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 (NO_x) 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

SUBMITTER: Li L 

PROVIDER: S-EPMC3627373 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature