### 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.