugitive Dust Emission Factors - November 2006 (PDF 444K)

Related Information - supporting data.

13.2.6 Abrasive Blasting

Final Section - Supplement D, October 1997 (PDF 69K) 

Background Document (PDF 301K) 

13.3 Explosives Detonation

Final Section - February 1980 (PDF 88K)

Technology Transfer Network 

Clearinghouse for Inventories & Emissions Factors

http://www.epa.gov/ttn/chief/ap42/ch13/index.html

Last updated on Wednesday, February 19, 2014

Chapter 13: Miscellaneous Sources, AP 42, Fifth Edition, Volume I | Clearinghouse for Emission Inventor... Page 1 of 2

http://www.epa.gov/ttn/chief/ap42/ch13/index.html 7/9/2014
AP 42 Emissions Factors by Chapter

13.4 Wet Cooling Towers

Final Section - January 1995 (PDF 114K)

13.5 Industrial Flares

Final Section - September 1991 (PDF 46K) 

Flare Efficiency Study, EPA 600/2-83-052 - July 1983 (PDF 3.2M)

Primary reference for the section. 

Chapter 13: Miscellaneous Sources, AP 42, Fifth Edition, Volume I | Clearinghouse for Emission Inventor... Page 2 of 2

http://www.epa.gov/ttn/chief/ap42/ch13/index.html 7/9/2014
13. MISCELLANEOUS SOURCES

This chapter contains emission factor information on those source categories that differ

substantially from, and hence cannot be grouped with, the other "stationary" sources discussed in this

publication. Most of these miscellaneous emitters, both natural and manmade, are truly area sources,

with their pollutant-generating process(es) dispersed over large land areas. Another characteristic of

these sources is the inapplicability, in most cases, of conventional control methods such as wet/dry

equipment, fuel switching, process changes, etc. Instead, control of these emissions, where possible at

all, may involve such techniques as modification of agricultural burning practices, paving with asphalt

or concrete, or stabilization of dirt roads. Finally, miscellaneous sources generally emit pollutants

intermittently, compared to most stationary point sources. For example, a wildfire may emit large

quantities of particulate and carbon monoxide for several hours or even days. But, when measured

against a continuous emitter over a long period of time its emissions may seem relatively minor.

Also, effects on air quality may be of relatively short duration.

1/95 Miscellaneous Sources 13.0-1
13.2 Fugitive Dust Sources

Significant atmospheric dust arises from the mechanical disturbance of granular material

exposed to the air. Dust generated from these open sources is termed "fugitive" because it is not

discharged to the atmosphere in a confined flow stream. Common sources of fugitive dust include

unpaved roads, agricultural tilling operations, aggregate storage piles, and heavy construction

operations.

For the above sources of fugitive dust, the dust-generation process is caused by 2 basic

physical phenomena:

1. Pulverization and abrasion of surface materials by application of mechanical force

through implements (wheels, blades, etc.).

2. Entrainment of dust particles by the action of turbulent air currents, such as wind erosion

of an exposed surface by wind speeds over 19 kilometers per hour (km/hr) (12 miles per

hour [mph]).

In this section of AP-42, the principal pollutant of interest is PM-10 — particulate matter (PM)

no greater than 10 micrometers in aerodynamic diameter (μmA). Because PM-10 is the size basis for

the current primary National Ambient Air Quality Standards (NAAQS) for particulate matter, it

represents the particle size range of the greatest regulatory interest. Because formal establishment of

PM-10 as the primary standard basis occurred in 1987, many earlier emission tests have been

referenced to other particle size ranges, such as:

TSP Total Suspended Particulate, as measured by the standard high-volume ("hi-vol") air

sampler, has a relatively coarse size range. TSP was the basis for the previous

primary NAAQS for PM and is still the basis of the secondary standard. Wind tunnel

studies show that the particle mass capture efficiency curve for the high-volume

sampler is very broad, extending from 100 percent capture of particles smaller than

10 μm to a few percent capture of particles as large as 100 μm. Also, the capture

efficiency curve varies with wind speed and wind direction, relative to roof ridge

orientation. Thus, high-volume samplers do not provide definitive particle size

information for emission factors. However, an effective cut point of 30 μm

aerodynamic diameter is frequently assigned to the standard high volume sampler.

SP Suspended Particulate, which is often used as a surrogate for TSP, is defined as PM

with an aerodynamic diameter no greater than 30 μm. SP may also be denoted as PM30.

IP Inhalable Particulate is defined as PM with an aerodynamic diameter no greater than

15 μm IP also may be denoted as PM-15.

FP Fine Particulate is defined as PM with an aerodynamic diameter no greater than

2.5 μm. FP may also be denoted as PM-2.5.

The impact of a fugitive dust source on air pollution depends on the quantity and drift

potential of the dust particles injected into the atmosphere. In addition to large dust particles that

1/95 Miscellaneous Sources 13.2-1
settle out near the source (often creating a local nuisance problem), considerable amounts of fine

particles also are emitted and dispersed over much greater distances from the source. PM-10

represents a relatively fine particle size range and, as such, is not overly susceptible to gravitational

settling.

The potential drift distance of particles is governed by the initial injection height of the

particle, the terminal settling velocity of the particle, and the degree of atmospheric turbulence.

Theoretical drift distance, as a function of particle diameter and mean wind speed, has been computed

for fugitive dust emissions. Results indicate that, for a typical mean wind speed of 16 km/hr

(10 mph), particles larger than about 100 μm are likely to settle out within 6 to 9 meters (20 to 30 feet

[ft]) from the edge of the road or other point of emission. Particles that are 30 to 100 μm in diameter

are likely to undergo impeded settling. These particles, depending upon the extent of atmospheric

turbulence, are likely to settle within a few hundred feet from the road. Smaller particles, particularly

IP, PM-10, and FP, have much slower gravitational settling velocities and are much more likely to

have their settling rate retarded by atmospheric turbulence.

Control techniques for fugitive dust sources generally involve watering, chemical stabilization,

or reduction of surface wind speed with windbreaks or source enclosures. Watering, the most common

and, generally, least expensive method, provides only temporary dust control. The use of chemicals to

treat exposed surfaces provides longer dust suppression, but may be costly, have adverse effects on

plant and animal life, or contaminate the treated material. Windbreaks and source enclosures are often

impractical because of the size of fugitive dust sources.

The reduction of source extent and the incorporation of process modifications or adjusted work

practices, both of which reduce the amount of dust generation, are preventive techniques for the

control of fugitive dust emissions. These techniques could include, for example, the elimination of

mud/dirt carryout on paved roads at construction sites. On the other hand, mitigative measures entail

the periodic removal of dust-producing material. Examples of mitigative control measures include

clean-up of spillage on paved or unpaved travel surfaces and clean-up of material spillage at conveyor

transfer points.

13.2-2 EMISSION FACTORS 1/95
 1/11 Miscellaneous Sources 13.2.1-1

13.2.1 Paved Roads 

13.2.1.1 General 

Particulate emissions occur whenever vehicles travel over a paved surface such as a road 

or parking lot. Particulate emissions from paved roads are due to direct emissions from vehicles 

in the form of exhaust, brake wear and tire wear emissions and resuspension of loose material on 

the road surface. In general terms, resuspended particulate emissions from paved roads originate 

from, and result in the depletion of, the loose material present on the surface (i.e., the surface 

loading). In turn, that surface loading is continuously replenished by other sources. At industrial 

sites, surface loading is replenished by spillage of material and trackout from unpaved roads and 

staging areas. Figure 13.2.1-1 illustrates several transfer processes occurring on public streets.

Various field studies have found that public streets and highways, as well as roadways at 

industrial facilities, can be major sources of the atmospheric particulate matter within an area.1-9

Of particular interest in many parts of the United States are the increased levels of emissions 

from public paved roads when the equilibrium between deposition and removal processes is 

upset. This situation can occur for various reasons, including application of granular materials 

for snow and ice control, mud/dirt carryout from construction activities in the area, and 

deposition from wind and/or water erosion of surrounding unstabilized areas. In the absence of 

continuous addition of fresh material (through localized track out or application of antiskid 

material), paved road surface loading should reach an equilibrium value in which the amount of 

material resuspended matches the amount replenished. The equilibrium surface loading value 

depends upon numerous factors. It is believed that the most important factors are: mean speed of 

vehicles traveling the road; the average daily traffic (ADT); the number of lanes and ADT per lane; 

the fraction of heavy vehicles (buses and trucks); and the presence/absence of curbs, storm 

sewers and parking lanes.10

The particulate emission factors presented in a previous version of this section of AP-42, 

dated October 2002, implicitly included the emissions from vehicles in the form of exhaust, brake 

wear, and tire wear as well as resuspended road surface material. EPA included these sources in 

the emission factor equation for paved roads since the field testing data used to develop the 

equation included both the direct emissions from vehicles and emissions from resuspension of 

road dust.

This version of the paved road emission factor equation only estimates particulate 

emissions from resuspended road surface material28. The particulate emissions from vehicle 

exhaust, brake wear, and tire wear are now estimated separately using EPA's MOVES 29 model. 

This approach eliminates the possibility of double counting emissions. Double counting results 

when employing the previous version of the emission factor equation in this section and MOVES 

to estimate particulate emissions from vehicle traffic on paved roads. It also incorporates the 

decrease in exhaust emissions that has occurred since the paved road emission factor equation was 

developed. Earlier versions of the paved road emission factor equation includes estimates of 

emissions from exhaust, brake wear, and tire wear based on emission rates for vehicles in the 1980 

calendar year fleet. The amount of PM released from vehicle exhaust has decreased since 1980 

due to lower new vehicle emission standards and changes in fuel characteristics. 
13.2.1-2 EMISSION FACTORS 1/11

13.2.1.2 Emissions And Correction Parameters 

Dust emissions from paved roads have been found to vary with what is termed the "silt 

loading" present on the road surface. In addition, the average weight and speed of vehicles 

traveling the road influence road dust emissions. The term silt loading (sL) refers to the mass of 

silt-size material (equal to or less than 75 micrometers [μm] in physical diameter) per unit area of 

the travel surface. The total road surface dust loading consists of loose material that can be 

collected by broom sweeping and vacuuming of the traveled portion of the paved road. The silt 

fraction is determined by measuring the proportion of the loose dry surface dust that passes through 

a 200-mesh screen, using the ASTM-C-136 method. Silt loading is the product of the silt fraction 

and the total loading, and is abbreviated "sL". Additional details on the sampling and analysis of 

such material are provided in AP-42 Appendices C.1 and C.2. 

The surface sL provides a reasonable means of characterizing seasonal variability in a paved 

road emission inventory. In many areas of the country, road surface loadings 11-21 are heaviest 

during the late winter and early spring months when the residual loading from snow/ice controls is 

greatest. As noted earlier, once replenishment of fresh material is eliminated, the road surface 

loading can be expected to reach an equilibrium value, which is substantially lower than the late 

winter/early spring values. 
1/11 Miscellaneous Sources 13.2.1-2 

Figure 13.2.1-1. Deposition and removal processes. 
13.2.1-4 EMISSION FACTORS 1/11

13.2.1.3 Predictive Emission Factor Equations10,29

The quantity of particulate emissions from resuspension of loose material on the road surface 

due to vehicle travel on a dry paved road may be estimated using the following empirical 

expression: 

 E = k (sL)0.91x (W)1.02 (1)

where: E = particulate emission factor (having units matching the units of k), 

k = particle size multiplier for particle size range and units of interest (see below), 

sL = road surface silt loading (grams per square meter) (g/m2), and 

W = average weight (tons) of the vehicles traveling the road. 

It is important to note that Equation 1 calls for the average weight of all vehicles traveling 

the road. For example, if 99 percent of traffic on the road are 2 ton cars/trucks while the 

remaining 1 percent consists of 20 ton trucks, then the mean weight "W" is 2.2 tons. More 

specifically, Equation 1 is not intended to be used to calculate a separate emission factor for each 

vehicle weight class. Instead, only one emission factor should be calculated to represent the 

"fleet" average weight of all vehicles traveling the road. 

The particle size multiplier (k) above varies with aerodynamic size range as shown in 

Table 13.2.1-1. To determine particulate emissions for a specific particle size range, use 

the appropriate value of k shown in Table 13.2.1-1. 

To obtain the total emissions factor, the emission factors for the exhaust, brake wear and 

tire wear obtained from either EPA's MOBILE6.2 27 or MOVES2010 29 model should be added to 

the emissions factor calculated from the empirical equation. 

Table 13.2.1-1. PARTICLE SIZE MULTIPLIERS FOR PAVED ROAD EQUATION 

Size rangea

 Particle Size Multiplier kb

g/VKT g/VMT lb/VMT

PM-2.5c 0.15 0.25 0.00054 

PM-10 0.62 1.00 0.0022 

PM-15 0.77 1.23 0.0027 

PM-30d 3.23 5.24 0.011 

a Refers to airborne particulate matter (PM-x) with an aerodynamic diameter equal to or less than 

x micrometers. 

b Units shown are grams per vehicle kilometer traveled (g/VKT), grams per vehicle mile traveled 

(g/VMT), and pounds per vehicle mile traveled (lb/VMT). The multiplier k includes unit 

conversions to produce emission factors in the units shown for the indicated size range from the 

mixed units required in Equation 1. 

c

 The k-factors for PM2.5 were based on the average PM2.5:PM10 ratio of test runs in Reference 30. 

d

 PM-30 is sometimes termed "suspendable particulate" (SP) and is often used as a surrogate for 

TSP. 
1/11 Miscellaneous Sources 13.2.1-5 

Equation 1 is based on a regression analysis of 83 tests for PM-10.3, 5-6, 8, 27-29, 31-36 Sources 

tested include public paved roads, as well as controlled and uncontrolled industrial paved roads. The 

majority of tests involved freely flowing vehicles traveling at constant speed on relatively level roads. 

However, 22 tests of slow moving or "stop-and-go" traffic or vehicles under load were available for 

inclusion in the data base.32-36 Engine exhaust, tire wear and break wear were subtracted from the 

emissions measured in the test programs prior to stepwise regression to determine Equation 1.37, 39 The 

equations retain the quality rating of A (D for PM-2.5), if applied within the range of source conditions 

that were tested in developing the equation as follows: 

Silt loading: 0.03 - 400 g/m2

0.04 - 570 grains/square foot (ft2

) 

Mean vehicle weight: 1.8 - 38 megagrams (Mg) 

2.0 - 42 tons 

Mean vehicle speed: 1 - 88 kilometers per hour (kph) 

 1 - 55 miles per hour (mph) 

The upper and lower 95% confidence levels of equation 1 for PM10 is best described with 

equations using an exponents of 1.14 and 0.677 for silt loading and an exponents of 1.19 and 0.85 

for weight. Users are cautioned that application of equation 1 outside of the range of variables and 

operating conditions specified above, e.g., application to roadways or road networks with speeds 

above 55 mph and average vehicle weights of 42 tons, will result in emission estimates with a 

higher level of uncertainty. In these situations, users are encouraged to consider an assessment of the 

impacts of the influence of extrapolation to the overall emissions and alternative methods that are 

equally or more plausible in light of local emissions data and/or ambient concentration or 

compositional data. 

To retain the quality rating for the emission factor equation when it is applied to a specific 

paved road, it is necessary that reliable correction parameter values for the specific road in question 

be determined. With the exception of limited access roadways, which are difficult to sample, the 

collection and use of site-specific silt loading (sL) data for public paved road emission inventories 

are strongly recommended. The field and laboratory procedures for determining surface material 

silt content and surface dust loading are summarized in Appendices C.1 and C.2. In the event that 

site-specific values cannot be obtained, an appropriate value for a paved public road may be 

selected from the values in Table 13.2.1-2, but the quality rating of the equation should be reduced 

by 2 levels. 

Equation 1 may be extrapolated to average uncontrolled conditions (but including natural 

mitigation) under the simplifying assumption that annual (or other long-term) average emissions are 

inversely proportional to the frequency of measurable (> 0.254 mm [ 0.01 inch]) precipitation by 

application of a precipitation correction term. The precipitation correction term can be applied on 

a daily or an hourly basis 26, 38.

For the daily basis, Equation 1 becomes: 

Eext = [ k (sL)0.91x (W)1.02 ] (1 – P/4N) (2) 

where k , sL, W, and S are as defined in Equation 1 and 

Eext = annual or other long-term average emission factor in the same units as k, 

P = number of "wet" days with at least 0.254 mm (0.01 in) of precipitation during the 

averaging period, and 
13.2.1-6 EMISSION FACTORS 1/11

N = number of days in the averaging period (e.g., 365 for annual, 91 for seasonal, 30 

for monthly). 

Note that the assumption leading to Equation 2 is based on analogy with the approach used to 

develop long-term average unpaved road emission factors in Section 13.2.2. However, Equation 2 

above incorporates an additional factor of "4" in the denominator to account for the fact that paved 

roads dry more quickly than unpaved roads and that the precipitation may not occur over the 

complete 24-hour day. 

For the hourly basis, equation 1 becomes:

Eext = [ k (sL)0.91x (W)1.02 ] (1 –1.2P/N) (3) 

where k , sL, W, and S are as defined in Equation 1 and 

E ext = annual or other long-term average emission factor in the same units as k, 

P = number of hours with at least 0.254 mm (0.01 in) of precipitation during the 

averaging period, and 

N = number of hours in the averaging period (e.g., 8760 for annual, 2124 for 

season 720 for monthly) 

Note: In the hourly moisture correction term (1-1.2P/N) for equation 3, the 1.2 multiplier is 

applied to account for the residual mitigative effect of moisture. For most applications, this 

equation will produce satisfactory results. Users should select a time interval to include 

sufficient "dry" hours such that a reasonable emissions averaging period is evaluated. For the 

special case where this equation is used to calculate emissions on an hour by hour basis, such as 

would be done in some emissions modeling situations, the moisture correction term should be 

modified so that the moisture correction "credit" is applied to the first hours following cessation 

of precipitation. In this special case, it is suggested that this 20% "credit" be applied on a basis of 

one hour credit for each hour of precipitation up to a maximum of 12 hours. 

Note that the assumption leading to Equation 3 is based on analogy with the approach 

used to develop long-term average unpaved road emission factors in Section 13.2.2. 

Figure 13.2.1-2 presents the geographical distribution of "wet" days on an annual basis for 

the United States. Maps showing this information on a monthly basis are available in the Climatic 

Atlas of the United States23 . Alternative sources include other Department of Commerce 

publications (such as local climatological data summaries). The National Climatic Data Center 

(NCDC) offers several products that provide hourly precipitation data. In particular, NCDC offers 

Solar and Meteorological Surface Observation Network 1961-1990 (SAMSON) CD-ROM, which 

contains 30 years worth of hourly meteorological data for first-order National Weather Service 

locations. Whatever meteorological data are used, the source of that data and the averaging period 

should be clearly specified. 

It is emphasized that the simple assumption underlying Equations 2 and 3 has not been 

verified in any rigorous manner. For that reason, the quality ratings for Equations 2 and 3 should 

be downgraded one letter from the rating that would be applied to Equation 1.
1/11 Miscellaneous Sources 13.2.1.2 

Figure 13.2.1-2. Mean number of days with 0.01 inch or more of precipitation in the United States. 
13.2.1-8 EMISSION FACTORS 1/11

Table 13.2.1-2 presents recommended default silt loadings for normal baseline conditions 

and for wintertime baseline conditions in areas that experience frozen precipitation with periodic 

application of antiskid material24. The winter baseline is represented as a multiple of the nonwinter baseline, depending on the ADT value for the road in question. As shown, a multiplier of 

4 is applied for low volume roads (< 500 ADT) to obtain a wintertime baseline silt loading of 4 X 

0.6 = 2.4 g/m2

. 

Table 13.2.1-2. Ubiquitous Silt Loading Default Values with Hot Spot 

Contributions from Anti-Skid Abrasives (g/m2

) 

ADT Category < 500 500-5,000 5,000-10,000 > 10,000 

Ubiquitous Baseline g/m2 0.6 0.2 0.06 0.03 

0.015 limited 

access 

Ubiquitous Winter Baseline 

Multiplier during months with 

frozen precipitation 

X4 X3 X2 X1 

Initial peak additive contribution 

from application of antiskid abrasive 

(g/m2

) 

2 2 2 2 

Days to return to baseline conditions 

(assume linear decay) 

7 3 1 0.5 

It is suggested that an additional (but temporary) silt loading contribution of 2 g/m2 occurs 

with each application of antiskid abrasive for snow/ice control. This was determined based on a 

typical application rate of 500 lb per lane mile and an initial silt content of 1 % silt content. 

Ordinary rock salt and other chemical deicers add little to the silt loading, because most of the 

chemical dissolves during the snow/ice melting process. 

To adjust the baseline silt loadings for mud/dirt trackout, the number of trackout points is 

required. It is recommended that in calculating PM10 emissions, six additional miles of road be 

added for each active trackout point from an active construction site, to the paved road mileage of 

the specified category within the county. In calculating PM2.5 emissions, it is recommended that 

three additional miles of road be added for each trackout point from an active construction site. 

It is suggested the number of trackout points for activities other than road and building 

construction areas be related to land use. For example, in rural farming areas, each mile of 

paved road would have a specified number of trackout points at intersections with unpaved 

roads. This value could be estimated from the unpaved road density (mi/sq. mi.). 

The use of a default value from Table 13.2.1-2 should be expected to yield only an orderof-magnitude estimate of the emission factor. Public paved road silt loadings are dependent 
1/11 Miscellaneous Sources 13.2.1-9

upon: traffic characteristics (speed, ADT, and fraction of heavy vehicles); road characteristics 

(curbs, number of lanes, parking lanes); local land use (agriculture, new residential construction) 

and regional/seasonal factors (snow/ice controls, wind blown dust). As a result, the collection 

and use of site-specific silt loading data is highly recommended. In the event that default silt 

loading values are used, the quality ratings for the equation should be downgraded 2 levels. 

Limited access roadways pose severe logistical difficulties in terms of surface sampling, 

and few silt loading data are available for such roads. Nevertheless, the available data do not 

suggest great variation in silt loading for limited access roadways from one part of the country to 

another. For annual conditions, a default value of 0.015 g/m2

 is recommended for limited access 

roadways.9,22 Even fewer of the available data correspond to worst-case situations, and elevated 

loadings are observed to be quickly depleted because of high traffic speeds and high ADT rates. 

A default value of 0.2 g/m2

 is recommended for short periods of time following application of 

snow/ice controls to limited access roads.22

The limited data on silt loading values for industrial roads have shown as much variability 

as public roads. Because of the variations of traffic conditions and the use of preventive 

mitigative controls, the data probably do not reflect the full extent of the potential variation in silt 

loading on industrial roads. However, the collection of site specific silt loading data from 

industrial roads is easier and safer than for public roads. Therefore, the collection and use of sitespecific silt loading data is preferred and is highly recommended. In the event that site-specific 

values cannot be obtained, an appropriate value for an industrial road may be selected from the 

mean values given in Table 13.2.1-3, but the quality rating of the equation should be reduced by 2 

levels. 

The predictive accuracy of Equation 1 requires thorough on-site characterization of road 

silt loading. Road surface sampling is time-consuming and potentially hazardous because of the 

need to block traffic lanes. In addition, large number of samples is required to represent spatial 

and temporal variations across roadway networks. Mobile monitoring is a new alternative silt 

loading or road dust emission characterization method for either paved or unpaved roads. It 

utilizes a test vehicle that generates and monitors its own dust plume concentration (mass basis) at 

a fixed sampling probe location. A calibration factor is needed for each mobile monitoring 

configuration (test vehicle and sampling system), to convert the relative dust emission intensity to 

an equivalent silt loading or emission factor. Typically, portable continuous particle 

concentration monitors do not comply with Federal Reference Method (FRM) standards. 

Therefore, a controlled study must be performed to correlate the portable monitor response to the 

road silt loading or size specific particle concentration measured with an approved FRM sampling 

system. In the calibration tests, multiple test conditions should be performed to provide an 

average correlation with known precision and to accommodate variations in road silt loading, 

vehicle speed, road dust characteristics and other road conditions that may influence mobile 

monitoring measurements or emissions characteristics. Because the paved road dust emissions 

are also dependent on the average vehicle weight for the road segment, it is important that the 

weight of the test vehicle correspond closely to the average vehicle weight for the road segment 

or be adjusted using the average vehicle weight relationship in Equation 1. In summary, it is 

believed that the Mobile Monitoring Method will provide improved capabilities to provide 

reliable temporally and spatially resolved silt loading or emissions factors with increased 

coverage, improved safety, reduced traffic interference and decreased cost. 40, 41, 42
Table 13.2.1-3 (Metric And English Units). TYPICAL SILT CONTENT AND LOADING VALUES FOR PA

INDUSTRIAL FACILITIES a

Industry

No. of 

Sites

No. Of 

Samples

Silt Content (%)

No. of 

Travel 

Lanes

Total Loading x 10-3

Range Mean Range Mean Unitsb 

Copper smelting 1 3 15.4-21.7 19.0 2 12.9 - 19.5 15.9 kg/km 

45.8 - 69.2 55.4 lb/mi

Iron and steel production 9 48 1.1-35.7 12.5 2 0.006 - 4.77 0.495 kg/km 

0.020 -16.9 1.75 lb/mi

Asphalt batching 1 3 2.6 - 4.6 3.3 1 12.1 - 18.0 14.9 kg/km 

43.0 - 64.0 52.8 lb/mi

Concrete batching 1 3 5.2 - 6.0 5.5 2 1.4 - 1.8 1.7 kg/km 

5.0 - 6.4 5.9 lb/mi

Sand and gravel processing 1 3 6.4 - 7.9 7.1 1 2.8 - 5.5 3.8 kg/km 

9.9 - 19.4 13.3 lb/mi

Municipal solid waste landfill 2 7 - 2 - 

Quarry 1 6 - 2 - 

Corn wet mills 3 15 - 2 - 

a References 1-2,5-6,11-13. Values represent samples collected from industrial roads. Public road silt loading value

in Table-13.2.1-2. Dashes indicate information not available.b Multiply entries by 1000 to obtain stated units; 

kilometer (kg/km) and pounds per mile (lb/mi). 

13.2.1.10 EMISSION FACTORS 1/11
1/11 Miscellaneous Sources 13.2.1-11 

13.2.1.4 Controls6,25

Because of the importance of the silt loading, control techniques for paved roads attempt 

either to prevent material from being deposited onto the surface (preventive controls) or to 

remove from the travel lanes any material that has been deposited (mitigative controls). Covering 

of loads in trucks, and the paving of access areas to unpaved lots or construction sites, are examples 

of preventive measures. Examples of mitigative controls include vacuum sweeping, water 

flushing, and broom sweeping and flushing. Actual control efficiencies for any - of these 

techniques can be highly variable. Locally measured silt loadings before and after the application 

of controls is the preferred method to evaluate controls. It is particularly important to note that 

street sweeping of gutters and curb areas may actually increase the silt loading on the traveled 

portion of the road. Redistribution of loose material onto the travel lanes will actually produce a 

short-term increase in the emissions. 

In general, preventive controls are usually more cost effective than mitigative controls. 

The cost-effectiveness of mitigative controls falls off dramatically as the size of an area to be 

treated increases. The cost-effectiveness of mitigative measures is also unfavorable if only a 

short period of time is required for the road to return to equilibrium silt loading condition. That is 

to say, the number and length of public roads within most areas of interest preclude any 

widespread and routine use of mitigative controls. On the other hand, because of the more 

limited scope of roads at an industrial site, mitigative measures may be used quite successfully 

(especially in situations where truck spillage occurs). Note, however, that public agencies could 

make effective use of mitigative controls to remove sand/salt from roads after the winter ends. 

Because available controls will affect the silt loading, controlled emission factors may be 

obtained by substituting controlled silt loading values into the equation. (Emission factors from 

controlled industrial roads were used in the development of the equation.) The collection of 

surface loading samples from treated, as well as baseline (untreated), roads provides a means to 

track effectiveness of the controls over time. The use of Mobile Monitoring Methodologies 

provide an improved means to track progress in controlling silt loading values. 

13.2.1.5 Changes since Fifth Edition 

The following changes were made since the publication of the Fifth Edition of AP-42: 

October 2002 

1) The particle size multiplier for PM2.5 was revised to 25% of PM10. The approximately 

55% reduction was a result of emission testing using FRM monitors. The monitoring 

was specifically intended to evaluate the PM-2.5 component of the emissions.

2) Default silt loading values were included in Table 13.2.1-2 replacing the Tables 

and Figures containing silt loading statistical information.

3) Editorial changes within the text were made indicating the possible causes of 

variations in the silt loading between roads within and among different locations. 

The uncertainty of using the default silt loading value was discussed. 
13.2.1-12 EMISSION FACTORS 1/11

4) Section 13.2.1.1 was revised to clarify the role of dust loading in 

resuspension. Additional minor text changes were made. 

5) Equations 2 and 3, Figure 13.2.1-2, and text were added to incorporate natural 

mitigation into annual or other long-term average emission factors. 

December 2003 

1) The emission factor equation was adjusted to remove the component of particulate 

emissions- from exhaust, brake wear, and tire wear. A parameter C representing these 

emissions was included in the predictive equation. The parameter C varied with 

aerodynamic size range of the particulate matter. Table 13.2.1-2 was added to 

present the new coefficients. 

2) The default silt loading values in Table 13.2.1-3 were revised to incorporate the 

results from a recent analysis of silt loading data. 

November 2006 

1) The PM2.5 particle size multiplier was revised to 15% of PM10 as the result of 

wind tunnel studies of a variety of dust emitting surface materials. 

2) References were rearranged and renumbered. 

January 2011 

1) The empirical predictive equation was revised. The revision is based upon stepwise 

regression of 83 profile emissions tests and an adjustment of individual test data for 

the exhaust; break wear and tire wear emissions prior to regression of the data. 

2) The C term is removed from the empirical predictive equation and Table 13.2.1-2 

with the C term values is removed since the exhaust; break wear and tire wear 

emissions were no longer part of the regressed data. 

3) The PM2.5 particle size multiplier was revised to 25% of PM10 since the PM10 test 

data used to develop the equation did not meet the necessary PM10 concentrations for 

a ratio of 15%. 

4) The lower speed of the vehicle speed range supported by the empirical predictive 

equation was revised to 1 mph. 

5) Information was added on an improved methodology to develop spatially and 

temporally resolved silt loadings or emissions factors by Mobile Monitoring 

Methodologies. 

References For Section 13.2.1 

1. D. R. Dunbar, Resuspension Of Particulate Matter, EPA-450/2-76-031, U. S. 

Environmental Protection Agency, Research Triangle Park, NC, March 1976. 

2. R. Bohn, et al., Fugitive Emissions From Integrated Iron And Steel Plants, EPA-600/2-78-

050, U. S. Environmental Protection Agency, Cincinnati, OH, March 1978. 
1/11 Miscellaneous Sources 13.2.1.13 

3. C. Cowherd, Jr., et al., Iron And Steel Plant Open Dust Source Fugitive Emission 

Evaluation, EPA-600/2-79-103, U. S. Environmental Protection Agency, Cincinnati, OH, 

May 1979. 

4. C. Cowherd, Jr., et al., Quantification Of Dust Entrainment From Paved Roadways, EPA450/3-77-027, U. S. Environmental Protection Agency, Research Triangle Park, NC, July 

1977. 

5. Size Specific Particulate Emission Factors For Uncontrolled Industrial And Rural Roads, 

EPA Contract No. 68-02-3158, Midwest Research Institute, Kansas City, MO, September 

1983. 

6. T. Cuscino, Jr., et al., Iron And Steel Plant Open Source Fugitive Emission Control 

Evaluation, EPA-600/2-83-110, U. S. Environmental Protection Agency, Cincinnati, OH, 

October 1983. 

7. J. P. Reider, Size-specific Particulate Emission Factors For Uncontrolled Industrial And 

Rural Roads, EPA Contract 68-02-3158, Midwest Research Institute, Kansas City, MO, 

September 1983. 

8. C. Cowherd, Jr., and P. J. Englehart, Paved Road Particulate Emissions, EPA-600/7-84-

077, U. S. Environmental Protection Agency, Cincinnati, OH, July 1984. 

9. C. Cowherd, Jr., and P. J. Englehart, Size Specific Particulate Emission Factors For 

Industrial And Rural Roads, EPA-600/7-85-051, U. S. Environmental Protection Agency, 

Cincinnati, OH, October 1985. 

10. Emission Factor Documentation For AP-42, Sections 11.2.5 and 11.2.6 — Paved Roads, 

EPA Contract No. 68-D0-0123, Midwest Research Institute, Kansas City, MO, March 1993. 

11. Evaluation Of Open Dust Sources In The Vicinity Of Buffalo, New York, EPA Contract 

No. 68-02-2545, Midwest Research Institute, Kansas City, MO, March 1979. 

12. PM-10 Emission Inventory Of Landfills In The Lake Calumet Area, EPA Contract 

No. 68-02-3891, Midwest Research Institute, Kansas City, MO, September 1987. 

13. Chicago Area Particulate Matter Emission Inventory — Sampling And Analysis, Contract 

No. 68-02-4395, Midwest Research Institute, Kansas City, MO, May 1988. 

14. Montana Street Sampling Data, Montana Department Of Health And Environmental 

Sciences, Helena, MT, July 1992. 

15. Street Sanding Emissions And Control Study, PEI Associates, Inc., Cincinnati, OH, 

October 1989. 

16. Evaluation Of PM-10 Emission Factors For Paved Streets, Harding Lawson Associates, 

Denver, CO, October 1991. 

17. Street Sanding Emissions And Control Study, RTP Environmental Associates, Inc., Denver, 

CO, July 1990. 
13.2.1-14 EMISSION FACTORS 1/11

18. Post-storm Measurement Results — Salt Lake County Road Dust Silt Loading Winter 

1991/92 Measurement Program, Aerovironment, Inc., Monrovia, CA, June 1992. 

19. Written communication from Harold Glasser, Department of Health, Clark County (NV). 

20. PM-10 Emissions Inventory Data For The Maricopa And Pima Planning Areas, EPA 

Contract No. 68-02-3888, Engineering-Science, Pasadena, CA, January 1987. 

21. Characterization Of PM-10 Emissions From Antiskid Materials Applied To Ice- And SnowCovered Roadways, EPA Contract No. 68-D0-0137, Midwest Research Institute, Kansas 

City, MO, October 1992. 

22. C. Cowherd, Background Document for Revisions to Fine Fraction Ratios Used for AP-42 

Fugitive Dust Emission Factors. Prepared by Midwest Research Institute for Western 

Governors Association, Western Regional Air Partnership, Denver, CO, February 1, 2006. 

23. Climatic Atlas Of The United States, U.S. Department of Commerce, Washington, D.C., 

June 1968. 

24. C. Cowherd, Jr., et al., Improved Activity Levels for National Emission Inventories of 

Fugitive Dust from Paved and Unpaved Roads, Presented at the 11th International Emission 

Inventory Conference, Atlanta, Georgia, April 2002. 

25. C. Cowherd, Jr., et al., Control Of Open Fugitive Dust Sources, EPA-450/3-88-008, U. 

S. Environmental Protection Agency, Research Triangle Park, NC, September 1988. 

26. Written communication (Technical Memorandum) from G. Muleski, Midwest Research 

Institute, Kansas City, MO, to B. Kuykendal, U. S. Environmental Protection Agency, 

Research Triangle Park, NC, September 27, 2001. 

27. EPA, 2002b. MOBILE6 User Guide, United States Environmental Protection Agency, 

Office of Transportation and Air Quality. EPA420-R-02-028, October 2002. 

28. Written communication (Technical Memorandum) from P. Hemmer, E.H. Pechan & 

Associates, Inc., Durham, NC to B. Kuykendal, U. S. Environmental Protection Agency, 

Research Triangle Park, NC, August, 21, 2003.

29. EPA, 2009, MOVES2010 User Guide, United States Environmental Protection Agency, 

Office of Transportation and Air Quality. EPA420B-09-041, December 2009.

30. Fugitive Particulate Matter Emissions, U.S. Environmental Protection Agency, 

Research Triangle Park, NC, Midwest Research Institute Project No. 4604-06, April 15, 

1997. 

31. Midwest Research Institute, Roadway Emissions Field Tests at U.S. Steel’s Fairless 

Works, U.S. Steel Corporation, Fairless Hills, PA, USX Purchase Order No. 146-

0001191-0068, May 1990. 
1/11 Miscellaneous Sources 13.2.1.15 

32. Paved Road Modifications to AP-42, Background Documentation For Corn Refiners 

Association, Inc. Washington, DC 20006, Midwest Research Institute Project No. 

310842, May 20, 2008. 

33. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Marshall, 

Minnesota Facility, McVehil-Monnett Associates, Midwest Research Institute Project 

No. 310212.1.001, July 6, 2001. 

34. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Columbus, 

Nebraska Facility, McVehil-Monnett Associates, Midwest Research Institute Project 

No. 310212.1.002. July 13, 2001. 

35. Emission Tests of Paved Road Traffic at Cargill Sweeteners North America Blair, 

Nebraska Facility, McVehil-Monnett Associates, Midwest Research Institute Project 

No. 310395.1.001. November 27, 2002. 

36. Emission Tests of Paved Road Traffic at ADM’s Marshall, Minnesota Facility, 

McVehil-Monnett Associates, Midwest Research Institute Project No. 310479.1.001. 

December 5, 2003. 

37. E-mail communication between Ron Myers of EPA/OAQPS/SPPD/MPG, RTP, NC and 

Prashanth Gururaja and Ed Glover of EPA/OTAQ/ASD/HDOC re. Diesel exhaust, tire 

and brake wear for low speed stop and go traffic; January 2009 through May 2009. 

38. Technical Memorandum from William B. Kuykendal to File, Subject: Decisions on 

Final AP-42 Section 13.2.1 “Paved Roads”, October 10, 2002. 

39. E-mail communication between Ron Myers of EPA/OAQPS/SPPD/MPG, RTP, NC and 

Gary Dolce and Rudolph Kapichak of EPA/OTAQ/ASD/HDOC re. Paved Road Test 

Data; October 12, 2010 through December 16, 2010. 

40. C. Cowherd, Mobile Monitoring Method Specifications, Prepared by Midwest Research 

Institute for Clark County Department of Air Quality and Environmental Management, 

Las Vegas, NV, February 6, 2009. 

41. C. Cowherd, Technical Support Document for Mobile Monitoring Technologies, 

Prepared by Midwest Research Institute for Clark County Department of Air Quality 

and Environmental Management, Las Vegas, NV, January 9, 2009. 

42. R. Langston, R. S. Merle Jr., et al.,Clark County (Nevada) Paved Road Dust Emission 

Studies in Support of Mobile Monitoring Technologies, Clark County Department of 

Air Quality and Environmental Management, Las Vegas, NV, December 22, 2008. 

43. Midwest Research Institute; Analysis of the Fine Fraction of Particulate Matter in 

Fugitive Dust; Western Governors’ Association - Western Regional Air Partnership 

(WRAP); October 12, 2005. 
Emission Factor Documentation for AP-42, 

Section 13.2.1

Paved Roads

Measurement Policy Group 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

January 2011
Emission Factor Documentation for AP-42, 

Section 13.2.1

Paved Roads

Measurement Policy Group 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

January 2011
NOTICE 

The information in this document has been funded by the Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency (EPA). This final report 

has been subjected to the Agency’s review, and it has been approved for publication as an 

EPA document. Mention of trade names or commercial products is not intended to constitute 

endorsement or recommendation for use.
iv

CONTENTS 

List of Figures ......................................................................................................................v 

List of Tables ..................................................................................................................... vi 

1 Introduction .......................................................................................................... 1-1 

2. Source Description ............................................................................................... 2-1 

 2.1 Public and industrial roads ................................................................ 2-1 

 2.2 Review of current paved road emission factors ................................ 2-1 

 2.2.1 September 1985 through January 1995. ................................. 2-1 

 2.2.2 January 1995 through October 2002 ....................................... 2-4 

 2.2.3 October 2002 through December 2003 ................................... 2-5 

 2.2.4 December 2003 through November 2006 ............................... 2-5 

 2.2.5 November 2006 through May 2010 ........................................ 2-6 

 2.2.6 May 2010 ................................................................................ 2-6 

3. General Data Review and Analysis ..................................................................... 3-1 

 3.1 Literature search and screening ........................................................ 3-1 

 3.2 Emission data quality rating system ................................................. 3-1 

 3.3 Emission factor quality rating system ............................................... 3-3 

 3.4 Methods of emission factor determination ....................................... 3-3 

 3.4.1 Mass Emission Measurements ................................................ 3-4 

 3.4.2 Emission Factor Derivation .................................................... 3-5 

 3.5 Emission factor quality rating scheme used in this study ................. 3-6 

4. AP-42 Section Development................................................................................ 4-1 

 4.1 Revisions to section narrative ........................................................... 4-1 

 4.2 Pollutant emission factor development ............................................. 4-1 

 4.2.1 Review of Specific Data Sets ................................................. 4-2 

 4.2.2 Emissions Factor Development. ........................................... 4-36 

 4.3 Development of other material in AP-42 section ........................... 4-69 

 4.4 References for Section 4 ................................................................. 4-70 

Appendix A Response to Comments ........................................................................... A-1 
v

LIST OF FIGURES 

Number .......................................................................................................................... Page 

4-1 PM10 Emissions Factor Data Base by Silt Loading (93 test runs). .................... 4-45 

4-2 PM10 Emissions Factor Data Base by Average Vehicle Weight (93 test runs). 4-46 

4-3 Silt Loading vs. Average Vehicle Weight (93 Test Runs). ................................ 4-47 

4-4 PM10 Emissions Factors by Vehicle Speed. ....................................................... 4-48 

4-5 Vehicle Speed vs Silt Loading. .......................................................................... 4-49 

4-6 Paved Road Dust Emissions Factors, All Data. ................................................. 4-50 

4-7 All Paved Road Data, Silt Loading by Vehicle Weight with EF. ...................... 4-51 

4-8 Paved Road Dust Emissions Factor Data Excluding Z-3. ................................. 4-52 

4-9 Cumulative Distribution of Predicted/Actual Ratios. ........................................ 4-57 

4-10 Cumulative Distribution – Predicted/Actual by Silt Loading. ........................... 4-58 

4-11 Cumulative Distribution – Predicted/Actual by Average Vehicle Weight. ....... 4-59 

4-12 Predicted vs Actual PM10 Emissions Factor by Silt Loading. .......................... 4-62 

4-13 Predicted vs Actual PM10 Emissions Factor by Average Vehicle Weight. ...... 4-63 

4-14 Predictive Accuracy by Silt Loading (unrestricted range). ................................ 4-64 

4-15 Predictive Accuracy by Silt Loading (restricted range). .................................... 4-65 

4-16 Predictive Accuracy by Average Vehicle Weight (unrestricted range). ............ 4-66 

4-17 Predictive Accuracy by Average Vehicle Weight (restricted range). ................ 4-67
vi

LIST OF TABLES 

Number .......................................................................................................................... Page 

4-1 Summary Information for Reference 15 .............................................................. 4-3 

4-2 Summary Information for Reference 17 .............................................................. 4-6 

4-3 Summary information for Reference 31 ............................................................ 4-11 

4-4 Detailed Information From Paved Road Tests for Reference 31 ....................... 4-11 

4-5 Summary Information for Reference 8 .............................................................. 4-12 

4-6 Detailed Information From Paved Road Tests for Reference 8 ......................... 4-13 

4-7 Summary of Paved Road Emission Factors for Reference 7 ............................. 4-15 

4-8 Detailed Information From Paved Road Tests for Reference 7 ......................... 4-16 

4-9 Summary of Paved Road Emission Factors for Reference 6 ............................. 4-18 

4-10 Detailed Information From Paved Road Tests for Reference 6 ......................... 4-19 

4-11 Detailed Information From Paved Road Tests for Reference 30 ....................... 4-22 

4-12 Summary of Emissions Data from MCP’s Marshall, 

 Minnesota Facility (Reference 33) ..................................................................... 4-24 

4-13 Summary of Emissions Data from MCP’s Columbus, 

 Nebraska Facility (Reference 34) ...................................................................... 4-26 

4-14 Summary of Emissions Data from Cargill’s Blair, 

 Nebraska Facility (Reference 35) ...................................................................... 4-27 

4-15 Summary of Emissions Data from ADM’s Marshall, 

 Minnesota Facility (Reference 36) .................................................................... 4-27 

4-16 Vehicle Fleet Assumption Used in 2003 MOBILE6.2 Model .......................... 4-28 

4-17 Final Paved Roads Emissions Factor Data Set ................................................. 4-41 

4-18 Correlation Matrix for log-transformed PM10 data ............................................ 4-53 

4-19 Regression Analysis using Silt Loading and Weight. ........................................ 4-55 

4-20 Comparison of Previous and New Equations for Estimating Paved Road Dust 

Emissions ........................................................................................................... 4-61 
1-1 

SECTION 1 

INTRODUCTION

 The document "Compilation of Air Pollutant Emissions Factors" (AP-42) has been 

published by the U.S. Environmental Protection Agency (EPA) since 1968. Supplements to AP42 have been routinely published to add new emission source categories and to update existing 

emission factors. AP-42 is periodically updated by EPA to respond to new emission factor needs 

of EPA, State, and local air pollution control programs and industry. 

 An emission factor relates the quantity (weight) of pollutants emitted to a unit of activity 

of the source. The uses for the emission factors reported in AP-42 include: 

 1. Estimates of area-wide emissions. 

 2. Estimates of emissions for a specific facility. 

 3. Evaluation of emissions relative to ambient air quality. 

 The purpose of this report is to compile the existing background report and supplements 

into a single report, provide an update of the background information from test reports and other 

information to support preparation of a revised AP-42 section to replace existing Section 13.2.1, 

"Paved Roads," dated November 2006. 

 The principal pollutant of interest in this report is “particulate matter” (PM), with special 

emphasis placed on “PM10” - particulate matter no greater than 10μmA (micrometers in 

aerodynamic diameter) and PM2.5. PM10 and PM2.5 form the basis for the current National 

Ambient Air Quality Standards (NAAQSs) for particulate matter. PM10 and PM2.5 thus represent 

the two size ranges of particulate matter that are of greatest regulatory interest. Nevertheless, 

formal establishment of PM10 and PM2.5 as the standard basis is relatively recent, and many 

emission tests have referenced other particle size ranges. Other size ranges employed in this 

report are: 

 TSP Total Suspended Particulate, as measured by the standard high-volume (hi-vol) air 

sampler. TSP was the basis for the previous NAAQSs for particulate matter. TSP consists of a 

relatively coarse particle size fraction. While the particle capture characteristics of the hi-vol 

sampler are dependent upon approach wind velocity, the effective D50 (i.e., 50% of the particles 

are captured and 50% are not) varies roughly from 25 to 50 μmA. 

 SP Suspended Particulate, which is used as a surrogate for TSP. Defined as PM no 

greater than 30 μmA. SP also may be denoted as “PM30.” 

 IP Inhalable Particulate, defined as PM no greater than 15 μmA. Throughout the late 

1970s and the early 1980s, it was clear that EPA intended to revise the NAAQSs to reflect a 

particle size range finer than TSP. What was not clear was the size fraction that would be 

eventually used, with values between 7 and 15 μmA frequently mentioned. Thus, many field 
1-2 

studies were conducted using IP emission measurements because it was believed that IP would 

be the basis for the new NAAQS. IP may also be represented by “PM15.” 

 FP Fine Particulate, defined as PM no greater than 2.5μmA. FP also may be denoted 

as “PM2.5.” 

 This background report consists of five sections. Section 1 provides an introduction to 

the report. Section 2 presents descriptions of the paved road source types and emissions from 

those sources as well as a brief history of the current AP-42 emission factors. Section 3 is a 

review of emissions data collection and analysis procedures; it describes the literature search, the 

screening of emission test reports, and the quality rating system for both emission data and 

emission factors. Section 4 details the development of paved road emission factors for the draft 

AP-42 section; it includes the review of specific data sets and the results of data analysis. Section 

5 presents the AP-42 section for paved roads. 
2-1 

SECTION 2

SOURCE DESCRIPTION 

Particulate emissions occur whenever vehicles travel 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, resuspension of material carried by the vehicle, 

deposits from undercarriages, engine exhaust gases or tire and brake wear. Depending on the 

road surface characteristics, vehicle mix, the most significant emissions may arise from the 

surface material loading (measured as mass of material per unit area), or a combination of 

engine exhaust, brake and tire emissions. Surface 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). Because of the 

importance of the surface loading, available control techniques either attempt to prevent 

material from being deposited on the surface or to remove (from the travel lanes) any material 

that has been deposited. 

2.1 PUBLIC AND INDUSTRIAL ROADS 

While the mechanisms of particle deposition and resuspension are largely the same for 

public and industrial roads, there can be major differences in surface loading characteristics, 

emission levels, traffic characteristics, and viable control options. For the purpose of 

estimating particulate emissions and determining control programs, the distinction between 

public and industrial roads is not a question of ownership but rather a question of surface 

loading and traffic characteristics. 

Although public roads generally 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, public roads in industrial 

areas can be often heavily loaded and traveled by heavy vehicles. In that instance, better 

emission estimates might be obtained by treating these roads as industrial roads through the 

use of a silt loading and average vehicle weight appropriate for the road segment. In extreme 

cases, public roads, industrial road, or parking lots may have such a high surface loadings that 

the paved surface is covered with loose material and in extreme cases is mistaken for an 

unpaved surface. In that event, use of a paved road emission factor may actually result in a 

higher estimate than that obtained from the unpaved road emission factor, and the road is better 

characterized as unpaved in nature rather than paved. 

2.2 REVIEW OF PAST AND CURRENT PAVED ROAD EMISSION FACTORS

 2.2.1 September 1985 through January 1995.

From September 1985 through January 1995, AP-42 currently contained two sections 

concerning paved road fugitive emissions. The first, Section 11.2.5, is entitled "Urban Paved 

Roads" and was first drafted in 1984 using test results from public paved roads. Emission 

factors are given in the form of the following equation: 
2-2 

 E = k (sL/0.5)p (2-1)

where: E = particulate emission factor (g/VKT) 

 s = surface material content silt, defined as particles < 75 μm 

 diameter (%) 

 L = surface material loading, defined as mass of particles per 

unit area of the travel surface (g/m2)

 k = base emission factor (g/VKT) 

p = exponent (dimensionless) 

The factors k and p are given by 

Particle size 

fraction

k (g/VKT) p

TSP 5.87 0.9

PM15 2.54 0.8 

PM10 2.28 0.8 

PM2.5 1.02 0.6 

The form of the emission factor model is reasonably consistent throughout all particle size 

fractions of interest. 

The urban paved road emission factors represented by Equation 2-1 did not change since 

their inclusion in the 4th Edition (September 1985) and the January 1995 revision. It should be 

noted that these emission factors were not quality rated "A" through "E." (See Section 3 for an 

overview of the AP-42 quality rating scheme.) 

Section 11.2.6, "Industrial Paved Roads," was first published in 1983 and was slightly 

modified in Supplement B (1988) to the 4th Edition. Section 11.2.6 contained three distinct 

sets of emission factor models as described below. 

 

 (2-2)

 For TSP, the following equation is recommended: 

where: E = emission factor (kg/VKT) 

 I = industrial augmentation factor (dimensionless) 

 n = number of traffic lanes (dimensionless) 

 s = surface material silt content (%) 

 L = surface material loading across all traffic lanes (kg/km) 

 W = average vehicle weight (Mg) 

The basic form of Equation 2-2 dates from a 1979 report and was originally included in 

Supplement 14 to AP-42 (May 1983). The version used in AP-42 was slightly revised in that 

0.7

7.228010

s

n

4 I 0.022 E ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛ = WL
2-3 

the leading term (i.e., 0.022 in Eq. [2-2]) was reduced by 14%. The industrial road 

augmentation factor (I) was included to take into account for higher emissions from industrial 

roads than from urban roads; it varied from 1 to 7. The emission factor equation was rated "B" 

for cases with I =1 and "D" otherwise. 

For smaller particle size ranges, models somewhat similar to those in Eq. (2-1) were 

recommended: 

 E = k (sL/12)

0.3

 (2-3) 

where: E = emission factor (kg/VKT) 

 k = base emission factor (kg/VKT), see below 

 sL = road surface silt loading (g/m2) 

The base emission factor (k) above varied with aerodynamic size range as follows: 

Particle size 

fraction

k (g/VKT)

PM15 0.28

PM10 2.22

PM2.5 0.081

 

These models represented by Equation 2-3 were first developed in 1984 from 15 emission tests 

of uncontrolled paved roads and they were rated "A." 

 During the development of Eq. (2-3), tests of light-duty traffic on heavily loaded road 

surfaces were identified as a separate subset, for which separate single-valued emission factors 

were developed. Section 11.2.6 recommended the following for light-duty (less than 4 tons) 

vehicles traveling over roads where the surface material was dry and the road was heavily 

loaded (silt loading greater than 15 g/m2): 

 E = k (2-4)

where: E = emission factor (kg/VKT) 

 k = single-valued factor depending on particle size range of interest 

 (see below) 

Particle size 

fraction

k (g/VKT)

PM15 0.12

PM10 0.093

 

The single-valued emission factors was quality rated "C."

During the time that AP-42 had four methods for estimating emissions from paved roads 

(Sections 11.2.5 and 11.2.6, AP-42 Fourth Edition, 1993), users of AP-42 noted difficulty selecting the 

appropriate emission factor model to use in their applications. For example, inventories of industrial 
2-4 

facilities (particularly of iron and steel plants) conducted throughout the 1980s yielded measured silt 

loading values substantially lower than those in the Section 11.2.6 data base. In extreme cases when 

the models were used with silt loading values outside the range for which they were developed, 

estimated PM10 emission factors were larger than the corresponding TSP emission factors. 

Furthermore, the distinction between "urban" and "industrial" paved roads was blurred. 

For the purpose of estimating emissions, it was gradually realized that source emission levels 

are not a question of ownership but rather a question of surface loading and traffic 

characteristics. Confirmatory evidence was obtained in a 1989 field program29which found that 

paved roads at an iron and steel facility far more closely resembled "urban" roads rather than 

"industrial" roads in terms of emission characteristics. 

Finally, it was unknown how well the emission factors of that time performed for cases 

of increased surface loading on public roads, such as after application of antiskid materials or 

within areas of trackout from unpaved areas.14 These situations were of considerable interest 

to several state and local regulatory agencies, most notably in the western United States. 

2.2.2 January 1995 through October 2002

The January 1995 update attempted to correct as many of the shortcomings of the 

previous versions as possible. To that end, the update employed an approach slightly different 

than that used in the past. In addition to reviewing test data obtained since the September 1988 

update8

, the test data used for both of the 1988 sections were also included for reexamination 

in the final data set. In assembling the data base, no distinction was made between public and 

industrial roads or between controlled and uncontrolled tests, with the anticipation that the 

reformulated emission factor will be applicable over a far greater range of source conditions. 

The inclusion of controlled tests represented a break with EPA previous guidelines for 

preparing AP-42 sections9

. Those guidelines presented a clear preference that only 

uncontrolled tests be used to develop an emission factor. However, the principal control 

measures for paved roads seek to reduce the value of an independent variable in the emission 

factor equation, i.e., the silt loading. 

 The revised emissions factor equation published in the January 1995 update of the 

paved road section included silt loading, average vehicle weight and a particle size multiplier 

as independent variables. The resulting equation was: 

 )3/()2/( 65.0 5.1 = kE WsL (2-5)

 where: E = particulate emission factor (having units matching the units of k) 

 k = particle size multiplier for particle size range and units of 

 interest (see below), 

 sL = road surface silt loading (grams per square meter) (g/m2), and 

 W = average weight (tons) of the vehicles traveling the road. 

The selection of the value for the independent variable for the particle size multiplier was 

based upon the units of the emissions factor desired and the size range for the emissions. 
2-5 

Particle Size Multipliers for Paved Road Equation

Size Range 

Multiplier k 

g/VKT g/VMT lb/VMT 

PM2.5 2.1 3.3 0.0073 

PM10 4.6 7.3 0.016 

PM15 5.5 9.0 0.020 

PM30 24 38 0.082 

2.2.3 October 2002 through December 2003

Prior to October 2002, the basis of the particle sizing information for paved roads 

emissions factors was high volume sampler impactors data. While the initial particle sizing 

was performed by cyclones, subsequent particle sizing was performed by slotted impactors. 

The impactor data had biases created by particle bounce and reintrainment. As such particle 

sizing below 10 μm was questioned. In October 2002, a three city paved and unpaved road 

emissions study was completed that evaluated particle sizing at 10 and 2.5μm and assessed the 

default values for silt loading. The results of the three city study formed the basis for revising 

the PM2.5 particle size multiplier k from 2.1 g/VKT (3.3 g/VMT or 0.0073 lb/VMT) to 1.1 

g/VKT (1.8 g/VMT or 0.0040 lb/VMT). The form of the predictive equation and the 

exponents for silt loading and average vehicle weight were unchanged. The changes in the 

October 2002 revision provided recommended default silt loading data for normal and worst 

case public paved roads based upon the updated silt loading values for public paved roads. 

The remaining numerical revisions that were made in the emissions factor for paved roads 

included an adjustment for the normal mitigation effects due to rain events. For long term 

average conditions, a 25% reduction in the particulate emissions was included for every day 

that there was measureable rain for that day. A similar adjustment was included that used 

hourly time intervals rather that a daily time interval. 

2.2.4 December 2003 through November 2006 

The December 2003 revision of the AP-42 Section for paved roads incorporated a 

constant in the predictive equation for particulate emissions factors. The AP-42 equations 

prior to December 2003 estimated PM emissions from re-entrained road dust, and vehicle 

exhaust, brake wear and tire wear emissions. In the December 2003 revision of the section, 

the component of emissions due to exhaust, brake wear and tire wear were separated from the 

composite fugitive dust emission factor equation. The first stated reason for the separation was 

to eliminate the possibility of double counting emissions. With the introduction of EPA’s 

Mobile6.2 model, estimates of PM emissions from exhaust, brake wear and tire wear were 

calculated based upon the vehicle mix, vehicle speed and road class. The double counting of 

emissions was a possibility when both the fugitive dust emission factors from AP-42 and 

Mobile6.2 were used to estimate emissions from vehicle traffic on paved roads. The second 

stated reason was to incorporate decreases in particulate matter emissions from the exhaust of 

newer vehicle models and fuel sources. Since the majority of data supporting the paved road 

emission factor equation was developed at the time prior to when the vehicles in the fleet 

incorporated significant reductions of particulate matter emissions. A technical memorandum 

provided the basis for estimating PM emissions due to exhaust, break wear and tire wear. The 
2-6 

technical memorandum used estimated emissions from a 1980’s model year vehicle fleet since 

the emissions tests supporting the emissions factors equation were performed in the early 

1980’s to early 1990’s. It was believed that since 1980, there have been and will continue to 

be improvements in vehicles and fuel that will result in a decrease in PM emissions from 

engine exhaust. Depending on the emissions factors units desired, the constant that was 

included in the emissions factor equation had values of 0.2119 g/VKT, 0.1317 g/VMT or 

0.00047 lb/VMT for PM30, PM15 and PM10 emissions. For PM2.5 emissions, depending on the 

required emissions factors units, the constant used in the equation had values of 0.1617 g/VKT, 

0.1005 g/VMT or 0.00036 lb/VMT. 

2.2.5 November 2006 through May 2010

In November 2006, the particle size multiplier k was lowered to 0.66 g/VKT, 1.1 g/VMT 

or 0.0024 depending on the needed units for the emissions factor. The revision was based 

upon a broad based assessment of the biases associated with the cyclone/impactor method for 

particulate sizes less than 10 μm in aerodynamic diameter. While the December 2003 update 

revised the particle size multiplier, the update was based upon limited test data. In addition, 

the impact of biased emissions factor ratios for PM2.5 impacted fugitive sources other than 

paved roads. The impact was due to particle bounce from the cascade impactor stages to the 

backup filter potentially inflating PM2.5 concentrations. The impact was possible even though 

steps were taken to minimize particle bounce in the earlier studies. The assessment study was 

sponsored by the Western Regional Air Partnership and conducted by the Midwest Research 

Institute (MRI). The testing was conducted at MRI’s Aerosol Test Facility (ATF) in Deramus 

Field Station in Grandview, Missouri using surface dust collected from seven locations in five 

western states. The tests provided the basis for comparing the average PM2.5 concentration and 

the collocated PM10 concentration. The study compared the fine fraction ratios derived from 

FRM samplers to those derived from the cyclone/impactor method. The cyclone/impactor 

samplers and operating method used in the study were the same as those that generated the 

original AP-42 emission factors and associated PM2.5 / PM10 ratios. The study consisted of 

100 test runs covering PM10concentration from approximately 0.3 mg/m3

 to 7 mg/m3. 

