AAAR 35th Annual Conference October 17 - October 21, 2016 Oregon Convention Center Portland, Oregon, USA
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Identifying PM2.5 and PM0.1 Sources and Estimating Their Health Impacts in the San Francisco Bay Area
XIN YU, Melissa Venecek, Michael Kleeman, University of California, Davis
Abstract Number: 561 Working Group: Source Apportionment
Abstract Numerous epidemiological studies have identified strong health associations with particles that have aerodynamic diameter < 2.5 µm (PM$_(2.5)). Multiple toxicity studies suggest that particles with aerodynamic diameter < 0.1 µm (PM$_(0.1)) may be even more dangerous to human health but confirmation of these results in epidemiology studies has been elusive. Accurate exposure estimates to PM$_(0.1) are difficult to generate partly because concentrations change over short distances which makes it impractical to use central site monitors to predict exposure for large populations. Furthermore, land use regression (LUR) models do not have enough input data to estimate exposure to PM$_(0.1). Recent studies have employed regional chemical transport models to provide the first realistic population exposure estimates for PM$_(0.1) over large populations suitable for epidemiology studies.
In this work, the University of California, Davis/California Institute of Technology (UCD/CIT) chemical transport model with 4km resolution was applied to predict PM$_(2.5) and PM$_(0.1) concentrations over the San Francisco Bay Area (SFBA) in California. Model predictions were compared to measurements of PM$_(2.5) mass and chemical composition to evaluate the accuracy of simulations. Predicted source contributions to primary PM$_(2.5) mass, PM$_(0.1) mass PM$_(0.1) EC, and PM$_(0.1) OC were compared to the analysis results from receptor-based Chemical Mass Balance (CMB) model. The contributions from most significant sources of PM$_(2.5) and PM$_(0.1) were assessed over the entire region with 4km resolution. The results from UCD/CIT model provided detailed spatial and temporal variations and enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of PM$_(2.5) and PM$_(0.1) in the SFBA.