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

Neil Wright; Katherine Newell; Kin Bong Hubert Lam; Om Kurmi; Zhengming Chen; Christiana Kartsonaki

doi:10.1016/j.ijheh.2021.113766

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

Neil Wright, Katherine Newell, Kin Bong Hubert Lam, Om Kurmi, Zhengming Chen, Christiana Kartsonaki

University of Oxford

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	113766
Journal	International Journal of Hygiene and Environmental Health
Volume	235
Early online date	24 May 2021
DOIs	https://doi.org/10.1016/j.ijheh.2021.113766
Publication status	Published - Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s)

Funder

Funding Information:
This research was funded in whole, or in part, by the Wellcome Trust [ 212946/Z/18/Z , 202922/Z/16/Z , 104085/Z/14/Z , 088158/Z/09/Z ]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Funding Information:
Professor Haidong Kan, School of Public Health Fudan University, Shanghai, for providing the fixed site monitoring data. Dr Steve Hung Lam Yim, Chinese University of Hong Kong, for providing all meteorological variables. Participants in the China Kadoorie Biobank study and the members of the survey teams in each of the 10 regional centres, as well as the project development and management teams based at Beijing, Oxford and the 10 regional centres. The CKB baseline survey and the first re-survey were supported by the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up has been supported by Wellcome grants to Oxford University (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z) and grants from the National Key Research and Development Program of China (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) and from the National Natural Science Foundation of China (91843302, 91846303, 81941018, 81390540). The UK Medical Research Council (MC_UU_00017/1, MC_UU_12026/2 MC_U137686851), Cancer Research UK (C16077/A29186, C500/A16896) and the British Heart Foundation (CH/1996001/9454), provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University for the project. UK Medical Research Council Global Challenges Research Fund (Foundation Award MR/P025080/1). Oxford-MRC Doctoral Training Partnership. This research was funded in whole, or in part, by the Wellcome Trust [212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Funding Information:
The CKB baseline survey and the first re-survey were supported by the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up has been supported by Wellcome grants to Oxford University ( 212946/Z/18/Z , 202922/Z/16/Z , 104085/Z/14/Z , 088158/Z/09/Z ) and grants from the National Key Research and Development Program of China ( 2016YFC0900500 , 2016YFC0900501 , 2016YFC0900504 , 2016YFC1303904 ) and from the National Natural Science Foundation of China ( 91843302 , 91846303 , 81941018 , 81390540 ). The UK Medical Research Council ( MC_UU_00017/1 , MC_UU_12026/2 MC_U137686851 ), Cancer Research UK ( C16077/A29186 , C500/A16896 ) and the British Heart Foundation ( CH/1996001/9454 ), provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University for the project.

Funding Information:
UK Medical Research Council Global Challenges Research Fund (Foundation Award MR/P025080/1 ).

Keywords

Ambient air pollution
Bayesian approach
Covariate misalignment
Integrated nested Laplace approximation
Stochastic partial differential equation

ASJC Scopus subject areas

Public Health, Environmental and Occupational Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Wright_et_al_Estimating_ambient_air_pollution_levelsFinal published version, 8.1 MBLicence: CC BY

Cite this

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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.",

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AU - Chen, Zhengming

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

Abstract

Bibliographical note

Funder

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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