2.2.6 May 2010 

This update recommends an updated equation for paved roads that is based upon additional test 

data that was conducted on roads with slow moving traffic and stop and go traffic. The 

emissions tests were performed for the Corn Refiners Association by Midwest Research 

Institute (MRI). The testing focused on PM10 emissions at four corn processing facilities. 

Unlike the development of earlier paved road equations, the equation development for this 

version adjusts the individual test data measured emissions by excluding exhaust emissions, 

tire wear emissions and brake wear emissions prior to the equation development. As a result, 

different values are subtracted from the results of each test based upon the average vehicle 

weights, average vehicle speed, ambient temperature, year of test and estimated mix of light 

duty and heavy duty vehicles.
3-1 

SECTION 3

GENERAL DATA REVIEW AND ANALYSIS

 To reduce the amount of literature collected to a final group of references from which 

emission factors could be developed, the following general criteria were used: 

 1. Emissions data must be from a primary reference: 

a. Source testing must be from a referenced study that does not reiterate 

information from previous studies. 

b. The document must constitute the original source of test data. For example, 

a technical paper was not included if the original study was contained in the 

previous document. If the exact source of the data could not be determined, 

the document was eliminated. 

 2. The referenced study must contain test results based on more than one test run. 

 3. The report must contain sufficient data to evaluate the testing procedures and source 

operating conditions. 

 A final set of reference materials was compiled after a thorough review of the pertinent 

reports, documents, and information according to these criteria. 

3.1 LITERATURE SEARCH AND SCREENING

 Review of available literature identified three paved road testing programs (presented 

later as Table 4-1) since the time of the last Section 11.2 update.8 The individual programs are 

discussed in detail in the next section. In addition, as discussed at the end of Section 2, earlier 

controlled industrial road test data were reexamined. The previous update8 noted that Eq. (2-4) 

yielded quite good estimates for emissions from vacuum swept and water flushed roads. 

Furthermore, it became apparent that previous distinctions between "industrial" and "urban" 

roads had become blurred as interest focused on heavily loaded urban roads (e.g., after 

snow/ice controls) and on cleaner industrial roads (as the result of plant-wide control 

programs). 

3.2 EMISSION DATA QUALITY RATING SYSTEM

 As part of the analysis of the emission data, the quantity and quality of the information 

contained in the final set of reference documents were evaluated. The following data are to be 

excluded from consideration: 

1. Test series averages reported in units cannot be converted to the selected reporting 

units. 

2. Test series representing incompatible test methods (i.e., comparison of EPA Method 5 

front-half with EPA Method 5 front- and back-half). 
3-2 

3. Test series of controlled emissions for which the control device is not specified. 

4. Test series in which the source process is not clearly identified and described. 

5. Test series in which it is not clear whether the emissions were measured before or after 

the control device. 

Test data sets that were not excluded were assigned a quality rating. The rating system 

used was that specified by EPA for preparing AP-42 sections.9 The data were rated as 

follows: 

A Multiple tests that were performed on the same source using sound 

methodology 

 and reported in enough detail for adequate validation. These tests do not 

 necessarily conform to the methodology specified in EPA reference test 

methods, 

 although these methods were used as a guide for the methodology actually used. 

B Tests that were performed by a generally sound methodology, but lack enough 

detail for adequate validation. 

C Tests that were based on an untested or new methodology or that lacked a 

significant amount of background data. 

D Tests that were based on a generally unacceptable method but may provide an 

order-of-magnitude value for the source. 

 The following criteria were used to evaluate source test reports for sound methodology 

and adequate detail: 

1. Source operation. The manner in which the source was operated is well 

documented in the report. The source was operating within typical parameters 

during the test. 

2. Sampling procedures. The sampling procedures conformed to a generally 

acceptable methodology. If actual procedures deviated from accepted methods, 

the deviations are well documented. When this occurred, an evaluation was 

made of the extent such alternative procedures could influence the test results. 

3. Sampling and process data. Adequate sampling and process data are 

documented in the report, and any variations in the sampling and process 

operation are noted. If a large spread between test results cannot be explained 

by information contained in the test report, the data are suspect and were given 

a lower rating. 

4. Analysis and calculations. The test reports contain original raw data sheets. 

The nomenclature and equations used were compared to those (if any) specified 

by EPA to establish equivalency. The depth of review of the calculations was 
3-3 

dictated by the reviewer's confidence in the ability and conscientiousness of the 

tester, which in turn was based on factors such as consistency of results and 

completeness of other areas of the test report. 

3.3 EMISSION FACTOR QUALITY RATING SYSTEM

 The quality of the emission factors developed from analysis of the test data was rated 

utilizing the following general criteria: 

A—Excellent: Developed only from A-rated test data taken from many randomly 

chosen facilities in the industry population. The source category is specific enough so 

that variability within the source category population may be minimized. 

B—Above average: Developed only from A-rated test data from a reasonable number 

of facilities. Although no specific bias is evident, it is not clear if the facilities tested 

represent a random sample of the industries. The source category is specific enough so 

that variability within the source category population may be minimized. 

C—Average: Developed only from A- and B-rated test data from a reasonable number 

of facilities. Although no specific bias is evident, it is not clear if the facilities tested 

represent a random sample of the industry. In addition, the source category is specific 

enough so that variability within the source category population may be minimized. 

D—Below average: The emission factor was developed only from A- and B-rated test 

data from a small number of facilities, and there is reason to suspect that these facilities 

do not represent a random sample of the industry. There also may be evidence of 

variability within the source category population. Limitations on the use of the 

emission factor are noted in the emission factor table. 

E—Poor: The emission factor was developed from C- and D-rated test data, and there 

is reason to suspect that the facilities tested do not represent a random sample of the 

industry. There also may be evidence of variability within the source category 

population. Limitations on the use of these factors are always noted. 

 The use of these criteria is somewhat subjective and depends to an extent on the 

individual reviewer. 

3.4 METHODS OF EMISSION FACTOR DETERMINATION

 Fugitive dust 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 size 

involved including particles which deposit immediately adjacent to the source. Standard 

source testing methods, which are designed for application to confined flows under steady 

state, forced-flow conditions, are not suitable for measurement of fugitive emissions unless the 

plume can be draw into a forced-flow system. The following presents a brief overview of 

applicable measurement techniques. More detail can be found in earlier AP-42 updates.8,10 
3-4 

3.4.1 Mass Emission Measurements

 Because it is usually impractical to enclose open dust sources or to capture the entire 

emissions plume, only the upwind-downwind and exposure profiling methods are suitable for 

measurement of particulate emissions from most open dust sources.10 These two methods are 

discussed separately below. 

 The basic procedure of the upwind-downwind method involves the measurement of 

particulate concentrations both upwind and downwind of the pollutant source. The number of 

upwind sampling instruments depends on the degree of isolation of the source operation of 

concern (i.e., the absence of interference from other sources upwind). 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 two downwind distances and three crosswind 

distances, at a minimum. The same sampling requirements pertain to line sources except that 

measurement need not be made at multiple crosswind distances. 

 Net downwind (i.e., downwind minus upwind) concentrations are used as input to 

dispersion equations (normally of the Gaussian type) to back calculate the particulate emission 

rate (i.e., source strength) required to generate the pollutant concentration measured. Emission 

factors are obtained by dividing the calculated emission rate by a source activity rate (e.g., 

number of vehicles, or weight of material transferred per unit time). A number of 

meteorological parameters must be concurrently recorded for input to this dispersion equation. 

At a minimum the wind direction and speed must be recorded on-site. 

 While the upwind-downwind method is applicable to virtually all types of sources, it 

has significant limitations with regard to development of source-specific emission factors. The 

major limitations are as follows: 

1. In attempting to quantify a large area source, overlapping of plumes from 

upwind (background) sources may preclude the determination of the specific 

contribution of the area source. 

2. Because of the impracticality of adjusting the locations of the sampling array 

for shifts in wind direction during sampling, it cannot be assumed that plume 

position is fixed in the application of the dispersion model. 

3. The usual assumption that an area source is uniformly emitting does not allow 

for realistic representation of spatial variation in source activity. 

4. The typical use of uncalibrated atmospheric dispersion models introduces the 

possibility of substantial error (a factor of three according to Reference 11) in 

the calculated emission rate, even if the stringent requirement of unobstructed 

dispersion from a simplified (e.g., constant emission rate from a single point) 

source configuration is met. 

 The other measurement technique, exposure profiling, offers distinct advantages for 
3-5 

source-specific quantification of fugitive emissions from open dust sources. The method uses 

the isokinetic profiling concept that is the basis for conventional (ducted) source testing. The 

passage of airborne pollutant immediately downwind of the source is measured directly by 

means of simultaneous multipoint sampling over the effective cross section of the fugitive 

emissions plume. This technique uses a mass-balance calculation scheme similar to EPA 

Method 5 stack testing rather than requiring indirect calculation through the application of a 

generalized atmospheric dispersion model. 

 For measurement of nonbuoyant fugitive emissions, profiling sampling heads are 

distributed over a vertical network positioned just downwind (usually about 5 m) from the 

source. If total particulate emissions are to be measured, sampling intakes are pointed into the 

wind and sampling velocity is adjusted to match the local mean wind speed, as monitored by 

anemometers distributed over height above ground level. 

 The size of the sampling grid needed for exposure profiling of a particular source may 

be estimated by observation of the visible size of the plume or by calculation of plume 

dispersion. Grid size adjustments may be required based on the results of preliminary testing. 

Particulate sampling heads should be symmetrically distributed over the concentrated portion 

of the plume containing about 90% of the total mass flux (exposure). For example, assuming 

that the exposure from a point source is normally distributed, the exposure values measured by 

the samplers at the edge of the grid should be about 25% of the centerline exposure. 

 To calculate emission rates using the exposure profiling technique, a conservation of 

mass approach is used. The passage of airborne particulate (i.e., the quantity of emissions per 

unit of source activity) is obtained by spatial integration of distributed measurements of 

exposure (mass/area) over the effective cross section of the plume. The exposure is the point 

value of the flux (mass/area/time) of airborne particulate integrated over the time of 

measurement. 

3.4.2 Emission Factor Derivation

 Emissions factors are typically derived from the ratio of the emissions to an activity 

level. It is assumed that the emissions are linearly proportional to the selected activity level. 

Usually the final emission factor for a given source operation, is the arithmetic average of the 

individual emission factors calculated from each test of that source type. In rare instances, the 

range of individual emission factor values is also presented. 

 As an improvement over the presentation of a final emission factor as a single-valued 

arithmetic mean, an emission factor may be presented in the form of a predictive equation 

derived by regression analysis of test data. The use of a predictive equation with a relatively 

good correlation coefficient (R2

) provides a means for improving the accuracy of the emissions 

factor in estimating the actual emissions when the independent variables are known. Such an 

equation mathematically relates emissions to parameters when characterize source conditions. 

These parameters may be grouped into three categories: 

1. Measures of source activity or energy expended (e.g., the speed and weight of a 

vehicle traveling on an unpaved road). 

2. Properties of the material being disturbed (e.g., the content of suspendable fines 
3-6 

in the surface material on an unpaved road). 

3. Climatic parameters (e.g., number of precipitation-free days per year on which 

emissions tend to be at a maximum). 

An emission factor equation is useful if it is successful in "explaining" much of the observed 

variance in emission factor values on the basis of corresponding variance sin specific source 

parameters. This enables more reliable estimates of source emissions on a site-specific basis. 

 A generic emission factor equation is one that is developed for a source operation 

defined on the basis of a single dust generation mechanism which crosses industry lines. An 

example would be vehicular traffic on unpaved roads. To establish its applicability, a generic 

equation should be developed from test data obtained in different industries. 

3.5 EMISSION FACTOR QUALITY RATING SCHEME USED IN THIS STUDY

 The uncontrolled emission factor quality rating scheme used in this study is somewhat 

different than was used in earlier updates8,11 of this section and represents a refinement of the 

rating system developed by EPA for AP-42 emission factors, as described in Section 3.3. The 

scheme entails the use of the same rating assessment of source test data quality followed by an 

initial rating assessment of the emission factor(s) based on the number and quality of the 

underlying source test data. 

 Test data that were developed from well documented, sound methodologies were 

assigned an A rating. Data generated by a methodology that was generally sound but either did 

not meet a minimum test system requirements or lacked enough detail for adequate validation 

received a B rating. 

 In evaluating whether an upwind-downwind sampling strategy qualified as a sound 

methodology, the following minimum test system requirements were used. At least five 

particulate measuring devices must be operated during a test, with one device located upwind 

and the other located at two downwind and three crosswind distances. The requirement of 

measurements at crosswind distances is waived for the case of line sources. Also wind 

direction and speed must be monitored concurrently on-site. 

 The minimum requirements for a sound exposure profiling program were the following. 

A one-dimensional, vertical 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 missions. At least one 

upwind sampler must be operated to measure background concentration, and wind speed must 

be measured on-site. 

 Neither the upwind-downwind nor the exposure profiling method can be expected to 

produce A-rated emissions data when applied to large, poorly defined area sources, or under 

very light and variable wind flow conditions. In these situations, data ratings based on degree 

of compliance with minimum test system requirements were reduced one letter. 

 Following the assignment of the individual source test quality ratings, the factor quality 
3-7 

rating of the single-valued emission factor will be evaluated. Recently approximately 20 “A” 

and “B” rated source test reports have been required to justify a factor quality rating of “A”. 

Each halving of the number of source test reports results in a one letter grade reduction in the

final factor quality rating. Several of the source test reports used as the basis for the emissions 

factor development include measurements conducted at different locations. To the extent that 

there are more than two tests at the different locations and that the different locations within a 

given reference represent differences in source conditions, each of the different source 

conditions will be counted as an independent test. The development of the paved road 

emissions factor differs from typical in that it includes the use of stepwise multiple non linear 

regression. Following the initial factor quality rating, the adjusted correlation coefficient will 

be used to increase the emissions factor quality rating. Only correlation coefficients above 0.4 

will be used to increase the emissions factor quality rating.
4-1 

SECTION 4 

AP-42 SECTION DEVELOPMENT 

4.1 REVISIONS TO SECTION NARRATIVE

 The AP-42 presented later in this background document is intended to replace the 

current version of Section 13.2.1 "Paved Roads" in AP-42. The last update of this section is 

dated November 2006. The general form of the emissions factor equation presented in the 

paved road section has been consistent since the January 1995 major revision. Since this 

date revisions have been made addressing the influence of rain events, estimating default silt 

loading levels for various classes of roads, separating particulate emissions associated with 

the roads verses those associated with the vehicles and addressing biases in the measurement 

of PM2.5 with devices that use impactors to perform particulate sizing. 

4.2 POLLUTANT EMISSION FACTOR DEVELOPMENT 

 This update to Sections 13.2.1 is planned to address the application of the emissions 

factor equation addressing only the component associated with paved road surface materials 

and at speeds lower than 10 miles per hour. In order to achieve this goal, the following 

general approach was taken 

1. Assemble the available test data for paved roads in a single data base, making 

no distinction between public and industrial roads or between controlled and 

uncontrolled roads. 

2. Develop PM10 and PM2.5 engine, tire wear and brake ware emissions 

estimates for each of the available data sets. For each of the available data 

sets, estimate the emissions associated with the road surface material by 

subtracting the engine, tire wear and brake wear from the measured PM10

emissions. 

2. Conduct a series of stepwise linear regression analyses of the revised and 

adjusted data base to assess the most critical parameters and to develop an 

emission factor model with: 

 • silt loading, 

 • mean vehicle weight, and, 

 • mean travel speeds 

 as potential correction parameters. 

3. Conduct an appropriate validation study of the reformulated model. 
4-2 

4.2.1 Review of Specific Data Sets 

4.2.1.1 Street Sanding Emissions And Control Study, PEI Associates, Inc., Cincinnati, 

OH, October 1989. (Reference 15) 

 This test program was undertaken to characterize PM10 emissions from six streets that 

were periodically sanded for anti-skid control within the Denver area. The primary objective 

was given as development of a predictive algorithm for clean and sanded streets, with a 

secondary objective stated as defining the effectiveness of control measures. Summary 

information is given in Table 4-1. 

 Sampling employed six to eight 8 PM10 samplers equipped with volumetric flow 

control. Samplers were arranged in two upwind/downwind configurations. The "basic" 

configuration consisted of six samplers arranged in identical patterns upwind and downwind 

of the test road, with one sampler and one pair of samplers at nominal distances of 20 and 5 

m, respectively, from the road. 

 The second configuration was used for tests of control measure effectiveness. The 

road segment was divided into two halves, corresponding to the treated and experimental 

control (untreated) portions. Identical sampling arrays were again used upwind and 

downwind on both halves, at nominal distances of 20 and 5 m. Because this array employed 

all eight samplers available, no collocation was possible for the second configuration. 

 In addition to the PM10 concentration measurements, several other types of samples 

were collected: 

• Wind speed/direction and incoming solar radiation were collected on-site, and 

the results were combined to estimate atmospheric stability class needed to 

calculate emission factors. 

• Colorado Air Pollution Control Division (APCD) representatives collected 

traffic data, including traffic counts, travel speeds, and percentage of heavyduty vehicles. 

• Vacuums with disposable paper bags were used to collect the loose material 

from the road surface. In addition to samples taken from the travel lanes, the 

field crew took daily samples of material adjacent to curbs and periodic 

duplicate samples. 

 The study collected PM10 concentration data on 24 different days and calculated a 

total of 69 different emission rates for baseline, sanded and controlled paved road surfaces. 

Emission factors were obtained by back-calculation from the CALINE3 dispersion model12 

together with a series of assumptions involving mixing widths and heights and an effective 

release height. Although data collected at the 20 m distance were used to evaluate results, the 

test report did not describe any sensitivity analysis to determine how dependent the emission 

rates were on the underlying assumptions. 
TABLE 4-1. SUMMARY INFORMATION FOR REFERENCE 15 

 PM10 emissio

Operation Location State Test dates No. of tests Geom. mean 

Vehicle traffic Colfax Colorado 3-4/89 17 1.33 Vehicle traffic York St. Colorado 4/89 1 1.07 Vehicle traffic Belleview Colorado 4/89 4 1.62 Vehicle traffic I-225 Colorado 4/89 9 0.31 Vehicle traffic Evans Colorado 5-6/89 29 1.06 Vehicle traffic Louisiana Colorado 6/89 7 0.96 

4-3
4-4 

 The testing program found difficulty in defining "upwind" concentrations for several 

of the runs, including cases with wind reversals or winds nearly parallel to the roadway 

orientation. A total of eight of the 69 tests required that either an average concentration from 

other test days or a downwind concentration be used to define "upwind" conditions. In 

addition, the test report described another seven runs as invalid for reasons such as wet road 

surfaces, nearby dust sources or concentrations increasing with downwind distance. 

 A series of stepwise regression analyses were conducted, with different predictive 

equations presented for (a) baseline conditions, (b) sanded roads, and (c) roads swept to 

remove the sand applied, and (d) all conditions combined. In each case, only one independent 

variable was included in the predictive equation: silt loading, for cases (a) and (d); and time 

since treatment, for (b) and (c). 

 In general, Reference 15 is reasonably well documented in terms of describing test 

conditions, sampling methodology, data reduction and analysis. A chief limitation lies in the 

fact that neither sampling configuration fully met minimum requirements for the upwinddownwind method presented in Section 3.4. Specifically, only two or three samplers were 

used downwind rather than the minimum of four. 

 Furthermore, a later report6 drawing upon the results from Reference 15 and 17 

effectively eliminated 24% of the combined baseline tests because of wind directions. In 

addition, the later report6 noted that the baseline data should be considered as "conservatively 

high" because roughly 70% of the data were calculated assuming the most unstable 

atmospheric class (which results in the highest back calculated emission factor). Because of 

these limitations, the emission data have been given an overall rating of "D." 

4.2.1.2 RTP Environmental Associates 1990. Street Sanding Emissions and Control 

Study, prepared for the Colorado Department of Health. July 1990. (Reference 17) 

 This test program was quite similar to that described in Reference 15 cited in 

paragraph 4.2.1.1 and used an essentially identical methodology. In fact, the two test reports 

are very similar in outline, and many passages in the two reports are identical. The primary 

objective was given as expanding the data base in Reference 15 to further develop predictive 

algorithms for clean and sanded streets. Summary information is given in Table 4-2. 

 The test program employed the same two basic PM10 sampling arrays as did Reference 

15. A third configuration was used for "profile" tests, in which additional samplers were 

placed at 10 and 20 ft heights. (Analysis of results from elevated samplers is not presented in 

Reference 17.) 

 As was the case in Reference 15, additional samples were collected including: 

• Wind speed/direction were collected on-site, and the results used in estimating 

atmospheric stability class needed to calculate emissions factors. (Unlike 

Reference 15, solar radiation measurements were not collected.) 

• Traffic data, including traffic counts, travel speeds, and percentages of heavyduty vehicles were collected. 

• Vacuums with disposable paper bags were used to collect the loose material 
4-5 

from the road surface. The program developed an extensive set of collocated 

samples of material along the edges of the roadway. 

 The study collected PM10 concentration data on 33 days and calculated a total of 131 

different emission rates for baseline, sanded and controlled paved road surfaces. Emission 

factors were obtained by back-calculation from the CALINE3 dispersion model12 together 

with essentially the same assumptions as those in Reference 15. This report also noted the 

same difficulty as Reference 15 in defining "upwind" concentrations in cases with wind 

reversals or winds nearly parallel to the roadway orientation. Unlike Reference 15, however, 

this report does not provide readily available information on how many tests used either an 

average concentration from other test days or a downwind concentration to define "upwind" 

conditions. Reference 6 does, however, describe seven tests as invalid because of filter 

problems or because upwind concentrations were higher than downwind values. 

 As with the Reference 15 program, a series of stepwise regression analyses were 

conducted. This test program combined data from Reference 15 and 17 and considered 

predictive equations for (a) baseline conditions, (b) sanded roads, and (c) roads swept to 

remove the sand applied, and (d) all conditions combined. 

 Unlike Reference 15, however, Reference 17 appears to present silt loading values that 

are based on wet sieving (see page 8 of the test report) rather than the dry sieving technique 

(as described in Appendix E to AP-42) routinely used in fugitive dust tests. (MRI could not 

obtain any clarifying information during telephone calls to the testing organization and the 

laboratory that analyzed the samples.) Wet sieving disaggregates composite particles and 

results from the two types of sieving are not comparable. 

 There is additional confusion over the silt loading values given in Reference 17 for 

cleaning tests. Specifically, the same silt loading value is associated with both the treatment 

and the experimental control. This point could not be clarified during telephone conversation 

with the testing organization. Attempts to clarify using test report appendices were 

unsuccessful. Two appendices appear to interchange silt loading with silt percentage. More 

importantly, it could not be determined whether the surface sample results reported in 

Appendix D to Reference 17 pertain to treated or the experimental control segment, and with 

which emission rate a silt loading should be associated. 
TABLE 4-2. SUMMARY INFORMATION FOR REFERENCE 17 

 PM10 emission facto

Operation Location State Test dates 

No. of test Geom. mean Ra

Vehicle traffic Mexico Colorado 2/90 3 2.75 1.0Vehicle traffic State Hwy 36 Colorado 1-3/90 13 1.31 0.1Vehicle traffic Colfax Colorado 2-4/90 41 1.32 0.2Vehicle traffic Park Rd. Colorado 4/90 11 1.26 0.6Vehicle traffic Evans Colorado 2-3/90 11 2.10 0.8Vehicle traffic Louisiana Colorado 1,3/90 9 3.24 1.4Vehicle traffic Jewell Colorado 1/90 1 6.36 6.3Vehicle traffic Bryon Colorado 4/90 3 8.38 5.5

4-6
4-7 

 Reference 17 contains substantial amounts of information, but is not particularly well 

documented in terms of describing test conditions, sampling methodology, data reduction 

and analysis. In addition, the same limitations mentioned in connection with Reference 15 

are equally applicable to Reference 17, as follows: 

• not meeting the minimum number of samplers. 

• numerous tests conducted under variable wind conditions. 

• frequent use (70% to 80% of the tests) of the most unstable atmospheric stability 

class in the CALINE 3 model which will result in the highest calculated emission 

rate. 

 Because of these limitations, emission rate data have been given an overall rating of 

"D." Furthermore, the silt loading data in this report are considered suspect for reasons noted 

above. 

4.2.1.3 T. Cuscino, Jr., et al., Iron And Steel Plant Open Source Fugitive Emission 

Control Evaluation, EPA 600/2 83 110, U. S. Environmental Protection Agency, 

Cincinnati, OH, October 1983. (Reference 6, ref_06c13s0201_2011.pdf)

 This study evaluated paved road control techniques at two different iron and steel 

plants. (See Tables 9 and 10 in Reference 8.) Data were quality rated as "A," and 

uncontrolled test results were incorporated into the data base for Section 11.2.6 published in 

1983. The only use of the controlled test results, however, was the following addition to 

Section 11.2.6.4 in 1988: 

"Although there are relatively few quantitative data on emissions from controlled 

paved roads, those that are available indicate that adequate estimates generally may 

be obtained by substituting controlled loading values into .. [Equations (2-2) and (2-

3)].... The major exception to this is water flushing combined with broom sweeping. 

In that case, the equations tend to overestimate emissions substantially (by an 

average factor of 4 or more)." 

 In the current update, the controlled emission factors have been used as part of the 

overall data base to develop predictive models. Although PM10 emission data are not 

specifically presented in the report, appropriate values were previously developed by lognormal interpolation of the PM15 and PM2.5 factors.8

4.2.1.4. G. E. Muleski, Measurement of Fugitive Dust Emissions from Prilled Sulfur Handling, 

Final Report, MRI Project No. 7995-L, Prepared for Gardinier, Inc., June 1984 (Reference 45) 

 This was first report identified to suggest that heavily loaded paved roads may be 

better considered as unpaved in terms of emission estimates. The program produced three 

tests of emissions from end-loader travel over paved surfaces. Two of the three tests were 

conducted on very heavily loaded surface, while the third was on a cleaned paved surface. 

(See Tables 20 and 21 of the 1987 update.)8

 No PM10 emission factors were reported; results were presented for total particulate 

(TP) and suspended particulate (SP, or PM30). Data were quality rated "A" in the 1987 report. 
4-8 

 Because no PM10 data were given, Test Report 5 data were most directly useful as 

independent data against which the TSP emission factor model (Eq. (2-2)) could be assessed. 

This comparison showed generally good agreement between predicted and observed with 

agreement becoming better as source conditions approached those in the underlying data 

base. 

 The 1987 update8 developed PM10 emission factors based on information contained 

in the test report. When compared to the single valued factors (Equation [2-4]), agreement 

for the first two tests was within a factor of approximately two. The third test — that of the 

cleaned surface — could not be used to assess the performance of either Eq. (2-1) or Eq. (2-

3) because the surface loading value could not be converted to the necessary units with 

information presented in the report. 

4.2.1.5 T. F. Eckle and D. L. Trozzo, Verification of the Efficiency of a Road-Dust 

Emission-Reduction Program by Exposure Profile Measurement, Presented at EPA/AISI 

Symposium on Iron and Steel Pollution Abatement, Cleveland, Ohio, October 1984. 

(Reference 46) 

 This paper discussed the development of an exposure profiling system as well as an 

evaluation of the effectiveness of a paved road vacuum sweeping program. Because no 

reference is made to an earlier test report, this paper is considered to be the original source of 

the test data. Although ten uncontrolled and five controlled tests are mentioned, test data are 

reported only in terms of averages. (See Tables 24 and 25 in Reference 8.) Only TSP 

emission factors are presented. Although data were obtained using a sound methodology, 

data were rated "C" because of inadequate detail in the paper. 

 Averaged data from Test Report 8 were used in an independent assessment of 

Eq. (2-2). Although only average emission levels could be compared, the data suggested 

that TSP emissions could be estimated within very acceptable limits. 

4.2.1.6 Roadway Emissions Field Tests at U.S. Steel’s Fairless Works, U.S. Steel 

Corporation, Fairless Hills, PA, USX Purchase Order No. 146-0001191-0068, May 1990. 

(Reference 31, ref_31c13s0201_2011.pdf).

 This 1989 field program used exposure profiling to characterize emissions from 

paved roads at an integrated iron and steel plant near Philadelphia, Pennsylvania, in 

November 1989. In many respects, this program arose because of uncertainties with paved 

road emission factor models used outside their range of applicability. During the preparation 

of an alternative emission reduction ("bubble") plan for the plant, questions arose about the 

use of AP-42 equations and other EPA guidance13 in estimating roadway emissions involved 

in the emissions trade. This program provided site-specific data to support the bubble plan. 

This testing program also represented the first exposure profiling data to supplement the 

AP-42 paved road data base since the 1984 revision. Site “C” was located along the main 

access route and had a mix of light- and medium-duty vehicles. Site “E” was located near 

the southwest corner of the plant and the traffic consisted mostly of plant equipment. Table 

4-3 provides summary information and Table 4-4 provides detailed information. 

 The program involved two paved road test sites. The first (site "C") was along the 

four-lane main access route to the plant. Average daily traffic (ADT) had been estimated as 

more than 4,000 vehicle passes per day, with most vehicles representative of "foreign" 
4-9 

equipment (i.e., cars, pickups, and semi-trailers rather than plant haul trucks and other 

equipment). Site "E," on the other hand, was located near the iron- and steel-making 

facilities and had both lower ADT and heavier vehicles than site "C." The plant regularly 

vacuum swept paved roads, and two cleaning frequencies (two times and five times per 

week) were considered during the test program. 

 Eight tests were conducted at Site C-1 and four tests were conducted at Site E-2. 

The paved road test sites were considered uncontrolled. The road width, moisture content, 

and mean number of wheels were not reported. The test data are assigned an “A” rating. 

Table 4-3 presents summary information and Table 4-4 presents detailed test information. 

Warm wire anemometers at two heights measured wind speed. 

 Depending on traffic characteristics of the road being tested, a 6 to 7.5 m high 

profiling array was used to measure downwind mass flux. This array consisted of four or 

five total particulate sampling heads spaced at 1.5 m heights and was positioned at a 

nominal 5 m distance downwind from the road. A high-volume sampler with a parallel-slot 

cascade impactor and a cyclone preseparator (cutpoint of 15 μmA) was employed to 

measure the downwind particle size distribution, and a standard high-volume sampler was 

utilized to determine the downwind mass fraction of total suspended particulate matter 

(TSP). The height for downwind sizing devices (2.2 m) was selected after review of prior 

test results. It approximated the height in a roadway dust plume at which half the mass 

emissions pass above and half below. The upwind (background) particle size distribution 

was determined with a high-volume cyclone/ impactor combination. Warm wire 

anemometers at two heights measured wind speed. 

Additional samples included: 

• Average wind speeds at two heights and wind direction at one height were recorded 

during testing to maintain isokinetic sampling. 

• Traffic data, including traffic counts, travel speeds, and vehicle class were recorded 

manually. 

• Vacuums with disposable paper bags were used to collect the loose material from the 

road surface. 

 The sampling equipment met the requirements of a sound exposure profiling 

methodology specified in Section 3.4 so that the emission test data are rated "A." The test 

report presents emission factors for total particulate (TP), total suspended particulate (TSP) 

and PM10, for the ten paved road emission tests conducted. 

 Reference 31 found that the emission factors and silt loadings more closely resembled 

those in the "urban" rather than the "industrial" data base. That is to say, emissions agreed 

more closely with factors estimated by the methods of September 1985 AP-42 Section 11.2.5 

than by methods in Section 11.2.6. Given the traffic rate of 4000 vehicles per day at Site 

"C," this finding was not terribly surprising. What was far more surprising was that 

emissions at Site "E" were also more "urban" than "industrial." Although the TSP and PM10 

models in Section 11.2.5 showed a slight tendency to underpredict, the Section 11.2.6 PM10 

model overestimated measured emissions by at least an order of magnitude. The 

performance of the industrial TSP model, on the other hand, was only slightly poorer than 

that for the urban TSP model. 
4-10 

 4.2.1.7 Midwest Research Institute, Paved Road Particulate Emissions - Source 

Category Report, for U.S. EPA, July 1984. (Reference 8, ref_08c13s0201_2011.pdf) 

 This document reports the results of testing of paved roads conducted in 1980 at sites 

in Kansas City, MO, St. Louis, MO, Tonganoxie, KS, and Granite City, IL. Paved road test 

sites included commercial/industrial roads, commercial/residential roads, expressways, and a 

street in a rural town. The expanded measurement program reported in this document was 

used to develop emission factors for paved roads and focused on the following particle sizes: 

PM15 (inhalable particulate matter [IP]), PM10, and PM2.5. 

 Total airborne PM emissions were characterized using an exposure profiler 

containing four sampling heads. High-volume samplers with size selective inlets (SSI) 

having a cutpoint of 15 μmA were used to characterize upwind and downwind PM15 

concentrations. A high- volume sampler with a SSI and a cascade impactor was also located 

downwind to characterize particle size distribution within the PM15 component. Upwind 

and downwind standard high- volume samplers measured TSP concentrations. Warm wire 

anemometers at two heights measured wind speed. 

 A total of 19 paved road emission tests were conducted in four cities. These 

included four tests of commercial/industrial paved roads, ten tests of commercial/residential 

paved roads, four expressway tests, and one test of a street in a rural town. Additionally, as 

part of this study, 81 dust samples were collected in 12 cities. The mean number of vehicle 

wheels was not reported. The test data are assigned an A rating. Table 4-5 presents 

summary test data and Table 4-6 presents detailed test information. 
TABLE 4-3. SUMMARY INFORMATION FOR REFERENCE 31 

Operation Location State Test dates No. of tests TSP emission factor, lb/VMT PM10 emission fa

Geom. mean Ran

ge Geom. mean 

Vehicle traffic AU-X 

(Unpaved road) 

PA 11/89 2 0.61 0.39-0.96 0.16 

Vehicle traffic Paved road PA 11/89 6 0.033 0.012-0.12 0.0095 Vehicle traffic Paved road PA 11/89 4 0.078 0.033-0.30 0.022 

1 lb/VMT = 281.9 g/VKT.

TABLE 4-4. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFEREN

Test runs PM10 emission factor, lb/VMT Duration, min 

Meteorology Vehicle characteristics 

Temperature, 

°F 

Mean wind 

speed, mph 

No. of vehicle 

passes 

Mean vehicle 

weight, ton 

Mean 

vehicle 

speeda

AU-C-3 0.00497 103 50 12 836 5.5 (27) AU-C-4 0.0355 147 63 11 1057 6.0 25 AU-C-5 0.0337 120 62 14 963 3.9 29 

AU-C-6c 0.00816c 187 39 14 685 6.2 (27) 

AU-C-7 0.000887 96 42 12 703 3.0 (27) AU-C-8 0.0174 218 40 15 779 2.0 (27) 

AU-E-1 0.00709 154 43 12 210 12 15 AU-E-2 0.0234 89 44 13 373 5.1 16 AU-E-3 0.0355 118 41 9.3 330 2.6 (15) AU-E-4 0.0199 130 41 9.3 364 2.6 (15) 

a Value in parentheses is the average speed measured for test road during the field exercise. b Test conducted on a paved road surface vacuum-swept five times per week. c Mean TSP/TP or PM10/TP ratio applied. 1 lb/VMT = 281.9 g/VKT. 1 g/m2 = 1.434 gr/ft2

4-11
TABLE 4-5. SUMMARY INFORMATION FOR REFERENCE 8

Operation State Test dates No. of tests 

PM15 emission factor, lb/VMT PM10 emission factor, lb/VMT PM2.5 emissio

Geom. mean Range Geom. mean Range Geom. mean 

Commercial/ 

Industrial 

MO 2/80 4 0.0078 0.0036 - 0.013 0.0068 0.0034 - 0.011 0.0045 

Commercial/ 

Residential 

MO, IL 2/80 10 0.0021 0.0006 - 0.012 0.0017 0.0004 - 0.0093 0.0011 

Expressway MO 5/80 4 0.0004 0.0002 - 0.0008 0.0004 0.0002 - 0.0007 0.0002 

Rural Town KS 3/80 1 0.031 0.031 0.025 0.025 0.005 

1 lb/VMT = 281.9 g/VKT. 

4-12
TABLE 4-6. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENC

Category Run test No. 

PM10 

emission 

factor, 

lb/VMT 

Duration, 

min. Temp., °F 

Mean 

wind 

speed, 

mph 

Road 

width, 

ft 

No. of 

vehicle 

passes 

Mean 

vehicle 

speed, 

mph 

Mean 

vehicle 

weight, 

tons 

Commercial/Industrial M-1 0.0110 120 28 7.4 44 2,627 30 5.6 Commercial/Industrial M-2 0.00340 86 27 6.5 44 2,166 30 3.8 Commercial/Industrial M-3 0.00781 120 28 7.8 44 2,144 30 4.5 Commercial/Industrial M-9 0.00712 136 50 7.4 44 3,248 30 4.1 Commercial/Residential M-4 0.000400 240 38 7.8 36 2,763 35 2.1 Commercial/Residential M-5 0.00153 226 53 2.2 36 2,473 35 2.2 Commercial/Residential M-6 0.00304 281 35 5.6 36 3,204 30 2.1 Commercial/Residential M-13 0.000680 194 60 2.7 22 5,190 35 2.7 Commercial/Residential M-14 0.00301 178 55 9.2 22 3,940 35 2.7 Commercial/Residential M-15 0.00323 135 77 11.4 22 4,040 35 2.7 Commercial/Residential M-17 0.00582 150 75 4.0 40 3,390 30 2.0 Commercial/Residential M-18 0.000800 172 75 5.1 40 3,670 30 2.0 Commercial/Residential M-19 0.000390 488 70 2.7 20 5,800 30 2.4 Expressway M-10 0.000390 182 60 2.9 96 11,148 55 4.5 Expressway M-11 0.000700 181 56 8.7 96 11,099 55 4.8 Expressway M-12 0.000190 150 65 4.7 96 9,812 55 3.8 Expressway M-16 0.000530 254 70 4.0 96 15,430 55 4.3 Rural Town M-8 0.0247 345 50 4.7 30 1,975 20 2.2 

1 lb/VMT = 281.9 g/VKT. 1 g/m2 = 1.434 gr/ft2 

4-13
4-14 

4.2.1.8 Midwest Research Institute, Size Specific Particulate Emission Factors for 

Uncontrolled Industrial and Rural Roads, for U. S. EPA, January 1983. (Reference 7, 

ref_07c13s0201_2011.pdf). 

 This document reports the results of testing conducted in 1981 and 1982 at industrial 

unpaved and paved roads and at rural unpaved roads. Unpaved industrial roads were tested at 

a sand and gravel processing facility in Kansas, a copper smelting facility in Arizona, and 

both a concrete batch and asphalt batch plant in Missouri. The study was conducted to 

increase the existing data base for size-specific PM emissions. The following particle sizes 

were of specific interest for the study: PM15, PM10, and PM2.5. 

 Exposure profiling was utilized to characterize total PM emissions. Five sampling 

heads, located at heights of up to 5 m, were deployed on the profiler. A standard high-volume 

sampler and a high-volume sampler with an SSI (cutpoint of 15 μmA) were also deployed 

downwind. In addition, two high-volume cyclone/impactors were operated to measure 

particle size distribution. A standard high-volume sampler, a high-volume sampler with an 

SSI, and a high-volume cyclone/impactor were utilized to characterize the upwind TSP and 

PM15 concentrations and the particle size distribution within the PM15 fraction. Wind speed 

was monitored with warm wire anemometers. 

 A total of 18 paved road tests and 21 unpaved road tests are completed. The test data 

are assigned an A rating. Industrial paved road tests were conducted as follows: three 

unpaved road tests at the sand and gravel processing plant, three paved road tests at the copper 

smelting plant, four paved road tests at the asphalt batch facility, and three paved road tests at 

the concrete batch facility. The industrial road tests were considered uncontrolled and were 

conducted with heavy duty vehicles at the sand and gravel processing plant and with medium 

duty vehicles at the asphalt batch, concrete batch, and copper smelting plants. Table 4-7 

presents summary test data and Table 4-8 presents detailed test information.
4-15 

TABLE 4-7. SUMMARY OF PAVED ROAD EMISSION FACTORS FOR REFERENCE 

Industrial 

category Type 

TP, lb/VMT PM15, lb/VMT PM10, lb/VMT P

Geo. 

mean Range Geo. mean Range Geo. mean Range Geo

mean 

Asphalt Batching Medium 

duty 

1.83 0.750-3.65 0.437 0.124-

0.741 

0.295 0.0801-

0.441 

0.130 

Concrete Batching 

Medium duty 

4.74 2.25-7.23 1.66 0.976-2.34 1.17 0.699-1.63 0.38

Copper Smelting Medium 

duty 

11.2 7.07-15.7 4.01 2.02-5.56 2.78 1.35-3.86 0.607 

Sand and Gravel Processing 

Medium Duty 

5.50 4.35-6.64 1.02 0.783-1.26 0.633 0.513-0.753 0.203 

1 lb/VMT = 281.9 g/VKT. 

4-15
4-16 

TABLE 4-8. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFER

Run 

No. 

Industrial 

category Traffic 

PM10

emission 

factor, 

lb/VMT 

Duration, 

min. 

Mean 

wind 

speed, 

mph 

Road 

width, 

ft 

No. of 

vehicle

passes

Vehicle characteristics

Moisture 

content, %

Mean vehicle weight, 

tons 

No. of 

wheels 

Mean 

vehicle 

speed, 

mph 

Y-1 Asphalt Batching Medium 

Duty 

0.257 274 5.37 13.8 47 3.6 6 10 0.22 

Y-2 Asphalt Batching Medium 

Duty 

0.401 344 4.70 14.1 76 3.7 7 10 0.51 

Y-3 Asphalt Batching Medium 

Duty 

0.0801 95 6.04 14.1 100 3.8 6.5 10 0.32 

Y-4 Asphalt Batching Medium 

Duty 

0.441 102 5.59 14.1 150 3.7 6 10 0.32 

Z-1 Concrete 

Batching 

Medium Duty 

0.699 170 6.71 24.3 149 8.0 10 10 a 

Z-2 Concrete 

Batching 

Medium Duty 

1.63 143 9.84 24.9 161 8.0 10 15 a 

Z-3 Concrete 

Batching 

Medium Duty 

4.01 109 9.62 24.9 62 8.0 10 15 a 

AC-4 Copper Smelting Medium 

Duty 

3.86 38 8.72 34.8 45 5.7 7.4 10 0.43 

AC-5 Copper Smelting Medium 

Duty 

3.13 36 9.62 34.8 36 7.0 6.2 15 0.43 

AC-6 Copper Smelting Medium 

Duty 

1.35 33 4.92 34.8 42 3.1 4.2 20 0.53 

AD-1 Sand and Gravel Heavy Duty 3.27 110 7.61 12.1 11 42 11 23 a AD-2 Sand and Gravel Heavy Duty 0.753 69 5.15 12.1 16 39 17 23 a AD-3 Sand and Gravel Heavy Duty 0.513 76 3.13 12.1 20 40 15 23 a 

1 lb/VMT = 281.9 g/VKT. 1 g/m2 = 1.434 gr/ft2a Not measured 

4-16
4-17 

4.2.1.9. Midwest Research Institute, Iron and Steel Plant Open Source Fugitive Emission

Control Evaluation, for U. S. EPA, August 1983, (Reference 6, 

ref_06c13s0201_2011.pdf).

 This test report centered on the measurement of the effectiveness of different control 

techniques for PM emissions from fugitive dust sources in the iron and steel industry. The 

test program was performed at two integrated iron and steel plants, one located in Houston, 

Texas, and the other in Middletown, Ohio. Control techniques to reduce emissions from 

paved roads, unpaved roads, and coal storage piles were evaluated. For paved roads, control 

techniques included vacuum sweeping, water flushing, and flushing with broom sweeping. 

Particle emission sizes of interest in this study were total PM, PM15, and PM2.5. 

 The exposure profiling method was used to measure paved road particulate emissions 

at the Iron and Steel plants. For this study, a profiler with four or five sampling heads located 

at heights of 1 to 5 m was deployed. Two high-volume cascade impactors with cyclone 

preseparators (cutpoint of 15 μmA), one at 1 m and the other at 3 m, measured the downwind 

particle size distribution. A standard high-volume sampler and an additional high-volume 

sampler fitted with a SSI (cutpoint of 15 μmA) were located downwind at a height 2 m. One 

standard high-volume sampler and two high-volume samplers with SSIs were located upwind 

for measurement of background concentrations of TSP and PM15. 

 Twenty-three paved road tests of controlled and uncontrolled emissions were 

performed. These included 11 uncontrolled tests, 4 vacuum sweeping tests, 4 water flushing 

tests, and 4 flushing and broom sweeping tests. For paved roads, this test report does not 

present vehicle speeds, mean number of wheels, or moisture contents. Because vehicle 

speeds above 15 MPH and moisture content are not expected to influence the emissions 

equation, the test data are assigned an A rating. Table 4-9 presents summary test data and 

Table 4-10 presents detailed test information. The PM10 emission factors presented in Table 

4-10 were calculated from the PM15 and PM2.5 data using logarithmic interpolation. 

After vacuum sweeping, emissions were reduced slightly more than 50 percent for two 

test runs and less than 16 percent for two test runs. Water flushing applied at 0.48 gal/yd2

achieved emission reductions ranging from 30 percent to 70 percent. Flushing at 0.48 gal/yd2

combined with broom sweeping resulted in emission reductions ranging from 35 percent to 

90 percent. 
TABLE 4-9. SUMMARY OF PAVED ROAD EMISSION FACTORS FOR REFERENCE 

Control 

method Location State Test date No. of tests TP, lb/VMT PM15, lb/VMT PM

Geo mean Range Geo mean Range Geo me

None A,D,F,J OH 7/80, 

10/80, & 

11/80 

7 1.22 0.29-5.50 0.38 0.13-2.14 0.10 

Vacuum Sweeping 

A OH 10/80 & 

11/80 

4 0.87 0.53-1.46 0.45 0.27-0.87 0.14 

Water Flushing 

D,L TX 6/81 4 1.43 1.30-1.74 0.47 0.32-0.65 0.08 

Flushing & Broom Sweep 

K,L,M TX 6/81 4 0.96 0.54-2.03 0.20 0.10-0.49 0.07 

None L,M TX 6/81 4 3.12 0.83-5.46 0.92 0.31-1.83 0.26 

1 lb/VMT = 281.9 g/VKT. 

4-18
TABLE 4-10. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENC

Site Test Run No. Control method PM10 emission factor, (lb/VMT) Duration (min.) Temp., (°F) Mean wind speed, (mph) No. of vehicle passes Mean vehicle weight, (tons) Si

A F-34 None 0.536 62 90 4.2 79 28 A F-35 None 0.849 127 90 7.5 130 25 A F-36 VS 0.147 335 50 5.9 263 8.3 A F-37 VS 0.209 241 50 4.8 199 17 A F-38 VS 0.430 127 50 4.5 141 18 A F-39 VS 0.686 215 50 6.4 190 18 D F-61 None 1.35 108 40 11.0 93 40 D F-62 None 0.929 77 45 12.1 94 36 D F-74 WF 1.32 205 50 9.0 67 29 

F F-27 None 0.357 91 100 9.5 158 14 F F-45 None 0.608 135 50 4.0 172 16 

J F-32 none 0.144 259 90 5.8 301 14 

K B-52 FBS 0.0946 60 90 2.9 119 12 

L B-50 FBS 0.230 104 90 5.6 123 9.4 L B-51 FBS 0.435 93 90 4.2 127 11 L B-54 WF 0.268 101 90 5.4 118 10 L B-55 WF 0.575 82 90 8.5 98 11 L B-56 WF 0.398 61 90 6.3 118 9.2 L B-58 None 1.08 96 90 6.7 67 18 

M B-53 FBS 0.161 81 90 5.3 72 20 M B-57 0.554 None 101 90 3.6 68 12 M B-59 0.993 None 114 90 6.1 67 11 M B-60 1.18 None 112 90 5.0 50 12 

aAverage of 2+ values bSample used for more than 1 run. c PM10 emission factors were calculated from the PM15 and PM2.5 data using logarithmic interpolation. 

VS = Vacuum sweeping; WF = Water flushing; FBS = Water flushing and broom sweeping; 1 lb/VMT = 281.9 g/VKT

1.434 gr/ft2

4-19
4-20 

4.2.1.10. Midwest Research Institute, Fugitive Particulate Matter Emissions for U.S. 

Environmental Protection Agency, Emission Factor and Inventory Group, April

15, 1997. (Reference 30, ref_30c13s0201_2011.pdf).

 This reference documents the performance of six field studies characterizing the 

vehicle emissions from three unpaved roads and three paved roads. Testing of unpaved roads 

was performed in Kansas City, MO; Raleigh, NC; and Reno, NV. Testing of paved roads was 

performed in Denver, CO; Raleigh, NC; and Reno, NV. Midwest Research Institute 

measured the emission rates for PM10 and PM2.5 at all six locations based upon a plume 

profiling methodology. The test data are assigned an A rating. 

 Plume profiling calculates emission rates using a conservation of mass approach. The 

passage of airborne particulate (i.e., the quantity of emissions per unit of source activity) is 

obtained by spatial integration of distributed measurements of exposure (mass/area) over the 

effective cross section of the plume. Exposure is the point value of the flux (mass/area time) 

of airborne particulate integrated over the time of measurement or, equivalently, the net 

particulate mass passing through a unit area normal to the mean wind direction during the test. 

The steps in the calculation procedure are as follows. The concentration of PM10 measured 

by a sampler is compared to the wind speed and corrected to standard conditions. The 

concentration for each sampler is multiplied by the wind velocity and sampling duration to 

obtain the exposure for each sampling height. The exposure is integrated over the plumeeffective cross section. The quantity obtained represents the total passage of airborne 

particulate matter (i.e., mass flux) due to the source. The exposure is set to zero at the 

maximum effective height of the plume where the net concentration equals zero). The 

maximum effective height of the plume is found by linear extrapolation of the uppermost net 

concentrations to a value of zero. Although at ground level the wind velocity is zero, for 

calculation, the exposure value at ground level is set equal to the value at a height of 1 m. The 

integration is then performed from 1 m to the plume height, H, using Simpson's 

approximation. 

 Testing in Denver CO was conducted to characterize emissions from a high speed (55 

mph speed limit) limited access interstate road and a medium speed (40 mph speed limit) one 

lane road (two lanes with a wide median). For this part of the study, a profiler with four or 

five sampling heads located at heights of 1, 3, 5 and 7 m were deployed. One high-volume 

cascade impactor with cyclone preseparators (cutpoint of 10 μmA) and two dichotomous 

samplers were used to measured the downwind particle size distribution. All of the particle 

sizing samplers were located at 2 m above ground level. A single set of the same sampling 

equipment was located at 2 m above ground level and upwind for measurement of background 

concentrations of TSP, PM10 and PM2.5. To the extent possible, each of the emission tests was 

performed during periods following snowfall, after the test road surface had dried. In most 

cases, sand application was ordered, because the relatively light snow conditions 

characteristic of the 1996 winter did not trigger routine sand application. 

 This test program also assessed the potential bias associated with particle sizing using 

the historical impactors that followed the cyclone pre-separator. The use of the dichotomous 

samplers consistently yielded a lower ratio of PM2.5 to PM10 ratio than were measured by the 

cyclone/impactor samplers. The PM2.5/ PM10 ratios measured by the dichotomous samplers 

are presented to the right of the PM10 emissions factors column in Table 4-11. Where two 
4-21 

values are presented in the column, these are the ratios measured at two different heights. The 

ratios range from 0.26 to 0.37. As a result of this study, the constant in the PM2.5 emissions 

factor equation was revised to 25% of the PM10 constant.

4.2.1.11. Paved Road Modifications to AP-42, Background Documentation For Corn

Refiners Association, Inc. Washington, DC 20006 MRI Project No. 310842, May 

20, 2008. (Reference 32, ref_32c13s0201_2011.pdf).

 The Corn Refiners Association (CRA) funded four paved road PM10 test programs 

because site conditions did not match source conditions underlying the AP-42 emission factor 

equation. The sites enforce speed limits of 5 or 15 mph and employ road sweeping programs 

to manage the build up of silt on the roadways. In addition, plants experience traffic queues 

(i.e., stop-and-go traffic) during periods with high corn receipts. The combination of heavy 

trucks (delivering corn to the facilities) and fairly low silt loading (sL) values on the plant 

roads was not typical of the AP-42 data base. Given these differences, the member companies 

undertook testing to develop more representative emission factors. Midwest Research 

Institute designed and conducted the test programs at all four facilities. 

 Reference 32 compiles test data and information from references 33, 34, 35 & 36. In 

addition, reference 32 proposes an expansion of the allowable speed parameters supported in 

the paved road equation. Lastly, reference 32 proposes a revised equation for paved roads to 

reflect the expanded test information. The data upon which the proposed equation was based 

included emissions associated with the trucks (engine exhaust, tire wear and brake wear) and 

with material deposited on the roadway. Since testing documented in references 7 through 10 

were conducted at facilities with very similar operating conditions using test procedures that 

were nearly identical, the following description provides background for all four test 

programs. 

 All four testing programs employed the same exposure profiling method used to 

develop the test data underlying the emission factor predictive equations for both paved and 

unpaved roads. In each program, a test plan was submitted to the state agency for comment 

and review prior to the start of testing. The final test reports and supporting information were 

also submitted to state agencies. Because low emission levels were expected (due to low sL 

and slow speeds), several precautions were taken to assure reliable quantification. First, long 

sampling durations were employed. Samplers were operated up to 5 hours to collect adequate 

sample mass. Second, to ensure adequate traffic during test periods, the facilities provided 

“drone” passes by corn semi-trailers. Drone traffic mimicked the actual traffic except those 

trucks returned to staging areas without emptying corn. In addition, testing applied “lessons 

learned” throughout the programs. For example, when it became apparent how difficult it 

could be to separate net PM10 concentrations (i.e., due to traffic on the road) from background 

(upwind) concentrations, changes were made in equipment deployment. The use of identical 

upwind and downwind vertical sampling arrays permitted better definition of the net 

contribution of roadway emissions. 
TABLE 4-11. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENC

Site 

Test 

Run 

No. 

Road 

Speed 1

PM10

emission 

factor, 

(g/VKT) 

PM2.5/ 

PM10 Ratio

Duration, 

min. 

Temp., 

°F 

Mean wind 

speed, mph

No. of 

vehicle passes

Mean 

vehicle 

weight, tons

Silt loa

g/m

CO BH-1 55 1.08 0.20 163 18 2.7 6,561 2.2 0.1CO BH-2 55 0.102 0.34 360 37 17.0 17,568 2.2 0.01CO BH-3 55 - 0.16 360 46 17.2 14,616 - 0.01CO BH-4 55 - Blank - - - - - CO BH-5 40 - Blank - - - - - CO BH-6 40 4.68 0.03 240 48 3.1 3,112 2.2 1.4NC BJ-6 45 0.301 0.27/0.34 450 71 8.2 14,670 2.2 0.0NC BJ-7 45 1.94 0.44/0.44 143 68 9.4 3,748 2.2 0.0NC BJ-9 45 0.6/0.14 178 71 5.3 4,616 2.2 0.0NC BJ-10 45 0.44/0.33 288 68 3.7 10,218 2.2 0.0

NV BJ-11 45 0.68/0.47 387 75 5.1 13,216 2.2 0.0NV BK-7 45 0.57 0.29/0.33 420 89 7.3 7,394 2.2 0.0NV BK-8 45 0.44 0.26/0.34 270 87 6.1 5,747 2.2 0.0NV BK-9 45 - 0.13/0.38 240 90 2.6 4,622 - 0.0

1 Road Speed is the posted speed limit for the road segment.

4-22
4-23 

In addition to PM10 concentrations, each sampling program samples included: 

• Measurement of average wind speeds at two heights and wind direction at one height 

for 5-minute intervals throughout the test period. 

• Manual recording of traffic counts by vehicle type. The host facilities provided 

information on vehicle weights and corn receipts. 

• Collection of road surface material by vacuums with disposable paper bags. The 

material collected within the bag was sieved to determine the surface silt loading. 

 Reference 32 states that the four test programs conducted by CRA produced 14 and 8 

PM10 emission factor values for slowly moving and stop-and-go traffic, respectively. Other 

observations in this report includes: that in all but one of the 22 cases, the AP-42 emission 

factor overestimated the measured value; that for some tests, “stop-and-go” emission factors 

were substantially greater than the “slowly moving” factor (presumably because of the diesel 

exhaust as trucks moved from a dead stop) but that there was no significant difference 

between “slowly moving” and “stop-and-go” results on average. 

 Furthermore, Tables 4-12, 4-13, and 4-15 use bold font to indicate those tests that 

used identical upwind and downwind vertical sampling arrays. Those tests provided better 

definition of net PM10 mass thus producing more accurate emission factors. Although these 

test results tended to be lower than the other emission factors, the two sets on average did not 

differ significantly.

4.2.1.12 Midwest Research Institute, Emission Tests of Paved Road Traffic at 

Minnesota Corn Processors Marshall, Minnesota Facility, McVehil-Monnett Associates, 

July 6, 2001. (Reference 33, ref_33c13s0201_2011.pdf).

 Truck traffic flow at the Minnesota Corn Processor’s (MCP’s) Marshall, Minnesota 

facility was characterized as either slowly moving (5 mph enforced speed limit) or stop-andgo in nature. In this testing program, data was collected over 5 days during April of 2001. 

During this period, three stop-and-go traffic situations and six slowly moving traffic 

instances were examined. Truck traffic progressing through the test site was held to two 

lanes for queued traffic. Silt content (sL, measured by MCP), truck weight, and number of 

passes, along with other pertinent data was recorded for each run. For all runs, a vertical 

network of samplers was operated downwind. The last test period used a vertical array of 

samplers upwind to better characterize upwind concentrations and to provide a more accurate 

calculation of the net PM10 emission factor. 

 The results of this testing program are summarized in Table 4-12. The test data are 

assigned an A rating. The test report remarked that the emission factors obtained were far 

below the value (0.453 lb/VMT) used in the plant emission inventory. Use of test-specific 

silt loading and vehicle weight did not significantly improve the predictive accuracy of the 

AP-42 factor. The tests found no discernable relationship between emission levels and either 

silt loading or vehicle weight. Finally, it was noted that the shape of the exposure profile 

was more likely due to diesel exhaust than re-entrained road dust. 
4-24 

Table 4-12. Summary of Emissions Data from MCP’s Marshall, Minnesota Facility 

(Reference 33)

Run Test condition 

Traffic 

rate 

(veh/hr) 

Traffic speed 

(mph)a

Mean vehicle 

weight, W 

(tons) 

Surface silt 

loading, sL 

(g/m2

) 

Measured PM10

emission factor 

(lb/VMT) 

CE-1 Stop-and-go 38 NA 36 1.16 0.059 

CE-2 Stop-and-go 32 NA 36 0.86 0.14 

CE-11 Slowly moving 35 5 12 1.34 0.34 

CE-3 Stop-and-go 47 NA 39 0.86 0.10 

CE-13 Slowly moving 48 5 13 1.34 0.051 

CE-15 Slowly moving 30 5 40 1.91 0.14 

CE-16 Slowly moving 28 5 40 1.41 0.17 

CE-17 Slowly moving 29 5 40 2.93 0.091 

CE-19 Slowly moving 61 5 38 0.76 0.041 

a

 Vehicle speed was maintained at the plant limit of 5 mph. NA = Not applicable. 

 Bold entries indicate that identical vertical sampling arrays were used to better isolate the 

source contribution. 

4.2.1.12. Midwest Research Institute, Emission Tests of Paved Road Traffic at Minnesota

Corn Processors Columbus, Nebraska Facility, McVehil-Monnett Associates, July

13, 2001. (Reference 34, ref_34c13s0201_2011.pdf).

 Truck traffic flow at MCP’s Columbus, Nebraska facility was characterized as either 

slowly moving (5 mph enforced speed limit) or stop-and-go in nature. Between June 12 and 

15, 2001, four tests each of stop-and-go and slowly moving traffic were performed. Trucks 

entered by the north gate and traveled past a vertical sampling array en route to a staggered 

queue at which a second vertical sampling array was positioned. In this way, testing 

evaluated both source conditions (stop-and-go and slowing moving) at once. Building on 

experience from testing at the MCP Marshall facility, the last two runs, CF-4 and CF-5, used 

identical upwind and downwind vertical sampling arrays to better characterize background 

concentrations. In that case, only one condition could be evaluated during a test. The results 

of the MCP Columbus test program are summarized in Table 4-13. The test data are 

assigned an “A” rating. 

4.2.1.13. Midwest Research Institute, Emission Tests of Paved Road Traffic at Cargill 

Sweeteners North America Blair, Nebraska Facility, McVehil-Monnett Associates, 

November 27, 2002. (Reference 35, ref_35c13s0201_2011.pdf). 

 This report describes a testing program conducted at Cargill’s Blair, Nebraska facility 

during August 2002. The plant used a regular sweeping program to reduce surface loadings 

on paved roads. Testing relied on regular corn truck traffic at the site, although the plant 
4-25 

provided a limited amount of “drone” traffic. The test data are assigned an “A” rating. 

