Copula-based spatio-temporal modeling of air pollutant data incorporating covariate dependence

Abstract

Elevated levels of PM10 are known to cause severe respiratory and cardiovascular diseases, and, in extreme cases, cancer and mortality. Despite various reduction policies implemented across different sectors, PM10 concentrations in South Korea continue to exceed the annual recommended limit set by the World Health Organization. Spatio-temporal PM10 concentrations may exhibit both spatial and temporal dependence. Additionally, interactions between PM10 and environmental factors can further influence the variability in PM10. Therefore, this study proposes a method that incorporates the spatio-temporal neighbors of covariates alongside those of PM10 by adopting an approach that captures spatio-temporal interactions through spatio-temporal neighbors. Vine copula was used to integrate pairwise dependence structures between a given location and its surrounding spatio-temporal neighbors. We applied the model to weekly average PM10 data for South Korea in 2019, using PM2.5, CO, population density, nighttime light intensity, land-use mix and air temperature as covariates. As PM10 exhibited skewness, its marginal distribution was modeled using the Gumbel and Generalized Extreme Value distributions. The proposed model outperformed a spatio-temporal mixed effects model, a kriging method, and alternative copula-based approaches, particularly in predicting the top 5% of extreme values, by effectively capturing tail dependence crucial for extreme value analysis. This study highlights the importance of utilizing vine copula to effectively model diverse dependence structures in spatio-temporal data while simultaneously accommodating spatial and temporal dimensions, including spatio-temporal dependence among covariates. The results underscore the broader applicability of the proposed approach to other fields where complex dependence structures are present.