13.2.1 Paved Roads

13.2.1.1 General

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

or parking lot. Particulate emissions from paved roads are due to direct emissions from vehicles in the form ofexhaust, brake wear and tire wear emissions and resuspension of loose material on the road surface. In general terms, resuspended particulate emissions from paved roads originate from, and result in the depletion of, the loose material present on the surface (i.e., the surface loading). In turn, that surface loading is continuously replenished by other sources. At industrial sites, surface loading is replenished by spillage ofmaterial and trackout from unpaved roads and staging areas. Figure 13.2.1-1 illustrates several transfer processes occurring on public streets.

Various field studies have found that public streets and highways, as well as roadways at industrial facilities, can be major sources ofthe atmospheric particulate matter within an area)9 Ofparticular interest in many parts ofthe United States are the increased levels of emissions from public paved roads when the equilibrium between deposition and removal processes is upset. This situation,can occur-for various reasons, including application of granular materials for snow and ice control, mud/dirt carryout from construction activities in the area, and deposition from wind and/or water erosion of surrounding unstabilized areas. In the absence of continuous addition offresh material (through localized track out or application of antiskid material), paved road surface loading should reach an equilibrium value in which the amount of material resuspended matches the amount replenished. The equilibrium surface loading value depends upon numerous factors. It is believed that the most importani 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.1°

The particulate emission factors presented in the previous version ofthis section ofAP 42, dated October 2002, implicitly included the emissions from vehicles in the form ofexhaust, 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 ofthe paved road emission factor equation only estimates particulate emissions from resuspended road surface material28. The particulate emissions from vehicle exhaust, brake wear, and tire wear are now estimated separately using EPA’s MOVES29 model. ~This~ approach eliminates the possibility of double counting emissions. Double counting results •~h~n employing the previous version ofthe emission factor equation in this section and MOVES to estimate particulate emissions from vehicle traffic on paved roads. It also incorporates the decrease in exhaust emissions that has occurred since the paved road emission fact~r,equation~was developed. Earlier versions ofthe paved road emission factor equation includes estiniaie~’~ emissions from exhaust, brake wear, and tire wear based on emission rates f~i~hic~es~inthe 1980 calendar year fleet. The amount of PM released from vehicle exhaust has decreasea since 1980 due to lower new vehicle emission standards and changes in fuel characteristics.

1/11 Miscellaneous Sources 13.2.1-113.2.1.2 Emissions And Correction Parameters

Dust emissions from paved roads have been found to vary with what is termed the “silt loading” present on the road surface. In addition, the average weight and speed ofvehicles traveling the road influence road dust emissions. The term silt loading (sL) refers to the mass of silt-size material (equal to or less than 75 micrometers [i.tm] in physical diameter) per unit area of the travel surface. The total road surface dust loading consists of loose material that can be collected by broom sweeping and vacuuming ofthe traveled portion ofthe paved road. The silt fraction is determined by measuring the proportion ofthe loose dry surface dust that passes through

a 200-mesh screen, using the ASTM-C-136 method. Silt loading is the product ofthe silt fraction and the total loading, and is abbreviated “sL”. Additional details on the sampling and analysis of such material are provided in AP-42 Appendices C.1 and C.2.

The surface sL provides a reasonable means ofcharacterizing seasonal variability in a paved road emission inventory. In many areas ofthe 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 offresh material is eliminated, the road surface loading can be expected to reach an equilibrium value, which is substantially lower than the late winter/early spring values.

13.2.1-2 EMISSION FACTORS 1/11-AC,)0CDCD0CCd)Cl)IIREMOVALREENTRMNMENT(J)WINDEROSION~jJpISPLACEMENT~RAINFALLRUNOFFTOCATCHBAShSTREETSWEEPINGwDEPOSITION(fJPAVUIENTWEARfloDEC11051510W03VEHICLERELATEDDEPOSITION03LITTER(~)MUDANDDIRTCAROVOUT(j)EROSIONFROMADJACENTAREAS(~)WILLS(:1)BiOLOGICALDEBRISQICECONTROLCOMPOUNDSb,-~--~~1-~;.--;’~*:~-‘Ø,.,~.~‘w-—.a•V—I,,eI—~‘~~_t®c•-4±-Figure13.2.1-1.Depositionandremovalprocesses.13.2.1.3 Predictive Emission Factor Equations’°’29

The quantity ofparticulate emissions from resuspension of loose material on the road surface due to vehicle travel on a dry paved road may be estimated using the following empirical expression:



### E = 

### k (sL)°9’ x (W)’°2 (1)

where: 

## E

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

## k

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

### sL

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

## W

= average weight (tons) ofthe vehicles traveling the road.

It is important to note that Equation 1 calls for the average weight of all vehicles traveling the road. For example, if 99 percent oftraffic on the road are 2 ton cars/trucks while the remaining 1 percent consists of20 ton trucks, then the mean weight “W” is 2.2 tons. More specifically, Equation 1 is not intended to be used to calculate a separate emission factor for each vehicle weight class. Instead, only one emission factor should be calculated to represent the “fleet” average weight of all vehicles traveling the road.

The particle size multiplier (k) above varies with aerodynamic size range as shown in Table 13.2.1-1. To determine particulate emissions for a specific particle size range, use the appropriate value ofk 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 MOBILE6.227 or MOVES2O 1029 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 rang& Particle Size Multiplier kb
~/VKT g/VMT lb/VMT PM~2.5c 0.15 0.25 0.00054
PM-b 0.62 1.00 0.0022
PM-is 0.77 1.23 0.0027 PM~30d 3.23 5.24 0.011

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

x micrometers

b Units shown are grams per vehicle kilometer traveled (g/VKT), grams per vehicle mile traveled (g/VMT), and pounds per vehicle mile traveled (lb/VMT). The multiplier k includes unit conversions to produce emission factors in the units shown for the indicated size range from the mixed units required in Equation 1.

C The k-factors for PM2.5 were based on the average PM25:PM10 ratio oftest runs in Reference 30.

d PM-30 is sometimes termed “suspendable particulate” (SP) and is often used as a surrogate for
TSP.

13.2. 1-4 EMISSION FACTORS 1/11Equation 1 is based on a regression analysis of83 tests for PM-l0.3’56’8’2729’3136 Sources

tested include public paved roads, as well as controlled and uncontrolled industrial paved roads. The majority oftests involved freely flowing vehicles traveling at constant speed on relatively level roads. However, 22 tests ofslow moving or “stop-and-go” traffic or vehicles under load were available for inclusion in the data base.3236 Engine exhaust, tire wear and break wear were subtracted from the emissions measured in the test programs prior to stepwise regression to determine Equation

~ The equations retain the quality rating ofA (D for PM-2.5), ifapplied within the range ofsource conditions that were tested in developing the equation as follows:

Silt loading: 0.03

- 400 g/m2 0.04

- 570 grains/square foot (ft2)

Mean vehicle weight: 1.8

- 38 megagrams (Mg) 2.0-42 tons

Mean vehicle speed: 1

- 88 kilometers per hour (kph) 1

- 55 miles per hour (mph)

The upper and lower 95% confidence levels ofequation 1 for PM10 is best described with equations using an exponents of 1.14 and 0.677 for silt loading and an exponents of 1.19 and 0.85 for weight. Users are cautioned that application ofequation 1 outside ofthe range ofvariables and operating conditions specified above, e.g., application to roadways or road networks with speeds above 55 mph and average vehicle weights of42 tons, will result in emission estimates with a higher level ofuncertainty. In these situations, users are encouraged to consider an assessment ofthe impacts ofthe influence of extrapolation to the overall emissions and alternative methods that are equally or more plausible in light of local emissions data and/or ambient concentration or compositional data.

To retain the quality rating for the emission factor equation when it is applied to a specific paved road, it is necessary that reliable correction parameter values for the specific road in question be determined. With the exception of limited access roadways, which are difficult to sample, the collection and use of site-specific silt loading (sL) data for public paved road emission inventories

are strongly recommended. The field and laboratory procedures for determining surface material silt content and surface dust loading are summarized in Appendices C. 1 and C.2. In the event that site-specific values cannot be obtained, an appropriate value for a paved public road may be selected from the values in Table 13.2.1-3, but the quality rating ofthe equation should be reduced by 2 levels.

Equation 1 may be extrapolated to average uncontrolled conditions (but including natural mitigation) under the simplif~’ing assumption that annual (or other long-term) average emissions are inversely proportional to the frequency of measurable (>0.254 mm [0.01 inch]) precipitation by application ofa precipitation correction term. The precipitation correction term can be applied on

a daily or an hourly basis 26,38

For the daily basis, Equation 1 becomes:

Eext

= 

### [k (sL)°91 x (W)1°2 j (1

— 

### P/4N) (2)

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

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

### P

= number of “wet” days with at least 0.254 mm (0.01 in) of precipitation during the averaging period, and
1/11 Miscellaneous Sources 13.2.1-5N number ofdays in the averaging period (e.g., 365 for annual, 91 for seasonal, 30 for monthly).

Note that the assumption leading to Equation 2 is based on analogy with the approach used to develop long-term average unpaved road emission factors in Section 13.2.2. However, Equation 2 above incorporates an additional factor of “4” in the denominator to account for the fact that paved roads dry more quickly than unpaved roads and that the precipitation may not occur over the complete 24-hour day.

For the hourly basis, equation 1 becomes:

Eext

= 

# { k (sL)°9’ x (W)’°2 

# 1 (1 —1 .2P/N) (3)

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


### E ext

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

### P number of hours with at least 0.254 mm (0.01 in) ofprecipitation during the averaging period, and 

### N

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

season 720 for monthly)

Note: In the hourly moisture correction term (1-1 .2P/N) for equation 3, the 1.2 multiplier is applied to account for the residual mitigative effect of moisture. For most applications, this equation will produce satisfactory results. Users should select a time interval to include sufficient “dry” hours such that a reasonable emissions averaging period is evaluated. For the special case where this equation is used to calculate emissions on an hour by hour basis, such as would be done in some emissions modeling situations, the moisture correction term should be modified so that the moisture correction “credit” is applied to the first hours following cessation ofprecipitation. In this special case, it is suggested that this 20% “credit” be applied on a basis of

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

Note that the assumption leading to Equation 3 is based on analogy with the approach used to develop long-term average unpaved road emission factors in Section 13.2.2. Figure 13.2.1-2 presents the geographical distribution of “wet” days on an annual basis for the United States. Maps showing this information on a monthly basis are available in the Climatic Atlas ofthe United States23. Alternative sources include other Department of Commerce publications (such as local climatological data summaries). The National Climatic Data Center (NCDC) offers several products that provide hourly precipitation data. In particular, NCDC offers Solar and Meteorological Surface Observation Network 1961-1990 (SAMSON) CD-ROM, which contains 30 years worth of hourly meteorological data for first-order National Weather Service locations. Whatever meteorological data are used, the source ofthat data and the averaging period should be clearly specified.

It is emphasized that the simple assumption underlying Equations 2 and 3 has not been verified in any rigorous manner. For that reason, the quality ratings for Equations 2 and 3 should be downgraded one letter from the rating that would be applied to Equation 1.

13.2.1-6 EMISSION FACTORS1210ALASA0taD-4C.)CDCD0C,)0C)CDGo~0‘)/7070~~90120c1201‘goleo~•.6’~2102401800~!OI~jqo—Figure13.2.1-2.Meannumberofdayswith0.01inchormoreofprecipitationintheUnitedStates.Table 13.2.1-2 presents recommended default silt loadings for normal baseline conditions

and for wintertime baseline conditions in areas that experience frozen precipitation with periodic application ofantiskid material24. The winter baseline is represented as a multiple ofthe 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 of4 X 0.6

= 2.4 g/m2.