 Eight PM10 emission tests were attempted. The test report describes difficulty 

encountered in isolating net PM10 mass due to traffic on the test road. During test plan 

review, the Nebraska Department of Environmental Quality requested a change in test site to 

allow two trucks to pass by at the same time. The original site would have permitted upwind 

monitoring in the immediate vicinity of the tests road, but this was not possible at the second 

location. Furthermore, steeply sloping ground on the upwind side of the test road prevented 

use of a vertical background sampling array (as used at the two MCP plants) to better isolate 

the source contribution. 

 The results are summarized in Table 4-14. Only two tests (CI-7 and CI-8) had net 

mass attributed to the source. In the remaining instances, the measured downwind PM10

concentrations were lower than upwind values. It was stated that this was believed to be an 

undesired result from moving the test source. Runs CI-7 and CI-8 showed the measured 

emission factor to be much lower than that predicted by the AP-42 equation. Comments in 

the report indicated that exposure profiles showed a maximum more likely due to diesel 

exhaust than from re-entrained surface road dust. 

4.2.1.14. Midwest Research Institute, Emission Tests of Paved Road Traffic at ADM’s 

Marshall, Minnesota Facility, McVehil-Monnett Associates, December 5, 2003. 

(Reference 36, ref_36c13s0201_2011.pdf). 

 The test program at ADM’s Marshall MN facility represented the last test by the 

Corn Refiners Association. By September 2003, the Marshall facility had implemented a 

road sweeping program. Three tests of PM10 emissions were conducted, one from stop-andgo traffic and two from slowly moving traffic. Because of experience gained from the 

earlier tests, identical vertical networks of samplers were operated downwind and upwind 

during each test. 

 The results of this testing program are summarized in Table 4-15. The test data are 

assigned an A rating. Measured emission factors were all significantly lower than that 

predicted by the AP-42 equation. The test report also remarked that the measured emission 

rates were independent of traffic rate, while the AP-42 factor implies a linear dependency 

between the emission and traffic rates. 

 The results are summarized in Table 4-14. Only two tests (CI-7 and CI-8) had net 

mass attributed to the source. In the remaining instances, the measured downwind PM10

concentrations were lower than upwind values. It was stated that this was believed to be an 

undesired result from moving the test source. Runs CI-7 and CI-8 showed the measured 

emission factor to be much lower than that predicted by the AP-42 equation. Comments in 

the report indicated that exposure profiles showed a maximum more likely due to diesel 

exhaust than from re-entrained surface road dust. 
Table 4-13. Summary of Emissions Data from MCP’s Columbus, Nebraska Facility (Refere

Runa Test condition 

Traffic rate 

(veh/hr) 

Traffic 

speed 

(mph)b

Mean vehicle 

weight, W (tons) 

Surface silt loading, 

sL (g/m2) 

Measured P

factor 

CF-1/N Low Speed 47 5.0 40 0.97 0CF-1/S Stop-and-go 47 NA 40 0.97 0CF-2/N Slowly moving 66 5.3 41 0.81 0CF-2/S Stop-and-go 66 NA 41 0.81 0CF-3/N Slowly moving 54 5.1 41 0.63 0.CF-3/S Stop-and-go 54 NA 41 0.63 0CF-4/N Slowly moving 86 4.7 41 1.1 0.CF-5/N Stop-and-go 52 NA 41 1.4 0a Suffix indicates whether tests was conducted on the North or South portion of the corn haul road. Trucks 

a queue toward the south; trucks entering the north gate traveled passed the north sampling array to reach 

b Speed of moving trucks determined by accumulating time required to travel a measured distance. NA = n

applicable. 

 Bold entries indicate that identical vertical sampling arrays were used to better isolate the source contribu

4-26
Table 4-14. Summary of Emissions Data from Cargill’s Blair, Nebraska Facility (Reference 3

Run Test condition 

Traffic rate

(veh/hr) 

Traffic speed 

(mph) a

Mean vehicle weight, 

W (tons) 

Surface silt loading, 

sL (g/m2

)b 

Measured PM

factor (lb

CI-1 Low Speed 45 13.4 / 16.8 

12.8 / 16.9 

26 0.06 - 

CI-2 Low Speed 45 26 0.06 - CI-3 Slowly moving 60d 13.6 / 12.7 13.5 / 15.5 27 0.06 - CI-4 Low Speed 60d 27 0.06 - CI-7 Slowly moving 47 15.2 / 16.2 13.6 / 16.1 27 0.05 0.00CI-8 Low Speed 47 27 0.05 0.00CI-11 Low Speed 56 13.5 / 12.7 27 0.025 - CI-12 Low Speed 56 27 0.25 - a Vehicle speed for inbound (loaded) /outbound (empty) trucks determined by accumulating time required to tr

measured distance. b Surface silt loading sample information provided by Cargill. 

c “-“ indicates that no net mass was attributed to the test road traffic. d Twenty of 238 total passes were by “drone” trucks. 

Table 4-15. Summary of Emissions Data from ADM’s Marshall, Minnesota Facility (Reference 

Run Test Condition 

Traffic rate 

(veh/hr) 

Traffic 

speed 

(mph)a

Mean vehicle 

weight, W 

(tons) 

Surface silt 

loading, sL 

(g/m2) 

Measured PM

emission fac

(lb/VMT) 

CM-1 Slowly moving 154 NA 40 0.72 0.014 CM-2 Stop-and-go 42 NA 40 0.72 0.14 CM-4 Slowly moving 156 5 40 0.70 0.016 

a Vehicles speeds maintained at plant limit of 5 mph. NA = not applicable. 

Bold entries indicate that identical vertical sampling arrays were used to better isolate the source 

contribution.

4-27
4-28 

4.2.1.15. E.H. Pechan & Associates, Inc., Recommendations for Emission Factor 

Equations in AP-42 Paved Roads Section: TECHNICAL MEMORANDUM August 21, 

2003. (Reference 28, ref_28c13s0201_2011.pdf).

 This technical memorandum documents the procedure that was used to separate the 

various components of paved road particulate matter emissions into two components. One 

component includes the emissions from exhaust, brake wear and tire wear. The other 

component includes the particulate matter reentrained from the road surface. The combined 

paved road particulate matter emissions were estimated with the empirical equation 

published in the October 2002 AP-42 Section for Paved Roads. The vehicle exhaust, 

brakewear and tirewear emission factors were obtained from the MOBILE6.2 model. A 

typical vehicle fleet and fuel source from 1980 was utilized for the model runs. The 

assumption included a vehicle fleet for July 1980, a gasoline sulfur content of 300 ppm, a 

diesel sulfur content of 500 ppm and no use of reformulated gas. The vehicle fleet 

assumptions used in the analysis are presented in Table 4-16. The model was run to estimate 

PM10 and PM2.5 emission factors in g/VMT for each vehicle class at speeds of 25, 30, 35, 40, 

45, 50, 55, and 60 mph. Within vehicle classes, the greatest standard deviation was lower 

than 0.04% of the emissions factor. Based on the low relative standard deviation, it was 

assumed that the vehicle speed was not a factor in exhaust, brakewear and tirewear PM 

emissions. Table 4-16 presents the vehicle fleet characteristics used in the model and the 

calculated average PM10 and PM2.5 emission factors for exhaust, brakewear and tirewear for 

each class of vehicle. 

Table 4-16: Vehicle Fleet Assumptions Used in 2003 MOBILE6.2 Model

VehicleType LDGV LDGT12 LDGT34 LDGT HDGV LDDV LDDT HDDV MC 

GVWR 3,075 4,105 7,000 35,000 3,705 6,000 70,000 550 

VMT Distribution 0.6748 0.1477 0.0758 0.0365 0.0088 0.0118 0.0352 0.0094 

PM10 Emissions 

Factor 

0.1053 0.1061 0.2746 0.1632 0.3825 0.7206 0.7206 2.1227 0.0922 

PM2.5 Emissions 

Factor 0.0686 0.0690 0.1851 0.1084 0.2576 0.6519 0.6521 1.9272 0.0590 

 The contractor developed “AP-42 Composite” PM10 and PM2.5 emission factors using 

the October 2002 AP-42 paved roads emission factor equation with the mean vehicle weight 

set at 3.74 tons (a value they indicated was typical of the 1980 paved road vehicle fleet. The 

contractor used silt loadings ranging from 0.02 to 400 g/m2 for calculating the emissions 

factors. The contractor also calculated the fleet average PM10 and PM2.5 emission factors for 

exhaust, brakewear and tirewear by summing the products of the VMT Distribution ratio and 

the PM10 and PM2.5 emission factors for each vehicle class. The calculated fleet average 

values were 0.2119 for PM10 and 0.1617 for PM2.5. The contractor then subtracted the fleet 

average emissions factors for exhaust, brakewear and tirewear from the “AP-42 Composite” 

emissions factors to produce an emission factor for only the re-entrained road dust 

component. The contractor noted that the while the stated applicable silt loadings for the 

October 2002 AP-42 paved road equation ranged from 0.02 to 400 g/m2

 the PM2.5 emissions 

factor became negative at silt loadings less than 0.029 g/m2. They stated that since negative 

emissions were not physically possible, the equation they recommended was only valid for 
4-29 

silt loading ranging from 0.03 to 400 g/m2. While no test data are associated with this report, 

the report does provide estimates of engine exhaust, tire wear and brake wear derived from 

an EPA emissions model which is based upon emissions testing by a validated test method 

on multiple vehicles for each type of vehicle. As a result, emissions estimates by vehicle 

class are assigned an A rating. Because the use of a national average vehicle fleet emissions 

estimate does not provide emissions that are representative of the mix of vehicle classes 

measured during the above test reports, the composite emissions estimates are assigned a C 

rating.

4.2.1.16. E-mail communication between Ron Myers of EPA/OAQPS/SPPD/MPG, 

RTP, NC and Prashanth Gururaja and Ed Glover of EPA/OTAQ/ASD/HDOC re. 

Diesel exhaust, tire and brake wear for low speed stop and go traffic; January 2009 

through May 2009. (Reference 37, ref_37c13s0201_2011.pdf).

 

 This e-mail communication and spreadsheet file concerns estimates of PM10

emissions associated with slow moving and stop and go diesel engine semi-trailer trucks. 

The purpose of the request was to provide a means to disaggregate the consolidated PM 

emissions measured of trucks during delivery of product at corn storage and transfer 

facilities. The request stated that the trucks were 18 wheel semitrailers of about ten years of 

age, were queued for the delivery of their load to a transfer or processing facility and that the 

estimated vehicle speed averaged about 1 mph but that they were stopped most of the time. 

PM2.5 emissions were estimated using the MOVES mobile source emissions model. The 

trucks modeled were approximately ten years old, traveling at an average of 1.5 mph on level 

pavement. Emissions were estimated at 11.06035 g/hour or 8.789778 g/VMT. PM10

emissions were estimated to be approximately 3% greater than PM2.5 emissions. While no 

test data are associated with this report, the report does provide estimates of engine exhaust, 

tire wear and brake wear derived from an EPA emissions model which is based upon 

emissions testing by a validated test method on multiple vehicles for the specific type of 

vehicle measured during the Corn Refiners Association Studies. As a result, emissions 

estimates for slow moving trucks are assigned an A rating. 

4.2.1.17. E-mail communication between Ron Myers of EPA/OAQPS/SPPD/MPG, 

RTP, NC and Gary Dolce, David Brzezinski and Rudolph Kapichak of 

EPA/OTAQ/ASD/HDOC re. vehicle exhaust, tire and brake wear for urban 

unrestricted road-types; October 2010 through December 2010. (Reference 39, 

ref_39c13s0201_2011.pdf).

 This e-mail communication and spreadsheet files concern improved estimates of 

PM10 emissions associated engine exhaust, tire wear and brake wear for free flowing traffic. 

The purpose of the estimates was to update the emissions estimates produced by E. H. 

Pechan using the 2003 version of MOBILE6.2. The emissions model used for this updated 

emissions estimates was the 2010 version of the MOVES model. Like the MOBILE6.2 

model, the emissions predicted with the MOVES model provide a means for disaggregating 

the emissions measured during the paved road field studies that measured emissions due to 

road surface dust, vehicle exhaust, break wear and tire wear. 

It is explained in the documentation that in order to develop an equation for road dust 

alone, estimates of the particulate emissions from vehicle exhaust, brake wear and tire wear 
4-30 

were required. The e-mail documentation states that the MOVES model includes significant 

new data about PM emissions from both light duty and heavy duty on‐road vehicles which 

allow MOVES to account for the influences of ambient temperature, vehicle speed, and 

vehicle deterioration on emissions. The documentation further states that none of those 

factors are accounted for in MOBILE6.2. 

Documentation includes information provided to OTAQ on the test date (sometimes 

month and year, sometimes just year), vehicle speed, ambient temperature, and average 

vehicle weight for each of the paved road field studies. The documentation states that OTAQ 

created a MOVES2010a model input file that approximated the information for the paved 

road field studies as closely as possible. The documentation also states that since 

MOVES2010a provides output for calendar years 1990 and 1999‐2050 alternative scenarios 

were developed to estimate emissions for years which MOVES2010a is not programmed to 

provide. 

The documentation states that the speed and ambient temperature measured during 

the field study provided additional independent variables used in the MOBILE2010a model 

to estimate emissions. The documentation indicates that an emissions estimate was produced 

for each of the individual tests by allocating all of the vehicle activity to a single 

MOBILE2010a speed bin which included the vehicle speed observed in the test. To reduce 

the number of number of total runs needed, temperatures for the individual tests were 

rounded to the nearest multiple of 5 degrees. In a small number of cases, vehicle speed or 

temperature data were not available for particular tests. In those cases, a vehicle speed of 25 

mph or an ambient temperature of 75 degrees was used. All other inputs to MOVES were 

national defaults. 

All vehicle and fuel type combinations (except for electric vehicles) were included. 

Emissions were generated only for the urban unrestricted road‐type. Emissions were 

generated for all PM10 pollutants (primary exhaust PM10 total, primary PM10 brake wear, 

and primary PM10 tire wear. Only running exhaust and crankcase running exhaust processes 

were included in the exhaust emissions calculations as the test sites did not include any 

starting or idling activity. Inventory results generated by MOVES source type (vehicle type) 

were divided by VMT to get emission factors by source type for each speed and temperature 

bin in the original test data. 

Emissions estimates for free flowing light duty vehicles and trucks are assigned an B 

rating since most of the test data were for model years which an alternative emissions 

scenario (year, vehicle mix and assumed degradion level) was used as the independent 

variables used in the MOVES model input file. While it is likely that vehicle emissions prior 

to 1990 had tailpipe emissions very similar to the 1990 model year, this can not be verified. 

Also, while the emissions for each test are comprised of a large number of vehicles and the 

emissions factor produced by the MOVES model are based upon a large number of 

supporting tests, it is unclear that the MOVES model is an accurate and precise indication of 

the vehicle exhaust, tire wear and brake wear emissions during each test series. 
4-31 

4.2.1.18. Midwest Research Institute; Analysis of the Fine Fraction of Particulate Matter 

in Fugitive Dust; Western Governors’ Association - Western Regional Air Partnership 

(WRAP); October 12, 2005. (Reference 43, ref_43c13s0201_2011.pdf).

 This project was conducted by Midwest Research Institute for the Western Regional 

Air Partnership to provide more accurate PM2.5 and PM10 fugitive dust emissions inventories 

for regional haze regulatory purposes to address the significant contribution of fugitive dust 

to visibility impairment. The results of this project were expected to affect the quantity of 

dust apportioned to the fine versus coarse size modes. It was stated that the results would be 

helpful in developing accurate emission inventories for PM nonattainment, maintenance, and 

action plan areas in the WRAP region. Finally, it was stated that the results may be used to 

seek modifications to the EPA’s AP-42 emission factors to ensure widespread availability of 

the information developed in the study. 

 During the first testing phase of the project, PM2.5 measurements using the highvolume cascade impactors were compared to simultaneous measurements obtained using 

EPA reference- method samplers for PM2.5. The tests were conducted in a flow-through 

wind tunnel and exposure chamber, where concentration level and uniformity were 

controlled. With the same test setup, a second phase of testing was performed with reference 

method samplers, for the purpose of measuring PM2.5 to PM10 ratios for fugitive dust from 

different geologic sources in the West. The testing provided information on the magnitude 

and variability of PM2.5 to PM10 ratios for source materials that were recognized as 

problematic with regard to application of mitigative dust control measures. 

 Three dust source materials were tested under the first Phase of the study. The three 

dust source materials included an Owens Dry Lake surface soil, and two Arizona road dust 

reference standards (one coarse and one fine fraction material). Fixed PM10 concentration 

levels in the range of 1, 2.5, and 5 milligrams per cubic meter (each with its naturally 

occurring PM2.5 level) were tested. It was stated that those PM10 concentration levels were 

selected as representative of dust plume concentrations under which major particle mass 

contributions to plume samples occur in emission factor development. The ratios of PM2.5 to 

PM10 for fugitive dust from different geologic soil types were measured. A total of seven 

source materials were tested. The materials included Alaska river bed sediment, Arizona 

alluvial channel, Arizona agricultural soil, New Mexico unpaved landfill road dust, New 

Mexico grazing soil, California Salton Sea shoreline soil, and Wyoming unpaved road 

surface material. Test results included the calculation of the average PM2.5 concentration and 

the collocated PM10 concentration. It was intended that any variation in PM2.5/ PM10 ratio be 

evaluated as a function of the test soil properties (for example, position in soil texture 

triangle). 

 A total of 100 individual tests were performed, including 17 blank runs (for quality 

assurance purposes). The results of the testing are well documented and the documentation 

is sufficient to assess that the study was well designed and implemented. This was a 

laboratory study designed to assess those emissions sources that were considered to have the 

greatest influence in PM10 and PM2.5 non attainment areas. As a result, the study is assigned 

a quality rating of B when applied within the bounds of the type of surface material that was 

available and for dust generation characteristics comparable to those used in the study. The 
4-32 

study included no paved road surface material and was weighted toward higher particulate 

matter concentrations. Since the study was a laboratory study, did not include any paved road 

surface materials, and was weighted toward higher particulate concentrations, it is assigned a 

quality rating of “D” when used for paved roads. 

 The results of the Phase I testing indicated that the PM2.5 concentrations measured by 

the cyclone/impactor system were consistently biased by a factor of about 2 relative the 

PM2.5 concentrations measured by the Partisol samplers. While there was some data 

separation of different test materials, the second phase testing showed a tendency of the 

measured PM2.5/ PM10 ratio to decrease with increasing PM10 concentration. At PM10

concentrations above 1.0 mg/m3 the PM2.5/ PM10 ratio was between 0.1 and 0.15. The 

PM2.5/ PM10 ratio increased to about 0.35 as the PM10 concentration approached about 0.5 

mg/m3. 

4.2.1.19. Midwest Research Institute; Background Document for Revisions to Fine 

Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors; Western Governors’ 

Association - Western Regional Air Partnership (WRAP); November 1, 2006. 

(Reference 44, http://www.epa.gov/ttn/chief/ap42/ch13/bgdocs/b13s02.pdf).

This report summarizes the results of the October 2005 WRAP study which evaluated 

the PM2.5/ PM10 ratio measured by the cyclone/impactor system and measured by the Partisol 

samplers. While no additional analyses of the laboratory study were performed, suggested 

PM2.5/ PM10 ratios were made for use in revising existing AP-42 emissions factor parameters 

for PM2.5 dust emissions factor equations in Sections 13.2.1 (paved roads), 13.2.2 (unpaved 

roads), 13.2.3 (material transfer and storage piles), 13.2.4 (windblown dust) and 13.2.5 

(industrial wind erosion). A revised PM2.5/PM10 ratio of 0.15 was recommended for the 

paved roads emissions factor. 

4.2.1.20. Technical Memorandum from William B. Kuykendal to File, Subject: 

Decisions on Final AP-42 Section 13.2.1 “Paved Roads”, October 10, 2002. (Reference 

38, ref_38c13s0201_2011.pdf). 

 This technical memorandum to the files summarizes and responds to comments on an 

October 2001, EPA proposed revision of Section 13.2.1 “Paved Roads” for AP-42 and 

request for comments. The memorandum also presents EPA’s decisions and rational 

supporting these decisions for the final changes leading to the final section. The proposed 

revisions to the section included an adjustment for rain events (comparable to the adjustment 

in the unpaved road section) which in essence “zeroed” the emissions on days that more than 

0.01 inch of rain was recorded. In addition, the proposed revisions included the separation of 

vehicle engine exhaust, breakwear and tirewear as recommended in the E. H. Pechan 

Technical Memorandum of August 21, 2003. The memorandum includes attachments with 

the detailed comments that lead to the final revision of the emissions factor equation. The 

final changes to the emissions factor equation included: 

• the subtraction of 0.2119 g/VMT for engine exhaust, brakewear and tirewear, 

• an adjustment of (1- (P/4N)) for rain events (P = number of rain days and N = number 

of days in period), and 

• an adjustment of (1- (1.2P/N)) for rain events (P = number of rain hours and N = 
4-33 

number of hours in period). 

4.2.1.21. Clark County (Nevada) Paved Road Dust Emission Studies in Support of Mobile 

Monitoring Technologies; R. Langston, R.S. Merle Jr, V. Etyemezian, H. Kuhns, J. 

Gillies, D. Zhu, D. Fitz, K. Bumiller, D.E. James and H. Teng; Clark County 

Department of Air Quality and Environmental Management, Desert Research Institute, 

University of California, Riverside, University of Nevada, Las Vegas; December 22, 

2008. 

(Reference 42, http://www.epa.gov/ttn/chief/ap42/ch13/related/Final_Test_Report.pdf). 

This report documents the fourth phase of a study by Clark County to investigate 

alternative ways of estimating PM10 emissions of surface dust entrained from paved roads. A 

new vehicle-mounted mobile sampling technology was tested in comparison with the 

traditional AP-42 method and its associated road surface sampling. In addition, the plume 

flux profiling method, was used to calibrate the mobile monitoring technology. 

Two versions of the mobile monitoring technology were tested—TRAKER and 

SCAMPER. Both technologies involve on-board sampling of the dust plume generated by a 

test vehicle. Both use continuous optical based PM10 particle monitors in conjunction with 

GPS systems, so that dust plume concentrations can be mapped on to the road system 

traveled by the test vehicle. The SCAMPER samples the plume in the wake of the test 

vehicle. The TRAKER I and II test vehicles sample the plumes from the front wheel wells of 

the respective vehicles. TRAKER II has a dilution system to provide for use on unpaved 

roads. All three units have samplers that monitor the PM10 concentration in front of the 

vehicle so that “background” PM10 can be subtracted. 

The referenced study evaluated mobile monitoring technologies in comparison with 

the traditional AP-42 methodology, but in a controlled measurement environment that 

included restricted vehicle movement, controlled vehicle speeds and controlled road surface 

material loadings. This was accomplished by dedicating half of a divided roadway as the test 

course for the 5-day field study. The stated specific study objectives were as follows: 

• Comparison of SCAMPER and TRAKER system measurements with emission 

measurements using a downwind flux tower. 

• Determination of the relationship between roadway silt loading and SCAMPER and 

TRAKER measurements at several standard vehicle speeds (25, 35 and 45 mph). 

• Comparison of SCAMPER and TRAKER measurements to AP-42 emission 

estimates. 

• Characterization of road surface silt depletion rate as a function of the number of 

vehicle passes. 

• Characterization of quantified emissions vs. quantified silt loading mass. 

• Data assessment and review for recommendations on performance specifications for 

vehicle-mounted mobile sampling systems. 

Particle concentration measurements formed the basis for the mobile monitoring 

technologies as well as the roadside emission flux measurements. A continuously recording 

optical light scattering particle monitor (DustTrak Model 8520, TSI Inc., Shoreview MN) 

was the basic instrument used for PM10 readings. A collocated mass-based reference monitor 
4-34 

was used to correct the DustTrak readings to equivalent PM10 mass-based concentrations, 

using a plume profiling tower with various reference, reference equivalent and DustTrak 

monitors at different heights. Canister vacuum cleaners with hard-floor inlets were used to 

recover applied soil from the roadway sites into pre-tared vacuum bags. Three soil recovery 

techniques were used during the study. Road dust emission factors were then calculated for 

the silt loadings using the 2006 AP-42 emission factor equation. A weight of 2.88 tons, 

based on the arithmetic average of the reported weights of the three mobile source vehicles 

was used to calculate the AP-42 emission factors from the silt loadings. 

Thirteen different experimental test conditions were performed. Most consisted of 

approximately 30 vehicle passes, with each pass identified by the mobile sampling 

technology. Each run consisted of three passes by each mobile sampling technology. Crosscomparisons were performed to determine the ratio between the DustTrak reading and the 

PM10 mass-based concentration measured by a collocated reference sampler. The correlation 

between the DustTrak and TEOM showed that DustTrak values would have to be multiplied 

by a factor of 2.8 ± 0.6 to obtain mass-equivalent PM10. A controlled laboratory tests was 

also used to obtain a relationship between the DustTrak measurements and mass-based 

measurements. These tests generated a DustTrak correction multiplier of 2.4, which was 

chosen for use in this program. 

Two conclusions were made from the test results obtained in the study, when 

comparing mobile monitoring technologies with the AP-42 methodology: 

• The calibrated mobile methods measured emission factors that were about 1.5 times 

higher than found with the AP-42 methodology when higher silt loadings were 

applied to the test road. 

• The mobile methods tracked each other quite well under most conditions. 

It was concluded that a different silt mobilization process occurred as a result of silt 

being distributed on top the embedded road surface aggregates and hence being more easily 

entrained by vehicle mechanical and aerodynamic shear. It was also stated that aged silt 

found on most roads is more likely to be embedded between the road surface aggregates. 

Another conclusion identified in the field study was that implementation of mobile 

monitoring technologies provide for much easier representation of spatially distributed 

roadway emission characteristics, while eliminating the need to divert traffic. 

4.2.1.22. Technical Support Document for Mobile Monitoring Technologies; Prepared 

For Clark County Department of Air Quality and Environmental Management; 

Chatten Cowherd; Midwest Research Institute; January 9, 2009. (Reference 41, 

http://www.epa.gov/ttn/chief/ap42/ch13/related/Mobile_Monitoring_TSD_010909.pdf). 

This report states that it documents a peer review process conducted to determine 

whether the mobile monitoring method is a suitable alternative to the traditional AP-42 

method for developing road dust emission factors. The report identifies seven individuals 

which were requested to review the series of Clark County test reports and to judge the value 

of mobile monitoring technologies in relation to the traditional approach for determining 

paved road dust emission factors. 

The items addressed in this document include: 
4-35 

• A summary of road dust entrainment dynamics, 

• A brief discussion of the basis of the current road dust emissions estimating method. 

Also described were the methods used to characterize the road surface silt loadings, 

the statistical methods used in developing the AP-42 emission factor equations and 

the use of roadside plume exposure profiling to quantify mass emissions rates. 

• A brief discussion of the methods used to estimate independent variables required for 

the AP-42 emissions factor equations, associated restrictions and the resulting 

limitations and a subjective assessment of the uncertainties. 

• A more in depth discussion of the two mobile monitoring technologies (the Desert 

Research Institute (DRI) and the CE-CERT version) is provided. The report 

identifies the presence of high background dust concentration and high wind speeds 

as two restrictions for the use of mobile monitoring. The report discusses the 

subjectively established calibration requirements for mobile monitoring. Calibration 

requirements identified include determining the relationship between concentrations 

measured by the instrument used for mobile monitoring and the Federal Register 

Measurement Method, the relationship between the concentrations measured at 

different vehicle speeds, different road dust characteristics and different vehicle 

weight during mobile monitoring and mass emissions measured by plume profiling. 

• The report provides a discussion comparing of the implementation of the traditional 

application of the emissions factor and the use of mobile monitoring to develop 

emissions inventories. 

• Lastly, the report provides the charge provided to the reviewers, an overview of 

comments by the reviewers and an indication of what changes will be made to 

address the reviewers concerns in a Specification for Mobile Monitoring document. 

While this document states that the purpose is to demonstrate that mobile monitoring 

is equivalent or superior to the traditional AP-42 methodology, it provides only subjective 

opinions of the author and the selected reviewers. While there were no quantitative 

indicators to compare the precision or accuracy of the mobile monitoring technologies over 

the normal range of road conditions (silt loadings, mix of vehicle weights, vehicle speed) and 

resultant emissions produced, the author and the majority of the reviewers concluded that the 

method was more accurate and precise than the traditions measurement and monitoring 

methods. The review does reveal that there is an understanding that there is a lack of 

precision and understanding of independent variables other than silt loading, weight and 

speed which influence road dust emissions. Several reviewers highlight the potential of 

mobile monitoring methods to replace or supplement the resource intensive and dangerous 

collection of representative silt loading information. Several reviewers also highlight the 

need for further development and standardization of mobile monitoring such that the method 

could be used for managing the road dust emissions where required. 

4.2.1.23. Mobile Monitoring Method Specifications; Prepared For Clark County 

Department of Air Quality and Environmental Management; Chatten Cowherd; 

Midwest Research Institute; February 6, 2009. (Reference 40,

http://www.epa.gov/ttn/chief/ap42/ch13/related/MM_Method_Specifications_020609.pdf). 

This document provides instructions for performing a standardized methodology for 

the construction of a mobile sampling platform, specifications for instrumentation used with 
4-36 

Federal Register Methods for PM10 or PM2.5, calibrations required to correlate the 

combined sampling platform and instrumentation with standardized plume profiling testing 

used to quantify mass emissions from roads and procedures for collecting information for use 

in road surface characteristics or emissions. 

4.2.2. EMISSIONS FACTOR DEVELOPMENT.

 A total of 103 individual tests are available. All tests quantified PM10 emissions. 

Lastly, plume profiling was the test method. Of these, 81 emissions tests included mean 

vehicle weight, road silt loading, and vehicle speed. The remaining tests included all of these 

parameters except vehicle speed. These emissions tests measured PM10 emissions associated 

with engine exhaust, tire wear, brake wear and material deposited on the road surface. Policy 

decisions within EPA make it necessary to separate particulate matter emissions associated 

with the operation of the vehicles (engine exhaust, tire wear and brake wear) and those 

associated with the road surface characteristics. These policy decisions are based in part on 

the recent and future efforts to control engine exhaust emissions. Many of the emissions 

tests performed to quantify particulate matter emissions from paved roads were conducted in 

the mid 1980’s to middle 1990’s. Several of the emissions studies have experienced 

comparable upwind and downwind concentrations with downwind particulate that appears to 

consist of a large percentage of organic or carbonaceous material. The first separation of 

vehicle associated emissions and pavement associated emissions was in the 2003 update. 

This update used the national VMT weighted fleet average PM10 emissions factor of 0.2119 

g/VMT to subtract from the existing emissions factor equation as a means of separating the 

emissions from engine exhaust, tire wear and brake wear from the composite paved road 

emissions factor. A fleet average vehicle weight of 3.75 tons is associated with this 

emissions factor. Since the average vehicle weight used in the development of the paved 

road emissions factor equation was about 10 tons, the PM10 emissions factor for engine 

exhaust, tire wear and brake wear probably underestimated these emissions. In addition, 

because of the range and variation in mean vehicle weight, the use of an average for 

adjustment value introduces excessive error in the estimated road dust emissions estimates. 

Improved test specific adjustments for vehicle exhaust, tire wear and brake wear can be made 

since (1) average vehicle weights are available for each test series, (2) PM10 emissions 

factors estimates for each vehicle class are available using the MOVES model and (3) PM10 

emissions estimates for slowly moving and stop and go truck traffic are available. By 

subtracting the estimated test specific vehicle emissions from the measured emissions prior 

to performing the stepwise multiple regression, emissions associated with the road surface 

material will be isolated. 

4.2.2.1. Compilation and Adjustment of Final Data Base.

 In keeping with the results from the data set review, a final data base was compiled 

by combining the following sets: 

1. The January 1983 EPA data base, 

2. the August 1983 EPA data base, 

3. the July 1984 EPA data base, 

4. the May 1990 USX data base, 
4-37 

5. the April 1997 EPA data base, and 

6. the May 2008 CRA data base. 

 While several of the test reports include detailed information on the number of light 

duty vehicles, moderate weight trucks and heavy weight trucks, none provide detailed 

information on vehicle class as used to estimate emissions of vehicle exhaust, tire wear and 

break wear. For this assessment the vehicle classes will be separated into two vehicle 

classes. One group of vehicle class will include the six classes of light duty vehicles/trucks 

and motorcycles. The other group of vehicle class includes gas and diesel heavy duty trucks. 

Other assumptions used to estimate vehicle associated emissions include: 

• The test fleet includes a mixture of light duty vehicles, heavy duty gas trucks and 

heavy duty diesel trucks when the average vehicle weight is less than 23 tons. 

• The test fleet includes a mixture of light duty vehicles and heavy duty diesel 

trucks when the average vehicle weight is between 23 tons and 35 tons. 

• The test fleet includes only heavy duty diesel trucks when the average vehicle 

weight is more than 35 tons. 

 First, the average vehicle weight and emissions are determined for the two classes of 

vehicles used to estimate the adjustment for the measured emissions. The vehicle weights 

and VMT distribution presented in Table 4-16 are used to calculate the average vehicle 

weight. The VMT adjusted gross vehicle weight is calculated for each class of vehicle by 

multiplying the VMT distribution by the average gross vehicle weight for the class. The 

individual vehicle class VMT adjusted gross vehicle weights are summed to arrive at the two 

VMT adjusted gross vehicle weights used in this assessment. For light duty vehicles, the 

VMT adjusted gross vehicle weight is 3320 pounds. For heavy duty trucks, the VMT 

adjusted gross vehicle weight is 3742 pounds. The sums of the VMT distributions for these 

two classes of vehicles are obtained by summing the individual VMT distributions for the 

two classes of vehicles used in this assessment. For light duty vehicles, the VMT 

distribution is 0.928. For heavy duty trucks, the VMT distribution is 0.0717. Dividing the 

VMT adjusted gross vehicle weights by the VMT distributions and converting to tons yields 

the average vehicle weights for the two classes of vehicles. For light duty vehicles, the 

average gross vehicle weight is 1.79 tons. For the combination of heavy duty gas and diesel 

trucks, the average gross vehicle weight is 26.09 tons. 

 Next, an algorithm is developed to provide test run specific ratios of light duty 

vehicles and heavy duty trucks. The algorithm is developed by solving the following two 

equations. 

 Wt = (RLD x WLD) + (RHD x RHD) 

 1.00 = RLD + RHD

where: Wt = Test report average vehicle weight 

 WLD = Average Light Duty Vehicle Weight (1.78848 tons) 

 RHD = Average Heavy Duty Truck Weight (26.09135 tons) 

 RLD = Light duty vehicle ratio 

 RHD = Heavy duty truck ratio 
4-38 

 For test runs where the average vehicle weight is less than 23 tons, the resulting 

algorithm to estimate the ratio of heavy duty gas/diesel trucks in each test series is: 

 RHD = (Wt - 1.78848) / (26.09135 - 1.78848) 

 For tests where the average vehicle weight is more than 23 tons, the resulting 

algorithm to estimate the ratio of heavy duty diesel trucks in each test series is: 

 RHD = (Wt - 1.78848) / (35 - 1.78848) 

 Run specific emissions estimates for vehicle exhaust, brake wear and tire wear are 

estimated using the EPA Office of Transportation and Air Quality MOVES (MOtor Vehicle 

Emission Simulator) 2010 model29. For all tests with vehicle speed greater than 10 mph only 

emissions for freely moving traffic is calculated. Emissions for a representative mix of light 

duty vehicles and for a representative mix of heavy duty trucks are calculated. For each test 

series, information on the date of the test, the location of the test program, ambient 

temperature during the test, average vehicle speed, and other general information required to 

generate a valid PM10 emissions calculation with the MOVES model. While the MOVES 

model has the ability to generate start up emissions, all test conditions are assumed to include 

only vehicles which have achieved normal operating temperatures. For all test series with 

average vehicle speeds greater than 10 mph, the MOVES model calculated only running 

exhaust, tire wear and brake wear emissions. For heavy duty vehicles, the running emissions 

ranged from 0.645 g/VMT to 4.896 g/VMT. For light duty vehicles, the running emissions 

ranged from 0.0196 g/VMT to 0.1324 g/VMT. For test series with average vehicle speeds 

below 9.9 mph, in addition to running exhaust, tire wear and brake wear emissions; exhaust 

emissions during acceleration and idling are included. A separate MOVES model run 

estimated the average emissions for the non steady state emissions at 11.06 g/hour. The 

emissions factor for this driving condition was calculated by dividing the hourly emissions 

by the average vehicle speed. Summing the product of emissions factors from heavy duty 

trucks and light duty vehicles and the ratio of heavy duty vehicles and light duty vehicles 

provides an estimate of the total engine exhaust; tire wear and brake wear emissions for the 

test run. 

 The test run specific emissions factor estimate for engine exhaust, tire wear and brake 

wear is subtracted from the test run measured emissions factor to produce the test run 

specific emissions factor due to road surface material. To allow log transformation of the 

data, values of zero or less were set to 0.01 g/VMT. Table 4-17 presents the final dependent 

and independent variables for all of the useable test series that were assembled for 

developing the paved road emissions factor equation. There were 10 test runs of the 103 

available data where downwind emissions were not measureable. Six of the data were 

associated with low speed traffic at corn refining facilities and four of the data were high or 

moderate speed urban traffic. None of these ten data were included in the data analyzed to 

estimate the predictive emissions factor equation. There were 3 out of the 103 available data 

sets where the estimated emissions from engine exhaust, tire wear and break wear were equal 

to or comparable to the measured emissions. Two of the three test runs were on roads where 

the average vehicle speed was 55 mph. Emissions of two additional test runs with vehicle 

speeds of 55 mph had engine exhaust, tire wear and break wear emissions greater than 160% 

of the road emissions. The silt level for one of the 55 mph test runs was greater than all 
4-39 

other 55 mph data sets and was performed to characterize emissions from a road that had 

been sanded for traction control. For slightly slower moving traffic (40 – 45 mph), three of 

the five test runs had significant percentage of engine exhaust; tire wear and brake wear 

emissions. One of the remaining two runs had silt levels greater than 60% of the entire data 

set and the test was performed to characterize emissions from a road that had been sanded 

for traction control. 

 Graphical presentations of the final PM10 data base are shown in Figures 4-1 through 

4-5. Because of the large range of silt loadings and estimated emissions factors, the data are 

plotted on a logarithmic scale for the first three figures. Figure 4-1 presents the data base by 

silt loading with five ranges of average vehicle weight depicted with different shape and 

color data points. The figure shows that with increasing silt loading there is an increase in 

the PM10 emissions factor. Figure 4-2 presents the data base by average vehicle weight with 

seven ranges of silt loading depicted with different shape and color data points. Although 

there is a significant overlap of the different vehicle weight data, there appears to be some 

relationship between average vehicle weight and the PM10 emissions factor. As with silt 

loading, it appears that the PM10 emissions factor increases with increasing vehicle weight. 

The wider spread of the data around the center line of the data makes the relationship more 

difficult to discern. Figure 4-3 presents the relationship between silt loading and average 

vehicle weight with eight ranges of emissions factors depicted with different shape and color 

data points. Although very poor, there appears to be a weak relationship between silt 

loading and vehicle weight. The cause of this relationship is probably due to the selection of 

the test location and parameters than any physical force that would cause this relationship. 

Figure 4-4 presents the relationship between average vehicle speed and the PM10 emissions 

factor. It appears that between 10 and 55 mph, the emissions factor decreases with 

increasing speed. Below 10 mph there does not appear to be a speed relationship. Figure 4-5 

presents the relationship between silt loading and vehicle speed with five ranges of PM10

emissions factors. The silt loading appears to decrease with increasing speed above 10 mph. 

In addition, there seems to be a clear increase in PM10 emissions factor as silt loading 

increases and speed decreases. Figure 4-6 presents a three dimensional view of the silt 

loading, vehicle weight and PM10 emissions factors. One data point seems to be very 

uncharacteristic of the general trend of the data. Figure 4-7 provides a two dimensional view 

of the data with the data identifier in the label. For three data points, the PM10 emissions 

factor is also included in the label. The point which has the uncharacteristic emissions is 

point Z-3 with a PM10 emissions factor of 1819 g/VMT. While this value is the highest 

emissions factor of all of the 92 test data, both the vehicle weight and silt loading for this run 

are near other data which are under 100 g/VMT. As a result, this data was flagged as a 

potential outlier. This data was reassessed following log transformation and the variation 

was determined to be comparable with other data and was included in the final data set used 

to estimate the predictive equation. Figure 4-8 presents the three dimensional view of the 

test data with silt loading, vehicle weight and PM10 emissions factor with test run Z-3 

removed. With point Z-3 removed, there appears to be two regimes of the data. Most of the 

data had silt loadings below 20 g/m2

 with few gaps down to 0.013 g/m2. There are ten data 

with silt loadings spread out from 50 g/m2

 to almost 400 g/m2 with no data between these 

two regimes. There appears to be one incline associated with the lower silt loading data and 

a significantly greater incline for the higher silt loading data. This greater incline is the result 

of a small number of data collected prior to 1983. These data have higher silt loadings that 

the default silt loading for the peak additive contribution value for roads with average daily 
4-40 

traffic volume counts of less than 500. While there may be a very small number of streets 

that reach this silt loading level, these are believed to be unrepresentative of typical well 

managed urban or rural roads during any season. As a result, these data are flagged as 

extreme values and were not included in the final data set used to estimate the predictive 

equation.
Table 4-17. Final Paved Roads Emissions Factor Data Set

Reference Run ID 

Silt loading 

(g/m2) 

Speed (mph) 

Weight 

(tons) 

Downwind 

Concentration 

mg/m3

Measured PM10

Emission factor 

(g/VMT) 

Estimated 

Fraction 

Heavy Duty 

Vehicles 

En

ti

 AUC3 0.42 27 5.5 0.011 2.25 0.153  AUC4 0.52 25 6 0.04 16.1 0.173  AUC5 0.23 29 3.9 0.07 15.3 0.087  AUC6 0.23 27 6.2 0.03 3.7 0.182  AUC7 0.26 27 3 0.01 0.402 0.050 USX 5/1990 AUC8 0.15 27 2 0.03 7.88 0.009  AUE1 4 15 12 0.01 3.22 0.420  AUE2 4 16 5.1 0.6 10.6 0.136  AUE3 2.2 15 2.6 0.08 16.1 0.033  AUE4 1.3 15 2.6 0.06 9.01 0.033  M-1 0.46 30 5.6 0.124 4.99 0.157  M-2 0.26 30 3.8 0.033 1.55 0.083  M-3 0.147 30 4.5 0.070 3.54 0.112  M-4 0.432 35 2.1 0.030 0.177 0.013  M-5 1.01 35 2.2 0.090 0.692 0.017  M-6 0.716 30 2.1 0.063 1.38 0.013  M-7 0.59 35 2.3 0.130 4.22 0.021  M-8 2.48 20 2.2 0.120 11.2 0.017  M-9 0.293 30 4.1 0.130 3.24 0.095 EPA 7/1984 M-10 0.022 55 4.5 0.104 0.177 0.112  M-11 0.022 55 4.8 0.080 0.322 0.124  M-12 0.022 55 3.8 0.080 0.084 0.083  M-13 0.11 35 2.7 0.065 0.306 0.038  M-14 0.079 35 2.7 0.030 1.37 0.038  M-15 0.049 35 2.7 0.090 1.47 0.038  M-16 0.022 55 4.3 0.060 0.241 0.103  M-17 0.809 30 2 0.056 2.64 0.009  M-18 0.731 30 2 0.080 0.37 0.009 

 M-19 0.929 30 2.4 0.050 0.177 0.025 

4-41
Table 4-17. (Continued) 

Reference Run ID 

Silt loading 

(g/m2) 

Speed (mph) 

Weight 

(tons) 

Downwind 

Concentration 

mg/m3

Measured PM10

Emission factor 

(g/VMT) 

Estimated 

Fraction 

Heavy Duty 

Vehicles 

En

ti

 Y1 90.7 10 3.6 117 0.075  Y2 76.1 10 3.7 182 0.079  Y3 193 10 3.8 36.3 0.083  Y4 193 10 3.7 200 0.079  Z1 11.3 10 8 317 0.256 EPA 1/1983 Z2 12.4 15 8 740 0.256  Z3 12.4 15 8 1820 0.256  AC4 287 10 5.7 1750 0.161  AC5 188 15 7 1420 0.214  AC6 399 20 3.1 613 0.054  AD1 94.8 23 42 1480 1.000  AD2 63.6 23 39 342 1.000  AD3 52.9 23 40 233 1.000  F34 2.78 NR 28 0.552 188 0.789  F35 2.03 NR 25 0.057 298 0.699  F36 0.201 NR 8.3 0.134 54.7 0.268  F37 0.417 NR 17 0.163 77.2 0.626  F38 0.218 NR 18 0.301 167 0.667  F39 0.441 NR 18 0.177 253 0.667 EPA 8/1983 F27 14.8 NR 14 0.531 130 0.502  F32 0.117 NR 14 0.138 53.1 0.502  F61 17.9 NR 40 0.327 463 1.000  F45 5.11 NR 16 0.744 212 0.585  F62 14.4 NR 36 0.294 317 1.000  F74 5.59 NR 29 0.114 545 0.819 

4-42
Table 4-17. (Continued)

Reference Run ID 

Silt loading 

(g/m2) 

Speed (mph) 

Weight 

(tons) 

Downwind 

Concentration 

mg/m3

Measured PM10

Emission factor 

(g/VMT) 

Estimated 

Fraction 

Heavy Duty 

Vehicles 

En

ti

 B50 13.6 NR 9.4 0.225 82.1 0.313  B51 13.6 NR 11 0.410 140 0.379  B52 7.19 NR 12 0.102 35.4 0.420  B54 3.77 NR 10 0.187 93.3 0.338 EPA 8/1983 B55 6.3 NR 11 0.295 183 0.379  B56 2.4 NR 9.2 0.229 126 0.305  B58 10.4 NR 18 0.190 368 0.667  B57 2.32 NR 12 0.358 195 0.420  B59 2.06 NR 11 0.149 348 0.379  B60 3.19 NR 12 0.339 439 0.420  BH1 0.184 55 2.2 0.233 1.08 0.017  BH2 0.0127 55 2.2 0.030 0.102 0.017  BH3 0.0127 55 2.2 0 0.017  BH6 1.47 40 2.2 0.300 4.68 0.017 EPA 4/1997 BJ6 0.06 45 2.2 0.045 0.301 0.017  BJ7 0.06 45 2.2 0.130 1.94 0.017  BJ9 0.06 45 2.2 0 0.017  BJ10 0.06 45 2.2 0 0.017  BJ11 0.06 45 2.2 0 0.017  BK7 0.082 45 2.2 0.033 0.57 0.017  BK8 0.082 45 2.2 0.033 0.44 0.017  CE-1 1.16 1 36 0.050 27 1.000  CE-2 0.86 1 36 0.075 64 1.000  CE-11 1.34 5 12 0.200 154 0.420 CRA 5/2008 CE-3 0.86 1 39 0.070 45 1.000  CE-15 1.91 5 40 0.065 63.5 1.000  CE-16 1.41 5 40 0.050 77.1 1.000  CE-17 2.93 5 40 0.040 41.3 1.000  CE-19 0.76 5 38 0.040 18.6 1.000 

4-43
Table 4-17. (Continued)

Reference Run ID 

Silt loading 

(g/m2) 

Speed (mph) 

Weight 

(tons) 

Downwind 

Concentration 

mg/m3

Measured PM10

Emission factor 

(g/VMT) 

Estimated 

Fraction 

Heavy Duty 

Vehicles 

En

ti

 CE-12 1.34 5 13 0.085 23.1 0.461  CF-1N 0.97 5 40 0.035 4.99 1.000  CF-1/South 0.97 1 40 0.040 19.5 1.000  CF-2N 0.81 5.3 41 0.044 16.3 1.000  CF-2/South 0.81 1 41 0.080 63.5 1.000  CF-3N 0.63 5.1 41 0.015 1.09 1.000  CF-3/South 0.63 1 41 0.025 23.1 1.000  CF-4N 1.1 4.7 41 0.019 3.08 1.000 CRA 5/2008 CF-5 1.4 1 41 0.030 16.3 1.000  CI-1 0.06 15.1 26 0 0.729  CI-2 0.06 14.85 26 0 0.729  CI-3 0.06 13.15 27 0 0.759  CI-4 0.06 14.5 27 0 0.759  CI-7 0.05 15.3 27 0.030 1.63 0.759  CI-8 0.05 15.3 27 0.030 2.99 0.759  CI-11 0.025 13.1 27 0 0.759  CI-12 0.25 13.1 27 0 0.759  CM-1 0.72 5 39.8 0.035 6.35 1.000  CM-2 0.72 1 39.6 0.050 63.5 1.000  CM-4 0.7 5 39.5 0.035 7.26 1.000 

4-44
4-45 

Figure 4-1. PM10 Emissions Factor Data Base by Silt Loading (93 test runs). 
Figure 4-2. PM10 Emissions Factor Data Base by Average Vehicle Weight (93 test run

4-46 
4-47 

Figure 4-3. Silt Loading vs. Average Vehicle Weight (93 Test Runs). 
4-48 

Figure 4-4. PM10 Emissions Factors by Vehicle Speed. 
4-49 

Figure 4-5. Vehicle Speed vs Silt Loading. 
4-50 

Figure 4-6. Paved Road Dust Emissions Factors, All Data. 
4-51 

Figure 4-7. All Paved Road Data, Silt Loading by Vehicle Weight with EF. 
4-52 

Figure 4-8. Paved Road Dust Emissions Factor Data Excluding Z-3. 
4-53 

4.2.2.2. Emission Factor Development.

 Stepwise multiple linear regression was used to develop a predictive model with the 

final data set. The potential correction factors included: 

 - silt loading, sL 

 - mean vehicle weight, W 

 - mean vehicle speed, S 

 All variables were log-transformed in order to obtain a multiplicative model as in the 

past. Table 4-18 presents the correlation matrix of the log-transformed independent and 

dependent variables. The most notable feature of the correlation matrix is the high degree of 

correlation between silt loading and emissions factors. The correlation between emissions 

factor, weight and speed is much lower than with silt loading. The high correlation between 

weight and speed is believed to be the result of the large data collected by the corn refiners 

association to characterize emissions at terminals. This suggests that obtaining accurate silt 

loading information is the most important independent variable to obtain for accurately 

estimating emissions factors. 

Table 4-18 Correlation Matrix for log-transformed PM10 data.

PM10 Emission 

factor (g/VMT) 

Silt loading 

(g/m2) 

Weight 

(tons) 

Speed 

(mph) 

PM10 Emission factor (g/VMT) 1

Silt loading (g/m2) 0.8010 1 

Weight (tons) 0.3280 -0.1841 1 

Speed (mph) -0.4066 -0.2785 -0.7784 1

 Initially several regression analysis were performed using the Data Analysis tools in 

MS Excel to evaluate a range of independent variables. The independent variables included 

silt loading, average vehicle weight, the product of silt loading and vehicle weight, the square 

of silt loading (after log transformation) and the square of the vehicle weight (after log 

transformation). In addition, the influence of including and excluding flagged test runs were 

explored. The primary criteria for selecting the most appropriate form and supporting data set 

was the predictive performance of the equation using the combination of the correlation 

coefficient, the P-value and the relative percent difference from the actual emissions factor for 

the test series with silt loadings and vehicle weights in the range of default values used in the 

national inventory. The stepwise regression was first performed using the “Regression” 

function in the “Analysis Tool” of Excel. It was determined that the use of the speed term 

either produced equations with P-values greater than 0.1 or produced equations with 

independent parameter relationships that were illogical (i.e. increased emissions with 

decreased weight). It was also determined that the inclusion of data with silt loadings greater 

than 20 g/m2

 produced equations which uniformly overestimated test data with lower silt 

loadings without a significant improvement in estimating the high silt loading data. Also, the 

exclusion of the ten data with high silt loadings did not significantly change the predictive 

accuracy of the equation for the ten high silt loading test runs. The 93 test data with positive 

measured emissions were provided to a statistician for subsequent analysis with SAS. 
4-54 

Several additional assessments were performed to determine an equation that provided a high 

correlation coefficient, a low average percent error for test series with targeted independent 

variables and which provided a reasonable level of predictive accuracy for test series where 

the independent variables were outside the targeted range. The equation which produced the 

highest correlation coefficient was one which forced the intercept to zero. This equation 

performed well and was consistent with engineering assessments of the physical influences 

on emissions. This equation used only silt loading and average vehicle weight as the 

independent variables. It was decided that the traditional scaling factors of 2 for silt loading 

and 3 for average vehicle weight were no longer required and resulted in simpler calculation 

of paved roads emissions factors. The resulting equation for PM10 is: 

( ) ( ) 021.1912.0 = WsL0.1EF

 Table 4-19 shows the statistical output. The predicted exponents for silt and weight are 

0.912 and 1.021 respectively and have a coefficient of determination (R2

) of 0.72. The standard 

error associated with the silt and weight terms are 0.12 and 0.08 respectively. As a result, it is 

expected that 95% of future data would fall within equations with exponents of 0.677 and 1.14 for 

the silt term and 0.852 and 1.19 for the weight term. 

The range of conditions which existed at the test sites used in developing the equation was as 

follows: 

 Silt loading: 0.03 - 400 g/m2 

 0.01 - 570 grains/square foot (ft2) 

 Mean vehicle weight: 1.8 - 38 megagrams (Mg) 

 2.0 - 42 tons 

 Mean vehicle speed: 1 - 88 kilometers per hour (kph) 

 1 - 55 miles per hour (mph) 

 
Table 4-19. Regression Analysis using Silt Loading and Weight. 

SUMMARY OUTPUT All positive test data, sL < 20 force 0, sL W 

Regression Statistics 

Multiple R 0.848347765 R Square 0.71969393 Adjusted R Square 0.703887682 Standard Error 1.921751464 Observations 83 ANOVA 

df SS MS F 

Significance 

F 

Regression 2 768.0593789 384.0296894 103.9849195 5.61978E-23 Residual 81 299.1434238 3.693128689 

Total 83 1067.202803 

 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 

Intercept 0 #N/A #N/A #N/A #N/A #N/A Weight (tons) 1.0212836 0.084774552 12.04705393 9.58964E-20 0.852608836 1.189958364 Silt loading (g/m2) 0.911843675 0.117787966 7.741399277 2.42283E-11 0.677482574 1.146204776 

4-55 
4-56 

 An assessment of the performance of the predictive equation is difficult since the 

range of silt loadings and the associated emissions factors spans five orders of magnitude. 

This is further complicated by the focus of many of the field tests. Approximately half of the 

field test locations were selected either due to concerns that these sources were major 

contributors to air quality impacts, or were selected because of elevated road silt levels to 

allow the measurement of a difference from background concentrations of particulate matter. 

Another complication is that PM emissions of the vehicle exhaust were not measured during 

the tests and a modeled average emission factor or rate was subtracted to arrive at the road 

dust emissions. 

 One can assess the performance of the predictive equation by calculating the average 

predicted to actual ratio and producing the cumulative distribution of these ratios. For the 

two parameter equation, the average predicted to actual ratio is 49. This is significantly 

lower than the average predicted to actual ratio of 315 for the previous equation when 

applied to the existing data. When limited to silt loading levels of 20 g/m2, the new 

equation produces average predicted vs actual ration of 38 compared to the previous 

equations ration of 221. It should be noted that the previous equation subtracted 0.2119 

g/VMT (the estimated national average engine exhaust, brake wear and tire wear emissions 

factor) from the previous equation which was based upon measured emissions. The new 

equation subtracts the estimated engine; brake wear and tire wear emissions estimated for 

each test run. These emissions average 1.565 g/VMT and range from 0.031 to 11.06 g/VMT 

depending on meteorological conditions, vehicle speed and vehicle weight determined during 

the test. Figure 4-9 depicts the cumulative distribution of the predicted to actual ratios for 

both the previous equation and the new equation. Figure 4-10 presents this same information 

but with ranges of silt loading depicted through the use of different shapes and colors for the 

markers of the data. Figure 4-11 is this same information but with ranges of vehicle weights 

depicted with different markers. It is difficult to discern any differences below the ratio of 

1.0. Above the ratio of 1.0 the increased range of the predicted vs actual ratio of the older 

equation is evident. The new equation appears to demonstrate an improved performance 

compared to the previous equation. 

 Another means of assessing the performance of the regression equations is to 

compare the calculated results of the equations to the actual value measured. With a large 

range of measured emissions factors, comparing the relative percent difference between the 

results of the equation and the measured value places the differences in the smallest 

measured value and the largest measured value on comparable terms. Two comparisons 

were made to assess the relative predictive performance of the existing equation to the 

previous equation. As shown with the average percent error for the entire population in 

Table 4-23, the new equation provides an order of magnitude improvement in estimating the 

actual measured emissions over the previous equation. Associated with the reduction in the 

percent difference from actual emissions is a 47 percent reduction in the emissions factor. 

When the performance of the equation is evaluated within classes of the independent 

variables of silt loading, average vehicle weight and speed; the new equation shows 

comparable or improved performance in all groups of the variables except two. 
4-57

Figure 4-9. Cumulative Distribution of Predicted/Actual Ratios. 
4-58

Figure 4-10. Cumulative Distribution – Predicted/Actual by Silt Loading. 
4-59

Figure 4-11. Cumulative Distribution – Predicted/Actual by Average Vehicle Weight. 
4-60 

 Figure 4-12 through Figure 4-9 provide graphical indications of the performance of 

the updated equation to estimate the actual emissions. The first figure shows the relationship 

of emissions to the road surface silt loading. Included in this figure is information on the 

average vehicle weight through the use of a different shape and color for different ranges of 

vehicle weight. While not shown, the previous equation had a greater spread than the new 

equations estimates. Figure 4-13 shows the influence of vehicle weight on the emissions 

factors. For all weight ranges, the spread of the data is much greater than is demonstrated in 

the figures with silt as the ordinate. Included in this figure is information on the silt loading 

associated with the test. One can see a general increase in emissions with silt loading. This 

is probably due to the greater correlation between silt loading and PM10 emissions factors 

than between average vehicle weight and PM10 emissions factors. Figure 4-9 shows the 

influence of speed on the emissions factors. As with vehicle weight, there is a greater spread 

of the emissions factor than when silt is the primary dependent variable graphed. One can 

also see a weak relationship between silt loading and average vehicle speed.
Table 4-20. Comparison of Previous and New Equations for Estimating Paved Road Dust Emissi

Predictive Performance of Paved Road Dust Emissions Equ

Average Relative Percent Difference 1 Relative Standard 

Old Equation vs Actual 

New Equation vs Actual 

Old Equation vs New Equation 

Old Equation vs Actual 

New Equatiovs Actual 

Population Average 31,378 3,142 -47 5.77 5By Classes of Silt Loading (g/m2) 

≤ 0.2 33,601 3,858 -71 2.12 1

 0.2 – 0.75 102,647 17,049 -62 3.71 11 0.75 – 1.5 3,236 669 -61 2.48 0 1.5 – 50 221 47 -45 3.57 0

≥ 50 248 253 73 1.81 0

By Classes of Average Weight (ton) 

2 - 3 467 333 40 2.09 03 – 5 718 289 350 1.71 05 – 10 -2 -41 53 -394.3 010 – 40 38,248 4,906 74,840 3.06 24

≥ 40 128,217 21,549 68,550 4.27 112

By Classes of Average Speed (mph) 

 < 10 90,216 15,112 -79 4.30 2010 – 25 54,063 7,034 -6 2.17 425 – 45 293 170 -41 2.41 0.1 45 1,041 662 -34 1.28 0.1 55 1,404 467 -114 1.57 0.1

4-61
4-62

Figure 4-12. Predicted vs Actual PM10 Emissions Factor by Silt Loading. 
4-63

Figure 4-13. Predicted vs Actual PM10 Emissions Factor by Average Vehicle Weight. 
4-64

Figure 4-14. Predictive Accuracy by Silt Loading (unrestricted range).
4-65

Figure 4-15. Predictive Accuracy by Silt Loading (restricted range).
4-66

Figure 4-16. Predictive Accuracy by Average Vehicle Weight (unrestricted range). 
4-67

Figure 4-17. Predictive Accuracy by Average Vehicle Weight (restricted range). 
4-68 

4.2.2.3 Emissions Factor Quality Rating Assessment.

 All of the source test data used to develop the emissions factor equation were rated A 

since the test procedures used were profiling tests and were all well documented. While only 

six reports are available that provide documentation of emissions factors for paved roads, 

these test reports contain the results of 17 different road conditions. The reports and the 

number of test conditions documented in the report are: 

• USX 5/1990 - 2 tests (sL ~.3 & sL > 2), 

• EPA 7/1984 - 2 tests (30 mph & 55 mph), 

• EPA 1/1983 - 4 tests (<15 mph, >20 mph, W < 3 tons, W 5-8 tons, W > 30 tons), 

• EPA 8/1983 - 2 tests for two parameter equation, 

• EPA 4/97 - 3 tests (speed 55, 45), 3 locations, and 

• CRA 5/2008 - 4 tests (4 locations, 2 speeds, ) 

 However, since the EPA 8/1983 report does not contain information on the average 

speed of the vehicles in the study, none of the tests documented in that report is usable for 

further data set groupings. The remaining five reports contain the results of 15 different road 

conditions. While all of the tests were performed on paved roads, the ranges of conditions (silt 

loading, vehicle speed and vehicle weight) were diverse. An assessment of the variation 

associated with the data and the impact of that variation on a single value emissions factor. 

