01126831

Component

https://doi.org/10.3141/2123-04

http://scholar.google.com...gett&publication_year=2009

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 underpredicted 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.

01147486

09-2258

English

pp 26-37

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309142526

Print

Figures (4) ; References (44) ; Tables (9)

Health hazards; Mathematical prediction; Particulates; Sampling

AERMOD (Computer model); CAL3QHC (Computer model); CALINE4 (Computer model)

Overprediction; Underprediction

London (England); Sacramento (California)

Environment; Highways; I15: Environment

TRIS, TRB, ATRI

Jan 30 2009 6:36PM

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