Table 13.2.1-2. Ubiquitous Silt Loading Default Values with Hot Spot Contributions from Anti-Skid Abrasives (~/m2~

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

Ubiquitous Baseline g/m2 0.6 0.2 0.06 0.03
0.015 limited

access
Ubiquitous Winter Baseline X4 X3 X2 Xl Multiplier during months with frozen precipitation

Initial peak additive contribution 2 2 2 2 from application of antiskid abrasive (g/m2)

Days to return to baseline conditions 7 3 1 0.5 

## (assume linear decay)

It is suggested that an additional (but temporary) silt loading contribution of2 g/m2 occurs with each application of antiskid abrasive for snow/ice control. This was determined based on a typical application rate of500 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 ofthe chemical dissolves during the snow/ice melting process.

To adjust the baseline silt loadings for mud/dirt trackout, the number oftrackout points is required. It is recommended that in calculating PM10 emissions, six additional miles of road be added for each active trackout point from an active construction site, to the paved road mileage of the specified category within the county. In calculating PM2.5 emissions, it is recommended that three additional miles of road be added for each trackout point from an active construction site.

It is suggested the number oftrackout 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 oftrackout 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 13.2.1-3 should be expected to yield only an order of-magnitude estimate ofthe emission factor. Public paved road silt loadings are dependent

13.2. 1-8 EMISSION FACTORSupon: traffic characteristics (speed, ADT, and fraction of heavy vehicles); road characteristics

(curbs, number of lanes, parking lanes); local land use (agriculture, new residential construction) and regional/seasonal factors (snow/ice controls, wind blown dust). As a result, the collection and use of site-specific silt loading data is highly recommended. In the event that default silt loading values are used, the quality ratings for the equation should be downgraded 2 levels.

Limited access roadways pose severe logistical difficulties in terms of surface sampling, and few silt loading data are available for such roads. Nevertheless, the available data do not

- suggest great variation in silt loading for limited access roadways from one part of the country to another. For annual conditions, a default value of 0.0 15 g/m2 is recommended for limited access roadways.9’22 Even fewer of the available data correspond to worst-case situations, and elevated loadings are observed to be quickly depleted because of high traffic speeds and high ADT rates. A default value of 0.2 g/m2 is recommended for short periods of time following application of snow/ice controls to limited access roads.22

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

as public roads. Because ofthe variations oftraffic conditions and the use ofpreventive mitigative controls, the data probably do not reflect the full extent ofthe potential variation in silt loading on industrial roads. However, the collection of site specific silt loading data from industrial roads is easier and safer than for public roads. Therefore, the collection and use of sitespecific silt loading data is preferred and is highly recommended. In the event that site-specific values cannot be obtained, an appropriate value for an industrial road may be selected from the

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

The predictive accuracy of Equation 1 requires thorough on-site characterization of road silt loading. Road surface sampling is time-consuming and potentially hazardous because ofthe need to block traffic lanes. In addition, a large number of samples are required to represent spatial and temporal variations across roadway networks. Mobile monitoring is a new alternative silt loading or road dust emission characterization method for either paved or unpaved roads. It utilizes a test vehicle that generates and monitors its own dust plume concentration (mass basis) at

a fixed sampling probe location. A calibration factor is needed for each mobile monitoring configuration (test vehicle and sampling system), to convert the relative dust emission intensity to

an equivalent silt loading or emission factor. Typically, portable continuous particle concentration monitors do not comply with Federal Reference Method (FRM) standards. Therefore, a controlled study must be performed to correlate the portable monitor response to the road silt loading or size specific particle concentration measured with an approved FRM sampling system. In the calibration tests, multiple test conditions should be performed to provide an average correlation with known precision and to accommodate variations in road silt loading, vehicle speed, road dust characteristics and other road conditions that may influence mobile monitoring measurements or emissions characteristics. Because the paved road dust emissions

are also dependent on the average vehicle weight for the road segment, it is important that the weight ofthe test vehicle correspond closely to the average vehicle weight for the road segment

or be adjusted using the average vehicle weight relationship in Equation 1. In summary, it is believed that the Mobile Monitoring Method will provide improved capabilities to provide reliable temporally and spatially resolved silt loading or emissions factors with increased coverage, improved safety, reduced traffic interference and decreased cost. 40,41,42