The average of all the adjusted emissions factors is 140 g/VMT and the standard deviation is 

387. A relative standard deviation of 3 is greater than many other factors. As a result, the 

number of tests needed to achieve the predictive accuracy of the mean is greater. The 

availability of 15 A or B rated test reports would normally justify an initial assignment of a 

factor rating of B. However, the greater variability of the underlying data justifies a single 

value factor rating of C. 

 The stepwise regression of the available data indicated that a large portion of the 

variation of the emissions factor was due to the large range of the road silt loading that existed 

at the test locations. The preliminary regressions produced equations with varying constants 

and exponents with correlation coefficient below 60%. By excluding the high silt loading data 

and forcing a zero intercept, the correlation coefficient (R2) for the final equation is 72%. 

This indicates that approximately 72% of the variations in the emissions factors are due to the 

silt level and average vehicle weight. As a result of the improved ability of the equation to 

estimate the measured values over the single value emissions factor, a quality rating of B is 

assigned to the equation. 

4.2.2.4 Assignment of equation parameters for PM30 and PM2.5.

 While several of the reports include measurements of PM2.5, the WRAP studies 

suggest that many of these measurements are in error due to particle bounce issues with the 

impactor stages. The results of the WRAP study indicated that the PM2.5 concentrations 

measured by the cyclone/impactor system were consistently biased by a factor of about 2 

relative the PM2.5 concentrations measured by the Partisol samplers. The second phase of the 

WRAP showed a tendency of the measured PM2.5/ PM10 ratio to decrease with increasing 
4-69 

PM10 concentration. At PM10 concentrations above 1.0 mg/m3 the PM2.5/ PM10 ratio was 

between 0.1 and 0.15. The PM2.5/ PM10 ratio increased to about 0.35 as the PM10

concentration approached about 0.5 mg/m3. While some of the paved road test data 

encountered concentrations above 1.0 mg/m3 much of the test data consisted of measured 

concentrations below 0.5 mg/m3. The paved road emissions factor for PM2.5 was revised to 

15% of the calculated PM10 emissions factor in 2008. It is not clear whether the WRAP study 

assessed the PM10 concentrations measured during the paved roads testing prior to their 

recommendations for revising the PM2.5 emissions factors. As shown in Table 4-17 the PM10

concentrations associated with 58 of the 71 test runs used to develop the three parameter 

emissions factor equation. Many of these test runs involve traffic volumes that would 

produce fairly constant particulate concentrations. Also, of these 58 test runs, only three runs 

were the highest PM10 concentrations greater than 0.5 mg/m3. An earlier report (Reference 5) 

measured PM2.5/ PM10 ratios during field tests. The range of PM2.5/ PM10 ratios was from 

0.25 to 0.37. Since essentially all of the measured PM10 concentrations used for the stepwise 

regression were below 0.5 mg/m3

 and the ratios measured during field sampling of paved road 

emissions were between 0.25 and 0.37, the recommended PM2.5 emissions factor is 25% of 

the PM10 emissions factor. Since there is little measured PM2.5 data, an emissions factor 

quality rating of “D” is assigned. 

 While a stepwise regression could be performed to estimate the PM30 emissions factor 

equation, it is believed that the number of available data would be significantly less and a 

comparable confidence in the resulting equation could not be achieved. The ratio of PM30 to 

PM10 presented in the present AP-42 section is 5.2 and is proposed for the revised equation. 

4.2.2.5. Assignment of a precipitation correction factor. 

 As is presented in Reference 38, a correction parameter for precipitation events was 

included in the revision of the AP-42 section in October 2002. As recommended in the 

Technical Memorandum to the files, the correction parameters are retained in this version of 

the AP-42 section. 

4.3 DEVELOPMENT OF OTHER MATERIAL IN AP-42 SECTION 

 Concurrent with the development of the revised AP-42 section for paved roads, a 

separate effort was conducted to assemble a silt loading data base for nonindustrial roads. 

Over the past 10 years, numerous organizations have collected silt loading samples from 

public paved roads. Unfortunately, uniformity—in sampling and analysis methodology as 

well as roadway classification schemes—has been sorely lacking in these studies. 

 Silt loading data were compiled in the following manner. Persons knowledgeable 

about PM10 at each EPA regional office were asked to identify sL data for public roads. In 

many instances, the EPA representatives identified state/local air regulatory personnel who 

were then asked to supply the data. Given that the relative importance of PM10 emissions 

from public sources is greater in the western United States, it is not surprising that most of the 

data are from that area of the country. What is surprising, perhaps, is that Montana has 

collected roughly two- thirds of all data. Furthermore, only Montana had data collected from 

the same road over extended periods of time, thus permitting examination of temporal 
4-70 

variation. 

 The assembled data set did not yield any readily identifiable, coherent relationship 

between silt loading and road class, average daily traffic (ADT), etc. Much of the difficulty is 

probably due to the fact that not all variables were reported by each organization. Further 

complicating the analysis is the fact that, in many parts of the country, paved road silt loading 

varies greatly over the course of the year. Recall that repeated sampling at Montana 

municipalities indicated a very noticeable annual cycle. Nevertheless, it is questionable 

whether the seasonal variation noted in the Montana data base could successfully predict 

variations for many other sites. While one could possibly expect similar variations for, say, 

Idaho or Wyoming roads, there is far less reason to suspect a similar cycle in, say, Maine or 

Michigan, in the absence of additional information. 

 Because no meaningful relationship could be established between sL and an 

independent variable, the decision was made to directly employ the nonindustrial data base in 

the AP-42 section. The draft AP-42 section presents the cumulative frequency distribution for 

the sL data base, with subdivisions into (a) low-ADT (< 5000 vehicles/day) and high-ADT 

roads and (b) first and second halves of the year. Suggested default values are based on the 

50th and 90th percentile values. 

 The second use of the assembled data set recognizes that the end users of AP-42 are 

the most capable in identifying which roads in the data base are similar to roads of interest to 

them. The draft AP-42 section presents the paved road surface loading values together with 

the city, state, road name, collection date (samples collected from the same road during the 

same month are averaged), road ADT if reported, classification of the roadway, etc. Readers 

of AP-42 are invited to review the data base and to select values that they deem appropriate 

for the roads and seasons of interest. 

4.4 References for Section 4

1. D. R. Dunbar, Resuspension Of Particulate Matter, EPA-450/2-76-031, U. S. 

Environmental Protection Agency, Research Triangle Park, NC, March 1976. 

2. R. Bohn, et al., Fugitive Emissions From Integrated Iron And Steel Plants, EPA600/2-78-050, U. S. Environmental Protection Agency, Cincinnati, OH, March 1978. 

3. C. Cowherd, Jr., et al., Iron And Steel Plant Open Dust Source Fugitive Emission 

Evaluation, EPA-600/2-79-103, U. S. Environmental Protection Agency, Cincinnati, 

OH, May 1979. 

4. C. Cowherd, Jr., et al., Quantification Of Dust Entrainment From Paved Roadways, 

EPA-450/3-77-027, U. S. Environmental Protection Agency, Research Triangle Park, 

NC, July 1977. 

5. Size Specific Particulate Emission Factors For Uncontrolled Industrial And Rural 

Roads, EPA Contract No. 68-02-3158, Midwest Research Institute, Kansas City, MO, 

September 1983. 
4-71 

6. T. Cuscino, Jr., et al., Iron And Steel Plant Open Source Fugitive Emission Control 

Evaluation, EPA-600/2-83-110, U. S. Environmental Protection Agency, Cincinnati, 

OH, October 1983. 

7. J. P. Reider, Size-specific Particulate Emission Factors For Uncontrolled Industrial 

And Rural Roads, EPA Contract 68-02-3158, Midwest Research Institute, Kansas 

City, MO, September 1983. 

8. C. Cowherd, Jr., and P. J. Englehart, Paved Road Particulate Emissions, EPA-600/7-

84-077, U. S. Environmental Protection Agency, Cincinnati, OH, July 1984. 

9. C. Cowherd, Jr., and P. J. Englehart, Size Specific Particulate Emission Factors For 

Industrial And Rural Roads, EPA-600/7-85-051, U. S. Environmental Protection 

Agency, Cincinnati, OH, September 1985. 

10. Emission Factor Documentation For AP-42, Sections 11.2.5 and 11.2.6 — Paved 

Roads, EPA Contract No. 68-D0-0123, Midwest Research Institute, Kansas City, MO, 

March 1993. 

11. Evaluation Of Open Dust Sources In The Vicinity Of Buffalo, New York, EPA 

Contract No. 68-02-2545, Midwest Research Institute, Kansas City, MO, March 1979. 

12. PM-10 Emission Inventory Of Landfills In The Lake Calumet Area, EPA Contract No. 

68-02-3891, Midwest Research Institute, Kansas City, MO, September 1987. 

13. Chicago Area Particulate Matter Emission Inventory — Sampling And Analysis, 

Contract No. 68-02-4395, Midwest Research Institute, Kansas City, MO, May 1988. 

14. Montana Street Sampling Data, Montana Department Of Health And Environmental 

Sciences, Helena, MT, July 1992. 

15. Street Sanding Emissions And Control Study, PEI Associates, Inc., Cincinnati, OH, 

October 1989. 

16. Evaluation Of PM-10 Emission Factors For Paved Streets, Harding Lawson 

Associates, Denver, CO, October 1991. 

17. Street Sanding Emissions And Control Study, RTP Environmental Associates, Inc., 

Denver, CO, July 1990. 

18. Post-storm Measurement Results — Salt Lake County Road Dust Silt Loading Winter 

1991/92 Measurement Program, Aerovironment, Inc., Monrovia, CA, June 1992. 

19. Written communication from Harold Glasser, Department of Health, Clark County 

(NV). 

20. PM-10 Emissions Inventory Data For The Maricopa And Pima Planning Areas, EPA 

Contract No. 68-02-3888, Engineering-Science, Pasadena, CA, January 1987. 
4-72 

21. Characterization Of PM-10 Emissions From Antiskid Materials Applied To Ice- And 

Snow-Covered Roadways, EPA Contract No. 68-D0-0137, Midwest Research 

Institute, Kansas City, MO, October 1992. 

22. C. Cowherd, Background Document for Revisions to Fine Fraction Ratios Used for 

AP-42 Fugitive Dust Emission Factors. Prepared by Midwest Research Institute for 

Western Governors Association, Western Regional Air Partnership, Denver, CO, 

February 1, 2006. 

23. Climatic Atlas Of The United States, U.S. Department of Commerce, Washington, 

D.C., June 1968. 

24. C. Cowherd, Jr., et al., Improved Activity Levels for National Emission Inventories of 

Fugitive Dust from Paved and Unpaved Roads, Presented at the 11th International 

Emission Inventory Conference, Atlanta, Georgia, April 2002. 

25. C. Cowherd, Jr., et al., Control Of Open Fugitive Dust Sources, EPA-450/3-88-008, U. 

S. Environmental Protection Agency, Research Triangle Park, NC, September 1988. 

26. Written communication (Technical Memorandum) from G. Muleski, Midwest 

Research Institute, Kansas City, MO, to B. Kuykendal, U. S. Environmental Protection 

Agency, Research Triangle Park, NC, September 27, 2001. 

27. EPA, 2002b. MOBILE6 User Guide, United States Environmental Protection Agency, 

Office of Transportation and Air Quality. EPA420-R-02-028, October 2002. 

28. Written communication (Technical Memorandum) from P. Hemmer, E.H. Pechan & 

Associates, Inc., Durham, NC to B. Kuykendal, U. S. Environmental Protection 

Agency, Research Triangle Park, NC, August, 21, 2003. 

29. EPA, 2009, MOVES2010 User Guide, United States Environmental Protection 

Agency, Office of Transportation and Air Quality. EPA420B-09-041, December 2009. 

30. Fugitive Particulate Matter Emissions, U.S. Environmental Protection Agency, 

Research Triangle Park, NC, Midwest Research Institute Project No. 4604-06, April 

15, 1997. 

31. Midwest Research Institute, Roadway Emissions Field Tests at U.S. Steel’s Fairless 

Works, U.S. Steel Corporation, Fairless Hills, PA, USX Purchase Order No. 146-

0001191-0068, May 1990. 

32. Paved Road Modifications to AP-42, Background Documentation For Corn Refiners 

Association, Inc. Washington, DC 20006, Midwest Research Institute Project No. 

310842, May 20, 2008. 

33. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Marshall, 

Minnesota Facility, McVehil-Monnett Associates, Midwest Research Institute Project 
4-73 

No. 310212.1.001, July 6, 2001. 

34. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Columbus, 

Nebraska Facility, McVehil-Monnett Associates, Midwest Research Institute Project 

No. 310212.1.002. July 13, 2001. 

35. Emission Tests of Paved Road Traffic at Cargill Sweeteners North America Blair, 

Nebraska Facility, McVehil-Monnett Associates, Midwest Research Institute Project 

No. 310395.1.001. November 27, 2002. 

36. Emission Tests of Paved Road Traffic at ADM’s Marshall, Minnesota Facility, 

McVehil-Monnett Associates, Midwest Research Institute Project No. 310479.1.001. 

December 5, 2003. 

37. E-mail communication between Ron Myers of EPA/OAQPS/SPPD/MPG, RTP, NC 

and Prashanth Gururaja and Ed Glover of EPA/OTAQ/ASD/HDOC re. Diesel exhaust, 

tire and brake wear for low speed stop and go traffic; January 2009 through May 2009. 

38. Technical Memorandum from William B. Kuykendal to File, Subject: Decisions on 

Final AP-42 Section 13.2.1 “Paved Roads”, October 10, 2002. 

39. E-mail communication between Ron Myers of EPA/OAQPS/SPPD/MPG, RTP, NC 

and Gary Dolce and Rudolph Kapichak of EPA/OTAQ/ASD/HDOC re. Paved Road 

Test Data; October 12, 2010 through December 16, 2010. 

40. C. Cowherd, Mobile Monitoring Method Specifications, Prepared by Midwest 

Research Institute for Clark County Department of Air Quality and Environmental 

Management, Las Vegas, NV, February 6, 2009. 

41. C. Cowherd, Technical Support Document for Mobile Monitoring Technologies, 

Prepared by Midwest Research Institute for Clark County Department of Air Quality 

and Environmental Management, Las Vegas, NV, January 9, 2009. 

42. R. Langston, R. S. Merle Jr., et al., Clark County (Nevada) Paved Road Dust Emission 

Studies in Support of Mobile Monitoring Technologies, Clark County Department of 

Air Quality and Environmental Management, Las Vegas, NV, December 22, 2008. 

43. Midwest Research Institute; Analysis of the Fine Fraction of Particulate Matter in 

Fugitive Dust; Western Governors’ Association - Western Regional Air Partnership 

(WRAP); October 12, 2005. 

44. Midwest Research Institute; Background Document for Revisions to Fine Fraction 

Ratios Used for AP-42 Fugitive Dust Emission Factors; Western Governors’ 

Association - Western Regional Air Partnership (WRAP); November 1, 2006. 

45. G. E. Muleski, Measurement of Fugitive Dust Emissions from Prilled Sulfur Handling, 

Final Report, MRI Project No. 7995-L, Prepared for Gardinier, Inc., June 1984 
4-74 

46. T. F. Eckle and D. L. Trozzo, Verification of the Efficiency of a Road-Dust EmissionReduction Program by Exposure Profile Measurement, Presented at EPA/AISI 

Symposium on Iron and Steel Pollution Abatement, Cleveland, Ohio, October 1984. 
A-1 

Appendix A 

Response to Comments 
Comments and responses on 2010 proposed revision of Section 13.2.1 Paved 

Roads. 

Commenter Page

Chatten Cowherd of Midwest Research Institute on behalf of the Center for the Study of Open Source 

Emissions (CSOSE)......................................................................................................................................1

Rebecca Kies and Courtney Bokenkroger Senior Statistician of Midwest Research Institute, Kansas City, 

MO. ............................................................................................................................................................... 5

Greg Muleski of Midwest Research Institute ............................................................................................... 7

Camille Sears for the Sierra Club ................................................................................................................. 9

David E. James, PhD PE; Associate Vice Provost for Academic Programs; UNLV, Las Vegas, NV.......14

Steve Zemba of Cambridge Environmental Inc for the National Asphalt Pavement Association..............16

Catharine Fitzsimmons, Chief, Air Quality Bureau and Lori Hanson Iowa Department of Natural 

Resources. ...................................................................................................................................................16

Pat Davis of MARAMA for the States of New Jersey, Delaware, Maryland and Massachusetts. .............17

Julie McDill (MARAMA), David Fees (Delaware), Julie Rand (New Jersey). ......................................... 18

Gary Garman of McVehil-Monnett. ........................................................................................................... 19
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 1 

Chatten Cowherd of Midwest Research Institute on behalf of the Center for the Study of 

Open Source Emissions (CSOSE) 

Comment: The general consensus among the Center for the Study of Open Source 

Emissions (CSOSE) participants who have worked in this field is that the proposed 

equation does not offer improved predictive capability but introduces additional data 

requirements to the paved road emission inventory process. 

Response: We disagree that the proposed equation does not offer improved predictive 

capability. The predictive equation published in November 2006 produced negative 

PM10 emissions at very low silt loadings and negative PM2.5 emissions estimates 

whenever a silt loading of less than 0.06 and average vehicle weight of 3.75 tons (or silt 

loading of 0.1 and vehicle weight of 3 tons). As presented in Table 4-23 of the draft 

background report, the 2006 equation had an average relative percent error of over 

27,000 compared to the proposed equation with a relative percent error of 1,200. Part of 

the error imbedded in the 2006 equation is due to the use of the estimated 1980's fleet 

average vehicle emissions (average vehicle weight of 3.75 tons) for adjustment of the 

equation presented in the 2003 revision of the AP-42 section. This average 

underestimated the vehicle emissions of the fleets measured in almost 2/3 of the paved 

road emissions test (58 of the 93 tests had average vehicle weights over 5 tons). Since 

the proposed revision provided a correction to each test series based upon the average 

vehicle weight presented in the test report and the correction used in the final revision 

includes variations in speed, ambient temperature, year of vehicle fleet; this error has 

been reduced. Combining the reduction in error of the test data with the use of a more 

traditional revised stepwise regression of the paved road emissions data, we believe the 

revised equation will provide a superior basis than the 2006 equation. 

Comment: There is also the broader issue of adopting mobile monitoring as the basis for 

more realistic emission inventorying of paved roads. 

Response: EPA agrees that the adoption of mobile monitoring to estimate either the silt 

loading of the road system or the emissions factor provides a significant advance in 

characterizing the system wide emissions and the variation that exists with different 

roads. The use of mobile monitoring offers the ability to characterize road classes which 

have been problematic in the past due to resource constraints and safety issues. The 

ability of mobile monitoring to provide a temporally and spatially resolved emissions 

estimates and to characterize significantly more miles of roadways than were possible by 

the traditional vacuuming, screening and weighing techniques is a distinct advantage. In 

addition, the mobile monitoring method provides an excellent means for tracking system 

wide management controls instituted to provide emissions reductions from roadway 

emissions. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 2 

In the final version of the AP-42 section we describe the mobile monitoring technique 

along with a brief assessment that mobile monitoring provides significant improvements 

in the estimation of road dust emissions caused by vehicle traffic. 

Comment: The proposed equation has a significant new data input requirement (vehicle 

speed) that increases the difficulty of generating paved road emission inventories. 

Response: We disagree; access to the average vehicle speed of road segments is an 

existing requirement for the accurate estimation of vehicle exhaust emissions in the 

MOVES model. While the incorporation of the vehicle speed for every road segment 

may increase the complexity of emissions inventory development, for most road systems 

emissions estimates can be assembled by grouping of road segments into a limited 

number of groups. 

The assessment of the influence of the speed term on the predictive accuracy of the 

resulting equation is a better criterion to determine whether this term should be used in 

the equation. Limited improvement (or degradation) in the predictive accuracy of the 

equation provides a more compelling rationale to exclude the speed term in the final 

equation than the alleged difficulty of generating the emissions inventory. The 

reassessment of the form of the emissions factor equation included the assessment of the 

influence of speed on the predictive accuracy of the equation, the improvement of the 

equation to address the variance which may be due to the independent parameters, and 

the statistical significance of each variable in predicting the dependent variable. 

Comment: Based on our discussions of the proposed equation and the technical analyses 

presented by EPA, we find the scientific foundation for the revision unconvincing. 

Response: The foundation upon which EPA proposed a revision of the paved road 

equation was a proposal by the Corn Refiners Association (CRA) to perform emissions 

tests to support the extension of the applicable source conditions. The Corn Refiners 

retained the services of Midwest Research Institute (MRI) in Kansas City to perform the 

emissions testing at lower average vehicle speeds to support the extension of the 

applicable source conditions. Twenty two usable profiling tests were performed. In 

addition to designing and conducting the emissions tests, MRI provided EPA with three 

options for incorporating the new data into the paved roads section. The Agency decided 

that returning to multiple estimation methods would recreate the problems that existed 

prior to 1995 when there was two AP-42 sections for paved roads and multiple methods 

within these two sections. 

When MRI drafted the AP-42 section that included the CRA data, it was highlighted by 

the Office of Transportation and Air Quality (OTAQ) that the proposal and adoption of a 

revised equation had conformity implications that needed to be addressed. Several issues 

associated with conformity were raised. These included the situation that areas 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 3 

containing low volume rural roads were predicted to have greater emissions of PM10 than 

the previous equation predicted. Another situation was that the revised equation may 

result in greater predicted emissions of PM2.5 under some conditions. The greater 

predicted emissions were the result of the existing equation generating negative 

emissions for high volume roads. In an assessment to understand the extent and 

significance of these issues, it was revealed that the vehicle exhaust, tire wear and break 

wear emissions components were not addressed properly. The estimates of vehicle 

exhaust, tire wear and break wear used in the 2003 revision did not account for the 

significant differences in these emissions during the available tests and in addition 

significantly mis characterized for the additional data provided by the corn refiners. For 

the historical data, the proposed revision incorporated test specific emissions estimates as 

calculated by MOBIL 6.2 and based upon the average vehicle weight reported for each 

test. For the CRA data, the proposed revision incorporated test specific emissions 

estimates as calculated by the MOVES model and based upon the average vehicle weight, 

vehicle speed and estimated acceleration rates. For consistency and for improved 

accuracy in predicting vehicle exhaust emissions, MOVES model estimates were 

calculated for the historical data. While the incorporation of the data provided by the 

Corn Refiners Association extended the capabilities of the equation to 1 mph, the Corn 

Refiners Association data highlighted the variable significance of exhaust emissions and 

the need to address these emission on a test by test basis. An additional advantage of 

determining road emissions prior to developing the road emissions equation is that the 

equation never predicted negative PM10 or PM2.5 emissions. 

Comment: Besides the problems stated above, we find difficulty in understanding the 

scientific basis for replacing the existing PM2.5/PM10 ratio published in 2006 with the 

ratio that was previously used by EPA. The ratio in the existing equation was accepted by 

EPA as an outcome of an experimental program supported by the Western Regional Air 

Partnership (WRAP). 

Response: In evaluating the data underlying the equation proposed in this revision, all of 

the data were assessed to understand the basis and representativeness of the data. The 

WRAP laboratory study was evaluated and was found to focus primarily on categories of 

emissions that would generate very large concentrations of dust emissions and focused 

primarily on western sources of these emissions. These types of emissions sources have 

a high probability of overloading air sampling devices that depend on impaction to 

collect particles of differing sizes. These sources are also predominately dominated by 

sources where the emissions may have large variations over time depending on the 

repetition rate of the activity which generates the emissions. Paved roads, especially 

those with high traffic volumes and those that have neared their normal aged equilibrium 

state generate dust emissions of greater consistency in concentration and particle size 

characteristics. Not only are these emissions more consistent, the emissions 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 4 

concentrations are much lower except when the roads silt loading is very high. These 

high silt loadings are not typical of public roads except for periods where sand is applied 

as anti skid material, natural forces exacerbate the normal soil loading on the road or in 

areas where there is a large track out of dirt from an adjacent unpaved area. 

The WRAP study included the collection of seven soil samples. The samples included 

sediment from Alaska, Alluvial Channel from Phoenix AZ, Agricultural Soil from 

Phoenix AZ, Road Dust from the Las Cruces Landfill in New Mexico, Grazing Soil from 

Radium Springs in New Mexico, Shoreline Soils from the Salton Sea in California and a 

Barrow Pit from Thunder Basin Mine in Wyoming. In addition, three additional samples 

which were used in the first Phase of the study were also used in the second Phase of the 

study. These three samples included a Standard fine Arizona Test dust, a Standard coarse 

Arizona Test dust and Lakebed Soil from Owens Dry Lake in California. For each of 

these samples, the WRAP study states that two five gallon containers of soil were 

collected. To collect this volume of sample from paved roads which are in equilibrium 

would require sweeping or vacuuming of multiple miles of roadway. Additionally, none 

of these samples are representative of aged material deposited on paved roads except for 

paved roads which have had anti-skid abrasives (such as sand) applied during winter or 

where significant windblown dust or track out dirt is deposited on paved roads. 

Most of the laboratory tests performed to assess the revised PM10/PM2.5 ratio to assign to 

historical data was conducted at PM10 concentrations above 2.5 mg/m3. The greatest 

downwind concentration measured in tests used to support the paved road equation 

development was 0.74 mg/m3

 in run ID F45. Of the tests conducted in the wind tunnel 

laboratory, only 15 percent of the samples were performed at concentrations below 0.74 

mg/m3. The lowest PM10 concentration measured during the laboratory study was 0.381 

mg/m3. Of the 80 profiling tests used to support the paved road emissions factor equation 

and where the downwind concentrations were available, only five had concentrations 

greater than 0.358 mg/m3. In addition, over 80% of the profiling tests had downwind 

concentrations less than 0.2 mg/m3

 and 60% had downwind concentrations less than 0.1 

mg/m3

. In the wind tunnel laboratory studies, the only particulate used to challenge the 

sampling devices was the material collected for the studies. The emissions measured 

during the paved road profiling tests was a combination of emissions from the road 

surface, engine exhaust, break wear and tire wear emissions. As presented in Table 4-17 

of the draft background document, vehicle emissions can be a significant component of 

the emissions measured by the profiling samplers. In three cases, the estimated exhaust, 

break wear and tire wear emissions exceed the measured emissions and were assigned an 

emissions factor of 0.01 g/VMT (see test runs M10, M12 and CF-3N). In an additional 

10% of the profiling tests, about half of the measured emissions were estimated to be 

exhaust break wear and tire wear emissions. And in approximately 35% of the profiling 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 5 

tests, the measured emissions were more than 10% exhaust; break wear and tire wear 

emissions. 

Based upon a more careful and thorough examination of the experimental design of the 

WRAP study and the profile measurements conducted to characterize paved road 

emissions it is concluded that EPA mistakenly accepted the conclusion that the PM2.5 to 

PM10 ratio for paved roads should be estimated at 15%. While the WRAP study provides 

a reasonable indicator that past measurements of the particle size distributions below 10 

μm are unreliable due to particle bounce and re-entrainment associated with impactors, it 

does not discredit PM2.5 to PM10 ratios established by field studies which used FRM or 

equivalent monitors for measuring PM2.5 to PM10 concentrations. While there were only 

twelve test runs conducted during the profiling tests documented in the April 15, 1997 

report by MRI for EPA, the PM2.5 to PM10 ratios determined at these three locations 

provide a superior estimate of a national ratio for estimating PM2.5 emissions than an 

extrapolation from the WRAP laboratory study. 

Rebecca Kies and Courtney Bokenkroger Senior Statistician of Midwest Research 

Institute, Kansas City, MO. 

Comment: The approach used by EPA to calculate the proposed paved road equation 

differs from standard least-squares regression procedures. MRI recommends that 

ordinary least squares regression procedures be used. 

Response: EPA used the non standard approach in an attempt to provide an improved 

predictor of emissions than the exponential form traditionally used for this section. In the 

traditional form of regressing the equation, the log transformed data would be regressed 

and include an intercept. Then when returned to normal space, the inverse log of the 

intercept constant would be the multiplier for the silt and weight terms. The regression 

terms for silt and weight would then be the exponents for those terms in the final 

equation. More sophisticated statistical software and individuals with more thorough 

knowledge in the application of stepwise non linear regression were not available at the 

time but were used in the equation development for the published final section. EPA 

used SAS which is more robust statistical software than Excel for developing the 

equation used in the final AP-42 section. With guidance from the statistician, EPA used 

Excel to explore limited alternative forms of the equation that could potentially provide 

an equation with better predictive accuracy. EPA assessed the influence of test data that 

potentially would adversely influence the resulting equation and assessed the use of 

composite factors in an attempt to alleviate the additional problems identified by MRI’s 

statisticians. These assessments led EPA to exclude ten test data where the silt loadings 

were greater than 20 g/m2

 and to exclude test data where field measurements could not 

quantify emissions due to traffic on the road. Additional regression methods available in 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 6 

SAS were evaluated following the exploratory assessment within Excel. The equation 

which had the best predictive accuracy was based upon the traditional least squares 

regression of the log transformed data with the intercept forced to zero. 

Comment: Additional concerns about gaps in the range of data surfaced during our 

statistical analysis. Notice the major holes highlighted by the circles in the speed-silt 

loading and speed-weight boxes. The dataset is missing low silt loading, low speed; low 

silt loading, high speed data; and low weight, low speed data. Ideally, the boxes relating 

silt loading, weight, and speed should be completely filled with data points in order to 

cover all ranges of possible occurrences and consider them to be independent factors in 

the model. 

Response: It is recognized that there are gaps in the data. In most cases, the contractor 

performing the study (MRI in all cases) and the studies sponsor (EPA, industry) was 

interested only in un-managed road systems at the test location. In some of these 

instances, the condition highlighted would not be expected due to the physical forces 

influencing the independent variables. For example, low silt loading would not be a 

normal condition when the average vehicle speeds are low since the aerodynamic energy 

imparted on the road surface would not be great enough to move the silt to the road 

shoulder. This situation of low silt loading and low average speed may be a possibility 

should there be active management of the silt loading on the road. Either the active 

management of the road silt loading lacks the frequency to achieve lower silt loadings or 

there was not a need to achieve these lower silt loadings. In other cases, the data may be 

missing due to safety concerns associated with the collection of one or more pieces of 

information. For example, the collection of data at roads with high speed and low silt 

loading requires extensive time to collect sufficient material to quantify the low silt 

loading. Should resources become available in the future improving the emissions factor 

for paved roads, the collection of test data to fill in these data gaps will be suggested. In 

addition, mobile monitoring methods may be a viable alternative to the vacuuming of 

roads to estimate the silt loading of roads where there are safety concerns. 

Comment: It is recommended that different modeling options be explored to find the 

best fit and set of predictors for the data provided. Two such options are: 

• Look at low speed and high speed models separately, potentially excluding 

vehicle speeds under 5 mph from equation development. 

• Use a composite factor of weight and speed together with either weight or speed 

as independent variables in the regression. This helps alleviate the problems due 

to multicollinearity between weight and speed seen in these data. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 7 

Response: EPA assessed different modeling options to find a best fit. A return to 

multiple sets of equations or values as predictors which introduce multiple results for 

similar independent variables has been shown to create confusion, "results in shopping 

for a fortuitous estimate" and adversarial debates. Any set of predictors should have 

nearly identical results for comparable independent parameters where there multiple 

predictors could be used. 

EPA evaluated the exclusion of atypical independent parameter conditions such as the 

very low speed conditions. Other conditions that were evaluated were very high silt 

loading conditions. It was decided to exclude emissions tests with silt loading levels over 

20 g/m2 due to the potential complexity of an equation needed to incorporate the different 

characteristics that these few data present. While these high silt loadings may have been 

representative of conditions which would be tolerated by the sources (or regulatory 

authorities) in the mid to early 1980's, they are unusual conditions and may not be 

reasonable to use in developing or assessing the best predictor for the more representative 

and dominant situations. It is believed that management practices would be implemented 

by sources and regulatory authorities to address extended durations of high silt loading 

conditions. Additionally, an assessment of the final equations ability to estimate the 

emissions of the ten tests with high silt loading. While there were changes in the percent 

difference from actual emissions for individual test runs, the average percent difference 

from actual emissions was almost the same as the 2006 equation. 

Greg Muleski of Midwest Research Institute 

Comment: The measured emission factor for CM-2 should be "63.5" rather than "6.35" 

so the independent variable in Table 4-17 should have been about 52 g/vmt (rather than 

the default value of 0.02 g/vmt). 

Response: The measured PM10 emissions factor in Table 4-15 was checked against the 

value reported in the test report. The value of 0.14 lb/VMT in the table was consistent 

with the value in the submitted test report. As indicated in the comment, there was an 

error in transcribing or units conversion to transfer the value from Table 4-15 to Table 4-

17. The emissions factor for the Corn Refiners Association test numbered CM-2 was 

revised from 6.35 grams/VMT to 63.5 grams/VMT in Table 4-17. As a result, the 

subtraction of the estimated vehicle exhaust, tire wear and break wear resulted in Road 

Dust Emissions of 52.44 grams/VMT rather than 0.02 grams/VMT. 

Comment: The two-step regression process described in Section 4.2.2.2 differs from 

standard stepwise multiple regression used in the past AP-42 updates. It is not clear how 

R-squared values at each step can be combined to obtain a meaningful value. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 8 

Response: As indicated by several comments from individuals at MRI, the multi-step 

regression used by EPA does not conform to traditional stepwise multiple regression 

techniques. More traditional techniques were used in the development of the equation 

used in the final section and SAS (which is more robust software for statistical analysis) 

was employed to assess the predictive accuracy of the final equation. 

Comment: The high degree of correlation between speed and weight precludes both 

being included as independent terms in the emission factor equation. 

Response: It was believed that the large number of tests where the road surface silt 

loading was artificially changed through either the addition of sand or through removal 

with mechanical means altered the normal correlation between the vehicle speed and the 

road silt loading. With the use of more robust statistical software, the presence of inter 

correlation between speed and silt loading was re assessed. In addition, the more robust 

software allows a better determination of the potential improvement of an equation which 

includes speed to predict road dust emissions. This assessment revealed that the use of 

the speed term was contraindicated and the final equation contains only silt loading and 

average vehicle weight as independent variables. 

Comment: The goal should be to develop a predictive tool for situations without 

measured emissions rather than trying to get the best fit for the set of measured 

emissions. 

Response: The use of Excel to generate the predictive equation made an evaluation of 

the capability for the equation to predict data that was not part of the existing data set 

difficult and labor intensive. The use of SAS allows for a more reliable assessment of the 

equations predictive capabilities. 

Comment: The geometric mean is the better choice than the arithmetic average when 

working with the predicted/observed ratios. 

Response: It is assumed that the use of the geometric mean is a metric to evaluate the 

predictive accuracy of the equation through the use of the average predicted to observed 

values. With the use of SAS, several indicators of the predictive capabilities of the 

resulting equation were evaluated. 

Comment: The document would have benefitted from a thorough review/edit prior to 

being posted on the CHIEF web site. 

Response: Prior to posting the final background report, the AP-42 Section and 

background report was reviewed and edited more thoroughly and the Table of contents 

was updated to provide an accurate indication of the contents of the chapters. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 9 

Camille Sears for the Sierra Club 

Comment: I have a few concerns regarding USEPA’s proposed revision to AP-42 

Section 13.2.1: 

• USEPA’s multiple regression analysis incorporating vehicle speed excludes a 

valuable data set for assessing paved road PM emissions from industrial facilities. 

• USEPA’s proposed revision to AP-42 Section 13.2.1 results in a very significant 

reduction in PM10 and PM2.5 emission factors from paved roads in industrial 

settings. 

• It is unclear whether USEPA’s proposed revision to AP-42 Section 13.2.1 

improves upon predictive performance of the existing 2006 emission factor. 

Response: The performance of the multiple stepwise regression of the data recognized 

that incorporation of the speed term involved the exclusion of 22 test runs. EPA 

recognized that the exclusion of these data could affect the resulting equation and decided 

to include the speed term since the correlation coefficient showed a modest improvement. 

Another commenter indicated that there are better software and process available than 

were used by EPA to develop the equation. EPA employed software more suited for 

stepwise multiple nonlinear regression than Microsoft Excel in the final equation 

development. EPA used this improved software for a more rigorous assessment of the 

influence of incorporating the speed term in the equation in this reassessment. (In EPA's 

reassessment, it was revealed that the speed term provided no improvement in the 

predictive accuracy of the resulting equation. As a result, the equation published in the 

final AP-42 section includes only silt loading and average vehicle weight). 

While EPA is cognizant of potential impact of any changes that may result in revising the 

emissions factors in AP-42, the primary goal of emissions factors development is to 

provide factors that provide as accurate of a prediction of the target population as 

possible. The underlying data has considerable variation even when several of the 

independent parameters are nearly identical. With the increased number of independent 

parameters, it is possible that some situations where emissions will be greater than the 

previous equation and some where emissions will be less. 

While there may be some situations where the predictive performance of the proposed 

equation performed poorer at predicting the underlying data, there were others where the 

predictive performance was improved. Several measures were used to assess the 

predictive performance of the revised equation and the final equation performs better than 

the previous equation. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 10 

Comment: USEPA excluded 22 tests performed at two integrated iron and steel plants 

due to lack of vehicular speed data. These iron and steel plant source tests are crucial for 

calculating fugitive dust emissions from industrial facilities, and excluding these data has 

a very significant impact on predicted paved road emission rates. As discussed in the 

following section, USEPA’s proposed revision to the paved road emission factor will 

reduce particulate emission calculations at typical industrial sites by roughly an order of 

magnitude. This large, and perhaps unrealistic, reduction in calculated industrial paved 

road emissions is an artifact of trying to develop an emission factor based on tests that 

must include vehicle speed data. 

Response: The exclusion of the 22 tests performed at iron and steel facilities did not 

significantly bias the equation. An evaluation of the predictive precision of the equation 

in the November 2006 version of the AP-42 Section for Paved Roads reveals that on 

average the equation over predicted the 92 individual data by over 11,000%. While 

approximately 50% of the predicted estimates underestimated the measured emissions 

and 50% overestimated emissions, overestimates were significantly greater than the 

underestimates. The 25 percentile value underestimated actual emissions by 54% while 

the 75 percentile value overestimated actual emissions by 713%. The equation using 

only silt loading and average vehicle weight which was rejected for the equation that 

included speed overestimates actual emissions by 1,429%. The equation that was 

proposed and includes the speed term overestimates actual emissions by only 890%. For 

both the previously published equation and the proposed equation, the majority of the 

overestimation appears to be associated with the lowest speeds, silt loading in the middle 

third of the data and in the highest average vehicle weights. In these categories, it 

appears that the previously published equation overestimates emissions more than the 

proposed equation. With respect to roads with greater average vehicle weights such as 

may be present at industrial facilities, the equation in the November 2006 AP-42 section 

tended to overestimate emissions more than the proposed equation. Table 1 below 

presents the independent parameter variables, estimated measured emissions, predicted 

emissions by the 2006 AP-42 equation, the equation considered in the proposal that 

includes only silt loading and average vehicle weight and the equation proposed that 

includes silt loading, average vehicle weight and speed (with an average speed of 35 mph 

assigned for unrecorded speeds). For those test conditions where average vehicle weight 

was greater than 8 tons, the 2006 AP-42 equation tended to overestimate actual emissions 

factors by about 350%. The equation that considered only silt loading and average 

vehicle weight tended to overestimate actual emissions factors by about 3%. The 

equation that considered silt loading, average vehicle weight and speed tended to 

underestimate actual emissions factors by about 12%. A comparison between the 

equation proposed for use and the equation that was considered but did not include the 

speed term shows that the exclusion of the 22 test data that were missing the average 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 11 

speed did not adversely affect the average predictive capabilities of the equation. As 

stated elsewhere, a more rigorous and capable statistical software package was used to 

develop the final equation used in the AP-42 section. 

For the equation published in the 2011 final AP-42 section, the predictive accuracy is 

slightly improved over the equation proposed in the draft AP-42 section. As presented in 

Table 2, the equation published in the final section provides a moderately better or worse 

predictor of actual emissions for a few tests, but does not provide a significantly different 

accuracy that the equation in the draft AP-42 section. While the equation presented in the 

AP-42 section published in 2006 overestimates actual emissions factors by 350%, the 

equation presented in the final 2011 section overestimates actual emissions by an average 

of 77%.
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 12 

Table 1. Performance of 2006 AP-42 equation and equations considered for 2010 draft s

Predicted Emissions (g/VMT) Percent differ

ID # 

Silt 

Loading 

Average 

Speed 

Average 

Weight 

Measured EF 

(g/VMT) 

Old AP-42 

(sL, W) 

Rejected Proposal 

(sL, W) 

Proposed 

(sL, W, s) 

Old AP-42 

(sL, W) 

Reje

AD1 94.8 23 42 1478.189 4696.25 1575.71 1156.43 218% F61 17.9 NR 40 461.174 1476.91 325.20 235.28 220% AD3 52.9 23 40 231.189 2987.29 881.27 636.21 1192% AD2 63.6 23 39 340.807 3241.81 1020.25 751.93 853% F62 14.4 NR 36 315.177 1094.65 241.87 179.78 247% F74 5.59 NR 29 543.498 427.73 83.19 63.42 -21% F34 2.78 NR 28 186.561 257.62 42.37 31.39 38% CI-7 0.05 15.3 27 0.589 17.71 1.02 0.53 2907% CI-8 0.05 15.3 27 1.949 17.71 1.02 0.53 809% F35 2.03 NR 25 296.721 177.11 28.62 21.73 -40% F38 0.218 NR 18 165.766 25.19 2.73 2.05 -85% F39 0.441 NR 18 251.766 39.95 5.21 4.09 -84% B58 10.4 NR 18 366.778 313.08 95.42 90.51 -15% F37 0.417 NR 17 76.038 35.33 4.70 3.76 -54% F45 5.11 NR 16 210.911 165.22 44.58 42.38 -22% F32 0.117 NR 14 52.170 11.42 1.22 0.98 -78% F27 14.8 NR 14 129.070 270.08 105.02 111.94 109% B57 2.32 NR 12 194.216 64.10 16.59 16.78 -67% B60 3.19 NR 12 438.216 78.89 22.24 22.93 -82% B52 7.19 NR 12 34.616 133.95 46.98 50.85 287% AUE1 4 15 12 2.286 91.43 27.39 24.99 3900% B59 2.06 NR 11 347.289 52.04 13.74 14.26 -85% B55 6.3 NR 11 182.289 107.84 38.43 42.66 -41% B51 13.6 NR 11 139.289 177.97 78.02 90.68 28% B54 3.77 NR 10 92.662 66.87 21.97 24.52 -28% B50 13.6 NR 9.4 81.506 140.54 67.62 83.43 72% B56 2.4 NR 9.2 125.421 43.92 13.44 15.07 -65% F36 0.201 NR 8.3 54.168 7.33 1.25 1.26 -86% 

Average 358% 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 13 

Table 2. Performance of 2006 AP-42 equation, equation proposed in 2010 draft section and Fin

Predicted Emissions (g/VMT) Percent diffe

ID #

Silt

Loading

AverageSpeed

Average

Weight

Measured EF

(g/VMT)

Old AP‐42

(sL, W)

Proposed

(sL, W, s)

Final

(sL, W)

Old AP‐42

(sL, W)

P

AD1 94.8 23 42 1478.189 4696.25 1156.43 2886.277 218% F61 17.9 NR 40 461.174 1476.91 235.28 600.570 220% AD3 52.9 23 40 231.189 2987.29 636.21 1613.169 1192% AD2 63.6 23 39 340.807 3241.81 751.93 1859.513 853% F62 14.4 NR 36 315.177 1094.65 179.78 442.254 247% F74 5.59 NR 29 543.498 427.73 63.42 149.639 -21% F34 2.78 NR 28 186.561 257.62 31.39 76.359 38% CI-7 0.05 15.3 27 0.589 17.71 0.53 1.886 2907% CI-8 0.05 15.3 27 1.949 17.71 0.53 1.886 809% F35 2.03 NR 25 296.721 177.11 21.73 51.060 -40% F38 0.218 NR 18 165.766 25.19 2.05 4.773 -85% F39 0.441 NR 18 251.766 39.95 4.09 9.073 -84% B58 10.4 NR 18 366.778 313.08 90.51 161.994 -15% F37 0.417 NR 17 76.038 35.33 3.76 8.133 -54% F45 5.11 NR 16 210.911 165.22 42.38 75.113 -22% F32 0.117 NR 14 52.170 11.42 0.98 2.093 -78% F27 14.8 NR 14 129.070 270.08 111.94 172.829 109% B57 2.32 NR 12 194.216 64.10 16.78 27.253 -67% B60 3.19 NR 12 438.216 78.89 22.93 36.436 -82% B52 7.19 NR 12 34.616 133.95 50.85 76.446 287% AUE1 4 15 12 2.286 91.43 24.99 44.785 3900% B59 2.06 NR 11 347.289 52.04 14.26 22.375 -85% B55 6.3 NR 11 182.289 107.84 42.66 62.006 -41% B51 13.6 NR 11 139.289 177.97 90.68 125.074 28% B54 3.77 NR 10 92.662 66.87 24.52 35.222 -28% B50 13.6 NR 9.4 81.506 140.54 83.43 106.525 72% B56 2.4 NR 9.2 125.421 43.92 15.07 21.429 -65% F36 0.201 NR 8.3 54.168 7.33 1.26 2.010 -86% 

Average 358% 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 14 

Comment: USEPA prepared a consequence analysis of the National Emission Inventory 

(“NEI”) resulting from their proposed revision.8 USEPA found that their revised paved 

road emission factor will significantly reduce PM10 emissions in the NEI (up to 200% 

reduction), while PM2.5 emissions are only slightly affected (some NEI calculations 

increase, some decrease). USEPA, however, did not examine the affect of their draft 

revised paved road equation on fugitive dust emissions from industrial sources. 

Response: The estimated impact on State Emissions Inventories and the NEI was 

performed as a tool for decisions which may need to be made to address conformity 

requirements. The Agency may provide States with extensions of times for adopting 

revised emissions estimates in their SIP and Transportation plans. These estimates were 

also produced to assist State and local agencies understand the potential impact that the 

revised emissions factors may have on their PM10 and PM2.5 inventories which are being 

prepared to address non attainment conditions and required SIP plan development. The 

emissions inventory impact estimates were not produced as a decision criteria for revision 

of the emissions factor equation. The only criteria used in assessing the proper equation 

to publish are the representativeness of the underlying test data and the comparison of the 

equation to the actual measured emissions. Although not presented in the background 

report, the performance of the equation was made by ordering the available test data by 

silt loading, average vehicle weight and by speed to evaluate whether there was any 

systematic bias which was driven by one or more outlying data. Table 4-23 of the 

background report for the proposed revision did include the average percent error for the 

2006 equation and the proposed equation. When arranged by weight, the 2006 equation 

produced errors of about 70,000 percent for vehicle weights of over 10 tons while the 

proposed equation produced errors of about 2,500 percent. The equation published in the 

final section produces errors of 5,000 percent for vehicle weights between 10 and 40 tons 

and errors of about 20,00 percent for vehicle weights over 40 tons. Although when 

limited to these high weight classes the performance appears to be worse, for lower 

weight classes the new equation demonstrates superior performance to both the previous 

published equation and the proposed equation. 

David E. James, PhD PE; Associate Vice Provost for Academic Programs; UNLV, Las 

Vegas, NV. 

Comment: In many parts of the country where there is significant rain or a rainy season, 

rain days may considerably effect estimated PM10 emissions in the inventory. However, 

for Las Vegas and other places like it in arid places, I tend to use a 'pessimistic' approach 

that doesn't include the rain days, since rain occurs sporadically, and what rain does fall is 

often very light. For the desert southwest, I think that it is best to look at the data without 

rain adjustments. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 15 

Response: It is recognized that the mitigation adjustment for rain events in AP-42 is 

imperfect. It is recognized that with very light rain events, the silt loading on paved roads 

may increase due to the removal of soil on the under carriage of vehicles. For most areas 

of the US, these very light rain events are offset with heavier rain events. Over a month 

to a year, these enrichment and mitigation events balance out. It should also be noted that 

the mitigation level is not based upon any measured data and is an "engineering or expert 

elicitation" estimate. 

The emissions factors and the adjustment factors in AP-42 are educated estimates of the 

national average value and do not include variations that may occur due to local and 

regional influences. While some variation in the emissions factors for paved road has 

been reduced through the incorporation of the independent variables silt loading, vehicle 

weight and number of rain events, the remaining variation is still substantial. EPA does 

not prohibit the use of alternative emissions factors or adjustments when accompanied by 

a scientifically credible rationale and supporting data. 

Comment: With locally derived data, we obtain results that are different from those that 

might be predicted using default silt loading data. The actual impact on total estimated 

PM10 emissions in an inventory or SIP would depend on how much VMT was assigned to 

each roadway category. 

Response: It is recognized that the default silt loading information presented in AP-42 

does not provide the precision and accuracy that may be needed to properly represent the 

influence of emissions from paved or unpaved roads. It is also recognized that the 

resources required collecting representative silt loadings for large numbers of roads is 

substantial. However, where roads are believed to be significant contributors to the 

levels of ambient air particulate matter, obtaining this information is valuable to 

accurately estimate emissions. To address the needs to obtain this information in a cost 

effective manner, we have included a discussion of the potential advantages of mobile 

monitoring to develop temporally and spatially resolved silt loading (or emissions) 

information. 

Comment: I also ran a hypothetical sensitivity analysis comparing arbitrary 

combinations of vehicle weight and silt loading, to see what the impacts of the new PM10

equation might be. 

Response: It is recognized that different road classes may have different silt loadings 

and the vehicles using these roads may have different average vehicle weights. These 

variables will have differing influences on the predicted emissions from these roads. As 

a result, the use of locally derived silt loading information is strongly encouraged. 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 16 

Steve Zemba of Cambridge Environmental Inc for the National Asphalt Pavement 

Association. 

Comment: The recommended default values for silt loading in draft Table 13.2.1-3, and 

particularly that for asphalt batching, may be too high for typical current applications. The 

recommended value is 120 g/m2, but, as you know, in EPA’s 2000 Emission Assessment Report 

for Hot Mix Asphalt Plants, a silt-loading value 3 g/m2 is suggested for paved roads at typical 

hot-mix asphalt production facilities. Also, site-specific measurements at a hot mix asphalt 

facility in Alexandria, Virginia in 2005 (using the sampling and analytical methods described in 

AP42 Appendix C) found a silt loading level of 0.5 g/m2. This facility, which we analyzed in 

detail for the City of Alexandria, employs aggressive dust suppression techniques. 

Response: Values presented in Table 13.2.1-3 are based upon road dust samples collected in the 

mid to late 1970's through the mid to late 1980's. It is unclear whether any management 

practices were used at these facilities to control the silt loading of the roads where these samples 

were collected. It is possible that current normal maintenance practices would achieve lower silt 

loadings than are presented in the table. Statements in the documentation included in the reports 

by the Corn Refiners Association and several other test programs used in the equation 

development indicate that there was active management of the road surface dust levels. As a 

result, the silt loading data collected during those test programs are lower than they would be 

otherwise. While there is no requirement to use the silt loading values provided in the tables of 

AP-42 updated silt loading data can be collected by any individual as long as they follow the 

procedures presented in the AP-42 appendices. It is recommended that in addition to 

documenting the sampling and analyses, the documentation include normal housekeeping 

practices and special monitoring and maintenance practices at the collection sites. While we 

cannot guarantee rapid incorporation of new silt loading data into the table, any reports 

submitted will be posted for use by subsequent users. 

Catharine Fitzsimmons, Chief, Air Quality Bureau and Lori Hanson Iowa Department of 

Natural Resources. 

Comment: The DNR supports the revision of this section to incorporate new data from 

corn wet mills and to account for mean vehicle speeds below 10 miles per hour. 

Response: Thanks for your support. 

Comment: The proposed form of the equation requires that a mobile source emissions 

model be run in order to determine a paved road emission factor. Obtaining the 

emissions factor for vehicle emissions in this manner will be problematic as the DNR 

does not have the resources to generate specific emissions factors for vehicle emissions 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 17 

by running MOVES20I0 for every construction permitting project that includes a paved 

haul road. The DNR suggests that either the empirical equation be developed to include 

vehicle emissions from engine exhaust, tire and brake wear, or that a table of default 

values be included in the section to account for vehicle emissions as an alternative to 

running a mobile source emission model. 

Response: While vehicle exhaust emissions may have been relatively stable for the last 

twenty or thirty years, several regulatory programs which cover mobile source emissions 

are expected to produce decreasing exhaust emissions over the next five to ten years. In 

addition, engine exhaust like road dust emissions is highly dependent on the road 

characteristics, meteorological conditions, vehicle speed, vehicle class and other 

environmental conditions. As a result, a default engine exhaust equation will result in 

unknown errors and may lead to incorrect decisions on different programs. While 

decisions for many programs may not require the accuracy that would occur with 

individual selection of the requisite parameters needed for the most accurate emissions 

estimates, this would be a decision that should be made for each application. While State 

agencies (Department of Transportation or Air Quality) may not have the resources or 

time to generate a project specific emissions estimate for every project, individual States 

are in a better position to develop default parameters (engine exhaust, silt and average 

vehicle weight) which is appropriate for use for projects with different sensitivities. 

Pat Davis of MARAMA for the States of New Jersey, Delaware, Maryland and 

Massachusetts. 

Comment: We have been examining the ERTAC/PECHAN emission factors for Road Dust 

and Maryland noticed that the PM2.5 emission factors were zeroed out for the following road 

types: 

• Urban Collector 

• Urban Minor Arterial 

• Urban Other Principal Arterial 

• Urban Other Freeways and Expressways 

• Urban Interstate 

Emission factors for PM10 were found and there was no mention in the documentation of 

why the PM2.5 emission factors were zeroed out, so we are bit confused. 

Response: As a result of a revision of the ratio of the PM2.5 to PM10 recommended by the 

Western States Air Resources Council (WESTAR) from 25% to 15%, the multiplier k in the 

predictive equation for PM2.5 was revised from 1.8 (for grams/VMT) to 1.1 (for grams/VMT) 

in the 2006 revision of the paved roads AP-42 Section. With a constant emissions factor of 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 18 

0.1617 subtracted for the vehicle exhaust break wear and tire wear emissions, these 

emissions result in a negative calculated road dust emission when one enters an average 

vehicle weight of 4 tons or less and a silt loading of 0.2 grams/square meter or less. While 

the k value used in the previous version of the equation resulted in negative emissions 

whenever the silt loading was less than 0.03 grams/square meter, this affected only Freeways, 

Expressways and Interstates and was believed to be rational since roadways with average 

speeds of 55 mph (and the normal level of silt for that speed) had a high number of tests with 

low measured emissions and were considered to be composed primarily of exhaust 

emissions. 

In the equation presented in the final version of this update, the estimated exhaust component 

was subtracted from each source test prior to the stepwise regressions of the test data to 

develop the predictive equation. As a result of the absence of vehicle exhaust, tire wear and 

break wear in the predictive equation, there are no conditions that will result in negative 

emissions for the road dust emissions. 

Julie McDill (MARAMA), David Fees (Delaware), Julie Rand (New Jersey). 

Comment: Here is Delaware's paved road dust spreadsheet for 2007, using the new 

equation. We got very detailed with this category; estimating emissions by month. 

Regarding the new equation, PM10 was reduced by 58% from the emissions submitted to 

MACTEC; while PM2.5 increased by 48%. I believe the PM2.5 increase is caused by two 

factors-first, the PM2.5/ PM10 ratio was increased to 25% (previously 15%). The second 

reason is that under the old equation, one had to apply a correction factor, C, to remove the 

exhaust, brake, and tire wear from the front part of the equation. By subtracting C at the end 

of the equation, the resulting PM2.5 value went negative for several roadway types. Of course 

we zeroed these out, but with the new method there is never a situation where the emission 

factor value can go negative. Having negative emission factors result from the use of the old 

equation was obviously a flaw in the method, so I expect the new equation is more accurate. 

I look forward to NJ's results when they apply the new equation, to see if they get changes 

similar to mine. 

New Jersey has similar results, but even more drastic for PM2.5. An increase in PM2.5 of 

350% and a decrease in PM10 of 46% I think one big cause is the difference in k factor, 

among other changes. The k factor for PM2.5 went down from the 2003 AP-42 to the 2006 

AP-42, and back up again in this new draft. We guessed at the new vehicle speed 

requirement, but a slight variation in speeds will not make that much of a difference. 

Response: It is correct that the k value and the C value both influence the predictive value 

for the emissions factor. In addition, the exponents associated with the silt loading and the 

average vehicle weight also influence the emissions estimates. It is also correct that the 
Comments and Responses on June 2010 Draft Revision 

of AP-42 Section 13.2.1 for Paved Roads. 

January 2011  Page 19 

updated equation will not generate a negative emissions factor since the vehicle emissions, 

tire wear and break wear will not be included in the equation development. Based upon an 

assessment of the predicted to actual emissions factor for each of the available emissions 

tests, the updated equation provides an improved estimate of the emissions compared to the 

previous equation. It is also believed that the return to the PM2.5 to PM10 ratio of 25% is a 

better indicator of the PM2.5 than the 15% ratio that was based upon laboratory assessment 

conducted for WESTAR. 

Gary Garman of McVehil-Monnett. 

Comment: It's good to see the paved road section is being revised. Thanks. It has been a 

challenge in the past explaining to industrial clients that paving a road would actually result 

in higher predicted emissions than if the road is left unpaved. I think we'll see more paving 

and actual emission reductions as a result of the new equation. A few editorial comments on 

the draft paved road section: 

Page 13.2.1-1, third paragraph, first sentence..change to "The particulate emission factors 

presented in a previous version.." 

Page 13.2.1-5, third paragraph, last sentence..change "Table 13.2.1-3" to "Table 13.2.1-2" 

Page 13.2.1-8, fifth paragraph, first sentence..change "Table 13.2.1-3" to "Table 13.2.1-

2" 

Page 13.2.1-9, second paragraph, second sentence..remove hyphen between "not" and 

"suggest" 

Table 13.2.1-3...the page number this table is on should be changed to 13.2.1.10. Also, 

total loading range for iron and steel should be 0.006-4.77, not 43.0-64.0. 

Page 13.2.1-11, first paragraph, fourth sentence..remove hyphen between "any" and "of" 

Thanks again. I look forward to this draft being finalized. 

Response: An assessment of the paved verses unpaved road equation performance will be 

conducted. A statement will be added to the paved road section explaining that under some 

high silt loading conditions the equation may predict higher emissions than for an unpaved 

road and that for these conditions the unpaved road equation should be used. The 

typographical errors will be corrected in the final version. 
Comments on Proposed Paved Road Equation

Cowherd, Chatten to: Ron Myers 08/31/2010 03:00 PM

Cc: "Kies, Rebecca", "Muleski, Greg"

History: This message has been forwarded.

Hello Ron,

Thank you for the opportunity to comment on the proposed revision to the paved road dust equation in

AP‐42 section 13.2.1.  The attached letter presents comments developed on behalf of the Center for the

Study of Open Source Emissions (CSOSE).  

As you know, the revised equation (proposed by EPA as a replacement for the existing equation) and its

technical foundation were topics of discussion during the August 18 teleconference hosted by the

CSOSE.  During this teleconference and in related information exchanges, the general consensus among

CSOSE participants who have worked in this field is that the proposed equation does not offer improved

predictive capability but introduces additional data requirements to the paved road emission inventory

process.  

There is also the broader issue of adopting mobile monitoring as the basis for more realistic emission

inventorying of paved roads.  In previous conversations, I believe that you have acknowledged the clear

advantages of mobile monitoring over the traditional AP‐42 method for determining paved road dust

emissions with its reliance on limited and difficult measurements of silt loading.  

We believe that the CSOSE constitutes a substantial resource in resolving these issues and in assisting

EPA with the goal of developing improved emission factors such as those applicable to paved road dust

emissions.

Please contact me with any questions or comments.

Sincerely,

Chat Cowherd

Chatten Cowherd, Jr., Ph.D.

Midwest Research Institute

425 Volker Boulevard

Kansas City, MO 64110

(816) 753‐7600 ext. 1586

(816) 360‐5346 direct dial

This message is intended exclusively for the individual or entity to which it is addressed.

