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# Emissions Factors & AP 42, Compilation of Air Pollutant Emission Factors

An emissions factor is a representative value that attempts to re late the quantity of a pollutant released to the atmosphere with an ac tivity associated with the release of that pollutant. These factors are usually expressed as the weight of pollutant divided by a unit weight, volume, dist ance, or duration of the activity emitting the pollutant (e.g., kilograms of particulate emitted per megagram of co al 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 cate gories. A source category is a spec ific industry sector or group of similar emitting sources. The emissi on factors have been developed an d 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 an d updates to the fifteen chapters available in Volume I, Stationary Point and Area Sources

. The latest emissions fact ors are available below on this website. Use the AP 42 Chapter webpage links below to access th e document by chapter

.

WebFIRE: Access to these emissions factors and other EP A reviewed stationary point and area source factors is also av ailable from the WebFIRE application . WebFIRE provides fast and complete access to the Agency’s air emission s factors information.

For information about emissions factor s 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 peri od ended December 14, 2009. AP 42, Fifth Edition Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Sources 

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7 / 9 / 201 4 htt p ://www.e p a. g ov/ttn/chief/a p 42/index.htm lAP 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 emissi ons factors, supported by the EPA, please see the table of contents below.

Emissions Factors Procedures Procedures for the Development of Emissions Fa ctors from Stationary Sources . This page includes both the existing procedures and the review dr aft 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 Si zed Emission Factors for Selected Sources -- October 1986 (PDF 2M) 

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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 

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# AP 42, Fifth Edition, Volume I Chapter 13: Miscellaneous Sources

13.0 Introduction to Miscellaneous Source s

13.1 Wildfires and Prescribed Burning
Fina l Section - Supplement B, October 1996 (PDF 99K) Related Information

13.2 Introduction to Fugitive Dust Source s Final Section - January 1995 (PDF 9K)

13.2.1 Paved Road s Final Section - January 2011 (PDF 400K) Background Documentation - January 2011 (PDF 1.7M) Related Information

13.2.2 Unpaved Road s Fina l 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 eq uation in Section 13.2.4." The reference to sect ion to 13.2.2 was an error.

13.2.4 Aggregate Handling and Storage Piles
Fina l 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
Fina l 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 Abrasiv e Blasting
Fina l Section - Supplement D, Octo ber 1997 (PDF 69K) Background Document (PDF 301K)

13.3 Explosives Detonation Final Section - February 1980 (PDF 88K) 

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7 / 9 / 201 4 htt p ://www.e p a. g ov/ttn/chief/a p 42/ch13/index.htm lAP 42 Emissions Factors by Chapter 13.4 Wet Cooling Tower s Fina l Section - January 1995 (PDF 114K )

13.5 Industrial Flare s Fina l Section - September 1991 (PDF 46K) Flare Efficiency Study, EPA 600/2

- 83

- 052 - July 1983 (PDF 3.2M) Primary reference for the section. 

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## 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 13.0-113.2 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 PMwith 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 13.2-1settle 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 emis sions 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 contin uously 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 st reets and highways, as well as roadways at industrial facilities, can be major sources of the atmospheric part iculate 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 surro unding unstabilized ar eas. In the absence of continuous addition of fresh material (through localized track out or application of antiskid material), paved road surface loading should reac h 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 material 28 . 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 emis sion 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 vers ions of the paved road emission factor equation includes estimates of emissions from exhaust, brake we ar, and tire wear based on emis sion rates for vehicles in the 1980 calendar year fleet. The am ount of PM released from vehicl e exhaust has decreased since 1980 due to lower new vehicle em ission 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 f ound 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 microm eters [μm] in physical di ameter) 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. Si lt loading is the product of the silt fraction and the total loading, and is a bbreviated "sL". Additional detail s 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 ch aracterizing 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 va lue, which is substantially lower than the late winter/early spring values.1/11 Miscellaneous Sources 13.2.1-2Figure 13.2.1-1. Deposition and removal processes.13.2.1-4 EMISSION FACTORS 1/11 13.2.1.3 Predictive Emission Factor Equations 10,29

The quantity of particulate emissions from resusp ension of loose material on the road surface due to vehicle travel on a dry paved road ma y be estimated using the following empirical expression: 

## E = k (sL) 0.91 x 

## (W) 1.02 (1)

where: 

## E = particulate emission factor (hav ing 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/m 2 ), and 

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

It is important to note that E quation 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, th en 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 shoul d 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 ran g e a Particle Size Multi p lier k b

g /VKT g /VMT lb/VMT

PM-2.5 c 0.15 0.25 0.00054
PM-10 0.62 1.00 0.0022
PM-15 0.77 1.23 0.0027
PM-30 d 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). Th e multiplier k includes unit conversions to produce emission fact ors in the units shown for the indicated size range from the mixed units required in Equation 1.

c The k-factors for PM 2.5 were based on the average PM 2.5 :PM 10 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 contro lled and uncontrolled industrial paved roads. The majority of tests involved freely flowing vehicles trav eling 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/m 2 0.04 - 570 grains/square foot (ft 2 )
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 PM 10 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., applicat ion 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, user s are encouraged to consider an assessment of the impacts of the influence of extrapolation to the overall emissions and alte rnative 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 fact or 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 a ccess roadways, which are difficult to sample, the collection and use of site-specifi c silt loading (sL) data for public paved road emission inventories are strongly recommended. The field and laborat ory procedures for determining surface material silt content and surface dust loadin g 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 fr equency of measurable (> 0.254 mm [ 0.01 inch]) precipitation by application of a precipitation correction term. Th e precipitation correction term can be applied on a daily or an hourly 

### basis 26, 38



### .
For the daily basis, Equation 1 becomes:



## E ext 

## = [ k (sL) 0.91 x 

## (W) 1.02 

## ] (1 – P /4 N ) (2)

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 "wet" days with at least 0.254 mm (0.01 in) of precipitation during the averaging period, and13.2.1-6 EMISSION FACTORS 1/11 N = number of days in the averaging peri od (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:



## E ext 

## = [ k (sL) 0.91 x 

## (W) 1.02 

## ] (1 –1.2 P / N ) (3)

where k

, sL

, W, and S are as defined in Equation 1 and



## E ext = annual or other long-term average em ission 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 effe ct 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 em issions averaging period is evaluated. For the special case where this equation is used to calc ulate 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 "credi t" is applied to the first hours following cessation of precipitation. In this special case, it is sugg ested that this 20% "credit" be applied on a basis of one hour credit for each hour of prec ipitation 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 ge ographical 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 States 23

. Alternative sources include other Department of Commerce publications (such as local climatological data summ aries). The National Climatic Data Center (NCDC) offers several products th at 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 assumpti on 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 rati ng that would be applied to Equation 1.1/11 Miscellaneous Sources 13.2.1.2Figure 13.2.1-2. Mean number of days with 0.01 inchor more of precipitation in the United States.13.2.1-8 EMISSION FACTORS 1/11 Table 13.2.1-2 presents recommended default si lt loadings for norma l baseline conditions and for wintertime baseline conditio ns in areas that experience fro zen precipitation with periodic application of antiskid material 24 . The winter baseline is repres ented 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/m 2

.

Table 13.2.1-2. Ubiquitous Silt Load ing Default Values with Hot Spot Contributions from Anti-Skid Abrasives (g/m 2 )
ADT Category < 500 500-5,000 5,000-10,000 > 10,000

Ubiquitous Baseline g/m 2 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/m 2 ) 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 te mporary) silt loading contribution of 2 g/m 2 occurs with each application of antiskid abrasive for s now/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 PM 10 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 PM 2.5 emissions, it is recommended that three additional miles of road be added for ea ch 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 dependent1/11 Miscellaneous Sources 13.2.1-9 upon: traffic characteristics (speed , ADT, and fraction of heavy ve hicles); road characteristics (curbs, number of lanes, parking lanes); local land use (agriculture, new residential construction) and regional/seasonal factors (sno w/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 fo r 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 limite d access roadways from one part of the country to another. For annual conditions, a default value of 0.015 g/m 2 is recommended for limited access roadways. 9,22 Even fewer of the available data corres pond 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/m 2 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 reflec t 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 safe r than for public roads. Therefor e, 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 qualit y rating of the equation should be reduced by 2 levels.

The predictive accuracy of Equation 1 requir es thorough on-site charac terization of road silt loading. Road surface sampling is time-consuming and potentially ha zardous 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 monito ring is a new alternative silt loading or road dust emission characterization me thod 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 fa ctor is needed for each mobile monitoring configuration (test vehicle and sa mpling system), to convert the re lative dust emission intensity to an equivalent silt loading or emission fact or. Typically, portable continuous particle concentration monitors do not comply with Federal Reference Met hod (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 prec ision and to accommodate variat ions in road silt loading, vehicle speed, road dust characteristics and othe r road conditions that may influence mobile monitoring measurements or emissions characterist ics. 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 vehicl e 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 cap abilities to provide reliable temporally and spatially resolved silt loading or emissions factors with increased coverage, improved sa fety, reduced traffic interference and decreased cost. 40, 41, 42Table 13.2.1-3 (Metric And English Units). TYPICALSILT CONTENT AND LOADING VALUES FOR PAVED ROADS ATINDUSTRIALFACILITIESaIndustryNo. ofSitesNo. OfSamplesSilt Content (%)No. ofTravelLanesTotal Loading x 10-3Silt Loading(g/m2)RangeMeanRangeMeanUnitsbRangeMeanCopper smelting1315.4-21.719.0212.9 - 19.515.9kg/km188-40029245.8 - 69.255.4lb/miIron and steel production9 481.1-35.712.520.006 - 4.770.495 kg/km0.09-799.70.020 -16.91.75lb/miAsphalt batching1 32.6 - 4.63.3112.1 - 18.014.9kg/km76-19312043.0 - 64.052.8lb/miConcrete batching1 35.2 - 6.05.521.4 - 1.81.7kg/km11-12125.0 - 6.45.9lb/miSand and gravel processing1 36.4 - 7.97.112.8 - 5.53.8kg/km53-95709.9 - 19.413.3lb/miMunicipal solid waste landfill2 7 - 2-1.1-32.07.4Quarry1 6 - 2-2.4-148.2Corn wet mills3 15 - 2-0.05 – 2.91.1aReferences 1-2,5-6,11-13. Values represent samples collected fromindustrialroads. Public road silt loading values are presentedin Table-13.2.1-2. Dashes indicate information not available.bMultiply entries by 1000 to obtain stated units; kilograms perkilometer (kg/km) and pounds per mile (lb/mi).

13.2.1.10 EMISSION FACTORS 1/111/11 Miscellaneous Sources 13.2.1-11 13.2.1.4 Controls 6,25

Because of the importance of the silt loading, control techniques for paved roads attempt either to prevent material from being deposited onto the surfac e (preventive controls) or to remove from the travel lanes any material that ha s 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. Ac tual 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 mate rial onto the travel lane s 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 retu rn to equilibrium silt loading condition. That is to say, the number and length of public roads w ithin most areas of interest preclude any widespread and routine use of mitigative contro ls. 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, cont rolled emission factors may be obtained by substituting controlled silt loading va lues 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 cont rols over time. The use of Mobile Monitoring Methodologies provide an improved means to track progr ess 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 PM 2.5 was revised to 25% of PM 10 . The approximately 55% reduction was a result of emission te sting 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 lo ading statistical information.

3) Editorial changes within the text were made indicating the possible causes of variations in the silt loading between ro ads 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 clar ify the role of dust loading in resuspension. Additional mi nor 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 ti re wear. A parameter C representing these emissions was included in the predictive equation. The parameter C varied with aerodynamic size range of the particulat e 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 PM 2.5 particle size multiplier was revised to 15% of PM 10 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 revise d. The revision is based upon stepwise regression of 83 profile emissi ons tests and an adjustment of individual test data for the exhaust; break wear and tire wear em issions prior to regr ession of the data.

2) The C term is removed from the empirica l 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 PM 2.5 particle size multiplier was revised to 25% of PM 10 since the PM 10 test data used to develop the equation did not meet the necessary PM 10 concentrations for a ratio of 15%.

4) The lower speed of the vehicle speed range supported by the em pirical predictive equation was revised to 1 mph.

5) Information was added on an improved methodology to develop spatially and temporally resolved silt loadings or em issions 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, Resear ch Triangle Park, NC, March 1976.

2. R. Bohn, et al., Fugitive Emissions From In tegrated 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 F ugitive 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, R esearch Triangle Park, NC, July 1977.

5. Size Specific Particulate Emission Factors For Uncontrolled Industrial And Rural Roads, EPA Contract No. 68-02-3158, Midwest Resear ch 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 Rese arch 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 Particulat e 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 Resear ch Institute, Kansas Ci ty, MO, March 1993.

11. Evaluation Of Open Dust Sources In The Vicinity Of Buffalo, New York, EPA Contract No. 68-02-2545, Midwest Research Ins titute, 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 Institut e, Kansas City, MO, September 1987.

13. Chicago Area Particulate Matter Emissi on Inventory — Sampling And Analysis, Contract No. 68-02-4395, Midwest Research Ins titute, 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 Haro ld 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-Sc ience, Pasadena, CA, January 1987.

21. Characterization Of PM-10 Emissions From An tiskid Materials Applied To Ice- And SnowCovered Roadways, EPA Contract No. 68-D0-0137, Midwes t 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 Commer ce, 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 11 th 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, Resear ch Triangle Park, NC, September 1988.

26. Written communication (Technical Memorandum) from G. Muleski, Midwest Research Institute, Kansas City, MO, to B. Kuykenda l, U. S. Environmental Protection Agency, Research Triangle Park, NC, September 27, 2001.

27. EPA, 2002b. MOBILE6 User Guid e, United States Enviro nmental Protection Agency, Office of Transportation and Air Quality. EPA420-R-02-028, October 2002.



### 28. Written communication (Techni cal Memorandum) from P. Hemmer, E.H. Pechan & Associates, Inc., Durham, NC to B. Kuykend al, U. S. Environmental Protection Agency, Research Triangle Park , NC, August, 21, 2003.



### 29. EPA, 2009, MOVES2010 User Guide, United States Environmenta l Protection Agency, Office of Transporta tion and Air Quality. EPA420B-09-041, December 2009.



### 30. Fugitive Particulate Matter Emissions, U.S. Environmental Protection Agency, Research Triangle Park, NC, Midwest Res earch Institute Project No. 4604-06, April 15, 1997.

31. Midwest Research Institute, Roadway Emissions Field Test s 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, Midw est 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, Midw est 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, Midw est 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 In stitute 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 Gl over 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/M PG, RTP, NC and Gary Dolce and Rudolph Kapich ak 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 fo r 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 Mob ile Monitoring Technologies , Clark County Department of Air Quality and Environmental Manage ment, Las Vegas, NV, December 22, 2008.

43. Midwest Research Institute; Analysis of the Fine Fracti on of Particulate Matter in Fugitive Dust; Western Governors’ Associ ation - 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 Agen c y

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 Agen c y

January 2011

### NOTICE

The information in this document has been funded by the Office of Air Quality Planning and Standards, U.S. Environmental Pr otection 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 sc heme 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 mate rial in AP-42 section ........................... 4-69

4.4 References for Section 4 ................................................................. 4-70
Appendix A Response to Comments ........................................................................... A-1v 

### LIST OF FIGURES

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

4-1 PM 10 

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

4-2 PM 10 

### Emissions Factor Data Base by Averag e Vehicle Weight ( 93 test runs). 4-46
4-3 Silt Loading vs. Average Vehicle Weight (93 Test Runs). ................................ 4-47

4-4 PM 10 

### 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 F actor Data Excluding Z-3. ................................. 4-52

4-9 Cumulative Distribution of Pr edicted/Actual Ratios. ........................................ 4-57

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

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

4-12 Predicted vs Actual PM10 Emi ssions 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 Vehicl e Weight (unrestricted range). ............ 4-66
4-17 Predictive Accuracy by Average Vehi cle Weight (restricted range). ................ 4-67vi 

### 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 Pave d Road Tests for Reference 8 ......................... 4-13
4-7 Summary of Paved Road Emissi on 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 Emissi on 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 Da ta from MCP’s Marshall,
Minnesota Facility (Reference 33) ..................................................................... 4-24
4-13 Summary of Emissions Da ta 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 M odel .......................... 4-28

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

4-18 Correlation Matrix for log-transformed PM 10 

### data ............................................ 4-53
4-19 Regression Analysis using Silt Loading and Weight. ........................................ 4-55
4-20 Comparison of Previous and New Equations for Es timating Paved Road Dust

Emissions ........................................................................................................... 4-611-1 

### 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 em ission source categories and to update existing emission factors. AP-42 is pe riodically updated by EPA to res pond to new emission factor needs of EPA, State, and loca l air pollution control programs and industry.

An emission factor relates the quantity (wei ght) of pollutants emitted to a unit of activity of the source. The uses for the emi ssion 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 ba ckground information from test reports and other information to support preparation of a revise d AP-42 section to replace existing Section 13.2.1, "Paved Roads," dated November 2006.

The principal pollutant of interest in this re port is “particulate matter” (PM), with special emphasis placed on “PM 10 

### ” - particulate matte r no greater than 10 μ mA (micrometers in aerodynamic diameter) and PM 2.5 

### . PM 10 

### and PM 2.5 

### form the basis for the current National Ambient Air Quality Standards (NAAQSs) for particulate matter. PM 10 

### and PM 2.5 

### thus represent the two size ranges of particulate matter that are of greatest regul atory interest. Nevertheless, formal establishment of PM 10 

### and PM 2.5 

### as the standard basis is relatively recent, and many emission tests have referenced ot her particle size ranges. Other size ranges employed in this report are:

TSP Total Suspended Particulate, as measur ed 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 th e particle capture charact eristics of the hi-vol sampler are dependent upon approach wind velocit y, the effective D50 (i.e., 50% of the particles are captured and 50% are not) varies roughly from 25 to 50 μ mA.

SP Suspended Particulate, which is used as a surrogate for TSP. Defined as PM no greater than 30 μ mA. SP also may be denoted as “PM 30 

### .”

IP Inhalable Particulate, de fined as PM no greater than 15 μ mA. Throughout the late 1970s and the early 1980s, it was clear that EPA intended to revise th e 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 μ mA frequently mentioned. Thus, many field1-2 

### studies were conducted using IP emission measur ements because it was believed that IP would be the basis for the new NAAQS. IP may also be represented by “PM 15 

### .”

FP Fine Particulate, define d as PM no greater than 2.5 μ mA. FP also may be denoted as “PM 2.5 

### .”

This background report consists of five sect ions. Section 1 provi des 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 collecti on and analysis procedures; it desc ribes the literature search, the screening of emission test repo rts, and the quality rating system for both emission data and emission factors. Section 4 details the developm ent of paved road emission factors for the draft AP-42 section; it includes the revi ew 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 pave d surface, such as public and industrial roads an d parking lots. These emission s may originate from material previously deposited on the travel surface, resu spension of material carried by the vehicle, deposits from undercarriages, engine exhaust ga ses or tire and brake wear. Depending on the road surface characteristics, ve hicle 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. Su rface loading is in turn replenished by other sources (e.g., pavement wear, deposition of ma terial from vehicles, deposition from other nearby sources, carryout from surrounding unpa ved areas, and litter). Because of the importance of the surface loading, available co ntrol 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 characte ristics, and viable control options. For th e purpose of estimating particulate emissions and determini ng 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 ha ve lower surface loadings than industrial roads, the fact that these road s have far greater traffic volume s may result in a substantial contribution to the measured air qua lity in certain areas. In addition, public roads in industrial areas can be often heavily load ed and traveled by heavy vehicl es. In that instance, better emission estimates might be obtai ned 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 roa d, or parking lots may have such a high surface loadings that the paved surface is covered wi th 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 unpav ed 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 p ublic 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 = p articulate emission factor (g/VKT)

s = surface material content si lt, defined as particles < 75 μ m diameter (%) L = surface material loading, defined as mass of particles per
unit area of the travel su r face ( g / m 2 ) 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
PM 15 

### 2.54 0.8
PM 10 

### 2.28 0.8
PM 2.5 

### 1.02 0.6

The form of the emission fact or model is reasonably consiste nt throughout a ll particle size fractions of interest.

The urban paved road emission factors repres ented by Equation 2-1 did not change since their inclusion in the 4th Edition (September 1985 ) and the January 1995 re vision. 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, "I ndustrial Paved Roads," was first pub lished in 1983 and was slightly modified in Supplement B (1988 ) to the 4th Edition. Secti on 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 augmen tation factor (dimensionless) n = number of traffic lanes (dimensionless) s = surface material silt content (%) L = surface material loadi ng across all traffic lanes (kg/km) W = average vehicle weight (Mg)

The basic form of Equation 2- 2 dates from a 1979 report a nd was originally included in Supplement 14 to AP-42 (May 198 3). The version used in AP-42 was slightly revised in that 0.7

7.228010 s

n 4 I 0.022 E ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= WL2-3 

### the leading term (i.e., 0.022 in Eq. [2-2]) was reduced by 14%. The industrial road augmentation factor (I) was includ ed to take into account for hi gher emissions from industrial roads than from urban road s; it varied from 1 to 7. The em ission factor equation was rated "B" for cases with I =1 and "D" otherwise.

For smaller particle size ranges, models so mewhat 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 si ze range as follows:

Particle size fraction k (g/VKT)

PM 15 

### 0.28
PM 10 

### 2.22
PM 2.5 

### 0.081

These models represented by Equa tion 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, fo r which separate single-valued emission factors were developed. Sectio n 11.2.6 recommended the following for light-duty (less than 4 tons) vehicles traveling over roads where the surfac e material was dry and the road was heavily loaded (silt loading greater than 15 g/m 2 

### ):

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)

PM 15 

### 0.12
PM 10 

### 0.093

The single-valued emission fa ctors 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 a pplications. For example, inventories of industrial2-4 facilities (particularly of iron and steel plants) conducted throughout the 198 0s yielded measured silt loading values substantially lower th an 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 gr adually realized that source emission levels are not a question of ownership but rather a question of surface loading and traffic characteristics. Confirmatory eviden ce was obtained in a 1989 field program 29 

### which 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 f actors 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, th e update employed an approach slightly different than that used in the past. In addition to re viewing test data obtained since the September 1988 update 8

, 

### 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 cont rolled and uncontrolled tests, w ith the anticipation that the reformulated emission factor will be applicable over a far greater range of source conditions.

The inclusion of controlled tests represente d a break with EPA previous guidelines for preparing AP-42 sections 9 

### . Those guidelines presented a clear preference that only uncontrolled tests be used to develop an emi ssion factor. However, the principal control measures for paved roads seek to reduce the valu e 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, aver age vehicle weight and a particle size multiplier as independent variables. The resulting equation was:

)3/()2/( 5.1 65.0 

### WsL kE = (2-5)



### where: E = pa rticulate emission factor (having units matching th e 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 emi ssions 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
PM 2.5 

### 2.1 3.3 0.0073
PM 10 

### 4.6 7.3 0.016

PM 15 

### 5.5 9.0 0.020
PM 30 

### 24 38 0.082

2.2.3 October 2002 through December 2003

Prior to October 2002, the basis of the pa rticle sizing informa tion for paved roads emissions factors was high volume sampler impact ors data. While the initial particle sizing was performed by cyclones, subs equent particle sizing was perf ormed by slotted impactors. The impactor data had biases created by particle bounce and reintrainment. As such particle sizing below 10 μm was questioned. In Octobe r 2002, a three city pa ved 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 st udy formed the basis for revising the PM 2.5 

### particle size multiplier k from 2.1 g/VK T (3.3 g/VMT or 0.0073 lb/VMT) to 1.1 g/VKT (1.8 g/VMT or 0.0040 lb/V MT). The form of the pr edictive equation and the exponents for silt loading and average vehicle weight were un changed. 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 s ilt loading values for public paved roads. The remaining numerical revisions that were ma de 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 partic ulate emissions was included for every day that there was measureable rain for that day. A similar adju stment 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 Secti on for paved roads incorporated a constant in the predictive e quation 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, brak e wear and tire wear we re 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. W ith the introduc tion of EPA’s Mobile6.2 model, estimates of PM emissions fr om exhaust, brake wear and tire wear were calculated based upon the vehicl e 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 traffi c 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 deve loped at the time prior to when the vehicles in the fleet incorporated significant reductions of particulat e matter emissions. A technical memorandum provided the basis for estimating PM emissions due to exhaust, br eak wear and tire wear. The2-6 

### technical memorandum used estimated emissions from a 1980’s model year vehicle fleet since the emissions tests supporting the emissions fa ctors equation were pe rformed in the early 1980’s to early 1990’s. It was believed that since 19 80, there have been and will continue to be improvements in vehicles and fuel that wi ll result in a decrease in PM emissions from engine exhaust. Depending on the emissions factors units de sired, the constant that was included in the emissions f actor equation had va lues of 0.2119 g/VKT, 0.1317 g/VMT or 0.00047 lb/VMT for PM 30 

### , PM 15 

### and PM 10 

### emissions. For PM 2.5 

### emissions, depending on the required emissions factors units, the constant us ed 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 multip lier k was lowered to 0.66 g/VKT, 1.1 g/VMT or 0.0024 depending on the needed units for the emissions fact or. The revision was based upon a broad based assessm ent of the biases associated with the cyclone/impactor method for particulate sizes less than 10 μ m 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 PM 2.5 

### impacted fugitive sources other than paved roads. The impact was du e to particle bounce from the cascade impactor stages to the backup filter potentially inflating PM 2.5 

### 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 Partne rship 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 test s provided the basis for comparing the average PM 2.5 

### concentration and the collocated PM 10 

### concentration. The stud y compared the fine frac tion ratios derived from FRM samplers to those derived from the cycl one/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 f actors and associated PM 2.5 

### / PM 10 

### ratios. The study consisted of 100 test runs covering PM 10 

### concentration from approximately 0.3 mg/m 3 

### to 7 mg/m 3



### .
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 PM 10 

### 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 resu lts of each test based upon the average vehicle weights, average vehicle speed, ambient temperatur e, 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 de veloped, the following general criteria were used:

1. Emissions data must be from a primary reference: a. Source testing must be from a re ferenced 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 th e 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 informa tion 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 ti me of the last Section 11.2 update. 8 

### The individual programs are discussed in detail in the next se ction. In addition, as discussed at the end of Section 2, earlier controlled industrial road test data were reexamined. The previous update 8 

### 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 focu sed 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 da ta, the quantity and quali ty of the information contained in the final set of re ference documents were evaluated. The following data are to be excluded from consideration:

1. Test series averages reported in units can not be converted to th e 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 ra ting. 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 performe d on the same source using sound methodology and reported in enough detail for adequate valid ation. 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 methodol ogy actually used.