1/11 Miscellaneous Sources 13.2.1-9Table13.2.1-3(MetricAndEnglishUnits).TYPICALSILTCONTENTANDLOADINGVALUESFORPAVEDROADSATINDUSTRIALFACILITIESaNo.ofSiltLoadingNo.ofNo.OfSiltContent(%)TravelTotalLoadingxi0~(g/m2)IndustrySitesSamplesRangeMeanLanesRangeMeanIUnitsbRangeIMeanCoppersmelting1315.4-21.719.0212.9-19.515.9kg/km188-40029245.8-69.255.4lb/miIronandsteelproduction9481.1-35.712.5243.0-64.00.495kg/km0.09-799.70.020-16.91.75lb/miAsphaltbatching132.6-4.63.3112.1-18.014.9kg/km76-19312043.0-64.052.8lb/miConcretebatching135.2-6.05.521.4-1.81.7kg/km11-12125.0-6.45.9lb/miSandandgravelprocessing136.4-7.97.112.8-5.53.8kg/km53-95709.9-19.413.3lb/miMunicipalsolidwastelandfill27-2-1.1-32.07.4Quarry16-2-2.4-148.2Cornwetmills315-2-0.05—2.91.1References1-2,5-6,11-13.Valuesrepresentsamplescollectedfromindustrialroads.PublicroadsiltloadingvaluesarepresentedinTable-13.2.1-2.Dashesindicateinformationnotavailable.~’Multiplyentriesby1000toobtainstatedunits;kilogramsperkilometer(kg/km)andpoundspermile(lb/mi).13.2.1.4 Controls6’25

Because ofthe importance ofthe silt loading, control techniques for paved roads attempt either to prevent material from being deposited onto the surface (preventive controls) or to

remove from the travel lanes any material that has been deposited (mitigative controls). Covering of loads in trucks, and the paving ofaccess areas to unpaved lots or construction sites, are examples ofpreventive measures. Examples of mitigative controls include vacuum sweeping, water flushing, and broom sweeping and flushing. Actual control efficiencies for any

- ofthese 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 ofgutters and curb areas may actually increase the silt loading on the traveled portion ofthe road. Redistribution of loose material onto the travel lanes will actually produce a short-term increase in the emissions.

In general, preventive controls are usually more cost effective than mitigative controls. The cost-effectiveness of mitigative controls falls offdramatically as the size of an area to be treated increases. The cost-effectiveness of mitigative measures is also unfavorable if only a short period oftime is required for the road to return to equilibrium silt loading condition. That is to say, the number and length of public roads within most areas of interest preclude any widespread and routine use of mitigative controls. On the other hand, because ofthe more limited scope ofroads at an industrial site, mitigative measures may be used quite successfully (especially in situations where truck spillage occurs). Note, however, that public agencies could make effective use of mitigative controls to remove sand/salt from roads after the winter ends.

Because available controls will affect the silt loading, controlled emission factors may be obtained by substituting controlled silt loading values into the equation. (Emission factors from controlled industrial roads were used in the development ofthe equation.) The collection of surface loading samples from treated, as well as baseline (untreated), roads provides a means to track effectiveness ofthe controls over time. The use of Mobile Monitoring Methodologies provide an improved means to track progress in controlling silt loading values.

13.2.1.5 Changes since Fifth Edition

The following changes were made since the publication ofthe Fifth Edition ofAP-42:

October 2002 1) The particle size multiplier for PM2.5 was revised to 25% ofPM10. The approximately 55% reduction was a result of emission testing using FRM monitors. The monitoring

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

2) Default silt loading values were included in Table 13.2.1-2 replacing the Tables and Figures containing silt loading statistical information.

3) Editorial changes within the text were made indicating the possible causes of variations in the silt loading between roads within and among different locations. The uncertainty of using the default silt loading value was discussed.

1/1 1 Miscellaneous Sources 13.2.1-114) Section 13.2.1.1 was revised to clarify the role ofdust loading in resuspension. Additional minor text changes were made.