This communication may contain information that is confidential, proprietary, privileged or otherwise legally exempt from disclosure.

If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all copies 

of the message.
Chatten Cowherd, Jr., Ph.D. 

Director 

ccowherd@mriresearch.org 

 (816) 360-5346 

August 31, 2010 

Mr. Ron Myers 

U.S. Environmental Protection Agency 

Research Triangle Park NC 27711 

RE: Proposed Revision to AP-42 Emission Factor Equation for Paved Road Dust

Dear Mr. Myers: 

The Center for Study of Open Source Emissions (CSOSE) is pleased with the opportunity 

to submit comments in response to EPA’s proposed revision of the emission factor equation in 

AP-42 Section 13.2.1. It should be noted that these comments were prepared by the undersigned 

as Director of CSOSE, taking into account verbal and written communications from interested 

members of the Center, including those provided during a presentation and discussion of this 

topic in the August 18 teleconference hosted by the Center. However, this letter was not 

circulated to CSOSE participants for review prior to submission. 

One of the goals of CSOSE is to promote transparency and collaboration in the 

documentation of test results and in the use of those results to derive effective tools for 

compliance with air quality standards. We believe that this goal is consistent with EPA’s stated 

goal to develop a self-sustaining emissions factors program that produces high quality, timely 

emissions factors, better indicates the precision and accuracy of emissions factors, encourages 

the appropriate use of emissions factors, and ultimately improves emissions quantification (see 

EPA’s Advance Notice of Proposed Rulemaking on “Emission Factors Program Improvements,” 

Oct. 14, 2009). 

We acknowledge the concerns of various parties related to the scientific foundation for the 

proposed equation as well as the increased effort required in developing vehicle speed data to 

include in paved road emission inventories. CSOSE participants have presented analyses 

demonstrating that the proposed equation does not provide an improved predictive capability 

above that provided by the current equation. In addition the proposed equation has a significant 

new data input requirement (vehicle speed) that increases the difficulty of generating paved road 

emission inventories and that has possible implications on projected effectiveness of current SIPmandated control strategies. 

Based on our discussions of the proposed equation and the technical analyses presented by 

EPA, we find the scientific foundation for the revision unconvincing. This leads us to question 

the process used in advancing this proposed equation. Our understanding of the rationale for 

revision of the existing equation might be clarified if there were evidence of an internal review 

process within EPA that raised issues and resolved them appropriately. 
Besides the problems stated above, we find difficulty in understanding the scientific basis 

for replacing the existing PM-2.5/PM-10 ratio published in 2006 with the ratio that was 

previously used by EPA. The ratio in the existing equation was accepted by EPA as an outcome 

of an experimental program supported by the Western Regional Air Partnership (WRAP). That 

experimental program included regular progress updates in WRAP teleconferences with 

participation from EPA representatives. To our knowledge, WRAP was never directly informed 

in advance that the stated conclusions of their study are now being discounted. 

We have encouraged others to present comments on the proposed equation that are 

supportive of the goal of providing improved emission factors. At the time of this writing, we 

are aware that separate comments are being submitted by Midwest Research Institute (Ms. 

Courtney Bokenkroger and Dr. Greg Muleski), by the Clark County Department of Air Quality 

and Environmental Management (Mr. Rodney Langston) and by the University of Nevada at Las 

Vegas (Dr. David James). 

We trust that EPA will publish all comments as well as the responses to each comment. 

This will be of great assistance to all in moving toward the best possible use of the test data in 

supporting a meaningful and appropriate emission factor equation for entrained dust from paved 

roads. 

In summary, we conclude that there is no compelling scientific justification for adopting the 

proposed emission factor equation as a replacement for the existing equation. This problem is 

compounded by the requirement for additional input data and the potential impact on current and 

future emission inventories as tools for compliance determination. We conclude that an internal 

EPA review may not have been performed prior to posting the proposed equation for public 

comment. Finally we emphasize the importance of publishing all comments submitted to EPA 

along with EPA’s responses to each comment.

If you have questions about these comments submitted on behalf of CSOSE, please contact 

the undersigned by email (ccowherd@mriresearch.org) or by telephone (816) 360-5346. We 

look forward to your responses to these comments. We believe that CSOSE constitutes a 

substantial resource in resolving the above issues and in assisting EPA with the goal of 

developing improved emission factors for open sources. Thank you again for the opportunity to 

submit comments on the proposed revision to the current AP-42 equation for paved road dust 

emissions. 

Sincerely yours, 

CENTER FOR STUDY OF OPEN SOURCE EMISSIONS

Chatten Cowherd, Jr., Ph.D. 

Director 
 

Thank you for the opportunity to comment on the proposed AP-42 paved roads section 13.2.1. 

Attached to this email are MRI’s comments resulting from statistical analysis of the proposed 

changes to the paved road equation by MRI Senior Statistician, Courtney Bokenkroger. These 

comments have been reviewed by myself, Chat Cowherd, and Greg Muleski. 

 

Please feel free to respond with any questions or comments. 

 

Sincerely, 

Becky Kies 

 

 

Rebecca Kies

Assistant Scientist

Midwest Research Institute

425 Volker Blvd. KCMO 64110 

(816) 360-3825 (direct) 

(816) 753-7600 x1818 

rkies@mriresearch.org 

 

This message is intended exclusively for the individual or entity to which it is addressed. 

This communication may contain information that is confidential, proprietary, privileged or otherwise legally exempt from disclosure. 

If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all copies of the message. 

Attachments:

Comments in Response to EPA Proposed Section 13.2.1 Paved Road Equation.pdf

https://rtairmail1.rtp.epa.gov/mail/rmyers.nsf/9ff539a1e24f5aaf852577890046a8f6/C48E... 10/22/2010
1 

Courtney Bokenkroger 

Senior Statistician 

816-360-5303 

August 31, 2010 

Mr. Ron Myers 

U.S. Environmental Protection Agency 

Research Triangle Park NC 27711 

RE: Draft AP-42 Section 13.2.1 Paved Roads 

Dear Mr. Myers: 

Midwest Research Institute (MRI) is pleased with the opportunity to submit comments in response to 

EPA’s proposed draft revisions to AP-42 Section 13.2.1 Paved Roads and corresponding background 

documents. We applaud EPA’s effort to improve the quality of the emission factor model for paved roads 

and appreciate your consideration of external comments. 

MRI has a productive history of work in air pollutant source testing, process characterization, and 

development of emission factors for EPA’s Emission Factor Handbook (AP-42). Besides serving for 

more than 25 years as an EPA contractor in the testing of ducted sources and in associated methods 

development, we have made unique contributions to the development and application of test methods for 

open (non-ducted) sources. The open sources that we have tested over the past 35 years include 

agricultural operations, paved and unpaved roads, construction activities, surface mining activities, 

military training operations, and open area wind erosion. Because of the large natural variability of these 

sources, MRI pioneered the concept of predictive emission factor equations rather than relying on simple 

averaging of test results for fugitive dust sources. This approach reduced the uncertainty of emission 

factor estimates for unpaved roads--as the largest contributor to the national PM-10 emission total--by up 

to two orders of magnitude. 

Our comments on the draft AP-42 Section 13.2.1 Paved Roads focus on a statistical analysis of the 

data set and procedure used to calculate the proposed new paved road emission factor equation and can be 

summarized as follows: 

The approach used by EPA to calculate the proposed paved road equation differs from 

standard least-squares regression procedures. MRI recommends that ordinary leastsquares regression procedures be used. 

In using ordinary least squares regression to compare models for only the field 

measurements that included vehicle speed, we find that inclusion of speed in the model 

takes away from the explanation of variance of the model (R2

) and that vehicle speed 

does not have a statistically significant relation to emission factor. 

It is recommended that different modeling options be explored to find the best predictive 

equation from the data provided. Two such options are: 

o Look at low speed and high speed models separately, potentially excluding 

vehicle speeds under 5 mph from equation development. 
2 

o Use a composite factor of weight and speed together with either weight or speed 

as independent variables in the regression. This helps alleviate the problem of the 

multicollinearity of weight and speed seen in these data. 

Model Comparison

The data set used by EPA to develop the proposed paved road equation included emission factor, silt 

loading, weight, and speed. Out of 93 total observations, 71 included speed data. The 71 observations that 

included speed data were the ones used by MRI for model comparison. 

It is not reasonable to compare the proposed model with other possible models for the data using the 

approach taken by EPA to calculate the proposed model. The double-regression approach used renders 

two different R-square values (one for each regression), neither of which accurately represent the 

proportion of variability explained by the final resulting model. 

The resulting equations obtained from running least-squares regression on the log transformed, 

normalized values with and without inclusion of speed on the set of 71 data points appear below. 

Regression without speed: EF = 6.51 *(silt loading/2)0.97 * (weight/3)0.36

Regression including speed: EF = 6.41 *(silt loading/2)0.97 * (weight/3)0.27 * (speed/30)-0.12

Variance Explained 

by Model

Variable p-value “Proportion of 

Variance Explained”

Regression 

without speed

R

2

= 0.6335 Silt loading < 0.0001 0.62673

Weight 0.0739 0.04621

Regression

including speed

R

2

= 0.6288 Silt loading < 0.0001 0.62673

Weight 0.3892 0.04621

Speed 0.7140 0.00202

The R-square value from a standard least-squares regression represents the proportion of variability 

explained by the model. When speed is included in the regression, the R-square is slightly lower than 

when speed is not included. This means that the model explains less of the variance seen in emission 

factor when speed is included than when it is not. 

The column labeled p-value represents the statistical significance of the factor in the prediction of the 

dependent variable (the lower the p-value, the greater the significance). In order to be considered 

statistically significant for inclusion in the model, generally p-values are less than or equal to 0.15. Note 

that the p-values for the equation that includes speed indicate that speed and weight are both statistically 

insignificant (this is because there is likely a relationship between weight and speed). When speed is not 

included, weight is statistically significant. 

The column labeled “proportion of variance explained” is the proportion of R-square that is explained 

by each individual variable. Speed contributes almost no additional “explanation of variance” to the 

model (i.e. speed doesn’t add much to the predictive power of the model). 
3 

Gaps in the Data

 Additional concerns about gaps in the range of data surfaced during our statistical analysis. Notice 

the major holes highlighted by the circles in the speed-silt loading and speed-weight boxes. The dataset is 

missing low silt loading, low speed; low silt loading, high speed data; and low weight, low speed data. 

Ideally, the boxes relating silt loading, weight, and speed should be completely filled with data points in 

order to cover all ranges of possible occurrences and consider them to be independent factors in the 

model. 

Conclusions and Recommendations

The proposed approach used by EPA to calculate the proposed paved road equation differs from 

standard regression procedures. The two-regression approach used results in two different R-square 

values, neither of which accurately represent the proportion of variability explained by the final resulting 

model. Additionally, different data sets were used to develop the two models. 

In using ordinary least-squares regression to compare data models for the same data, inclusion of 

speed in the model does not significantly add to the explanation of variance in emission factor. Also, 

speed does not have a statistically significant relationship with emission factor. 

The low-speed data (≤ 5 mph) have an un-proportionally large effect on the fit of the model. This is 

of concern because there are not enough low speed data to represent all ranges of weight and silt loading. 
4 

Because the correlation between the log-transformed, normalized weight and speed in the model is 

approximately -0.78, inclusion of both factors introduces issues related to multicollinearity. The problem 

with having highly correlated variables in a model is that the coefficients are easily influenced by the 

dataset used in estimation and may not be meaningfully interpreted because they are not independent. 

It is recommended that different modeling options be explored to find the best fit and set of predictors 

for the data provided. Two such options are: 

Look at low speed and high speed models separately, potentially excluding vehicle 

speeds under 5 mph from equation development. 

Use a composite factor of weight and speed together with either weight or speed as 

independent variables in the regression. This helps alleviate the problems due to 

multicollinearity between weight and speed seen in these data. 

If you have questions or comments about this evaluation of the proposed paved road equation in EPA 

AP-42 Section 13.2.1, please contact the undersigned by email (cbokenkroger@mriresearch.org) or 

telephone (816- 360-5303). We look forward to your response on this matter. 

 Sincerely yours, 

 MIDWEST RESEARCH INSTITUTE 

 Courtney Bokenkroger 

 Senior Statistician 
Comments on Section 13.2.1 draft

Muleski, Greg to: Ron Myers 08/30/2010 02:58 PM

History: This message has been forwarded.

Ron

Thank you for the opportunity to comment on the draft paved road emission 

factor. Based on my analysis for the Corn Refiner Association member 

companies, I know that the revision moves the power on the "mean vehicle 

weight term" in the right direction.

My specific comments are as follows:

1. The measured emission factor for CM-2 should be "63.5" rather than "6.35" 

so the independent variable in Table 4-17 should have been about 52 g/vmt 

(rather than the default value of 0.02 g/vmt).

2. The two-step regression process described in Section 4.2.2.2 differs from 

standard stepwise multiple regression used in the past AP-42 updates. It is 

not clear how R-squared values at each step can be combined to obtain a 

meaningful value.

3. The high degree of correlation between speed and weight precludes both 

being included as independent terms in the emission factor equation. 

Furthermore, it is not clear what inclusion of speed does for the model. The 

goal should be to develop a predictive tool for situations without measured 

emissions rather than trying to get the best fit for the set of measured 

emissions.

4. The geometric mean is the better choice than the arithmetic average when 

working with the predicted/observed ratios.

5. The draft background document is in rough shape. It would have been better 

to have posted only Section 4 to avoid confusion arising from the table of 

contents, references, etc. The document would have benefitted from a thorough 

review/edit prior to being posted on the CHIEF web site.

Please feel free to contact me with any questions or comments.

Greg Muleski

This message is intended exclusively for the individual or entity to which it 

is addressed.

This communication may contain information that is confidential, proprietary, 

privileged or otherwise legally exempt from disclosure.

If you have received this message in error, please notify the sender 

immediately by facsimile, e-mail or phone and delete all copies of the 

message.
FW: Message from KMBT_421

Muleski, Greg to: Ron Myers 08/26/2010 09:52 AM

History: This message has been replied to and forwarded.

Ron

Sorry I missed your phone call. I've attached 2 annotated pages from your 

draft background document that show the problem.

-----Original Message-----

From: copier211h@mriresearch.org [mailto:copier211h@mriresearch.org]

Sent: Thursday, August 26, 2010 8:50 AM

To: Muleski, Greg

Subject: Message from KMBT_421

This message is intended exclusively for the individual or entity to which it 

is addressed.

This communication may contain information that is confidential, proprietary, 

privileged or otherwise legally exempt from disclosure.

If you have received this message in error, please notify the sender 

immediately by facsimile, e-mail or phone and delete all copies of the 

message.



From: "Camille Sears" <camille.marie@sbcglobal.net> 

To: Ron Myers/RTP/USEPA/US@EPA 

Date: Monday, August 30, 2010 11:53PM 

Subject: Re: AP-42 13.2.1 

History: This message has been forwarded.

Hi Ron, 

Attached are our comments. Please let me know if you have questions. 

Your help is greatly appreciated! 

Best wishes, 

Camille 

Attachments:

SC-13.2.1.comments.pdf

Page 1 of 1

https://rtairmail1.rtp.epa.gov/mail/rmyers.nsf/9ff539a1e24f5aaf852577890046a8f6/56708... 10/22/2010








Table 1A

AP-42 Section 13.2.1: Paved Roads

Comparison of Existing and Draft Paved Road PM10 Emission Factors

(Silt loading resuspension only)

Setting sL (g/m2

) W (tons) S (mph) Draft E 

(lb/VMT)

Existing E 

(lb/VMT)

Draft E / 

Existing E

Public 0.2 3.75 25 0.0017 0.0045 0.38

Public 0.2 3.75 35 0.0018 0.0045 0.40

Public 0.2 3.75 45 0.0019 0.0045 0.42

Public 0.6 3.75 25 0.0050 0.0098 0.52

Public 0.6 3.75 35 0.0053 0.0098 0.55

Public 0.6 3.75 45 0.0055 0.0098 0.57

Industrial 0.6 10 5 0.0066 0.0441 0.15

Industrial 0.6 10 15 0.0078 0.0441 0.18

Industrial 0.6 10 25 0.0085 0.0441 0.19

Industrial 0.6 20 5 0.0095 0.1255 0.08

Industrial 0.6 20 15 0.0113 0.1255 0.09

Industrial 0.6 20 25 0.0122 0.1255 0.10

Industrial 0.6 30 5 0.0117 0.2309 0.05

Industrial 0.6 30 15 0.0140 0.2309 0.06

Industrial 0.6 30 25 0.0152 0.2309 0.07

Industrial 2.0 10 5 0.0213 0.0969 0.22

Industrial 2.0 10 15 0.0254 0.0969 0.26

Industrial 2.0 10 25 0.0276 0.0969 0.28

Industrial 2.0 20 5 0.0308 0.2749 0.11

Industrial 2.0 20 15 0.0367 0.2749 0.13

Industrial 2.0 20 25 0.0398 0.2749 0.14

Industrial 2.0 30 5 0.0382 0.5055 0.08

Industrial 2.0 30 15 0.0455 0.5055 0.09

Industrial 2.0 30 25 0.0494 0.5055 0.10

Industrial 5.0 10 5 0.0523 0.1762 0.30

Industrial 5.0 10 15 0.0624 0.1762 0.35

Industrial 5.0 10 25 0.0677 0.1762 0.38

Industrial 5.0 20 5 0.0756 0.4992 0.15

Industrial 5.0 20 15 0.0901 0.4992 0.18

Industrial 5.0 20 25 0.0977 0.4992 0.20

Industrial 5.0 30 5 0.0937 0.9174 0.10

Industrial 5.0 30 15 0.1117 0.9174 0.12

Industrial 5.0 30 25 0.1212 0.9174 0.13

Industrial 10.0 10 5 0.1032 0.2767 0.37

Industrial 10.0 10 15 0.1230 0.2767 0.44

Industrial 10.0 10 25 0.1335 0.2767 0.48

Industrial 10.0 20 5 0.1490 0.7835 0.19

Industrial 10.0 20 15 0.1777 0.7835 0.23

Industrial 10.0 20 25 0.1928 0.7835 0.25

Industrial 10.0 30 5 0.1847 1.4398 0.13

Industrial 10.0 30 15 0.2203 1.4398 0.15

Industrial 10.0 30 25 0.2390 1.4398 0.17

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions

sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

For comparison purposes, no rain adjustments or control efficiencies applied
Table 1B

AP-42 Section 13.2.1: Paved Roads

Comparison of Existing and Draft Paved Road PM2.5 Emission Factors

(Silt loading resuspension only)

Setting sL (g/m2

) W (tons) S (mph) Draft E 

(lb/VMT)

Existing E 

(lb/VMT)

Draft E / 

Existing E

Public 0.2 3.75 25 0.0004 0.0004 1.08

Public 0.2 3.75 35 0.0004 0.0004 1.14

Public 0.2 3.75 45 0.0005 0.0004 1.19

Public 0.6 3.75 25 0.0012 0.0012 1.06

Public 0.6 3.75 35 0.0013 0.0012 1.12

Public 0.6 3.75 45 0.0014 0.0012 1.16

Industrial 0.6 10 5 0.0016 0.0063 0.26

Industrial 0.6 10 15 0.0019 0.0063 0.30

Industrial 0.6 10 25 0.0021 0.0063 0.33

Industrial 0.6 20 5 0.0023 0.0185 0.13

Industrial 0.6 20 15 0.0028 0.0185 0.15

Industrial 0.6 20 25 0.0030 0.0185 0.16

Industrial 0.6 30 5 0.0029 0.0343 0.08

Industrial 0.6 30 15 0.0034 0.0343 0.10

Industrial 0.6 30 25 0.0037 0.0343 0.11

Industrial 2.0 10 5 0.0053 0.0142 0.37

Industrial 2.0 10 15 0.0063 0.0142 0.44

Industrial 2.0 10 25 0.0068 0.0142 0.48

Industrial 2.0 20 5 0.0076 0.0410 0.19

Industrial 2.0 20 15 0.0091 0.0410 0.22

Industrial 2.0 20 25 0.0098 0.0410 0.24

Industrial 2.0 30 5 0.0094 0.0755 0.12

Industrial 2.0 30 15 0.0112 0.0755 0.15

Industrial 2.0 30 25 0.0122 0.0755 0.16

Industrial 5.0 10 5 0.0129 0.0261 0.49

Industrial 5.0 10 15 0.0154 0.0261 0.59

Industrial 5.0 10 25 0.0167 0.0261 0.64

Industrial 5.0 20 5 0.0186 0.0746 0.25

Industrial 5.0 20 15 0.0222 0.0746 0.30

Industrial 5.0 20 25 0.0241 0.0746 0.32

Industrial 5.0 30 5 0.0231 0.1373 0.17

Industrial 5.0 30 15 0.0275 0.1373 0.20

Industrial 5.0 30 25 0.0299 0.1373 0.22

Industrial 10.0 10 5 0.0255 0.0412 0.62

Industrial 10.0 10 15 0.0303 0.0412 0.74

Industrial 10.0 10 25 0.0329 0.0412 0.80

Industrial 10.0 20 5 0.0368 0.1172 0.31

Industrial 10.0 20 15 0.0438 0.1172 0.37

Industrial 10.0 20 25 0.0476 0.1172 0.41

Industrial 10.0 30 5 0.0456 0.2157 0.21

Industrial 10.0 30 15 0.0543 0.2157 0.25

Industrial 10.0 30 25 0.0590 0.2157 0.27

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions

sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

For comparison purposes, no rain adjustments or control efficiencies applied
Table 2A

AP-42 Section 13.2.1: Paved Roads

Comparison of Existing and CRA-Proposed Paved Road PM10 Emission Factors

(Silt loading resuspension only)

Setting sL (g/m2

) W (tons) S (mph) CRA E 

(lb/VMT)

Existing E 

(lb/VMT)

CRA E / 

Existing E

Public 0.2 3.75 25 0.0039 0.0045 0.86

Public 0.2 3.75 35 0.0039 0.0045 0.86

Public 0.2 3.75 45 0.0039 0.0045 0.86

Public 0.6 3.75 25 0.0100 0.0098 1.03

Public 0.6 3.75 35 0.0100 0.0098 1.03

Public 0.6 3.75 45 0.0100 0.0098 1.03

Industrial 0.6 10 5 0.0225 0.0441 0.51

Industrial 0.6 10 15 0.0225 0.0441 0.51

Industrial 0.6 10 25 0.0225 0.0441 0.51

Industrial 0.6 20 5 0.0396 0.1255 0.32

Industrial 0.6 20 15 0.0396 0.1255 0.32

Industrial 0.6 20 25 0.0396 0.1255 0.32

Industrial 0.6 30 5 0.0549 0.2309 0.24

Industrial 0.6 30 15 0.0549 0.2309 0.24

Industrial 0.6 30 25 0.0549 0.2309 0.24

Industrial 2.0 10 5 0.0598 0.0969 0.62

Industrial 2.0 10 15 0.0598 0.0969 0.62

Industrial 2.0 10 25 0.0598 0.0969 0.62

Industrial 2.0 20 5 0.1044 0.2749 0.38

Industrial 2.0 20 15 0.1044 0.2749 0.38

Industrial 2.0 20 25 0.1044 0.2749 0.38

Industrial 2.0 30 5 0.1447 0.5055 0.29

Industrial 2.0 30 15 0.1447 0.5055 0.29

Industrial 2.0 30 25 0.1447 0.5055 0.29

Industrial 5.0 10 5 0.1250 0.1762 0.71

Industrial 5.0 10 15 0.1250 0.1762 0.71

Industrial 5.0 10 25 0.1250 0.1762 0.71

Industrial 5.0 20 5 0.2179 0.4992 0.44

Industrial 5.0 20 15 0.2179 0.4992 0.44

Industrial 5.0 20 25 0.2179 0.4992 0.44

Industrial 5.0 30 5 0.3016 0.9174 0.33

Industrial 5.0 30 15 0.3016 0.9174 0.33

Industrial 5.0 30 25 0.3016 0.9174 0.33

Industrial 10.0 10 5 0.2179 0.2767 0.79

Industrial 10.0 10 15 0.2179 0.2767 0.79

Industrial 10.0 10 25 0.2179 0.2767 0.79

Industrial 10.0 20 5 0.3797 0.7835 0.48

Industrial 10.0 20 15 0.3797 0.7835 0.48

Industrial 10.0 20 25 0.3797 0.7835 0.48

Industrial 10.0 30 5 0.5254 1.4398 0.36

Industrial 10.0 30 15 0.5254 1.4398 0.36

Industrial 10.0 30 25 0.5254 1.4398 0.36

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions

sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

CRA = Corn Refiners Association

For comparison purposes, no rain adjustments or control efficiencies applied
Table 2B

AP-42 Section 13.2.1: Paved Roads

Comparison of Existing and CRA-Proposed Paved Road PM2.5 Emission Factors

(Silt loading resuspension only)

Setting sL (g/m2

) W (tons) S (mph) CRA E 

(lb/VMT)

Existing E 

(lb/VMT)

CRA E / 

Existing E

Public 0.2 3.75 25 0.0003 0.0004 0.73

Public 0.2 3.75 35 0.0003 0.0004 0.73

Public 0.2 3.75 45 0.0003 0.0004 0.73

Public 0.6 3.75 25 0.0012 0.0012 1.02

Public 0.6 3.75 35 0.0012 0.0012 1.02

Public 0.6 3.75 45 0.0012 0.0012 1.02

Industrial 0.6 10 5 0.0030 0.0063 0.48

Industrial 0.6 10 15 0.0030 0.0063 0.48

Industrial 0.6 10 25 0.0030 0.0063 0.48

Industrial 0.6 20 5 0.0056 0.0185 0.30

Industrial 0.6 20 15 0.0056 0.0185 0.30

Industrial 0.6 20 25 0.0056 0.0185 0.30

Industrial 0.6 30 5 0.0078 0.0343 0.23

Industrial 0.6 30 15 0.0078 0.0343 0.23

Industrial 0.6 30 25 0.0078 0.0343 0.23

Industrial 2.0 10 5 0.0085 0.0142 0.60

Industrial 2.0 10 15 0.0085 0.0142 0.60

Industrial 2.0 10 25 0.0085 0.0142 0.60

Industrial 2.0 20 5 0.0151 0.0410 0.37

Industrial 2.0 20 15 0.0151 0.0410 0.37

Industrial 2.0 20 25 0.0151 0.0410 0.37

Industrial 2.0 30 5 0.0211 0.0755 0.28

Industrial 2.0 30 15 0.0211 0.0755 0.28

Industrial 2.0 30 25 0.0211 0.0755 0.28

Industrial 5.0 10 5 0.0182 0.0261 0.70

Industrial 5.0 10 15 0.0182 0.0261 0.70

Industrial 5.0 10 25 0.0182 0.0261 0.70

Industrial 5.0 20 5 0.0319 0.0746 0.43

Industrial 5.0 20 15 0.0319 0.0746 0.43

Industrial 5.0 20 25 0.0319 0.0746 0.43

Industrial 5.0 30 5 0.0443 0.1373 0.32

Industrial 5.0 30 15 0.0443 0.1373 0.32

Industrial 5.0 30 25 0.0443 0.1373 0.32

Industrial 10.0 10 5 0.0319 0.0412 0.77

Industrial 10.0 10 15 0.0319 0.0412 0.77

Industrial 10.0 10 25 0.0319 0.0412 0.77

Industrial 10.0 20 5 0.0558 0.1172 0.48

Industrial 10.0 20 15 0.0558 0.1172 0.48

Industrial 10.0 20 25 0.0558 0.1172 0.48

Industrial 10.0 30 5 0.0774 0.2157 0.36

Industrial 10.0 30 15 0.0774 0.2157 0.36

Industrial 10.0 30 25 0.0774 0.2157 0.36

Notes:

E = resuspension emission factor; calculations exclude vehicle exhaust, brake wear, and tire wear emissions

sL = silt loading; W = mean vehicle weight; S = mean vehicle speed

CRA = Corn Refiners Association

For comparison purposes, no rain adjustments or control efficiencies applied
Fw: Dave's comments on the Excel workbook - do not need to be mentioned 

on the call

dave.james to: Ron Myers 08/18/2010 03:07 PM

Dear Ron,

Please find attached some comments on the proposed new AP42 paved road equation 

A) I think that, on tab PM10 Paved Roads EF's, column Z, the

column labeled "Percent Total Emissions Factor Increase" uses the formula (column x - column s) / 

column x

to calculate percent changes. I think this should be, instead (column x - column s) / column s, so that

the percent change is calculated relative to the 2006 emissions factor equation instead

of the proposed new 2010 emissions factor equation

Column AD is the recalculated percent reduction for the rain corrected EF's based on this suggested 

equation revision

B) For the desert southwest, I think that it is best to look at the data without rain adjustments

C) In my edited tab "PM10 Paved Roads EF's" I have added several columns, AB, AC, and AD

(1) Column AB is the calculated raw reduction of 2010 dry EF's compared to 2006 dry EF's. (column u - 

column o)

(2) Column AC is the calculated percent reduction of 2010 dry EF's compared to 2006 dry EF's using

the equation (column u - column o)/column o

D) based on the 7,632 row data set in the tab PM10 Paved Roads EF's

(1) The new 2010 dry EF's are much lower overall than the 2006 dry EF's. see the chart in the new tab 

labeled "compareNewOLDPM10EF's"

(2) The reductions of dry 2010 EF's compared to dry 2006 EF's linearly increase in magnitude with the 

magnitude of the original 2006 emissions factor (see the chart in the new tab labeled "reductions" - 

calculated in column AB)

(3) When I plot the percentage changes of the dry 2010 PM 10 EF's calculated ) above against 2006 

emissions factors, they are all around 70-80% (see the chart new tab labeled "percent reductions")

E) Athough national data might show reductions, since the new equation 

1) raises the influence of silt loading (new exponent 0.98, old exponent 0.65)

2) lowers the influence of vehicle weight (new exponent 0.53, old exponent 1.5)

3) adds in an influence of vehicle speed,

4) eliminates the influence of the correction factor for exhaust brake and tire wear,

I would recommend that any assessment of the impact of the proposed new equation be

based on locally sampled data and not use the national data.

Thank you for the opportunity to comment.
Sincerely,

Dave

David E. James, PhD PE

Associate Vice Provost for Academic Programs

Office of the Vice Provost for Academic Affairs

Box 451099

4505 South Maryland Parkway

Las Vegas, NV 89154-1099

Direct Line (702) 895-5804 Main Office (702) 895-1267

FAX (702) 895-3670 FDH 704 Mail Stop 1099

email: dave.james@unlv.edu

http://provost.unlv.edu/acadaffairs.html
Re: Fw: Dave's comments on the EPA Excel workbook - some additional follow 

up thoughts

dave.james to: Ron Myers 09/15/2010 02:44 PM

Cc: Rodney Langston, Russell Merle

Hi Ron,

Thank you for your good email below and for the additional information.

I apologize for taking so long to get back to you with my thoughts and responses. Here they are:

A) Understood about the zeros being problematic

B) In many parts of the country where there is significant rain or a rainy season, rain days may 

considerably effect estimated PM10 emissions in the inventory. However, for Las Vegas and other places 

like it in arid places,

I tend to use a 'pessimistic' approach that doesn't include the rain days, since rain occurs sporadically, 

and what rain does fall is often very light.

C) I'm glad that my extra columns in your Excel workbook are helpful

D) Yes, from the default data it looks like many of the estimated EF's would go down with the new 

proposed equation

E) Since we last corresponded, 

1) I ran some calculations for Clark County's AP42 measured 2003-2006 silt loading data set using

their locally derived fleet weights. Please see the attached file "comparison20062010_AP42 road dust 

EFs2003_2006.pdf"

If you examine the bottom-most table on the page, where percentage EF changes are computed, that the 

net impact of the new proposed equation on Clark County's estimated paved road dust PM-10 emissions 

would be to 

a) increase estimated PM10 emissions as grams/VMT 23% on local roads, 

b) decrease them by 3% on collector roads (probably not significant), and 

c) increase them by 1% on minor arterials (also probably not significant).

With locally derived data, we obtain results that are different from those that might be predicted using 

default silt loading data. The actual impact on total estimated PM10 emissions in an inventory or SIP 

would depend on how much VMT was assigned to each roadway category.

2) I also ran a hypothetical sensitivity analysis comparing arbitrary combinations of vehicle weight and silt 

loading, to see what the impacts of the new PM10 equation might be. Please see the attached file 

"new2010EFsensitivityanalysis.pdf"

Table 1 shows the 2006 equation predicted PM10 emissions 

Table 2 shows the proposed 2010 equation predicted PM10 emissions

Table 3 shows the changes in predicted emissions (2010 EF - 2006 EF)

and

Table 4 shows the Percentage changes, (2010 EF - 2006 EF)/2006 EF

Table 4 shows that the net effect of using the new proposed 2010 equation is that predicted
PM10 emissions 

a) increase for lower silt loadings at all fleet average vehicle weights, and 

b) decrease for higher silt loadings, espeically at lower fleet average vehicle weights

I hope that these preliminary calculations are helpful. I have also sent them as PDF and as the original 

Excel files to

my research sponsors, Clark County Dept of Air Quality and Environmental Management.

Sincerely,

Dave

David E. James, PhD PE

Associate Vice Provost for Academic Programs

Office of the Vice Provost for Academic Affairs

Box 451099

4505 South Maryland Parkway

Las Vegas, NV 89154-1099

Direct Line (702) 895-5804 Main Office (702) 895-1267

FAX (702) 895-3670 FDH 704 Mail Stop 1099

email: dave.james@unlv.edu

http://provost.unlv.edu/acadaffairs.html

From: Myers.Ron@epamail.epa.gov

To: dave.james@unlv.edu

Date: 08/18/2010 06:34 PM

Subject: Re: Fw: Dave's comments on the Excel workbook - do not need to be mentioned on the call

Dave:

Thanks for looking at the proposed revisions of the paved road equation.

First, I was trying to replicate the emissions estimates that are being made 

for the 2008 NEI, any rain adjustments or other mitigation that I included in 

the spreadsheet are the same as I estimated were used in the NEI emissions 

estimates. As with you I would not have included as much mitigation for rain 

and "Street Sweeping" and other silt management as there is is used in the NEI 

estimates.

A. You are correct. I should have divided by the estimated 2008 emissions as 

calculated with the existing AP-42. I think this was a hold over from when I 

was just looking at the road dust emissions estimates. When looking only at 

road dust emissions, all the zero emissions estimates is problematic since 

dividing by zero only generates errors in Excel. I added in the vehicle 

emissions when I saw how many 2008 NEI estimates were zero.
B. I would tend to agree with you as there are not many rain days. As I 

stated above, I don't know what mitigation is included in the "adjusted" 

emissions data in the NEI. Frankly to documentation of the NEI emissions 

estimates doesn't help me much to recreate their emissions estimates (see 

paved_roads_2294000000_documentation.doc which is attached).

C. Thanks for the calculations. I did these calculation only because a few 

internal EPA people suggested that I provide State/local agencies with some 

information to provide an indication of how this change might affect their 

inventories.

D. My original assessment also showed that the revised equation generates much 

lower PM10 estimates than the previous equation. From a combined emisions 

inventory perspective and use in the modeling for SIP development this should 

get support from inventory developers, modelers and Air Quality Assessors as 

it has always been difficult to explain how fugitive dust emissions are the 

majority of the emissions in the inventory but comprise less than 10% of the 

emissions on PM monitors. This will not get the inventory there but it goes 

in the right direction. I agree that for best emissions estimates, locally 

derived silt loadings are needed. However, no one wants to develop these and 

would rather complain that EPA's default values aren't good enough and they 

want better defaults. There is so much variation in silt levels on roads no 

single number is good enough for every road.

_________________________________

Ron Myers

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Sector Policy and Programs Division

Monitoring Policy Group, D243-05

RTP NC 27711

Tel. 919.541.5407

Fax 919.541.1039

E-mail myers.ron@epa.gov

dave.james---08/18/2010 03:10:06 PM---Dear Ron, This is a resend, using a 

compressed version of the Excel file to reduce

From: 

dave.james@unlv.edu 

To: 

Ron Myers/RTP/USEPA/US@EPA 

Date: 

08/18/2010 03:10 PM 

Subject: 

Fw: Dave's comments on the Excel workbook - do not need to be mentioned on the 

call 
Dear Ron, 

This is a resend, using a compressed version of the Excel file to reduce the 

file size, 

in case my earlier send was rejected. 

Please find attached some comments on the proposed new AP42 paved road 

equation 

A) I think that, on tab PM10 Paved Roads EF's, column Z, the 

column labeled "Percent Total Emissions Factor Increase" uses the formula 

(column x - column s) / column x 

to calculate percent changes. I think this should be, instead (column x - 

column s) / column s, so that 

the percent change is calculated relative to the 2006 emissions factor 

equation instead 

of the proposed new 2010 emissions factor equation 

Column AD is the recalculated percent reduction for the rain corrected EF's 

based on this suggested equation revision 

B) For the desert southwest, I think that it is best to look at the data 

without rain adjustments 

C) In my edited tab "PM10 Paved Roads EF's" I have added several columns, AB, 

AC, and AD 

(1) Column AB is the calculated raw reduction of 2010 dry EF's compared to 

2006 dry EF's. (column u - column o) 

(2) Column AC is the calculated percent reduction of 2010 dry EF's compared to 

2006 dry EF's using 

the equation (column u - column o)/column o 

D) based on the 7,632 row data set in the tab PM10 Paved Roads EF's 

(1) The new 2010 dry EF's are much lower overal l than the 2006 dry EF's. see 

the chart in the new tab labeled "compareNewOLDPM10EF's" 

(2) The reductions of dry 2010 EF's compared to dry 2006 EF's linearly 

increase in magnitude with the magnitude of the original 2006 emissions factor 

(see the chart in the new tab labeled "reductions" - calculated in column AB) 

(3) When I plot the percentage changes of the dry 2010 PM 10 EF's calculated 

) above against 2006 emissions factors, they are all around 70-80% (see the 

chart new tab labeled "percent reductions") 

E) Athough national data might show reductions, since the new equation 

1) raises the influence of silt loading (new exponent 0.98, old exponent 0.65) 

2) lowers the influence of vehicle weight (new exponent 0.53, old exponent 

1.5) 

3) adds in an influence of vehicle speed, 

4) eliminates the influence of the correction factor for exhaust brake and 

tire wear, 

I would recommend that any assessment of the impact of the proposed new 

equation be 

based on locally sampled data and not use the national data. 
Thank you for the opportunity to comment. 

Sincerely, 

Dave 

David E. James, PhD PE

Associate Vice Provost for Academic Programs

Office of the Vice Provost for Academic Affairs

Box 451099

4505 South Maryland Parkway

Las Vegas, NV 89154-1099

Direct Line (702) 895-5804 Main Office (702) 895-1267

FAX (702) 895-3670 FDH 704 Mail Stop 1099

email: dave.james@unlv.edu 

http://provost.unlv.edu/acadaffairs.html

*********************** ATTACHMENT NOT DELIVERED *******************

This Email message contained an attachment named 

djedit_Impact_of_revised_paved_roads_pm_emission_factors_on_NEI.xls.zip 

which may be a computer program. This attached computer program could

contain a computer virus which could cause harm to EPA's computers, 

network, and data. The attachment has been deleted.

This was done to limit the distribution of computer viruses introduced

into the EPA network. EPA is deleting all computer program attachments

sent from the Internet into the agency via Email.

If the message sender is known and the attachment was legitimate, you

should contact the sender and request that they rename the file name

extension and resend the Email with the renamed attachment. After

receiving the revised Email, containing the renamed attachment, you can

rename the file extension to its correct name.

For further information, please contact the EPA Call Center at

(866) 411-4EPA (4372). The TDD number is (866) 489-4900.

*********************** ATTACHMENT NOT DELIVERED 

***********************[attachment "paved_roads_2294000000_documentation.doc" 

deleted by Dave James/UNLV] 
From: Steve Zemba <zemba@cambridgeenvironmental.com> 

To: Ron Myers/RTP/USEPA/US@EPA 

cc: gfore@hotmix.org, Mike <ames@cambridgeenvironmental.com>, Laura Green 

<green@cambridgeenvironmental.com>, HMarks@hotmix.org 

Date: Tuesday, August 31, 2010 02:32PM 

Subject: Comment on AP42 Paved Roads Draft Section 13.2.1 

History: This message has been replied to and forwarded.

Dear Ron, 

I write to provide the attached comment on the draft AP42 section on 

Paved Road dust emissions. As described in the comment, NAPA (who 

sponsored the review) is potentially interested in collecting data to 

provide more representative parameters for applications to the asphalt 

pavement industry. We would appreciate your advice on how best to 

gather these data so that they could be submitted for consideration in 

the AP42 section. 

Thanks for your help and consideration, 

Steve 

-- 

Stephen G. Zemba, Ph.D., P.E. 

Senior Engineer 

*Cambridge Environmental Inc* 

58 Charles Street 

Cambridge, MA 02141 

Office: 617-225-0810 x34 M-W 518-306-4603 Th-F 

Cell: 339-223-9328 

Fax: 617-225-0813 

http://www.CambridgeEnvironmental.com

AP42PavedRoadsSectionComment083110.pdf

Type: application/pdf 

Name: 

AP42PavedRoadsSectionComment0

Page 1 of 1

https://rtairmail1.rtp.epa.gov/mail/rmyers.nsf/9ff539a1e24f5aaf852577890046a8f6/8F3D... 10/22/2010
Cambridge Environmental Inc 

 

58 Charles Street Cambridge, Massachusetts 02141 

617-225-0810 www.CambridgeEnvironmental.com

August 31, 2010 

Ronald Myers 

U.S. Environmental Protection Agency 

109 T.W. Alexander Drive 

Mail Code: D243-05 

Research Triangle Park, NC 27709 

Dear Ron, 

It was a pleasure speaking with you again recently – thank you for the background information on the 

draft update to the AP42 section on Paved Road emissions (Section 13.2.1). 

I have reviewed the draft update on behalf of the National Asphalt Pavement Association (NAPA), and 

write to comment on a specific aspect of interest. I believe that the recommended default values for siltloading in draft Table 13.2.1-3, and particularly that for asphalt batching, may be too high for typical 

current applications. The recommended value is 120 g/m2

, but, as you know, in EPA’s 2000 Emission 

Assessment Report for Hot Mix Asphalt Plants, a silt-loading value 3 g/m2

 is suggested for paved roads at 

typical hot-mix asphalt production facilities. Also, site-specific measurements at a hot mix asphalt 

facility in Alexandria, Virginia in 2005 (using the sampling and analytical methods described in AP42 

Appendix C) found a silt loading level of 0.5 g/m2

. This facility, which we analyzed in detail for the City 

of Alexandria, employs aggressive dust suppression techniques. 

More generally, as you know, best management practices (BMPs) such as water spraying and road 

sweeping can effectively control dust emissions; by the same token, the absence of these practices can 

indeed result in dusty roads. Perhaps the value of 120 g/m2

, which appears to be based on older data, 

derives from testing at one or more facilities that failed to employ BMPs. If so, then perhaps 120 g/m2 

could be considered to be a default value in the absence of BMPs, whereas the value of 3 g/m2, as used in 

EPA’s Emission Assessment Report, could be a default value in the presence of typical BMPs. 

Of course, more data are always better. In that regard, we have spoken with representatives from NAPA , 

and they have expressed potential willingness to coordinate a study to provide updated values for silt 

loading and other emission factor parameters that reflect current practices in the hot-mix asphalt industry. 

At your convenience, might we schedule a call to discuss whether this would be of interest to you and 

your colleagues at the Agency? 

Thank you for your consideration, and best regards. 

Sincerely, 

Stephen G. Zemba, Ph.D., P.E. 

Senior Engineer 
IDNR comment on proposed AP 42 Section 13.2.1 Paved Roads

Hanson, Lori [DNR] to: Ron Myers 08/20/2010 10:45 AM

Cc: "McGraw, Jim [DNR]"

History: This message has been forwarded.

Mr. Myers,

I have attached the Iowa Department of Natural Resources comments on the proposed revision toAP42

section 13.2.1 on paved roads. Thank you for the opportunity to provide comments, Lori Hanson


To refresh your memory we noticed that a number of the PM2.5 emission factors 

were zeroed out for a number of road types. Can you please tell us why the 

road types listed below were zeroed out?

Urban Collector

Urban Minor Arterial

Urban Other Principal Arterial

Urban Other Freeways and Expressways

Urban Interstate

Thanks,

Pat Davis

-----Original Message-----

From: Huntley.Roy@epamail.epa.gov [mailto:Huntley.Roy@epamail.epa.gov] 

Sent: Tuesday, July 13, 2010 1:38 PM

To: Myers.Ron@epamail.epa.gov

Cc: Pat Davis

Subject: Fw: PECHAN/ERTAC Road Dust Emissions

Ron, could you answer Pat question?

Roy Huntley

Environmental Engineer

Emission Inventory and Analysis Group

Mail Drop (C339-02)

Environmental Protection Agency

RTP, NC 27711

Voice - 919 541-1060

Fax - 919 541-0684

Office C341H

----- Forwarded by Roy Huntley/RTP/USEPA/US on 07/13/2010 01:24 PM -----

|>

| From: Pat Davis <pdavis@marama.org> 

|

|>

| To: Roy Huntley/RTP/USEPA/US@EPA 

|

>

|>

| Cc: 

Judy Rand <Judy.Rand@dep.state.nj.us>, Julie McDill <jmcdill@marama.org>, 

Pat Davis <pdavis@marama.org>, "Fees David F. (DNREC)"<David.Fees@state.de.us>,|

"WRBARNARD@mactec.com" <WRBARNARD@mactec.com>, Walter Simms <wsimms@mde.state.md.us>,

"kenneth.santlal@state.ma.us"<kenneth.santlal@state.ma.us |
the following road types:

Urban Collector

Urban Minor Arterial

Urban Other Principal Arterial

Urban Other Freeways and Expressways

Urban Interstate

Emission factors for PM10 were found and there was no mention in the

documentation of why the PM2.5 emission factors were zeroed out, so we

are bit confused.

We were hoping that you might have answer for us, or be able to point us

in the direction of someone who might know why the PM2.5 emission

factors are zeroed out.

Thanks, and I hope you are well!

Pat Davis
FW: [chief] Proposed revisions to AP 42 section 13.2.1 Paved Roads

Julie McDill to: Ron Myers 06/23/2010 03:29 PM

History: This message has been replied to.

Hello Ron,

I called and left a message about possibly getting on a call with the MARAMA 

states in the next couple of weeks to discuss proposed changes to the Paved 

Road PM emissions estimation method. 

Please respond to let me know if and when that might be possible. I can set 

up a conference call and distribute a slide set. It would be best sometime 

between July 7 and 16th. What follows (and the attachment) are some emails 

that give you a flavor of the changes that states are finding as a result of 

the new calculations. As you probably know, the PM emission from paved roads 

has always posed problems in modeling. In general, modelers take our 

inventories and reduced the paved road emissions by about 90% before running 

the model.

Thanks for your help.

Julie McDill

MARAMA

________________________________________

From: Judy Rand [Judy.Rand@dep.state.nj.us]

Sent: Wednesday, June 16, 2010 4:43 PM

To: Julie McDill; Pat Davis; rthunell@mde.state.md.us; David.Fees@state.de.us

Cc: Nicholle Worland; WRBARNARD@mactec.com; kenneth.santlal@state.ma.us

Subject: RE: [chief] Proposed revisions to AP42 section 13.2.1 Paved Roads

Thanks Dave. We have come up with similar results, but even more drastic for 

PM2.5. An increase in PM2.5 of 350% and a decrease in PM10 of 46% I think 

one big cause is the difference in k factor, among other changes. The k 

factor for PM2.5 went down from the 2003 AP-42 to the 2006 AP-42, and back up 

again in this new draft. We guessed at the new vehicle speed requirement, but 

a slight variation in speeds will not make that much of a difference.

See NJ's attached calcs and compare spreadsheet. I won't be in til Monday. 

If you want to have a call either Nicholle can cover it tomorrow, or we are in 

on Monday.

Judy

>>> "Fees David F. (DNREC)" <David.Fees@state.de.us> 6/16/2010 2:02 PM >>>

Roger,

Here is Delaware's paved road dust spreadsheet for 2007, using the new 

equation. We got very detailed with this category; estimating emissions by 

month.

Regarding the new equation, PM10 was reduced by 58% from the emissions 

submitted to MACTEC; while PM2.5 increased by 48%. I believe the PM2.5 

increase is caused by two factors-first, the PM2.5/PM10 ratio was increased to 

25% (previously 15%). The second reason is that under the old equation, one 

had to apply a correction factor, C, to remove the exhaust, brake, and tire 

wear from the front part of the equation. By subtracting C at the end of the 

equation, the resulting PM2.5 value went negative for several roadway types. 

Of course we zeroed these out, but with the new method there is never a 
situation where the emission factor value can go negative. Having negative

emission factors result from the use of the old equation was obviously a flaw 

in the method, so I expect the new equation is more accurate.

I look forward to NJ's results when they apply the new equation, to see if 

they get changes similar to mine.

If you have any questions about the calculations within the spreadsheet, just 

give a call.

Regards,

Dave

David F. Fees, P.E.

Managing Engineer

Emission Inventory Development Program

Air Quality Management Section, DNREC

tel. (302) 739-9402, fax (302) 739-3106

e-mail: david.fees@state.de.us<mailto:david.fees@state.de.us>

Blue Skies Delaware; Clean Air for Life

From: Roger Thunell [mailto:rthunell@mde.state.md.us]

Sent: Monday, June 14, 2010 3:00 PM

To: Judy Rand; Julie McDill; Pat Davis

Cc: WRBARNARD@mactec.com; Fees David F. (DNREC); kenneth.santlal@state.ma.us

Subject: RE: [chief] Proposed revisions to AP42 section 13.2.1 Paved Roads

Judy/Dave/Kenneth:

Could any of you send me a spreadsheet calculating emissions in this manner?

I am not sure if we are using the latest methods or not.

Thanks

Roger

>>> Pat Davis <pdavis@marama.org> 6/14/2010 12:54 PM >>>

Thanks a lot for sending this along, Judy. Please let us know what you find 

when you look at the changes in emissions.

Pat

-----Original Message-----

From: Judy Rand [mailto:Judy.Rand@dep.state.nj.us]

Sent: Monday, June 14, 2010 9:16 AM

To: Julie McDill; Pat Davis

Cc: WRBARNARD@mactec.com; rthunell@mde.state.md.us; David.Fees@state.de.us; 

kenneth.santlal@state.ma.us

Subject: Fwd: [chief] Proposed revisions to AP42 section 13.2.1 Paved Roads

Pat and Julie,

We are going to look at this to see how it affects emissions. In the past, 

each change to this category has changed emission calculations.

Thanks,

Judy

Judy Rand, PE

Environmental Engineer

NJDEP Air Quality Planning

(609) 984-1950

jrand@dep.state.nj.us
FW: Proposed revisions to AP 42 section 13.2.1 Paved Roads

Julie McDill to: Ron Myers 06/30/2010 04:25 PM

Hi Ron,

Here is the announcement for our call next week. Can you send me a slide set 

by noon next Tuesday and I will distribute it to the group and post it on our 

ftp.

Thanks,

Julie

________________________________________

From: Julie McDill

Sent: Tuesday, June 29, 2010 3:21 PM

To: Paul.Bodner@ct.gov; mark.prettyman@state.de.us; David.Fees@state.de.us; 

jessica.daniels@dc.gov; melanie.loyzim@maine.gov; rthunell@mde.state.md.us; 

kenneth.santlal@state.ma.us; david.healy@des.nh.gov; 

judy.rand@dep.state.nj.us; Nicholle.Worland@dep.state.nj.us; 

jdbarnes@gw.dec.state.ny.us; rwstanna@gw.dec.state.ny.us; sbogart@state.pa.us; 

karen.slattery@dem.ri.gov; jeff.merrell@state.vt.us; 

Thomas.Foster@deq.virginia.gov; laura.boothe@ncdenr.gov; 

Robert.J.Betterton@wv.gov; mcconnell.robert@epamail.epa.gov; 

Forde.Raymond@epamail.epa.gov; kremer.janet@epamail.epa.gov; 

huntley.roy@epa.gov; Susan Wierman

Cc: cooke.donald@epamail.epa.gov; burkhart.richard@epamail.epa.gov; 

Garcia.Ariel@epamail.epa.gov; Kelly.Bob@epamail.epa.gov; 

Salomone.Jenna@epamail.epa.gov; Wieber.Kirk@epamail.epa.gov; 

Moltzen.Michael@epamail.epa.gov; Laurita.Matthew@epamail.epa.gov; 

Feingersh.Henry@epamail.epa.gov; Kremer.Janet@epamail.epa.gov; 

Ellsworth.Todd@epamail.epa.gov; Leon-Guerrero.Tim@epamail.epa.gov; 

Cripps.Christopher@epamail.epa.gov; Rehn.Brian@epamail.epa.gov; 

Kotsch.Martin@epamail.epa.gov; Dolce.Gary@epamail.epa.gov; 

Kapichak.Rudolph@epamail.epa.gov; Houyoux.Marc@epamail.epa.gov; 

Timin.Brian@epamail.epa.gov; Stackhouse.Butch@epamail.epa.gov; 

Broadwell.Valerie@epamail.epa.gov; Ling.Michael@epamail.epa.gov; 

Fox.Tyler@epamail.epa.gov; Cook.Leila@epamail.epa.gov; 

Spink.Marcia@epamail.epa.gov; Wayland.Richard@epamail.epa.gov; 

Hemby.James@epamail.epa.gov; Wilkie.Walter@epamail.epa.gov; 

Fernandez.Cristina@epamail.epa.gov; Ruvo.Richard@epamail.epa.gov; 

Werner.Raymond@epamail.epa.gov; arnold.anne@epamail.epa.gov; 

Baker.William@epamail.epa.gov; Arnold.David@epamail.epa.gov; 

Conroy.Dave@epamail.epa.gov

Subject: FW: Proposed revisions to AP42 section 13.2.1 Paved Roads

Hello all,

This email is to announce a teleconference on July 7 at 2:30 PM Eastern 

concerning the proposed change to the equation used to estimate PM 10 and 2.5 

emissions from paved roads. Ron Myers of OAQPS will provide a presentation on 

the development of the new equation and will answer your questions. Modellers 

and planners from MANE-VU state agencies along with some USEPA regional staff 

have been invited. Call in information is as follows:

Number: 866-202-1783

Code: *5743656* - Make sure you press * before and after the number.

Date: July 7

Time: 2:30 - 4:00 P.M. Eastern

BACKGROUND FOR THE CALL

This equation is used to calculate emissions for the area source modeling 

inventory. Delaware and New Jersey have already done some preliminary 

calculations and find the new equation results in very different values than 

the old equation. I attach their spreadsheets for your review. Toward the 

bottom of this email are texts of emails discussing the differences. In 

addition is the text distributed by NACAA which provides links to materials 
for your formal comment to USEPA.

As you are no doubt aware, modellers have applied a transport fraction 

reduction to fugitive road dust emissions in the past to bring the calculated 

impact on ambient PM in line with measured concentrations. The new equation 

may require a revision to the transport fraction calculation. I have invited 

our NY modellers to join the call to hear the discussion so that they can 

consider any impact on the transport fraction calculation.

The new equation is proposed, so we can decide to use the old calculation 

method for our modeling inventory. That is what is in our current draft area 

source inventory files. However, States will then face a disconnect with the 

model for future emission calculations. At any rate, it seems to me that all 

states should use the same methodology so that the inventory is consistant 

accross our region.

Julie McDill

MARAMA

Relevant Email texts

________________________________________

From: Judy Rand [Judy.Rand@dep.state.nj.us]

Sent: Wednesday, June 16, 2010 4:43 PM

Thanks Dave. We have come up with similar results, but even more

drastic for PM2.5. An increase in PM2.5 of 350% and a decrease in PM10

of 46% I think one big cause is the difference in k factor, among other

changes. The k factor for PM2.5 went down from the 2003 AP-42 to the

2006 AP-42, and back up again in this new draft. We guessed at the new

vehicle speed requirement, but a slight variation in speeds will not

make that much of a difference.

See NJ's attached calcs and compare spreadsheet. I won't be in til

Monday. If you want to have a call either Nicholle can cover it

tomorrow, or we are in on Monday.

Judy

---------------

From: "Fees David F. (DNREC)" <David.Fees@state.de.us> 6/16/2010 2:02 PM

Roger,

Regarding the new equation, PM10 was reduced by 58% from the emissions

submitted to MACTEC; while PM2.5 increased by 48%. I believe the PM2.5

increase is caused by two factors-first, the PM2.5/PM10 ratio was

increased to 25% (previously 15%). The second reason is that under the

old equation, one had to apply a correction factor, C, to remove the

exhaust, brake, and tire wear from the front part of the equation. By

subtracting C at the end of the equation, the resulting PM2.5 value went

negative for several roadway types. Of course we zeroed these out, but

with the new method there is never a situation where the emission factor

value can go negative. Having negative emission factors result from the

use of the old equation was obviously a flaw in the method, so I expect

the new equation is more accurate.

I look forward to NJ's results when they apply the new equation, to see

if they get changes similar to mine.

If you have any questions about the calculations within the spreadsheet,

just give a call.

Regards,

Dave

------------------------------

TO: NACAA EMISSIONS & MODELING COMMITTEE

Please information below regarding a proposed revision of the AP-42 paved 

roads section. The proposed draft can be found here - 

http://www.epa.gov/ttn/chief/ap42/ch13/index.html; scroll down to section 

13.2.1, paved roads. EPA will take comments on the draft until July 30, 2010. 

For more information, please contact Ron Myers at myers.ron@epa.gov.