B Tests that were performed by a gene rally sound methodolog y, 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 generall y 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 procedur es deviated from accepted methods, the deviations are well documented. Wh en this occurred, an evaluation was made of the extent such alternative pro cedures could influence the test results.

3. Sampling and process data . Adequate sampling and process data are documented in the report, and any va riations in the sampling and process operation are noted. If a large spread be tween test results cannot be explained by information contained in the test repo rt, the data are susp ect 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 de pth of review of the calculations was3-3 

### dictated by the reviewer's confidence in the ability and cons cientiousness of the tester, which in turn was based on fact ors such as consiste ncy 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 indus try population. The source cate gory is specif ic enough so that variability within the source category population may be minimized.

B—Above average : Developed only from A-rated te st data from a reasonable number of facilities. Although no specific bias is evid ent, 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 evid ent, it is not clear if the facilities tested represent a random sample of the industry. In addition, th e source category is specific enough so that variability within the s ource category populatio n may be minimized.

D—Below average : The emission factor was develope d only from A- and B-rated test data from a small number of facilities, and ther e 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 sour ce category populatio n. Limitations on the use of the emission factor are noted in the emission factor table.

E—Poor : The emission factor was developed fr om C- and D-rated test data, and there is reason to suspect that the facilities test ed do not represent a random sample of the industry. There also may be evidence of va riability within the source category population. Limitations on the use of these factors are always noted.

The use of these crit eria is somewhat subjective a nd depends to an extent on the individual reviewer.

3.4 METHODS OF EMISSION FACTOR DETERMINATIO N

Fugitive dust emission rates and particle size distributions are difficult to quantify because of the diffuse and variable nature of su ch sources and the wide range of particle size involved including particles which deposit immedi ately adjacent to the source. Standard source testing methods, which are designed for application to confin ed flows under steady state, forced-flow conditions, are not suitable fo r 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. 8,103-4 

### 3.4.1 Mass Emission Measurements

Because it is usually impractical to encl ose open dust sources or to capture the entire emissions plume, only the upwind-downwind and exposure profiling methods are suitable for measurement of particulate emissi ons from most open dust sources. 10 

### These two methods are discussed separately below.

The basic procedure of th e upwind-downwind method invo lves the measurement of particulate concentrations both upwind and downwind of the polluta nt source. The number of upwind sampling instruments depends on the de gree of isolation of the source operation of concern (i.e., the absence of in terference from other sources upwind). Increasing the number of downwind instruments improv es 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 tw o downwind distances and three crosswind distances, at a minimum. The same sampling requ irements 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 t ype) to back calculate the particulate emission rate (i.e., source strength) required to generate the pollutant concentratio n 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 tim e). A number of meteorological parameters must be concurrently recorded for input to this disper sion equation.
At a minimum the wind direction and speed must be recorded on-site.

While the upwind-downwind method is applicab le to virtually all types of sources, it has significant limitations with re gard to development of source- specific emission factors. The major limitations are as follows:

1. In attempting to quantify a large ar ea source, ove rlapping of plumes from upwind (background) sources may preclude the determination of the specific contribution of the area source.

2. Because of the impracticality of adju sting the locations of the sampling array for shifts in wind direct ion during sampling, it cannot be assu med that plume position is fixed in the application of the dispersion model.

3. The usual assumption that an area sour ce is uniformly emitting does not allow for realistic representation of spa tial variation in source activity.

4. The typical use of uncalibrated atmo spheric dispersion models introduces the possibility of substantial error (a factor of three according to Reference 11) in the calculated emission rate, even if th e stringent requirem ent 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 dis tinct advantages for3-5 

### source-specific quantification of fugitive emissions from open dust sources. The method uses the isokinetic profiling concept that is the basi s for conventional (ducte d) source testing. The passage of airborne pollutant immediately downwind of the s ource is measured directly by means of simultaneous multipoint sampling over th e effective cross section of the fugitive emissions plume. This technique uses a ma ss-balance calculation scheme similar to EPA Method 5 stack testing ra ther than requiring indi rect calculation throug h 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 em issions are to be measured, samp ling intakes are pointed into the wind and sampling velocity is adjusted to ma tch the local mean wind speed, as monitored by anemometers distributed over he ight above ground level.

The size of the sampling grid needed for e xposure profiling of a particular source may be estimated by observation of the visible si ze of the plume or by calculation of plume dispersion. Grid size adjustments may be required based on the re sults of preliminary testing. Particulate sampling heads should be symmetrica lly distributed over the concentrated portion of the plume containing about 90 % of the total mass flux (expos ure). For example, assuming that the exposure from a point source is normal ly distributed, the exposu re 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 expo sure 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 obt ained by spatial integration of distributed measurements of exposure (mass/area) over the effective cross sect ion of the plume. The exposure is the point value of the flux (mass/area/time) of airbor ne 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 lin early 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 calcula ted from each test of that sour ce 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 pr esented in the form of a predictive equation derived by regression analysis of test data. The use of a pred ictive equation with a relatively good correlation coefficient (R 2 

### ) 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 pa rameters when characterize source conditions. These parameters may be grouped into three categories:

1. Measures of source activity or energy expended (e.g., the sp eed and weight of a vehicle traveling on an unpaved road).

2. Properties of the material being distur bed (e.g., the content of suspendable fines3-6 

### 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 us eful if it is successful in "e xplaining" much of the observed variance in emission factor valu es on the basis of corresponding variance sin specific source parameters. This enables more reliable estimate s 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 traffi c on unpaved roads. To estab lish 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 updates 8,11 

### of this section and repr esents a refinement of the rating system developed by EPA for AP-42 emissi on factors, as described in Section 3.3. The scheme entails the use of the same rating assessme nt of source test data quality followed by an initial rating assessment of the emission fact or(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 gene rated 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-downwin d sampling strategy qualified as a sound methodology, the following minimu m test system requirements were used. At least five particulate measuring devices mu st be operated during a test, with one device located upwind and the other located at two downwind and three crosswind di stances. The requirement of measurements at crosswind distances is waived for the case of line sources. Also wind direction and speed must be mo nitored concurrently on-site.

The minimum requirements for a sound expo sure 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 poi nt sources while a two-dimensio nal array of at least five samplers is required for quantif ication of fixed virtual point source missions. At least one upwind sampler must be operated to measure ba ckground concentration, and wind speed must be measured on-site.

Neither the upwind-downwind nor the expo sure 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 th e individual source test qualit y ratings, the factor quality3-7 

### rating of the single-valued emi ssion factor will be evaluated. Recently approximately 20 “A” and “B” rated source test reports have been required to justif y a factor quality rating of “A”. Each halving of the number of source test report s results in a one letter grade reduction in the final factor quality rating. Several of the source test reports used as th e 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 lo cations and that the different locations within a given reference represent differences in s ource 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 bac kground 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 sinc e the January 1995 major revision. Since this date revisions have been made addressing the in fluence of rain events, estimating default silt loading levels for various classes of roads, se parating particulate emi ssions associated with the roads verses those associated with the vehicles and addressing biases in the measurement of PM 2.5 

### with devices that use impactor s 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 pa ved roads in a single data base, making no distinction between pub lic and industrial roads or between controlled and uncontrolled roads. 2. Develop PM 10 

### and PM 2.5 

### 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 a nd brake wear from the measured PM 10 

### emissions. 2. Conduct a series of step wise linear regression anal yses 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 PM 10 

### 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 algor ithm 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 PM 10 

### samplers equipped with volumetric flow control. Samplers were arranged in two upw ind/downwind configurations. The "basic" configuration consisted of si x samplers arranged in identic al patterns upwind and downwind of the test road, with one samp ler and one pair of samplers at nominal distances of 20 and 5 m, respectively, from the road.

The second configuration was used for test s 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 collocati on was possible for the second configuration.

In addition to the PM 10 

### concentration measurements, se veral 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 Divisi on (APCD) representatives collected traffic data, including tra ffic counts, travel speeds, and percentage of heavyduty vehicles. • Vacuums with disposable paper bags we re 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 mate rial adjacent to curbs and periodic duplicate samples.

The study collected PM 10 

### concentration data on 24 diffe rent days and calculated a total of 69 different emission rates for baseline , sanded and controlled paved road surfaces. Emission factors were obtained by back-cal culation from the CALINE3 dispersion model 12 

### together with a series of assumptions involvi ng mixing widths and heights and an effective release height. Although da ta collected at the 20 m distance were used to evaluate results, the test report did not describe any sensitivity an alysis to determine how dependent the emission rates were on the underlying assumptions.TABLE 4-1. SUMMARY INFORMATION FOR REFERENCE 15PM10emission factor (g/VKT)OperationLocation State Test datesNo. of tests Geom. mean RangeVehicle trafficColfax Colorado 3-4/8917 1.33 0.53-9.01Vehicle trafficYork St. Colorado 4/891 1.07 1.07Vehicle trafficBelleview Colorado 4/894 1.62 1.10-4.77Vehicle trafficI-225 Colorado 4/899 0.31 0.17-0.51Vehicle trafficEvans Colorado 5-6/8929 1.06 0.21-7.83Vehicle trafficLouisiana Colorado 6/897 0.96 0.42-1.73

4-34-4 

### The testing program found difficulty in de fining "upwind" concen trations for several of the runs, including cases with wind reversals or winds nearly para llel to the roadway orientation. A total of eight of th e 69 tests required that either an average concentration from other test days or a downwind concentration be used to defi ne "upwind" conditions. In addition, the test report described another seven runs as invalid for reasons such as wet road surfaces, nearby dust sources or concentra tions 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 fu lly met minimum requirements for the upwinddownwind method presented in Section 3.4. Speci fically, only two or three samplers were used downwind rather than the minimum of four.

Furthermore, a later report 6 

### drawing upon the results from Reference 15 and 17 effectively eliminated 24% of the combined ba seline tests because of wind directions. In addition, the later report 6 

### 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 Departme nt 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 identi cal methodology. In fact, the two test reports are very similar in outline, a nd many passages in the two reports are identical. The primary objective was given as expanding the data base inReference 15 to further develop predictive algorithms for clean and sanded streets. Su mmary information is given in Table 4-2.

The test program employed the same two basic PM 10 

### 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 resu lts from elevated samplers is not presented in Reference 17.)

As was the case in Reference 15, additio nal 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 meas urements were not collected.)

• Traffic data, including traffic counts, tr avel speeds, and percentages of heavyduty vehicles were collected.

• Vacuums with disposable paper bags were used to collect the loose material4-5 

### from the road surface. The program developed an extensive set of collocated samples of material along the edges of the roadway.

The study collected PM 10 

### concentration data on 33 days and calculated a total of 131 different emission rates for ba seline, sanded and controlled pa ved road surfaces. Emission factors were obtained by back-calcula tion from the CALINE3 dispersion model 12 

### 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 para llel to the roadway orientati on. 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 concentrati ons were higher than downwind values.

As with the Reference 15 program, a seri es of stepwise regression analyses were conducted. This test program combined data from Reference 15 and 17 and considered predictive equations for (a) ba seline conditions, (b) sanded ro ads, and (c) roads swept to remove the sand applied, and (d ) all conditions combined.

Unlike Reference 15, however, Reference 17 app ears to present silt loading values that are based on wet sieving (see page 8 of the test report) ra ther 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 informati on during telephone calls to th e testing organization and the laboratory that analyzed the samples.) Wet sieving disaggregates composite particles and results from the two types of si eving are not comparable.

There is additional confusion over the silt loading values given in Reference 17 for cleaning tests. Specifically, the same silt loadi ng value is associated with both the treatment and the experimental control. This point co uld not be clarified during telephone conversation with the testing organization. Attempts to clarify using test report appendices were unsuccessful. Two appendices appear to interchang e silt loading with si lt percentage. More importantly, it could not be determined whet her the surface sample results reported in Appendix D to Reference 17 pertain to treated or the experimental cont rol segment, and with which emission rate a silt lo ading should be associated.TABLE 4-2. SUMMARY INFORMATION FOR REFERENCE 17PM10emission factor (g/VKT)Operation Location State Test datesNo. of test Geom. mean RangeVehicle traffic Mexico Colorado 2/90 32.75 1.08-6.45Vehicle traffic State Hwy 36Colorado 1-3/90 13 1.31 0.14-4.18Vehicle traffic Colfax Colorado 2-4/90 41 1.32 0.27-5.04Vehicle traffic Park Rd. Colorado 4/90 11 1.26 0.69-3.33Vehicle traffic Evans Colorado 2-3/90 11 2.10 0.87-7.27Vehicle traffic Louisiana Colorado 1,3/90 9 3.24 1.40-5.66Vehicle traffic Jewell Colorado 1/90 1 6.36 6.36Vehicle traffic Bryon Colorado 4/90 3 8.38 5.53-14.72

4-64-7 

### Reference 17 contains substantial amounts of information, but is not particularly well documented in terms of describing test c onditions, sampling methodology, data reduction and analysis. In addition, the same limitati ons 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 mode l 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 t echniques at two diffe rent iron and steel plants. (See Tables 9 and 10 in Reference 8.) Data were quality rated as "A," and uncontrolled test results were in corporated into the data ba se for Section 11.2.6 published in 1983. The only use of the cont rolled test results, however, was the following addition to Section 11.2.6.4 in 1988:

"Although there are relatively few quantitativ e data on emissions from controlled paved roads, those that are available indicate that ade quate estimates generally may be obtained by substituting controlled loadi ng values into .. [Equa tions (2-2) and (2- 3)].... The major exception to this is wate r flushing combined with broom sweeping. In that case, the equations tend to over estimate 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 PM 10 

### emission data are not specifically presented in the re port, appropriate values were previously developed by lognormal interpolation of the PM 15 

### and PM 2.5 

### 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 em ission estimates. The program produced three tests of emissions from end-loader travel ove r 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 PM 10 

### emission factors were reported; result s were presented for total particulate (TP) and suspended particulate (SP, or PM 30 

### ). Data were quality rate d "A" in the 1987 report.4-8 

### Because no PM 10 

### data were given, Test Report 5 data were most directly useful as independent data against which the TSP emissi on factor model (Eq. (2-2)) could be assessed. This comparison showed generally good agr eement between predicte d and observed with agreement becoming better as source conditions approached those in the underlying data base.

The 1987 update 8 

### developed PM 10 

### emission factors based on information contained in the test report. When compared to the si ngle valued factors (Equation [2-4]), agreement for the first two tests was within a factor of a pproximately two. The third test — that of the cleaned surface — could not be used to assess th e 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 Ab atement, 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 me ntioned, test data are reported only in terms of averages. (See Tabl es 24 and 25 in Reference 8.) Only TSP emission factors are presented. Although da ta were obtained using a sound methodology, data were rated "C" because of inad equate detail in the paper.

Averaged data from Test Report 8 were used in an independent assessment of Eq. (2-2). Although only average emission leve ls could be compared, the data suggested that TSP emissions could be esti mated within very acceptable limits.

4.2.1.6 Roadway Emissions Field Te sts at U.S. Steel’s Fair less Works, U.S. Steel Corporation, Fairless Hills, PA, US X Purchase Order No. 146-0001191-0068, May 1990. (Reference 31, ref_31c13s0201_2011.pdf).

This 1989 field program used exposure pr ofiling to characterize emissions from paved roads at an integrated iron and steel plant near Ph iladelphia, Pennsylvania, in November 1989. In many respects, this program arose because of un certainties with paved road emission factor models used outside thei r 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 guidance 13 

### 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 fi rst exposure profiling da ta to supplement the AP-42 paved road data base since the 1984 re vision. 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 traffi c consisted mostly of plant equipment. Table 4-3 provides summary information and Tabl e 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 th e plant. Average daily traffic (ADT) had been estimated as more than 4,000 vehicle passes per day, with most vehicles repres entative 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 unc ontrolled. The road wi dth, 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 ma ss 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 μ mA) was employed to measure the downwind particle size distribut ion, and a standard high-volume sampler was utilized to determine the downwind mass frac tion 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 ro adway dust plume at which half the mass emissions pass above and half below. The upwind (background) part icle 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 hei ght were recorded during testing to maintain isokinetic sampling.

• Traffic data, including traffi c 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 part iculate (TP), total suspended particulate (TSP) and PM 10 

### , for the ten paved road emission tests conducted.

Reference 31 found that the emission factor s and silt loadings more closely resembled those in the "urban" rather than the "industrial" data base. Th at is to say, emissions agreed more closely with factors estimated by th e methods of September 1985 AP-42 Section 11.2.5 than by methods in Section 11.2.6. Given the tra ffic rate of 4000 vehicles per day at Site "C," this finding was not terribly surprisi ng. What was far more surprising was that emissions at Site "E" were also more "urb an" than "industrial." Although the TSP and PM 10 

### models in Section 11.2.5 showed a slight tendency to underpredict, the Section 11.2.6 PM 10 

### 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, Pave d Road Particulate Emissions - Source Category Report, for U.S. EPA, Ju ly 1984. (Reference 8, ref_08c13s0201_2011.pdf)

This document reports the results of test ing 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, comm ercial/residential roads, expressways, and a street in a rural town. The expanded measur ement program reported in this document was used to develop emission factors for paved ro ads and focused on the following particle sizes:
PM 15 

### (inhalable particulate matter [IP]), PM 10 

### , and PM 2.5



### .

Total airborne PM emissions were ch aracterized using an exposure profiler containing four sampling heads. High-volume samplers with size selective inlets (SSI) having a cutpoint of 15 μ mA were used to characterize upwind and downwind PM 15 

### concentrations. A high- volume sampler with a SSI and a cascade impactor was also located downwind to characterize particle size distribution within the PM 15 

### 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 te sts, 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 m ean number of vehicle wheels was not reported. The test data are assigned an A rating. Table 4-5 presents summary test data and Ta ble 4-6 presents detail ed test information.TABLE 4-3. SUMMARY INFORMATION FOR REFERENCE 31OperationLocationStateTestdatesNo. oftestsTSP emission factor, lb/VMTPM10emission factor, lb/VMTGeom. mean Range Geom. mean RangeVehicle trafficAU-X(Unpaved road)PA 11/89 2 0.61 0.39-0.960.160.14-0.18Vehicle trafficPaved roadPA 11/89 6 0.033 0.012-0.12 0.0095 0.0009-0.036Vehicle trafficPaved roadPA 11/89 4 0.078 0.033-0.30 0.022 0.0071-0.0361 lb/VMT = 281.9g/VKT.TABLE 4-4. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE 31Test runsPM10emissionfactor, lb/VMTDuration,minMeteorologyVehicle characteristicsSiltloading,g/m2Silt, %Temperature,°FMean windspeed, mphNo. of vehiclepassesMean vehicleweight, tonMeanvehiclespeedaAU-C-30.00497103 50 12 836 5.5(27)0.42 10AU-C-40.0355147 63 11 1057 6.0 25 0.52 12AU-C-50.0337120 62 14 963 3.9 29 0.23 9.7AU-C-6c0.00816c187 3914685 6.2(27)0.23b8.6AU-C-70.00088796 42 12 703 3.0(27)0.26b7.7AU-C-80.0174218 40 15 779 2.0(27)0.15b9.9AU-E-10.00709154 43 12 210 12 15 4.0 17AU-E-20.023489 4413373 5.1 16 4.0 17AU-E-30.0355118 41 9.3 330 2.6(15)2.2 18AU-E-40.0199130 41 9.3 364 2.6(15)1.3 15a 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

4-11TABLE 4-5. SUMMARY INFORMATION FOR REFERENCE 8OperationStateTestdatesNo. oftestsPM15emission factor, lb/VMTPM10emission factor, lb/VMTPM2.5emission factor, lb/VMTGeom. mean Range Geom. mean Range Geom. mean RangeCommercial/IndustrialMO2/804 0.0078 0.0036 - 0.013 0.0068 0.0034 - 0.011 0.0045 0.0030 - 0.0063Commercial/ResidentialMO, IL2/8010 0.0021 0.0006 - 0.012 0.0017 0.0004 - 0.0093 0.0011 0.0002 - 0.0037ExpresswayMO5/804 0.0004 0.0002 - 0.0008 0.0004 0.0002 - 0.0007 0.0002 0.0001 - 0.0003Rural TownKS3/801 0.031 0.031 0.025 0.025 0.005 0.0051 lb/VMT = 281.9 g/VKT.

4-12TABLE 4-6. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE8CategoryRun testNo.PM10emissionfactor,lb/VMTDuration,min.Temp.,°FMeanwindspeed,mphRoadwidth,ftNo. ofvehiclepassesMeanvehiclespeed,mphMeanvehicleweight,tonsSiltloading,g/m2Silt (%)Commercial/IndustrialM-10.0110 120 28 7.4 442,627 30 5.6 0.46 10.7Commercial/IndustrialM-20.00340 86 27 6.5 442,166 30 3.8 0.26 6.2Commercial/IndustrialM-30.00781 120 28 7.8 442,144 30 4.5 0.15 3.5Commercial/IndustrialM-90.00712 136 50 7.4 443,248 30 4.1 0.29 12.2Commercial/ResidentialM-40.000400 240 38 7.8 362,763 35 2.1 0.43 18.8Commercial/ResidentialM-50.00153 226 53 2.2 362,473 35 2.2 1.00 21.4Commercial/ResidentialM-60.00304 281 35 5.6 363,204 30 2.1 0.68 21.7Commercial/ResidentialM-130.000680 194 60 2.7 225,190 35 2.7 0.11 13.7Commercial/ResidentialM-140.00301 178 55 9.2 223,940 35 2.7 0.079 -Commercial/ResidentialM-150.00323 135 77 11.4 224,040 35 2.7 0.047 8.1Commercial/ResidentialM-170.00582 150 75 4.0 403,390 30 2.0 0.83 5.7Commercial/ResidentialM-180.000800 172 75 5.1 403,670 30 2.0 0.73 7.1Commercial/ResidentialM-190.000390 488 70 2.7 205,800 30 2.4 0.93 8.6ExpresswayM-100.000390 182 60 2.9 9611,148 55 4.5 0.022 -ExpresswayM-110.000700 181 56 8.7 9611,099 55 4.8 0.022 -ExpresswayM-120.000190 150 65 4.7 969,812 55 3.8 0.022 -ExpresswayM-160.000530 254 70 4.0 9615,430 55 4.3 0.022 -Rural TownM-80.0247 345 50 4.7 301,975 20 2.2 2.50 14.51 lb/VMT = 281.9 g/VKT.1 g/m2 = 1.434 gr/ft2

4-134-14 

### 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 test ing conducted in 1981 a nd 1982 at industrial unpaved and paved roads and at ru ral unpaved roads. Unpaved indu strial roads were tested at a sand and gravel processing facility in Kans as, a copper smelting facility in Arizona, and both a concrete batch and as phalt batch plant in Missouri . The study was conducted to increase the existing data base for size-speci fic PM emissions. The following particle sizes were of specific interest for the study: PM 15 

### , PM 10 

### , and PM 2.5



### .
Exposure profiling was utilized to characte rize total PM emissions. Five sampling heads, located at heights of up to 5 m, were deployed on the pr ofiler. A standard high-volume sampler and a high-volume sampler with an SSI (cutpoint of 15 μ mA) were also deployed downwind. In addition, two high-volume cyclone/impactor s were operated to measure particle size distribution. A st andard high-volume sampler, a high-volume sampler with an SSI, and a high-volume cy clone/impactor were utilized to characterize the upwind TSP and
PM 15 

### concentrations and the particle size distribution within the PM 15 

### fraction. Wind speed was monitored with warm wire anemometers.

A total of 18 paved road te sts and 21 unpaved road tests ar e completed. The test data are assigned an A rating. Indu strial paved road tests were conducted as follows: three unpaved road tests at the sand a nd gravel processing plan t, three paved road tests at the copper smelting plant, four paved road te sts at the asphalt batch facilit y, and three paved road tests at the concrete batch facili ty. The industrial road tests were considered uncontrolled and were conducted with heavy dut y 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.4-15TABLE 4-7. SUMMARY OF PAVED ROAD EMISSION FACTORS FOR REFERENCE 7IndustrialcategoryTypeTP, lb/VMTPM15, lb/VMTPM10, lb/VMTPM2.5, lb/VMTGeo.meanRange Geo. mean RangeGeo.meanRangeGeo.meanRangeAsphalt Batching Mediumduty1.83 0.750-3.65 0.437 0.124-0.7410.295 0.0801-0.4410.130 0.0427-0.214ConcreteBatchingMediumduty4.74 2.25-7.23 1.66 0.976-2.34 1.17 0.699-1.63 0.381 0.200-0.562Copper Smelting Mediumduty11.2 7.07-15.7 4.01 2.02-5.56 2.78 1.35-3.86 0.607 0.260-0.846Sand and GravelProcessingMediumDuty5.50 4.35-6.64 1.02 0.783-1.26 0.633 0.513-0.753 0.203 0.194-0.2111 lb/VMT = 281.9 g/VKT.

