Emissions Modeling with MOVES and EMFAC to Assess the Potential for a Transportation Project to Create Particulate Matter Hot Spots

Transcription

1 0 0 Emissions Modeling with MOVES and EMFAC to Assess the Potential for a Transportation Project to Create Particulate Matter Hot Spots Stephen Reid*, Song Bai, Yuan Du, Garnet Erdakos, Lynn Baringer, Douglas Eisinger, Michael McCarthy Sonoma Technology, Inc. N. McDowell Blvd., Suite D Petaluma, CA -0 Tel: Fax: sreid@sonomatech.com, sbai@sonomatech.com, ydu@sonomatech.com, gerdakos@sonomatech.com, lbaringer@sonomatech.com, doug@sonomatech.com, mmcarthy@sonomatech.com Karin Landsberg Washington State Department of Transportation Maple Park Ave. SE Olympia, WA 0 Tel: 0.0. Fax: landsbk@wsdot.wa.gov *Corresponding author Word count:,0 words text + tables/figures x 0 words (each) =,0 words July, 0

2 Reid et al. 0 ABSTRACT In particulate matter (PM) nonattainment and maintenance areas, quantitative hot-spot analyses are required to assess air quality impacts of transportation projects that are identified as projects of local air quality concern (POAQC). In its 00 rulemaking, the U.S. Environmental Protection Agency (EPA) identified sample projects that would likely be POAQCs, including a new highway project with annual average daily traffic (AADT) greater than,000 and at least % diesel truck traffic. The objective of this study was to identify project characteristics that can reasonably exclude the project from consideration as a POAQC. Scenario analyses were performed for a hypothetical project that featured a freeway with high-occupancy vehicle (HOV) lanes being added and baseline traffic activity of,000 AADT and % diesel truck traffic. The MOVES and EMFAC emissions models were used to quantify PM and PM. emissions for a 00 analysis and to evaluate the impact of fleet turnover and truck percentages on project-level emissions from 00 to 0. The team found that fleet turnover effects sharply reduce projectlevel PM. emissions over time. For an analysis year of 0, impacts from a highway project with,000 AADT and % trucks are approximately 0% less than impacts from such a project in 00. In contrast, fleet turnover effects do not substantially reduce PM emissions, as reentrained road dust emissions and tire wear and brake wear emissions increasingly dominate project-level inventories over time, and these emissions vary little by analysis year. Keywords: PM, hot-spot analysis, project-level, future-year projections, MOVES, EMFAC

3 Reid et al INTRODUCTION Motor vehicle traffic is a major source of particulate matter (PM) emissions in the United States, with vehicles contributing to direct emissions of PM with an aerodynamic diameter smaller than. μm (PM. ) and PM with an aerodynamic diameter smaller than μm (PM ), as well as to the secondary formation of PM. in the atmosphere. Roadway-related sources of vehicular PM emissions include exhaust, the mechanical wear of tires and brakes, and the resuspension of road dust (). In general, motor vehicles contribute % to % of total PM. emissions in urban areas in the United States (). These emissions can result in elevated PM concentrations in the near-road environment; key factors controlling concentration levels are emission rates, which depend on traffic volume, speed, and vehicle fleet mix, and meteorological conditions (e.g., wind speed and atmospheric stability) (, ). Atmospheric reactions, deposition, and production also affect concentrations but occur on much longer time scales (). Because of the potential for localized hot-spots of elevated PM concentrations in the near-road environment, hot-spot analyses assessing the potential near-field air quality impacts of transportation projects may be required in areas that historically have not met one or more of the federal National Ambient Air Quality Standards (NAAQS) for PM or PM.. The goal of such hot-spot analyses is to demonstrate that a transportation project meets Clean Air Act (CAA) transportation conformity requirements set by the U.S. Environmental Protection Agency (EPA). These requirements ensure that federally supported transportation projects will meet state and local goals with respect to attaining and maintaining relevant NAAQS. In March 00, the EPA issued a final rule called PM. and PM Hot-Spot Analysis in Project-Level Transportation Determinations for the New PM. and Existing PM National Ambient Air Quality Standards (PM hot-spot rule) (). With the final PM hot-spot rule, the EPA and the Federal Highway Administration (FHWA) also jointly released Transportation Conformity Guidance for Qualitative Hot-spot Analyses in PM. and PM Nonattainment Areas, which served as interim guidance until appropriate methods could be developed for quantitative assessments (). In 0, the EPA issued Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM. and PM Nonattainment and Maintenance Areas (PM quantitative hot-spot analysis guidance) (). The 00 PM hot-spot rule, supported by the 0 PM quantitative hot-spot analysis guidance, requires quantitative PM hot-spot analyses to assess near-road air quality impacts of selected transportation projects in PM and PM. nonattainment and maintenance areas. Quantitative PM hot-spot analyses are required for projects of local air quality concern (POAQC), which typically include projects with substantial diesel truck or bus activity. The 00 final rule lists types of projects that are of air quality concern [see 0 CFR.(b)()], and in the preamble to this rulemaking, the EPA provided the following examples to illustrate potential POAQCs: A project on a new highway or expressway that serves a significant volume of diesel truck traffic, such as facilities with annual average daily traffic (AADT) greater than,000, where % or more of such AADT is diesel truck traffic New exit ramps and other highway facility improvements to connect a highway or expressway to a major freight, bus, or intermodal terminal Expansion of an existing highway or other facility that affects a congested intersection (operated at Level-of-Service D, E, or F) that has a significant increase in the number of diesel trucks

