title: Estimating Spatiotemporal Variability of Ambient Air Pollutant Concentrations with A Hierarchical Model.
abstract: Studies have linked exposure to air pollutants to short-term and sub-chronic health outcomes. However, individual-level air pollution exposure is difficult to measure at a high spatial and temporal resolution and for larger populations due to limitations in sampling techniques. We presented a hierarchical model to capture spatiotemporal variability of nitrogen dioxide (NO2) and nitrogen oxides (NOx) concentrations in Southern California by combining high temporal resolution data from routine monitoring stations with high spatial resolution data from investigator-initiated episodic measurements. In this model, the spatiotemporal field of concentrations was first decomposed into a mean and residual and the mean representing the seasonal trend was further decomposed into a constant and varying temporal basis functions. The mean of the spatially varying coefficients of temporal basis functions were modeled by local covariates using non-linear generalized additive model and least square fitting using measurements from both routine monitoring and additional episodic sampling locations, while the spatially-correlated residuals of the coefficients were co-kriged. We found traffic, land-use and wind accounted for a large portion of the variance (beyond 35%) for the long-term average trend of concentrations. Spatial residuals accounted for a large portion of the variance of the temporal components (about 30% for NO2and 20% for NOx). Leave-one-out cross validation produced an R2of 0.84 for NO2and 0.81 for NOxwhen comparing the modeled weekly concentration with the observed trends at all routine monitoring stations.
authors.affiliation: Program in Public Health, College of Health Sciences, University of California, Irvine, USA ; State Key Lab of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China.
authors.forename: Lianfa
authors.initials: L
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Wu
authors.forename: Jo Kay
authors.initials: JK
authors.lastname: Ghosh
authors.forename: Beate
authors.initials: B
authors.lastname: Ritz
keywords: air pollution; generalized additive model; nitrogen dioxide; nitrogen oxides; spatiotemporal variability; temporal trend