4-154-16TABLE 4-8. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE 7RunNo.IndustrialcategoryTrafficPM10emissionfactor,lb/VMTDuration,min.Meanwindspeed,mphRoadwidth,ftNo. ofvehiclepassesVehicle characteristicsMoisturecontent, %Siltloading,g/m2Silt, %Meanvehicleweight,tonsNo. ofwheelsMeanvehiclespeed,mphY-1 Asphalt Batching MediumDuty0.257 274 5.37 13.8 47 3.6 6 10 0.22 91 2.6Y-2 Asphalt Batching MediumDuty0.401 344 4.70 14.1 76 3.7 7 10 0.51 76 2.7Y-3 Asphalt Batching MediumDuty0.0801 95 6.04 14.1 100 3.8 6.5 10 0.32 193 4.6Y-4 Asphalt Batching MediumDuty0.441 102 5.59 14.1 150 3.7 6 10 0.32 193 4.6Z-1 ConcreteBatchingMediumDuty0.699 170 6.71 24.3 149 8.0 10 10 a 11.3 6.0Z-2 ConcreteBatchingMediumDuty1.63 143 9.84 24.9 161 8.0 10 15 a 12.4 5.2Z-3 ConcreteBatchingMediumDuty4.01 109 9.62 24.9 62 8.0 10 15 a 12.4 5.2AC-4 Copper Smelting MediumDuty3.86 38 8.72 34.8 45 5.7 7.4 10 0.43 287 19.8AC-5 Copper Smelting MediumDuty3.13 36 9.62 34.8 36 7.0 6.2 15 0.43 188 15.4AC-6 Copper Smelting MediumDuty1.35 33 4.92 34.8 42 3.1 4.2 20 0.53 400 21.7AD-1 Sand and Gravel Heavy Duty 3.27 110 7.61 12.1 11 42 11 23 a 94.8 6.4AD-2 Sand and Gravel Heavy Duty 0.753 69 5.15 12.1 16 39 17 23 a 63.6 7.9AD-3 Sand and Gravel Heavy Duty 0.513 76 3.13 12.1 20 40 15 23 a 52.6 7.01 lb/VMT = 281.9 g/VKT.1 g/m2= 1.434 gr/ft2aNot measured

4-164-17 

### 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 measuremen t 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 Middlet own, Ohio. Control techniqu es to reduce emissions from paved roads, unpaved roads, and coal storage pile s were evaluated. For paved roads, control techniques included vacuum sw eeping, water flushing, and flus hing with broom sweeping. Particle emission sizes of interest in this study were total PM, PM 15 

### , and PM 2.5



### .
The exposure profiling method was used to measure paved road particulate emissions at the Iron and Steel plants. Fo r this study, a profiler with four or five samplin g heads located at heights of 1 to 5 m was deployed. Two high-volume cascade impactors with cyclone preseparators (cutpoint of 15 μ mA), 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 μ mA) were located downwind at a height 2 m. One standard high-volume sampler a nd two high-volume samplers w ith SSIs were located upwind for measurement of background concentrations of TSP and PM 15



### .
Twenty-three paved road tests of controlled and un controlled emissions were performed. These included 11 uncontrolled tests, 4 vacuum sweeping te sts, 4 water flushing tests, and 4 flushing and broom sweeping tests. For paved road s, this test report does not present vehicle speeds, mean number of wheel s, or moisture contents. Because vehicle speeds above 15 MPH and moisture content are not expected to influence the emissions equation, the test data are assi gned an A rating. Table 4-9 pr esents summary test data and Table 4-10 presents detailed test information. The PM 10 

### emission factors presented in Table 4-10 were calculated from the PM 15 

### and PM 2.5 

### data using logarithmic interpolation.

After vacuum sweeping, emission s were reduced slightly more than 50 percent for two test runs and less than 16 percent for two te st runs. Water flushi ng applied at 0.48 gal/yd 2 

### achieved emission reductions ranging from 30 pe rcent to 70 percent. Flushing at 0.48 gal/yd 2 

### combined with broom sweeping resulted in em ission reductions ranging from 35 percent to 90 percent.TABLE 4-9. SUMMARY OF PAVED ROAD EMISSION FACTORS FOR REFERENCE 6ControlmethodLocation State Test dateNo. oftestsTP, lb/VMT PM15, lb/VMTPM2.5, lb/VMTGeo mean Range Geo mean Range Geo mean RangeNone A,D,F,J OH 7/80,10/80, &11/807 1.22 0.29-5.50 0.38 0.13-2.14 0.10 0.04-0.52VacuumSweepingA OH 10/80 &11/804 0.87 0.53-1.46 0.45 0.27-0.87 0.14 0.08-0.26WaterFlushingD,L TX 6/81 4 1.43 1.30-1.74 0.47 0.32-0.65 0.08 0.08-0.09Flushing &BroomSweepK,L,M TX 6/81 4 0.96 0.54-2.03 0.20 0.10-0.49 0.07 0.04-0.13None L,M TX 6/81 4 3.12 0.83-5.46 0.92 0.31-1.83 0.26 0.06-0.621 lb/VMT = 281.9 g/VKT.

4-18TABLE 4-10. DETAILED INFORMATION FROM PAVED ROAD TESTS FOR REFERENCE6SiteTestRun No.ControlmethodPM10emissionfactor, (lb/VMT)Duration(min.)Temp.,(°F)Mean windspeed, (mph)No. ofvehicle passesMeanvehicle weight,(tons)Silt loading,(g/m2)Silt, %AF-34None0.536 62 90 4.2 79 28 2.79 16AF-35None0.849 127 90 7.5 130 25 2.03 10.4AF-36VS0.147 335 50 5.9 263 8.3 0.202 18.3AF-37VS0.209 241 50 4.8 199 17 0.043 26.4AF-38VS0.430 127 50 4.5 141 18 0.217 27.9AF-39VS0.686 215 50 6.4 190 18 0.441 19.6DF-61None1.35 108 40 11.0 93 40 17.9 21.0DF-62None0.929 77 45 12.1 94 36 14.4 20.3DF-74WF1.32 205 50 9.0 67 29 5.59 9.45aFF-27None0.357 91 100 9.5 158 14 17.7 35.7FF-45None0.608 135 50 4.0 172 16 5.11 28.4JF-32none0.144 259 90 5.8 301 14 0.117 13.4KB-52FBS0.0946 60 90 2.9 119 12 7.19 34.3LB-50FBS0.230 104 90 5.6 123 9.4 13.6 28.2bLB-51FBS0.435 93 90 4.2 127 11 13.6 28.2bLB-54WF0.268 101 90 5.4 118 10 3.77 22.6LB-55WF0.575 82 90 8.5 98 11 6.29 19.6aLB-56WF0.398 61 90 6.3 118 9.2 2.40 11.2LB-58None1.08 96 90 6.7 67 18 10.4 17.9MB-53FBS0.161 81 90 5.3 72 20 -- 9.94MB-570.554None 101 90 3.6 68 12 2.32 6.45aMB-590.993None 114 90 6.1 67 11 2.06 14.0aMB-601.18None 112 90 5.0 50 12 3.19 13.5aAverage of 2+ valuesbSample used for more than 1 run.cPM10emission factors were calculated from the PM15andPM2.5data using logarithmic interpolation.VS = Vacuum sweeping; WF = Water flushing; FBS = Waterflushing and broom sweeping; 1 lb/VMT = 281.9 g/VKT; 1 g/m2=1.434 gr/ft2

4-194-20 

### 4.2.1.10. Midwest Research Institute, Fugitive Particulate Matter Emissions for U.S. Environmental Protection Agency, Emissi on Factor and Inventory Group, April 15, 1997. (Reference 30, ref_30c13s0201_2011.pdf).
This reference documents the performanc e of six field studies characterizing the vehicle emissions from three unpaved roads and three paved roads. Te sting of unpaved roads was performed in Kansas Cit y, MO; Raleigh, NC; and Reno, NV. Testing of paved roads was performed in Denver, CO; Raleigh, NC; a nd Reno, NV. Midwest Research Institute measured the emission rates for PM 10 

### and PM 2.5 

### at all six locations based upon a plume profiling methodology. The test da ta are assigned an A rating.

Plume profiling calculates emission rates us ing a conservation of mass approach. The passage of airborne particulate (i.e., the quant ity 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 pl ume. Exposure is the point valu e of the flux (mass/area time) of airborne particulate integr ated over the time of measurement or, equivalently, the net particulate mass passing through a unit area normal to the mean wi nd direction during the test. The steps in the calculation procedure ar e as follows. The concentration of PM 10 

### 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 sa mpling height. The exposure is integrated over the plumeeffective cross section. The quantity obtained repr esents the total passage of airborne particulate matter (i.e., mass flux ) due to the source. The expo sure is set to zero at the maximum effective height of the plume wher e the net concentration equals zero). The maximum effective height of th e plume is found by linear extra polation 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 c onducted to characterize emi ssions 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 μ mA) and two dichotomous samplers were used to measured the downwind pa rticle size distribution. All of the particle sizing samplers were located at 2 m above ground level. A si ngle set of the same sampling equipment was located at 2 m above ground le vel and upwind for meas urement of background concentrations of TSP, PM 10 

### and PM 2.5 

### . 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, becau se the relatively li ght snow conditions characteristic of the 19 96 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 PM 2.5 

### to PM 10 

### ratio than were measured by the cyclone/impactor samplers. The PM 2.5 

### / PM 10 

### ratios measured by th e dichotomous samplers are presented to the right of the PM 10 

### emissions factors column in Table 4-11. Where two4-21 

### values are presented in the colu mn, 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 PM 2.5 

### emissions factor equation was revised to 25% of the PM 10 

### constant.

4.2.1.11. Paved Road Modifi cations to AP-42, Background Documentation For Corn Refiners Association, Inc. Washingto n, DC 20006 MRI Project No. 310842, May 20, 2008. (Reference 32, ref_32c13s0201_2011.pdf).

The Corn Refiners Association (CRA) funded four paved road PM 10 

### 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 expe rience traffic queues (i.e., stop-and-go traffic) duri ng periods with high corn receipts. The combination of heavy trucks (delivering corn to the facilities) and fa irly low silt loading (sL) values on the plant roads was not typical of the AP -42 data base. Given these di fferences, the member companies undertook testing to develop more representative emission fa ctors. Midwest Research Institute designed and conducted the test programs at all four facilities.

Reference 32 compiles test data and inform ation from references 33, 34, 35 & 36. In addition, reference 32 proposes an expansion of the allowable sp eed 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 document ed in references 7 through 10 were conducted at facilities with very similar operati ng conditions using te st procedures that were nearly identical , the following description provide s 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 f actor predictive equations for both paved and unpaved roads. In each program, a test plan was submitted to the st ate agency for comment and review prior to the start of testing. The fi nal test reports and supp orting information were also submitted to state agencies . Because low emission levels we re expected (due to low sL and slow speeds), several precauti ons were taken to assure relia ble 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, th e facilities provided “drone” passes by corn semi-trailers. Drone traffic mimicked the actual traffic except those trucks returned to staging areas without emptyi ng corn. In addition, testing applied “lessons learned” throughout the programs. For example, when it became apparent how difficult it could be to separate net PM 10 

### concentrations (i.e ., due to traffic on th e road) from background (upwind) concentrations, change s 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 FROMPAVED ROAD TESTS FOR REFERENCE 30SiteTestRunNo.RoadSpeed1PM10emissionfactor,(g/VKT)PM2.5/PM10RatioDuration,min.Temp.,°FMean windspeed, mphNo. ofvehicle passesMeanvehicleweight, tonsSilt loading,g/m2Silt, %CO BH-1 55 1.08 0.20 163 18 2.7 6,561 2.2 0.184 9.4CO BH-2 55 0.102 0.34 360 37 17.0 17,568 2.2 0.0127 41.0CO BH-3 55 - 0.16 360 46 17.2 14,616 - 0.0127 41.0CO 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 1.47 1.2NC BJ-6 45 0.301 0.27/0.34 450 71 8.2 14,670 2.2 0.060 52NC BJ-7 45 1.94 0.44/0.44 143 68 9.4 3,748 2.2 0.060 52NC BJ-9 45 0.6/0.14 178 71 5.3 4,616 2.2 0.060 52NC BJ-10 45 0.44/0.33 288 68 3.7 10,218 2.2 0.060 52NV BJ-11 45 0.68/0.47 387 75 5.1 13,216 2.2 0.060 52NV BK-7 45 0.57 0.29/0.33 420 89 7.3 7,394 2.2 0.082 3.4NV BK-8 45 0.44 0.26/0.34 270 87 6.1 5,747 2.2 0.082 3.4NV BK-9 45 - 0.13/0.38 240 90 2.6 4,622 - 0.082 3.41Road Speed is the posted speed limit for the roadsegment.

4-224-23 

### In addition to PM 10 

### concentrations, each sampling program samples included:

• Measurement of average wind speeds at two heights and wind direction at one height for 5-minute intervals th roughout 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 v acuums with disposable paper bags. The material collected within the bag was siev ed to determine the surface silt loading.

Reference 32 states that the four test programs conduc ted by CRA produced 14 and 8
PM 10 

### emission factor values for slowly moving and stop-and-go tr affic, 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 movi ng” 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 “sto p-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. Thos e tests provided better definition of net PM 10 

### 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 en forced 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 situatio ns 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 pertin ent data was recorded for each run. For all r uns, a vertical network of samplers was operated downwind. The last test period used a vertical array of samplers upwind to better characterize upwind co ncentrations and to provide a more accurate calculation of the net PM 10 

### emission factor.

The results of this testing program are su mmarized 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/V MT) used in the plant emission i nventory. Use of test-specific silt loading and vehicle weight did not signif icantly improve the pred ictive accuracy of the AP-42 factor. The tests found no di scernable 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/m 2 ) Measured PM 10 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 pl ant 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 Columb us, Nebraska Facility , McVehil-Monnett Associates, July 13, 2001. (Reference 34, ref_34c13s0201_2011.pdf).

Truck traffic flow at MCP’s Columbus, Nebr aska 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 sl owly 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 fac ility, the last two runs , CF-4 and CF-5, used identical upwind and downwind ve rtical sampling arrays to be tter characterize background concentrations. In that case, on ly one condition could be evalua ted during a test . The results of the MCP Columbus test program are summa rized 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 testi ng program conducted at Cargill’ s Blair, Nebraska facility during August 2002. The plant used a regular sweeping program to reduce surf ace loadings on paved roads. Testing relied on regular corn truck traffic at the site, a lthough the plant4-25 

### provided a limited amount of “d rone” traffic. The test data are assigned an “A” rating.

Eight PM 10 

### emission tests were attempted. The test report describes difficulty encountered in isolating net PM 10 

### mass due to traffic on the te st 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 test s road, but this was not possible at the second location. Furthermore, steeply sloping ground on the upwind side of the te st 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 re maining instances, the measured downwind PM 10 

### concentrations were lower than upw ind values. It was stated that this was believed to be an undesired result from moving th e test source. Runs CI-7 an d 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-entra ined surface road dust.

4.2.1.14. Midwest Research Institute, Emission Tests of Paved Road Traffic at ADM’s Marshall, Minnesota Facility , McVehil-Monnett Associ ates, 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 2 003, the Marshall facil ity had implemented a road sweeping program. Three tests of PM 10 

### emissions were conduc ted, one from stop-andgo traffic and two from slowly moving traffi c. 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 su mmarized 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 re maining instances, the measured downwind PM 10 

### concentrations were lower than upw ind values. It was stated that this was believed to be an undesired result from moving th e test source. Runs CI-7 an d 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-entra ined surface road dust.Table 4-13. Summary of Emissions Data from MCP’s Columbus, Nebraska Facility (Reference 34)RunaTest conditionTraffic rate(veh/hr)Trafficspeed(mph)bMean vehicleweight, W (tons)Surface silt loading,sL (g/m2)Measured PM10emissionfactor (lb/VMT)CF-1/N Low Speed 47 5.0 400.970.011CF-1/S Stop-and-go 47 NA 400.970.043CF-2/N Slowly moving 66 5.3 410.810.036CF-2/S Stop-and-go 66 NA 410.810.14CF-3/N Slowly moving 54 5.1 410.630.0024CF-3/S Stop-and-go 54 NA 410.630.051CF-4/NSlowlymoving86 4.7 41 1.1 0.0068CF-5/N Stop-and-go 52 NA 411.40.036aSuffix indicates whether tests was conducted on the North or South portion of thecorn haul road. Trucks were held ina queue toward the south; trucks entering the north gatetraveled passed the north sampling array to reach the queue.bSpeed of moving trucks determined by accumulating time required to travel a measured distance. NA = notapplicable.Boldentries indicate that identical vertical sampling arrays were used tobetter isolate the source contribution.

4-26Table 4-14. Summary of Emissions Data from Cargill’s Blair, Nebraska Facility (Reference 35)Run Test conditionTraffic rate(veh/hr)Traffic speed(mph)aMean vehicle weight,W (tons)Surface silt loading,sL (g/m2)bMeasuredPM10emissionfactor (lb/VMT)cCI-1 Low Speed 4513.4 / 16.812.8 / 16.926 0.06 -CI-2 Low Speed 45260.06-CI-3Slowlymoving60d13.6 / 12.713.5 / 15.527 0.06 -CI-4 Low Speed 60d27 0.06 -CI-7Slowlymoving4715.2 / 16.213.6 / 16.127 0.05 0.0036CI-8 Low Speed 47270.050.0066CI-11 Low Speed 5613.5 / 12.727 0.025 -CI-12 Low Speed 56270.25-aVehicle speed for inbound (loaded) /outbound (empty) trucks determined by accumulating time required to travel ameasured distance.bSurface silt loading sample information provided by Cargill.c“-“ indicates that no net mass was attributed to the test road traffic.dTwenty of 238 total passeswere by “drone” trucks.Table 4-15. Summary of Emissions Data from ADM’s Marshall, Minnesota Facility (Reference 36)Run Test ConditionTraffic rate(veh/hr)Trafficspeed(mph)aMean vehicleweight, W(tons)Surface siltloading, sL(g/m2)MeasuredPM10emission factor(lb/VMT)CM-1Slowlymoving154 NA 40 0.72 0.014CM-2 Stop-and-go 42 NA 40 0.720.14CM-4Slowlymoving156 5 40 0.70 0.016aVehicles speeds maintained at plant limit of 5 mph. NA = not applicable.Boldentries indicate that identical vertical sampling arrays were used tobetter isolate the sourcecontribution.

4-274-28 

### 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 pr ocedure that was used to separate the various components of paved road particulate matter emissions into two components. One component includes the emissi ons from exhaust, brake wear and tire wear. The other component includes the particulate matter reentrai ned from the road surf ace. The combined paved road particulate matter emissions we re estimated with the empirical equation published in the October 2002 AP-42 Section for Paved Roads. The vehicle exhaust, brakewear and tirewear emissi on factors were obtained fr om 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 5 00 ppm and no use of reformulat ed gas. The vehicle fleet assumptions used in the analysis are presente d in Table 4-16. The model was run to estimate
PM 10 

### and PM 2.5 

### emission factors in g/VMT for each ve hicle class at spee ds of 25, 30, 35, 40, 45, 50, 55, and 60 mph. W ithin vehicle classes, the greate st standard deviation was lower than 0.04% of the emi ssions factor. Based on the low rela tive standard de viation, it was assumed that the vehicle speed was not a fact or in exhaust, brak ewear and tirewear PM emissions. Table 4-16 presents the vehicle fleet characteristics used in the model and the calculated average PM 10 

### and PM 2.5 

### emission factors for exhaust, brakewear and tirewear for each class of vehicle.

Table 4-16: Vehicle Fleet Assu mptions 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


### PM 10 Emissions
Factor 0.1053 0.1061 0.2746 0.1632 0.3825 0.7206 0.7206 2.1227 0.0922



### PM 2.5 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” PM 10 

### and PM 2.5 

### emission factors using the October 2002 AP-42 paved roads emission fact or equation with the mean vehicle weight set at 3.74 tons (a value they indicated was typi cal of the 1980 paved road vehicle fleet. The contractor used silt loadi ngs ranging from 0.02 to 400 g/m 2 

### for calculating the emissions factors. The contractor also calculated the fleet average PM 10 

### and PM 2.5 

### emission factors for exhaust, brakewear and tirewear by summing th e products of the VMT Distribution ratio and the PM 10 

### and PM 2.5 

### emission factors for each vehicle cl ass. The calculated fleet average values were 0.2119 for PM 10 

### and 0.1617 for PM 2.5 

### . The contractor then subtracted the fleet average emissions factors for exhaust, brakew ear and tirewear from the “AP-42 Composite” emissions factors to produce an emission f actor for only the re-entrained road dust component. The contractor noted that the whil e the stated applicable silt loadings for the October 2002 AP-42 paved road e quation ranged from 0.02 to 400 g/m 2 

### the PM 2.5 

### emissions factor became negative at si lt loadings less than 0.029 g/m 2



### . They stated that since negative emissions were not physically possible, the equation they recomme nded was only valid for4-29 

### silt loading ranging from 0.03 to 400 g/m 2 

### . While no test data are as sociated with this report, the report does provide estimates of engine exha ust, tire wear and br ake wear derived from an EPA emissions model which is based upon em issions testing by a va lidated 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 mi x of vehicle classes measured during the above test reports, the composite emissions estimates are assigned a C rating.

4.2.1.16. E-mail communicati on 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. (Ref erence 37, ref_37c13s0201_2011.pdf).

This e-mail communication and spreadsh eet file concerns estimates of PM 10 

### emissions associated with slow moving and stop and go diesel engine se mi-trailer trucks. The purpose of the request was to provide a means to disaggregate the consolidated PM emissions measured of trucks during deliver y 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 processi ng facility and that the estimated vehicle speed averaged about 1 mph but that they we re stopped most of the time.
PM 2.5 

### emissions were estimated using the MOVE S 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. PM 10 

### emissions were estimated to be approximately 3% greater than PM 2.5 

### 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 A ssociation Studies. As a result, emissions estimates for slow moving trucks are assigned an A rating.

4.2.1.17. E-mail communication between Ro n Myers of EPA/OAQPS/SPPD/MPG, RTP, NC and Gary Dolce, David Br zezinski 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
PM 10 

### 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. Th e 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 M OVES model provide a means for disaggregating the emissions measured during the paved road fi eld studies that measured emissions due to road surface dust, vehicle exha ust, break wear and tire wear.

It is explained in the docume ntation that in order to deve lop an equation for road dust alone, estimates of the particulate emissions from vehicle exhaust, brak e wear and tire wear4-30 

### 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 docum entation further states that none of those factors are accounted for in MOBILE6.2.

Documentation includes information provide d to OTAQ on the test date (sometimes month and year, sometimes just year), vehi cle 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 y ears which MOVES2010a is not programmed to provide.

The documentation states that the speed and ambient temperature measured during the field study provided additi onal independent variables us ed in the MOBILE2010a model to estimate emissions. The documentation indica tes that an emissions estimate was produced for each of the individual tests by allocati ng all of the vehicle activity to a single MOBILE2010a speed bin which included the vehicl e 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 PM 10 

### total, primary PM 10 

### brake wear, and primary PM 10 

### tire wear. Only running exhaust and crankcase running exhaust processes were included in the exhaust emissions calculations as the te st sites did not include any starting or idling activity. Inve ntory results generated by MOVE S 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 du ty vehicles and trucks are assigned an B rating since most of the test data were fo r model years which an alternative emissions scenario (year, vehicle mix and assumed degr adion level) was used as the independent variables used in the M OVES 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 comp rised of a large number of vehicles and the emissions factor produced by the MOVES m odel 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. (R eference 43, ref_43c13s0201_2011.pdf).

This project was conducted by Midwest Rese arch Institute for the Western Regional Air Partnership to provide more accurate PM 2.5 

### and PM 10 

### fugitive dust emissions inventories for regional haze regulatory purposes to addre ss the significant contri bution 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 mode s. It was stated that the results would be helpful in developing accurate emission invent ories for PM nonattainment, maintenance, and action plan areas in the WRAP re gion. Finally, it was stated that the results may be used to seek modifications to the EPA’s AP-42 emission f actors to ensure widespread availability of the information developed in the study.

During the first testing phase of the project, PM 2.5 

### measurements using the highvolume cascade impactors were compared to simultaneous measurements obtained using EPA reference- method samplers for PM 2.5 

### . The tests were conduc ted in a flow-through wind tunnel and exposure chamber, where c oncentration 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 PM 2.5 

### to PM 10 

### ratios for fugitive dust from different geologic sources in the West. The testing provided information on the magnitude and variability of PM 2.5 

### to PM 10 

### ratios for source materials that were recognized as problematic with regard to application of mitigative dust control measures.