4 Reid et al Similar highway projects that involve a significant increase in the number of diesel transit buses and diesel trucks The 00 PM hot-spot rule stated that an interagency consultation process among departments of transportation (DOTs), the EPA, and state and local agencies should be used to identify projects needing PM hot-spot analyses (). If a project is a POAQC, conducting a PM hot-spot analysis requires several major work steps: gathering diesel and gasoline vehicle travel data, estimating emissions via the EPA s MOVES model or the California Air Resources Board s (CARB) EMFAC model (in California only), acquiring and processing meteorological and background PM data, running an air dispersion model such as AERMOD, and processing model output. Real-world examples of this process include an analysis of the interchange of I- with Southport Road by the Indiana Department of Transportation () and an analysis of the High Desert Corridor project by the California Department of Transportation (). Because the steps involved in a POAQC analysis involve data collection efforts and complex modeling tasks, a complete analysis may take several months. Moreover, the proposed project and its build alternatives may be revised during the analysis process, requiring additional data collection and modeling work. Therefore, transportation project analysts need information to help identify projects that are not likely to be POAQCs. To help provide such information, the team performed scenario analyses for a hypothetical transportation project, which was roughly based on a hypothetical project developed by the EPA for PM hot-spot analysis training purposes. This hypothetical project features a freeway with high-occupancy vehicle (HOV) lanes being added in each direction. For this work, traffic activity data developed by the EPA was adjusted to match the POAQC example of,000 AADT and % diesel truck traffic. From this starting point, the team estimated PM and PM. emissions for the hypothetical project for a 00 base year (to match the year of EPA s rulemaking), for additional analysis years ranging from 00 to 0, and for a range of vehicle fleet compositions (i.e., percentage of diesel trucks). The team then compared scenario-specific emission results with the 00 baseline results to evaluate the impact of fleet turnover (i.e., the introduction of newer, cleaner vehicles into the fleet over time) and truck percentages on potential project-level air quality impacts. These analyses were designed to provide answers to questions such as: Given the 00 rulemaking year, what PM and PM. emission levels might be expected of a 00 project with AADT of,000 and at least % diesel truck traffic? How would a 00 project with AADT of,000 and % trucks compare to the 00 project in terms of PM emission levels and concentrations? What is the influence of diesel truck percentage on project-level air quality impacts, and how might fleet turnover effects offset those impacts? What is the contribution of non-exhaust emissions processes (e.g., re-entrained dust, tire wear, brake wear) to project-level air quality impacts, and how do those contributions vary for different analysis years? Scenario analyses were performed using the MOVES and EMFAC models to quantify exhaust, tire wear, and brake wear emissions, and methods from the EPA s AP- emission factors handbook () were used to estimate emissions from re-entrained road dust.

