Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA
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 f...
Main Authors: | , , , , |
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Format: | Journal article |
Language: | English |
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Elsevier
2021
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_version_ | 1797081643463213056 |
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author | Wright, N Newell, K Lam, KIN Chen, Z Kartsonaki, C |
author_facet | Wright, N Newell, K Lam, KIN Chen, Z Kartsonaki, C |
author_sort | Wright, N |
collection | OXFORD |
description | 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 disperse air pollutant monitors and weather stations. A two-stage approach is used to address misalignment of weather covariate data. |
first_indexed | 2024-03-07T01:17:01Z |
format | Journal article |
id | oxford-uuid:8f09a130-51c4-413c-a9f1-93d0360f2b2b |
institution | University of Oxford |
language | English |
last_indexed | 2024-03-07T01:17:01Z |
publishDate | 2021 |
publisher | Elsevier |
record_format | dspace |
spelling | oxford-uuid:8f09a130-51c4-413c-a9f1-93d0360f2b2b2022-03-26T23:01:41ZEstimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLAJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8f09a130-51c4-413c-a9f1-93d0360f2b2bEnglishSymplectic ElementsElsevier2021Wright, NNewell, KLam, KINChen, ZKartsonaki, CSpatio–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 disperse air pollutant monitors and weather stations. A two-stage approach is used to address misalignment of weather covariate data. |
spellingShingle | Wright, N Newell, K Lam, KIN Chen, Z Kartsonaki, C Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
title | Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
title_full | Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
title_fullStr | Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
title_full_unstemmed | Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
title_short | Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
title_sort | estimating ambient air pollutant levels in suzhou through the spde approach with r inla |
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