Three dust source materials were tested unde r 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 on e fine fraction material). Fixed PM 10 

### concentration levels in the range of 1, 2.5, and 5 milligrams per cubic meter (each with its naturally occurring PM 2.5 

### level) were tested. It was stated that those PM 10 

### concentration levels were selected as representative of dust plume concentrations under which major particle mass contributions to plume samples occur in emissi on factor development. The ratios of PM 2.5 

### to PM 10 

### 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 la ndfill road dust, New Mexico grazing soil, California Salton S ea shoreline soil, and Wyoming unpaved road surface material. Test results includ ed the calculation of the average PM 2.5 

### concentration and the collocated PM 10 

### concentration. It was inte nded that any variation in PM 2.5 

### / PM 10 

### ratio be evaluated as a function of the test soil pr operties (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 te sting 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 PM 10 

### and PM 2.5 

### non attainment areas. As a result, the study is assigned a quality rating of B when applied within the bou nds of the type of surface material that was available and for dust generation characteristics comparable to those used in the study. The4-32 

### study included no paved road surface material and was weighted toward higher particulate matter concentrations. Since the study was a laboratory study, di d not include any paved road surface materials, and was weighted toward highe r 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 PM 2.5 

### concentrations measured by the cyclone/impactor system were consistently biased by a factor of about 2 relative the
PM 2.5 

### 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 PM 2.5 

### / PM 10 

### ratio to decrease with increasing PM 10 

### concentration. At PM 10 

### concentrations above 1.0 mg/m3 the PM 2.5 

### / PM 10 

### ratio was between 0.1 and 0.15. The
PM 2.5 

### / PM 10 

### ratio increased to about 0.35 as the PM 10 

### 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 Fu gitive Dust Emission Factors ; Western Governors’ Association - Western Regional Air Partnership (WRAP); November 1, 2006. (Reference 44, http://www.epa.gov/ttn/chi ef/ap42/ch13/bgdocs/b13s02.pdf).

This report summarizes the results of the October 2005 WRAP study which evaluated the PM 2.5 

### / PM 10 

### ratio measured by the cyclone/impactor system and measured by the Partisol samplers. While no additional analyses of the laboratory study were performed, suggested
PM 2.5 

### / PM 10 

### ratios were made for use in revising existing AP-42 emissions factor parameters for PM 2.5 

### dust emissions factor equations in Sections 13.2.1 (paved roads), 13.2.2 (unpaved roads), 13.2.3 (material transfer and st orage piles), 13.2.4 (windblown dust) and 13.2.5 (industrial wind erosi on). A revised PM 2.5 

### /PM 10 

### 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 S ection 13.2.1 “Paved Roads” , October 10, 2002. (Reference 38, ref_38c13s0201_2011.pdf).

This technical memorandum to the files su mmarizes and responds to comments on an October 2001, EPA proposed revision of S ection 13.2.1 “Paved Ro ads” for AP-42 and request for comments. The memorandum also pr esents EPA’s decisions and rational supporting these decisions for th e final changes leading to th e 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 esse nce “zeroed” the emissions on days that more than 0.01 inch of rain was recorded. In addition, th e proposed revisions included the separation of vehicle engine exhaust, breakwear and tirew ear as recommended in the E. H. Pechan Technical Memorandum of August 21, 2003. The memora ndum includes attachments with the detailed comments that lead to the final revision of the em issions 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 E mission 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 Environmenta l Management, Desert Research Institute, University of California, Riverside, Univ ersity of Nevada, Las Vegas; December 22, 2008. (Reference 42, http://www.epa.gov/ttn/chief/ap 42/ch13/related/Final_ Test_Report.pdf).

This report documents the fourth phase of a study by Clark C ounty to investigate alternative ways of estimating PM 10 

### emissions of surface dust entrained from paved roads. A new vehicle-mounted mobile sampling technol ogy 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 calib rate the mobile monitoring technology.

Two versions of the mobile monitori ng 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 PM 10 

### particle monitors in conjunction with GPS systems, so that dust plume concentra tions 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. TRAK ER 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” PM 10 

### 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, controll ed vehicle speeds and controlled road surface material loadings. This was accomplished by dedi cating 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 sy stem 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 deplet ion rate as a functio n of the number of vehicle passes.

• Characterization of quantified emissions vs. quantified silt loading mass.

• Data assessment and review for recomme ndations on performance specifications for vehicle-mounted mobile sampling systems.

Particle concentration measurements formed the basis for the mobile monitoring technologies as well as the ro adside emission flux measuremen ts. A continuously recording optical light scattering particle monitor (D ustTrak Model 8520, TSI Inc., Shoreview MN) was the basic instrument used for PM 10 

### readings. A collocated mass-based reference monitor4-34 

### was used to correct the DustTr ak readings to equivalent PM 10 

### mass-based concentrations, using a plume profiling tower with various refe rence, reference equivalent and DustTrak monitors at different heights. Canister vacuum cleaners with ha rd-floor inlets were used to recover applied soil from the ro adway 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 emissi on 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 emi ssion factors from the silt loadings.

Thirteen different experiment al test conditions were perf ormed. Most consisted of approximately 30 vehicle passes, with each pass identified by the mobile samplingtechnology. Each run consisted of three passes by each mobile sampling technology. Crosscomparisons were performed to determine the ratio between the DustTrak reading and the
PM 10 

### mass-based concentration measured by a collo cated reference sampler. The correlation between the DustTrak and TEOM showed that Du stTrak values would have to be multiplied by a factor of 2.8 ± 0.6 to obtain mass-equivalent PM 10 

### . A controlled laboratory tests was also used to obtain a relati onship between the DustTrak measurements and mass-based measurements. These tests generated a Dust Trak correction multiplier of 2.4, which was chosen for use in this program.

Two conclusions were made from the te st results obtained in the study, when comparing mobile monitoring techno logies with the AP-42 methodology:

• The calibrated mobile methods measured em ission factors that were about 1.5 times higher than found with the AP-42 met hodology 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 mob ilization process occurred as a result of silt being distributed on top the embedded road surf ace aggregates and hence being more easily entrained by vehicle mechanical and aerodynamic shear. It was also st ated that aged silt found on most roads is more likely to be em bedded between the road surface aggregates. Another conclusion identified in the field study was that implementation of mobile monitoring technologies provide for much easie r representation of sp atially distributed roadway emission characteristics, while e liminating the need to divert traffic.

4.2.1.22. Technical Support Document for Mobile Monitoring Technologies ; Prepared For Clark County Department of Air Qu ality 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 su itable 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 th e traditional approa ch 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 curre nt 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 prof iling to quantify mass emissions rates.

• A brief discussion of the methods used to estimate independent variables required for the AP-42 emissions factor equations, asso ciated restrictions and the resulting limitations and a subjective a ssessment of the uncertainties.

• A more in depth discussion of the two m obile 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 require ments for mobile monitoring. Calibration requirements identified include determini ng the relationship between concentrations measured by the instrument used for mob ile monitoring and the Federal Register Measurement Method, the relationship between the concentrations measured at different vehicle speeds, different road dust characteristics a nd 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 m obile 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 Speci fication for Mobile Monitoring document.

While this document states that the purpos e is to demonstrate that mobile monitoring is equivalent or superior to the traditi onal AP-42 methodology, it provides only subjective opinions of the author and the selected revi ewers. While there were no quantitative indicators to compare the precision or accur acy 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 th e reviewers concluded that the method was more accurate and precise than th e 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 vari ables other than silt loading, weight and speed which influence road dust emissions. Se veral reviewers highlig ht the potential of mobile monitoring methods to replace or s upplement the resource in tensive and dangerous collection of representative silt loading information. Severa l reviewers also highlight the need for further development and standardizatio n of mobile monitoring such that the method could be used for managing the ro ad 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 inst ructions for performing a standardized methodology for the construction of a mobile sampling platform, specifications for instrumentation used with4-36 

### Federal Register Methods for PM10 or PM 2.5, calibrations required to correlate the combined sampling platform and instrumentati on 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 individua l tests are available. All tests quantified PM 10 

### emissions. Lastly, plume profiling was the test method. Of these, 81 emissions tests included mean vehicle weight, road silt loadin g, and vehicle speed. The remaining tests included all of these parameters except vehicle speed. These emissions tests measured PM 10 

### emissions associated with engine exhaust, tire wear, brake wear and material deposited on the road surface. Policy decisions within EPA make it necessary to se parate 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 decision s are based in part on the recent and future efforts to control engi ne exhaust emissions. Many of the emissions tests performed to quantify part iculate matter emissions from paved roads were conducted in the mid 1980’s to middle 1990’s . Several of the emission s studies have experienced comparable upwind and downwind concentrations with downwind particulate that appears to consist of a large percentage of organic or carbonaceous materi al. The first separation of vehicle associated emissions and pavement associated emissions wa s in the 2003 update. This update used the national VMT weighted fleet average PM 10 

### 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 a nd brake wear from the composite paved road emissions factor. A fleet aver age vehicle weight of 3.75 tons is associated with this emissions factor. Since the average vehicle we ight used in the deve lopment of the paved road emissions factor equati on was about 10 tons, the PM 10 

### emissions factor for engine exhaust, tire wear and brake wear probably u nderestimated these emi ssions. In addition, because of the range and vari ation 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 ex haust, tire wear and brake wear can be made since (1) average vehicle weights are av ailable for each test series, (2) PM 10 

### emissions factors estimates for each vehicle class ar e available using the MOVES model and (3) PM 10 

### emissions estimates for slowly moving and st op 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 wi th 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 wei ght 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 an d diesel heavy duty trucks. Other assumptions used to estimate ve hicle associated emissions include:

• The test fleet includes a mixture of light duty vehicles, hea vy duty gas trucks and heavy duty diesel trucks when the averag e 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 weig ht 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 emi ssions 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 vehi cle weights are summed to arrive at the two VMT adjusted gross vehicle wei ghts used in this assessment. For light duty vehicles, the VMT adjusted gross vehicle weight is 3320 pounds. For heavy du ty trucks, the VMT adjusted gross vehicle weight is 3742 pounds. The su ms of the VMT distributions for these two classes of vehicles are obtained by summing the individua l VMT distributions for the two classes of vehicles used in this a ssessment. For light duty vehicles, the VMT distribution is 0.928. Fo r heavy duty trucks, the VMT distribution is 0. 0717. Dividing the VMT adjusted gross vehicle weights by the VMT distributions and conver ting to tons yields the average vehicle weights for the two classe s of vehicles. For lig ht duty vehicles, the average gross vehicle weight is 1.79 tons. For the combination of heavy duty gas and diesel trucks, the average gross vehi cle weight is 26.09 tons.

Next, an algorithm is developed to provi de test run specific ratios of light duty vehicles and heavy duty trucks. The algorithm is developed by solving the following two equations.

W t = (R LD x W LD ) + (R HD x R HD )

1.00 = R LD + R HD
where: W t = Test report average vehicle weight

W LD = Average Light Duty Vehicle Weight (1.78848 tons)

R HD = Average Heavy Duty Truck Weight (26.09135 tons)

R LD = Light duty vehicle ratio

R HD = Heavy duty truck ratio4-38 

### 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:

R HD = (W t - 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 dut y diesel trucks in each test series is:

R HD = (W t - 1.78848) / (35 - 1.78848)



### Run specific emissions estimates for vehicl e exhaust, brake wear and tire wear are estimated using the EPA Office of Tr ansportation and Air Quality MOVES (MOtor Vehicle Emission Simulator) 2010 model 29 

### . 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 sp eed, and other general information required to generate a valid PM 10 

### emissions calculation with the MOVES model. While the MOVES model has the ability to generate start up emissions, all test co nditions are assumed to include only vehicles which have achieved normal operati ng temperatures. For all test series with average vehicle speeds greater than 10 mph, the MOVES model ca lculated 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 te st series with average vehicle speeds below 9.9 mph, in addition to running exhaust, tire wear and brake w ear emissions; exhaust emissions during acceleration and idling ar e 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 dr iving condition was calculated by dividing the hourly emissions by the average vehicle speed. Summing the pr oduct of emissions factors from heavy duty trucks and light duty vehicles and the ratio of heavy duty vehi cles and light duty vehicles provides an estimate of the to tal engine exhaust; tire wear an d brake wear emissions for the

test run.

The test run specific emissions factor estimat e for engine exhaust, tire wear and brake wear is subtracted from the test run measur ed emissions factor to produce the test run specific emissions factor due to road surface ma terial. To allow log transformation of the data, values of zero or less we re set to 0.01 g/VMT. Table 4- 17 presents the final dependent and independent variables for all of the useab le 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 refini ng facilities and f our of the data were high or moderate speed urban traffic. None of these ten data were in cluded 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 additi onal test runs with vehicle speeds of 55 mph had engine exha ust, tire wear and break wear emission s greater than 160% of the road emissions. The silt level for one of the 55 mph test runs was greater than all4-39 

### 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 tr affic (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 si lt 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 presentati ons of the final PM 10 

### 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 pres ents the data base by silt loading with five ranges of average vehicle weight depi cted with different shape and color data points. The figure shows that with in creasing silt loading there is an increase in the PM 10 

### emissions factor. Fi gure 4-2 presents the data base by average vehicle weight with seven ranges of silt loading depicted with di fferent shape and color data points. Although there is a significant overlap of the different vehicle weight da ta, there appears to be some relationship between average vehicle weight and the PM 10 

### emissions factor. As with silt loading, it appears that the PM 10 

### 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 th e relationship between si lt loading and average vehicle weight with eight ranges of emissions f actors depicted with different shape and color data points. Although very poor, there appe ars 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 be tween average vehicle speed and the PM 10 

### emissions factor. It appears that be tween 10 and 55 mph, the emissions factor decreases with increasing speed. Below 10 mph there does not a ppear to be a speed relationship. Figure 4-5 presents the relationship between silt loadi ng and vehicle speed with five ranges of PM 10 

### emissions factors. The silt loading appears to decrease with increasing speed above 10 mph. In addition, there seems to be a clear increase in PM 10 

### emissions factor as silt loading increases and speed decreases. Figure 4-6 pr esents a three dimensional view of the silt loading, vehicle weight and PM 10 

### 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 PM 10 

### emissions factor is also included in the label. The point which has the uncharacteristic emissions is point Z-3 with a PM 10 

### 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 da ta and was included in th e 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 PM 10 

### 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/m 2 

### with few gaps down to 0.013 g/m 2 

### . There are ten data with silt loadings spread out from 50 g/m 2 

### to almost 400 g/m 2 

### 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 coll ected prior to 1983. These data have higher silt loadings that the default silt loading for the peak additive contribution value for roads with average daily4-40 

### traffic volume counts of less than 500. While there may be a very small number of streets that reach this silt loading le vel, 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 SetReference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)AUC3 0.42 27 5.5 0.0112.25 0.153 0.3298 1.920AUC4 0.52 25 6 0.0416.1 0.173 0.3537 15.746AUC5 0.23 29 3.9 0.0715.3 0.087 0.1941 15.106AUC6 0.23 27 6.2 0.033.7 0.182 0.3961 3.304AUC7 0.26 27 3 0.010.402 0.050 0.1653 0.237USX 5/1990 AUC8 0.15 27 2 0.037.88 0.009 0.0936 7.786AUE1 4 15 12 0.013.22 0.420 0.9337 2.286AUE2 4 16 5.10.610.6 0.136 0.3709 10.229AUE3 2.2 15 2.6 0.0816.1 0.033 0.1804 15.920AUE4 1.3 15 2.6 0.069.01 0.033 0.1804 8.830M-1 0.46 30 5.6 0.1244.99 0.157 0.3610 4.629M-2 0.26 30 3.8 0.0331.55 0.083 0.2486 1.301M-30.147 30 4.5 0.0703.54 0.112 0.2845 3.256M-40.432 35 2.1 0.0300.177 0.013 0.0927 0.084M-5 1.01 35 2.2 0.0900.692 0.017 0.0749 0.617M-60.716 30 2.1 0.0631.38 0.013 0.1043 1.276M-7 0.59 35 2.3 0.1304.22 0.021 0.1146 4.105M-8 2.48 20 2.2 0.12011.2 0.017 0.1063 11.094M-90.293 30 4.1 0.1303.24 0.095 0.2190 3.021EPA 7/1984 M-10 0.022 55 4.5 0.1040.177 0.112 0.1798 0.010M-11 0.022 55 4.8 0.0800.322 0.124 0.2009 0.121M-12 0.022 55 3.8 0.0800.084 0.083 0.1403 0.010M-13 0.11 35 2.7 0.0650.306 0.038 0.0988 0.207M-14 0.079 35 2.7 0.0301.37 0.038 0.1044 1.266M-15 0.049 35 2.7 0.0901.47 0.038 0.0886 1.381M-16 0.022 55 4.3 0.0600.241 0.103 0.1581 0.083M-17 0.809 30 2 0.0562.64 0.009 0.0501 2.590M-18 0.731 30 2 0.0800.37 0.009 0.0501 0.320M-19 0.929 30 2.4 0.0500.177 0.025 0.0791 0.098

4-41Table 4-17. (Continued)Reference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)Y190.7 10 3.6117 0.075 0.2274 116.773Y276.1 10 3.7182 0.079 0.2359 181.764Y3193 10 3.836.3 0.083 0.2443 36.056Y4193 10 3.7200 0.079 0.2359 199.764Z111.3 10 8317 0.256 0.6096 316.390EPA 1/1983 Z2 12.4 15 8740 0.256 0.5697 739.430Z312.4 15 81820 0.256 0.5697 1819.430AC4 287 10 5.71750 0.161 0.4090 1749.591AC5 188 15 71420 0.214 0.4852 1419.515AC6 399 20 3.1613 0.054 0.1466 612.853AD1 94.8 23 421480 1.000 1.8114 1478.189AD2 63.6 23 39342 1.000 1.8114 340.189AD3 52.9 23 40233 1.000 1.8114 231.189F34 2.78 NR 28 0.552188 0.789 1.4388 186.561F35 2.03 NR 25 0.057298 0.699 1.2790 296.721F360.201 NR 8.3 0.13454.7 0.268 0.5320 54.168F370.417 NR 17 0.16377.2 0.626 1.1617 76.038F380.218 NR 18 0.301167 0.667 1.2339 165.766F390.441 NR 18 0.177253 0.667 1.2339 251.766EPA 8/1983 F27 14.8 NR 14 0.531130 0.502 0.9292 129.071F320.117 NR 14 0.13853.1 0.502 0.9292 52.171F61 17.9 NR 40 0.327463 1.000 1.8261 461.174F45 5.11 NR 16 0.744212 0.585 1.0896 210.910F62 14.4 NR 36 0.294317 1.000 1.8226 315.177F74 5.59 NR 29 0.114545 0.819 1.5012 543.499

4-42Table 4-17. (Continued)Reference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)B50 13.6 NR 9.4 0.22582.1 0.313 0.5936 81.506B51 13.6 NR 11 0.410140 0.379 0.7108 139.289B52 7.19 NR 12 0.10235.4 0.420 0.7836 34.616B54 3.77 NR 10 0.18793.3 0.338 0.6379 92.662EPA 8/1983 B55 6.3 NR 11 0.295183 0.379 0.7108 182.289B56 2.4 NR 9.2 0.229126 0.305 0.5794 125.421B58 10.4 NR 18 0.190368 0.667 1.2221 366.778B57 2.32 NR12 0.358195 0.420 0.7836 194.216B59 2.06 NR 11 0.149348 0.379 0.7108 347.289B60 3.19 NR 12 0.339439 0.420 0.7836 438.216BH10.184 55 2.2 0.2331.08 0.017 0.0306 1.049BH20.0127 55 2.2 0.0300.102 0.017 0.0306 0.071BH30.0127 55 2.20 0.017 0.0305BH6 1.47 40 2.2 0.3004.68 0.017 0.0343 4.646EPA 4/1997 BJ6 0.06 45 2.2 0.0450.301 0.017 0.0336 0.267BJ7 0.06 45 2.2 0.1301.94 0.017 0.0336 1.906BJ9 0.06 45 2.20 0.017 0.0305BJ10 0.06 45 2.20 0.017 0.0305BJ11 0.06 45 2.20 0.017 0.0305BK70.082 45 2.2 0.0330.57 0.017 0.0336 0.536BK80.082 45 2.2 0.0330.44 0.017 0.0336 0.406CE-1 1.16 1 36 0.05027 1.00011.06 15.940CE-2 0.86 1 36 0.07564 1.00011.06 52.940CE-11 1.34 5 12 0.200154 0.4202.212 151.788CRA 5/2008 CE-3 0.86 1 39 0.07045 1.00011.06 33.940CE-15 1.91 5 40 0.06563.5 1.0002.212 61.288CE-16 1.41 5 40 0.05077.1 1.0002.212 74.888CE-17 2.93 5 40 0.04041.3 1.0002.212 39.088CE-19 0.76 5 38 0.04018.6 1.0002.212 16.388

4-43Table 4-17. (Continued)Reference Run IDSilt loading(g/m2)Speed(mph)Weight(tons)DownwindConcentrationmg/m3Measured PM10Emission factor(g/VMT)EstimatedFractionHeavy DutyVehiclesEstimatedEngine, brake,tire emissionfactor(g/VMT)EstimatedPM10RoadDust Emissionfactor(g/VMT)CE-12 1.34 5 13 0.08523.1 0.4612.212 20.888CF-1N 0.97 5 40 0.0354.99 1.0002.212 2.778CF-1/South 0.97 1 40 0.04019.5 1.00011.06 8.440CF-2N 0.81 5.3 41 0.04416.3 1.000 2.0868 14.213CF-2/South 0.81 1 41 0.08063.5 1.00011.06 52.440CF-3N 0.63 5.1 41 0.0151.09 1.000 2.1686 0.010CF-3/South 0.63 1 41 0.02523.1 1.00011.06 12.040CF-4N 1.1 4.7 41 0.0193.08 1.000 2.3532 0.727CRA 5/2008 CF-5 1.4 1 41 0.03016.3 1.00011.06 5.240CI-1 0.06 15.1 260 0.729 1.0008CI-2 0.06 14.85 260 0.729 1.0008CI-3 0.06 13.15 270 0.759 1.0410CI-4 0.06 14.5 270 0.759 1.0410CI-7 0.05 15.3 27 0.0301.63 0.759 1.0409 0.589CI-8 0.05 15.3 27 0.0302.99 0.759 1.0409 1.949CI-11 0.025 13.1 270 0.759 1.0410CI-12 0.25 13.1 270 0.759 1.0410CM-1 0.72 5 39.8 0.0356.35 1.0002.212 4.138CM-2 0.72 1 39.6 0.05063.5 1.00011.06 52.440CM-4 0.7 5 39.5 0.0357.26 1.0002.212 5.048

4-444-45Figure 4-1. PM10Emissions Factor Data Base by Silt Loading (93 test runs).Figure 4-2. PM10Emissions Factor Data Base by Average Vehicle Weight (93 test runs).

4-464-47Figure 4-3. Silt Loading vs. AverageVehicle Weight (93 Test Runs).4-48Figure 4-4. PM10Emissions Factors by Vehicle Speed.4-49Figure 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 Emissi ons Factor Data Excluding Z-3.4-53 

### 4.2.2.2. Emission Fact or 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 orde r to obtain a multiplicative model as in the past. Table 4-18 presents th e correlation matrix of the lo g-transformed independent and dependent variables. The most notable feature of the correlation matrix is the high degree of correlation between silt loading and emissions f actors. The correlation between emissions factor, weight and speed is much lower than with silt loading. Th e high correlation between weight and speed is beli eved to be the result of the large data collected by the corn refiners association to characterize emissi ons 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 PM 10 

### data.

PM 10 

### Emission factor (g/VMT) Silt loading (g/m 2 

### ) Weight (tons) Speed (mph)

PM 10 

### Emission factor (g/VMT) 1 Silt loading (g/m 2 

### ) 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 variab les. The independent variables included silt loading, average vehicle we ight, the product of silt loading and vehicle weight, the square of silt loading (after log transformation) and the square of the vehi cle weight (after log transformation). In addition, th e influence of including and excluding flagged test runs were explored. The primary criteria for selecting th e most appropriate form and supporting data set was the predictive performance of the equati on using the combinati on 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 weig hts 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 relations hips 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/m 2 

### produced equations which uniformly overestimated te st data with lower silt loadings without a signif icant improvement in estimating the high silt loading data. Also, the exclusion of the ten data with high silt loadings di d not significantly ch ange the predictive accuracy of the equation for the ten high silt loading test runs. The 93 test data with positive measured emissions were provid ed to a statistician for subs equent analysis with SAS.4-54 

### Several additional assessments we re performed to determine an equation that provided a high correlation coefficient, a low average percent er ror for test series with targeted independent variables and which provided a re asonable level of predictive ac curacy for test series where the independent variables were outside the targ eted range. The equa tion which produced the highest correlation coefficient was one which forced the inter cept to zero. This equation performed well and was consiste nt with engineering assessment s of the physical influences on emissions. This e quation used only silt loading and average vehicle weight as the independent variables. It was decided that th e 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 emissi ons factors. The re sulting equation for PM 10 

### is:


## ( ) ( ) 021.1912.0 

### WsL0.1EF =

Table 4-19 shows the statisti cal output. The predicted expo nents for silt and weight are 0.912 and 1.021 respectively and have a coefficient of determination (R 2 

### ) of 0.72. The standard error associated with the silt and weight terms are 0.12 and 0.08 respec tively. As a result, it is expected that 95% of future data would fall with in equations with exponent s 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 exis ted 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 andWeight.SUMMARY OUTPUTAll positive testdata, sL < 20 force 0, sL WRegression StatisticsMultiple R 0.848347765R Square0.71969393Adjusted R Square 0.703887682Standard Error 1.921751464Observations 83ANOVAdf SSMSFSignificanceFRegression2 768.0593789 384.0296894 103.9849195 5.61978E-23Residual 81299.14342383.693128689Total83 1067.202803Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0#N/A#N/A#N/A#N/A#N/AWeight (tons) 1.0212836 0.084774552 12.04705393 9.58964E-20 0.852608836 1.189958364Silt loading (g/m2) 0.911843675 0.117787966 7.741399277 2.42283E-11 0.677482574 1.146204776

4-554-56 

### An assessment of the performance of th e predictive equation is difficult since the range of silt loadings and the associated emissions factors span s five orders of magnitude. This is further complicated by the focus of many of the field test s. Approximately half of the field test locations were selected either due to concerns that these sources were major contributors to air quality impact s, or were selected because of elevated road silt levels to allow the measurement of a difference from bac kground concentrations of particulate matter. Another complication is that PM emissions of the vehicle exha ust 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 pred ictive equation by calculating the average predicted to actual ratio and pr oducing the cumulative distributi on of these ratios. For the two parameter equation, the aver age 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 actu al ration of 38 compar ed 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 ex haust, 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 meteorologi cal conditions, vehicle speed and vehicle weight determined during the test. Figure 4-9 depicts th e cumulative distribu tion 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 th e use of different shapes and colors for the markers of the data. Figure 4-11 is this same information but with ra nges of vehicle weights depicted with different markers. It is diffic ult 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 ne w equation appears to demons trate an improved performance compared to the previous equation.

