Long-term Particulate Matter Modeling for Health Effects Studies in California – Part II: Concentrations and Sources of Ultrafine Organic Aerosols
Jianlin Hu1, Shantanu Jathar2, Hongliang Zhang3, Qi Ying4, Shu-Hua Chen5, Christopher D. Cappa6, and Michael J. Kleeman61Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China 2Department of Mechanical Engineering, Colorado State University, Fort Collins CO, USA 3Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge LA, USA 4Zachry Department of Civil Engineering, Texas A&M University, College Station TX, USA 5Department of Land, Air, and Water Resources, University of California, Davis. One Shields Avenue, Davis, CA, USA 6Department of Civil and Environmental Engineering, University of California, Davis. One Shields Avenue, Davis CA, USA
Received: 08 Oct 2016 – Accepted for review: 06 Dec 2016 – Discussion started: 12 Dec 2016
Abstract. Organic aerosol (OA) is a major constituent of ultrafine particulate matter (PM0.1). Recent epidemiological studies have identified associations between PM0.1 OA and premature mortality and low birth weight. In this study, the source-oriented UCD/CIT model was used to simulate the concentrations and sources of primary organic aerosols (POA) and secondary organic aerosols (SOA) in PM0.1 in California for a 9-year (2000–2008) modeling period with 4 km horizontal resolution to provide more insights about PM0.1 OA for health effects studies. As a related quality control, predicted monthly average concentrations of fine particulate matter (PM2.5) total organic carbon at six major urban sites had mean fractional bias of −0.31 to 0.19 and mean fractional errors of 0.4 to 0.59. The predicted ratio of PM2.5 SOA/OA was lower than estimates derived from chemical mass balance (CMB) calculations by a factor of 2 ~ 3, which suggests the potential effects of processes such as POA volatility, additional SOA formation mechanism, and missing sources. OA in PM0.1, the focus size fraction of this study, is dominated by POA. Wood smoke is found to be the single biggest source of PM0.1 OA in winter in California, while meat cooking, mobile emissions (gasoline and diesel engines), and other anthropogenic sources (mainly solvent usage and waste disposal) are the most important sources in summer. Biogenic emissions are predicted to be the largest PM0.1 SOA source, followed by mobile sources and other anthropogenic sources, but these rankings are sensitive to the SOA model used in the calculation. Air pollution control programs aiming to reduce the PM0.1 OA concentrations should consider controlling solvent usage, waste disposal, and mobile emissions in California, but these findings should be revisited after the latest science is incorporated into the SOA exposure calculations. The spatial distributions of SOA associated with different sources are not sensitive to the choice of SOA model, although the absolute amount of SOA can change significantly. Therefore, the spatial distributions of PM0.1 POA and SOA over the 9-year study period provide useful information for epidemiological studies to further investigate the associations with health outcomes.
Hu, J., Jathar, S., Zhang, H., Ying, Q., Chen, S.-H., Cappa, C. D., and Kleeman, M. J.: Long-term Particulate Matter Modeling for Health Effects Studies in California – Part II: Concentrations and Sources of Ultrafine Organic Aerosols, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2016-903, in review, 2016.