11 /06 Miscellaneous Sources 13.2.2-1 13.2.2 Unpaved Roads 13.2.2.1 General When a vehicle travels an unpaved road, the force of the wheels on the road surface causes pulverization of surface material. Particles are lifted and dropped from the rolling wheels, and the road surface is exposed to strong air currents in turbulent shear with the surface. The turbulent wake behind the vehicle continues to act on the 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 inthis 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 Calculation 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 haveshown 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 data from this table can potentially introduce considerable error. Use of this data is strongly discouraged when it is feasible to obtain locally gathered data. Since the silt content of a rural dirt road will vary with geographic location, it should be measured for use in projecting emissions. As a conservative 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

aSee discussion in text. As noted earlier, the models presented as Equations 1a and 1b were developed from tests of traffic on unpaved surfaces. Unpaved roads have a hard, generally nonporous surface that usually dries quickly after a rainfall or watering, because of traffic- enhanced natural evaporation. (Factors influencing how fast a road dries are discussed in Section 13.2.2. 3, below.) The quality ratings given above pertain to the mid-range of the measured source conditions for 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 Wearb 

### 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 determinedfor 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 emissions 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 and 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 material is reduced in proportion to the traffic volume; and 4. The moisture content of the road surface material varies between the extremes observed in the area. The CHIEF Web site (http://www.epa.gov/ttn/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 relatively short stretches of unpaved road with at least several hundred vehicle passes per day. Furthermore, if the newly paved road is located near unpaved areas or is used to transport material, it is essential that the control plan address routine cleaning of the newly paved road surface. The control efficiencies achievable by paving can be estimated by comparing emission factors for unpaved and paved road conditions. The predictive emission factor equation for paved roads, given in Section 13.2.1, requires estimation of the silt 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 (berms) also must be taken into account in estimating the control efficiency of paving. Other improvement methods cover the road surface with another material that has a lower silt content. Examples include placing gravel or slag on a dirt road. Control efficiency can be estimated by comparing the emission factors obtained using the silt contents before and after improvement. The silt content of the road surface should be determined 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 relationship between the instantaneous control efficiency due to watering and the resulting increase insurface 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 dries, characterization of emissions from watered roadways is best done by collecting road surface material samples at various times between water truck passes. (Appendices C.1 and C.2 present the sampling and analysis procedures.) The moisture content measured can then be associated with a control 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 evaporation" throughout the United States. Figure 13.2.2-4 presents the geographical distribution of the percentageof 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 roaddust 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 beenthe 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. 20Several 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 appropriate. 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 exhaust, 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.