Another means of assessing the performa nce 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, compar ing the relative percent difference between the results of the equation and the measured valu e places the differences in the smallest measured value and the largest measured valu e 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 pr ovides an order of magnitude improvement in estimating the actual measured emissions over th e previous equation. Associated with the reduction in the percent difference from actual emissions is a 47 percent reduction in th e emissions factor. When the performance of the equation is evaluated within classes of the independent variables of silt loading, average vehicl e weight and speed; the new equation shows comparable or improved performance in al l groups of the variables except two.4-57Figure 4-9. Cumulative Distribution of Predicted/Actual Ratios.4-58Figure 4-10. Cumulative Distribution –Predicted/Actual by Silt Loading.4-59Figure 4-11. Cumulative Distribution – Predicted/Actual by Average Vehicle Weight.4-60 

### Figure 4-12 through Figure 4-9 provide gra phical indications of the performance of the updated equation to estimate the actual emissions. The first figure shows the relationship of emissions to the road surf ace silt loading. Included in th is figure is information on the average vehicle weight through th e 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 vehi cle weight on the emissions factors. For all weight ranges, the spread of th e data is much greater th an 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 emissi ons with silt lo ading. This is probably due to the greater co rrelation between silt loading and PM 10 

### emissions factors than between average vehicle weight and PM 10 

### 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 th e primary dependent variab le graphed. One can also see a weak relationsh ip between silt loading and average vehicle speed.Table 4-20. Comparison of Previous and New Equations for Estimating Paved Road Dust Emissions.Predictive Performance of Paved Road DustEmissions EquationsAverage Relative Percent Difference1Relative Standard DeviationOld Equation vsActualNew Equationvs ActualOld Equation vsNew EquationOld Equationvs ActualNew Equationvs ActualOld Equation vsNew EquationPopulation Average31,378 3,142-47 5.775.84-1.2By Classes of Silt Loading (g/m2)≤0.233,601 3,858-71 2.121.38-0.620.2 – 0.75102,647 17,049-62 3.71 11.54-0.350.75 – 1.53,236669-61 2.480.41-0.461.5 – 5022147-45 3.570.11-0.46≥5024825373 1.810.271.20By Classes of Average Weight (ton)2 - 346733340 2.090.43-1.623 – 5718289350 1.710.729.915 – 10-2-4153 -394.30.37-0.7410 – 4038,248 4,906 74,840 3.06 24.73-0.18≥40128,217 21,549 68,550 4.27 112.84-0.22By Classes of Average Speed (mph)< 1090,216 15,112-79 4.30 20.67-0.0710 – 2554,063 7,034-6 2.174.94-15.1125 – 45293170-41 2.41 0.139-0.45451,041662-34 1.28 0.198-0.05551,404467-114 1.57 0.114-0.60

4-614-62Figure 4-12. Predicted vs Actual PM10Emissions Factor by Silt Loading.4-63Figure 4-13. Predicted vs Actual PM10Emissions Factor byAverage Vehicle Weight.4-64Figure 4-14. Predictive Accuracy bySilt Loading (unrestricted range).4-65Figure 4-15. Predictive Accuracy bySilt Loading (restricted range).4-66Figure 4-16. Predictive Accuracy by Average Vehicle Weight (unrestricted range).4-67Figure 4-17. Predictive Accuracy by Average Vehicle Weight (restricted range).4-68 

### 4.2.2.3 Emissions Factor Qu ality Rating Assessment.



### All of the source test data used to devel op the emissions factor equation were rated A since the test procedures used were profiling te sts and were all well documented. While only six reports are available that provide documen tation of emissions factors for paved roads, these test reports contain the results of 17 di fferent road conditions. The reports and the number of test co nditions 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 (s peed 55, 45), 3 locations, and

• CRA 5/2008 - 4 tests (4 locations, 2 speeds, )
However, since the EPA 8/1983 report doe s not contain inform ation on the average speed of the vehicles in the study, none of the tests documente d in that report is usable for further data set groupings . The remaining five re ports 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 vehi cle weight) were dive rse. An assessment of the variation associated with the data and the impact of th at variation on a single va lue emissions factor. The average of all the adjusted emissions fact ors is 140 g/VMT and the standard deviation is 387. A relative standard deviati on of 3 is greater than many ot her factors. As a result, the number of tests needed to achieve the predic tive accuracy of the mean is greater. The availability of 15 A or B rate d 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 th e 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, th e correlation coefficient (R2) fo r the final equa tion is 72%. This indicates that approximately 72% of the vari ations in the emissions factors are due to the silt level and average vehicle weight. As a resu lt of the improved ability of the equation to estimate the measured values over the single va lue emissions factor, a quality rating of B is assigned to the equation.

4.2.2.4 Assignment of equation parameters for PM 30 

### and PM 2.5 

### .



### While several of the reports include measurements of PM 2.5 

### , the WRAP studies suggest that many of these measurements are in error due to particle bounce issues with the impactor stages. The results of th e WRAP study indicated that the PM 2.5 

### concentrations measured by the cyclone/impact or system were consistently biased by a factor of about 2 relative the PM 2.5 

### concentrations measured by the Partis ol samplers. The second phase of the WRAP showed a tendency of the measured PM 2.5 

### / PM 10 

### ratio to decrease with increasing4-69 

### PM 10 

### concentration. At PM 10 

### concentrations above 1.0 mg/m3 the PM 2.5 

### / PM 10 

### ratio was between 0.1 and 0.15. The PM 2.5 

### / PM 10 

### ratio increased to about 0.35 as the PM 10 

### 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. Th e paved road emissions factor for PM 2.5 

### was revised to 15% of the calculated PM 10 

### emissions factor in 2008. It is not clear whether the WRAP study assessed the PM 10 

### concentrations measured during the paved roads testing prior to their recommendations for revising the PM 2.5 

### emissions factors. As shown in Table 4-17 the PM 10 

### concentrations associated with 58 of the 71 te st runs used to develop the three parameter emissions factor equation. Many of these te st runs involve traffi c volumes that would produce fairly constant particulate concentrations . Also, of these 58 test runs, only three runs were the highest PM 10 

### concentrations greater than 0.5 mg/m 3. An earlier report (Reference 5) measured PM 2.5 

### / PM 10 

### ratios during field test s. The range of PM 2.5 

### / PM 10 

### ratios was from 0.25 to 0.37. Since essentia lly all of the measured PM 10 

### concentrations used for the stepwise regression were below 0.5 mg/m 3 

### and the ratios measured during field sampling of paved road emissions were between 0.25 and 0.37, the recommended PM 2.5 

### emissions factor is 25% of the PM 10 

### emissions factor. Since th ere is little measured PM 2.5 

### data, an emissions factor quality rating of “D” is assigned.

While a stepwise regression could be performed to estimate the PM 30 

### emissions factor equation, it is believed that the number of available data w ould be significantly less and a comparable confidence in the resulting equati on could not be achieve d. The ratio of PM 30 

### to PM 10 

### presented in the present AP-42 section is 5. 2 and is proposed for the revised equation.

4.2.2.5. Assignment of a preci pitation correction factor.



### As is presented in Reference 38, a corr ection 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 parame ters 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, numero us organizations have collecte d silt loading samples from public paved roads. Unfortun ately, 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 PM 10 

### at each EPA regional office were asked to identify sL data for public roads. In many instances, the EPA representatives identifi ed state/local air regu latory personnel who were then asked to supply the data. Gi ven that the relative importance of PM 10 

### 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 M ontana had data collected from the same road over extended periods of ti me, thus permitting examination of temporal4-70 

### variation.

The assembled data set did not yield any readily identifiable, coherent relationship between silt loading and ro ad class, average daily traffic (ADT), etc. Much of the difficulty is probably due to the fact that not all variable s 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. Ne vertheless, it is questionable whether the seasonal variation noted in the Montana data base coul d 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 si milar cycle in, say, Maine or Michigan, in the absence of additional information.

Because no meaningful relationship coul d be established between sL and an independent variable, the decision was made to directly employ th e nonindustrial data base in the AP-42 section. The draft AP -42 section presents the cumula tive frequency distribution for the sL data base, with subdivisions into (a ) low-ADT (< 5000 vehi cles/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 assemb led 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 pave d road surface loading values together with the city, state, road name, coll ection 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 th e 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, Res earch 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 Protec tion Agency, Cincinnati, OH, March 1978.

3. C. Cowherd, Jr., et al., Iron And Steel Pl ant Open Dust Source Fugitive Emission Evaluation, EPA-600/2-79-103, U. S. Environmental Prot ection 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. Envi ronmental Protection Agency, Research Triangle Park, NC, July 1977.

5. Size Specific Particulate Emission Factor s For Uncontrolled I ndustrial 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 Sour ce Fugitive Emission Control Evaluation, EPA-600/2-83-110, U. S. Environm ental Protection Agency, Cincinnati, OH, October 1983.

7. J. P. Reider, Size-specific Particulate Emission Factor s For Uncontro lled 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 Protecti on Agency, Cincinnati, OH, July 1984.

9. C. Cowherd, Jr., and P. J. Englehart, Si ze 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, Midwes t 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 Institut e, Kansas City, MO, September 1987.

13. Chicago Area Particulate Matter Emi ssion Inventory — Sampling And Analysis, Contract No. 68-02-4395, Midwest Research Institute, Kansas City, MO, May 1988.

14. Montana Street Sampling Data, Montana De partment Of Health And Environmental Sciences, Helena, MT, July 1992.

15. Street Sanding Emissions And Control St udy, PEI Associates, Inc., Cincinnati, OH, October 1989.

16. Evaluation Of PM-10 Emission Factor s 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 La ke County Road Dust Silt Loading Winter 1991/92 Measurement Program, Aerovironment, Inc., Monrovia, CA, June 1992.

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

20. PM-10 Emissions Inventory Data For Th e Maricopa And Pima Planning Areas, EPA Contract No. 68-02-3888, Engineering-Sc ience, Pasadena, CA, January 1987.4-72 

### 21. Characterization Of PM-10 Emissions From Antiskid Mate rials 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 Re visions to Fine Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors. Prep ared by Midwest Research Institute for Western Governors Association, Western Regional Air Partne rship, Denver, CO, February 1, 2006.

23. Climatic Atlas Of The United States, U. S. Department of Co mmerce, 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, At lanta, 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, Res earch 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 Envir onmental Protection Agency, Office of Transportation and Air Qual ity. EPA420-R-02-028, October 2002.

28. Written communication (Technical Memorandum) from P. Hemmer, E.H. Pechan & Associates, Inc., Durham, NC to B. K uykendal, 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 Ai r Quality. EPA420B-09-041, December 2009.

30. Fugitive Particulate Matter Emissions, U.S. Environmental Protection Agency, Research Triangle Park, NC, Midwest Res earch Institute Project No. 4604-06, April 15, 1997.

31. Midwest Research Institut e, Roadway Emissions Field Te sts at U.S. Steel’s Fairless Works, U.S. Steel Corporation, Fairle ss 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 In stitute Project No. 310842, May 20, 2008.

33. Emission Tests of Paved Road Traffic at Minnesota Corn Processors Marshall, Minnesota Facility, McVehil-Monnett Associ ates, Midwest Research Institute Project4-73 

### 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 Res earch Institute Project No. 310479.1.001. December 5, 2003.

37. E-mail communication between Ron My ers 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 a nd go traffic; January 2009 through May 2009.

38. Technical Memorandum from William B. Kuykendal to File, S ubject: Decisions on Final AP-42 Section 13.2.1 “Paved Roads”, October 10, 2002.

39. E-mail communication between Ron My ers 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 Me thod Specifications, Prepared by Midwest Research Institute for Clark County Depart ment of Air Quality and Environmental Management, Las Vegas, NV, February 6, 2009.

41. C. Cowherd, Technical Support Docume nt for Mobile Monitoring Technologies, Prepared by Midwest Research Institute for Clark County Department of Air Quality and Environmental Management, La s Vegas, NV, January 9, 2009.

42. R. Langston, R. S. Merle Jr., et al., Cl ark County (Nevada) Paved Road Dust Emission Studies in Support of Mobile Monitoring T echnologies, Clark County Department of Air Quality and Environmental Manageme nt, Las Vegas, NV, December 22, 2008.

43. Midwest Research Institute; Analysis of the Fine Fracti on of Particulate Matter in Fugitive Dust; Western Governors’ Associ ation - Western Regional Air Partnership (WRAP); October 12, 2005.

44. Midwest Research Instit ute; Background Document for Revisions to Fine Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors; Western Governors’ Association - Western Regional Air Pa rtnership (WRAP); November 1, 2006.

45. G. E. Muleski, Measurement of Fugitive Dust Emissions from Prilled Sulfur Handling, Final Report, MRI Project No. 7995-L, Pr epared for Gardinier, Inc., June 19844-74 

### 46. T. F. Eckle and D. L. Trozzo, Verificati on of the Efficiency of a Road-Dust EmissionReduction Program by Exposure Profile M easurement, 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 be half of the Center for the Study of Open Source Emissions (CSOSE)

...................................................................................................................................... 1

Rebecca Kies and Courtney Bokenkroger Senior Sta tistician 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 Provos t 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 Institut e on behalf of the Center for the Study of Open Source Emissions (CSOSE)

Comment: The general consensus among the Cent er for the Study of Open Source Emissions (CSOSE) participants who have wo rked 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 e quation does not offer improved predictive capability. The predictive equation publis hed in November 2006 produced negative

PM 10 

### emissions at very low silt loadings and negative PM 2.5 

### 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 per cent error of over 27,000 compared to the proposed equation with a relative percent erro r 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 fl eets 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 th e test report and the correcti on used in the final revision includes variations in speed, ambient temperatur e, year of vehicle fleet; this error has been reduced. Combining the reduction in erro r 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 supe rior basis than the 2006 equation.

Comment: There is also the broader issue of ad opting mobile monitoring as the basis for
more realistic emission inve ntorying of paved roads.

Response: EPA agrees that the adoption of mobile monitoring to estimate either the silt loading of the road system or the emissi ons 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 te mporally and spatially resolved emissions estimates and to characterize significantly more miles of roadways than were possible by the traditional vacuuming, screen ing and weighing techniques is a distinct advantage. In addition, the mobile monitori ng method provides an excellent means for tracking system wide management controls instituted to provide emissions reduc tions 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 m onitoring provides significant improvements
in the estimation of road dust emi ssions caused by vehicle traffic.

Comment: The proposed equation has a significant new data input requirement (vehicle speed) that increases the difficulty of ge nerating paved road emission inventories.

Response: We disagree; access to the average vehi cle speed of road segments is an existing requirement for the accurate estima tion 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 inve ntory 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 sp eed term on the predictive accuracy of the resulting equation is a better criterion to dete rmine whether this term should be used in the equation. Limited improvement (or degr adation) in the pred ictive 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 f actor 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 a nd 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 appli cable source conditions. The Corn Refiners
retained the services of Midw est Research Institute (MRI) in Kansas City to perform the emissions testing at lower average vehicl e speeds to support th e extension of the applicable source conditions. Twenty two usable profiling tests were performed. In addition to designing and conduc ting the emissions tests, MR I 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 in cluded 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 th at 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.

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### containing low volume rural roads were pred icted to have greater emissions of PM 10 

### than the previous equation predicted. Another s ituation was that the revised equation may result in greater predicted emissions of PM 2.5 

### 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 wa s revealed that the vehicle e xhaust, tire wear and break wear emissions components were not addr essed 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 te sts and in addition significantly mis characterized for the additiona l data provided by the corn refiners. For the historical data, the proposed revision incor porated test specific emissions estimates as calculated by MOBIL 6.2 and based upon the av erage vehicle weight reported for each test. For the CRA data, the proposed revisi on incorporated test specific emissions estimates as calculated by the MOVES model and based upon the average vehicle weight, vehicle speed and estimated acceleration ra tes. For consistency and for improved accuracy in predicting vehicle exhaust emissions, MOVES model estimates were
calculated for the historical data. While th e incorporation of the data provided by the Corn Refiners Association extended the capab ilities of the equati on to 1 mph, the Corn Refiners Association data highlighted the va riable 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 developi ng the road emissions equation is that the equation never predicted negative PM 10 

### or PM 2.5 

### emissions.

Comment: 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. Th e ratio in the existing equation was accepted by EPA as an outcome of an experimental pr ogram supported by the Western Regional Air Partnership (WRAP).

Response: In evaluating the data underlying the e quation proposed in this revision, all of
the data were assessed to understand the basi s and representativene ss of the data. The WRAP laboratory study was eval uated and was found to focus primarily on categories of emissions that would generate very large c oncentrations of dust emissions and focused primarily on western sources of these emissi ons. These types of emissions sources have a high probability of overloading air samp ling devices that depend on impaction to collect particles of differing sizes. These sources are also predom inately dominated by sources where the emissions may have la rge variations over time depending on the repetition rate of the activity which genera tes the emissions. Pave d roads, especially those with high traffic volumes and those that have neared their nor mal aged equilibrium state generate dust emissions of greater cons istency 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 th e roads silt loading is very high. These high silt loadings are not typi cal of public roads except for periods where sand is applied as anti skid material, natural forces exacerba te 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 So ils from the Salton S ea 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 st udy were also used in the second Phase of the study. These three samples included a Standard fi ne Arizona Test dust, a Standard coarse
Arizona Test dust and Lakebed Soil from Owen s 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 vol ume of sample from paved ro ads 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 abrasive s (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 perf ormed to assess the revised PM 10 

### /PM 2.5 

### ratio to assign to
historical data was conducted at PM 10 

### concentrations above 2.5 mg/m 3 

### . The greatest
downwind concentration measured in tests used to support the paved road equation development was 0.74 mg/m 3 

### in run ID F45. Of the test s conducted in the wind tunnel laboratory, only 15 percent of the samples we re performed at concentrations below 0.74 mg/m 3 

### . The lowest PM 10 

### concentration measured dur ing the laboratory study was 0.381 mg/m 3 

### . 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/m 3 

### . In addition, over 80% of th e profiling tests had downwind concentrations less than 0.2 mg/m 3 

### and 60% had downwind con centrations less than 0.1 mg/m 3 

### . In the wind tunnel laboratory studies, the only particulate us ed 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 emi ssions can be a significant component of the emissions measured by the profiling samplers . In three cases, the estimated exhaust,
break wear and tire wear emi ssions 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 th e 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 th an 10% exhaust; break wear and tire wear
emissions.

Based upon a more careful and thorough examin ation of the experimental design of the WRAP study and the profile measurements conducted to characterize paved road
emissions it is concluded that EPA mistak enly accepted the conclusion that the PM 2.5 

### to
PM 10 

### 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 PM 2.5 

### to PM 10 

### ratios established by field studies which used FRM or equivalent monitors for measuring PM 2.5 

### to PM 10 

### concentrations. While there were only twelve test runs conducted during the prof iling tests documented in the April 15, 1997 report by MRI for EPA, the PM 2.5 

### to PM 10 

### ratios determined at these three locations provide a superior estimate of a national ratio for estimating PM 2.5 

### emissions than an extrapolation from the WRAP laboratory study.

Rebecca Kies and Courtney Bokenkroger Se nior Statistician of Midwest Research Institute, Kansas City, MO.

Comment: The approach used by EPA to calcula te the proposed paved road equation differs from standard least-squares regr ession procedures. MRI recommends that ordinary least squares regr ession 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 us ed for this section. In the traditional form of regressing the equation, th e log transformed data would be regressed and include an intercept. Then when retu rned 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 i ndividuals with more thorough knowledge in the application of stepwise non lin ear regression were not available at the time but were used in the equation devel opment 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. W ith guidance from the statistician, EPA used Excel to explore limited alternative forms of the equation that coul d potentially provide an equation with better predictive accuracy. EP A assessed the influence of test data that potentially would adversely influence the re sulting equation and a ssessed the use of composite factors in an attempt to allevi ate the additional problems identified by MRI’s
statisticians. These assessments led EPA to ex clude ten test data wh ere the silt loadings were greater than 20 g/m 2 

### and to exclude test data wher e 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 explorator y assessment within Excel. The equation which had the best predictive accuracy wa s based upon the traditional least squares regression of the log transformed data with the intercept forced to zero.

Comment: Additional concerns about gaps in th e range of data surfaced during our statistical analysis. Notice the major holes highlighted by the circle s 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 complete ly filled with data points in order to cover all ranges of possible o ccurrences 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 highlight ed would not be expected due to the physical forces influencing the independent variables. Fo r 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 loadi ng and low average speed may be a possibility should there be active management of the si lt 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 su fficient 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 mode ls 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 regressi on. 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 m odeling 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 de bates. 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 inde pendent 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/m 2 

### due to the potential comple xity 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 s ources (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 loadi ng. 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 s hould have been about 52 g/vmt (rather than the default value of 0.02 g/vmt).

Response: The measured PM 10 

### emissions factor in Table 4-15 was checked against the value reported in the test report. The valu e 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 tr ansfer the value from Table 4-15 to Table 4-
17. The emissions factor for the Corn Refi ners 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 de scribed 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.

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### 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 soft ware for statistical analysis) was employed to assess the predictive accuracy of the final equation.

Comment: The high degree of correlation betwee n speed and weight precludes both being included as independent term s in the emission factor equation.

Response: It was believed that the large numbe r of tests where the road surface silt loading was artificially changed through eith er 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 robus t statistical software, the presence of inter correlation between speed and silt loading wa s re assessed. In addition, the more robust
software allows a better determination of th e 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 fi nal equation contains only silt loading and average vehicle weight as independent variables.

Comment: The goal should be to develop a pred ictive 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 pr edictive 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 pred icted/observed ratios.

Response: It is assumed that the use of the geom etric mean is a metric to evaluate the predictive accuracy of the e quation through the use of the av erage predicted to observed values. With the use of SAS, several indi cators 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 bac kground report, the AP-42 Section and background report was reviewed and edited mo re thoroughly and the Table of contents was updated to provide an accurate indicat ion 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 Sec tion 13.2.1 results in a very significant

reduction in PM 10 

### and PM 2.5 

### emission factors from paved roads in industrial settings.

• It is unclear whether USEPA’s propos ed revision to AP-42 Section 13.2.1

improves upon predictive performance of the existing 2006 emission factor.

Response: The performance of the multiple stepwi se regression of the data recognized that incorporation of the speed term invol ved 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 correlati on coefficient showed a modest improvement.
Another commenter indicated that there are better software and pr ocess 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 softwa re 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 sp eed term provided no improvement in the predictive accuracy of the resu lting 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 poten tial 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 consider able variation even when several of the independent parameters are ne arly identical. With the in creased number of independent parameters, it is possible that some situati ons 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 unde rlying data, there were others where the predictive performance was improved. Seve ral measures were used to assess the predictive performance of the revised equati on 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 ir on and steel plant source tests are crucial for calculating fugitive dust emissions from industria l facilities, and excl uding these data has a very significant impact on pr edicted 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 typi cal industrial sites by roughly an order of magnitude. This large, and perhaps unrealis tic, reduction in calcul ated industrial paved road emissions is an artifact of trying to develop an emission fact or based on tests that must include vehicle speed data.

