Estimating spatiotemporal variability of ambient air pollutant concentrations with a hierarchical model

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 NO2 and 20% for NOx). Leave-one-out cross validation produced an R2 of 0.84 for NO2 and 0.81 for NOx when comparing the modeled weekly concentration with the observed trends at all routine monitoring stations.

► Hierarchical spatiotemporal model predicted weekly NO2 and NOx concentrations well.
► Episodic measurements supplemented routine time-series measurements in modeling.
► Land-use, meteorology, and roadway were important in concentration prediction.