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• W2022237285 abstract "Scientific evidence has increasingly shown an association between particulate matter (PM) and adverse human health impacts. Accurately predicting near-road PM 2.5 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 PM 2.5 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 PM 2.5 concentrations. Screen plots and statistical tests indicated that, at the Sacramento site, CALINE4 and CAL3QHC performed moderately well, while AERMOD under-predicted PM 2.5 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." @default.
• W2022237285 created "2016-06-24" @default.
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• W2022237285 date "2009-01-01" @default.
• W2022237285 modified "2023-09-24" @default.
• W2022237285 title "Predicting Near-Road PM_2.5 Concentrations" @default.
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• W2022237285 doi "https://doi.org/10.3141/2123-04" @default.
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