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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 |
| 关键词 | Bayesian hierarchical modelExceedance probabilityExposure mapGMRFGRFINLAParticulate matter |
| 语种 | 英语 |
| scopus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; concentration (composition); particulate matter; spatiotemporal analysis; territory; aerosol; article; artifact; cross validation; Italy; optical depth; particulate matter 10; population exposure; prediction; probability; stochastic model; uncertainty; validation process; Italy |
| 来源期刊 | ATMOSPHERIC ENVIRONMENT
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/248581 |
| 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). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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