5 Reid et al METHODS The EPA s PM quantitative hot-spot analysis guidance outlines the technical requirements for completing these project-level assessments. Specifically, EPA guidance describes nine analysis steps, of which the first four are relevant for this work.. Determine the need for a PM hot-spot analysis This step involves identifying whether the project of interest is a POAQC, which would trigger the need for a hot-spot analysis. This determination must be made according to transportation conformity regulation requirements, including interagency consultation. The main purpose of this study is to offer insights that help with making POAQC determinations.. Determine the approach, models, and data to be used This step determines general analysis scales and approaches, such as the relevant PM NAAQS, the project area to be analyzed, emissions and dispersion models to be used, project-specific data sources, and the schedule for conducting the analysis and for points of consultation.. Estimate on-road motor vehicle emissions This step focuses on emissions modeling, which includes preparing project-level traffic data and using the EPA s MOVES model or the CARB s EMFAC model to estimate exhaust, tire wear, and brake wear emissions from onroad vehicles.. Estimate emissions from road dust, construction, and additional sources This step involves estimating emissions from other emissions sources, such as re-entrained road dust, when applicable. Typically, road dust is of concern for PM impacts rather than PM. impacts. For a hypothetical freeway project with,000 AADT and,000 diesel trucks (% of the AADT), the team estimated mobile source PM and PM. emissions for 00 and later analysis years (0 through 0) using the MOVES0 and EMFAC0 emissions models. For consistency, the team used a common set of assumptions to configure MOVES and EMFAC. For example, the analysis time period for each year modeled was a January weekday (to represent worst-case conditions for PM emissions), and meteorological data (hourly temperature and relative humidity) for Fresno County, California, were used as input for both MOVES and EMFAC. For speed data, the team used the average speed values by roadway link from the EPA s original sample project. These speeds were not adjusted for scenarios with increased AADT so that the impact of changes in traffic volumes alone could be isolated. The 00 analysis serves as a baseline comparison point for all other analysis scenarios; the project s traffic activity corresponds to what the EPA identified as a POAQC in its 00 PM hot-spot rule. For this project-level assessment, the team used both models to generate emission rates (e.g., grams of PM. per mile) for exhaust, tire wear, and brake wear processes. The team then combined these emission rates with project-level activity data to estimate emissions. In addition, re-entrained dust emissions were calculated using a CARB method () for paved-road dust emissions that is based on Chapter.. of the EPA s AP- emission factors handbook (). The AP- road dust emissions equation requires several inputs, including roadway silt loading, average weight of vehicles accessing the road, and precipitation data. Values for average vehicle weight were derived from the fleet information in MOVES and EMFAC, so road dust emissions vary slightly between the models. To gain further insight into project characteristics that would likely exclude a project from consideration as a POAQC, the team developed various project scenarios and estimated

6 Reid et al. 0 their emissions for comparison with 00 baseline values. The scenarios account for impacts of fleet turnover, as well as changes in total traffic and diesel truck volumes (Table ). TABLE Summary of Emissions Modeling Scenarios Scenario Description Fleet turnover,000 AADT with % trucks in 00, 0, 0, 00, 0, 00, 0 Increased AADT 0, 0, 0, 0, 0% increases in the overall AADT of,000 vehicles in 00, 0, 0, 00, 0, 00, 0 (% trucks for each scenario) Higher fraction of trucks,000 AADT with 0 and 0% trucks in 00, 0, 0, 0 RESULTS 00 Baseline Emissions For PM, total 00 MOVES- and EMFAC-based emissions estimates for the hypothetical project are nearly identical, totaling 0.0 and. kg/day, respectively. For both sets of emissions, tire wear and re-entrained road dust emissions are about equal, with approximately half of the total PM emissions being associated with road dust (Figure ). However, MOVES exhaust emissions estimates for 00 are about 0% higher than the EMFAC-based estimates, and EMFAC brake wear estimates are. times higher than the MOVES-based estimates. For PM., total MOVES- and EMFAC-based emissions (including re-entrained road dust) for the 00 hypothetical project are. and. kg/day, respectively. Because MOVES and EMFAC do not calculate re-entrained road dust emissions directly, the team used the AP- method to calculate emissions from this process, which were then added to MOVES and EMFAC emissions estimates for exhaust, tire wear, and brake wear. Road dust emissions would typically not be considered for a PM. hot-spot analysis unless road dust represented a significant PM. source in the project region. MOVES- and EMFAC-based PM. emissions without road dust total. and. kg/day, respectively. For subsequent analyses shown in this paper, PM. emissions will include only exhaust, brake wear, and tire wear components. For PM., MOVES produces higher exhaust emissions than EMFAC, while EMFAC produces much higher brake wear emissions. In MOVES, a PM /PM. brake wear ratio of is assumed, while EMFAC uses a PM /PM. ratio of.; this difference results in EMFAC-based PM. brake wear emissions that are eight times higher than the MOVES-based estimate.

