Spatiotemporal joint analysis of PM_2.5 and Ozone in California with INLA approach

The substantial threat of concurrent air pollutants to public health is increasingly severe under climate change. To identify the common drivers and extent of spatiotemporal similarity of PM_2.5 and ozone (O₃), this paper proposed a log Gaussian–Gumbel Bayesian hierarchical model allowing for sharing a stochastic partial differential equation and autoregressive model of order one (SPDE-AR(1)) spatiotemporal interaction structure. The proposed model, implemented by the approach of integrated nested Laplace approximation (INLA), outperforms in terms of estimation accuracy and prediction capacity for its increased parsimony and reduced uncertainty, especially for the shared O₃ sub-model. Besides the consistently significant influence of temperature (positive), extreme drought (positive), fire burnt area (positive), gross domestic product (GDP) per capita (positive), and wind speed (negative) on both PM_2.5 and O₃, surface pressure and precipitation demonstrate positive associations with PM_2.5 and O₃, respectively. While population density relates to neither. In addition, our results demonstrate similar spatiotemporal interactions between PM_2.5 and O₃, indicating that the spatial and temporal variations of these pollutants show relatively considerable consistency in California. Finally, with the aid of the excursion function, we see that the areas around the intersection of San Luis Obispo and Santa Barbara counties are likely to exceed the unhealthy O₃ level for USG simultaneously with other areas throughout the year. Our findings provide new insights for regional and seasonal strategies in the co-control of PM_2.5 and O₃. Our methodology is expected to be utilized when interest lies in multiple interrelated processes in the fields of environment and epidemiology.