5) Equations 2 and 3, Figure 13.2.1-2, and text were added to incorporate natural mitigation into annual or other long-term average emission factors.

December 2003 1) The emission factor equation was adjusted to remove the component ofparticulate emissions- from exhaust, brake wear, and tire wear. A parameter C representing these emissions was included in the predictive equation. The parameter C varied with aerodynamic size range ofthe particulate matter. Table 13.2.1-2 was added to present the new coefficients.

2) The default silt loading values in Table 13.2.1-3 were revised to incorporate the results from a recent analysis ofsilt loading data.

November 2006

1) The PM2.5 particle size multiplier was revised to 15% of PM10 as the result of wind tunnel studies ofa variety ofdust emitting surface materials.

2) References were rearranged and renumbered.

December 2010

1) The empirical predictive equation was revised. The revision is based upon stepwise regression of 83 profile emissions tests and an adjustment of individual test data for the exhaust; break wear and tire wear emissions prior to regression ofthe data.

2) The C term is removed from the empirical predictive equation and Table 13.2.1-2 with the C term values is removed since the exhaust; break wear and tire wear emissions were no longer part ofthe regressed data.

3) The PM2.5 particle size multiplier was revised to 25% of PM10 since the PM10 test data used to develop the equation did not meet the necessary PM10 concentrations for

a ratio of 15%.

4) The lower speed ofthe vehicle speed range supported by the empirical predictive equation was revised to 1 mph.

5) Information was added on an improved methodology to develop spatially and temporally resolved silt loadings or emissions factors by Mobile Monitoring Methodologies.

References For Section 13.2.1

1. D. R. Dunbar, Resuspension OfParticulate Matter, EPA-450/2-76-03 1, U. S. Environmental Protection Agency, Research Triangle Park, NC, March 1976.

2. R. Bohn, et al., Fugitive Emissions From Integrated Iron And Steel Plants, EPA-600/2-78- 050, U. S. Environmental Protection Agency, Cincinnati, OH, March 1978.

13.2.1-12 EMISSIONFACTORS 1/113. C. Cowherd, Jr., et al., Iron And Steel Plant Open Dust Source Fugitive Emission Evaluation, EPA-600/2-79- 103, U. S. Environmental Protection Agency, Cincinnati, OH, May 1979.

4. C. Cowherd, Jr., et al., Quantification OfDust Entrainment From Paved Roadways, EPA450/3-77-027, U. S. Environmental Protection Agency, Research Triangle Park, NC, July 1977.

5. Size Spec~fIc Particulate Emission Factors For Uncontrolled Industrial And Rural Roads, EPA Contract No. 68-02-3158, Midwest Research Institute, Kansas City, MO, September 1983.

6. T. Cuscino, Jr., et al., Iron And Steel Plant Open Source Fugitive Emission Control Evaluation, EPA-600/2-83-1 10, U. S. Environmental Protection Agency, Cincinnati, OH, October 1983.

7. J. P. Reider, Size-spec~/Ic Particulate Emission Factors For Uncontrolled Industrial And Rural Roads, EPA Contract 68-02-3 158, Midwest Research Institute, Kansas City, MO, September 1983.

8. C. Cowherd, Jr., and P. 3. 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 Spec~/ic Particulate Emission Factors For Industrial And Rural Roads, EPA-600/7-85-038, U. S. Environmental Protection Agency, Cincinnati, OH, September 1985.

10. Emission Factor Documentation ForAP-42, Sections 11.2.5 and 11.2.6— Paved Roads, EPA Contract No. 68-D0-0123, Midwest Research Institute, Kansas City, MO, March 1993.

11. Evaluation OfOpen Dust Sources In The Vicinity OfBuffalo, New York, EPA Contract No. 68-02-2545, Midwest Research Institute, Kansas City, MO, March 1979.

12. PM-JO Emission Inventory OfLandfills In The Lake CalumetArea, EPA Contract No. 68-02-389 1, Midwest Research Institute, Kansas City, MO, September 1987.

13. Chicago Area Particulate Matter Emission Inventory

— Sampling AndAnalysis, Contract No. 68-02-4395, Midwest Research Institute, Kansas City, MO, May 1988.