__________________
AP-42 k factors (g/mile)

2002 2007 (Existing) 2007(new) % Change 2003 2006 

Annual pm-10 tpy 37,606.28 38,210.45 20,532.18 -46% PM-10 7.3000 7.3000 

pm-2.5 tpy 3,788.42 1,142.03 5,110.37 347% PM-2.5 1.8000 1.1000 

Summer pm-10 tpd 115.11 105.70 56.75 -46%

pm-2.5 tpd 11.56 3.13 14.12 351%

Winter pm-10 tpd 95.87 101.69 54.74 -46%

pm-2.5 tpd 9.69 3.13 13.63 336%

Spring pm-10 tpd 99.94 105.08 56.41 -46%

pm-2.5 tpd 10.07 3.11 14.04 351%

Fall pm-10 tpd 101.03 106.23 57.08 -46%

pm-2.5 tpd 10.18 3.15 14.21 352%

Emissions Comparison
2007 CAP Emissions Calculations

Kent New Castle Sussex Kent New Castle Sussex

Rural Oth. Princ. Art.January 3.0368 3.8504 5.6417 0.7558 0.9584 1.4042

February 2.8324 3.6256 5.2929 0.7050 0.9024 1.3174March 3.3463 4.3680 6.3490 0.8329 1.0872 1.5802

April 3.4705 4.4744 6.6084 0.8638 1.1137 1.6448

May 4.3379 5.4584 8.1201 1.0797 1.3586 2.0210

June 4.4049 5.1675 8.5339 1.0964 1.2862 2.1240July 5.1486 5.3205 10.4483 1.2815 1.3243 2.6005

August 4.8552 5.4994 10.3512 1.2084 1.3688 2.5764

September 4.2558 5.1069 7.7932 1.0592 1.2711 1.9397October 3.6182 4.6311 6.9531 0.9006 1.1527 1.7306November 3.2676 4.3388 6.2977 0.8133 1.0799 1.5675December 2.9585 4.0132 5.7959 0.7364 0.9989 1.4426

45.5327 55.8543 88.1854 11.3329 13.9019 21.9489

Rural Minor ArterialJanuary 19.7917 0.9224 2.9397 4.9261 0.2296 0.7317

February 19.4746 0.9004 2.7698 4.8472 0.2241 0.6894March 7.3003 1.0547 1.7424 1.8170 0.2625 0.4337

April 6.7427 1.0176 1.8092 1.6782 0.2533 0.4503

May 7.5361 1.1867 2.1881 1.8757 0.2954 0.5446

June 6.8577 1.0829 2.5017 1.7069 0.2695 0.6226July 7.5020 1.1643 1.5249 1.8672 0.2898 0.3795

August 7.2545 1.1817 2.8365 1.8056 0.2941 0.7060

September 7.2050 1.1851 2.0514 1.7933 0.2950 0.5106October 7.0923 1.1383 1.7639 1.7652 0.2833 0.4390November 6.6536 1.0271 1.5527 1.6561 0.2556 0.3865December 6.7191 0.9796 1.4285 1.6723 0.2438 0.3556

110.1298 12.8408 25.1086 27.4108 3.1960 6.2494

Rural Major Collector

January 17.4130 10.8453 81.4407 4.3340 2.6993 20.2702

February 15.2798 9.2386 74.5901 3.8031 2.2994 18.5651March 6.6067 4.1449 31.0497 1.6444 1.0317 7.7281

April 6.3955 4.2942 30.7597 1.5918 1.0688 7.6560

May 8.4396 5.6208 36.4193 2.1006 1.3990 9.0646

June 8.9101 5.3072 11.1730 2.2177 1.3209 2.7809July 8.7305 6.1122 13.2889 2.1730 1.5213 3.3075

August 7.9809 5.1746 12.3685 1.9864 1.2879 3.0785

September 9.0665 5.4730 33.1489 2.2566 1.3622 8.2506October 8.0543 4.6414 30.6447 2.0047 1.1552 7.6273November 6.8855 3.8170 28.0237 1.7138 0.9500 6.9750December 5.8669 3.4370 27.2418 1.4602 0.8555 6.7804

109.6292 68.1062 410.1489 27.2862 16.9513 102.0842

Rural Minor Collector

PM10-FIL (TPY) PM2.5-FIL (TPY)
2007 CAP Emissions Calculations

Kent New Castle Sussex Kent New Castle Sussex

PM10-FIL (TPY) PM2.5-FIL (TPY)

January 7.5825 3.1843 9.1770 1.8872 0.7926 2.2841

February 6.6536 2.7126 8.4051 1.6561 0.6751 2.0920March 2.8769 1.2170 3.4988 0.7160 0.3029 0.8708

April 2.7849 1.2609 3.4661 0.6931 0.3138 0.8627

May 3.6750 1.6504 4.1039 0.9147 0.4108 1.0214

June 3.8799 1.5583 4.0969 0.9657 0.3878 1.0197July 3.8017 1.7946 4.8727 0.9462 0.4467 1.2128

August 3.4753 1.5194 4.5352 0.8650 0.3782 1.1288

September 3.9480 1.6069 3.7353 0.9826 0.4000 0.9297October 3.5072 1.3628 3.4532 0.8729 0.3392 0.8595November 2.9983 1.1207 3.1578 0.7463 0.2789 0.7860December 2.5547 1.0092 3.0697 0.6359 0.2512 0.7640

47.7380 19.9970 55.5718 11.8818 4.9772 13.8316

Rural LocalJanuary 72.2816 14.2450 182.5225 17.9906 3.5455 45.4290

February 63.4268 13.0827 167.1691 15.7866 3.2562 41.6076March 20.6871 5.3708 17.8860 5.1489 1.3368 4.4517

April 20.0256 4.9021 17.7190 4.9843 1.2201 4.4102

May 26.4262 6.0689 20.9791 6.5774 1.5105 5.2216

June 9.5065 4.9294 20.9434 2.3661 1.2269 5.2127July 27.3372 5.0483 24.9096 6.8041 1.2565 6.1999

August 24.9900 5.2372 23.1843 6.2199 1.3035 5.7705

September 9.6733 6.0687 19.0953 2.4076 1.5105 4.7527

October 25.2198 5.8458 17.6527 6.2771 1.4550 4.3937November 21.5599 5.2538 47.3761 5.3662 1.3077 11.7917December 18.3704 5.0929 46.0544 4.5723 1.2676 11.4627

339.5044 81.1457 605.4913 84.5011 20.1968 150.7040

Urban InterstateJanuary 0.0000 11.7187 0.0000 0.0000 2.9167 0.0000

February 0.0000 12.5883 0.0000 0.0000 3.1332 0.0000March 0.0000 13.3265 0.0000 0.0000 3.3169 0.0000

April 0.0000 14.0185 0.0000 0.0000 3.4891 0.0000

May 0.0000 15.5068 0.0000 0.0000 3.8596 0.0000

June 0.0000 14.5005 0.0000 0.0000 3.6091 0.0000July 0.0000 15.7325 0.0000 0.0000 3.9158 0.0000

August 0.0000 16.9323 0.0000 0.0000 4.2144 0.0000

September 0.0000 14.9430 0.0000 0.0000 3.7192 0.0000October 0.0000 13.8368 0.0000 0.0000 3.4439 0.0000November 0.0000 13.6508 0.0000 0.0000 3.3976 0.0000December 0.0000 12.6716 0.0000 0.0000 3.1539 0.0000

0.0000 169.4261 0.0000 0.0000 42.1694 0.0000

Urban Oth. Freeway

January 2.2724 2.2051 0.0000 0.5656 0.5488 0.0000
2007 CAP Emissions Calculations

Kent New Castle Sussex Kent New Castle Sussex

PM10-FIL (TPY) PM2.5-FIL (TPY)

February 2.1240 2.3687 0.0000 0.5287 0.5896 0.0000

March 2.6382 2.5076 0.0000 0.6566 0.6241 0.0000

April 2.6584 2.6378 0.0000 0.6617 0.6565 0.0000

May 3.3363 2.9179 0.0000 0.8304 0.7262 0.0000

June 3.5141 2.7285 0.0000 0.8747 0.6791 0.0000July 4.2085 2.9604 0.0000 1.0475 0.7368 0.0000

August 4.1031 3.1861 0.0000 1.0213 0.7930 0.0000

September 3.2980 2.8118 0.0000 0.8209 0.6998 0.0000October 2.6345 2.6036 0.0000 0.6557 0.6480 0.0000November 2.3741 2.5686 0.0000 0.5909 0.6393 0.0000December 2.1895 2.3844 0.0000 0.5450 0.5935 0.0000

35.3512 31.8807 0.0000 8.7988 7.9349 0.0000

Urban Oth. Princ. Art.January 1.3373 13.5266 3.0648 0.3328 3.3667 0.7628

February 1.2472 13.0598 2.8754 0.3104 3.2505 0.7157March 1.4735 14.9285 3.4491 0.3668 3.7156 0.8585

April 1.5282 14.5413 3.5900 0.3804 3.6193 0.8935

May 1.9102 16.7660 4.4112 0.4754 4.1730 1.0979

June 1.9397 15.0529 4.6360 0.4828 3.7466 1.1539July 2.2672 15.1950 5.6760 0.5643 3.7820 1.4127

August 2.1380 15.2299 5.6233 0.5321 3.7906 1.3996

September 1.8740 15.1373 4.2336 0.4664 3.7676 1.0537October 1.5933 14.6302 3.7772 0.3966 3.6414 0.9401November 1.4389 13.7450 3.4212 0.3581 3.4211 0.8515December 1.3028 13.7174 3.1486 0.3243 3.4142 0.7837

20.0502 175.5298 47.9065 4.9904 43.6886 11.9237

Urban Minor ArterialJanuary 4.3310 4.8764 2.4339 1.0780 1.2137 0.6058

February 4.2617 4.8104 2.2835 1.0607 1.1973 0.5683March 4.6884 5.7734 1.3887 1.1669 1.4370 0.3456

April 4.3304 5.8589 0.7328 1.0778 1.4582 0.1824

May 4.8399 7.1419 0.9004 1.2046 1.7776 0.2241

June 4.4042 6.2220 0.9463 1.0962 1.5486 0.2355July 4.8180 6.2667 1.1586 1.1992 1.5597 0.2884

August 4.6591 6.1525 1.1478 1.1596 1.5313 0.2857

September 4.6273 6.2988 0.8642 1.1517 1.5677 0.2151October 4.5549 5.6747 0.7710 1.1337 1.4124 0.1919November 4.2731 4.7114 0.6983 1.0636 1.1727 0.1738December 4.3152 4.3381 1.2677 1.0740 1.0797 0.3155

54.1031 68.1252 14.5932 13.4660 16.9561 3.6322

Urban Collector

January 31.7118 16.1893 32.0745 7.8929 4.0294 7.9832

February 30.9520 0.0378 29.3765 7.7038 3.4325 7.3117
2007 CAP Emissions Calculations

Kent New Castle Sussex Kent New Castle Sussex

PM10-FIL (TPY) PM2.5-FIL (TPY)

March 3.7702 9.2060 3.7580 0.9384 2.2913 0.9353April 3.4498 9.5377 3.7229 0.8586 2.3739 0.9266

May 3.8650 12.4840 4.4079 0.9620 3.1072 1.0971

June 3.5323 11.7876 4.4004 0.8792 2.9339 1.0952July 3.6517 13.5754 5.2337 0.9089 3.3789 1.3026

August 11.2659 11.4931 4.8712 2.8040 2.8606 1.2124

September 3.5772 12.1557 4.0121 0.8903 3.0255 0.9986

October 3.5078 10.3087 3.7090 0.8731 2.5658 0.9231November 10.2321 8.4777 11.0368 2.5467 2.1101 2.7470December 10.0602 7.6337 10.7289 2.5039 1.9000 2.6704

119.5761 122.8866 117.3317 29.7620 34.0090 29.2033

Urban LocalJanuary 43.1321 149.3299 23.6401 10.7354 37.1675 5.8839

February 42.0987 137.1463 21.6515 10.4782 34.1351 5.3890March 16.6866 56.3019 9.0129 4.1532 14.0133 2.2433

April 15.2685 51.3884 8.9287 3.8003 12.7903 2.2223

May 17.1063 63.6201 10.5715 4.2577 15.8347 2.6312

June 15.6337 51.6748 10.5535 3.8912 12.8616 2.6267July 16.1621 52.9218 12.5521 4.0227 13.1720 3.1242

August 15.3230 54.9018 11.6827 3.8138 13.6648 2.9078

September 15.8323 63.6183 9.6223 3.9406 15.8343 2.3949October 15.5252 61.2816 8.8953 3.8641 15.2527 2.2140November 13.9169 55.0759 8.1345 3.4639 13.7081 2.0246December 13.6832 53.3890 7.9076 3.4057 13.2883 1.9682

240.3684 850.6498 143.1529 59.8266 211.7228 35.6301

All Roadway Types PM10-PRI PM2

January 202.8901 230.8934 342.9350 50.4984 57.4683 85.3550 776.7185 19

February 188.3509 199.5712 314.4139 46.8797 53.0954 78.2562 702.3359 17March 70.0744 118.1994 78.1346 17.4412 29.4193 19.4473 266.4084 6

April 66.6544 113.9317 77.3368 16.5900 28.3571 19.2488 257.9230 6

May 81.4724 138.4216 92.1014 20.2781 34.4525 22.9236 311.9955 7

June 62.5833 120.0116 67.7849 15.5767 29.8703 16.8714 250.3798 6July 83.6273 126.0918 79.6648 20.8145 31.3837 19.8282 289.3839 7

August 86.0449 126.5080 76.6007 21.4162 31.4873 19.0656 289.1537 7

September 63.3575 134.4055 84.5562 15.7694 33.4529 21.0457 282.3192 7October 75.3075 125.9550 77.6201 18.7437 31.3496 19.3193 278.8826 6November 73.6000 113.7869 109.6988 18.3187 28.3210 27.3035 297.0857 7December 68.0204 108.6660 106.6430 16.9300 27.0465 26.5430 283.3294 7

1121.9831 1656.4422 1507.4902 279.2565 415.7040 375.2074 4285.9155 107

STATEWID
comments to draft AP-42 paved road section

Gary Garman 

t

o

:

Ron Myers 06/24/2010 12:58 PM

Please respond to ggarman

History: This message has been replied to and forwarded.

Ron,

It's good to see the paved road section is being revised. Thanks. It has been a challenge in the 

past explaining to industrial clients that paving a road would actually result in higher predicted 

emissions than if the road is left unpaved. I think we'll see more paving and actual emission 

reductions as a result of the new equation. A few editorial comments on the draft paved road 

section:

Page 13.2.1-1, third paragraph, first sentence..change to "The particulate emission factors 

presented in a previous version.."

Page 13.2.1-5, third paragraph, last sentence..change "Table 13.2.1-3" to "Table 13.2.1-2"

Page 13.2.1-8, fifth paragraph, first sentence..change "Table 13.2.1-3" to "Table 13.2.1-2"

Page 13.2.1-9, second paragraph, second sentence..remove hyphen between "not" and "suggest"

Table 13.2.1-3...the page number this table is on should be changed to 13.2.1.10. Also, total 

loading range for iron and steel should be 0.006-4.77, not 43.0-64.0.

Page 13.2.1-11, first paragraph, fourth sentence..remove hyphen between "any" and "of"

Thanks again. I look forward to this draft being finalized.

Gary

-- 

Gary Garman

Environmental Scientist

McVehil-Monnett Associates, Inc.

44 Inverness Drive East, Bldg C

Englewood, CO 80112

303-790-1332
11/06 Miscellaneous Sources 13.2.2-1

13.2.2 Unpaved Roads

13.2.2.1 General

When a vehicle travels an unpaved road, the force of the wheels on the road surface causes

pulverization of surface material. Particles are lifted and dropped from the rolling wheels, and the road

surface is exposed to strong air currents in turbulent shear with the surface. The turbulent wake behind

the vehicle continues to act on the road surface after the vehicle has passed.

The particulate emission factors presented in the previous draft version of this section of AP-42,

dated October 2001, implicitly included the emissions from vehicles in the form of exhaust, brake wear,

and tire wear as well as resuspended road surface material25. EPA included these sources in the emission

factor equation for unpaved public roads (equation 1b in this section) since the field testing data used to

develop the equation included both the direct emissions from vehicles and emissions from resuspension of

road dust. 

This version of the unpaved public road emission factor equation only estimates particulate

emissions from resuspended road surface material 23, 26. The particulate emissions from vehicle exhaust,

brake wear, and tire wear are now estimated separately using EPA’s MOBILE6.2 24. This approach

eliminates the possibility of double counting emissions. Double counting results when employing the

previous version of the emission factor equation in this section and MOBILE6.2 to estimate particulate

emissions from vehicle traffic on unpaved public roads. It also incorporates the decrease in exhaust

emissions that has occurred since the unpaved public road emission factor equation was developed. The

previous version of the unpaved public road emission factor equation includes estimates of emissions

from exhaust, brake wear, and tire wear based on emission rates for vehicles in the 1980 calendar year

fleet. The amount of PM released from vehicle exhaust has decreased since 1980 due to lower new

vehicle emission standards and changes in fuel characteristics.

13.2.2.2 Emissions Calculation And Correction Parameters1-6

The quantity of dust emissions from a given segment of unpaved road varies linearly with the

volume of traffic. Field investigations also have shown that emissions depend on source parameters that

characterize the condition of a particular road and the associated vehicle traffic. Characterization of these

source parameters allow for “correction” of emission estimates to specific road and traffic conditions

present on public and industrial roadways.

Dust emissions from unpaved roads have been found to vary directly with the fraction of silt

(particles smaller than 75 micrometers [:m] in diameter) in the road surface materials.1

 The silt fraction

is determined by measuring the proportion of loose dry surface dust that passes a 200-mesh screen, using

the ASTM-C-136 method. A summary of this method is contained in Appendix C of AP-42. Table

13.2.2-1 summarizes measured silt values for industrial unpaved roads. Table 13.2.2-2 summarizes

measured silt values for public unpaved roads. It should be noted that the ranges of silt content vary over

two orders of magnitude. Therefore, the use of data from this table can potentially introduce considerable

error. Use of this data is strongly discouraged when it is feasible to obtain locally gathered data.

Since the silt content of a rural dirt road will vary with geographic location, it should be measured

for use in projecting emissions. As a conservative approximation, the silt content of the parent soil in the

area can be used. Tests, however, show that road silt content is normally lower than in the surrounding

parent soil, because the fines are continually removed by the vehicle traffic, leaving a higher percentage

of coarse particles.
13.2.2-2 EMISSION FACTORS 11/06

Other variables are important in addition to the silt content of the road surface material. For

example, at industrial sites, where haul trucks and other heavy equipment are common, emissions are

highly correlated with vehicle weight. On the other hand, there is far less variability in the weights of

cars and pickup trucks that commonly travel publicly accessible unpaved roads throughout the United

States. For those roads, the moisture content of the road surface material may be more dominant in

determining differences in emission levels between, for example a hot, desert environment and a cool,

moist location.

The PM-10 and TSP emission factors presented below are the outcomes from stepwise linear

regressions of field emission test results of vehicles traveling over unpaved surfaces. Due to a limited

amount of information available for PM-2.5, the expression for that particle size range has been scaled

against the result for PM-10. Consequently, the quality rating for the PM-2.5 factor is lower than that for

the PM-10 expression.
11/06 Miscellaneous Sources 13.2.2-3

Table 13.2.2-1. TYPICAL SILT CONTENT VALUES OF SURFACE MATERIAL

ON INDUSTRIAL UNPAVED ROADSa

Industry

Road Use Or

Surface Material

Plant

Sites

No. Of

Samples

Silt Content (%)

Range Mean

Copper smelting Plant road 1 3 16 - 19 17

Iron and steel production Plant road 19 135 0.2 - 19 6.0

Sand and gravel processing Plant road 1 3 4.1 - 6.0 4.8

Material storage

area 1 1 - 7.1

Stone quarrying and processing Plant road 2 10 2.4 - 16 10

Haul road to/from

pit 4 20 5.0-15 8.3

Taconite mining and processing Service road 1 8 2.4 - 7.1 4.3

Haul road to/from

pit

1 12 3.9 - 9.7 5.8

Western surface coal mining Haul road to/from

pit

3 21 2.8 - 18 8.4

Plant road 2 2 4.9 - 5.3 5.1

Scraper route 3 10 7.2 - 25 17

Haul road

 (freshly graded) 2 5 18 - 29 24

Construction sites Scraper routes 7 20 0.56-23 8.5

Lumber sawmills Log yards 2 2 4.8-12 8.4

Municipal solid waste landfills Disposal routes 4 20 2.2 - 21 6.4

a

References 1,5-15.
13.2.2-4 EMISSION FACTORS 11/06

(1a)

(1b)

The following empirical expressions may be used to estimate the quantity in pounds (lb) of

size-specific particulate emissions from an unpaved road, per vehicle mile traveled (VMT):

For vehicles traveling on unpaved surfaces at industrial sites, emissions are estimated from the following

equation:

and, for vehicles traveling on publicly accessible roads, dominated by light duty vehicles, emissions may

be estimated from the following:

where k, a, b, c and d are empirical constants (Reference 6) given below and 

E = size-specific emission factor (lb/VMT)

s = surface material silt content (%)

W = mean vehicle weight (tons)

M = surface material moisture content (%) 

 S = mean vehicle speed (mph)

 C = emission factor for 1980's vehicle fleet exhaust, brake wear and tire wear.

The source characteristics s, W and M are referred to as correction parameters for adjusting the emission

estimates to local conditions. The metric conversion from lb/VMT to grams (g) per vehicle kilometer

traveled (VKT) is as follows:

1 lb/VMT = 281.9 g/VKT

The constants for Equations 1a and 1b based on the stated aerodynamic particle sizes are shown in

Tables 13.2.2-2 and 13.2.2-4. The PM-2.5 particle size multipliers (k-factors) are taken from

Reference 27.
11/06 Miscellaneous Sources 13.2.2-5

Table 13.2.2-2. CONSTANTS FOR EQUATIONS 1a AND 1b

Constant

Industrial Roads (Equation 1a) Public Roads (Equation 1b)

PM-2.5 PM-10 PM-30* PM-2.5 PM-10 PM-30*

k (lb/VMT) 0.15 1.5 4.9 0.18 1.8 6.0

a 0.9 0.9 0.7 1 1 1

b 0.45 0.45 0.45 - - -

c - - - 0.2 0.2 0.3

d --- 0.5 0.5 0.3

Quality Rating BBBBBB

*Assumed equivalent to total suspended particulate matter (TSP)

“-“ = not used in the emission factor equation

Table 13.2.2-2 also contains the quality ratings for the various size-specific versions of Equation 1a and

1b. The equation retains the assigned quality rating, if applied within the ranges of source conditions,

shown in Table 13.2.2-3, that were tested in developing the equation:

Table 13.2.2-3. RANGE OF SOURCE CONDITIONS USED IN DEVELOPING EQUATION 1a AND

1b

Emission Factor

Surface Silt

Content, %

Mean Vehicle

Weight

Mean Vehicle

Speed Mean

No. of

Wheels

Surface

Moisture

Content,

Mg ton km/hr mph %

Industrial Roads

(Equation 1a) 1.8-25.2 1.8-260 2-290 8-69 5-43 4-17a 0.03-13

Public Roads

(Equation 1b)

1.8-35 1.4-2.7 1.5-3 16-88 10-55 4-4.8 0.03-13

a

 See discussion in text.

As noted earlier, the models presented as Equations 1a and 1b were developed from tests of

traffic on unpaved surfaces. Unpaved roads have a hard, generally nonporous surface that usually dries

quickly after a rainfall or watering, because of traffic-enhanced natural evaporation. (Factors influencing

how fast a road dries are discussed in Section 13.2.2.3, below.) The quality ratings given above pertain to

the mid-range of the measured source conditions for the equation. A higher mean vehicle weight and a

higher than normal traffic rate may be justified when performing a worst-case analysis of emissions from

unpaved roads. 

The emission factors for the exhaust, brake wear and tire wear of a 1980's vehicle fleet (C) was

obtained from EPA’s MOBILE6.2 model 23. The emission factor also varies with aerodynamic size range
13.2.2-6 EMISSION FACTORS 11/06

as shown in Table 13.2.2-4

Table 13.2.2-4. EMISSION FACTOR FOR 1980'S VEHICLE FLEET 

EXHAUST, BRAKE WEAR AND TIRE WEAR

Particle Size Rangea

C, Emission Factor for

Exhaust, Brake Wear

and Tire Wearb

lb/VMT

PM2.5 0.00036

PM10 0.00047

PM30

c 0.00047

a Refers to airborne particulate matter (PM-x) with an aerodynamic diameter equal to or less

than x micrometers.

b Units shown are pounds per vehicle mile traveled (lb/VMT). c PM-30 is sometimes termed "suspendable particulate" (SP) and is often used as a surrogate

for TSP.

It is important to note that the vehicle-related source conditions refer to the average weight,

speed, and number of wheels for all vehicles traveling the road. For example, if 98 percent of traffic on

the road are 2-ton cars and trucks while the remaining 2 percent consists of 20-ton trucks, then the mean

weight is 2.4 tons. More specifically, Equations 1a and 1b are not intended to be used to calculate a

separate emission factor for each vehicle class within a mix of traffic on a given unpaved road. That is, in

the example, one should not determine one factor for the 2-ton vehicles and a second factor for the 20-ton

trucks. Instead, only one emission factor should be calculated that represents the "fleet" average of 2.4

tons for all vehicles traveling the road. 

Moreover, to retain the quality ratings when addressing a group of unpaved roads, it is necessary

that reliable correction parameter values be determined for the road in question. The field and laboratory

procedures for determining road surface silt and moisture contents are given in AP-42 Appendices C.1

and C.2. Vehicle-related parameters should be developed by recording visual observations of traffic. In

some cases, vehicle parameters for industrial unpaved roads can be determined by reviewing maintenance

records or other information sources at the facility.

In the event that site-specific values for correction parameters cannot be obtained, then default

values may be used.In the absence of site-specific silt content information, an appropriate mean value

from Table 13.2.2-1 may be used as a default value, but the quality rating of the equation is reduced by

two letters. Because of significant differences found between different types of road surfaces and

between different areas of the country, use of the default moisture content value of 0.5 percent in

Equation 1b is discouraged. The quality rating should be downgraded two letters when the default

moisture content value is used. (It is assumed that readers addressing industrial roads have access to the

information needed to develop average vehicle information in Equation 1a for their facility.)

The effect of routine watering to control emissions from unpaved roads is discussed below in

Section 13.2.2.3, “Controls”. However, all roads are subject to some natural mitigation because of

rainfall and other precipitation. The Equation 1a and 1b emission factors can be extrapolated to annual
11/06 Miscellaneous Sources 13.2.2-7

(2)

average uncontrolled conditions (but including natural mitigation) under the simplifying assumption that

annual average emissions are inversely proportional to the number of days with measurable (more than

0.254 mm [0.01 inch]) precipitation:

where: 

Eext = annual size-specific emission factor extrapolated for natural mitigation, lb/VMT

E = emission factor from Equation 1a or 1b

P = number of days in a year with at least 0.254 mm (0.01 in) of precipitation (see

below)

Figure 13.2.2-1 gives the geographical distribution for the mean annual number of “wet” days for the

United States.

Equation 2 provides an estimate that accounts for precipitation on an annual average basis for the

purpose of inventorying emissions. It should be noted that Equation 2 does not account for differences in

the temporal distributions of the rain events, the quantity of rain during any event, or the potential for the

rain to evaporate from the road surface. In the event that a finer temporal and spatial resolution is desired

for inventories of public unpaved roads, estimates can be based on a more complex set of assumptions. 

These assumptions include: 

1. The moisture content of the road surface material is increased in proportion to the quantity of

water added;

2. The moisture content of the road surface material is reduced in proportion to the Class A pan

evaporation rate;

3. The moisture content of the road surface material is reduced in proportion to the traffic

volume; and

4. The moisture content of the road surface material varies between the extremes observed in the

area. The CHIEF Web site (http://www.epa.gov/ttn/chief/ap42/ch13/related/c13s02-2.html) has a file

which contains a spreadsheet program for calculating emission factors which are temporally and spatially

resolved. Information required for use of the spreadsheet program includes monthly Class A pan

evaporation values, hourly meteorological data for precipitation, humidity and snow cover, vehicle traffic

information, and road surface material information.

It is emphasized that the simple assumption underlying Equation 2 and the more complex set of

assumptions underlying the use of the procedure which produces a finer temporal and spatial resolution

have not been verified in any rigorous manner. For this reason, the quality ratings for either approach

should be downgraded one letter from the rating that would be applied to Equation 1. 

13.2.2.3 Controls18-22

A wide variety of options exist to control emissions from unpaved roads. Options fall into the

following three groupings:

1. Vehicle restrictions that limit the speed, weight or number of vehicles on the road;
13.2.2-8 EMISSION FACTORS 11/06

2. Surface improvement, by measures such as (a) paving or (b) adding gravel or slag to a dirt

road; and

3. Surface treatment, such as watering or treatment with chemical dust suppressants.

Available control options span broad ranges in terms of cost, efficiency, and applicability. For example,

traffic controls provide moderate emission reductions (often at little cost) but are difficult to enforce. 

Although paving is highly effective, its high initial cost is often prohibitive. Furthermore, paving is not

feasible for industrial roads subject to very heavy vehicles and/or spillage of material in transport. 

Watering and chemical suppressants, on the other hand, are potentially applicable to most industrial roads

at moderate to low costs. However, these require frequent reapplication to maintain an acceptable level of

control. Chemical suppressants are generally more cost-effective than water but not in cases of temporary

roads (which are common at mines, landfills, and construction sites). In summary, then, one needs to

consider not only the type and volume of traffic on the road but also how long the road will be in service

when developing control plans. 

Vehicle restrictions. These measures seek to limit the amount and type of traffic present on the

road or to lower the mean vehicle speed. For example, many industrial plants have restricted employees

from driving on plant property and have instead instituted bussing programs. This eliminates emissions

due to employees traveling to/from their worksites. Although the heavier average vehicle weight of the

busses increases the base emission factor, the decrease in vehicle-miles-traveled results in a lower overall

emission rate. 
11/06 Miscellaneous Sources 13.2.2-9 Figure 13.2.2-1. Mean number of days with 0.01 inch or more of precipitation in United States.
13.2.2-10 EMISSION FACTORS 11/06

Surface improvements. Control options in this category alter the road surface. As opposed to the

“surface treatments” discussed below, improvements are relatively “permanent” and do not require

periodic retreatment. 

The most obvious surface improvement is paving an unpaved road. This option is quite

expensive and is probably most applicable to relatively short stretches of unpaved road with at least

several hundred vehicle passes per day. Furthermore, if the newly paved road is located near unpaved

areas or is used to transport material, it is essential that the control plan address routine cleaning of the

newly paved road surface. 

The control efficiencies achievable by paving can be estimated by comparing emission factors for

unpaved and paved road conditions. The predictive emission factor equation for paved roads, given in

Section 13.2.1, requires estimation of the silt loading on the traveled portion of the paved surface, which

in turn depends on whether the pavement is periodically cleaned. Unless curbing is to be installed, the

effects of vehicle excursion onto unpaved shoulders (berms) also must be taken into account in estimating

the control efficiency of paving.

Other improvement methods cover the road surface with another material that has a lower silt

content. Examples include placing gravel or slag on a dirt road. Control efficiency can be estimated by

comparing the emission factors obtained using the silt contents before and after improvement. The silt

content of the road surface should be determined after 3 to 6 months rather than immediately following

placement. Control plans should address regular maintenance practices, such as grading, to retain larger

aggregate on the traveled portion of the road. 

Surface treatments refer to control options which require periodic reapplication. Treatments fall

into the two main categories of (a) “wet suppression” (i. e., watering, possibly with surfactants or other

additives), which keeps the road surface wet to control emissions and (b) “chemical stabilization/

treatment”, which attempts to change the physical characteristics of the surface. The necessary

reapplication frequency varies from several minutes for plain water under summertime conditions to

several weeks or months for chemical dust suppressants. 

Watering increases the moisture content, which conglomerates particles and reduces their

likelihood to become suspended when vehicles pass over the surface. The control efficiency depends on

how fast the road dries after water is added. This in turn depends on (a) the amount (per unit road surface

area) of water added during each application; (b) the period of time between applications; (c) the weight,

speed and number of vehicles traveling over the watered road during the period between applications; and

(d) meteorological conditions (temperature, wind speed, cloud cover, etc.) that affect evaporation during

the period. 
11/06 Miscellaneous Sources 13.2.2-11

Figure 13.2.2-2 presents a simple bilinear relationship between the instantaneous control

efficiency due to watering and the resulting increase in surface moisture. The moisture ratio "M" (i.e., the

x-axis in Figure 13.2.2-2) is found by dividing the surface moisture content of the watered road by the

surface moisture content of the uncontrolled road. As the watered road surface dries, both the ratio M and

the predicted instantaneous control efficiency (i.e., the y-axis in the figure) decrease. The figure shows

that between the uncontrolled moisture content and a value twice as large, a small increase in moisture

content results in a large increase in control efficiency. Beyond that, control efficiency grows slowly with

increased moisture content.

Given the complicated nature of how the road dries, characterization of emissions from watered

roadways is best done by collecting road surface material samples at various times between water truck

passes. (Appendices C.1 and C.2 present the sampling and analysis procedures.) The moisture content

measured can then be associated with a control efficiency by use of Figure 13.2.2-2. Samples that reflect

average conditions during the watering cycle can take the form of either a series of samples between

water applications or a single sample at the midpoint. It is essential that samples be collected during

periods with active traffic on the road. Finally, because of different evaporation rates, it is recommended

that samples be collected at various times during the year. If only one set of samples is to be collected,

these must be collected during hot, summertime conditions.

When developing watering control plans for roads that do not yet exist, it is strongly

recommended that the moisture cycle be established by sampling similar roads in the same geographic

area. If the moisture cycle cannot be established by similar roads using established watering control

plans, the more complex methodology used to estimate the mitigation of rainfall and other precipitation

can be used to estimate the control provided by routine watering. An estimate of the maximum daytime

Class A pan evaporation (based upon daily evaporation data published in the monthly Climatological

Data for the state by the National Climatic Data Center) should be used to insure that adequate watering

capability is available during periods of highest evaporation. The hourly precipitation values in the

spreadsheet should be replaced with the equivalent inches of precipitation (where the equivalent of 1 inch

of precipitation is provided by an application of 5.6 gallons of water per square yard of road). 

Information on the long term average annual evaporation and on the percentage that occurs between May

and October was published in the Climatic Atlas (Reference 16). Figure 13.2.2-3 presents the

geographical distribution for "Class A pan evaporation" throughout the United States. Figure 13.2.2-4

presents the geographical distribution of the percentage of this evaporation that occurs between May and

October. The U. S. Weather Bureau Class A evaporation pan is a cylindrical metal container with a depth

of 10 inches and a diameter of 48 inches. Periodic measurements are made of the changes of the water

level.

The above methodology should be used only for prospective analyses and for designing watering

programs for existing roadways. The quality rating of an emission factor for a watered road that is based

on this methodology should be downgraded two letters. Periodic road surface samples should be

collected and analyzed to verify the efficiency of the watering program.

As opposed to watering, chemical dust suppressants have much less frequent reapplication

requirements. These materials suppress emissions by changing the physical characteristics of the existing

road surface material. Many chemical unpaved road dust suppressants form a hardened surface that binds

particles together. After several applications, a treated road often resembles a paved road except that the

surface is not uniformly flat. Because the improved surface results in more grinding of small particles,

the silt content of loose material on a highly controlled surface may be substantially higher than when the

surface was uncontrolled. For this reason, the models presented as Equations 1a and 1b cannot be used to

estimate emissions from chemically stabilized roads. Should the road be allowed to return to an
13.2.2-12 EMISSION FACTORS 11/06

uncontrolled state with no visible signs of large-scale cementing of material, the Equation 1a and 1b

emission factors could then be used to obtain conservatively high emission estimates. 

Figure 13.2.2-2. Watering control effectiveness for unpaved travel surfaces

0

25

50

75

100

012345

Moisture Ratio, M 

Control Efficiency (%)
11/06 Miscellaneous Sources 13.2.2-13

The control effectiveness of chemical dust suppressants appears to depend on (a) the dilution rate

used in the mixture; (b) the application rate (volume of solution per unit road surface area); (c) the time

between applications; (d) the size, speed and amount of traffic during the period between applications;

and (e) meteorological conditions (rainfall, freeze/thaw cycles, etc.) during the period. Other factors that

affect the performance of dust suppressants include other traffic characteristics (e. g., cornering, track-on

from unpaved areas) and road characteristics (e. g., bearing strength, grade). The variabilities in the

above factors and differences between individual dust control products make the control efficiencies of

chemical dust suppressants difficult to estimate. Past field testing of emissions from controlled unpaved

roads has shown that chemical dust suppressants provide a PM-10 control efficiency of about 80 percent

when applied at regular intervals of 2 weeks to 1 month. 
13.2.2-14 EMISSION FACTORS 11/06

Figure 13.2.2-3. Annual evaporation data.
11/06 Miscellaneous Sources 13.2.2-15 Figure 13.2.2-4. Geographical distribution of the percentage of evaporation occurring between May and Oc
13.2.2-16 EMISSION FACTORS 11/06

Table 13.2-2-5. EXAMPLE OF AVERAGE CONTROLLED EMISSION FACTORS

FOR SPECIFIC CONDITIONS

Period

Ground Inventory,

gal/yd2

Average Control

Efficiency, %a

Average Controlled

Emission Factor,

lb/VMT

May 0.037 0 7.1

June 0.073 62 2.7

July 0.11 68 2.3

August 0.15 74 1.8

September 0.18 80 1.4

a From Figure 13.2.2-5, #10 :m. Zero efficiency assigned if ground inventory is less than 0.05 gal/yd2

.

1 lb/VMT = 281.9 g/VKT. 1 gal/yd2

 = 4.531 L/m2.

Petroleum resin products historically have been the dust suppressants (besides water) most widely

used on industrial unpaved roads. Figure 13.2.2-5 presents a method to estimate average control

efficiencies associated with petroleum resins applied to unpaved roads.20 Several items should be noted:

1. The term "ground inventory" represents the total volume (per unit area) of petroleum resin

concentrate (not solution) applied since the start of the dust control season.

2. Because petroleum resin products must be periodically reapplied to unpaved roads, the use of

a time-averaged control efficiency value is appropriate. Figure 13.2.2-5 presents control efficiency values

averaged over two common application intervals, 2 weeks and 1 month. Other application intervals will

require interpolation.

3. Note that zero efficiency is assigned until the ground inventory reaches 0.05 gallon per square

yard (gal/yd2

). Requiring a minimum ground inventory ensures that one must apply a reasonable amount

of chemical dust suppressant to a road before claiming credit for emission control. Recall that the ground

inventory refers to the amount of petroleum resin concentrate rather than the total solution.

As an example of the application of Figure 13.2.2-5, suppose that Equation 1a was used to

estimate an emission factor of 7.1 lb/VMT for PM-10 from a particular road. Also, suppose that, starting

on May 1, the road is treated with 0.221 gal/yd2

 of a solution (1 part petroleum resin to 5 parts water) on

the first of each month through September. Then, the average controlled emission factors, shown in

Table 13.2.2-5, are found.

Besides petroleum resins, other newer dust suppressants have also been successful in controlling

emissions from unpaved roads. Specific test results for those chemicals, as well as for petroleum resins

and watering, are provided in References 18 through 21.
1

1/06 Miscellaneous Sources 13.2.2-17Figure 13.2.2-5. Average control efficiencies over common application intervals.
13.2.2-18 EMISSION FACTORS 11/06

13.2.2.4 Updates Since The Fifth Edition

The Fifth Edition was released in January 1995. Revisions to this section since that date are

summarized below. For further detail, consult the background report for this section (Reference 6).

October 1998 (Supplement E)– This was a major revision of this section. Significant changes to

the text and the emission factor equations were made.

October 2001 – Separate emission factors for unpaved surfaces at industrial sites and publicly

accessible roads were introduced. Figure 13.2.2-2 was included to provide control effectiveness estimates

for watered roads.

December 2003 – The public road emission factor equation (equation 1b) was adjusted to remove

the component of particulate emissions from exhaust, brake wear, and tire wear. The parameter C in the

new equation varies with aerodynamic size range of the particulate matter. Table 13.2.2-4 was added to

present the new coefficients. 

January 2006 – The PM-2.5 particle size multipliers (i.e., factors) in Table 13.2.2-2 were

modified and the quality ratings were upgraded from C to B based on the wind tunnel studies of a variety

of dust emitting surface materials.

References For Section 13.2.2

1. C. Cowherd, Jr., et al., Development Of Emission Factors For Fugitive Dust Sources,

EPA-450/3-74-037, U. S. Environmental Protection Agency, Research Triangle Park, NC,

June 1974.

2. R. J. Dyck and J. J. Stukel, "Fugitive Dust Emissions From Trucks On Unpaved Roads",

Environmental Science And Technology, 10(10):1046-1048, October 1976.

3. R. O. McCaldin and K. J. Heidel, "Particulate Emissions From Vehicle Travel Over Unpaved

Roads", Presented at the 71st Annual Meeting of the Air Pollution Control Association, Houston,

TX, June 1978.

4. C. Cowherd, Jr, et al., Iron And Steel Plant Open Dust Source Fugitive Emission Evaluation,

EPA-600/2-79-013, U. S. Environmental Protection Agency, Cincinnati, OH, May 1979.

5. G. Muleski, Unpaved Road Emission Impact, Arizona Department of Environmental Quality,

Phoenix, AZ, March 1991.

6. Emission Factor Documentation For AP-42, Section 13.2.2, Unpaved Roads, Final Report, Midwest

Research Institute, Kansas City, MO, September 1998.

7. T. Cuscino, Jr., et al., Taconite Mining Fugitive Emissions Study, Minnesota Pollution Control

Agency, Roseville, MN, June 1979.

8. Improved Emission Factors For Fugitive Dust From Western Surface Coal Mining Sources,

2 Volumes, EPA Contract No. 68-03-2924, Office of Air Quality Planning and Standards, U. S.

Environmental Protection Agency, Research Triangle Park, NC.
11/06 Miscellaneous Sources 13.2.2-19

9. T. Cuscino, Jr., et al., Iron And Steel Plant Open Source Fugitive Emission Control Evaluation,

EPA-600/2-83-110, U. S. Environmental Protection Agency, Cincinnati, OH, October 1983.

10. Size Specific Emission Factors For Uncontrolled Industrial And Rural Roads, EPA Contract

No. 68-02-3158, Midwest Research Institute, Kansas City, MO, September 1983.

11. C. Cowherd, Jr., and P. Englehart, Size Specific Particulate Emission Factors For Industrial And

Rural Roads, EPA-600/7-85-038, U. S. Environmental Protection Agency, Cincinnati, OH,

September 1985.

12. PM-10 Emission Inventory Of Landfills In The Lake Calumet Area, EPA Contract 68-02-3891, Work

Assignment 30, Midwest Research Institute, Kansas City, MO, September 1987.

13. Chicago Area Particulate Matter Emission Inventory — Sampling And Analysis, EPA Contract

No. 68-02-4395, Work Assignment 1, Midwest Research Institute, Kansas City, MO, May 1988.

14. PM-10 Emissions Inventory Data For The Maricopa And Pima Planning Areas, EPA Contract

No. 68-02-3888, Engineering-Science, Pasadena, CA, January 1987.

15. Oregon Fugitive Dust Emission Inventory, EPA Contract 68-D0-0123, Midwest Research Institute,

Kansas City, MO, January 1992.

16. Climatic Atlas Of The United States, U. S. Department Of Commerce, Washington, DC, June 1968.

17. National Climatic Data Center, Solar And Meteorological Surface Observation Network 1961-1990;

3 Volume CD-ROM. Asheville, NC, 1993.

18. C. Cowherd, Jr. et al., Control Of Open Fugitive Dust Sources, EPA-450/3-88-008,

U. S. Environmental Protection Agency, Research Triangle Park, NC, September 1988.

19. G. E. Muleski, et al., Extended Evaluation Of Unpaved Road Dust Suppressants In The Iron And

Steel Industry, EPA-600/2-84-027, U. S. Environmental Protection Agency, Cincinnati, OH,

February 1984.

20. C. Cowherd, Jr., and J. S. Kinsey, Identification, Assessment And Control Of Fugitive Particulate

Emissions, EPA-600/8-86-023, U. S. Environmental Protection Agency, Cincinnati, OH, August

1986.

21. G. E. Muleski and C. Cowherd, Jr., Evaluation Of The Effectiveness Of Chemical Dust Suppressants

On Unpaved Roads, EPA-600/2-87-102, U. S. Environmental Protection Agency, Cincinnati, OH,

November 1986.

22. Fugitive Dust Background Document And Technical Information Document For Best Available

Control Measures, EPA-450/2-92-004, Office Of Air Quality Planning And Standards, U. S.

Environmental Protection Agency, Research Triangle Park, NC, September 1992.

23. Written communication (Technical Memorandum) from P. Hemmer, E.H. Pechan & Associates,

Inc., Durham, NC to B. Kuykendal, U. S. Environmental Protection Agency, Research Triangle

Park, NC, August, 21, 2003.
13.2.2-20 EMISSION FACTORS 11/06

24. MOBILE6 User Guide, United States Environmental Protection Agency, Office of Transportation

and Air Quality. EPA420-R-02-028, October 2002.

25. Written communication (Technical Memorandum) from G. Muleski, Midwest Research Institute,

Kansas City, MO, to B. Kuykendal, U. S. Environmental Protection Agency, Research Triangle

Park, NC, Subject “Unpaved Roads”, September 27, 2001.

26. Written communication (Technical Memorandum) from W. Kuykendal, U. S. Environmental

Protection Agency, to File, Subject “Decisions on Final AP-42 Section 13.2.2 Unpaved Roads”,

November 24, 2003.

27. C. Cowherd, Background Document for Revisions to Fine Fraction Ratios &sed for AP-42 Fugitive

Dust Emission Factors. Prepared by Midwest Research Institute for Western Governors Association,

Western Regional Air Partnership, Denver, CO, February 1, 2006.
 Background Document for Revisions to Fine 

Fraction Ratios Used for AP-42 Fugitive Dust 

Emission Factors 

Prepared by 

Midwest Research Institute 

(Chatten Cowherd, MRI Project Leader) 

For 

Western Governors’ Association 

Western Regional Air Partnership (WRAP) 

1515 Cleveland Place, Suite 200 

Denver, Colorado 80202 

Attn: Richard Halvey 

MRI Project No. 110397 

February 1, 2006 

Finalized November 1, 2006 
Responses to Comments Received on Proposed AP-42 Revisions 

Commenter 

and Date 

Source 

Category 

Comment Response 

John Hayden, 

National Stone, 

Sand and Gravel 

Association 

(NSSGA); June 

14, 2006 

Unpaved 

Roads 

NSSGAsponsored tests 

(report dated Oct. 

15, 2004) at 

California 

aggregate 

producing plants 

support the 

proposed fine 

fractions. 

This comment reference a test report prepared 

by Air Control Techniques for the National 

Stone, Sand & Gravel Association, dated 

October 4, 2004. The report gives the results of 

tests to determine unpaved road emissions 

factors for controlled (wet suppression only) 

haul roads at two aggregate processing plants. 

A variation of the plume profiling method using 

TEOM continuous monitors with PM-2.5 and 

PM-10 inlets was employed. Tests with road 

surface moisture content below 1.5 percent 

were considered to be uncontrolled. 

Based on the example PM-10 concentration 

profiles presented in the report, the maximum 

roadside PM-10 dust concentrations in the 

subject study were in the range of 300 

micrograms per cubic meter. This is an order of 

magnitude lower than the concentrations 

typically found in other unpaved road emission 

factor studies. 

For the range of plume concentrations 

measured in the NSSGA-sponsored test 

program, an average fine fraction (PM-2.5/PM10 ratio) of 0.15 was reported. This fine fraction 

value is consistent with the results of the MRI 

dust tunnel testing in the same concentration 

range. At plume concentrations more typical of 

unpaved road emission factor studies, the 

proposed value of 0.1 is applicable. 

There is no need for any revisions to the 

proposed changes to AP-42 as a result of the 

cited study. 

Hao Quinn, 

Sacramento 

Metro AQMD; 

July 20, 2006 

Paved vs. 

unpaved 

roads 

For a particular 

industrial facility, 

the PM-10 

emission factor 

equations show 

higher emissions 

from paved roads 

rather than 

unpaved roads. 

This comment does not relate to the 

proposed changes to the fine particle 

fractions. 

It is possible that the emissions from a heavily 

loaded paved road can exceed emissions from 

an unpaved road with a low-to-moderate silt 

content at the same industrial facility, even if 

traveled by the same vehicles. This is the case 

in the cited example, for which the paved road 

silt loading is 70 g/m2

. 
Commenter 

and Date 

Source 

Category 

Comment Response 

Brian Leahy, 

Horizon 

Environmental; 

July 26, 2006 

Unpaved 

roads 

The k value for 

PM-2.5 does not 

appear to have 

changed in the 

proposed 

revision. 

The latest (2003) approved AP-42 k values for 

PM-2.5 in Table 13.2.2-2 are 0.23 and 0.27 

lb/VMT for industrial and public roads, 

respectively. The proposed values are 0.15 and 

0.18 lb/VMT, which are equivalent to 10 percent 

of the respective k values for PM-10. 

There is no need for revisions to the proposed 

changes to AP-42 as a result of this comment. 

The conversion 

of proposed k 

values from 

g/VMT to g/VKT 

does not appear 

correct 

Regarding the revised k values for PM-2.5, 

when the k value of 0.66 g/VKT is multiplied by 

1.6 km/mi, it becomes 1.06 g/VMT, which 

rounds to 1.1 g/VKT given in the proposed 

revision. Because the k values are given only to 

two significant figures, the converted values can 

vary by up to five digits in the second figure, 

depending on which direction the units 

conversion is made. For example, when k value 

of 1.1 g/VKT is divided by 1.6 km/mi, the 

resulting value rounds to 0.69 g/VKT, but if 1.06 

g/VKT is divided by 1.6 km/mi, the resulting 

value rounds to 0.66 g/VKT. 

There is no need for revisions to the proposed 

changes to AP-42 as a result of this comment. 

Shengxin Jin, 

NYSDOT 

Environmental 

Analysis Bureau; 

undated 

Paved 

roads 

The stated silt 

loading impact of 

antiskid abrasive 

does not appear 

correct 

This comment does not relate to the 

proposed changes to the fine particle 

fractions. 

The commenter is correct in that 500 lb/mi of 

antiskid abrasive with a 1% silt content 

produces a silt loading in the range of 0.5 g/m2

rather than 2 g/m2

. EPA may elect to make a 

separate modification to correct this discrepancy 

at a later time. 
 

1

Proposed Revisions to Fine Fraction Ratios 

Used for AP-42 Fugitive Dust Emission Factors 

ABSTRACT 

 A number of fugitive dust studies have indicated that the PM2.5 / PM10 ratios 

measured by US EPA federal reference method (FRM) samplers are significantly lower 

than predicted by AP-42 emission factors. As a result, the PM2.5 emission estimates are 

biased high. The controlled exposure study described in this report was conducted to 

compare fine fraction ratios derived from FRM samplers to those derived from the 

cyclone/impactor method that had been used to develop AP-42 emission factors for 

fugitive dust sources. The study was conducted by the Midwest Research Institute using 

the same cyclone/impactor samplers and operating method that generated the original 

AP-42 emission factors and associated PM2.5 / PM10 ratios. This study was sponsored by 

the Western Regional Air Partnership. 

 The study found that concentration measurements used to develop PM2.5 emission 

factors in AP-42 were biased high by a factor of two, as compared to PM2.5

measurements from FRM samplers. This factor-of-two bias helps to explain why 

researchers have often seen a discrepancy in the proportion of fugitive dust found in 

PM2.5 emission inventories and modeled ambient air impacts, as compared to the 

proportion on ambient filter samples. This study also shows that the PM2.5 / PM10 ratios 

for fugitive dust should be in the range of 0.1 to 0.15. Currently, the ratios in AP-42 

range from 0.15 to 0.4 for most fugitive dust sources. 

 It is recommended that the results of this study be used to revise the AP-42 PM2.5

emission factors for the following four fugitive dust source categories: paved roads, 

unpaved roads (public and industrial), aggregate handling and storage piles, and 

industrial wind erosion (AP-42 Sections 13.2.1, 13.2.2, 13.2.4, & 13.2.5, respectively). 

Emission estimates for other fugitive dust producing activities, such as construction and 

demolition will also be affected since they are based on these four source categories. 

INTRODUCTION 

The Dust Emissions Joint Forum (DEJF) of the Western Regional Air Partnership 

(WRAP) is engaged in gathering and improving data pertaining to the PM2.5 and PM10

components of fugitive dust emissions. Most of the PM2.5 emission factors in EPA’s AP42 guidance for fugitive dust sources (USEPA, 2005) were determined by using highvolume samplers, each fitted with a cyclone precollector and cascade impactor. 

Typically, AP-42 recommends that PM2.5 emission factors for dust sources be calculated 
 

2

by using PM10 emission factor equations along with PM2.5/PM10 ratios that have been 

published by EPA in AP-42. 

Beginning with the introduction of the cyclone/impactor method, it was realized 

particle bounce from the cascade impactor stages to the backup filter may have resulted 

in inflated PM2.5 concentrations, even though steps were taken to minimize particle 

bounce. This led to an EPA-funded field study in the late 1990s (MRI, 1997) to gather 

comparative particle sizing data in dust plumes downwind of paved and unpaved roads 

around the country. The test results indicated that dichotomous samplers produced 

consistently lower PM2.5/PM10 ratios than generated with the cyclone/impactor system. 

Dichotomous samplers are federal reference method (FRM) samplers that are used to 

measure compliance with federal air quality standards for particulate matter measured as 

PM2.5 and PM10. Pending the eventual collection of additional data, the decision was 

made that the true ratios would best be represented by an averaging of the 

cyclone/impactor data with the dichotomous sampler data. 

Based on the results of the EPA-funded field program, modifications were made to 

the appropriate sections of AP-42 for dust emissions from paved and unpaved roads. The 

PM2.5/PM10 ratio for emissions from unpaved roads (dominated by fugitive dust) was 

reduced from 0.26 to 0.15, and the PM2.5/PM10 ratio for the dust component of emissions 

from paved roads was reduced from 0.46 to 0.25. In the 2003 revision to AP-42, the nondust component of paved road emissions was assigned a PM2.5/PM10 ratio of 0.76, 

accounting for vehicle exhaust and brake and tire wear. 

Subsequent to the modifications of the PM2.5/PM10 ratios in AP-42, additional field 

test results (mostly from ambient air samplers) indicated that further reductions to the 

ratios were warranted (Pace, 2005). For example, ambient air monitoring data suggested 

that the fine fraction dust mass is of the order of 10 percent of the PM10 mass, based on 

chemical fingerprinting of the collected fine and coarse fractions of PM10 impacted by 

dust sources. It is important to note, however, that particle size data applicable to fugitive 

dust emission factors should be gathered either from the emissions plume or near the 

point where emissions are generated (within 10 m of the downwind edge of the source). 

METHODOLOGY 

This led DEJF to fund Midwest Research Institute (MRI) in conducting a controlled 

study of particle sizing in dust plumes. The objective of the study was to resolve the fine 

particle bias in the cyclone/impactor system, so that reliable PM2.5/PM10 ratios could be 

developed for as many dust source categories as possible. For this purpose, an air 

exposure chamber connected to a recirculating supply air stream was used in conjunction 

with a fluidization system for generating well-mixed dust plumes from a variety of 

western soils and road surface materials. R&P Model 2000 Partisol samplers were 

selected as the ground-truthing FRM samplers for PM10 and PM2.5. 
 

3

This study was performed in two phases (see below), as described in the attached test 

report (Cowherd and Donaldson, 2005). The test report serves as the background 

document to support the recommended revisions to AP-42, and it contains all the quality 

assurance procedures and results of the testing. 

 

Phase I – Compare PM2.5 Measured by Cyclone/Impactor to FRM Sampler 

In the first testing phase of the project, PM2.5 measurements using the high-volume 

cascade impactors were compared to simultaneous measurements obtained with EPA 

FRM samplers for PM2.5. As stated above, these tests were conducted in a flow-through 

wind tunnel and exposure chamber, where the PM10 concentration level and uniformity 

were controlled. The results of the tests provided the basis for quantifying more 

effectively any sampling bias associated with the cascade impactor system. 

 

Phase 2 – Compare PM2.5 to PM10 Ratios for Different Geologic Soils 

With the same test setup, a second phase of testing was performed with reference 

method samplers, for the purpose of measuring PM2.5 to PM10 ratios for fugitive dust 

from different geologic sources in the West. This testing provided needed information on 

the magnitude and variability of this ratio, especially for source materials that are 

recognized as problematic with regard to application of mitigative dust control measures. 

RESULTS 

The tests that were performed are listed in Tables 6 and 7 of the attached report. The 

Phase I tests were performed in March and April of 2005. The Phase II tests were 

performed in June through August of 2005. A total of 100 individual tests were 

performed, including 17 blank runs (for quality assurance purposes). The raw and 

intermediate test data are summarized in the tables presented in Appendix A of the 

attached report. 

Based on the 100 wind tunnel tests that were performed in the wind tunnel study, the 

findings support the following conclusions: 

1. PM2.5 concentrations measured by the high-volume cyclone/impactor system 

used to develop AP-42 emission factors for fugitive dust sources have a positive 

bias by a factor of 2, as compared to the PM2.5 concentration measurements from 

reference-method samplers (see Figure 1). The geometric mean bias is 2.01 and 

the arithmetic mean bias is 2.15. 

2. The PM2.5 bias associated with the cyclone/impactor system, as measured under 

controlled laboratory conditions with dust concentrations held at nearly steady 

values, closely replicates the bias observed in the prior EPA-funded field study 

at distributed geographic locations across the country. 
 

4

3. The PM2.5/PM10 ratios measured by the FRM samplers in the current study for a 

variety of western soils show a decrease in magnitude with increasing PM10

concentration (see Figure 2). Soils with a nominally spherical shape are 

observed to have somewhat lower ratios (at given PM10 concentrations) than 

soils with angular shape. A very similar dependence of PM2.5/PM10 ratio on 

PM10 concentration was also observed in the prior field study that used 

dichotomous samplers as FRM devices. 

4. The test data from the current study support a PM2.5/PM10 ratio in the range of 

0.1 to 0.15 for typical uncontrolled fugitive dust sources (see Figure 2). The 

PM2.5/PM10 ratio of 0.1 is also supported by numerous other studies including 

the prior EPA-funded field study that used dichotomous samplers as reference 

devices. It is possible that a ratio as low as 0.05 (as was found in the prior field 

tests of unpaved road emission factors) might be appropriate for very dusty 

sources, but this would require extrapolation of the current test data from the 

wind tunnel study. 

DISCUSSION 

Peer Review 

The test report on the wind tunnel study (Cowherd and Donaldson, 2005) was issued 

first in draft form for external peer review. Three peer reviewers (having no prior contact 

with the study) were selected by the DEJF: Patrick Gaffney (California Air Resources 

Board), John Kinsey (U.S. Environmental Protection Agency), and Mel Zeldin (Private 

Consultant). In addition, peer review comments were provided by Duane Ono (Great 

Basin UAPCD) and Richard Countess (Countess Environmental) who helped to develop 

this study. After the review comments on the draft test report were received, comment/ 

response logs were prepared by MRI, listing each comment and the response to each 

comment. The next step was to modify the draft test report in accordance with the 

responses to the review comments. The final test report was issued on October 12, 2005. 

Recommended Particle Size Ratios 

Based on the results of the WRAP/DEJF study (see attached test report) and the prior 

EPA-funded field study, it is proposed that new PM2.5/PM10 ratios be adopted for several 

categories of (uncontrolled) fugitive dust sources, as addressed in AP-42. The proposed 

ratios (given to the nearest 0.05) are summarized in Table 1. It should be noted that these 

fine fraction ratios and the emission factors could change in the future if field studies 

show other differences than those identified through this study. 

The proposed PM2.5/PM10 ratios in Table 1, apply to dry surface materials, having 

moisture contents in the range of 1% or less. Such materials when exposed to energetic 

disturbances produce dust plumes with core PM10 concentrations in the range of 5,000 

micrograms per cubic meter, near the point of emissions generation. The wind tunnel test 

data show that dust plumes with lower core concentrations have higher PM2.5/PM10
 

5

ratios. This might occur, for example, at higher soil (or other surface material) moisture 

contents. However, the emissions from such sources typically are substantially lower 

with correspondingly less impact on the ambient environment. 

Table 1. Proposed Particle Size Ratios for AP-42 

PM2.5/PM10 Ratio Fugitive dust source category AP-42 

section Current Proposed 

Paved Roads 13.2.1 0.25 0.15 

Unpaved Roads (Public & Industrial) 13.2.2 0.15 0.1 

Construction & Demolition – 0.208 1 0.1 

Aggregate Handling & Storage Piles 13.2.4 0.314 0.1 (traffic) 

0.15 (transfer) 

Industrial Wind Erosion 13.2.5 0.40 0.15 

Agricultural Tilling – 0.222 2 0.2 (no 

change) 

Open Area Wind Erosion – - 0.15 

Notes: 

1

 AP-42 Section 13.2.3 suggests using emission factors for individual dust 

producing activities, e.g., materials handling and unpaved roads. The WRAP 

Fugitive Dust Handbook recommends using a fine fraction ratio of 0.208 from 

a report prepared for the US EPA, Estimating Particulate Matter Emissions 

from Construction Operations (MRI, 1999). 

2

 Agricultural tilling was dropped from the 5th edition of AP-42. The WRAP 

Fugitive Dust Handbook recommends using a fine fraction ratio of 0.222 from 

Section 7.4 of the California Air Resources Board’s Emission Inventory 

Methodology (CARB, 2003). 

The justification for each proposed ratio in Table 1 is provided by source category in 

the sections below. In each case, reference is made to test reports that contain supporting 

data. 

Paved Roads 

For the dust component of particulate emissions from paved roads, a PM2.5/PM10

ratio of 0.15 is recommended. The proposed ratio is based on the factor-of-two bias in 

the cyclone/impactor data for the wind tunnel study, which tested western soils and road 

surface materials. As shown in Table 1, the current AP-42 ratio is 0.25. It should be 

recalled that the nondust component of paved road particulate emissions has been 

assigned a much higher ratio of 0.76, based on inputs from the EPA’s MOBILE 6 model. 
 

6

Unpaved Roads 

For the dust component of particulate emissions from unpaved roads, which 

dominates the total particulate emissions from this source category, a PM2.5/PM10 ratio of 

0.1 is recommended. The proposed ratio is justified from the test results of the wind 

tunnel study for a variety of western surface materials. It is also consistent with the 

factor-of-two bias in the cyclone/impactor data from the wind tunnel study and with the 

results of the prior field study that used dichotomous samplers as FRM devices (MRI, 

1997). 

Construction and Demolition 

The dust component of particulate emissions from construction and demolition 

dominate the total particulate emissions from this source category. A PM2.5/PM10 ratio of 

0.1 is recommended for dust emissions from construction and demolition. The proposed 

ratio is justified by the fact that the dominant dust source associated with construction 

and demolition projects is emissions from vehicle travel over unpaved surfaces. This is 

shown by case studies that calculate particulate emissions from representative 

construction activities (road, building, and nonbuilding construction). For example, the 

fine fraction ratio for scraper travel averages about 0.2 (Muleski et al., 2005), before 

correcting for the factor-of- two bias in the cyclone/impactor system. Moreover this 

includes the diesel emissions that are contained within the fine fraction component. 

It should be noted that if large open areas are disturbed (such as in land clearing) and 

left unprotected, and the areas are exposed to high winds, open area wind erosion can 

also be an important contributor to dust emissions from this source category. The 

recommended fine fraction ratio identified below should be used for the open area wind 

erosion component. 