7 Reid et al. 0 0 FIGURE Baseline PM (left) and PM. (right) emissions for a hypothetical 00 freeway project with an AADT of,000 vehicles, % of which are diesel trucks. The re-entrained road dust emission levels shown in Figure can vary by region, even if projects have similar traffic activity, because silt loading values are region-specific. However, these 00 emission levels provide a useful baseline illustration for understanding impacts associated with the EPA s hypothetical highway project with,000 AADT and,000 diesel trucks, as well as evaluating traffic activity levels required to produce similar project-level emissions in other years. Fleet Turnover Scenarios Beyond 00, vehicle exhaust emissions decrease significantly as a result of federal and California emissions standards. To examine the impacts of fleet turnover, the team estimated PM and PM. emissions for several analysis years from 0 to 0, holding the vehicle fleet constant at,000 AADT and % diesel trucks. For PM, MOVES-based emissions estimates for the hypothetical project decrease from 0.0 kg/day in 00 to. kg/day in 0, a reduction of about %. EMFAC-based PM estimates decrease from. kg/day to. kg/day, a reduction of about %. PM emissions reductions are associated with the exhaust portion of the emissions inventory, with emissions for tire wear, brake wear, and re-entrained road dust remaining nearly constant across all analysis years (Figure ). For both the MOVES and EMFAC models, tire wear and brake wear emission rates change little over time, so the emissions from these processes do not decrease with fleet turnover, as is the case with exhaust emissions. As a result of these trends, the contribution of the non-exhaust processes increases sharply over time, rising from % in 00 to % in 0 for MOVES-based estimates. Notably, the contribution of re-entrained road dust alone rises from % in 00 to % in 0. For EMFAC-based estimates, a sharp decrease in exhaust emissions occurs between 0 and 0 due to the impact of California diesel regulations. After 0, further fleet turnover benefits are minimal, and project-level emissions remain nearly constant. For the MOVES-based estimates, decreases in exhaust emissions are more gradual over time; however, project-level

8 Reid et al. PM emissions change little beyond 00. These findings suggest that, for PM, fleet turnover benefits are largely limited to near-term years, and project-level emissions are increasingly dominated by non-exhaust processes (especially re-entrained road dust) over time. 0 0 FIGURE PM emissions for a hypothetical freeway project with an AADT of,000 vehicles, % of which are diesel trucks. For PM., re-entrained road dust is less frequently considered, and project-level emissions are more influenced by exhaust emissions than is the case with PM. MOVES-based PM. emissions estimates for the hypothetical project are cut approximately in half between 00 and 0 and are reduced by % between 00 and 0 (decreasing from. kg/day to 0. kg/day). EMFAC-based PM. estimates are also cut approximately in half between 00 and 0 and are reduced by about 0% between 00 and 0 (decreasing from. kg/day to. kg/day). Figure shows that EMFAC-based brake wear PM. emission estimates are consistently about eight times higher than MOVES-based estimates, limiting the overall reduction in project-level emissions. In addition, the contribution of brake wear and tire wear to the overall EMFAC-based PM. inventory rises from 0% in 00 to % in 0. For MOVES, the increase in the contribution of these processes to the overall inventory is less pronounced but also significant, rising from % in 00 to % in 0. As was the case for PM, EMFAC-based exhaust PM. emissions decrease sharply between 0 and 0, resulting in project-level emissions that remain nearly constant after 0. For the MOVES-based estimates, decreases in exhaust emissions are more gradual over time, and significant fleet turnover benefits are observed in 00 and 0. These findings suggest that, for projects outside California, fleet turnover results in sharp PM. reductions over time for the hypothetical project with,000 AADT and % trucks. However, these fleet turnover benefits are somewhat limited for California projects due to the high brake wear emissions estimates produced by EMFAC and the modest decreases in exhaust emissions that occur after 0.

