Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We firstly propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017--2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold.

翻译：极度不健康的空气质量与多种疾病密切相关。针对潜在关联原因的分析及准确预测的需求日益增长，以期为环境机构制定公共政策策略提供建议。本文旨在对2017年至2021年间西班牙本土342个代表性监测站点采集的日均PM10浓度中，中度和极端高浓度值的时空模式进行建模。我们首先提出并比较了一系列关于年最大PM10浓度的贝叶斯层次广义极值模型，这些模型包含海拔、温度、降水量、水汽压和人口密度的固定效应，以及采用随机偏微分方程方法和滞后一阶动态自回归组件构建的时空随机效应。基于WAIC、DIC及其他准则，以2017-2020年前四年数据为训练集、2021年最后一年数据为测试集，选出了具有良好预测能力的最优模型。我们将最优模型的结构引入到年均与年最大PM10浓度的联合贝叶斯建模中，并证实某些预测因子（降水量、水汽压和人口密度）对不同尺度下的PM10浓度具有相似影响，而其他预测因子（海拔和温度）则呈现相反效应。研究结果通过网格尺度的超常函数识别出空气质量较差的热点区域，表明马德里自治区以及西班牙西北部和南部的部分地区可能暴露于严重空气污染中，同时超过预警风险阈值。