title: Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.
abstract: Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, Washington, United States.
authors.forename: Meng
authors.initials: M
authors.lastname: Wang
authors.affiliation: Department of Statistics, University of Washington , Seattle, Washington, United States.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Sampson
authors.affiliation: Jiangsu 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.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Hu
authors.affiliation: Department of Civil and Environmental Engineering, University of California , Davis, California, United States.
authors.forename: Michael
authors.initials: M
authors.lastname: Kleeman
authors.affiliation: Department of Biostatistics, University of Washington , Seattle, Washington, United States.
authors.forename: Joshua P
authors.initials: JP
authors.lastname: Keller
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, Washington, United States.
authors.forename: Casey
authors.initials: C
authors.lastname: Olives
authors.affiliation: Department of Biostatistics, University of Washington , Seattle, Washington, United States.
authors.forename: Adam A
authors.initials: AA
authors.lastname: Szpiro
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, Washington, United States.
authors.forename: Sverre
authors.initials: S
authors.lastname: Vedal
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, Washington, United States.
authors.forename: Joel D
authors.initials: JD
authors.lastname: Kaufman
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis; D004784:Environmental Monitoring / Q000379:methods; D015141:Los Angeles; D008956:Models, Chemical; D008962:Models, Theoretical; D010126:Ozone / Q000032:analysis*; D052638:Particulate Matter / Q000032:analysis
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D010126:Ozone