9 Reid et al. 0 0 FIGURE PM. emissions for a hypothetical freeway project with an AADT of,000 vehicles, % of which are diesel trucks. Another important finding related to these PM and PM. emissions results is that the most uncertain aspects of the emissions inventories become more important over time. Although exhaust emissions have been researched extensively through engine-testing programs, relatively little research has focused on PM emissions from re-entrained road dust, tire wear, and brake wear. Tire and brake wear emission rates in the MOVES and EMFAC models are based on data from two published studies (, ), and the large differences in brake wear emissions estimates between the two models highlight the uncertainty associated with these estimates. Increased AADT The next set of scenarios modeled increased overall AADT while holding the truck percentage constant at %. For analysis years 00 to 0, the team evaluated emissions for overall traffic volumes ranging from,000 to 0,000 AADT, with truck volumes ranging from,000 (% of,000) to 0,000 (% of 0,000). For each scenario evaluated, PM emissions calculations include re-entrained road dust emissions, while PM. emissions do not include road dust. For a given fleet mix and analysis year, and constant travel speeds, a linear relationship exists between traffic volumes and emissions (Figure ). For example, modeling the 00 sample project with MOVES and using an AADT of 0,000 vehicles (a 0% increase over the,000 baseline) results in a PM emissions estimate of kg/day, which is a 0% increase over the baseline value of 0 kg/day. Almost all the substantial decrease in emissions across all traffic volumes occurs by 0. In addition, with both MOVES and EMFAC, doubling the traffic volume to 0,000 AADT in 0 produces PM. emissions that are lower than the PM. emissions produced by a volume of,000 AADT in 00. This further illustrates the fleet turnover benefit of PM. estimates shown earlier (Figure ). However, for PM (where re-entrained road dust is a key emissions source), doubling traffic volumes produces emissions estimates that are higher than the 00 baseline across all analysis years.

10 Reid et al. 0 FIGURE PM emissions estimates by AADT and analysis year. PM emissions include reentrained road dust, while PM. emissions do not. In addition, because of fleet turnover effects, producing emission levels equivalent to 00 requires higher traffic volumes in later years. To examine this effect, the team held the truck percentage constant at % and calculated the overall AADT required to generate emissions totals in later years that are equivalent to the 00 baseline emissions. Producing project-level PM emissions equivalent to those from the 00 analysis year in 00 would require traffic volumes of,000 vehicles for MOVES-based analyses and,000 vehicles for EMFAC-based analyses (Figure ). By 0, traffic volumes of 00,000 for MOVES-based analyses and,000 for EMFAC-based analyses would be required to match 00 emission levels, increases of 0% and 0%, respectively. For PM. emissions, which are dominated by exhaust emissions and, therefore, impacted to a greater extent by fleet turnover, the changes in traffic volumes are even more extreme. By 00, MOVES-based PM. estimates would require an AADT of 00,000 vehicles to reach 00 emission levels, while EMFAC-based PM. estimates would require an AADT of 0,000 to reach 00 emission levels (also shown in Figure ). Note that this analysis illustrates traffic volumes required to produce emissions equivalent to the 00 baseline scenario, holding travel speeds constant. Actual project analyses would adjust traffic speeds to appropriately reflect roadway capacity and traffic volume changes specific to the project s characteristics. By 0, reaching 00 emission levels requires an AADT of. million vehicles for MOVES-based analyses; this number is almost times higher than the baseline volume of,000 vehicles. For EMFAC-based PM. estimates, a 0 AADT of approximately 0,000 vehicles would be required to reach 00 emission levels; this number is more than three times higher than the baseline volume. The differences in MOVES- and EMFAC-based traffic volumes are primarily driven by the higher brake wear emissions estimated by EMFAC, as brake wear emissions are not impacted by fleet turnover and change little by analysis year.

