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DOI	10.1016/j.atmosenv.2021.118192
	Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach
	Fioravanti G.; Martino S.; Cameletti M.; Cattani G.
发表日期	2021
ISSN	1352-2310
卷号	248
英文摘要	This paper illustrates the main results of a spatio-temporal interpolation process of PM10 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: 0.22–1.07μg/m3; RMSE: 4.9–13.9μg/m3). The model final output is a set of 365 gridded (1 km × 1 km) daily PM10 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 PM10 surfaces. The paper presents also two illustrative examples of practical applications of our model, exceedance probability and population exposure maps. © 2021 Elsevier Ltd
英文关键词	Hierarchical systems; Interpolation; Stochastic models; Stochastic systems; Aerosol optical depths; Bayesian hierarchical model; Exceedance probability; Laplace approximation; Meteorological variables; Spatio-temporal interpolations; Spatio-temporal modelling; Stochastic partial differential equation; Predictive analytics; aerosol; article; artifact; cross validation; Italy; optical depth; particulate matter 10; population exposure; prediction; probability; stochastic model; uncertainty; validation process
语种	英语
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169002
作者单位	Italian Institute for Environmental Protection and Research, Via Vitaliano Brancati 48, Rome, 00144, Italy; Norges Teknisk-Naturvitenskapelige Universitet, Trondheim, Norway; University of Bergamo, Bergamo, Italy
推荐引用方式 GB/T 7714	Fioravanti G.,Martino S.,Cameletti M.,et al. Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach[J],2021,248.
APA	Fioravanti G.,Martino S.,Cameletti M.,&Cattani G..(2021).Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach.Atmospheric Environment,248.
MLA	Fioravanti G.,et al."Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach".Atmospheric Environment 248(2021).