A1168
Title: Spatio-temporal joint modelling on moderate and extreme air pollution in Spain
Authors: Chengxiu Ling - Xi\'an Jiaotong-Liverpool University (China) [presenting]
Abstract: Very unhealthy air quality may cause numerous diseases. Extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for environmental agencies. The aim is to model the spatial and temporal pattern of both moderate and extreme PM10 from 342 monitors throughout mainland Spain from 2017 to 2021. Bayesian hierarchical generalized extreme models of annual maxima PM10, including both fixed effects and spatiotemporal random effect with SPDE-AR(1) model, are proposed. The similar and different effects of interrelated factors are identified through a joint Bayesian model of annual mean and annual maxima PM10, which may bring the power of statistical inference of body data to the tail analysis with the implementation of the INLA algorithm. Under WAIC, DIC and other criteria, the best model is selected with good predictive ability. The findings are applied to identify the hot-spot regions with extremely poor air quality using excursion functions. It suggests that the community of Madrid and the northwestern boundary of Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold. The joint model also provides evidence that precipitation, vapour pressure and population density influence comparably while altitude and temperature impact oppositely.
