a b s t r a c tFuture air pollution emissions in the year 2030 were estimated for the San Joaquin Valley (SJV) in central California using a combined system of land use, mobile, off-road, stationary, area, and biogenic emissions models. Four scenarios were developed that use different assumptions about the density of development and level of investment in transportation infrastructure to accommodate the expected doubling of the SJV population in the next 20 years. Scenario 1 reflects current land-use patterns and infrastructure while scenario 2 encouraged compact urban footprints including redevelopment of existing urban centers and investments in transit. Scenario 3 allowed sprawling development in the SJV with reduced population density in existing urban centers and construction of all planned freeways. Scenario 4 followed currently adopted land use and transportation plans for the SJV. The air quality resulting from these urban development scenarios was evaluated using meteorology from a winter stagnation event that occurred on December 15th, 2000 to January 7th 2001. Predicted base-case PM2.5 mass concentrations within the region exceeded 35 mg m À3 over the 22-day episode. Compact growth reduced the PM2.5 concentrations by w1 mg m À3 relative to the base-case over most of the SJV with the exception of increases (w1 mg m À3 ) in urban centers driven by increased concentrations of elemental carbon (EC) and organic carbon (OC).Low-density development increased the PM2.5 concentrations by 1e4 mg m À3 over most of the region, with decreases (0.5e2 mg m À3 ) around urban areas. Population-weighted average PM2.5 concentrations were very similar for all development scenarios ranging between 16 and 17.4 mg m À3 . Exposure to primary PM components such as EC and OC increased 10e15% for high density development scenarios and decreased by 11e19% for low-density scenarios. Patterns for secondary PM components such as nitrate and ammonium ion were almost exactly reversed, with a 10% increase under low-density development and a 5% decrease under high density development. The increased human exposure to primary pollutants such as EC and OC could be predicted using a simplified analysis of population-weighted primary emissions. Regional planning agencies should develop thresholds of population-weighted primary emissions exposure to guide the development of growth plans. This metric will allow them to actively reduce the potential negative impacts of compact growth while preserving the benefits.

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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.