Aggregate Handling and Storage Piles 

 

Although usually not a major source in comparison with traffic around storage piles, 

the transfer of aggregate associated with bucket loaders and unloaders or conveyor 

transfer points is addressed directly in this section of AP-42. A PM2.5/PM10 ratio of 0.15 

is recommended for transfer operations. This is half the current value in AP-42 and 

reflects adjustment for the factor-of-two bias in the cyclone/impactor test results. 

The dominant dust component of particulate emissions from aggregate handling and 

storage piles typically consists of loader and truck traffic around the storage piles. AP-42 

refers the reader to the unpaved roads section to find appropriate emission factors. A 

PM2.5/PM10 ratio of 0.1 is recommended for this source. The proposed ratio is consistent 

with that recommended above for traffic on unpaved surfaces. 
 

7

Industrial Wind Erosion 

For the dust component of particulate emissions from industrial wind erosion, a 

PM2.5/PM10 ratio of 0.15 is recommended. Industrial wind erosion is associated with 

crushed aggregate materials, such as coal or metallic ore piles. Examples would include 

open storage piles at mining operations. The proposed ratio is justified by portable wind 

tunnel tests of industrial aggregate materials which produced PM2.5/PM10 ratios averaging 

0.4, as indicated by the current AP-42 fine fraction ratio given in Table 1. When these 

results are corrected for the bias associated with the cyclone/impactor system at very high 

PM10 concentrations observed in the effluent from the portable wind tunnel (exceeding 

10,000 μg/m3), the result is 0.15. 

Agricultural Tilling 

For the dust component of particulate emissions from agricultural tilling and related 

land preparation activities, which dominates the total particulate emissions from this 

source category, no new PM2.5/PM10 ratio can be recommended at this time, because of 

the lack of published test data. However, the current factor of 0.2, as listed in Table 1, 

appears to be generally consistent with the results of the current wind tunnel tests. It was 

found that the agricultural soils tested in the wind tunnel produced slightly higher ratios 

than the other test materials. In addition, the dust plume core concentrations from 

agricultural operations are generally observed to be less intense because of the lower 

equipment speeds involved and the lack of repeated travel over the same routes. 

Open Area Wind Erosion 

For the dust component of particulate emissions from open area wind erosion (not 

currently addressed in AP-42), a PM2.5/PM10 ratio of 0.15 is recommended. Open area 

wind erosion is associated with exposed soils that have been disturbed, removing the 

protection afforded by natural crusting. Examples would include freshly tilled 

agricultural fields prior to planting of crops. The proposed ratio is justified by wind 

tunnel tests of exposed soils (MRI, 1994), which produced PM2.5/PM10 ratios averaging 

0.3. When these results are corrected for the bias associated with the cyclone/impactor 

system, the ratio becomes 0.15. This is consistent with the PM2.5/PM10 ratios in the range 

of 0.12 measured during dust storms on Owens Dry Lake (Ono, 2005). 

Specific Revisions to AP-42 

This section presents a listing of specific revisions to AP-42, for the purpose of 

incorporating the proposed PM2.5/PM10 ratios. As shown in Table 2, five subsections of 

AP-42 Section 13.2, Fugitive Dust, are impacted by the proposed changes. However, one 

of the five sections (13.2.3, Heavy Construction Operations) is impacted only indirectly 

because it refers to other sections of AP-42 for fugitive dust emission factors. 
 

8

In most cases, the change in the PM2.5/PM10 ratio is accomplished by changing the 

appropriate PM-2.5 particle size multiplier (k-factor) for the respective emission factor 

equation. In addition, the changes need to be referenced to the WRAP test report 

(Cowherd and Donaldson, 2005). 

Table 2. Specific revisions to AP-42 that are incorporated 

into the AP-42 sections included in Attachment A. 

Source 

category 

Subsection Title Revision Comments 

13.2.1.3 Predictive 

Emission Factor 

Equation 

In Table 13.2.1-1, reduce 

k values for PM-2.5 by 40 

percent, e.g., the new 

value is 1.1 g/VMT (and 

equivalent values for the 

other units) 

Add ref. number 

for WRAP test 

report 

13.2.1.5 Changes since 

Fifth Edition 

Modify statement (1) to 

reflect change in fine 

fraction 

13.2.1 Paved 

Roads 

References Add WRAP test report as 

Ref. 22 

13.2.2.2 Emission 

Calculation and 

Correction 

Parameters 

In Table 13.2.2-2, reduce 

k values for PM-2.5 by 

33%, e.g., the new value 

is 0.15 lb/VMT for 

industrial roads and 

0.18 lb/VMT for public 

roads (and equivalent 

values for the other units) 

Add ref. number 

for WRAP test 

report 

13.2.2 

Unpaved 

Roads 

13.2.2.4 Updates since 

Fifth Edition 

Add sentences describing 

change in fine fraction 

 References Add WRAP test report 

13.2.3 Heavy 

Construction 

Operations 

– – No changes required Refers to other 

AP-42 sections for 

emission factors 

13.2.4.3 Predictive 

Emission Factor 

Equations 

In k-factor table for 

Equation 1 for transfer 

operations, change PM2.5 multiplier to 0.053 

(dimensionless) 

Add ref. number 

for WRAP test 

report 

13.2.4 

Aggregate 

Handling and 

Storage Piles 

References Add WRAP test report 

13.2.5.2 Emissions and 

Correction 

Parameters 

In k-factor table for 

Equation 1, change 

PM-2.5 multiplier to 0.075 

(dimensionless) 

Add ref. number 

for WRAP test 

report 

13.2.5 

Industrial Wind 

Erosion 

References Add WRAP test report 
 

9

CONCLUSION 

 This study found that concentration measurements used to develop PM2.5 emission 

factors for AP-42 were biased high by a factor of two, as compared to PM2.5

measurements from FRM samplers. This factor-of-two bias helps to explain why 

researchers have often seen a similar discrepancy in the proportion of fugitive dust found 

in PM2.5 emission inventories and modeled ambient impacts, as compared to the 

proportion observed on ambient filter samples. This study also shows that the PM2.5 / 

PM10 ratios for fugitive dust should be in the range of 0.1 to 0.15. Currently, the fine 

fraction ratios in AP-42 range from 0.15 to 0.4 for most fugitive dust sources. 

 It is recommended that the results of this study by used to revise the AP-42 PM2.5

emission factors for the following four fugitive dust source categories: paved roads, 

unpaved roads (public and industrial), aggregate handling and storage piles, and 

industrial wind erosion (AP-42 Sections 13.2.1, 13.2.2, 13.2.4, & 13.2.5, respectively). 

Emission estimates for other fugitive dust producing activities, such as construction and 

demolition, will also be affected since they are based on these four source categories. It 

is recommended that revisions to the current AP-42 sections for these fugitive dust 

sources be adopted as shown in Attachment A to this report. 

IMPLICATIONS 

The proposed revisions to AP-42 are needed to ensure the most accurate PM2.5 and 

PM10 fugitive dust emissions inventories that are possible for regional haze regulatory 

purposes, given the available resources and the significant contribution of fugitive dust to 

visibility impairment. In particular, the revisions will affect the quantity of dust 

apportioned to the fine (PM2.5) versus coarse (PM2.5-10) size modes, which have 

significantly different effects on visibility and long-range transport potentials. This will 

reduce PM2.5 emission estimates for fugitive dust sources to about half their current level. 

It will also increase the coarse-mode size fraction for fugitive dust, which would be 

important in the event that a PM coarse standard is adopted by the US EPA and emission 

inventories are developed. 

The revisions will be helpful in developing accurate emission inventories for PM 

nonattainment, maintenance, and action plan areas throughout the country. Finally, the 

proposed modifications to the fine fractions associated with EPA’s AP-42 emission 

factors will ensure widespread availability of the most recent and accurate scientific 

information. 

References 
 

10

Cowherd, C. and J. Donaldson. 2005. Analysis of the Fine Fraction of Particulate 

Matter in Fugitive Dust. Final report prepared for the Western Governors’ Association, 

Western Regional Air Partnership (WRAP), MRI Project No. 110397, October 12, 2005. 

[Describes wind tunnel study to determine fine fraction ratios]

Midwest Research Institute. 1994. OU3 Wind tunnel Study: Test Report. Prepared for 

EG&G Rocky Flats, Golden CO. [Describes portable wind tunnel tests of emissions 

from soils and sediments] 

Midwest Research Institute. 1997. Fugitive Particulate Matter Emissions. Final report 

prepared for the U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards. Research Triangle Park NC. April, 1997. [Prior emission factor field 

study for paved and unpaved roads, comparing performance of cyclone/impactor 

system with reference method samplers for PM2.5] 

Muleski, G. E., C. Cowherd, and J. S. Kinsey. 2005. “Particulate Emissions from 

Construction Activities,” J. Air & Waste Manage. Assoc. 55: 772-783. [Summarizes 

field test results for emissions from major components of construction projects]

Ono, Duane. 2005. “Ambient PM2.5/PM10 ratios for Dust Events from the Keeler 

Dunes.” Great Basin UAPCD, Bishop, CA. [Describes FRM test results for highwind events on Owens Dry Lake] 

Pace, T. G. 2005. “Examination of Multiplier Used to Estimate PM2.5 Fugitive Dust 

Emissions from PM10.” Presented at the EPA Emission Inventory Conference. Las 

Vegas NV. April 2005. [Summarizes other field studies that can be used to develop 

PM2.5/PM10 ratios for fugitive dust emissions] 

USEPA. 2005. Compilation of Air Pollutant Emission Factors, AP-42. 6th Edition. 

Research Triangle Park, NC. [EPA’s emission factor handbook] 

CARB, 2003. Emission Inventory Procedural Manual Volume III: Methods for 

Assessing Area Source Emissions, California Air Resources Board, Sacramento, CA. 

November. [Summarizes the recommended calculation procedures for agricultural 

emissions and other sources] 

Midwest Research Institute. 1999. Estimating Particulate Matter Emissions from 

Construction Operations. Prepared for USEPA, Research Triangle Park NC, September. 

[Gives field test results for construction operations] 
11

Figure 1. Phase I test results show that the Cyclone/ Impactor method measured PM2.5 concentrations that were t

those measured by Federal Reference Method samplers when simultaneously exposed to the well-mixed dust env

tunnel. 

PM 2.5 Ratios for Cyclone and Partisols

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0123456

PM 10 Concentration (mg/m3)

Cyclone Concentration / Partisol Concentration ( PM 2.5) 
12

Figure 2. Phase II tests show that the PM2.5/PM10 ratio decreased with increasing PM concentrations, and could bthe range of 0.1 at concentrations that are typical of fugitive dust emission plumes. 

PM 2.5 / PM 10 Ratio vs PM 10 Concentration

0.0000.0500.1000.1500.2000.2500.3000.3500.4000.450

0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000

PM 10 Concentration (mg/m3)

PM 2.5 / PM 10 Ratio

AKSLaRTARS
Technical Memorandum

December 8, 2003

To: File

From: William B. Kuykendal

Emission Factor and Inventory Group

Office of Air Quality Planning and Standards

Environmental Protection Agency

Subject: Decisions on Final AP-42 Section 13.2.2 Unpaved Roads

In October 2001, EPA published a draft Section 13.2.2 “Unpaved Roads” for AP-42 and

requested comments. This memorandum documents the decisions that EPA has taken that have

resulted in the November 2003 final Section 13.2.2 “Unpaved Roads” for AP-42.

EPA received a number of comments on the October 2001 draft section. EPA evaluated these

comments and concluded that additional technical work was needed to resolve some of the issues

raised by the commentors. Resource constraints have prevented EPA from doing the technical

work; it is unlikely that resources will become available in the near future. Since there are two

versions of the unpaved road section available (Final Section dated September 1998, Draft

Section dated October 2001), EPA has been recommending that for most applications, the draft

October 2001 section should be used. EPA has decided to finalize the October 2001 draft with

currently available information, rather than continue with the ambiguous situation of having two

sections. When resources become available, EPA will revise the unpaved road section in a more

comprehensive manner.

EPA has made two changes to the October 2001 draft in the December 2003 final section:

1. The October 2001 draft had two options for the constants and exponents in the

“Public Roads” equation, Equation 1b. A complete discussion of these options is

included in the Technical Memorandum from G. Muleski to B. Kuykendal on

Unpaved Roads, dated September 27, 2001. EPA has decided to select “Option

1" because it gives a somewhat better empirical correlation.

2. In the December 2003 final paved road section, EPA included a term in Equation

1 that subtracted the direct vehicle components of engine exhaust, brake wear and

tire wear from the total emissions from the road. These components, engine

exhaust, brake and tire wear are now estimated using MOBILE6.2. The result

being that the AP-42 paved road equation now estimates reentrained road dust

emissions only rather that all components emitted by vehicle activity on paved

roads. A complete discussion is included in the Technical Memorandum from P.

Hemmer, E.H. Pechan & Associates, Inc., to B. Kuykendal, U. S. Environmental

Protection Agency, August, 21, 2003. The same logic also applies in the case of
unpaved roads and EPA has included the same term in the unpaved roads public

road equation, Equation 1b. Users should note that the consequences in the

unpaved road case are much less than with paved roads. In the case of unpaved

roads, reentrained road dust dominates all of the other emission components, in

most cases by at least two orders of magnitude. Thus the calculation of a negative

value using the new unpaved road equation should never occur.
13.2.3 Heavy Construction Operations

13.2.3.1 General

Heavy construction is a source of dust emissions that may have substantial temporary impact

on local air quality. Building and road construction are 2 examples of construction activities with high

emissions potential. Emissions during the construction of a building or road can be associated with

land clearing, drilling and blasting, ground excavation, cut and fill operations (i.e., earth moving), and

construction of a particular facility itself. Dust emissions often vary substantially from day to day,

depending on the level of activity, the specific operations, and the prevailing meteorological

conditions. A large portion of the emissions results from equipment traffic over temporary roads at

the construction site.

The temporary nature of construction differentiates it from other fugitive dust sources as to

estimation and control of emissions. Construction consists of a series of different operations, each

with its own duration and potential for dust generation. In other words, emissions from any single

construction site can be expected (1) to have a definable beginning and an end and (2) to vary

substantially over different phases of the construction process. This is in contrast to most other

fugitive dust sources, where emissions are either relatively steady or follow a discernable annual

cycle. Furthermore, there is often a need to estimate areawide construction emissions, without regard

to the actual plans of any individual construction project. For these reasons, following are methods by

which either areawide or site-specific emissions may be estimated.

13.2.3.2 Emissions And Correction Parameters

The quantity of dust emissions from construction operations is proportional to the area of land

being worked and to the level of construction activity. By analogy to the parameter dependence

observed for other similar fugitive dust sources,1 one can expect emissions from heavy construction

operations to be positively correlated with the silt content of the soil (that is, particles smaller than

75 micrometers [μm] in diameter), as well as with the speed and weight of the average vehicle, and to

be negatively correlated with the soil moisture content.

13.2.3.3 Emission Factors

Only 1 set of field studies has been performed that attempts to relate the emissions from

construction directly to an emission factor.1-2 Based on field measurements of total suspended

particulate (TSP) concentrations surrounding apartment and shopping center construction projects, the

approximate emission factors for construction activity operations are:

E = 2.69 megagrams (Mg)/hectare/month of activity

E = 1.2 tons/acre/month of activity

These values are most useful for developing estimates of overall emissions from construction

scattered throughout a geographical area. The value is most applicable to construction operations with:

(1) medium activity level, (2) moderate silt contents, and (3) semiarid climate. Test data were not

sufficient to derive the specific dependence of dust emissions on correction parameters. Because the

above emission factor is referenced to TSP, use of this factor to estimate particulate matter (PM) no

greater than 10 μm in aerodynamic diameter (PM-10) emissions will result in conservatively high

1/95 Miscellaneous Sources 13.2.3-1
estimates. Also, because derivation of the factor assumes that construction activity occurs 30 days per

month, the above estimate is somewhat conservatively high for TSP as well.

Although the equation above represents a relatively straightforward means of preparing an

areawide emission inventory, at least 2 features limit its usefulness for specific construction sites.

First, the conservative nature of the emission factor may result in too high an estimate for PM-10 to be

of much use for a specific site under consideration. Second, the equation provides neither information

about which particular construction activities have the greatest emission potential nor guidance for

developing an effective dust control plan.

For these reasons, it is strongly recommended that when emissions are to be estimated for a

particular construction site, the construction process be broken down into component operations.

(Note that many general contractors typically employ planning and scheduling tools, such as critical

path method [CPM], that make use of different sequential operations to allocate resources.) This

approach to emission estimation uses a unit or phase method to consider the more basic dust sources

of vehicle travel and material handling. That is to say, the construction project is viewed as consisting

of several operations, each involving traffic and material movements, and emission factors from other

AP-42 sections are used to generate estimates. Table 13.2.3-1 displays the dust sources involved with

construction, along with the recommended emission factors.3

In addition to the on-site activities shown in Table 13.2.3-1, substantial emissions are possible

because of material tracked out from the site and deposited on adjacent paved streets. Because all

traffic passing the site (i. e., not just that associated with the construction) can resuspend the deposited

material, this "secondary" source of emissions may be far more important than all the dust sources

actually within the construction site. Furthermore, this secondary source will be present during all

construction operations. Persons developing construction site emission estimates must consider the

potential for increased adjacent emissions from off-site paved roadways (see Section 13.2.1, "Paved

Roads"). High wind events also can lead to emissions from cleared land and material stockpiles.

Section 13.2.5, "Industrial Wind Erosion", presents an estimation methodology that can be used for

such sources at construction sites.

13.2.3.4 Control Measures4

Because of the relatively short-term nature of construction activities, some control measures

are more cost effective than others. Wet suppression and wind speed reduction are 2 common

methods used to control open dust sources at construction sites, because a source of water and material

for wind barriers tend to be readily available on a construction site. However, several other forms of

dust control are available.

Table 13.2.3-2 displays each of the preferred control measures, by dust source.3-4 Because

most of the controls listed in the table modify independent variables in the emission factor models, the

effectiveness can be calculated by comparing controlled and uncontrolled emission estimates from

Table 13.2.3-1. Additional guidance on controls is provided in the AP-42 sections from which the

recommended emission factors were taken, as well as in other documents, such as Reference 4.

13.2.3-2 EMISSION FACTORS 1/95
1/95 Miscellaneous Sources 13.2.3-3 Table 13.2.3-1. RECOMMENDED EMISSION FACTORS FOR CONSTRUCTION OPERATIONSa Construction Phase Dust-generating Activities Recommended Emission Factor Comments 

I. Demolition and debris removal 1. Demolition of buildings or other (natural) obstacles such as trees, boulders, etc. a. Mechanical dismemberment ("headache ball") of existing structures NA 

b. Implosion of existing structures NA 

c. Drilling and blasting of soil Drilling factor in Table 11.9-4 Blasting factor NA Blasting factor in Tables 11.9

and 11.9-2 not considered appropriate for general construction activities 

d. General land clearing Dozer equation (overburden) in Tables 11.9-1 and 11.9-2 

2. Loading of debris into trucks Material handling emission factor equation in Section 13.2.4 3. Truck transport of debris Unpaved road emission factor in Section 13.2.2, or paved road emission factor in Section 13.2.1 4. Truck unloading of debris Material handling emission factor equation in Section 13.2.4 May occur offsite 
13.2.3-4 EMISSION FACTORS 1/95 Table 13.2.3-1 (cont.). Construction Phase Dust-generating Activities Recommended Emission Factor Comments 

II. Site Preparation (earth moving) 1. Bulldozing Dozer equation (overburden) in Tables 11.9-1 and 11.9-2 

2. Scrapers unloading topsoil Scraper unloading factor in Table 11.9-4 3. Scrapers in travel Scraper (travel mode) expression in Tables 11.9-1 and 11.9-2 4. Scrapers removing topsoil 5.7 kg/vehicle kilometer traveled (VKT) (20.2 lb/vehicle mile traveled [VMT]) 5. Loading of excavated material into trucks Material handling emission factor equation in Section 13.2.4 6. Truck dumping of fill material, road base, or other materials Material handling emission factor equation in Section 13.2.4 May occur offsite 7. Compacting Dozer equation in Tables 11.9-1 and 11.9-2 Emission factor downgraded because o

differences in operatinequipment 

8. Motor grading Grading equation in Tables 11.9-1 and 11.9-2 
1/95 Miscellaneous Sources 13.2.3-5 Table 13.2.3-1 (cont.). Construction Phase Dust-generating Activities Recommended Emission Factor Comments 

III. General Construction 1. Vehicular traffic Unpaved road emission factor in Section 13.2.2, or paved road emission factor in Section 13.2.1 

2. Portable plants a. Crushing Factors for similar material/operations in Section 11.19.2 

b. Screening Factors for similar material/operations in Section 11.19.2 c. Material transfers Material handling emission factor equation in Section 13.2.4 

3. Other operations Factors for similar material/operations in the Mineral Products Industry, Chapter 11 of this document 

a NA = not applicable. b Refers to how many additional letters the emission factor should be downrated (beyond the guidance given in the other sec

application to construction activities. For example, "-2" means that an A-rated factor should be considered of C quality in 

construction emissions. All emission factors assumed to have site-specific input values; otherwise, additional downgrading be employed. Note that no rating can be lower than E. c First value for cases with independent variables within range given in AP-42 section; second value for cases with at least 1 

range. d Rating for emission factor given. Reference 5. e In the event that individual operations cannot be identified, one may very conservatively overestimate PM-10 emissions by 
Table 13.2.3-2. CONTROL OPTIONS FOR GENERAL CONSTRUCTION

OPEN SOURCES OF PM-10

Emission Source Recommended Control Method(s)

Debris handling Wind speed reduction

Wet suppressiona

Truck transportb Wet suppression

Paving

Chemical stabilizationc

Bulldozers Wet suppressiond

Pan scrapers Wet suppression of travel routes

Cut/fill material handling Wind speed reduction

Wet suppression

Cut/fill haulage Wet suppression

Paving

Chemical stabilization

General construction Wind speed reduction

Wet suppression

Early paving of permanent roads

a Dust control plans should contain precautions against watering programs that confound trackout

problems. b Loads could be covered to avoid loss of material in transport, especially if material is transported

offsite. c Chemical stabilization usually cost-effective for relatively long-term or semipermanent unpaved

roads.

d Excavated materials may already be moist and not require additional wetting. Furthermore, most

soils are associated with an "optimum moisture" for compaction.

References For Section 13.2.3

1. C. Cowherd, Jr., et al., Development Of Emissions Factors For Fugitive Dust Sources,

EPA-450/3-74-03, U. S. Environmental Protection Agency, Research Triangle Park, NC,

June 1974.

2. G. A. Jutze, et al., Investigation Of Fugitive Dust Sources Emissions And Control,

EPA-450/3-74-036a, U. S. Environmental Protection Agency, Research Triangle Park, NC,

June 1974.

3. Background Documentation For AP-42 Section 11.2.4, Heavy Construction Operations, EPA

Contract No. 69-D0-0123, Midwest Research Institute, Kansas City, MO, April 1993.

4. C. Cowherd, et al., Control Of Open Fugitive Dust Sources, EPA-450/3-88-008,

U. S. Environmental Protection Agency, Research Triangle Park, NC, September 1988.

13.2.3-6 EMISSION FACTORS 1/95
5. M. A. Grelinger, et al., Gap Filling PM-10 Emission Factors For Open Area Fugitive Dust

Sources, EPA-450/4-88-003, U. S. Environmental Protection Agency, Research Triangle Park,

NC, March 1988.

1/95 Miscellaneous Sources 13.2.3-7
11/06 Miscellaneous Sources 13.2.4-1

13.2.4 Aggregate Handling And Storage Piles

13.2.4.1 General

Inherent in operations that use minerals in aggregate form is the maintenance of outdoor

storage piles. Storage piles are usually left uncovered, partially because of the need for frequent

material transfer into or out of storage.

Dust emissions occur at several points in the storage cycle, such as material loading onto the

pile, disturbances by strong wind currents, and loadout from the pile. The movement of trucks and

loading equipment in the storage pile area is also a substantial source of dust.

13.2.4.2 Emissions And Correction Parameters

The quantity of dust emissions from aggregate storage operations varies with the volume of

aggregate passing through the storage cycle. Emissions also depend on 3 parameters of the condition

of a particular storage pile: age of the pile, moisture content, and proportion of aggregate fines.

When freshly processed aggregate is loaded onto a storage pile, the potential for dust emissions

is at a maximum. Fines are easily disaggregated and released to the atmosphere upon exposure to air

currents, either from aggregate transfer itself or from high winds. As the aggregate pile weathers,

however, potential for dust emissions is greatly reduced. Moisture causes aggregation and cementation

of fines to the surfaces of larger particles. Any significant rainfall soaks the interior of the pile, and

then the drying process is very slow.

Silt (particles equal to or less than 75 micrometers [:m] in diameter) content is determined by

measuring the portion of dry aggregate material that passes through a 200-mesh screen, using

ASTM-C-136 method.1 Table 13.2.4-1 summarizes measured silt and moisture values for industrial

aggregate materials.
13.2.4-2 EMISSION FACTORS 11/06 Table 13.2.4-1. TYPICAL SILT AND MOISTURE CONTENTS OF MATERIALS AT VARIOUS INDUST

Industry No. Of Facilities Material Silt Content (%) Moistu

No. Of Samples Range Mean No. Of Samples 

Iron and steel production 9 Pellet ore 13 1.3 - 13 4.3 11 

Lump ore 9 2.8 - 19 9.5 6 Coal 12 2.0 - 7.7 4.6 11 Slag 3 3.0 - 7.3 5.3 3 Flue dust 3 2.7 - 23 13 1 Coke breeze 2 4.4 - 5.4 4.9 2 Blended ore 1 — 15 1 Sinter 1 — 0.7 0 Limestone 3 0.4 - 2.3 1.0 2 

Stone quarrying and processing 2 Crushed limestone 2 1.3 - 1.9 1.6 2 

Various limestone products 8 0.8 - 14 3.9 8 

Taconite mining and processing 1 Pellets 9 2.2 - 5.4 3.4 7 

Tailings 2 ND 11 1 

Western surface coal mining 4 Coal 15 3.4 - 16 6.2 7 

Overburden 15 3.8 - 15 7.5 0 Exposed ground 3 5.1 - 21 15 3 

Coal-fired power plant 1 Coal (as received) 60 0.6 - 4.8 2.2 59 Municipal solid waste landfills 4 Sand 1 — 2.6 1 

Slag 2 3.0 - 4.7 3.8 2 Cover 5 5.0 - 16 9.0 5 Clay/dirt mix 1 — 9.2 1 Clay 2 4.5 - 7.4 6.0 2 Fly ash 4 78 - 81 80 4 Misc. fill materials 1 — 12 1 

a References 1-10. ND = no data.
11/06 Miscellaneous Sources 13.2.4-3

13.2.4.3 Predictive Emission Factor Equations

Total dust emissions from aggregate storage piles result from several distinct source activities

within the storage cycle:

1. Loading of aggregate onto storage piles (batch or continuous drop operations).

2. Equipment traffic in storage area.

3. Wind erosion of pile surfaces and ground areas around piles.

4. Loadout of aggregate for shipment or for return to the process stream (batch or continuous

drop operations). 

Either adding aggregate material to a storage pile or removing it usually involves dropping the

material onto a receiving surface. Truck dumping on the pile or loading out from the pile to a truck

with a front-end loader are examples of batch drop operations. Adding material to the pile by a

conveyor stacker is an example of a continuous drop operation.
13.2.4-4 EMISSION FACTORS 11/06

(1)

The quantity of particulate emissions generated by either type of drop operation, per kilogram

(kg) (ton) of material transferred, may be estimated, with a rating of A, using the following empirical

expression:11

where:

E = emission factor

k = particle size multiplier (dimensionless)

U = mean wind speed, meters per second (m/s) (miles per hour [mph])

M = material moisture content (%)

The particle size multiplier in the equation, k, varies with aerodynamic particle size range, as follows:

Aerodynamic Particle Size Multiplier (k) For Equation 1

< 30 :m < 15 :m < 10 :m < 5 :m < 2.5 :m

0.74 0.48 0.35 0.20 0.053a

a

 Multiplier for < 2.5 :m taken from Reference 14.

The equation retains the assigned quality rating if applied within the ranges of source

conditions that were tested in developing the equation, as follows. Note that silt content is included,

even though silt content does not appear as a correction parameter in the equation. While it is

reasonable to expect that silt content and emission factors are interrelated, no significant correlation

between the 2 was found during the derivation of the equation, probably because most tests with high

silt contents were conducted under lower winds, and vice versa. It is recommended that estimates from

the equation be reduced 1 quality rating level if the silt content used in a particular application falls

outside the range given:

Ranges Of Source Conditions For Equation 1

Silt Content

(%)

Moisture Content

(%)

Wind Speed

m/s mph

0.44 - 19 0.25 - 4.8 0.6 - 6.7 1.3 - 15

To retain the quality rating of the equation when it is applied to a specific facility, reliable

correction parameters must be determined for specific sources of interest. The field and laboratory

procedures for aggregate sampling are given in Reference 3. In the event that site-specific values for
11/06 Miscellaneous Sources 13.2.4-5

correction parameters cannot be obtained, the appropriate mean from Table 13.2.4-1 may be used, but

the quality rating of the equation is reduced by 1 letter.

For emissions from equipment traffic (trucks, front-end loaders, dozers, etc.) traveling between

or on piles, it is recommended that the equations for vehicle traffic on unpaved surfaces be used (see

Section 13.2.2). For vehicle travel between storage piles, the silt value(s) for the areas among the piles

(which may differ from the silt values for the stored materials) should be used.

Worst-case emissions from storage pile areas occur under dry, windy conditions. Worst-case

emissions from materials-handling operations may be calculated by substituting into the equation

appropriate values for aggregate material moisture content and for anticipated wind speeds during the

worst case averaging period, usually 24 hours. The treatment of dry conditions for Section 13.2.2,

vehicle traffic, "Unpaved Roads", follows the methodology described in that section centering on

parameter p. A separate set of nonclimatic correction parameters and source extent values

corresponding to higher than normal storage pile activity also may be justified for the worst-case

averaging period.

13.2.4.4 Controls12-13

Watering and the use of chemical wetting agents are the principal means for control of

aggregate storage pile emissions. Enclosure or covering of inactive piles to reduce wind erosion can

also reduce emissions. Watering is useful mainly to reduce emissions from vehicle traffic in the

storage pile area. Watering of the storage piles themselves typically has only a very temporary slight

effect on total emissions. A much more effective technique is to apply chemical agents (such as

surfactants) that permit more extensive wetting. Continuous chemical treating of material loaded onto

piles, coupled with watering or treatment of roadways, can reduce total particulate emissions from

aggregate storage operations by up to 90 percent.12

References For Section 13.2.4

1. C. Cowherd, Jr., et al., Development Of Emission Factors For Fugitive Dust Sources,

EPA-450/3-74-037, U. S. Environmental Protection Agency, Research Triangle Park, NC,

June 1974.

2. R. Bohn, et al., Fugitive Emissions From Integrated Iron And Steel Plants, EPA-600/2-78-050,

U. S. Environmental Protection Agency, Cincinnati, OH, March 1978.

3. C. Cowherd, Jr., et al., Iron And Steel Plant Open Dust Source Fugitive Emission Evaluation,

EPA-600/2-79-103, U. S. Environmental Protection Agency, Cincinnati, OH, May 1979.

4. Evaluation Of Open Dust Sources In The Vicinity Of Buffalo, New York, EPA Contract

No. 68-02-2545, Midwest Research Institute, Kansas City, MO, March 1979.

5. C. Cowherd, Jr., and T. Cuscino, Jr., Fugitive Emissions Evaluation, MRI-4343-L, Midwest

Research Institute, Kansas City, MO, February 1977.

6. T. Cuscino, Jr., et al., Taconite Mining Fugitive Emissions Study, Minnesota Pollution Control

Agency, Roseville, MN, June 1979.

7. Improved Emission Factors For Fugitive Dust From Western Surface Coal Mining Sources,

2 Volumes, EPA Contract No. 68-03-2924, PEDCo Environmental, Kansas City, MO, and

Midwest Research Institute, Kansas City, MO, July 1981.

8. Determination Of Fugitive Coal Dust Emissions From Rotary Railcar Dumping, TRC, Hartford,

CT, May 1984.

9. PM-10 Emission Inventory Of Landfills In the Lake Calumet Area, EPA Contract 

No. 68-02-3891, Midwest Research Institute, Kansas City, MO, September 1987.
13.2.4-6 EMISSION FACTORS 11/06

10. Chicago Area Particulate Matter Emission Inventory — Sampling And Analysis, EPA Contract

No. 68-02-4395, Midwest Research Institute, Kansas City, MO, May 1988.

11. Update Of Fugitive Dust Emission Factors In AP-42 Section 11.2, EPA Contract 

No. 68-02-3891, Midwest Research Institute, Kansas City, MO, July 1987.

12. G. A. Jutze, et al., Investigation Of Fugitive Dust Sources Emissions And Control,

EPA-450/3-74-036a, U. S. Environmental Protection Agency, Research Triangle Park, NC,

June 1974.

13. C. Cowherd, Jr., et al., Control Of Open Fugitive Dust Sources, EPA-450/3-88-008,

U. S. Environmental Protection Agency, Research Triangle Park, NC, September 1988.

14. C. Cowherd, Background Document for Revisions to Fine Fraction Ratios &sed for AP-42

Fugitive Dust Emission Factors. Prepared by Midwest Research Institute for Western

Governors Association, Western Regional Air Partnership, Denver, CO, February 1, 2006.
13.2.5 Industrial Wind Erosion

13.2.5.1 General1-3

Dust emissions may be generated by wind erosion of open aggregate storage piles and exposed

areas within an industrial facility. These sources typically are characterized by nonhomogeneous

surfaces impregnated with nonerodible elements (particles larger than approximately 1 centimeter [cm]

in diameter). Field testing of coal piles and other exposed materials using a portable wind tunnel has

shown that (a) threshold wind speeds exceed 5 meters per second (m/s) (11 miles per hour [mph]) at

15 cm above the surface or 10 m/s (22 mph) at 7 m above the surface, and (b) particulate emission

rates tend to decay rapidly (half-life of a few minutes) during an erosion event. In other words, these

aggregate material surfaces are characterized by finite availability of erodible material (mass/area)

referred to as the erosion potential. Any natural crusting of the surface binds the erodible material,

thereby reducing the erosion potential.

13.2.5.2 Emissions And Correction Parameters

If typical values for threshold wind speed at 15 cm are corrected to typical wind sensor height

(7 - 10 m), the resulting values exceed the upper extremes of hourly mean wind speeds observed in

most areas of the country. In other words, mean atmospheric wind speeds are not sufficient to sustain

wind erosion from flat surfaces of the type tested. However, wind gusts may quickly deplete a

substantial portion of the erosion potential. Because erosion potential has been found to increase

rapidly with increasing wind speed, estimated emissions should be related to the gusts of highest

magnitude.

The routinely measured meteorological variable that best reflects the magnitude of wind gusts

is the fastest mile. This quantity represents the wind speed corresponding to the whole mile of wind

movement that has passed by the 1 mile contact anemometer in the least amount of time. Daily

measurements of the fastest mile are presented in the monthly Local Climatological Data (LCD)

summaries. The duration of the fastest mile, typically about 2 minutes (for a fastest mile of 30 mph),

matches well with the half-life of the erosion process, which ranges between 1 and 4 minutes. It

should be noted, however, that peak winds can significantly exceed the daily fastest mile.

The wind speed profile in the surface boundary layer is found to follow a logarithmic

distribution:

where:

u(z) (1) u

0.4

ln z

zo

(z > zo)

u = wind speed, cm/s

u* = friction velocity, cm/s

z = height above test surface, cm

zo = roughness height, cm

0.4 = von Karman’s constant, dimensionless

11/06 Miscellaneous Sources 13.2.5-1
The friction velocity (u*) is a measure of wind shear stress on the erodible surface, as determined from

the slope of the logarithmic velocity profile. The roughness height (zo) is a measure of the roughness

of the exposed surface as determined from the y intercept of the velocity profile, i. e., the height at

which the wind speed is zero. These parameters are illustrated in Figure 13.2.5-1 for a roughness

height of 0.1 cm.

Figure 13.2.5-1. Illustration of logarithmic velocity profile.

Emissions generated by wind erosion are also dependent on the frequency of disturbance of the

erodible surface because each time that a surface is disturbed, its erosion potential is restored. A

disturbance is defined as an action that results in the exposure of fresh surface material. On a storage

pile, this would occur whenever aggregate material is either added to or removed from the old surface.

A disturbance of an exposed area may also result from the turning of surface material to a depth

exceeding the size of the largest pieces of material present.

13.2.5.3 Predictive Emission Factor Equation4

The emission factor for wind-generated particulate emissions from mixtures of erodible and

nonerodible surface material subject to disturbance may be expressed in units of grams per square

meter (g/m2) per year as follows:

Emission factor (2) k

N

i 1

Pi

13.2.5-2 EMISSION FACTORS 11/06
where:

k = particle size multiplier

N = number of disturbances per year

Pi = erosion potential corresponding to the observed (or probable) fastest mile of wind for

the ith period between disturbances, g/m2

The particle size multiplier (k) for Equation 2 varies with aerodynamic particle size, as follows:

Aerodynamic Particle Size Multipliers For Equation 2

30 μm <15 μm <10 μm <2.5 μm

1.0 0.6 0.5 0.075a

This distribution of particle size within the under 30 micrometer (μm) fraction is comparable to

the distributions reported for other fugitive dust sources where wind speed is a factor. This is

illustrated, for example, in the distributions for batch and continuous drop operations encompassing a

number of test aggregate materials (see Section 13.2.4).

In calculating emission factors, each area of an erodible surface that is subject to a different

frequency of disturbance should be treated separately. For a surface disturbed daily, N = 365 per year,

and for a surface disturbance once every 6 months, N = 2 per year.

The erosion potential function for a dry, exposed surface is:

where:

(3)

P = 58 (u ut )

2 25 (u ut )

P = 0 for u ≤ut

u* = friction velocity (m/s)

ut = threshold friction velocity (m/s)

Because of the nonlinear form of the erosion potential function, each erosion event must be treated

separately.

Equations 2 and 3 apply only to dry, exposed materials with limited erosion potential. The

resulting calculation is valid only for a time period as long or longer than the period between

disturbances. Calculated emissions represent intermittent events and should not be input directly into

dispersion models that assume steady-state emission rates.

For uncrusted surfaces, the threshold friction velocity is best estimated from the dry aggregate

structure of the soil. A simple hand sieving test of surface soil can be used to determine the mode of

the surface aggregate size distribution by inspection of relative sieve catch amounts, following the

procedure described below.

11/06 Miscellaneous Sources 13.2.5-3
FIELD PROCEDURE FOR DETERMINATION OF THRESHOLD FRICTION VELOCITY

(from a 1952 laboratory procedure published by W. S. Chepil):

1. Prepare a nest of sieves with the following openings: 4 mm, 2 mm, 1 mm, 0.5 mm,

and 0.25 mm. Place a collector pan below the bottom (0.25 mm) sieve.

2. Collect a sample representing the surface layer of loose particles (approximately 1 cm

in depth, for an encrusted surface), removing any rocks larger than about 1 cm in

average physical diameter. The area to be sampled should be not less than 30 cm by

30 cm.

3. Pour the sample into the top sieve (4-mm opening), and place a lid on the top.

4. Move the covered sieve/pan unit by hand, using a broad circular arm motion in the

horizontal plane. Complete 20 circular movements at a speed just necessary to achieve

some relative horizontal motion between the sieve and the particles.

5. Inspect the relative quantities of catch within each sieve, and determine where the

mode in the aggregate size distribution lies, i. e., between the opening size of the sieve

with the largest catch and the opening size of the next largest sieve.

6. Determine the threshold friction velocity from Table 13.2.5-1.

The results of the sieving can be interpreted using Table 13.2.5-1. Alternatively, the threshold friction

velocity for erosion can be determined from the mode of the aggregate size distribution using the

graphical relationship described by Gillette.5-6 If the surface material contains nonerodible elements

that are too large to include in the sieving (i. e., greater than about 1 cm in diameter), the effect of the

elements must be taken into account by increasing the threshold friction velocity.10

Table 13.2.5-1 (Metric Units). FIELD PROCEDURE FOR DETERMINATION OF

THRESHOLD FRICTION VELOCITY

Tyler Sieve No. Opening (mm) Midpoint (mm) u*

t (cm/s)

5 4

9 2 3 100

16 1 1.5 76

32 0.5 0.75 58

60 0.25 0.375 43

Threshold friction velocities for several surface types have been determined by field

measurements with a portable wind tunnel. These values are presented in Table 13.2.5-2.

13.2.5-4 EMISSION FACTORS 11/06
Table 13.2.5-2 (Metric Units). THRESHOLD FRICTION VELOCITIES

Material

Threshold

Friction

Velocity

(m/s)

Roughness

Height (cm)

Threshold Wind Velocity At

10 m (m/s)

zo = Act zo = 0.5 cm

Overburdena 1.02 0.3 21 19

Scoria (roadbed material)a 1.33 0.3 27 25

Ground coal (surrounding

coal pile)a 0.55 0.01 16 10

Uncrusted coal pilea 1.12 0.3 23 21

Scraper tracks on coal pilea,b 0.62 0.06 15 12

Fine coal dust on concrete padc 0.54 0.2 11 10

a Western surface coal mine. Reference 2.

b Lightly crusted. c Eastern power plant. Reference 3.

The fastest mile of wind for the periods between disturbances may be obtained from the

monthly LCD summaries for the nearest reporting weather station that is representative of the site in

question.7 These summaries report actual fastest mile values for each day of a given month. Because

the erosion potential is a highly nonlinear function of the fastest mile, mean values of the fastest mile

are inappropriate. The anemometer heights of reporting weather stations are found in Reference 8, and

should be corrected to a 10-m reference height using Equation 1.

To convert the fastest mile of wind (u+) from a reference anemometer height of 10 m to the

equivalent friction velocity (u*), the logarithmic wind speed profile may be used to yield the following

equation:

where:

(4) u 0.053 u10

u* = friction velocity (m/s)

u = fastest mile of reference anemometer for period between disturbances (m/s) 10

This assumes a typical roughness height of 0.5 cm for open terrain. Equation 4 is restricted to

large relatively flat piles or exposed areas with little penetration into the surface wind layer.

If the pile significantly penetrates the surface wind layer (i. e., with a height-to-base ratio

exceeding 0.2), it is necessary to divide the pile area into subareas representing different degrees of

exposure to wind. The results of physical modeling show that the frontal face of an elevated pile is

exposed to wind speeds of the same order as the approach wind speed at the top of the pile.

11/06 Miscellaneous Sources 13.2.5-5
For 2 representative pile shapes (conical and oval with flattop, 37-degree side slope), the ratios

of surface wind speed (us

) to approach wind speed (ur) have been derived from wind tunnel studies.9

The results are shown in Figure 13.2.5-2 corresponding to an actual pile height of 11 m, a reference

(upwind) anemometer height of 10 m, and a pile surface roughness height (zo) of 0.5 cm. The

measured surface winds correspond to a height of 25 cm above the surface. The area fraction within

each contour pair is specified in Table 13.2.5-3.

Table 13.2.5-3. SUBAREA DISTRIBUTION FOR REGIMES OF us

/ur

a

Pile Subarea

Percent Of Pile Surface Area

Pile A Pile B1 Pile B2 Pile B3

0.2a 5 5 3 3

0.2b 35 2 28 25

0.2c NA 29 NA NA

0.6a 48 26 29 28

0.6b NA 24 22 26

0.9 12 14 15 14

1.1 NA NA 3 4

a NA = not applicable.

The profiles of us

/ur in Figure 13.2.5-2 can be used to estimate the surface friction velocity

distribution around similarly shaped piles, using the following procedure:

1. Correct the fastest mile value (u+) for the period of interest from the anemometer

height (z) to a reference height of 10 m using a variation of Equation 1: u10

where a typical roughness height of 0.5 cm (0.005 m) has been assumed. If a siteu (5) 10 u ln (10/0.005)

ln (z/0.005)

specific roughness height is available, it should be used.

2. Use the appropriate part of Figure 13.2.5-2 based on the pile shape and orientation to

the fastest mile of wind, to obtain the corresponding surface wind speed distribution

(us )

u (6) s

(us)

ur

u10

13.2.5-6 EMISSION FACTORS 11/06
Figure 13.2.5-2. Contours of normalized surface windspeeds, us

/ur.

11/06 Miscellaneous Sources 13.2.5-7
3. For any subarea of the pile surface having a narrow range of surface wind speed, use a

variation of Equation 1 to calculate the equivalent friction velocity (u*):

(7) u

0.4us

25

ln0.5

0.10u s

From this point on, the procedure is identical to that used for a flat pile, as described above.

Implementation of the above procedure is carried out in the following steps:

1. Determine threshold friction velocity for erodible material of interest (see

Table 13.2.5-2 or determine from mode of aggregate size distribution).

2. Divide the exposed surface area into subareas of constant frequency of disturbance (N).

3. Tabulate fastest mile values (u+) for each frequency of disturbance and correct them to

10 m (u+) using Equation 5.5 10

4. Convert fastest mile values (u10) to equivalent friction velocities (u*), taking into

account (a) the uniform wind exposure of nonelevated surfaces, using Equation 4, or

(b) the nonuniform wind exposure of elevated surfaces (piles), using Equations 6 and

7.

5. For elevated surfaces (piles), subdivide areas of constant N into subareas of constant

u* (i. e., within the isopleth values of us

/ur in Figure 13.2.5-2 and Table 13.2.5-3) and

determine the size of each subarea.

6. Treating each subarea (of constant N and u*) as a separate source, calculate the erosion

potential (Pi

) for each period between disturbances using Equation 3 and the emission

factor using Equation 2.

7. Multiply the resulting emission factor for each subarea by the size of the subarea, and

add the emission contributions of all subareas. Note that the highest 24-hour (hr)

emissions would be expected to occur on the windiest day of the year. Maximum

emissions are calculated assuming a single event with the highest fastest mile value for

the annual period.

The recommended emission factor equation presented above assumes that all of the erosion

potential corresponding to the fastest mile of wind is lost during the period between disturbances.

Because the fastest mile event typically lasts only about 2 minutes, which corresponds roughly to the

half-life for the decay of actual erosion potential, it could be argued that the emission factor

overestimates particulate emissions. However, there are other aspects of the wind erosion process that

offset this apparent conservatism:

1. The fastest mile event contains peak winds that substantially exceed the mean value

for the event.

2. Whenever the fastest mile event occurs, there are usually a number of periods of

13.2.5-8 EMISSION FACTORS 11/06
11/06 Miscellaneous Sources 13.2.5-9

S rr h

m

= +

= +

=

π 2 2

2 2

2

314 14 6 14 6 110

838

. ( .) ( .) ( .)

slightly lower mean wind speed that contain peak gusts of the same order as the fastest mile wind

speed.

 Of greater concern is the likelihood of overprediction of wind erosion emissions in the case of

surfaces disturbed infrequently in comparison to the rate of crust formation.

13.2.5.4 Example 1: Calculation for wind erosion emissions from conically shaped coal pile

A coal burning facility maintains a conically shaped surge pile 11 m in height and 29.2 m in base

diameter, containing about 2000 megagrams (Mg) of coal, with a bulk density of 800 kilograms per cubic

meter (kg/m3) (50 pounds per cubic feet [lb/ft3

]). The total exposed surface area of the pile is calculated as

follows:

Coal is added to the pile by means of a fixed stacker and reclaimed by front-end loaders operating

at the base of the pile on the downwind side. In addition, every 3 days 250 Mg (12.5 percent of the stored

capacity of coal) is added back to the pile by a topping off operation, thereby restoring the full capacity of

the pile. It is assumed that (a) the reclaiming operation disturbs only a limited portion of the surface area

where the daily activity is occurring, such that the remainder of the pile surface remains intact, and (b) the

topping off operation creates a fresh surface on the entire pile while restoring its original shape in the area

depleted by daily reclaiming activity.

Because of the high frequency of disturbance of the pile, a large number of calculations must be

made to determine each contribution to the total annual wind erosion emissions. This illustration will use

a single month as an example.

Step 1: In the absence of field data for estimating the threshold friction velocity, a value of

1.12 m/s is obtained from Table 13.2.5-2. 

Step 2: Except for a small area near the base of the pile (see Figure 13.2.5-3), the entire pile

surface is disturbed every 3 days, corresponding to a value of N = 120 per year. It will be shown that the

contribution of the area where daily activity occurs is negligible so that it does not need to be treated

separately in the calculations.

Step 3: The calculation procedure involves determination of the fastest mile for each period of

disturbance. Figure 13.2.5-4 shows a representative set of values (for a 1-month period) that are assumed

to be applicable to the geographic area of the pile location. The values have been separated into 3-day

periods, and the highest value in each period is indicated. In this example, the anemometer height is 7 m,

so that a height correction to 10 m is needed for the fastest mile values. From Equation 5,

Step 4: The next step is to convert the fastest mile value for each 3-day period into 
Figure 13.2.5-3. Example 1: Pile surface areas within each wind speed regime.

13.2.5-10 EMISSION FACTORS 11/06
Figure 13.2.5-4. Example daily fastest miles wind for periods of interest.

11/06 Miscellaneous Sources 13.2.5-11
equivalent friction velocities for each surface wind regime (i. e., us

/ur ratio) of the pile, using

Equations 6 and 7. Figure 13.2.5-3 shows the surface wind speed pattern (expressed as a fraction of

the approach wind speed at a height of 10 m). The surface areas lying within each wind speed regime

are tabulated below the figure.

The calculated friction velocities are presented in Table 13.2.5-4. As indicated, only 3 of the

periods contain a friction velocity which exceeds the threshold value of 1.12 m/s for an uncrusted coal

pile. These 3 values all occur within the us

/ur = 0.9 regime of the pile surface.

Table 13.2.5-4 (Metric And English Units). EXAMPLE 1:

CALCULATION OF FRICTION VELOCITIES

3-Day Period

u+

7

u+

10

u* = 0.1u+ (m/s)

s

mph m/s mph m/s us/ur: 0.2 us/ur: 0.6 us/ur: 0.9

1 14 6.3 15 6.6 0.13 0.40 0.59

2 29 13.0 31 13.7 0.27 0.82 1.23

3 30 13.4 32 14.1 0.28 0.84 1.27

4 31 13.9 33 14.6 0.29 0.88 1.31

5 22 9.8 23 10.3 0.21 0.62 0.93

6 21 9.4 22 9.9 0.20 0.59 0.89

7 16 7.2 17 7.6 0.15 0.46 0.68

8 25 11.2 26 11.8 0.24 0.71 1.06

9 17 7.6 18 8.0 0.16 0.48 0.72

10 13 5.8 14 6.1 0.12 0.37 0.55

Step 5: This step is not necessary because there is only 1 frequency of disturbance used in the

calculations. It is clear that the small area of daily disturbance (which lies entirely within the us

/ur =

0.2 regime) is never subject to wind speeds exceeding the threshold value.

Steps 6 and 7: The final set of calculations (shown in Table 13.2.5-5) involves the tabulation

and summation of emissions for each disturbance period and for the affected subarea. The erosion

potential (P) is calculated from Equation 3.

For example, the calculation for the second 3-day period is:

P 58(u ut )

2 25(u ut )

P2 58(1.23 1.12)2 25(1.23 1.12)

0.70 2.75 3.45 g/m2

13.2.5-12 EMISSION FACTORS 11/06
Table 13.2.5-5 (Metric Units). EXAMPLE 1: CALCULATION OF PM-10 EMISSIONSa

3-Day Period u* (m/s)

u* - ut

*

(m/s) P (g/m2) ID

Pile Surface

Area

(m2)

kPA

(g)

2 1.23 0.11 3.45 A 101 170

3 1.27 0.15 5.06 A 101 260

4 1.31 0.19 6.84 A 101 350

TOTAL 780

a Where ut

* = 1.12 m/s for uncrusted coal and k = 0.5 for PM-10.

The emissions of particulate matter greater than 10 μm (PM-10) generated by each event are

found as the product of the PM-10 multiplier (k = 0.5), the erosion potential (P), and the affected area

of the pile (A).

As shown in Table 13.2.5-5, the results of these calculations indicate a monthly PM-10

emission total of 780 g.

13.2.5.5 Example 2: Calculation for wind erosion from flat area covered with coal dust

A flat circular area 29.2 m in diameter is covered with coal dust left over from the total

reclaiming of a conical coal pile described in the example above. The total exposed surface area is

calculated as follows:

This area will remain exposed for a period of 1 month when a new pile will be formed.

s

π

4

d2 0.785 (29.2)2 670 m2

Step 1: In the absence of field data for estimating the threshold friction velocity, a value of

0.54 m/s is obtained from Table 13.2.5-2.

Step 2: The entire surface area is exposed for a period of 1 month after removal of a pile and

N = 1/yr.

Step 3: From Figure 13.2.5-4, the highest value of fastest mile for the 30-day period (31 mph)

occurs on the 11th day of the period. In this example, the reference anemometer height is

7 m, so that a height correction is needed for the fastest mile value. From Step 3 of the previous

example, u+ = 1.05 u+, so that u+ = 33 mph. 10 7 10

Step 4: Equation 4 is used to convert the fastest mile value of 14.6 m/s (33 mph) to an

equivalent friction velocity of 0.77 m/s. This value exceeds the threshold friction velocity from Step 1

so that erosion does occur.

Step 5: This step is not necessary, because there is only 1 frequency of disturbance for the

entire source area.

11/06 Miscellaneous Sources 13.2.5-13
Steps 6 and 7: The PM-10 emissions generated by the erosion event are calculated as the

product of the PM-10 multiplier (k = 0.5), the erosion potential (P) and the source area (A). The

erosion potential is calculated from Equation 3 as follows:

Thus the PM-10 emissions for the 1-month period are found to be:

P 58(u ut )

2 25(u ut )

P 58(0.77 0.54)2 25(0.77 0.54)

3.07 5.75

8.82 g/m2

E = (0.5)(8.82 g/m2)(670 m2)

= 3.0 kg

References For Section 13.2.5

1. C. Cowherd, Jr., "A New Approach To Estimating Wind Generated Emissions From Coal

Storage Piles", Presented at the APCA Specialty Conference on Fugitive Dust Issues in the

Coal Use Cycle, Pittsburgh, PA, April 1983.

2. K. Axtell and C. Cowherd, Jr., Improved Emission Factors For Fugitive Dust From Surface

Coal Mining Sources, EPA-600/7-84-048, U. S. Environmental Protection Agency, Cincinnati,

OH, March 1984.

3. G. E Muleski, "Coal Yard Wind Erosion Measurement", Midwest Research Institute, Kansas

City, MO, March 1985.

4. Update Of Fugitive Dust Emissions Factors In AP-42 Section 11.2 — Wind Erosion, MRI No.

8985-K, Midwest Research Institute, Kansas City, MO, 1988.

5. W. S. Chepil, "Improved Rotary Sieve For Measuring State And Stability Of Dry Soil

Structure", Soil Science Society Of America Proceedings, 16:113-117, 1952.

6. D. A. Gillette, et al., "Threshold Velocities For Input Of Soil Particles Into The Air By Desert

Soils", Journal Of Geophysical Research, 85(C10):5621-5630.

7. Local Climatological Data, National Climatic Center, Asheville, NC.

8. M. J. Changery, National Wind Data Index Final Report, HCO/T1041-01 UC-60, National

Climatic Center, Asheville, NC, December 1978.

9. B. J. B. Stunder and S. P. S. Arya, "Windbreak Effectiveness For Storage Pile Fugitive Dust

Control: A Wind Tunnel Study", Journal Of The Air Pollution Control Association,

38:135-143, 1988.

10. C. Cowherd, Jr., et al., Control Of Open Fugitive Dust Sources, EPA 450/3-88-008, U. S.

Environmental Protection Agency, Research Triangle Park, NC, September 1988.

13.2.5-14 EMISSION FACTORS 11/06
11/06 Miscellaneous Sources 13.2.5-15

11. C. Cowherd, Background Document for Revisions to Fine Fraction Ratios Used for AP-42

Fugitive Dust Emission Factors. Prepared by Midwest Research Institute for Western Governors

Association, Western Regional Air Partnership, Denver, CO, February 1, 2006.
9/97 Metallurgical Industry 13.2.6-1

13.2.6 Abrasive Blasting

13.2.6.1 General1-2

Abrasive blasting is the use of abrasive material to clean or texturize a material such as metal or

masonry. Sand is the most widely used blasting abrasive. Other abrasive materials include coal slag, smelter

slags, mineral abrasives, metallic abrasives, and synthetic abrasives. Industries that use abrasive blasting

include the shipbuilding industry, automotive industry, and other industries that involve surface preparation

and painting. The majority of shipyards no longer use sand for abrasive blasting because of concerns about

silicosis, a condition caused by respiratory exposure to crystalline silica. In 1991, about 4.5 million tons of

abrasives, including 2.5 million tons of sand, 1 million tons of coal slag, 500 thousand tons of smelter slag,

and 500 thousand tons of other abrasives were used for domestic abrasive blasting operations.

13.2.6.2 Process Description1-9

Abrasive blasting systems typically include three essential components: an abrasive container (i. e.,

blasting pot); a propelling device; and a blasting nozzle or nozzles. The exact equipment used depends to a

large extent on the specific application and type(s) of abrasive.

Three basic methods can be used to project the abrasive towards the surface being cleaned: air

pressure; centrifugal wheels; or water pressure. Air blast (or dry) systems use compressed air to propel the

abrasive using either a suction-type or pressure-type process. Centrifugal wheel systems use a rotating

impeller to mechanically propel the abrasive by a combination of centrifugal and inertial forces. Finally, the

water (or wet) blast method uses either air pressure or water pressure to propel an abrasive slurry towards the

cleaned surface.

Abrasive materials used in blasting can generally be classified as sand, slag, metallic shot or grit,

synthetic, or other. The cost and properties associated with the abrasive material dictate its application. The

following discusses the general classes of commonly used abrasives.

Silica sand is commonly used for abrasive blasting where reclaiming is not feasible, such as in

unconfined abrasive blasting operations. Sand has a rather high breakdown rate, which can result in

substantial dust generation. Worker exposure to free crystalline silica is of concern when silica sand is used

for abrasive blasting.

Coal and smelter slags are commonly used for abrasive blasting at shipyards. Black Beauty ,

TM

which consists of crushed slag from coal-fired utility boilers, is a commonly used slag. Slags have the

advantage of low silica content, but have been documented to release other contaminants, including

hazardous air pollutants (HAP), into the air.

Metallic abrasives include cast iron shot, cast iron grit, and steel shot. Cast iron shot is hard and

brittle and is produced by spraying molten cast iron into a water bath. Cast iron grit is produced by crushing

oversized and irregular particles formed during the manufacture of cast iron shot. Steel shot is produced by

blowing molten steel. Steel shot is not as hard as cast iron shot, but is much more durable. These materials

typically are reclaimed and reused. 
13.2.6-2 EMISSION FACTORS 9/97

Synthetic abrasives, such as silicon carbide and aluminum oxide, are becoming popular substitutes

for sand. These abrasives are more durable and create less dust than sand. These materials typically are

reclaimed and reused.

Other abrasives include mineral abrasives (such as garnet, olivine, and staurolite), cut plastic, glass

beads, crushed glass, and nutshells. As with metallic and synthetic abrasives, these other abrasives are

generally used in operations where the material is reclaimed. Mineral abrasives are reported to create

significantly less dust than sand and slag abrasives.

The type of abrasive used in a particular application is usually specific to the blasting method. Dry

blasting is usually done with sand, metallic grit or shot, aluminum oxide (alumina), or silicon carbide. Wet

blasters are operated with either sand, glass beads, or other materials that remain suspended in water.

13.2.6.3 Emissions And Controls1,3,5-11

Emissions —

 Particulate matter (PM) and particulate HAP are the major concerns relative to abrasive blasting. 

Table 13.2.6-1 presents total PM emission factors for abrasive blasting as a function of wind speed. Higher

wind speeds increase emissions by enhanced ventilation of the process and by retardation of coarse particle

deposition.

Table 13.2.6-1 also presents fine particulate emission factors for abrasive blasting. Emission factors

are presented for PM-10 and PM-2.5, which denote particles equal to or smaller than 10 and 2.5 microns in

aerodynamic diameter, respectively. Emissions of PM of these size fractions are not significantly wind-speed

dependent. Table 13.2.6-1 also presents an emission factor for controlled emissions from an enclosed

abrasive blasting operation controlled by a fabric filter; the blasting media was 30/40 mesh garnet.

Limited data from Reference 3 give a comparison of total PM emissions from abrasive blasting using

various media. The study indicates that, on the basis of tons of abrasive used, total PM emissions from

abrasive blasting using grit are about 24 percent of total PM emissions from abrasive blasting with sand. 