Response: The exclusion of the 22 tests perfor med at iron and stee l 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, overestimat es were significantly greater than the underestimates. The 25 percen tile value underestimated actu al 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 reje cted for the equation that included speed overestimates actual emi ssions by 1,429%. The equation that was proposed and includes the speed term overest imates 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 averag e vehicle weights. In these categories, it appears that the previously published equati on overestimates emissions more than the proposed equation. With respect to roads with greater average vehi cle weights such as may be present at industrial facilities, th e equation in the November 2006 AP-42 section
tended to overestimate emissions more th an the proposed equation. Table 1 below presents the independent parameter variables, estimated measured emissions, predicted emissions by the 2006 AP-42 equation, the equa tion considered in the proposal that includes only silt loading and average vehicl e weight and the equation proposed that includes silt loading, average ve hicle weight and speed (with an average speed of 35 mph assigned for unrecorded speeds). For those test conditions wh ere average vehicle weight was greater than 8 tons, the 2006 AP-42 equati on tended to overestimate actual emissions factors by about 350%. The equation that co nsidered only silt loading and average vehicle weight tended to overestimate act ual emissions factors by about 3%. The equation that considered silt loading, aver age vehicle weight and speed tended to underestimate actual emissions factors by about 12%. A comparison between the equation proposed for use and the equation th at was considered but did not include the speed term shows that the ex clusion 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 pr edictive 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 overestimat es actual emissions factors by 350%, the equation presented in the final 2011 section ov erestimates actual emissions by an average
of 77%.Comments and Responses onJune 2010 Draft Revisionof AP-42 Section 13.2.1 for Paved Roads.January 2011Page 12Table 1. Performance of 2006 AP-42 equation andequations considered for 2010 draft section revision.Predicted Emissions (g/VMT)Percent difference from MeasuredID #SiltLoadingAverageSpeedAverageWeightMeasured EF(g/VMT)Old AP-42(sL, W)Rejected Proposal(sL, W)Proposed(sL, W, s)Old AP-42(sL, W)Rejected Proposal(sL, W)Proposed(sL, W, s)AD194.823421478.1894696.251575.711156.43218%7%-22%F6117.9NR40461.1741476.91325.20235.28220%-29%-49%AD352.92340231.1892987.29881.27636.211192%281%175%AD263.62339340.8073241.811020.25751.93853%200%121%F6214.4NR36315.1771094.65241.87179.78247%-23%-43%F745.59NR29543.498427.7383.1963.42-21%-85%-88%F342.78NR28186.561257.6242.3731.3938%-77%-83%CI-70.0515.3270.58917.711.020.532907%73%-11%CI-80.0515.3271.94917.711.020.53809%-48%-73%F352.03NR25296.721177.1128.6221.73-40%-90%-93%F380.218NR18165.76625.192.732.05-85%-98%-99%F390.441NR18251.76639.955.214.09-84%-98%-98%B5810.4NR18366.778313.0895.4290.51-15%-74%-75%F370.417NR1776.03835.334.703.76-54%-94%-95%F455.11NR16210.911165.2244.5842.38-22%-79%-80%F320.117NR1452.17011.421.220.98-78%-98%-98%F2714.8NR14129.070270.08105.02111.94109%-19%-13%B572.32NR12194.21664.1016.5916.78-67%-91%-91%B603.19NR12438.21678.8922.2422.93-82%-95%-95%B527.19NR1234.616133.9546.9850.85287%36%47%AUE1415122.28691.4327.3924.993900%1098%993%B592.06NR11347.28952.0413.7414.26-85%-96%-96%B556.3NR11182.289107.8438.4342.66-41%-79%-77%B5113.6NR11139.289177.9778.0290.6828%-44%-35%B543.77NR1092.66266.8721.9724.52-28%-76%-74%B5013.6NR9.481.506140.5467.6283.4372%-17%2%B562.4NR9.2125.42143.9213.4415.07-65%-89%-88%F360.201NR8.354.1687.331.251.26-86%-98%-98%Average358%3%-12%Comments and Responses onJune 2010 Draft Revisionof AP-42 Section 13.2.1 for Paved Roads.January 2011Page 13Table 2. Performance of 2006 AP-42 equation, equation proposed in 2010 draft section and Final 2010 section.PredictedEmissions(g/VMT)PercentdifferencefromMeasuredID#SiltLoadingAverageSpeedAverageWeightMeasuredEF(g/VMT)OldAP‐42(sL,W)Proposed(sL,W,s)Final(sL,W)OldAP‐42(sL,W)Proposed(sL,W.s)Final(sL,W)AD194.823421478.1894696.251156.432886.277218%-22%95%F6117.9NR40461.1741476.91235.28600.570220%-49%30%AD352.92340231.1892987.29636.211613.1691192%175%598%AD263.62339340.8073241.81751.931859.513853%121%447%F6214.4NR36315.1771094.65179.78442.254247%-43%40%F745.59NR29543.498427.7363.42149.639-21%-88%-72%F342.78NR28186.561257.6231.3976.35938%-83%-59%CI-70.0515.3270.58917.710.531.8862907%-11%220%CI-80.0515.3271.94917.710.531.886809%-73%-3%F352.03NR25296.721177.1121.7351.060-40%-93%-83%F380.218NR18165.76625.192.054.773-85%-99%-97%F390.441NR18251.76639.954.099.073-84%-98%-96%B5810.4NR18366.778313.0890.51161.994-15%-75%-56%F370.417NR1776.03835.333.768.133-54%-95%-89%F455.11NR16210.911165.2242.3875.113-22%-80%-64%F320.117NR1452.17011.420.982.093-78%-98%-96%F2714.8NR14129.070270.08111.94172.829109%-13%34%B572.32NR12194.21664.1016.7827.253-67%-91%-86%B603.19NR12438.21678.8922.9336.436-82%-95%-92%B527.19NR1234.616133.9550.8576.446287%47%121%AUE1415122.28691.4324.9944.7853900%993%1859%B592.06NR11347.28952.0414.2622.375-85%-96%-94%B556.3NR11182.289107.8442.6662.006-41%-77%-66%B5113.6NR11139.289177.9790.68125.07428%-35%-10%B543.77NR1092.66266.8724.5235.222-28%-74%-62%B5013.6NR9.481.506140.5483.43106.52572%2%31%B562.4NR9.2125.42143.9215.0721.429-65%-88%-83%F360.201NR8.354.1687.331.262.010-86%-98%-96%Average358%-12%77%

## 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 analys is of the National Emission Inventory (“NEI”) resulting from their proposed revisi on.8 USEPA found that their revised paved road emission factor will significantly reduce PM 10 

### emissions in the NEI (up to 200% reduction), while PM 2.5 

### emissions are only slightly a ffected (some NEI calculations increase, some decrease). USEPA, however, did not examine the a ffect of their draft revised paved road equation on fugitive dust emissions from industrial sources.

Response: The estimated impact on State Em issions 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 Stat es 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 agenci es understand the potenti al impact that the revised emissions factors may have on their PM 10 

### and PM 2.5 

### 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 asse ssing the proper equation to publish are the representativeness of the unde rlying 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 ma de 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 ve hicle 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 vehi cle weights over 40 t ons. Although when limited to these high weight classes the perf ormance 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 Pr ovost for Academic Programs; UNLV, Las Vegas, NV.

Comment: In many parts of the country where ther e is significant rain or a rainy season, rain days may considerably effect estimated PM 10 

### emissions in the inventory. However, for Las Vegas and other places lik e 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 th ink 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 ad justment for rain events in AP-42 is imperfect. It is recognized that with very li ght 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 o ffset 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 measur ed data and is an "engineering or expert
elicitation" estimate.

The emissions factors and the adjustment fact ors 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 th e independent variables silt loading, vehicle weight and number of rain events, the remain ing variation is still substantial. EPA does not prohibit the use of altern ative emissions factors or adjustments when accompanied by a scientifically credible rationale and supporting data.

Comment: With locally derived data, we obtain resu lts that are different from those that might be predicted using default silt loadi ng data. The actual impact on total estimated

PM 10 

### emissions in an inventory or SIP woul d 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 th at 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 s ilt loadings for large numbers of roads is substantial. However, where roads are believ ed to be significant contributors to the levels of ambient air particulate matter, obt aining 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 discus sion of the potential advantages of mobile monitoring to develop temporally and spatia lly resolved silt loading (or emissions)
information.

Comment: I also ran a hypothetical sensitiv ity analysis comparing arbitrary combinations of vehicle weight and silt load ing, to see what the impacts of the new PM 10 

### 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 vehi cle 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 t ypical 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, empl oys aggressive dust su ppression techniques.

Response: Values presented in Table 13.2.1-3 are base d upon road dust samples collected in the mid to late 1970's through the mid to late 1980' s. It is unclear wh ether any management practices were used at these facilities to control the silt loading of the roads where these samples were collected. It is possible th at current normal main tenance practices woul d achieve lower silt loadings than are presented in the table. Statem ents in the documentati on included in the reports by the Corn Refiners Associa tion and several other test programs used in the equation development indicate that there was active manage ment of the road surface dust levels. As a result, the silt loading data coll ected during those test programs are lower than they would be
otherwise. While there is no re quirement to use the silt loading va lues 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 Bu reau and Lori Hanson Iowa Department of
Natural Resources.

Comment: The DNR supports the revision of this section to incorpor ate 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 requi res that a mobile source emissions model be run in order to determine a pa ved road emission factor. Obtaining the
emissions factor for vehicle emissions in th is manner will be problematic as the DNR does not have the resources to generate speci fic 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 th e 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 sect ion to account for vehicle emi ssions 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, se veral regulatory programs which cover mobile source emissions are expected to produce decreasi ng exhaust emissions over the next five to ten years. In addition, engine exhaust like road dust em issions is highly dependent on the road characteristics, meteorological conditions , vehicle speed, vehicle class and other environmental conditions. As a result, a defa ult engine exhaust equation will result in unknown errors and may lead to incorrect decisions on different programs. While decisions for many programs may not requ ire the accuracy that would occur with individual selection of the requisite parame ters needed for the most accurate emissions estimates, this would be a decision that shoul d be made for each application. While State agencies (Department of Transportation or Ai r 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 pa rameters (engine exhaus t, 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 PM 2.5 

### 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 PM 10 

### were found and there was no me ntion in the documentation of why the PM 2.5 

### emission factors were zeroed ou t, so we are bit confused.

Response: As a result of a revision of the ratio of the PM 2.5 

### to PM 10 

### recommended by the Western States Air Resources Council (WESTAR) from 25% to 15%, the multiplier k in the predictive equation for PM 2.5 

### was revised from 1.8 (for gram s/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 br eak 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 load ing of 0.2 grams/square meter or less. While the k value used in the previ ous 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 fo r 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 th e stepwise regressions of the test data to develop the predictive equa tion. 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 (Del aware), 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, PM 10 

### was reduced by 58% from the emissions submitted to MACTEC; while PM 2.5 

### increased by 48%. I believe the PM 2.5 

### increase is caused by two factors-first, the PM 2.5 

### / PM 10 

### ratio was increased to 25% (p reviously 15%). The second reason is that under the old equa tion, one had to apply a correcti on factor, C, to remove the exhaust, brake, and tire wear from the front pa rt of the equation. By subtracting C at the end of the equation, the resulting PM 2.5 

### 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. Ha ving negative emission factors re sult 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 appl y the new equation, to s ee if they get changes
similar to mine.

New Jersey has similar results, but even more drastic for PM 2.5 

### . An increase in PM 2.5 

### of
350% and a decrease in PM 10 

### of 46% I think one big cause is the difference in k factor, among other changes. The k factor for PM 2.5 

### went down from the 2003 AP-42 to the 2006 AP-42, and back up again in this new draf t. We guessed at the new vehicle speed requirement, but a slight varia tion 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 e xponents associated with the silt loading and the average vehicle weight also influence the emissi ons 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 em issions 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 provi des an improved estimate of the emissions compared to the previous equation. It is also be lieved that the return to the PM 2.5 

### to PM 10 

### ratio of 25% is a
better indicator of the PM 2.5 

### 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 pavi ng 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..cha nge to "The particulate emission factors presented in a previous version.."

Page 13.2.1-5, third paragraph, last sentence..c hange "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 sent ence..remove hyphen between "not" and "suggest"

Table 13.2.1-3...the page number this tabl e 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 senten ce..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 pa ved 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 c onditions the unpaved road equa tion should be used. The typographical errors will be co rrected 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 “Emissi on 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 roademission 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, w e 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 hav e 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,

C ENTER FOR 

### S TUDY OF 

### O PEN 

### S OURCE 

### E MISSIONS



### Chatten Cowherd, Jr., Ph.D. DirectorFrom: "Kies
To: Ron Myers/RTP/USEPA/US@EPA

Date: Tuesday, August 31, 2010 11:17AM Subject: Statistical Comments on Draft AP-42 Section 13.2.1
History: This message has been forwarded.

Ron,

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 Seni or 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 discl osure. If you have received this message in error, please notify the sender immediately by facsimile, e-mail or phone and delete all c opies of the message.

Attachments:

Comments in Response to EPA Proposed Section 13.2.1 Paved Road Equation.pdf 

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...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 fromstandard 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 (R 2 ) 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 M odel 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 t he 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 meaningful ly 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 StatisticianComments 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 

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...

## Camille Sears 502 W. Lomita Ave., Ojai, CA 93023

Tel: (805) 646

- 2588 e

- mail: camille.marie@sbcglobal.net



### August 30, 2010

Mr. Ron Myers U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

Subject: Proposed Revision to AP

- 42 Section 13.2.1

– Paved Roads

Dear Mr. M yers,

Thank you for the opportunity to provide comments on the proposed revision to AP

- 42 Section
13.2.1. I have reviewed the proposed AP

- 42 revisions and associated reference documents and on behalf of Sierra Club offer the following brief comments.

1. Introduction

The existing USEPA air pollution emission factor for fugitive dust from vehicle traffic on paved
roads is as follows : 1



### E = k (S L 

### /2) 0.65 

### * (W/3) 1.5



### – C

Where: E = annual or other long

- term average emission factor in the same units as k

k = particle size multiplier (from Table 13.2.1

- 1, k = 0.0024 lb /vehicl e mile traveled (VMT) for

PM 2.5 

### and 0.016 lb /VMT for PM 10 

### )

S L 

### = road surface silt loading (g/m 2 

### )

W = average weight of vehicles (tons)

C = emission factor for 1980s vehicle fleet exhaust, brake wear, and tire wear (from Table 13.2.1

- 2,
C = 0. 00036 lb/VMT for PM 2.5 

### and 0.00047 lb/VM T for PM 10 

### )

The existing version of AP

- 42 Section 13.2.1 appears to be based on 64 source tests p erformed prior to 1995, the date when the paved road emission fa ctor first takes its current form.

In July 2008, the Corn Refiners Association (“CRA”) proposed a revision to AP

- 42 Section 13.2.1.

CRA’ s proposed revision is based on 22 additional source tests performed at ethanol plants in 2001

1 USEPA, Offic e of Air Quality Planning and Standards, AP

- 42, Section 13.2.1, Paved Roads, November 2006 , p. 13.2.1

- 4.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 2



### through 2003. 2 

### CRA rec alculated a paved road emission factor including the 64 source tests used by USEPA as the bases for the existing emission factor, plus the 22 additional CRA source tests (for a total of 86 tests). Based on their r egression analyses, CRA proposed a rev ise d paved road emission
factor with the following form: 3



### E = k (S L 

### /2) 0. 8 

### * (W/3) 0.8



### – C

CRA also proposed a revised particle size multiplier ( k), where k = 0.0034 lb / VMT for PM 2.5 

### and
0.023 lb /VMT for PM 10 

### )

The additional 22 tests performed by the CRA incl ude :

**•** Nine tests performed on roads at the Minnesota Corn Processors facility, Marshall, Minnesota, during April 2001;

**•** Eight tests performed on roads at the Minnesota Corn Processors facility, Columbus, Nebraska, during June 2001;

**•** Two tests performed on ro ads at the Cargill Sweeteners North America facility, Blair, Nebraska, during August 2002;

**•** Three tests performed on roads at ADM’s facility, Marshall, Minnesota, during September 2003 (this is the same facility as the April 2001 tests).

In May 2010, USEPA developed and proposed a revision to AP

- 42 Section 13.2.1, “Paved Roads.”
From USEPA:

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 g o 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. 4



### USEPA’s update to AP

- 42 Section 13.2.1, however, incorporates other data than that collected by
the Corn Refiners Association, and, more importantly, USEPA’s update excludes important data that have been used in developing the existing paved road emission factor . In summary, USEPA’s 2010 update to AP

- 42 Section 13. 2.1 incorp o rates the following data base changes:

**•** Including the 22 CRA tests performed in 2001 through 2003;

**•** Including three tests performed on public roads in Denver, Colorado, during March 1996;

2 Corn Refiners Association, Paved Road Modifications at AP

- 42, Background Documentation, MRI Project No. 310842, July 18, 2008, p. 4.

3 Id., p. 20.

4 USEPA, Emission factor Documentation for AP

- 42, Section 13.2.1, Paved Roads, Draft, June 2010, p. 2

- 9.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 3



### • Including two tests performed on public roads in Raleigh, North Carolina

, during April 1996;

**•** Including two tests performed on public roads in Reno, Nevada

, during June 1996;

**•** Excluding 22 tests performed at two i ntegrated iron and steel plants

– one located in Houston, Texas, and the other in Middletown, Ohio (dur ing 1980 and 1981)

. 5



### USEPA developed a proposed multiple regression equation based on paved road silt loading, mean vehicle weight, and vehicle speed

. The existing version of AP

- 42 Section 13.2.1 is based on regression analyses of silt loading and mean v ehicle weight. Since vehicle speed was not measured
at the 22 tests from the two i ntegrated iron and steel plants ( Houston, Texas and Middletown, Ohio during 1980 and 1981) , these tests were excluded from the data set.

USEPA’s proposed revision to AP

- 42 Section 13.2.1 , which is based on 71 individual source tests,
takes the form: 6



### E = k (S L 

### /2) 0. 98 

### * (W/3) 0.53 

### * (S /3 0 ) 0.16



### Where: E = annual or other long

- term average emission factor in the same units as k

k = particle size multiplier; k = 0 .0037 lb / VMT for PM 2.5 

### and 0 .015 lb /VMT for PM 10



### S L 

### = road surface silt loading (g/m 2 

### )

W = average weight of vehicles (tons)

S = average vehicle speed (miles per hour)

This equation does not incorporate emissions from engine exhaust and brake and tire wear , which
will ne ed to be estimated and added using USEPA’s MOBILE6.2 or MOVES2010 models.

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
PM 10 

### and PM 2.5 

### 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.

5 Id., p. 4

- 18.

6 USEPA, Office of Air Quality Planning and Standards, AP

- 42, Draft Section 13.2.1, Paved Roads , p. 13.2.1

- 4.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 4



### 2

. Key Industrial Source Tests are Excluded from USEPA’s Revised Factor

USEPA’s proposed revision to the paved road emission factor includes a third variable, mean vehicle speed

. Vehicle speed, however, does not appear to be an important predictive aid to the overall emission factor equation . This is evidenced by vehicle speed having a small (0.16) exponential te rm in USEPA’s propose d paved road emission factor. Furthermore, t he CRA, in their analyses of the source test data, state :

Taken together, these observations indicate that (a) silt loading and vehicle weight may be used as independent variables and that (b) inclusion of spee d would add very
little to the predictive cap ability of the model. 7



### I understand that USEPA has been asked to include vehicle speed in the revised paved road emission
factor. Doing so , however, excludes valuable source tests that were performed without measuring vehicle speed. In particular, USEPA is e xcluding 22 tests performed at two i ntegrated 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 in dustrial 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
calcula tions 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 vehicl e speed data.

3

. USEPA’s Proposed Update will Result in a Roughly Order of Magnitude Emission Reduction at Industrial Sites

In addition to developing an updated paved road emission factor, USEPA prepared a consequence analysis of the National Emission In ventory (“NEI”) resulting from their proposed revision

. 8



### USEPA found that their revised paved road emission factor will significantly reduce PM 10 

### emissions in the NEI (up to 200% reduction), while PM 2.5 

### emissions are only slightly affected (some NEI
calcu lation s increase, some decrease). USEPA, however, did not examine the affect of their draft revised paved road equation on fugitive dust emissions from industrial sources.

I prepared two tables that compare the existing paved road emission factor with US EPA’s proposed revision

– one for PM 10 

### (Table 1A), and one for PM 2.5 

### (Table 1B). These tables include a range of silt loading, vehicle weight, and vehicle speed conditions. For each set of silt loading, weight, and speed, I calculated the emission factor using both the existing and proposed paved road emission factor. As can be seen, the reduction in calculated emissions for industrial sites using the revised
factor is very large

– about an order of magnitude lower for PM 10 

### and somewhat less for PM 2.5



### .

7 Cor n Refiners Association , Paved Road Modifications at AP

- 42, Background Documentation, MRI Project No. 310842, July 18, 2008, p. 15

. 8 See Excel spreadsheet: Impact_of_revised_paved_roads_pm_emission_factors_on_NEI.xls.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 5



### USEPA’s choice to go ahead with their proposed paved road emission factor would have serious ramifications for NAAQS and PSD increment compliance . This is particularly true for proposed major sources of PM 10 

### and PM 2.5 

### which have paved haul road emission sources. Using USEPA’s proposed revision, s ources that are currently being scrutinized for PM 10 

### PSD increment and PM 2.5 

### NAAQS compliance would most likely be well below any regulatory design concentrations, even with significantly relaxed control measures

. Again, USEP A’s proposed revision is largely due to excluding a significant portion of the existing industrial source test data base, and is not due to any
tests that contradict the excluded data. In effect , USEPA ’s revision would be “sweeping under the rug” what is perhaps the greatest impact caused by many industrial sources.

In terms of the modeling analyses for NAAQS and PSD increment compliance, the 24

- hour PM 10 

### PSD increment, which is 30 micrograms per cubic meter “μg/m 3 

### ,” is almost always the m ost problematic regulatory design concentration. Proposed industrial sources, such as coal

- fired power plants, pig iron facilities, coal

- to

- liquid operations, coal

- to

- synthetic gas plants, and lime production facilities, often cause air impacts that are q uite close to exceeding the 24

- hour PM 10 

### PSD increment. It is common to see proposed PSD permit application modeled impacts co n suming some 80 to 99% of the allowable 24

- hour PM 10 

### PSD increment. The majority of this modeled impact is caused by low

- level o pen source fugitive emissions, including paved haul roads.

There is no basis to assume that the existing paved road emission factor overpredicts fugitive dust emissions from these major sources. And as we discussed earlier, it is common for major sources of emissions to be permitted without any PSD pre

- construction or post

- construction ambient air monitoring, even when such requirements are triggered by PM 10 

### significant monitoring concentrations identified in 40 CFR 52.21(i). Thus, there is no current wa y to verify whether source
PM 10 

### impacts at the fenceline are realistically handled by the applied fugitive dust emission factor and subsequent air dispersion modeling.

I have also prepared two tables that compare the existing paved road emission factor wi th the CRA’s proposed revision

– one for PM 10 

### (Table 2A), and one for PM 2.5 

### (Table 2B). While CRA’s proposed revision results in lower industrial source PM 10 

### and PM 2.5 

### emission factors, they are not nearly as severe as the changes proposed by USEPA.

The CRA source tests , however, include an apparent contradiction. CRA’s source tests were designed for low vehicular speeds and stop

- and

- go conditions. 9 

### But CRA also acknowledges that “inclusion of speed would add very little to the predictive capability of the model.” 10 

### So, the basis for including CRA’s source tests in AP

- 42 Section 13.2.1 seems unnecessary.

Revising AP

- 42 Section 13.2.1, using either CRA’s or USEPA’s proposed revisions, will greatly reduce calculated fugitive dust emissions at most industr ial facilities. This would make it easier for applicants to meet regulatory design concentration compliance, and to do so with fewer emission controls. These revisions, however, are based on data that are not representative of the majority of

9 Corn Refiners Association, Paved Road Modifications at AP

- 42, Background Documentation, MRI Project No. 310842, July 18, 2008, p. 4

. 10 Id., p. 15.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 6



### major emissi on sources. For example, the CRA source tests are for ethanol plants with low to very low silt loading levels. These conditions are not representative of the scores of proposed coal

- fired

power projects that have recently submitted permit applications to State agencies . And USEPA’s modification of the source test data base, to add public road source tests and to eliminate the integrated steel plant tests, probably makes things even worse. The silt loading levels (and associated emission factors) measure d at the integrated steel plant sites are representative of many industrial facilities. 11 

### Excluding these data will weigh the equation in a manner that reduces predictive performa n ce for most industrial plants.

4

. USEPA’s Proposed Update may not Improve P redictive Performance

As part of the proposed revision to AP

- 42 Section 13.2.1, it would be helpful if USEPA present ed performance analyses of both the existing and proposed paved road emission factors. Furthermore, it would be helpful if USEPA presente d performance criteria for sub

- categories of emission sources, such as public roads, industrial roads wi th low silt loading levels, and perhaps industrial roads with higher silt loading levels. From this analysis, USEPA and the reviewing public could get a better idea of whether the proposed changes will provide better predictive capability than does the existing
method. And just as important

, would be information on predictive performance for each sub

category of emission sources. In other words, we cou ld tell whether improving performance for one source category, ethanol plants for example, would have a detrimental effect on emission prediction for other industrial sources with higher silt loadings.

Likewise, focusing on performance of public roads, wi th vehicle speed included, greatly affects industrial source emission rates. But what effect does it have on the predictive performance of
industrial sources? As we discussed earlier, the coefficient of determination (r 2 

### ) is not particularly great for th e proposed revision (all data sets included)

. It would be useful to examine the predictive performance on various subsets of the existing data base, with both the existing and proposed
emission factors.

5. Other Factors Affecting USEPA’s Paved Road Emiss ion Factor

Following are a few observations that will affect the predictive emission factor equation when used
on industrial s ources

. I believe USEPA should address these concerns prior to revising their existing paved road emission factor.

**•** The paved ro ad emission factor should consider whether the road shoulder is paved and whether there is a source of dust fallout present

. For example, facilities with dust

- generating storage piles, and truck traffic moving between these piles, are likely to have high particulate
emission rates. This is particularly true for f acilities with unpaved road shoulders.

11 USEPA, Emission factor Documentation for AP

- 42, Section 13.2.1, Paved Roads, Draft, June 2010, pp. 4

- 42 to 4

- 45.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 7



### • Some vehicles have exhaust pipes pointing skyward, others are parallel to the ground, and still others pointing down to the ground. Downward

- pointing exhau st can exacerbate resuspension of dust, as I have often observed with forklifts and delivery vehicles with such an exhaust configuration. It is unclear whether industrial vehicles with downward

- pointing
exhaust are accounted for in the paved road emission factor.

**•** In developing the revised emission factor, USEPA subtracted a “C” term from the CRA source tests. This results in very small or even negative emission rates for certain tests. 12 

### Given the plume rise of exhaust from the slow

- moving CRA test vehicl es, it is possible that most, if not all, of the exhaust plume passed above the downwind air samplers. In other words, the “ C ” term used by USEPA may be too large for the CRA (and other ) source tests. USEPA should reevaluate to what extent, if any, exhaust, and brake and tire wear impact the downwind profile measurements.

