Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concentration measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the observed concentrations. Local regression models were specified to the concentration-distance relationship and analysis of variance was used to determine the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more standard background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to underpredict (be lower than) actual background. Changes in pollutant concentrations with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concentration by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter number concentrations); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concentrations).

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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 2006 rulemaking, the U.S. Environmental Protection Agency identified sample projects that would likely be POAQCs, including a new highway project with annual average daily traffic (AADT) greater than 125,000 and at least 8% diesel truck traffic. The objective of this study was to identify project characteristics that could reasonably exclude the project from consideration as a POAQC. Scenario analyses were performed for a hypothetical project that featured a new freeway with four mixed-flow lanes and baseline traffic activity of 125,000 AADT and 8% diesel truck traffic. The MO Vehicle Emission Simulator and the Emission FACtors models were used to quantify PM10 and PM2.5 emissions for a 2006 analysis and to evaluate the impact of fleet turnover and truck percentages on project-level emissions from 2006 to 2035. Fleet turnover effects sharply reduce project-level PM2.5 emissions over time. For an analysis year of 2015, impacts from a highway project with 125,000 AADT and 8% trucks are approximately 50% less than impacts from such a project in 2006. In contrast, fleet turnover effects do not substantially reduce PM10 emissions, since re-entrained 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.

Scientific evidence has increasingly shown an association between particulate matter (PM) and adverse human health impacts. Accurately predicting near-road PM2.5 concentrations is therefore important for project-level transportation conformity and health risk analysis. This study assessed the capability and performance of three dispersion models–-CALINE4, CAL3QHC, and AERMOD–-in predicting near-road PM2.5 concentrations. The comparative assessment included identifying differences among the three models in relation to methodology and data requirements. An intersection in Sacramento, California, and a busy road in London were used as sampling sites to evaluate how model predictions differed from observed PM2.5 concentrations. Screen plots and statistical tests indicated that, at the Sacramento site, CALINE4 and CAL3QHC performed moderately well, while AERMOD under-predicted PM2.5 concentrations. For the London site, both CALINE4 and CAL3QHC resulted in overpredictions when incremental concentrations due to on-road emission sources were low, while underpredictions occurred when incremental concentrations were high. The street canyon effect and receptor location likely contributed to the relatively poor performance of the models at the London site.

This article presents a new analysis approach to design and evaluate motor vehicle inspection and maintenance (I/M) programs. The new approach, called I/M-Design, uses real-world data to provide two resources not previously available: (1) a transparent framework to quantitatively illustrate the range of emission reductions available from I/M, and (2) a sensitivity analysis tool to evaluate how key variables affect I/M performance. In addition, the approach satisfies a policy-making information need-how to convey, in a logical and straightforward manner, the expected benefits from I/M without relying on modeling tools inaccessible to those outside the air quality field. The material presented in this article illustrates the new approach by estimating hydrocarbon (HC) emission reduction benefits available from enhanced

Accelerated penetration of on-road electric vehicles offers regional and community-scale air quality benefits; however, such benefits have not been previously quantified regarding environmental justice communities near major roads. This study evaluated six 2040 electric vehicle scenarios and quantified concentration reductions of nitrogen dioxide and fine particulate matter (diameter less than 2.5 µm) for southern California environmental justice communities near Interstate 710. Findings showed that aggressive electric vehicle penetration (85% electric vehicle share) reduced nitrogen dioxide and fine particulate matter concentrations more in communities with more people of color (1.9 ppb and 1.1 μg m−3) than in communities with more White residents (1.6 ppb and 0.94 μg m−3). Aggressive electric vehicle penetration reduced pollution exposure disparity by 30% for nitrogen dioxide and 14% for fine particulate matter. Disparity reductions were also found based on educational attainment. Results suggest policies that encourage accelerated electric vehicle penetration will address inequalities in air pollution and help achieve environmental justice.