This paper illustrates the main results of a spatio-temporal interpolation process of ${\text{PM}}_{10}$ concentrations at daily resolution using a set of 410 monitoring sites, distributed throughout the Italian territory, for the year 2015. The interpolation process is based on a Bayesian hierarchical model where the spatial-component is represented through the Stochastic Partial Differential Equation (SPDE) approach with a lag-1 temporal autoregressive component (AR1). Inference is performed through the Integrated Nested Laplace Approximation (INLA). Our model includes 11 spatial and spatio-temporal predictors, including meteorological variables and Aerosol Optical Depth. As the predictors’ impact varies across months, the regression is based on 12 monthly models with the same set of covariates. The predictive model performance has been analyzed using a cross-validation study. Our results show that the predicted and the observed values are well in accordance (correlation range: 0.79–0.91; bias: 0.22–1.07$\mathrm{\mu g}/{\text{m}}^3$; RMSE: 4.9–13.9$\mathrm{\mu g}/{\text{m}}^3$). The model final output is a set of 365 gridded (1 km $\times$ 1 km) daily ${\text{PM}}_{10}$ maps over Italy equipped with an uncertainty measure. The spatial prediction performance shows that the interpolation procedure is able to reproduce the large scale data features without unrealistic artifacts in the generated ${\text{PM}}_{10}$ surfaces. The paper presents also two illustrative examples of practical applications of our model, exceedance probability and population exposure maps.

本文说明了时空插值过程的主要结果 ${\text{下午}}_{10}$使用一组410个监测点以每日分辨率对2015年的浓度进行分布，这些监测点分布在整个意大利领土上，用于2015年。插值过程基于贝叶斯层次模型，其中空间分量通过随机偏微分方程（SPDE）方法表示具有滞后1的时间自回归分量（AR1）。通过集成嵌套拉普拉斯逼近（INLA）进行推理。我们的模型包括11个时空预测因子，包括气象变量和气溶胶光学深度。由于预测变量的影响会随月份变化，因此回归是基于具有相同协变量集的12个每月模型。预测模型的性能已使用交叉验证研究进行了分析。$\mathrm{微克}/{\text{米}}^3$; RMSE：4.9–13.9$\mathrm{微克}/{\text{米}}^3$）。模型的最终输出是一组365网格（1 km$\times$ 每天1公里） ${\text{下午}}_{10}$带有不确定性度量的意大利地图。空间预测性能表明，插值过程能够重现大规模数据特征而不会在生成的图像中产生不真实的伪像${\text{下午}}_{10}$表面。本文还提供了我们模型的实际应用的两个说明性示例，即超越概率和人口暴露图。