6

. Concluding Remarks

USEPA’s proposed revision to AP

- 42 Section 13.2.1 excludes a valuable industrial source paved road data base simply because vehicle speed was not included in the study. USEPA’s revised emission factor will result in a roughly order of magnitude emission reduction in industrial source paved road emissions

. This very significant change may not be appropriate given that a key data set was excluded from the regression analyses.

USEPA may be trying to fit too many source categories into a one

- size

- fits

- all emission factor. Under the umbrella of “paved roads” fits urban freeways, local street traffic, industrial sites with a wide

- range of truck sizes and weights, parking lots, and all shapes and sizes of vehicles using these paved surfaces

. I understand tha t USEPA has a very difficult task in develop ing a paved road
emission factor that meets the n eeds of all affected sources. It is likely that “clean” roads are downward

- biasing the emission factor for high

- emitting facilities. And the opposite is also tru e

– industrial roads with high silt loading are likely upward

- biasing the emission factor for cleaner roads with lighter vehicles.

I offer the suggestion that US EPA should consider develop ing separate paved road emission factors for public and industrial roads

. It may not be too far

- fetched to examine separate emission factors
for sub

- categories of industrial source paved road emissions as well. Also , USEPA may want to focus on silt loading and vehicle weight, as variability in vehicle speed seems to hav e a less significant impact on predicted emission performance.

Until USEPA has addressed whether the severe reduction in industrial source paved road emission calculations is warranted , I believe that the existing AP

- 42 paved road emission factor should

c ontinue to be used

.

12 Id.Revisions to AP

- 42, 13.2.1 August 30, 2010 Page

- 8



### I greatly appreciate your help in reviewing and commenting on t he proposed revisions to AP

- 42 Section 13.2.1

. Please contact me if you have any questions or require additional information.

Sincerely

,

Camille Sears

### 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/m 2 ) 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/m 2 ) 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/m 2 ) 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/m 2 ) 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 appliedFw: Dave's comments on the Excel workbook - do not need to be mentioned on the call dave. j ames 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 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. j ames@unlv.edu http :// provost

. unlv

. edu / acadaffairs

. htmlRe: Fw: Dave's comments on the EPA Excel workbook - some additional follow up thoughts dave. j ames 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 predictedPM10 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. j ames@unlv.edu http :// provost

. unlv

. edu / acadaffairs

. html

From: Myers.Ron@epamail.epa.gov

To: dave. j ames@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
callDear 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

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*********************** 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: AP42PavedRoadsSectionComment 0 Pa g e 1 of 1

10/22/2010 htt p s://rtairmail1.rt p .e p a. g ov/mail/rm y ers.nsf/9ff539a1e24f 5aaf852577890046a8f6/8F3D

...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 Pave d Road emissions (Section 13.2.1).

I have reviewed the draft update on behalf of th e National Asphalt Pavement Association (NAPA), andwrite 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 th at for asphalt batching, may be too high for typical current applications. The recommended value is 120 g/m 2 , but, as you know, in EPA’s 2000 Emission Assessment Report for Hot Mix Asphalt Plants, a silt-loading value 3 g/m 2 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/m 2 . This facility, which we analyzed in detail for the City of Alexandria, employs aggressive dust suppression techniques.

More generally, as you know, best management pr actices (BMPs) such as wa ter 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/m 2 , 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/m 2

could be considered to be a default value in the absence of BMPs, whereas the value of 3 g/m 2 , 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 coor dinate a study to provide updated values for silt loading and other emission factor parameters that refl ect 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 EngineerIDNR 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 HansonRE: PECHAN/ERTAC Road Dust Emissions

Pat Davis t

o

: Roy Huntley, Ron Myers 07/26/2010 01:22 PM

History: This message has been replied to.

Hi Ron,

Have you had a chance to look into this issue?

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 ||------------> | Date: 07/13/2010 12:02 PM | | | Subject: PECHAN/ERTAC Road Dust Emissions |

>Hi Roy,

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.

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 DavisFW: [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 asituation where the emission factor value can go negative

. H aving 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.usFW: 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 materialsfor your formal comment to USEP A

.

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/ap4 2/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)% Change2003 2006 2010Annual pm-10 tpy37,606.28 38,210.45 20,532.18-46%PM-107.3000 7.3000 6.79pm-2.5 tpy3,788.42 1,142.03 5,110.37347%PM-2.51.8000 1.1000 1.69Summerpm-10 tpd115.11 105.70 56.75-46%pm-2.5 tpd11.56 3.13 14.12351%Winter pm-10 tpd95.87 101.69 54.74-46%pm-2.5 tpd9.69 3.13 13.63336%Spring pm-10 tpd99.94 105.08 56.41-46%pm-2.5 tpd10.07 3.11 14.04351%Fall pm-10 tpd101.03 106.23 57.08-46%pm-2.5 tpd10.18 3.15 14.21352%Emissions Comparison2007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexRural Oth. Princ. Art.January3.0368 3.8504 5.6417 0.7558 0.9584 1.4042February2.8324 3.6256 5.2929 0.7050 0.9024 1.3174March3.3463 4.3680 6.3490 0.8329 1.0872 1.5802April3.4705 4.4744 6.6084 0.8638 1.1137 1.6448May4.3379 5.4584 8.1201 1.0797 1.3586 2.0210June4.4049 5.1675 8.5339 1.0964 1.2862 2.1240July5.1486 5.3205 10.4483 1.2815 1.3243 2.6005August4.8552 5.4994 10.3512 1.2084 1.3688 2.5764September4.2558 5.1069 7.7932 1.0592 1.2711 1.9397October3.6182 4.6311 6.9531 0.9006 1.1527 1.7306November3.2676 4.3388 6.2977 0.8133 1.0799 1.5675December2.9585 4.0132 5.7959 0.7364 0.9989 1.442645.5327 55.8543 88.1854 11.3329 13.9019 21.9489Rural Minor ArterialJanuary19.7917 0.9224 2.9397 4.9261 0.2296 0.7317February19.4746 0.9004 2.7698 4.8472 0.2241 0.6894March7.3003 1.0547 1.7424 1.8170 0.2625 0.4337April6.7427 1.0176 1.8092 1.6782 0.2533 0.4503May7.5361 1.1867 2.1881 1.8757 0.2954 0.5446June6.8577 1.0829 2.5017 1.7069 0.2695 0.6226July7.5020 1.1643 1.5249 1.8672 0.2898 0.3795August7.2545 1.1817 2.8365 1.8056 0.2941 0.7060September7.2050 1.1851 2.0514 1.7933 0.2950 0.5106October7.0923 1.1383 1.7639 1.7652 0.2833 0.4390November6.6536 1.0271 1.5527 1.6561 0.2556 0.3865December6.7191 0.9796 1.4285 1.6723 0.2438 0.3556110.1298 12.8408 25.1086 27.4108 3.1960 6.2494Rural Major CollectorJanuary17.4130 10.8453 81.4407 4.3340 2.6993 20.2702February15.2798 9.2386 74.5901 3.8031 2.2994 18.5651March6.6067 4.1449 31.0497 1.6444 1.0317 7.7281April6.3955 4.2942 30.7597 1.5918 1.0688 7.6560May8.4396 5.6208 36.4193 2.1006 1.3990 9.0646June8.9101 5.3072 11.1730 2.2177 1.3209 2.7809July8.7305 6.1122 13.2889 2.1730 1.5213 3.3075August7.9809 5.1746 12.3685 1.9864 1.2879 3.0785September9.0665 5.4730 33.1489 2.2566 1.3622 8.2506October8.0543 4.6414 30.6447 2.0047 1.1552 7.6273November6.8855 3.8170 28.0237 1.7138 0.9500 6.9750December5.8669 3.4370 27.2418 1.4602 0.8555 6.7804109.6292 68.1062 410.1489 27.2862 16.9513 102.0842Rural Minor CollectorPM10-FIL (TPY)PM2.5-FIL (TPY)2007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexPM10-FIL (TPY) PM2.5-FIL (TPY)January7.5825 3.1843 9.1770 1.8872 0.7926 2.2841February6.6536 2.7126 8.4051 1.6561 0.6751 2.0920March2.8769 1.2170 3.4988 0.7160 0.3029 0.8708April2.7849 1.2609 3.4661 0.6931 0.3138 0.8627May3.6750 1.6504 4.1039 0.9147 0.4108 1.0214June3.8799 1.5583 4.0969 0.9657 0.3878 1.0197July3.8017 1.7946 4.8727 0.9462 0.4467 1.2128August3.4753 1.5194 4.5352 0.8650 0.3782 1.1288September3.9480 1.6069 3.7353 0.9826 0.4000 0.9297October3.5072 1.3628 3.4532 0.8729 0.3392 0.8595November2.9983 1.1207 3.1578 0.7463 0.2789 0.7860December2.5547 1.0092 3.0697 0.6359 0.2512 0.764047.7380 19.9970 55.5718 11.8818 4.9772 13.8316Rural LocalJanuary72.2816 14.2450 182.5225 17.9906 3.5455 45.4290February63.4268 13.0827 167.1691 15.7866 3.2562 41.6076March20.6871 5.3708 17.8860 5.1489 1.3368 4.4517April20.0256 4.9021 17.7190 4.9843 1.2201 4.4102May26.4262 6.0689 20.9791 6.5774 1.5105 5.2216June9.5065 4.9294 20.9434 2.3661 1.2269 5.2127July27.3372 5.0483 24.9096 6.8041 1.2565 6.1999August24.9900 5.2372 23.1843 6.2199 1.3035 5.7705September9.6733 6.0687 19.0953 2.4076 1.5105 4.7527October25.2198 5.8458 17.6527 6.2771 1.4550 4.3937November21.5599 5.2538 47.3761 5.3662 1.3077 11.7917December18.3704 5.0929 46.0544 4.5723 1.2676 11.4627339.5044 81.1457 605.4913 84.5011 20.1968 150.7040Urban InterstateJanuary0.0000 11.7187 0.0000 0.0000 2.9167 0.0000February0.0000 12.5883 0.0000 0.0000 3.1332 0.0000March0.0000 13.3265 0.0000 0.0000 3.3169 0.0000April0.0000 14.0185 0.0000 0.0000 3.4891 0.0000May0.0000 15.5068 0.0000 0.0000 3.8596 0.0000June0.0000 14.5005 0.0000 0.0000 3.6091 0.0000July0.0000 15.7325 0.0000 0.0000 3.9158 0.0000August0.0000 16.9323 0.0000 0.0000 4.2144 0.0000September0.0000 14.9430 0.0000 0.0000 3.7192 0.0000October0.0000 13.8368 0.0000 0.0000 3.4439 0.0000November0.0000 13.6508 0.0000 0.0000 3.3976 0.0000December0.0000 12.6716 0.0000 0.0000 3.1539 0.00000.0000 169.4261 0.0000 0.0000 42.1694 0.0000Urban Oth. FreewayJanuary2.2724 2.2051 0.0000 0.5656 0.5488 0.00002007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexPM10-FIL (TPY) PM2.5-FIL (TPY)February2.1240 2.3687 0.0000 0.5287 0.5896 0.0000March2.6382 2.5076 0.0000 0.6566 0.6241 0.0000April2.6584 2.6378 0.0000 0.6617 0.6565 0.0000May3.3363 2.9179 0.0000 0.8304 0.7262 0.0000June3.5141 2.7285 0.0000 0.8747 0.6791 0.0000July4.2085 2.9604 0.0000 1.0475 0.7368 0.0000August4.1031 3.1861 0.0000 1.0213 0.7930 0.0000September3.2980 2.8118 0.0000 0.8209 0.6998 0.0000October2.6345 2.6036 0.0000 0.6557 0.6480 0.0000November2.3741 2.5686 0.0000 0.5909 0.6393 0.0000December2.1895 2.3844 0.0000 0.5450 0.5935 0.000035.3512 31.8807 0.0000 8.7988 7.9349 0.0000Urban Oth. Princ. Art.January1.3373 13.5266 3.0648 0.3328 3.3667 0.7628February1.2472 13.0598 2.8754 0.3104 3.2505 0.7157March1.4735 14.9285 3.4491 0.3668 3.7156 0.8585April1.5282 14.5413 3.5900 0.3804 3.6193 0.8935May1.9102 16.7660 4.4112 0.4754 4.1730 1.0979June1.9397 15.0529 4.6360 0.4828 3.7466 1.1539July2.2672 15.1950 5.6760 0.5643 3.7820 1.4127August2.1380 15.2299 5.6233 0.5321 3.7906 1.3996September1.8740 15.1373 4.2336 0.4664 3.7676 1.0537October1.5933 14.6302 3.7772 0.3966 3.6414 0.9401November1.4389 13.7450 3.4212 0.3581 3.4211 0.8515December1.3028 13.7174 3.1486 0.3243 3.4142 0.783720.0502 175.5298 47.9065 4.9904 43.6886 11.9237Urban Minor ArterialJanuary4.3310 4.8764 2.4339 1.0780 1.2137 0.6058February4.2617 4.8104 2.2835 1.0607 1.1973 0.5683March4.6884 5.7734 1.3887 1.1669 1.4370 0.3456April4.3304 5.8589 0.7328 1.0778 1.4582 0.1824May4.8399 7.1419 0.9004 1.2046 1.7776 0.2241June4.4042 6.2220 0.9463 1.0962 1.5486 0.2355July4.8180 6.2667 1.1586 1.1992 1.5597 0.2884August4.6591 6.1525 1.1478 1.1596 1.5313 0.2857September4.6273 6.2988 0.8642 1.1517 1.5677 0.2151October4.5549 5.6747 0.7710 1.1337 1.4124 0.1919November4.2731 4.7114 0.6983 1.0636 1.1727 0.1738December4.3152 4.3381 1.2677 1.0740 1.0797 0.315554.1031 68.1252 14.5932 13.4660 16.9561 3.6322Urban CollectorJanuary31.7118 16.1893 32.0745 7.8929 4.0294 7.9832February30.9520 0.0378 29.3765 7.7038 3.4325 7.31172007 CAP Emissions CalculationsKentNew Castle Sussex KentNew Castle SussexPM10-FIL (TPY) PM2.5-FIL (TPY)March3.7702 9.2060 3.7580 0.9384 2.2913 0.9353April3.4498 9.5377 3.7229 0.8586 2.3739 0.9266May3.8650 12.4840 4.4079 0.9620 3.1072 1.0971June3.5323 11.7876 4.4004 0.8792 2.9339 1.0952July3.6517 13.5754 5.2337 0.9089 3.3789 1.3026August11.2659 11.4931 4.8712 2.8040 2.8606 1.2124September3.5772 12.1557 4.0121 0.8903 3.0255 0.9986October3.5078 10.3087 3.7090 0.8731 2.5658 0.9231November10.2321 8.4777 11.0368 2.5467 2.1101 2.7470December10.0602 7.6337 10.7289 2.5039 1.9000 2.6704119.5761 122.8866 117.3317 29.7620 34.0090 29.2033Urban LocalJanuary43.1321 149.3299 23.6401 10.7354 37.1675 5.8839February42.0987 137.1463 21.6515 10.4782 34.1351 5.3890March16.6866 56.3019 9.0129 4.1532 14.0133 2.2433April15.2685 51.3884 8.9287 3.8003 12.7903 2.2223May17.1063 63.6201 10.5715 4.2577 15.8347 2.6312June15.6337 51.6748 10.5535 3.8912 12.8616 2.6267July16.1621 52.9218 12.5521 4.0227 13.1720 3.1242August15.3230 54.9018 11.6827 3.8138 13.6648 2.9078September15.8323 63.6183 9.6223 3.9406 15.8343 2.3949October15.5252 61.2816 8.8953 3.8641 15.2527 2.2140November13.9169 55.0759 8.1345 3.4639 13.7081 2.0246December13.6832 53.3890 7.9076 3.4057 13.2883 1.9682240.3684 850.6498 143.1529 59.8266 211.7228 35.6301All RoadwayTypesPM10-PRI PM2.5-PRIJanuary202.8901 230.8934 342.9350 50.4984 57.4683 85.3550776.7185 193.3217February188.3509 199.5712 314.4139 46.8797 53.0954 78.2562702.3359 178.2313March70.0744 118.1994 78.1346 17.4412 29.4193 19.4473266.4084 66.3078April66.6544 113.9317 77.3368 16.5900 28.3571 19.2488 257.9230 64.1958May81.4724 138.4216 92.1014 20.2781 34.4525 22.9236311.9955 77.6543June62.5833 120.0116 67.7849 15.5767 29.8703 16.8714250.3798 62.3184July83.6273 126.0918 79.6648 20.8145 31.3837 19.8282289.3839 72.0263August86.0449 126.5080 76.6007 21.4162 31.4873 19.0656289.1537 71.9690September63.3575 134.4055 84.5562 15.7694 33.4529 21.0457282.3192 70.2680October75.3075 125.9550 77.6201 18.7437 31.3496 19.3193278.8826 69.4126November73.6000 113.7869 109.6988 18.3187 28.3210 27.3035297.0857 73.9433December68.0204 108.6660 106.6430 16.9300 27.0465 26.5430283.3294 70.51941121.9831 1656.4422 1507.4902 279.2565 415.7040 375.2074 4285.9155 1070.1679STATEWIDEcomments 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

p ast 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

p resented in a p revious 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

303-790-133211 /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 ro ad surface after the vehicle has passed.

The particulate emission factors presented in the pr evious draft version of this section of AP-42, dated October 2001, implicitly included the emissions fro m vehicles in the form of exhaust, brake wear, and tire wear as well as resuspended road surface material 25 . EPA included these sources in the emission factor equation for unpaved public road s (equation 1b in this section) since the field testing data used to develop the equation included both the direct emissi ons from vehicles and emissions from resuspension of road dust.

This version of the unpaved public road emi ssion 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 road s. 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 emi ssion 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 Calcula tion And Correction Parameters 1-6

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 depe nd 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 emissi on 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 loo se 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 da ta 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 va ry with geographic location, it should be measured for use in projecting emissions. As a conservative appr oximation, 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 e xpression 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 ROADS a

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 unpa ved 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 conversi on 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 partic le 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 su spended particulate matter (TSP) “-“ = not used in the emission factor equation

Table 13.2.2-2 also contains the quality ratings for th e 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 4-17 a 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 Equa tions 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 th e equation. A higher mean vehicle weight and a higher than normal traffic rate may be justified wh en 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 range13.2.2-6 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 Range a 

### C , Emission Factor for Exhaust, Brake Wear and Tire Wear b



### lb/VMT

PM 2.5 

### 0.00036

PM 10 

### 0.00047

PM 30 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 particul ate" (SP) and is often used as a surrogate for TSP.

It is important to note that the vehicle-relate d source conditions refer to the average weight, speed, and number of wheels for all vehicles traveling th e road. For example, if 98 percent of traffic on the road are 2-ton cars and trucks while the remaini ng 2 percent consists of 20-ton trucks, then the mean weight is 2.4 tons. More speci fically, 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 mois ture contents are given in AP-42 Appendices C.1 and C.2. Vehicle-related parameters should be developed by recording vi sual observations of traffic. In some cases, vehicle parameters for industrial unpaved ro ads can be determined by reviewing maintenance records or other information sources at the facility.

In the event that site-specific values for correction parameters ca nnot be obtained, then default values may be used.In the absence of site-specific s ilt 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 f ound between different types of road surfaces and between different areas of the country, use of the de fault moisture content value of 0.5 percent in Equation 1b is discouraged. The quality rating shoul d be downgraded two letters when the default moisture content value is used. (It is assumed th at 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 em issions from unpaved roads is discussed below in Section 13.2.2.3, “Controls”. However, all road s are subject to some natural mitigation because of rainfall and other precipitation. The Equation 1a a nd 1b emission factors can be extrapolated to annual11 /06 Miscellaneous Sources 13.2.2-7 (2) average uncontrolled conditions (but including natu ral 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:

E ext = 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 not ed that Equation 2 does not account for differences in the temporal distributions of the rain events, the quan tity 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 ma terial is increased in proportion to the quantity of water added; 2. The moisture content of the road surface ma terial is reduced in proportion to the Class A pan evaporation rate; 3. The moisture content of the road surface ma terial is reduced in proportion to the traffic volume; and 4. The moisture content of the road surface ma terial varies between the extremes observed in the area. The CHIEF Web site (http://www.epa.gov/ttn/c hief/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 preci pitation, 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 whic h produces a finer temporal and spatial resolution have not been verified in any rigorous manner. Fo r this reason, the quality ratings for either approach should be downgraded one letter from the rati ng that would be applied to Equation 1.

13.2.2.3 Controls 18-22

A wide variety of options exist to control emi ssions 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 ve hicles and/or spillage of material in transport. Watering and chemical suppressants, on the other hand, ar e potentially applicable to most industrial roads at moderate to low costs. However, these require fre quent reapplication to maintain an acceptable level of control. Chemical suppressants are generally more co st-effective than water but not in cases of temporary roads (which are common at mines, landfills, and c onstruction sites). In summary, then, one needs to consider not only the type and volume of traffic on th e 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 ins tituted 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-trav eled results in a lower overall emission rate.11 /06 Miscellaneous Sources 13.2.2-9Figure 13.2.2-1. Mean number of days with 0.01inch or more of precipitation in United States.13.2.2-10 EMISSION FACTORS 11 /06 Surface improvements . Control options in this category alte r 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 pavi ng an unpaved road. This option is quite expensive and is probably most applicable to rela tively 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 em ission factor equation for paved roads, given in Section 13.2.1, requires estimation of the silt loadi ng on the traveled portion of the paved surface, which in turn depends on whether the paveme nt is periodically cleaned. Unless curbing is to be installed, the effects of vehicle excursion onto unpaved shoulders (ber ms) 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 afte r 3 to 6 months rather than immediately following placement. Control plans should address regular mainte nance 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 phy sical characteristics of the surface. The necessary reapplication frequency varies from several minut es 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 ove r 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) th e period of time between applications; (c) the weight, speed and number of vehicles traveling over the watere d 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 rela tionship 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 su rface 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 dr ies, characterization of emissions from watered roadways is best done by collecting road surface ma terial samples at various times between water truck passes. (Appendices C.1 and C.2 present the sampling a nd analysis procedures.) The moisture content measured can then be associated with a control efficien cy 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, becau se 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 estimat e the mitigation of rainfall and other precipitation can be used to estimate the control provided by rou tine watering. An estimate of the maximum daytime Class A pan evaporation (based upon daily evaporati on data published in the monthly Climatological Data for the state by the National Climatic Data Cent er) should be used to insure that adequate watering capability is available during periods of highest ev aporation. The hourly precipitation values in the spreadsheet should be replaced with the equivalent in ches 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 evaporati on and on the percentage that occurs between May and October was published in the Climatic Atl as (Reference 16). Figure 13.2.2-3 presents the geographical distribution for "Class A pan evapora tion" 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 evaporati on 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 le tters. Periodic road surface samples should be collected and analyzed to verify the efficiency of the watering program.

As opposed to watering, chemical dust suppressan ts 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 treat ed 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 controlle d 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 an13.2.2-12 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, MControl Efficiency (%)11 /06 Miscellaneous Sources 13.2.2-13 The control effectiveness of chemical dust suppr essants appears to depend on (a) the dilution rate used in the mixture; (b) the application rate (vol ume 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 othe r traffic characteristics (e. g., cornering, track-on from unpaved areas) and road characteristics (e. g., b earing 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 1 1 /0 6Figure 13.2.2-3. Annual evaporation data.11 /06 Miscellaneous Sources 13.2.2-15Figure 13.2.2-4. Geographical distribution of the percentage of evaporation occurring between May and October.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/yd 2 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/yd 2

.

1 lb/VMT = 281.9 g/VKT. 1 gal/yd 2 = 4.531 L/m 2

. 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 petroleu m 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 pe riodically reapplied to unpaved roads, the use of a time-averaged control efficiency value is appropria te. Figure 13.2.2-5 presents control efficiency values averaged over two common application intervals, 2 w eeks and 1 month. Other application intervals will require interpolation.

3. Note that zero efficiency is assigned un til the ground inventory reaches 0.05 gallon per square yard (gal/yd 2 ). Requiring a minimum ground inventory ensures that one must apply a reasonable amount of chemical dust suppressant to a road before claimi ng credit for emission control. Recall that the ground inventory refers to the amount of petroleum r esin 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/yd 2 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 suppr essants 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 /0 6 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. Re visions 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 exha ust, brake wear, and tire wear. The parameter C in the new equation varies with aerodynamic size range of th e particulate matter. Table 13.2.2-4 was added to present the new coefficients.

January 2006 – The PM-2.5 particle size multiplie rs (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, "Particulat e 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 Pr otection 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, S ection 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 Du st 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 Prot ection 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 Pr otection 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 Technica l 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) fro m 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 testprogram, 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/m 2

.