The study also indicates that total PM emissions from abrasive blasting using shot are about 10 percent of

total PM emissions from abrasive blasting with sand.

Hazardous air pollutants, typically particulate metals, are emitted from some abrasive blasting

operations. These emissions are dependent on both the abrasive material and the targeted surface.

Controls —

A number of different methods have been used to control the emissions from abrasive blasting. 

Theses methods include: blast enclosures; vacuum blasters; drapes; water curtains; wet blasting; and reclaim

systems. Wet blasting controls include not only traditional wet blasting processes but also high pressure

water blasting, high pressure water and abrasive blasting, and air and water abrasive blasting. For wet

blasting, control efficiencies between 50 and 93 percent have been reported. Fabric filters are used to control

emissions from enclosed abrasive blasting operations.
9/97 Metallurgical Industry 13.2.6-3

Table 13.2.6-1. PARTICULATE EMISSION FACTORS FOR ABRASIVE BLASTINGa

EMISSION FACTOR RATING: E

Source Particle size lb/1,000 lb abrasive

Emission factor,

Sand blasting of mild steel Total PM

panels 5 mph wind speed 27 b

(SCC 3-09-002-02) 10 mph wind speed 55

15 mph wind speed 91

PM-10 13 c

PM-2.5 1.3 c

Abrasive blasting of unspecified 

metal parts, controlled with a 

fabric filter Total PM 0.69 d

(SCC 3-09-002-04)

a One lb/1,000 lb is equal to 1 kg/Mg. Factors represent uncontrolled emissions, unless noted. 

SCC = Source Classification Code.

Reference 10. b

Emissions of PM-10 and PM-2.5 are not significantly wind-speed dependent. c

Reference 11. Abrasive blasting with garnet blast media. d

References For Section 13.2.6

 1. C. Cowherd and J. Kinsey, Development Of Particulate And Hazardous Emission Factors For 

Outdoor Abrasive Blasting, EPA Contract No. 68-D2-0159, Midwest Research Institute, Kansas

City, MO, June 1995.

 2. Written communication from J. D. Hansink, Barton Mines Corporation, Golden, CO, to Attendees of

the American Waterways Shipyard Conference, Pedido Beach, AL, October 28, 1991.

 3. South Coast Air Quality Management District, Section 2: Unconfined Abrasive Blasting, Draft

Document, El Monte, CA, September 8, 1988.

 4. A. W. Mallory, “Guidelines For Centrifugal Blast Cleaning”, J. Protective Coatings And Linings,

1(1), June 1984.

 5. B. Baldwin, “Methods Of Dust-Free Abrasive Blast Clearing”, Plant Engineering, 32(4),

February 16, 1978.

 6. B. R Appleman and J. A. Bruno, Jr., “Evaluation Of Wet Blast Cleaning Units”, J. Protective

Coatings And Linings, 2(8), August 1985.
13.2.6-4 EMISSION FACTORS 9/97

 7. M. K. Snyder and D. Bendersky, Removal Of Lead-Based Bridge Paints, NCHRP Report 265,

Transportation Research Board, Washington, DC, December 1983.

 8. J. A. Bruno, “Evaluation Of Wet Abrasive Blasting Equipment”, Proceedings Of The 2nd Annual

International Bridge Conference, Pittsburgh, PA, June 17-19, 1985.

 9. J. S. Kinsey, 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, September 11, 1989.

10. J. S. Kinsey, S. Schliesser, P. Murowchick, and C. Cowherd, Development Of Particulate Emission

Factors For Uncontrolled Abrasive Blasting Operations, EPA Contract No. 68-D2-0159, Midwest

Research Institute, Kansas City, MO, February 1995.

11. Summary Of Source Test Results, Poly Engineering, Richmond, CA, Bay Area Air Quality

Management District, San Francisco, CA, November 19, 1990.

12. Emission Factor Documentation For AP-42 Section 13.2.6, Abrasive Blasting, Final Report,

Midwest Research Institute, Cary, NC, September 1997.
Emission Factor Documentation for AP-42

Section 13.2.6

Abrasive Blasting

Final Report

For U. S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Emission Factor and Inventory Group

EPA Contract 68-D2-0159

Work Assignment No. 4-02

MRI Project No. 4604-02

September 1997
Emission Factor Documentation for AP-42

Section 13.2.6

Abrasive Blasting

Final Report

For U. S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Emission Factor and Inventory Group

Research Triangle Park, NC 27711

Attn: Mr. Ron Myers (MD-14)

EPA Contract 68-D2-0159

Work Assignment No. 4-02

MRI Project No. 4604-02

September 1997

ii

NOTICE

The information in this document has been funded wholly or in part by the United States

Environmental Protection Agency under Contract No. 68-D2-0159 to Midwest Research Institute. It has

been reviewed by the Office of Air Quality Planning and Standards, U. S. Environmental Protection Agency,

and has been approved for publication. Mention of trade names or commercial products does not constitute

endorsement or recommendation for use.
iii

PREFACE

This report was prepared by Midwest Research Institute (MRI) for the Office of Air Quality

Planning and Standards (OAQPS), U. S. Environmental Protection Agency (EPA), under Contract

No. 68-D2-0159, Work Assignment Nos. 2-01 and 4-02. Mr. Ron Myers was the requester of the work.

Approved for:

MIDWEST RESEARCH INSTITUTE

Roy Neulicht

Program Manager

Environmental Engineering Department

Jeff Shular

Director, Environmental Engineering

 Department

September, 1997
iv
v

TABLE OF CONTENTS

Page

1. INTRODUCTION ............................................................... 1-1

2. INDUSTRY AND PROCESS DESCRIPTION ........................................ 2-1

2.1 INDUSTRY CHARACTERIZATION .......................................... 2-1

2.2 PROCESS DESCRIPTION ................................................... 2-1

2.2.1 Types of Abrasives .................................................... 2-1

2.2.2 Blasting Methods ..................................................... 2-2

2.3 DUST CONTROL TECHNIQUES ............................................. 2-9

2.3.1 Blast Enclosures ...................................................... 2-9

2.3.2 Vacuum Blasters ...................................................... 2-9

2.3.3 Drapes .............................................................. 2-9

2.3.4 Water Curtains ....................................................... 2-9

2.3.5 Wet Blasting ......................................................... 2-11

2.3.6 Centrifugal Blasters ................................................... 2-13

2.4 REFERENCES FOR SECTION 2 .............................................. 2-13

3. GENERAL DATA REVIEW AND ANALYSIS ....................................... 3-1

3.1 LITERATURE SEARCH AND SCREENING .................................... 3-1

3.2 DATA QUALITY RATING SYSTEM .......................................... 3-1

3.3 EMISSION FACTOR QUALITY RATING SYSTEM ............................. 3-2

3.4 REFERENCES FOR SECTION 3 .............................................. 3-3

4. EMISSION FACTOR DEVELOPMENT ............................................ 4-1

4.1 REVIEW OF SPECIFIC DATA SETS .......................................... 4-1

4.1.1 Reference 1 .......................................................... 4-2

4.1.2 Reference 2 .......................................................... 4-2

4.1.3 Reference 3 .......................................................... 4-2

4.1.4 Reference 4 .......................................................... 4-3

4.1.5 Reference 5 .......................................................... 4-3

4.1.6 Reference 6 .......................................................... 4-3

4.2 RESULTS OF DATA ANALYSIS ............................................. 4-3

4.3 DEVELOPMENT OF CANDIDATE EMISSION FACTORS ........................ 4-7

4.4 REFERENCES FOR SECTION 4 .............................................. 4-11

5. PROPOSED AP-42 SECTION 13.2.6 ............................................... 5-1
vi

LIST OF FIGURES

Figure Page

2-1a. Suction blast nozzle assembly ................................................ 2-4

2-1b. Suction-tape blasting machine ................................................ 2-4

2-2. Pressure-type blasting machine ............................................... 2-5

2-3a. Wet blasting machine ....................................................... 2-6

2-3b. Adapter nozzle converting a dry blasting unit to a wet blasting unit ................... 2-6

2-4. Hydraulic blasting nozzle .................................................... 2-6

2-5. Schematic of vacuum blaster head ............................................. 2-10

2-6. Nozzle for air abrasive wet blast .............................................. 2-11

2-7. Water curtain device for abrasive blast nozzle .................................... 2-12

LIST OF TABLES

Table Page

2-1. MEDIA COMMONLY USED IN ABRASIVE BLASTING ........................ 2-2

2-2. FLOW RATE OF SAND THROUGH A BLASTING NOZZLE AS A

FUNCTION OF NOZZLE PRESSURE AND INTERNAL DIAMETER .............. 2-8

2-3. BULK DENSITY OF COMMON ABRASIVES ................................. 2-8

4-1. REFERENCE DOCUMENTS REVIEWED DURING LITERATURE SEARCH ....... 4-1

4-2. SUMMARY OF TEST DATA FOR ABRASIVE BLASTING OPERATIONS ........ 4-4

4-3. SUMMARY OF AVAILABLE CONTROL EFFICIENCY DATA FOR ABRASIVE 

BLASTING OPERATIONS ................................................. 4-6

4-4. SUMMARY OF PM TEST DATA FROM REFERENCE 1 ........................ 4-8

4-5. SUMMARY OF EMISSION FACTORS FOR PM METALS ...................... 4-9

4-6. SUMMARY OF EMISSION FACTORS FOR PM-10 METALS ................... 4-9

4-7. SUMMARY OF EMISSION FACTORS FOR PM-2.5 METALS ................... 4-10

4-7. SUMMARY OF EMISSION FACTORS FOR PM-2.5 METALS ................... 4-10

4-8. CANDIDATE PM-10 AND PM-2.5 EMISSION FACTORS ....................... 4-10

4-9. CANDIDATE TOTAL PM EMISSION FACTORS DIFFERENTIATED

BY WIND SPEED ......................................................... 4-10

4-10. CANDIDATE EMISSION FACTOR FOR GARNET BLASTING .................. 4-11
1-1

1. INTRODUCTION

The document Compilation of Air Pollutant Emission Factors (AP-42) has been published by the

U. S. Environmental Protection Agency (EPA) since 1972. Supplements to AP-42 are issued to add new

emission source categories and to update existing emission factors. The EPA also routinely updates AP-42 in

response to the needs of Federal, State, and local air pollution control programs and industry.

An emission factor relates the quantity (weight) of pollutants emitted to a unit of source activity. 

Emission factors reported in AP-42 are used to:

1. Estimate areawide emissions.

2. Estimate emissions for a specific facility.

3. Evaluate emissions relative to ambient air quality.

This report provides background information from test reports and other information to support

preparation of a new AP-42 section for abrasive blasting. The information in the proposed AP-42 section is

based on a review of the available literature for particulate phase air pollutants produced by abrasive blasting

operations. 

This report contains five sections. Following the introduction, Section 2 describes abrasive blasting

equipment, practices, and allied processes. Section 3 describes data collection and rating procedures, and

Section 4 describes the emission factor development. Section 5 presents the proposed AP-42 section.
2-1

2. INDUSTRY AND PROCESS DESCRIPTION

2.1 INDUSTRY CHARACTERIZATION1

Abrasive blasting is used for a variety of surface cleaning and texturing operations, mostly involving

metallic target materials. Sand is the most widely used blasting abrasive. Other abrasive materials include

coal slag, smelter slags, mineral abrasives, metallic abrasives, and synthetic abrasives. Industries that use

abrasive blasting include the shipbuilding industry, automotive industry, and other industries that involve

surface preparation and painting. The majority of shipyards no longer use sand for abrasive blasting because

of concerns about silicosis, a condition caused by respiratory exposure to crystalline silica. In 1991, about

4.5 million tons of abrasives, including 2.5 million tons of sand, 1 million tons of coal slag, 500 thousand

tons of smelter slag, and 500 thousand tons of other abrasives, were used for domestic abrasive blasting

operations.

2.2 PROCESS DESCRIPTION1-8

The following sections briefly describe the types of abrasives, blasting methods, and dust control

techniques commonly used in outdoor abrasive blasting. 

2.2.1 Types of Abrasives1-2

Abrasive materials are generally classified as: sand, slag, metallic shot or grit, synthetic, or other. 

The cost and properties associated with the abrasive material dictate its application. The following discusses

the general classes of common abrasives.

Silica sand is commonly used for abrasive blasting where reclaiming is not feasible, such as in

unconfined abrasive blasting operations. Sand has a rather high breakdown rate, which can result in

substantial dust generation. Worker exposure to free crystalline silica is of concern when silica sand is used

for abrasive blasting.

Coal and smelter slags are commonly used for abrasive blasting at shipyards. Black Beauty ,

TM

which consists of crushed slag from coal-fired utility boilers, is a commonly used slag. Slags have the

advantage of low silica content, but have been documented to release other contaminants, including

hazardous air pollutants (HAP), into the air.

Metallic abrasives include cast iron shot, cast iron grit, and steel shot. Cast iron shot is hard and

brittle and is produced by spraying molten cast iron into a water bath. Cast iron grit is produced by crushing

oversized and irregular particles formed during the manufacture of cast iron shot. Steel shot is produced by

blowing molten steel. Steel shot is not as hard as cast iron shot, but is much more durable. These materials

typically are reclaimed and reused.

Synthetic abrasives, such as silicon carbide and aluminum oxide, are becoming popular substitutes

for sand. These abrasives are more durable and create less dust than sand. These materials typically are

reclaimed and reused.

Other abrasives include mineral abrasives (such as garnet, olivine, and staurolite), cut plastic, glass

beads, crushed glass, and nutshells. As with metallic and synthetic abrasives, these other abrasives are
2-2

TABLE 2-1. MEDIA COMMONLY USED IN ABRASIVE BLASTING2

Type of medium Sizes normally available Applications

Glass beads 8 to 10 sizes from 30- to 440-mesh;

also many special gradations

Decorative blending; light deburring;

peening; general cleaning; texturing;

noncontaminating

Aluminum oxide 10 to 12 sizes from 24- to 325-mesh Fast cutting; matte finishes; descaling

and cleaning of coarse and sharp

textures

Garnet 6 to 8 sizes (wide-band screening) from

16- to 325-mesh

Noncritical cleaning and cutting;

texturing; noncontaminating for

brazing steel and stainless steel

Crushed glass 5 sizes (wide-band screening) from 30-

to 400-mesh

Fast cutting; low cost; short life;

abrasive; noncontaminating

Steel shot 12 or more sizes (close gradation) from

8- to 200-mesh

General-purpose rough cleaning

(foundry operation, etc.); peening

Steel grit 12 or more sizes (close gradation) from

10- to 325-mesh

Rough cleaning; coarse textures;

foundry welding applications; some

texturing

Cut plastic 3 sizes (fine, medium, coarse); definitesize particlesDeflashing of thermoset plastics;

cleaning; light deburring

Crushed nutshells 6 sizes (wide-band screening) Deflashing of plastics; cleaning; very

light deburring; fragile parts

generally used in operations where the material is reclaimed. Mineral abrasives are reported to create

significantly less dust than sand and slag abrasives.

The type of abrasive used in a particular application is usually specific to the blasting method. Dry

abrasive blasting is usually done with sand, aluminum oxide, silica carbide, metallic grit, or shot. Wet

blasting is usually done with sand, glass beads, or any materials that will remain suspended in water. 

Table 2-1 lists common abrasive materials and their applications.

2.2.2 Blasting Methods2-8

Abrasive blasting systems typically include three basic components: an abrasive container (i.e.,

blasting pot), a propelling device, and an abrasive blasting nozzle(s). The exact equipment used depends on

the application.

The three propelling methods used in abrasive blasting systems are: centrifugal wheels, air pressure,

or water pressure. Centrifugal wheel systems use centrifugal and inertial forces to mechanically propel the

abrasive media. Air blast systems use compressed air to propel the abrasive to the surface being cleaned. 3 4

Finally, the water blast method uses either compressed air or high pressure water.5
2-3

The compressed air suction, the compressed air pressure, and the wet abrasive blasting systems utilize the air

blast method. Hydraulic blasting systems utilize the water blast method.

In compressed air suction systems, two rubber hoses are connected to a blasting gun. One hose is

connected to the compressed-air supply and the other is connected to the bottom of the abrasive supply tank

or “pot.” The gun (Figure 2-1a) consists of an air nozzle that discharges into a larger nozzle. The high

velocity air jet (expanding into the larger nozzle) creates a partial vacuum in the chamber. This vacuum

draws the abrasive into the outer nozzle and expels it through the discharge opening. Figure 2-1b shows a

typical suction type blasting machine.

The compressed air pressure system consists of a pressure tank (pot) in which the abrasive is

contained. The use of a pressure tank forces abrasive through the blast hose rather than siphoning it as

described above. The compressed air line is connected to both the top and bottom of the pressure tank. This

allows the abrasive to flow by gravity into the discharge hose without loss of pressure (see Figure 2-2).

Finally, wet abrasive blasting systems (Figure 2-3a) use a specially designed pressure tank. The

mixture of abrasive and water is propelled by compressed air. An alternate method uses a pressure tank and

a modified abrasive blasting nozzle. This modified abrasive blasting nozzle is shown in Figure 2-3b.

Hydraulic blasting incorporates a nozzle similar to that described above for air suction systems,

except that high pressure water is used as the propelling media instead of compressed air. A diagram of this

type of nozzle is shown in Figure 2-4.

Pressure blast systems generally give a faster, more uniform finish than suction blast systems. They

also produce high abrasive velocities with less air consumption than suction systems. Pressure blast systems

can operate at pressures as low as 1 psig to blast delicate parts and up to 125 psig to handle the most

demanding cleaning and finishing operations.2

Suction blast systems are generally selected for light-to-medium production requirements, limited

space, and moderate budgets. These systems can blast continuously without stopping for abrasive changes

and refills.2

The amount of sand used during blasting operations can be estimated using Table 2-2. By knowing

the inside diameter of the nozzle (inches) and the air pressure supplied (psig), the sand flow rate is provided. 

For different abrasives and nozzle diameters, Equation 2-1 can be used.2
2-4

Figure 2-1a. Suction blast nozzle assembly.

Figure 2-1b. Suction-tape blasting machine.
2-5

Figure 2-2. Pressure-type blasting machine.
2-6

Figure 2-3a. Wet blasting machine.

Figure 2-3b. Adapter nozzle converting a dry blasting unit to a wet blasting unit.
2-7

Figure 2-4. Hydraulic blasting nozzle.
m

@

a

' m

@

s

×

(Da)

2

(Ds)

2

×

Da

Ds

m

@

a

m

@

s

2-8

(2-1)

TABLE 2-2. FLOW RATE OF SAND THROUGH A BLASTING NOZZLE AS A

FUNCTION OF NOZZLE PRESSURE AND INTERNAL DIAMETER2

Nozzle

internal

diameter, in.

Sand flow rate through nozzle, lb/hr

Nozzle pressure, psig

30 40 50 60 70 80 90 100

1/8 28 35 42 49 55 63 70 77

3/16 65 80 94 107 122 135 149 165

1/4 109 138 168 195 221 255 280 309

5/16 205 247 292 354 377 420 462 507

3/8 285 355 417 477 540 600 657 720

7/16 385 472 560 645 755 820 905 940

1/2 503 615 725 835 945 1,050 1,160 1,265

5/8 820 990 1,170 1,336 1,510 1,680 1,850 2,030

3/4 1,140 1,420 1,670 1,915 2,160 2,400 2,630 2,880

1 2,030 2,460 2,900 3,340 3,780 4,200 4,640 5,060

TABLE 2-3. BULK DENSITY OF COMMON ABRASIVES2

Type of abrasive Density, lb/ft3

Aluminum oxides 160

Sand 99

Steel 487

where:

 = mass flow rate (lb/hr) of abrasive with nozzle internal diameter Da

 = mass flow rate (lb/hr) of sand with nozzle internal diameter D from Table 2-2 s

D = actual nozzle internal diameter (in.) a

D = nozzle internal diameter (in.) from Table 2-2 s

D = bulk density of sand (lb/ft )

s

3

D = bulk density of abrasive (lb/ft )

a

3

The densities of several different abrasives are shown in Table 2-3.
2-9

2.3 DUST CONTROL TECHNIQUES2,4,6,7

A variety of techniques have been used to contain and recover the debris generated during abrasive

cleaning operations. These techniques may be categorized into the following: blast enclosures, vacuum

blasters, drapes, water curtains, wet blasters, and centrifugal blasters. Brief descriptions of each are provided

below. A more detailed discussion of each method can be found in Reference 6.

2.3.1 Blast Enclosures

Blast enclosures are designed to completely enclose one or more abrasive blast operations, thereby

confining the blast debris. The enclosure floor is usually equipped with funnels to divert the captured debris

into adjacent trucks. In one design, a ventilation system is used to remove the airborne dust from the

enclosure with the particles removed from the effluent airstream by a wet scrubber. The enclosures are

moved as the work progresses.

Blast enclosures can be very effective in containing and recovering abrasive blast debris. However,

they are specifically designed for a particular application, relatively expensive, and tend to slow down the

overall cleaning rate due to the time required to move the enclosure as the work progresses.

Some leakage of abrasive and paint debris can occur at the joints between the blast enclosure and the

structure being cleaned. Although attempts have been made to seal the joints with canvas, this is usually not

very effective, particularly when the blast is directed into these areas. A better method to minimize leakage

from enclosure joints is to fasten a flexible seal made of rubber, plastic, or thin metal to the inside edges of

the enclosure walls. The end of the flexible seal rests on the structure being cleaned, thus reducing the escape

of airborne dust.

2.3.2 Vacuum Blasters

Vacuum blasters are designed to remove paint and other surface coatings by abrasive blasting and

simultaneously collect and recover the spent abrasive and paint debris with a capture and collection system

surrounding the blast nozzle (Figure 2-5). In this type of system, the abrasive is automatically reclaimed and

reused as work progresses. Vacuum blasters are made in a variety of sizes but even the smaller units are

comparatively heavy and awkward to use. Furthermore, the production rates of the small units are low, and

costs are relatively high.

2.3.3 Drapes

Porous drapes (or curtains) on both sides of a truss-type structure (e.g., bridge) have been used to

divert debris downward into a barge or lined net under the blasting operation. The top of the drapes are tied

to the top of the structure. This technique is relatively inexpensive but also not very effective because dust

penetrates the porous drape and spillage occurs due to wind effects.

2.3.4 Water Curtains

In this technique, a water header with a series of nozzles is installed along the edges of the structure

being blasted. The water spray from the nozzles is directed downward creating a water curtain to collect

debris from abrasive blasting performed below the header. The debris is subsequently washed down to the

ground. This technique is relatively inexpensive and does reduce the amount of airborne dust.
2-10

Figure 2-5. Schematic of vacuum blaster head.
2-11

Figure 2-6. Nozzle for air abrasive wet blast.

However, one disadvantage is that the debris-laden water spills onto the ground (or into the water under a

bridge) creating additional contamination and clean-up problems.

One method used to solve the spillage problem associated with water curtains involves the placement

of troughs under the spray pattern to catch the water/abrasive mixture and divert it to an appropriate

container (e.g., tank truck) for disposal. For low structures, the troughs can be placed on the ground. For

high structures, the troughs can be supported from the structure itself. To minimize wind effects, porous

drapes can be added, extending from the blast area down to the troughs.

2.3.5 Wet Blasting

Wet blasting techniques include: wet abrasive blasting; high-pressure water blasting; high-pressure

water and abrasive blasting; and air and water abrasive blasting. The type of wet blasting method used

depends on the application.

Wet abrasive blasting is accomplished by adding water to conventional abrasive blasting nozzles as

shown in Figure 2-6. High-pressure water blast systems include an engine-driven, high-pressure pump, highpressure hose, and a gun equipped with a spray nozzle. If abrasives are introduced to this type of system,

high-pressure water and abrasive blasting is provided. Finally, in air and water abrasive blasting systems,

each of the three materials can be varied over a wide range, making them very versatile. Compared to dry

blasting, all wet blasting techniques produce substantially lower dust emissions.

Most wet abrasive blasters mix the water with the abrasive prior to impact on the surface. This

interaction can cause the rate of surface cleaning to be lower than with dry abrasive blasting. To solve this

problem, a retrofit device (design to minimize premixing of the water with the abrasive blast) has been

developed to fit over the end of conventional abrasive blast nozzles. This device is shown in Figure 2-7.

The two principal parts of the device (Figure 2-7) are a swirl chamber and an exit nozzle. The swirl

chamber is equipped with a tangential water inlet. The incoming water swirls around the inside of the

chamber and then out the exit nozzle. Centrifugal force causes the water to form a hollow cone pattern
2-12

Figure 2-7. Water curtain device for abrasive blast nozzle.
2-13

around the abrasive blast stream. The angle of the water cone is controlled principally by the shape of the

exit nozzle and centrifugal forces.

The above device is expected to be an improvement over traditional wet abrasive blasting. The

modified water nozzle design provides a water curtail around the abrasive/airstream. Thus, the cleaning

effectiveness of the abrasive/airstream should not be substantially affected. The device is simple to install

and operate with conventional abrasive blasting equipment.

2.3.6 Centrifugal Blasters

Finally, centrifugal blasters use high-speed rotating blades to propel the abrasive against the surface

to be cleaned. These blasters also retrieve and recycle the abrasive by the use of a capture and collection

system which allows little abrasive or paint debris to escape. Present centrifugal blasters are designed

primarily for large, flat, horizontal surfaces such as ship decks. Some have been designed for use on large

vertical surfaces such as ship hulls and storage tanks. Some effort has been made to develop small hand-held

units for use on bridges and similar structures.

2.4 REFERENCES FOR SECTION 2

1. Written communication from J. D. Hansink, Barton Mines Corporation, Golden, CO, to Attendees of

the American Waterways Shipyard Conference, Pedido Beach, AL, October 28, 1991.

2. South Coast Air Quality Management District, Section 2: Unconfined Abrasive Blasting, Draft

Document, El Monte, CA, September 8, 1988.

3. A. W. Mallory, “Guidelines for Centrifugal Blast Cleaning,” J. Protective Coatings and Linings,

1(1), June 1984.

4. B. Baldwin, “Methods of Dust-free Abrasive Blast Clearing,” Plant Engineering, 32(4),

February 16, 1978.

5. B. R Appleman and J. A. Bruno, Jr., “Evaluation of Wet Blast Cleaning Units,” J. Protective

Coatings and Linings, 2(8), August 1985.

6. M. K. Snyder and D. Bendersky, Removal of Lead-based Bridge Paints, NCHRP Report 265,

Transportation Research Board, Washington, DC, December 1983.

7. J. A. Bruno, “Evaluation of Wet Abrasive Blasting Equipment,” Proceedings of the 2nd Annual

International Bridge Conference, Pittsburgh, PA, June 17-19, 1985.

8. J. S. Kinsey, 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, September 11, 1989.
3-1

3. GENERAL DATA REVIEW AND ANALYSIS

3.1 LITERATURE SEARCH AND SCREENING

The first step of this investigation was a search of the available literature relating to the particulate

emissions associated with open abrasive blasting. This search included data contained in the open literature

(e.g., National Technical Information Service); source test reports and background documents located in the

files of the EPA's Office of Air Quality Planning and Standards (OAQPS); data base searches (e.g.,

SPECIATE); and MRI's own files (Kansas City and North Carolina). The search was an update of the

extensive information collection effort performed in 1989 as reported in Reference 1.

To evaluate candidate documents for acceptability as sources of emission data, the following general

criteria were used:

1. Emissions data must be taken only from a primary reference:

a. Source testing data must be obtained directly from a referenced study that does not reiterate

information from previous studies.

b. The document must constitute the original source (or publication) of the test data.

2. The report must contain sufficient data to evaluate the testing procedures and source operating

conditions.

A final set of reference materials was compiled after a thorough review of the pertinent reports,

documents, and information according to the above criteria. This set of documents was further analyzed to

derive candidate emission factors for abrasive blasting operations.

3.2 DATA QUALITY RATING SYSTEM

As part of MRI's analysis, the final set of reference documents was evaluated as to the quantity and

quality of data. The following data were always excluded from consideration:

1. Test series averages reported in units that cannot be converted to the selected reporting units.

2. Test series representing incompatible test methods.

3. Test series in which the control device (or equipment) is not specified.

4. Test series in which the abrasive blasting process is not clearly identified and described.

5. Test series in which it is not clear whether the emissions were measured before or after the control

device.

If there was no reason to exclude a particular data set, each was assigned a rating as to its quality. 

The rating system used was that specified by the EPA's Office of Air Quality Planning and Standards

(OAQPS) for the preparation of AP-42 Sections. The data were rated as follows: 2
3-2

A—Multiple tests performed on the same source using sound methodology and reported in enough

detail for adequate validation. These tests do not necessarily have to conform to the methodology specified

by EPA reference test methods, although such were certainly used as a guide.

B—Tests that are performed by a generally sound methodology, but they lack enough detail for

adequate validation.

C—Tests that are based on an untested or new methodology or that lack a significant amount of

background data.

D—Tests that are based on a generally unacceptable method, but the method may provide an orderof-magnitude value for the source.

The following criteria were used to evaluate source test reports for sound methodology and adequate

detail:

1. Source operation. The manner in which the source was operated is well documented in the report. 

The source was operating within typical parameters during the test.

2. Sampling procedures. The sampling procedures conformed to a generally accepted methodology. 

If actual procedures deviated from accepted methods, the deviations were well documented. 

3. Sampling and process data. Adequate sampling and process data were documented in the report. 

Many variations may be unnoticed and occur without warning during testing. Such variations can induce

wide deviations in sampling results. If a large spread between test results cannot be explained by information

contained in the test report, the data are suspect and were given a lower rating.

4. Analysis and calculations. The test reports contain original raw data sheets. The nomenclature

and equations used were compared to those specified by EPA (if any) to establish equivalency. The depth of

review of the calculations was dictated by the reviewer's confidence in the ability and conscientiousness of the

tester, which in turn was based on factors such as consistency of results and completeness of other areas of

the test report.

3.3 EMISSION FACTOR QUALITY RATING SYSTEM

The quality of the emission factors developed from analysis of the test data was rated utilizing the

following general criteria:

A—Excellent: Developed from A- and B-rated source test data taken from many randomly chosen

facilities in the industry population. The source category is specific enough so that variability within the

source category population may be minimized.

B—Above average: Developed only from A- or B-rated test data from a reasonable number of

facilities. Although no specific bias is evident, it is not clear if the facilities tested represent a random sample

of the industries. The source category is specific enough so that variability within the source category

population may be minimized.

C—Average: Developed only from A-, B- and/or C-rated test data from a reasonable number of

facilities. Although no specific bias is evident, it is not clear if the facilities tested represent a random sample
3-3

of the industry. In addition, the source category is specific enough so that variability within the source

category population may be minimized.

D—Below average: The emission factor was developed only from A-, B-, and/or C-rated test data

from a small number of facilities, and there is reason to suspect that these facilities do not represent a random

sample of the industry. There also may be evidence of variability within the source category population. 

Limitations on the use of the emission factor are noted in the emission factor table.

E—Poor: The emission factor was developed from C- and D-rated test data, and there is reason to

suspect that the facilities tested do not represent a random sample of the industry. There also may be

evidence of variability within the source category population. Limitations on the use of these factors are

footnoted.

The use of these criteria is somewhat subjective and depends to an extent upon the individual

reviewer. Details of the rating of each candidate emission factor are provided in Section 4. 

3.4 REFERENCES FOR SECTION 3

1. J. S. Kinsey, 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,

September 11, 1989.

2. Procedures for Preparing Emission Factor Documents, EPA-454/R-95-015, Office of Air Quality

Planning and Standards, U. S. Environmental Protection Agency, Research Triangle Park, NC,

May 1997.
4-1

TABLE 4-1. REFERENCE DOCUMENTS REVIEWED DURING LITERATURE SEARCH

Samimi, B., “Silica Dust in Sandblasting Operations,” Ph.D. Thesis, Tulane University, 1973.

Samimi, B., et al., “Dust Sampling Results at a Sandblasting Yard Using Stan-Blast in the New

Orleans Region: A Preliminary Report,” NIOSH-00036278, New Orleans, LA, 1974.

Samimi, B., et al., “The Efficiency of Protective Hoods Used by Sandblasters to Reduce Silica Dust

Exposure,” Am. Indus. Hyg. Assn. J., 36(2), February 1975.

Landrigan, P. J., et al., “Health Hazard Evaluation Report on the Tobin-Mystic River Bridge,”

TA80-099-859, NIOSH Report to City Boston Department of Health and Hospitals, Boston, MA,

July 25, 1980.

Bareford, P. E., and F. A. Record, “Air Monitoring at the Bourne Bridge Cape Cod Canal,

Massachusetts,” Final Report, Contract No. DACW 33-79-C-0126, U.S. Army Corps of Engineers,

New England Division, Waltham, MA, January 1982.

Beddows, N. A., “Lead Hazards and How to Control Them,” Natl. Safety News, 128(6), December

1983.

Lehner, E., et al., Memo to D. M. Moline, Department of Public Utilities, Division of Environmental

Services, City of Toledo, OH, January 31, 1985.

WhiteMetal, Inc., “Protecting Our Environment with the Jet Stripper,” Houston, TX, June 1987.

South Coast Air Quality Management District, “Section 2: Unconfined Abrasive Blasting,” Draft

Document, El Monte, CA, September 8, 1988.

4. EMISSION FACTOR DEVELOPMENT

4.1 REVIEW OF SPECIFIC DATA SETS

In the prior information search of the literature for documents on the subject of abrasive blasting,

37 individual documents were identified for further evaluation. Upon subsequent review of these 1

documents, 15 were determined to contain some type of applicable air monitoring data. Of these 15 documents, only 9 contained data which were found to be potentially useful in the development of candidate

emission factors. Those documents are listed in Table 4-1.

Besides the documents listed in Table 4-1, the ongoing literature search yielded seven additional test

reports, as listed below.

1. Kinsey, J. S., et al., “Development of Particulate Emission Factors for Uncontrolled Abrasive

Blasting Operations,” U. S. Environmental Protection Agency, Research Triangle Park, NC, February 1995.

2. NEESA 2-161. Particulate and Chromium Emission Testing at Plastic Media Blasting Facility,

BLDG 25, Naval Aviation Depot, Naval Air Station, Alameda, CA, Naval Energy and Environmental

Support Activity, Port Hueneme, CA, May 1990.

3. Determination of Particulate Emission Rates & Baghouse Removal Efficiency, Hamilton

Foundry, Harrison, Ohio, K&B Design, Inc., Cincinnati, OH, September 3, 1991.

4. Written Communication from D. Borda, The Hamilton Foundry & Machine Co., Harrison, OH, to

L. Gruber, Southwestern Ohio Air Pollution Control Agency, Cincinnati, OH, November 27, 1990.
4-2

5. Summary of Source Test Results, Hunter Schlesser Sandblasting, San Leanardo, CA, Bay Area

Air Quality Management District, San Francisco, CA, March 3, 1993.

6. Summary of Source Test Results, Poly Engineering, Richmond, CA, Bay Area Air Quality

Management District, San Francisco, CA, November 19, 1990.

One additional report ( Peart, J., et al. [title unknown] Federal Highway Administration, Washington, DC,

1995.) was requested from the Federal Highway Administration in March 1995 but never received. 

References 2 through 6 (listed above) document emission tests on enclosed abrasive blasting operations. 

Brief reviews of References 1 through 6 are provided in the following paragraphs.

4.1.1 Reference 1

The most definitive study in terms of data quality and documentation was reported by Kinsey et al.,

as cited above. The reported (uncontrolled) emission factors were based on actual air emissions data from a

pilot-scale test facility within which full-scale abrasive blasting (surface cleaning) was performed. This

entailed the construction and use of a low speed wind tunnel that was large enough to house commercially

available abrasive (sand) blasting equipment. Conventional EPA stack sampling and analysis procedures

were used in each test to determine emissions of particulate matter (PM) and HAP metals generated by

abrasive blasting of mild steel panels (automobile hoods and tank sides) with silica sand. The ten HAP

metals are arsenic (As), beryllium (Be), cadmium (Cd), cobalt (Co), chromium (Cr), manganese (Mn), nickel

(Ni), lead (Pb), antimony (Sb), and selenium (Se). Iron (Fe) emissions also were measured. Duplicate test

runs were conducted at each of nine test conditions covering the nominal range of wind speeds (5, 10, and

15 mph) and types of cleaned surfaces (precleaned, painted, and rusted). Emissions and facility operating

data were collected for each test condition. Finally, uncontrolled PM emission factors were developed for

each test condition. The data from this document are assigned an A rating. The EPA reference test methods

were used, adequate detail was provided, and no problems were reported.

4.1.2 Reference 2

This reference documents an emission test conducted on an enclosed abrasive blasting operation at a

California Naval Aviation Depot. Particulate matter, chromium, and hexavalent chromium emissions were

measured at the outlets of two fabric filters that control emissions from the blasting operations. A modified

EPA Method 5 sampling train was used to measure PM emissions, and CARB Method 425 was used to

measure chromium and hexavalent chromium. The blasting operations use plastic media as the blasting

abrasive. The test report does not include process rates, and emission factors could not be developed from

the data. The PM concentrations measured during the test averaged 3.61 mg/dscm (0.00158 gr/dscf). The

chromium concentrations averaged 0.00187 mg/dscm (8.17x10-7 gr/dscf) and the hexavalent chromium

concentrations averaged 0.000950 mg/dscm (4.12x10 gr/dscf). These data are not rated for use in -7

developing emission factors.

4.1.3 Reference 3

This reference documents an emission test conducted on an enclosed abrasive blasting operation at

Hamilton Foundry in Harrison, Ohio, on August 20 and 21, 1991. Particulate matter emissions were

measured at the inlet and outlet of a fabric filter that controls emissions from the blasting operations and

several other plant processes. The fabric filter collection efficiency was 99.9 percent during testing. The

results from this test are not useful because several processes are ducted to the fabric filter that was tested.
4-3

4.1.4 Reference 4

This reference documents an emission test conducted on an enclosed abrasive blasting operation at

Hamilton Foundry in Harrison, Ohio, on October 30, 1990. Particulate matter emissions were measured at

the inlet and outlet of a fabric filter that controls emissions from the blasting operations and several other

plant processes. The fabric filter collection efficiency was 99.9 percent during testing. The results from this

test are not useful because several processes are ducted to the fabric filter that was tested.

4.1.5 Reference 5

This reference documents an emission test conducted on an enclosed abrasive blasting operation at

Hunter Schlesser Sandblasting in San Leanardo, CA, on February 10, 1993. Particulate matter emissions

were measured at the outlet of a fabric filter that controls emissions from blasting operations. Three CARB

Method 5 test runs were completed, and the average PM concentration was 2.3 mg/dscm (0.001 gr/dscf). 

Glass beads were used as the blast media, and the targeted surfaces included two large motor shields and

several handrails. Process rates are not provided in the report.

4.1.6 Reference 6

This reference documents an emission test conducted on an enclosed abrasive blasting operation at

Poly Engineering in Richmond, CA, on February 10, 1993. Filterable PM emissions were measured at the

outlet of a fabric filter that controls emissions from blasting operations. Three CARB Method 5 test runs

were completed, and the average PM concentration was 0.055 gr/dscf. A CARB certified 30/40 mesh garnet

was used as the blast media, and the targeted surface was unspecified parts. Process rates are provided (lb/hr

of abrasive) in the report, and emission factors were developed in units of lb/1,000 lb of abrasive used. The

test report contains incomplete documentation of the stack test data.

The data from this report are assigned a C rating because of the level of detail provided in the report. 

The test methodology appeared to be sound and no problems were reported. However, sufficient data are not

included in the report to allow for a complete review of the test.

4.2 RESULTS OF DATA ANALYSIS

The individual data sets were evaluated using the criteria and rating system developed by the EPA's

Office of Air Quality Planning and Standards for the development of AP-42 emission factors. This scheme

entails the rating of test data quality followed by the rating of the adequacy of the data base relative to the

characterization of uncontrolled emissions from the source.

A summary of the available test data for uncontrolled and controlled abrasive blasting operations are

provided in Tables 4-2 and 4-3.

A number of comments should be made with regard to the data contained in Tables 4-2 and 4-3. In

the case of Table 4-2, only four of the twelve data sets contained enough information to develop PM and/or

lead emission factors for abrasive blasting operations. Six of the other studies involved some type of

industrial hygiene or ambient air monitoring in the vicinity of the blasting operation. None of the industrial

hygiene/ambient air studies characterized the blasting operation in sufficient detail for further analysis and

emission factor development. Finally, two of the tests did not include process rates. Two
4-4

TABLE 4-2. SUMMARY OF TEST DATA FOR ABRASIVE BLASTING OPERATIONSa

Reference Type of operation Type of fraction, concentration, quality mass/source

document tested abrasive Sampler location μmA mg/m rating extent Comments

Particle size average Data factor,

b

Time weighted Emission

3

Samini, 1973; Outdoor sandblasting Silica sand Within 5 yd (4.6 m) of TP 1.46-76.8 NR N/A 31 samples; no process data

Samini et al., at two steel sandblaster

1975 fabrication yards < 11 11.8 NR N/A 16 sample average; no process data

RP 0.109-8.93 NR N/A 29 samples; no process data

Samini et al., Abrasive cleaning of Stan-Blast < 5 yd (4.6 m) from TP 10.2 NR N/A Sampling time = 185 min

1974 ship hull source

Sandblaster's chest RP 88.8 NR N/A Sampling time = 181 min; blasting time =

< 10 yd (9.1 m) from RP 2.26-9.88 NR N/A No process data available

source

RP 4.58 NR N/A Blasting time = 180 min; no process data

< 11 6.98 NR N/A

150 min; no process data

Landrigan et Abrasive bridge Grit (Black 27 m downwind of TSP (Pb) 0.0129 NR N/A Data for a 6.1-h sampling period during

al., 1980 cleaning of lead-based Beauty) bridge which canvas shroud was not in place for a

paint 2-h period; Pb contributions from paint

chips, vehicle exhaust, and grit; no process

data available

Bareford and Abrasive bridge Sand Center of plume exiting TP — D 57-455 lb/h/ 2.5% Pb for particles < 2.4 μm; sand

Record, 1982 cleaning of lead-based sandblasting bay sandblaster usage—700 lb/h per blaster (no exact

paint throughput available)

TP (Pb) ! D 1.5-4.8 lb/h/ < 1% Pb for particles > 75 μm; sand

< 10 — D 24 lb/h/ Sand usage—700 lb/h per blaster (no exact

< 10 (Pb) — D 0.46 lb/h/ Sand usage—700 lb/h per blaster (no exact

sandblaster usage—700 lb/h per blaster (no exact

sandblaster throughput available)

sandblaster throughput available)

throughput available)

Beddows, General abrasive Grit Breathing zone TP 3-30+ NR N/A 8-h time-weighted averages; grit from coal

1983 blasting of lead-based samples slag typically contains from 20-40 μg of Pb/g

paint of material; grit from copper smelting can

contain up to 6,000 μg Pb/g of material; no

process data reported

Lehner et al., Abrasive bridge Sand 300-400 ft (91-122 m) TSP 0.339-0.482 NR N/A 24-h time-weighted averages; no process

1985 cleaning of lead-based downwind of bridge data or controls specified; assumed to be

paint TSP (Pb) 0.00122- NR N/A essentially uncontrolled

0.00215
4-5

TABLE 4-2. (continued)

Reference Type of operation Type of fraction, concentration, quality mass/source

document tested abrasive Sampler location μmA mg/m rating extent Comments

Particle size average Data factor,

b

Time weighted Emission

3

WhiteMetal Outdoor blasting of 30-60 mesh 5 ft (1.5 m) downwind TSP 257.61 NR N/A Hi-vols installed downwind of dry blasting

Inc., 1987 steel panels coated (0.59-0.25 operation to demonstrate control

with lead-based paint mm) silica 50 ft (15 m) downwind effectiveness of “Jet Stripper”; no sampling

sand TSP 45.99 NR N/A time or process data reported

100 ft (30 m)

downwind

200 ft (61 m)

downwind

500 ft (152 m)

downwind

TSP 6.18 NR N/A

TSP 2.71 NR N/A

TSP 0.90 NR N/A

South Coast Outdoor abrasive Sand In ventilation system TP N/A D 0.041 lb/lb Emission factors determined by source test

Air Quality blasting duct sand of an uncontrolled indoor blasting operation

Management using a quasi-stack technique; original test

District, 1988 Grit TP N/A D 0.010 lb/lb report not available

Shot TP N/A D 0.004 lb/lb

Other TP N/A D 0.010 lb/lb

grit

shot

abrasive

Kinsey et al., Blasting of molded 30-50 mesh 40 ft (12 m) downwind TP, < 10, See Reference 1 A See Table 4-4 Emission factors determined by source tests

1995 steel panels, silica sand < 2.5 in low speed wind tunnel using standard test

painted, cleaned, or methods for total particulate, particle size

rusted distribution, and iron and 10 HAP metals

NEESA Enclosed blasting of Plastic Fabric filter stack TP 3.61 NR N/A Fabric filter-controlled plastic media blast

2-161, 1990 aircraft parts room. No process data. Chromium conc. of

0.00187 mg/m and Cr conc. of 0.00095 3 +6

mg/m3

Hunter Enclosed blasting of Glass beads Fabric filter stack TP 2.3 NR N/A Fabric filter-controlled glass bead blast room. 

Schlesser motor shields and No process data.

Sandblasting, handrails

1993

Poly Enclosed blasting of Garnet Fabric filter stack TP 126 C 0.00069 lb/lb 1,740 lb/hr of abrasive used to blast

Engineering, unspecified parts garnet 700 lb/hr of parts

1990

From references listed in Table 4-1. N/A = not available or not applicable. NR = not rated. a

TP = total particulate matter. RP = respirable particulate matter (# 3.5 μmA) as determined using a 10-mm nylon cyclone followed by a 37-mm filter cassette. TSP = total b

 suspended particulate matter (# 30-50 μmA) as determined by a high volume air sampler.
4-6

TABLE 4-3. SUMMARY OF AVAILABLE CONTROL EFFICIENCY DATA FOR ABRASIVE BLASTING OPERATIONSa

Reference Type of operation Type of technology fraction, control

document tested abrasive employed Sampler location μmA Uncontrolled Controlled efficiency Comments

Control size Measured

Particle

b

Average dust concentration,

mg/m3

WhiteMetal Inc., Outdoor blasting of 30-60 mesh Water jet blasting 5 ft (1.5 m) TSP 257.6 42.3 84 Comparison of

1987 steel panels coated (250-590 μm) nozzle (i.e., “Jet downwind uncontrolled and

with lead-base paint silica sand Stripper”) 50 ft (15 m) TSP 46.0 3.3 93 controlled dust

downwind concentrations

100 ft (30 m) TSP 6.2 0.55 91 assumes identical

downwind test conditions;

200 ft (61 m) TSP 2.7 0.32 88 original test data not

downwind available; no process

500 ft (152 m) TSP 0.90 0.19 79 data or sampling

downwind time reported.

So. Coast Air Outdoor abrasive All Wet blasting (as ! TP NA NA 50% No basis of control

Quality blasting compared to dry estimate provided

Management blasting)

District, 1988

From references listed in Table 4-1. NA = not available. a

TSP = total suspended particulate matter (- #30-50 μmA) as determined by a high volume air sampler. TP = total particulate matter. b
4-7

additional studies (not shown in Table 4-2) had sufficient information to develop emission factors, but the

stacks that were tested ducted emissions from abrasive blasting and other sources.

Several problems were also noted with the Bareford and Record and South Coast AQMD emission

factor studies contained in Table 4-2. Both sets of emission factors were generally of poor quality and thus

were given a D rating based on the criteria discussed above. The emission factors from these studies are not

presented in the AP-42 section, but the South Coast AQMD study provides some valuable information on

"relative dustiness" (the amount of PM emitted by the various blast media) of several abrasives. The study

indicates that total PM emissions from abrasive blasting using grit are about 24 percent of total PM

emissions from abrasive blasting with sand. The study also indicates that total PM emissions from abrasive

blasting using shot are about 10 percent of total PM emissions from abrasive blasting with sand. This

information is presented in the text of the AP-42 section.

With regard to Table 4-3, only two data sets were identified which address control efficiency applied

to abrasive blasting operations. Both data sets were found to be extremely limited in scope and of poor

quality. As with the data for uncontrolled emissions, documentation of process operation was nonexistent in

both cases. However, the control efficiencies presented in these documents are discussed in the AP-42

section.

Table 4-4 provides an overall summary of the particulate emission factors developed in the study by

Kinsey, et al. As shown in Table 4-4, the emission factors for total PM tend to increase with wind speed for

each of the three types of mild steel surfaces blasted. Because the emissions contained no condensible

fraction, the total PM was collected entirely as “filterable” PM. The emission factors for PM-10, on the other

hand, show a tendency to decrease when the wind speed exceeds 10 mph. No substantial difference in

particulate emissions was observed, however, by either the type of surface cleaned or coating removed by the

abrasive.

The emission factors for five HAP metals and Fe are summarized in Tables 4-5, 4-6, and 4-7 for the

total PM, PM-10, and PM-2.5 particle size fractions, respectively. Except for Fe, these emission factors are

of the order of 10 kg per kg of sand. Five other HAP metals (As, Be, Co, Sb, and Se) were generally not !6

detected above blank levels.

4.3 DEVELOPMENT OF CANDIDATE EMISSION FACTORS

Based primarily on lack of documentation of the abrasive blasting process operation associated with

most of the tests summarized in Tables 4-2 and 4-3 (as noted above), only References 1 and 6 were used for

developing candidate PM emission factors. Reference 1 addresses only silica sand as a blasting medium, and

Reference 6 quantifies fabric filter-controlled PM emissions from blasting with garnet.

Regarding overall PM emissions from the Reference 1 abrasive blasting tests, no significant

dependence on the surface condition of the mild steel target panels was observed. Moreover, only the factors

for total PM emissions showed a consistent dependence on wind speed.

The candidate emission factors for PM-10 and PM-2.5 were derived (using Reference 1 data) as

simple averages of the results from the sand blasting of the three target panels, as shown in Table 4-8. The

candidate emission factors for total PM were differentiated by wind speed, as shown in Table 4-9. 
4-8

TABLE 4-4. SUMMARY OF PM TEST DATA FROM REFERENCE 1a

Operating

condition

Test

runs

Total PM

emission factor, 

kg/kg sand

PM-10 emission

factor,

kg/kg sandb

PM-2.5

emission

factor,

kg/kg sandc

Result of mass

balance, %

closured

Clean surface

 5 mph 17/18 0.029 0.017 0.0024 100

 10 mph 9/10 0.068 0.0081 0.0022 95

 15 mph 23/24 0.092 0.0045 0.00090 86

Average emission factor 0.063 0.0099 0.0018

Painted surface

 5 mph 15/16 0.027 0.0059 0.0010 99

 10 mph 7/8 0.070 0.052 0.00086 98

 15 mph 21/22 0.091 0.0091 0.0013 79

Average emission factor 0.063 0.022 0.0011

Oxidized surface

 5 mph 19/20 0.025 0.0057 0.0018 100

 10 mph 11/12 0.026 0.014 0.0011 100

 15 mph 25/26 0.089 0.0030 0.00026 82

Average emission factor 0.047 0.0074 0.0011

All results to two significant figures. Sand blasting only. Data are A-rated. a

Particles #10 μm in aerodynamic diameter (equivalent unit density spheres). b

Particles #2.5 μm in aerodynamic diameter (equivalent unit density spheres). c

total sand recovered + total particulate emissions Percent closure = = 100 d _____________________________________________________

total sand fed to tunnel
4-9

TABLE 4-5. SUMMARY OF EMISSION FACTORS FOR PM METALS

Operating condition Test run

Total emission factor, kg/kg sand

Cadmium Chromium Iron Manganese Nickel Lead

Clean surface

5 mph 17/18 1.8e-06 2.5e-06 2.8e-04 1.5e-06 2.0e-06 1.8e-06

10 mph 9/10 7.0e-07 6.5e-06 5.1e-04 2.9e-06 4.9e-06 1.3e-06

15 mph 23/24 1.8e-06 9.6e-06 4.2e-04 2.3e-06 8.0e-06 3.9e-06

Average emission factor 1.4e-06 6.2e-06 4.0e-04 2.3e-06 5.0e-06 2.4e-06

Painted surface

5 mph 15/16 9.5e-07 4.3e-06 2.9e-04 2.0e-06 2.0e-06 7.1e-06

10 mph 7/8 1.1e-06 8.7e-06 3.5e-04 4.0e-06 4.7e-06 1.4e-05

15 mph 21/22 6.3e-06 1.9e-05 5.1e-04 4.0e-06 2.7e-05 2.0e-05

Average emission factor 2.8e-06 1.1e-05 3.8e-04 3.3e-06 1.1e-05 1.4e-05

Oxidized surface

5 mph 19/20 6.4e-07 1.4e-06 6.2e-04 4.2e-06 1.3e-06 1.6e-05

10 mph 11/12 1.2e-06 5.2e-06 1.6e-03 1.2e-05 7.1e-06 7.8e-06

15 mph 25/26 1.6e-06 7.2e-06 1.3e-03 4.5e-06 8.3e-06 2.3e-05

Average emission factor 1.1e-06 4.6e-06 1.2e-03 7.1e-06 5.5e-06 1.5e-05

TABLE 4-6. SUMMARY OF EMISSION FACTORS FOR PM-10 METALS

Operating condition Test run

PM-10 emission factor, kg/kg sand

Cadmium Chromium Iron Manganese Nickel Lead

Clean surface

5 mph 17/18 1.8e-06 2.4e-06 2.1e-04 1.3e-06 2.0e-06 1.8e-06

10 mph 9/10 a 6.4e-06 3.1e-04 2.1e-06 4.4e-06 1.3e-06

15 mph 23/24 1.3e-06 9.5e-06 2.7e-04 1.6e-06 7.6e-06 3.9e-06

Average emission factor a 6.1e-06 2.6e-04 1.7e-06 4.7e-06 2.3e-06

Painted surface

5 mph 15/16 4.8e-07 4.0e-06 1.8e-04 1.4e-06 1.9e-06 3.5e-06

10 mph 7/8 a 8.0e-06 2.8e-04 3.2e-06 4.2e-06 1.0e-05

15 mph 21/22 2.9e-06 1.8e-05 3.0e-04 3.0e-06 2.6e-05 7.9e-06

Average emission factor a 6.1e-06 2.6e-04 1.7e-06 4.7e-06 2.3e-06

Oxidized surface

5 mph 19/20 3.7e-7 1.4e-06 3.8e-04 2.4e-06 1.2e-06 7.0e-06

10 mph 11/12 a 5.1e-06 8.2e-04 6.6e-06 6.3e-06 5.6e-06

15 mph 25/26 2.2e-07 6.9e-06 4.8e-04 2.0e-06 7.8e-06 8.4e-06

Average emission factor a 4.5e-06 5.6e-04 3.7e-06 5.1e-06 7.0e-06

Cadmium was not detected in any of the particle sizing fractions and therefore the calculations could not a

 be performed.
4-10

TABLE 4-7. SUMMARY OF EMISSION FACTORS FOR PM-2.5 METALS

Operating condition Test run

PM-2.5 emission factor, kg/kg sand

Cadmium Chromium Iron Manganese Nickel Lead

Clean surface

5 mph 17/18 1.4e-06 1.5e-06 1.1e-04 1.5e-07 1.1e-06 1.1e-06

10 mph 9/10 a 3.3e-06 2.0e-04 2.4e-07 1.6e-06 1.2e-06

15 mph 23/24 8.0e-07 5.4e-06 1.8e-04 7.0e-08 3.0e-06 3.9e-06

Average emission factor a 3.4e-06 1.7e-04 1.5e-07 1.9e-06 2.1e-06

Painted surface

5 mph 15/16 2.1e-07 2.1e-06 1.0e-04 2.9e-06 8.6e-07 2.8e-06

10 mph 7/8 a 4.0e-06 1.6e-04 1.2e-06 1.5e-06 5.6e-06

15 mph 21/22 7.6e-08 7.4e-06 1.5e-04 1.2e-07 8.1e-06 6.3e-06

Average emission factor a 4.5e-06 1.4e-04 5.4e-07 3.5e-06 4.9e-06

Oxidized surface

5 mph 19/20 3.1e-07 3.2e-07 1.4e-04 4.2e-07 4.2e-07 4.5e-06

10 mph 11/12 a 3.0e-06 1.9e-04 2.4e-07 3.4e-06 4.9e-06

15 mph 25/26 3.1e-09 3.7e-06 2.2e-04 8.6e-08 4.0e-06 6.6e-06

Average emission factor a 2.4e-06 1.8e-04 2.5e-07 2.6e-06 5.3e-06

Cadmium was not detected in any of the particle sizing fractions and therefore the calculations could not a

 be performed.

TABLE 4-8. CANDIDATE PM-10 AND PM-2.5 EMISSION FACTORS

PM emission factors, kg/kg sand

Surface PM-10 PM-2.5

Precleaned 0.0099 0.0018

Painted 0.022 0.0011

Oxidized 0.0074 0.0011

Average 0.013 0.0013
4-11

TABLE 4-9. CANDIDATE TOTAL PM EMISSION FACTORS DIFFERENTIATED

BY WIND SPEED

Wind speed

Emission factor (kg/kg sand) by surface type

Precleaned Painted Oxidized Average

5 mph 0.029 0.027 0.025 0.027

10 mph 0.068 0.070 0.026 0.055

15 mph 0.092 0.091 0.089 0.091

TABLE 4-10. CANDIDATE EMISSION FACTOR FOR GARNET BLASTING

Source Control

No. of

tests

EMISSION

FACTOR

RATING

Total PM

emission factor,

kg/kg of abrasive

used

Reference

No.

Enclosed blasting of unspecified

metal parts with 30/40 mesh garnet

Fabric filter 1 E 0.00069 6

All of these candidate emission factors are assigned E ratings because they are based on data from a single

study.

Data from Reference 6 were used to calculate an emission factor for fabric filter-controlled abrasive

(garnet) blasting. This emission factor is shown in Table 4-10. 

Because the emissions of HAP metals are strongly dependent on the target material composition and

its surface condition, no specific candidate emission factors are proposed.

4.4 REFERENCES FOR SECTION 4

 1. J. S. Kinsey, 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,

September 11, 1989.

 2. NEESA 2-161. Particulate and Chromium Emission Testing at Plastic Media Blasting Facility,

BLDG 25, Naval Aviation Depot, Naval Air Station, Alameda, CA, Naval Energy and Environmental

Support Activity, Port Hueneme, CA, May 1990.

 3. Determination of Particulate Emission Rates & Baghouse Removal Efficiency, Hamilton Foundry,

Harrison, Ohio, K&B Design, Inc., Cincinnati, OH, September 3, 1991.

 4. Written Communication from D. Borda, The Hamilton Foundry & Machine Co., Harrison, OH, to L.

Gruber, Southwestern Ohio Air Pollution Control Agency, Cincinnati, OH, November 27, 1990.

 5. Summary of Source Test Results, Hunter Schlesser Sandblasting, San Leanardo, CA, Bay Area Air

Quality Management District, San Francisco, CA, March 3, 1993.
4-12

 6. Summary of Source Test Results, Poly Engineering, Richmond, CA, Bay Area Air Quality

Management District, San Francisco, CA, November 19, 1990.

 7. Samimi, B., “Silica Dust in Sandblasting Operations,” Ph.D. Thesis, Tulane University, 1973.

 8. Samimi, B., et al., “Dust Sampling Results at a Sandblasting Yard Using Stan-Blast in the New Orleans

Region: A Preliminary Report,” NIOSH-00036278, New Orleans, LA, 1974.

 9. Samimi, B., et al., “The Efficiency of Protective Hoods Used by Sandblasters to Reduce Silica Dust

Exposure,” Am. Indus. Hyg. Assn. J., 36(2), February 1975.

10. Landrigan, P. J., et al., “Health Hazard Evaluation Report on the Tobin-Mystic River Bridge,”

TA80-099-859, NIOSH Report to City Boston Department of Health and Hospitals, Boston, MA,

July 25, 1980.

11. Bareford, P. E., and F. A. Record, “Air Monitoring at the Bourne Bridge Cape Cod Canal,

Massachusetts,” Final Report, Contract No. DACW 33-79-C-0126, U.S. Army Corps of Engineers,

New England Division, Waltham, MA, January 1982.

12. Beddows, N. A., “Lead Hazards and How to Control Them,” Natl. Safety News, 128(6), December

1983.

13. Lehner, E., et al., Memo to D. M. Moline, Department of Public Utilities, Division of Environmental

Services, City of Toledo, OH, January 31, 1985.

14. WhiteMetal, Inc., “Protecting Our Environment with the Jet Stripper,” Houston, TX, June 1987.

15. South Coast Air Quality Management District, “Section 2: Unconfined Abrasive Blasting,” Draft

Document, El Monte, CA, September 8, 1988.
5-1

5. PROPOSED AP-42 SECTION 13.2.6

The following pages contain the proposed new AP-42 section for abrasive blasting as it would

actually appear in the document.