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利用 R-INLA 通过 SPDE 方法估算苏州地区的环境空气污染物水平。

Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA.

机构信息

Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.

Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.

出版信息

Int J Hyg Environ Health. 2021 Jun;235:113766. doi: 10.1016/j.ijheh.2021.113766. Epub 2021 May 24.

DOI:10.1016/j.ijheh.2021.113766
PMID:34044249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8223501/
Abstract

Spatio-temporal models of ambient air pollution can be used to predict pollutant levels across a geographical region. These predictions may then be used as estimates of exposure for individuals in analyses of the health effects of air pollution. Integrated nested Laplace approximations is a method for Bayesian inference, and a fast alternative to Markov chain Monte Carlo methods. It also facilitates the SPDE approach to spatial modelling, which has been used for modelling of air pollutant levels, and is available in the R-INLA package for the R statistics software. Covariates such as meteorological variables may be useful predictors in such models, but covariate misalignment must be dealt with. This paper describes a flexible method used to estimate pollutant levels for six pollutants in Suzhou, a city in China with dispersed air pollutant monitors and weather stations. A two-stage approach is used to address misalignment of weather covariate data.

摘要

可以使用时空模型来预测地理区域内的污染物水平。然后，可以将这些预测用作空气污染健康影响分析中个体暴露的估计值。集成嵌套拉普拉斯近似法是一种贝叶斯推断方法，是马尔可夫链蒙特卡罗方法的快速替代方法。它还促进了 SPDE 方法在空间建模中的应用，该方法已用于污染物水平的建模，并且可在 R 统计软件的 R-INLA 包中使用。在这些模型中，气象变量等协变量可能是有用的预测因子，但必须处理协变量不对齐的问题。本文描述了一种灵活的方法，用于估算中国苏州市六种污染物的水平，苏州市有分散的空气污染物监测器和气象站。使用两阶段方法解决天气协变量数据不对齐的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/156e8099ed11/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/90be258e6d33/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/241584dc656d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/bfad6ab8e6d6/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/ff0873f673ce/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/3917e8576207/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/ff56f1d5eaa5/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/156e8099ed11/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/90be258e6d33/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/241584dc656d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/bfad6ab8e6d6/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/ff0873f673ce/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/3917e8576207/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/ff56f1d5eaa5/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2de/8223501/156e8099ed11/gr7.jpg

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