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

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

16. Evaluation OfPM-JO 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.

1/11 Miscellaneous Sources 13.2.1.1318. Post-storm Measurement Results

— Salt Lake County Road Dust Silt Loading Winter 1991/92 Measurement Program, Aerovironment, Inc., Monrovia, CA, June 1992.

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

20. PM-JO Emissions Inventory Data For The Maricopa And Pima Planning Areas, EPA Contract No. 68-02-3888, Engineering-Science, Pasadena, CA, January 1987.

21. Characterization OfPM-i 0 Emissions From Antiskid Materials Applied To Ice- And SnowCovered Roadways, EPA Contract No. 68-D0-0 137, Midwest Research Institute, Kansas City, MO, October 1992.

22. C. Cowherd, Background Documentfor Revisions to Fine Fraction Ratios UsedforAP-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 OfThe United States, U.S. Department ofCommerce, Washington, D.C., June 1968.

24. C. Cowherd, Jr., et at., ImprovedActivity Levelsfor National Emission Inventories of Fugitive Dustfrom Paved and Unpaved Roads, Presented at the

~ International Emission Inventory Conference, Atlanta, Georgia, April 2002.

25. C. Cowherd, Jr., et at., Control OfOpen Fugitive Dust Sources, EPA-450/3-88-008, U. S. Environmental Protection Agency, Research Triangle Park, NC, September 1988.

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

27. EPA, 2002b. MOBILE6 User Guide, United States Environmental Protection Agency, Office ofTransportation and Air Quality. EPA42O-R-02-028, October 2002.

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

29. EPA, 2009, MOVES2O1O User Guide, United States Environmental Protection Agency, Office ofTransportation and Air Quality. EPA42OB-09-041, December 2009.



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

### 1997.

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

### Works, U.S. Steel Corporation, Fairless Hills, PA, 

### USX Purchase Order No. 146- 0001191-0068, May 1990.

13.2.1-14 EMISSION FACTORS

### 32. PavedRoadMod~fIcations 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 ofPavedRoad Traffic at Minnesota Corn Processors Marshall, Minnesota Facility, McVehil-Monnett Associates, Midwest Research Institute Project No. 310212.1.001, July 6,2001.

34. Emission Tests ofPavedRoad 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 ofPavedRoad 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 ofPavedRoad Traffic atADM’s Marshall, Minnesota Facility, McVehil-MonnettAssociates, Midwest Research Institute Project No. 310479.1.001. December 5, 2003.

37. E-mail communication between Ron Myers ofEPA/OAQPS/SPPD/MPG, RTP, NC and Prashanth Gururaja and Ed Glover ofEPA/OTAQ/ASD/HDOC re. Diesel exhaust, 

### tire and brake wearfor low speed stop andgo traffic; January 2009 through May 2009.

38. Technical Memorandum from William B. Kuykendal to File, Subject: Decisions on FinalAP-42 Section 13.2.1 “Paved Roads “, 

### October 10, 2002.

39. E-mail communication between Ron Myers ofEPA/OAQPS/SPPD/MPG, RTP, NC and Gary Dolce and Rudolph Kapichak ofEPA/OTAQ/ASD/HDOC re. Paved Road Test Data; October 12, 2010 through December 16, 2010.

40. C. Cowherd, Mobile Monitoring Method Spec~flcations, Prepared by Midwest Research Institute for Clark County Department ofAir Quality and Environmental Management, Las Vegas, NV, February 6, 2009.

41. C. Cowherd, Technical Support Documentfor Mobile Monitoring Technologies, Prepared by Midwest Research Institute for Clark County Department ofAir Quality and Environmental Management, Las Vegas, NV, January 9, 2009.

42. R. Langston, R. S. Merle Jr., et al.,Clark County (Nevada) PavedRoad Dust Emission Studies in Support ofMobile Monitoring Technologies, Clark County Department of Air Quality and Environmental Management, Las Vegas, NV, December 22, 2008.

1/11 Miscellaneous Sources 13.2.1.15