### 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/m 2 rather than 2 g/m 2 . 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 PM 2.5 

### / PM 10 

### ratios measured by US EPA federal reference met hod (FRM) samplers are significantly lower than predicted by AP-42 emission factors. As a result, the PM 2.5 

### 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 ope rating method that gene rated the original AP-42 emission factors and associated PM 2.5 

### / PM 10 

### ratios. This study was sponsored by the Western Regional Air Partnership.

The study found that concentr ation measurements used to develop PM 2.5 

### emission factors in AP-42 were biased high by a factor of two, as compared to PM 2.5 

### measurements from FRM samplers. This f actor-of-two bias helps to explain why researchers have often seen a discrepanc y in the proportion of fugitive dust found in
PM 2.5 

### emission inventories and modeled ambient air impacts, as compared to the proportion on ambient filter samples. This study also shows that the PM 2.5 

### / PM 10 

### 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 PM 2.5 

### emission factors for the following four fugitive dust source categories: paved roads, unpaved roads (public and industrial), aggr egate handling and storage piles, and industrial wind erosion (AP-42 Sect ions 13.2.1, 13.2.2, 13.2.4, & 13.2.5, respectively). Emission estimates for other fugitive dust produc ing activities, such as construction and demolition will also be affected since they ar e 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 PM 2.5 

### and PM 10 

### components of fugitive dust emissions. Most of the PM 2.5 

### emission factors in EPA’s AP42 guidance for fugitive dust sources (USEPA , 2005) were determined by using highvolume samplers, each fitted with a cyclone precollect or and cascade impactor. Typically, AP-42 recommends that PM 2.5 

### emission factors for dust sources be calculated

### 2 by using PM 10 

### emission factor equations along with PM 2.5 

### /PM 10 

### ratios that have been published by EPA in AP-42.

Beginning with the introduction of the cy clone/impactor method, it was realized particle bounce from the cascade impactor stag es to the backup filter may have resulted in inflated PM 2.5 

### concentrations, even though steps we re taken to minimize particle bounce. This led to an EPA-funded field study in the late 1990s (M RI, 1997) to gather comparative particle sizing data in dust plumes downwind of pa ved and unpaved roads around the country. The test results indicated that dichotomous samplers produced consistently lower PM 2.5 

### /PM 10 

### 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 st andards for particulate matter measured as
PM 2.5 

### and PM 10 

### . 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 fiel d program, modifications were made to the appropriate sections of AP-42 for dust emissions from paved and unpaved roads. The
PM 2.5 

### /PM 10 

### ratio for emissions from unpaved roads (dominated by fugitive dust) was reduced from 0.26 to 0.15, and the PM 2.5 

### /PM 10 

### ratio for the dust component of emissionsfrom 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 PM 2.5 

### /PM 10 

### ratio of 0.76, accounting for vehicle exhaust and brake and tire wear.

Subsequent to the modifications of the PM 2.5 

### /PM 10 

### ratios in AP-42, additional field test results (mostly from ambient air samplers ) indicated that furthe r 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 PM 10 

### mass, based on chemical fingerprinting of the collect ed fine and coarse fractions of PM 10 

### impacted by dust sources. It is important to note, however, that particle size data applicable to fugitive dust emission factors should be gathered ei ther 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/imp actor system, so that reliable PM 2.5 

### /PM 10 

### ratios could be developed for as many dust source categories as possible. For this purpose, an air exposure chamber connected to a recirculati ng supply air stream wa s used in conjunction with a fluidization system for generating we ll-mixed dust plumes from a variety of western soils and road surface materials. R&P Model 2000 Partisol samplers were selected as the ground-trut hing FRM samplers for PM 10 

### and PM 2.5



### .

### 3 This study was performed in two phases (see be low), as described in the attached test report (Cowherd and Donaldson, 2005). Th e 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 PM 2.5 

### Measured by Cyclone/Impactor to FRM Sampler

In the first testing phase of the project, PM 2.5 

### measurements using the high-volume cascade impactors were compared to simu ltaneous measurements obtained with EPA FRM samplers for PM 2.5 

### . As stated above, these tests were conducted in a flow-through wind tunnel and exposure chamber, where the PM 10 

### concentration level and uniformity were controlled. The results of the test s provided the basis for quantifying more effectively any sampling bias associat ed with the cascade impactor system.

Phase 2 – Compare PM 2.5 

### to PM 10 

### 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 PM 2.5 

### to PM 10 

### ratios for fugitive dust from different geologic sources in the West. This testing provided needed information on the magnitude and variability of this rati o, especially for source materials that are recognized as problematic with regard to appl ication of mitigative dust control measures.



## RESULTS

The tests that were performed are listed in Ta bles 6 and 7 of the attached report. The Phase I tests were performed in March a nd 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 r uns (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. PM 2.5 

### concentrations measured by the hi gh-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 PM 2.5 

### 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 PM 2.5 

### 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 obs erved in the prior EPA-funded field study at distributed geographic lo cations across the country.

### 4 3. The PM 2.5 

### /PM 10 

### ratios measured by the FRM sample rs in the current study for a variety of western soils show a decr ease in magnitude with increasing PM 10 

### concentration (see Figure 2). Soils with a nominally spherical shape are observed to have somewhat lower ratios (at given PM 10 

### concentrations) than soils with angular shape. A very similar dependence of PM 2.5 

### /PM 10 

### ratio on
PM 10 

### 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 PM 2.5 

### /PM 10 

### ratio in the range of 0.1 to 0.15 for typical uncontrolled fug itive dust sources (see Figure 2). The
PM 2.5 

### /PM 10 

### 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 requi re extrapolation of the cu rrent test data from the wind tunnel study.



## DISCUSSION

Peer Review

The test report on the wind tunnel study (C owherd and Donaldson, 2005) was issued first in draft form for external peer review. Three peer re viewers (having no prior contact with the study) were select ed by the DEJF: Patrick Gaffn ey (California Air Resources Board), John Kinsey (U.S. Environmental Pr otection Agency), and Mel Zeldin (Private Consultant). In addition, p eer review comments were pr ovided by Duane Ono (Great Basin UAPCD) and Richard Countess (Countess Environmental) who helped to develop this study. After the review comments on th e 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 fi nal test report was issued on October 12, 2005.

Recommended Particle Size Ratios

Based on the results of the WRAP/DEJF study (see attached test re port) and the prior EPA-funded field study, it is proposed that new PM 2.5 

### /PM 10 

### ratios be adopted for several categories of (uncontrolled) fugitive dust sour ces, 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 co uld change in the future if field studies show other differences than thos e identified through this study.

The proposed PM 2.5 

### /PM 10 

### 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 PM 10 

### 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 lowe r core concentrations have higher PM 2.5 

### /PM 10

### 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

PM 2.5 /PM 10 Ratio Fugitive dust source category AP-42 section Current Proposed

Paved Roads 13.2.1 0.25 0.15

Unpaved Roads (Public & I ndustrial) 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, Es timating Particulate Matter Emissions from Construction Operations (MRI, 1999).

2 Agricultural tilling was dropped from the 5 th 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 co ntain supporting data.

Paved Roads

For the dust component of particulate emissions from paved roads, a PM 2.5 

### /PM 10 

### ratio of 0.15 is recommended. The proposed rati o 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 pave d 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 emissi ons from this source category, a PM 2.5 

### /PM 10 

### ratio of 0.1 is recommended. The proposed ratio is jus tified from the test results of the wind tunnel study for a variety of western surface mate rials. 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 st udy that used dichotomous samplers as FRM devices (MRI, 1997).

Construction and Demolition

The dust component of particulate emi ssions from construction and demolition dominate the total particulate emissi ons from this source category. A PM 2.5 

### /PM 10 

### ratio of 0.1 is recommended for dust emissions from construction and demolition. The proposed ratio is justified by the fact that the domi nant dust source associat ed with construction and demolition projects is emissions from vehi cle travel over unpaved surfaces. This is shown by case studies that calculate part iculate emissions from representative construction activities (road, building, and nonbuilding construc tion). For example, the fine fraction ratio for scraper travel averag es about 0.2 (Muleski et al., 2005), before correcting for the factor-of- two bias in th e cyclone/impactor system. Moreover this includes the diesel emissions that are contai ned within the fine fraction component.

It should be noted that if la rge 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 contri butor to dust emissions from this source category. The recommended fine fraction ratio identified be low should be used for the open area wind erosion component.

Aggregate Handling and Storage Piles

Although usually not a major source in comp arison with traffic around storage piles, the transfer of aggregate a ssociated with bucket loader s and unloaders or conveyor transfer points is addressed direc tly in this section of AP-42. A PM 2.5 

### /PM 10 

### 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 bi as in the cyclone/impactor test results.

The dominant dust component of particulat e 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 secti on to find appropriate emission factors. A
PM 2.5 

### /PM 10 

### ratio of 0.1 is recommended for this s ource. The proposed ratio is consistent with that recommended above for traffic on unpaved surfaces.

### 7 Industrial Wind Erosion

For the dust component of particulate em issions from industrial wind erosion, a
PM 2.5 

### /PM 10 

### ratio of 0.15 is recommended. Industr ial 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 pr oposed ratio is justified by portable wind tunnel tests of industrial aggreg ate materials which produced PM 2.5 

### /PM 10 

### ratios averaging 0.4, as indicated by the current AP-42 fine fract ion ratio given in Table 1. When these results are corrected for the bias associated with the cyclone/impactor system at very high
PM 10 

### concentrations observed in the effluent from the portable wind tunnel (exceeding 10,000 μ g/m 3 

### ), the result is 0.15.

Agricultural Tilling

For the dust component of particulate emissi ons from agricultural tilling and related land preparation activities, which dominates the total particulate emissions from this source category, no new PM 2.5 

### /PM 10 

### ratio can be recommended at this time, because of the lack of published test data. However, th e current factor of 0.2, as listed in Table 1, appears to be generally consistent with the re sults of the current wind tunnel tests. It was found that the agricultura l soils tested in the wind tunnel produced slightly higher ratios than the other test materials. In additi on, 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 trav el over the same routes.

Open Area Wind Erosion

For the dust component of particulate em issions from open area wind erosion (not currently addressed in AP-42), a PM 2.5 

### /PM 10 

### ratio of 0.15 is recommended. Open area wind erosion is associated with exposed so ils 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 PM 2.5 

### /PM 10 

### 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 PM 2.5 

### /PM 10 

### 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 PM 2.5 

### /PM 10 

### ratios. As shown in Ta ble 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 Constructi on 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 PM 2.5 

### /PM 10 

### ratio is accomplished by changing the appropriate PM-2.5 particle size multiplier (k -factor) for the respect ive 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 transferoperations, 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 concen tration measurements used to develop PM 2.5 

### emission factors for AP-42 were biased high by a factor of two, as compared to PM 2.5 

### measurements from FRM samplers. This f actor-of-two bias helps to explain why researchers have often seen a similar discrepa ncy in the proportion of fugitive dust found in PM 2.5 

### emission inventories and modeled ambient impacts, as compared to the proportion observed on ambient filter samples. This study also shows that the PM 2.5 

### /
PM 10 

### ratios for fugitive dust shoul d 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 PM 2.5 

### emission factors for the following four fugitive dust source categories: paved roads, unpaved roads (public and industrial), aggr egate handling and storage piles, and industrial wind erosion (AP-42 Sect ions 13.2.1, 13.2.2, 13.2.4, & 13.2.5, respectively). Emission estimates for other fugitive dust produc ing activities, such as construction and demolition, will also be affected since they ar e based on these four source categories. It is recommended that revisions to the curr ent AP-42 sections for these fugitive dust sources be adopted as shown in A ttachment A to this report.



## IMPLICATIONS

The proposed revisions to AP-42 are n eeded to ensure the most accurate PM 2.5 

### and
PM 10 

### 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, th e revisions will affect the quantity of dust apportioned to the fine (PM 2.5 

### ) versus coarse (PM 2.5-10 

### ) size modes, which have significantly different effects on visibility and long-range tr ansport potentials. This will reduce PM 2.5 

### 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 fractio ns 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 (WRA P), 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, comp aring performance of cyclone/impactor system with reference meth od samplers for PM2.5]

Muleski, G. E., C. Cowherd, and J. S. Ki nsey. 2005. “Particulate Emissions from Construction Activities ,” J. Air & Waste Manage. Assoc. 55: 772-783. [Summarizes field test results for emissions from majo r 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 PM 2.5 

### Fugitive Dust Emissions from PM 10 

### .” Presented at the EPA Emi ssion Inventory Conference. Las Vegas NV. April 2005. [Summarizes other field studies that can be used to develop
PM 2.5 

### /PM 10 

### 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 calculat ion 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]11Figure 1. Phase I test results show thatthe Cyclone/ Impactor method measured PM2.5concentrations that were two times higher thanthose measured by Federal Reference Method samplers when simultaneously exposed to the well-mixed dust environment in the windtunnel.PM 2.5 Ratios for Cyclone and Partisols00.511.522.533.544.50123456PM 10 Concentration (mg/m3)

Cyclone Concentration / Partisol Concentration ( PM 2.5)AZ FineAZ CoarseOwens DLB12Figure 2. Phase II tests show that the PM2.5/PM10ratio decreased with increasing PM concentrations, and could be expected to be inthe range of 0.1 at concentrations that are typical of fugitive dust emission plumes.PM 2.5 / PM 10 Ratio vs PM 10 Concentration0.0000.0500.1000.1500.2000.2500.3000.3500.4000.4500.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000PM 10 Concentration (mg/m3)

PM 2.5 / PM 10 RatioAZ Ag SoilKnik RiverSedimentsLas Cruces LandfillRoadThunder Basin MineAZ Alluvial ChannelRadium SpringsSalton Sea

## 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 constraint s 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 con tinue 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 13.2.3-1estimates. 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 Measures 4

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 1/951/95 Miscellaneous Sources 13.2.3-3Table 13.2.3-1. RECOMMENDED EMISSION FACTORS FOR CONSTRUCTION OPERATIONSaConstruction Phase Dust-generating Activities Recommended Emission Factor CommentsRatingAdjustmentbI. Demolition and debrisremoval1. Demolition of buildings orother (natural) obstacles suchas trees, boulders, etc.a. Mechanicaldismemberment("headache ball") ofexisting structures NA—b. Implosion of existingstructuresNA—c. Drilling and blasting ofsoilDrilling factor in Table 11.9-4Blasting factor NABlasting factor in Tables 11.9-1and 11.9-2 not consideredappropriate for generalconstruction activities-1NAd. General land clearing Dozerequation (overburden) inTables 11.9-1 and 11.9-2-1/-2c2. Loading of debris into trucks Material handling emissionfactor equation in Section 13.2.4-0/-1c3. Truck transport of debris Unpaved road emission factor inSection 13.2.2, or paved roademission factor in Section 13.2.1-0/-1c4. Truck unloading of debrisMaterial handling emissionfactor equation in Section 13.2.4May occur offsite -0/-1c13.2.3-4 EMISSION FACTORS 1/95Table 13.2.3-1 (cont.).Construction Phase Dust-generating Activities Recommended Emission Factor CommentsRatingAdjustmentbII. Site Preparation(earth moving)1. Bulldozing Dozer equation (overburden) inTables 11.9-1 and 11.9-2-1/-2c2. Scrapers unloading topsoil Scraper unloading factor inTable 11.9-4-13. Scrapers in travelScraper (travel mode) expressionin Tables 11.9-1 and 11.9-2-0/-1c4. Scrapers removing topsoil 5.7kg/vehicle kilometer traveled(VKT) (20.2 lb/vehicle miletraveled [VMT])Ed5. Loading of excavated materialinto trucksMaterial handling emission factorequation in Section 13.2.4-0/-1c6. Truck dumping of fill material,road base, or other materialsMaterial handling emission factorequation in Section 13.2.4May occur offsite-0/-1c7. CompactingDozer equation inTables 11.9-1 and 11.9-2Emission factordowngraded because ofdifferences in operatingequipment-1/-2c8. Motor gradingGrading equation in Tables 11.9-1and 11.9-2-1/-2c1/95 Miscellaneous Sources 13.2.3-5Table 13.2.3-1 (cont.).Construction Phase Dust-generating Activities Recommended Emission Factor CommentsRatingAdjustmentbIII. GeneralConstruction1. Vehicular traffic Unpaved road emission factor inSection 13.2.2, or paved road emissionfactor in Section 13.2.1-0/-1c-0/-1c2. Portable plantsa. CrushingFactors for similar material/operations inSection 11.19.2-1/-2cb. ScreeningFactors for similar material/operations inSection 11.19.2-1/-2cc. Material transfers Material handling emission factorequation in Section 13.2.4-0/-1c3. Other operationsFactors for similar material/operations inthe Mineral Products Industry, Chapter11 of this document—aNA = not applicable.bRefers to how many additional letters theemission factor should be downrated (beyondthe guidance given in the other sectionsof AP-42) forapplication to construction activities. For example, "-2" meansthat an A-rated factor should be considered of C quality in estimatingconstruction emissions. All emission factors assumed to have site-specific input values; otherwise, additional downgrading of one letter shouldbe employed. Note that no rating can be lower than E.cFirst value for cases with independent variables within rangegiven in AP-42 section; second value for cases with at least 1 variable outside therange.dRating for emission factor given. Reference 5.eIn the event that individual operations cannot be identified, onemay very conservatively overestimate PM-10 emissions by usingEquation 1.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 suppression a

Truck transport b Wet suppression Paving
Chemical stabilization c

Bulldozers Wet suppression d

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 1/955. 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 13.2.3-711 /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 unc overed, partially because of the need for frequent material transfer into or out of storage.

Dust emissions occur at several points in the st orage cycle, such as material loading onto the pile, disturbances by strong wind currents, and loa dout 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. Emissi ons 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 ont o 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 redu ced. 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 /06Table 13.2.4-1. TYPICAL SILT AND MOISTURE CONTENTS OF MATERIALS AT VARIOUS INDUSTRIESaIndustryNo. OfFacilities MaterialSilt Content (%) Moisture Content (%)No. OfSamples Range MeanNo. OfSamples Range MeanIron and steel production 9 Pellet ore 13 1.3 - 13 4.3 11 0.64 - 4.0 2.2Lump ore 9 2.8 - 19 9.5 6 1.6 - 8.0 5.4Coal 12 2.0 - 7.7 4.6 11 2.8 - 11 4.8Slag 3 3.0 - 7.3 5.3 3 0.25 - 2.0 0.92Flue dust 3 2.7 - 23 13 1 — 7Coke breeze 2 4.4 - 5.4 4.9 2 6.4 - 9.2 7.8Blended ore 1 — 15 1 — 6.6Sinter 1 — 0.7 0 — —Limestone 3 0.4 - 2.3 1.0 2 ND 0.2Stone quarrying and processing 2 Crushed limestone 2 1.3 - 1.9 1.6 2 0.3 - 1.1 0.7Various limestone products 8 0.8 - 14 3.9 8 0.46 - 5.0 2.1Taconite mining and processing 1 Pellets 9 2.2 - 5.4 3.4 7 0.05 - 2.0 0.9Tailings 2 ND 11 1 — 0.4Western surface coal mining 4 Coal 15 3.4 - 16 6.2 7 2.8 - 20 6.9Overburden 15 3.8 - 15 7.5 0 — —Exposed ground 3 5.1 - 21 15 3 0.8 - 6.4 3.4Coal-fired power plant 1 Coal (as received) 60 0.6 - 4.8 2.2 59 2.7 - 7.4 4.5Municipal solid waste landfills 4 Sand 1 — 2.6 1 — 7.4Slag 2 3.0 - 4.7 3.8 2 2.3 - 4.9 3.6Cover 5 5.0 - 16 9.0 5 8.9 - 16 12Clay/dirt mix 1 — 9.2 1 — 14Clay 2 4.5 - 7.4 6.0 2 8.9 - 11 10Fly ash 4 78 - 81 80 4 26 - 29 27Misc. fill materials 1 — 12 1 — 11aReferences 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 pile s (batch or continuous drop operations). 2. Equipment traffic in storage area. 3. Wind erosion of pile surf aces and ground areas around piles. 4. Loadout of aggregate for shipment or for retu rn 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 th e pile or loading out from the pile to a truck with a front-end loader are examples of batch dr op 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 estimat ed, 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, vari es 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 0.053 a

a Multiplier for < 2.5 : m taken from Reference 14.

The equation retains the assigned quality rati ng 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 emissi on 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 speci fic sources of interest. The field and laboratory procedures for aggregate sampling are given in Referen ce 3. In the event that site-specific values for11 /06 Miscellaneous Sources 13.2.4-5 correction parameters cannot be obtained, the appr opriate 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, fr ont-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. Th e 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 ac tivity also may be justified for the worst-case averaging period.

13.2.4.4 Controls 12-13

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 th emselves 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. C ontinuous chemical treating of material loaded onto piles, coupled with watering or treatment of ro adways, 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 Protec tion 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 Pr otection 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 Du st From Western Surface Coal Mining Sources, 2 Volumes, EPA Contract No. 68-03-2924, PEDC o 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 Protec tion 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 General 1-3

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) a t 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: (1) u(z) u 0.4 ln z

z o (z > z o )

u = wind speed, cm/s

u * = friction velocity, cm/s

z = height above test surface, cm

z o = roughness height, cm 0.4 = von Karman’s constant, dimensionless

11 /06 Miscellaneous Sources 13.2.5-1The 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 (z o ) 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 Equation 4

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/m 2 ) per year as follows:

(2) Emission factor k N

i 1 P i

13.2.5-2 EMISSION FACTORS 11 /06where:

k = particle size multiplier N = number of disturbances per year
P i = erosion potential corresponding to the observed (or probable) fastest mile of wind for the ith period between disturbances, g/m 2

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. 075 a

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 u t ) 2 25 (u u t )

P = 0 for u ≤ u t

u * = friction velocity (m/s)

u t = 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 13.2.5-3FIELD 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 /06Table 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)

z o = Act z o = 0.5 cm

Overburden a 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 pile a 1.12 0.3 23 21

Scraper tracks on coal pile a,b 0.62 0.06 15 12

Fine coal dust on concrete pad c 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 u 10

u * = friction velocity (m/s)

= fastest mile of reference anemometer for period between disturbances (m/s) u 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 13.2.5-5For 2 representative pile shapes (conical and oval with flattop, 37-degree side slope), the ratios of surface wind speed (u s ) to approach wind speed (u r ) 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 (z o ) 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 u s /u r 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 u s /u r 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: u 10

where a typical roughness height of 0.5 cm (0.005 m) has been assumed. If a site(5) u 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 (u
s )

(6) u s (u s )

u r u 10

13.2.5-6 EMISSION FACTORS 11 /06Figure 13.2.5-2. Contours of normalized surface windspeeds, u s /u r .

11 /06 Miscellaneous Sources 13.2.5-73. 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 0.4 u s 25
ln0.5 0.10 u 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 (u 10 ) 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 u s /u r 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 (P i ) 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 /0611/06 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/m 3) (50 pounds per cubic feet [lb/ft 3 ]). 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 t opping 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 p ile 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 a nnual 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 valu e 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 intoFigure 13.2.5-3. Example 1: Pile surface areas within each wind speed regime.

13.2.5-10 EMISSION FACTORS 11 /06Figure 13.2.5-4. Example daily fastest miles wind for periods of interest.

11 /06 Miscellaneous Sources 13.2.5-11equivalent friction velocities for each surface wind regime (i. e., u s /u r 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 u s /u r = 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 u s /u r : 0.2 u s /u r : 0.6 u s /u r : 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 u s /u r = 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 u t ) 2 25(u u t )

P 2 58(1.23 1.12) 2 25(1.23 1.12)

0.70 2.75 3.45 g/m 2

13.2.5-12 EMISSION FACTORS 11 /06Table 13.2.5-5 (Metric Units). EXAMPLE 1: CALCULATION OF PM-10 EMISSIONS a

3-Day Period u * (m/s) u * - u t *
(m/s) P (g/m 2 ) ID Pile Surface
Area (m 2 ) 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 u t * = 1.12 m/s for uncrusted coal an d 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 d 2 0.785 (29.2) 2 670 m 2

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 13.2.5-13Steps 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 u t ) 2 25(u u t )

P 58(0.77 0.54) 2 25(0.77 0.54)

3.07 5.75

8.82 g/m 2

E = (0.5)(8.82 g/m 2 )(670 m 2 )

= 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 Part nership, Denver, CO, February 1, 2006.9/97 Metallurgical Industry 13.2.6-1

13.2.6 Abrasive Blasting

13.2.6.1 General 1-2

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 Description 1-9

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 Controls 1,3,5-11

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 BLASTING a

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 1997ii 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, 1997ivv 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

............................................... 5-1vi 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

.................. 4-111-1 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 CHARACTERIZATION 1

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 DESCRIPTION 1-8

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 Abrasives 1-2

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 are2-2 TABLE 2-1. MEDIA COMMONLY USED IN ABRASIVE BLASTING 2

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 particles Deflashing 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 Methods 2-8

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. 52-3

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. 22-4 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 × ( D

a ) 2

( D

s ) 2 × D a D s

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 DIAMETER 2

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 ABRASIVES 2

Type of abrasive Density, lb/ft 3

Aluminum oxides 160

Sand 99

Steel 487 where:

= mass flow rate (lb/hr) of abrasive with nozzle internal diameter D a

= 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 TECHNIQUES 2,4,6,7

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 asshown 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 pattern2-12 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: 23-2 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 sample3-3

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 1documents, 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 gr/dscf) and the hexavalent chromium -7 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. Two4-4 TABLE 4-2. SUMMARY OF TEST DATA FOR ABRASIVE BLASTING OPERATIONS a

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 0.002154-5 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/m 3

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 OPERATIONS a

Reference Type of operation Type of technology fraction, control document tested abrasive employed Sampler location μmA efficiency Comments Uncontrolled Controlled Control size Measured Particle

b Average dust concentration,

mg/m 3

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. b4-7

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 1 a

Operating condition Test

runs Total PM emission factor, kg/kg sand PM-10 emission

factor, kg/kg sand b PM-2.5

emission factor, kg/kg sand c Result of mass

balance, % closure d

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 tunnel4-9 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 0.00134-11 TABLE 4-9. CANDIDATE TOTAL PM EMISSION FACTORS DIFFERENTIATED
BY WIND SPEED

Wind speed Emission factor (kg/kg sand) by surface type
Average

Precleaned Painted Oxidized
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.