11 Reid et al. FIGURE Projected traffic volumes needed to produce 00-equivalent emissions. (Scales are different for MOVES- and EMFAC-estimated PM. emissions.) Increased Diesel Truck Traffic The final set of scenarios modeled increased truck percentage for the baseline traffic volume of,000 vehicles. For analysis years 00, 0, 0, and 0, the team evaluated emissions for truck percentages of %, 0%, and 0%. MOVES- and EMFAC-based results are calculated for PM (Figure ). Because increased truck volumes significantly impact both exhaust and reentrained road dust emissions, the overall PM emissions inventories increase sharply as the truck percentage increases. Increasing the truck percentage from % to 0% results in MOVESpredicted PM emissions increasing by a factor of across all future projected years. In addition, for both MOVES and EMFAC, modeling a truck percentage of even 0% results in total PM emissions across all analysis years that are higher than the 00 baseline of 0 kg/day (based on % trucks in 00).

12 Reid et al. FIGURE Projected PM emissions changes associated with increased truck volume in future year scenarios. For PM., in contrast, the absence of re-entrained road dust emissions and the considerable decrease in exhaust emissions over time offsets the impact of increased truck traffic volumes. For example, in 0, total MOVES-based PM. emissions for the 0% truck scenario are less than the 00 baseline PM. emissions with % trucks. For the 0% truck scenario, by 00, total MOVES-based PM. emissions are less than the 00 baseline emissions (Figure ). Similarly, for the EMFAC-based PM. results, by 0, emissions for both the 0% and 0% truck scenarios are less than the 00 baseline. These findings indicate that a current-year (0) California transportation project with,000 AADT and 0% trucks has lower PM. impacts than the hypothetical 00 POAQC with,000 AADT and % trucks.

13 Reid et al. 0 FIGURE Projected PM. emissions changes associated with increased truck volume in future year scenarios. DISCUSSION The results of this study yield a number of key insights that may be helpful in POAQC determinations. First, the results highlight the importance of project location and relevant NAAQS standards to POAQC determinations. For projects in PM nonattainment areas, reentrained road dust emissions (and, to a lesser extent, tire wear and brake wear emissions) increasingly dominate project-level inventories over time, and these emissions vary little by analysis year. Therefore, fleet turnover effects and congestion relief will neither provide significant emissions reductions over time, nor allow build scenarios to compare favorably with no-build scenarios. However, for projects in PM. nonattainment areas, the picture is very different. Exhaust emissions dominate the project-level inventory (especially for MOVES-based analyses), and for the year 0, impacts from a highway project with,000 AADT and % trucks are already approximately 0% less than impacts from such a project in 00. In addition, fleet turnover means that, by 00 and beyond, even projects with,000 AADT and 0% (0,000) diesel trucks are likely to produce PM. emissions equivalent to or less than the emissions from the 00 baseline project with,000 trucks. Another important insight is the linear relationship between traffic activity and PM emissions (assuming a consistent vehicle fleet and travel speeds). This linear relationship, combined with the various scenarios analyzed for this study, may allow project analysts to quickly estimate PM impacts associated with their project and compare those impacts with the 00 hypothetical project. For example, suppose an analyst is reviewing a highway project with

14 Reid et al. 0 a 0 analysis year in a PM. nonattainment area. The analyst could use the data illustrated in Figures through to qualitatively assess where the project s traffic volumes for diesel and other vehicles are comparable, and whether the 0 volumes would be expected to result in emissions substantially less than the 00 baseline case developed here. The analyst could then use such a comparison during the conformity interagency consultation process to help determine whether the project was a POAQC that required the more rigorous evaluation steps established by the EPA. Another important insight for POAQC determinations is that current emissions modeling techniques have limitations with regard to estimates of emissions from re-entrained road dust, tire wear, and brake wear; with time, these processes will become increasingly important at the project level. Additional research is needed to refine the team s understanding of these emissions and to assess and, as needed, improve the associated modeling techniques for these processes. ACKNOWLEDGMENTS The authors would like to acknowledge funding support from participants in the Near- Road Air Quality Research Pooled Fund. Pooled Fund participants include Departments of Transportation from the states of Arizona, California, Texas, Virginia, and Washington, as well as the Federal Highway Administration. The objective of the Pooled Fund is to expand knowledge regarding near-road air quality issues and improve the ability of state DOT staff to address those issues. The findings and conclusions included in this paper are those of the authors and do not necessarily reflect the requirements or position of any governmental agency.

15 Reid et al REFERENCES. Gertler, A. W., J. A. Gillies, and W. R. Pierson. An assessment of the mobile source contribution to PM and PM. in the United States. Water Air and Soil Pollution, (-), 0-, Oct.. McCarthy, M. C., D. S. Eisinger, H. R. Hafner, L. R. Chinkin, P. T. Roberts, K. N. Black, N. N. Clark, P. H. McMurry, and A. M. Winer. Particulate matter: a strategic vision for transportationrelated research. Environ. Sci. Technol., 0(), -, doi:./es0i (STI- 00-), September. Available at Zhu, Y., W. C. Hinds, S. Kim, S. Shen, and C. Sioutas. Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmos. Environ., (), -, doi:./s- (0)00-0, September.. Baldauf, R., E. Thoma, M. Hays, R. Shores, J. S. Kinsey, B. Gullet, S. Kimbrough, V. Isakov, T. Long, R. Snow, A. Khlystov, J. Weinstein, F.-L. Chen, R. Seila, D. Olson, I. Gilmour, S.-H. Cho, N. Watkins, P. Rowley, and J. Bang. Traffic and meteorological impacts on near-road air quality: summary of methods and trends from the Raleigh near-road study. J. Air Waste Manage.,, -, July.. Hagler, G. S. W., R. W. Baldauf, E. D. Thoma, T. R. Long, R. F. Snow, J. S. Kinsey, L. Oudejans, and B. K. Gullett. Ultrafine particles near a major roadway in Raleigh, North Carolina: downwind attenuation and correlation with traffic-related pollutants. Atmos. Environ.,, -, doi:./j.atmosenv U.S. Environmental Protection Agency. PM. and PM hot-spot analyses in project-level transportation conformity determinations for the new PM. and existing PM national ambient air quality standards, EPA-HQ-OAR Final rule prepared by the U.S. Environmental Protection Agency, Research Triangle Park, NC, 00.. U.S. Environmental Protection Agency, and Federal Highway Administration. Transportation conformity guidance for qualitative hot-spot analyses in PM. and PM nonattainment and maintenance areas, EPA0-B U.S. Environmental Protection Agency. Transportation conformity guidance for quantitative hotspot analyses in PM. and PM nonattainment and maintenance areas: Appendices, EPA-0-B Guidance document prepared by the Transportation and Regional Programs Division, Office of Transportation and Air Quality, 0.. Indiana Department of Transportation. I- Section PM. hot spot case study. 0.. California Department of Transportation. Quantitative PM hot-spot analysis: high desert corridor, EA: 0-00U. Prepared by California Department of Transportation, District, Division of Design, Office of Environmental Design, Los Angeles, CA, 0.. U.S. Environmental Protection Agency. Emissions Factors & AP California Air Resources Board. Section., Entrained paved road dust, paved road travel (Updated July )... Garg, B. D., S. H. Cadle, P. A. Mulawa, P. J. Groblicki, C. Laroo, and G. A. Parr. Brake wear particulate matter emissions. Environ. Sci. Technol., (), -.. Sanders, P. G., N. Xu, T. M. Dalka, and M. M. Maricq. Airborne brake wear debris: Size distributions, composition, and a comparison of dynamometer and vehicle tests. Environ. Sci. Technol., (), 00-0, Sep.