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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wright, Neil [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
Integrated nested Laplace approximation |
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| Übergeordnetes Werk: |
Enthalten in: Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite - Zhang, Wei ELSEVIER, 2014, official journal of the German Society of Hygiene, Environmental and Public Health Sciences (Gesellschaft für Hygiene, Umweltmedizin und Präventivmedizin, GHUP), München |
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| Übergeordnetes Werk: |
volume:235 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.ijheh.2021.113766 |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 650 | 7 | |a Bayesian approach |2 Elsevier | |
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| 700 | 1 | |a Newell, Katherine |4 oth | |
| 700 | 1 | |a Lam, Kin Bong Hubert |4 oth | |
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| 700 | 1 | |a Chen, Zhengming |4 oth | |
| 700 | 1 | |a Kartsonaki, Christiana |4 oth | |
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10.1016/j.ijheh.2021.113766 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001454.pica (DE-627)ELV054374286 (ELSEVIER)S1438-4639(21)00081-X DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 610 VZ 530 620 VZ 52.56 bkl Wright, Neil verfasserin aut Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Bayesian approach Elsevier Covariate misalignment Elsevier Integrated nested Laplace approximation Elsevier Stochastic partial differential equation Elsevier Ambient air pollution Elsevier Newell, Katherine oth Lam, Kin Bong Hubert oth Kurmi, Om oth Chen, Zhengming oth Kartsonaki, Christiana oth Enthalten in Elsevier Zhang, Wei ELSEVIER Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite 2014 official journal of the German Society of Hygiene, Environmental and Public Health Sciences (Gesellschaft für Hygiene, Umweltmedizin und Präventivmedizin, GHUP) München (DE-627)ELV017446082 volume:235 year:2021 pages:0 https://doi.org/10.1016/j.ijheh.2021.113766 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.56 Regenerative Energieformen alternative Energieformen VZ AR 235 2021 0 |
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10.1016/j.ijheh.2021.113766 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001454.pica (DE-627)ELV054374286 (ELSEVIER)S1438-4639(21)00081-X DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 610 VZ 530 620 VZ 52.56 bkl Wright, Neil verfasserin aut Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Bayesian approach Elsevier Covariate misalignment Elsevier Integrated nested Laplace approximation Elsevier Stochastic partial differential equation Elsevier Ambient air pollution Elsevier Newell, Katherine oth Lam, Kin Bong Hubert oth Kurmi, Om oth Chen, Zhengming oth Kartsonaki, Christiana oth Enthalten in Elsevier Zhang, Wei ELSEVIER Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite 2014 official journal of the German Society of Hygiene, Environmental and Public Health Sciences (Gesellschaft für Hygiene, Umweltmedizin und Präventivmedizin, GHUP) München (DE-627)ELV017446082 volume:235 year:2021 pages:0 https://doi.org/10.1016/j.ijheh.2021.113766 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.56 Regenerative Energieformen alternative Energieformen VZ AR 235 2021 0 |
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10.1016/j.ijheh.2021.113766 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001454.pica (DE-627)ELV054374286 (ELSEVIER)S1438-4639(21)00081-X DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 610 VZ 530 620 VZ 52.56 bkl Wright, Neil verfasserin aut Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Bayesian approach Elsevier Covariate misalignment Elsevier Integrated nested Laplace approximation Elsevier Stochastic partial differential equation Elsevier Ambient air pollution Elsevier Newell, Katherine oth Lam, Kin Bong Hubert oth Kurmi, Om oth Chen, Zhengming oth Kartsonaki, Christiana oth Enthalten in Elsevier Zhang, Wei ELSEVIER Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite 2014 official journal of the German Society of Hygiene, Environmental and Public Health Sciences (Gesellschaft für Hygiene, Umweltmedizin und Präventivmedizin, GHUP) München (DE-627)ELV017446082 volume:235 year:2021 pages:0 https://doi.org/10.1016/j.ijheh.2021.113766 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.56 Regenerative Energieformen alternative Energieformen VZ AR 235 2021 0 |
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10.1016/j.ijheh.2021.113766 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001454.pica (DE-627)ELV054374286 (ELSEVIER)S1438-4639(21)00081-X DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 610 VZ 530 620 VZ 52.56 bkl Wright, Neil verfasserin aut Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Bayesian approach Elsevier Covariate misalignment Elsevier Integrated nested Laplace approximation Elsevier Stochastic partial differential equation Elsevier Ambient air pollution Elsevier Newell, Katherine oth Lam, Kin Bong Hubert oth Kurmi, Om oth Chen, Zhengming oth Kartsonaki, Christiana oth Enthalten in Elsevier Zhang, Wei ELSEVIER Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite 2014 official journal of the German Society of Hygiene, Environmental and Public Health Sciences (Gesellschaft für Hygiene, Umweltmedizin und Präventivmedizin, GHUP) München (DE-627)ELV017446082 volume:235 year:2021 pages:0 https://doi.org/10.1016/j.ijheh.2021.113766 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.56 Regenerative Energieformen alternative Energieformen VZ AR 235 2021 0 |
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10.1016/j.ijheh.2021.113766 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001454.pica (DE-627)ELV054374286 (ELSEVIER)S1438-4639(21)00081-X DE-627 ger DE-627 rakwb eng 630 VZ 640 VZ 610 VZ 530 620 VZ 52.56 bkl Wright, Neil verfasserin aut Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Bayesian approach Elsevier Covariate misalignment Elsevier Integrated nested Laplace approximation Elsevier Stochastic partial differential equation Elsevier Ambient air pollution Elsevier Newell, Katherine oth Lam, Kin Bong Hubert oth Kurmi, Om oth Chen, Zhengming oth Kartsonaki, Christiana oth Enthalten in Elsevier Zhang, Wei ELSEVIER Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite 2014 official journal of the German Society of Hygiene, Environmental and Public Health Sciences (Gesellschaft für Hygiene, Umweltmedizin und Präventivmedizin, GHUP) München (DE-627)ELV017446082 volume:235 year:2021 pages:0 https://doi.org/10.1016/j.ijheh.2021.113766 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.56 Regenerative Energieformen alternative Energieformen VZ AR 235 2021 0 |
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Enthalten in Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite München volume:235 year:2021 pages:0 |
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Apoptosis in MCF-7 breast cancer cells induced by S-alkenylmercaptocysteine (CySSR) species derived from Allium tissues in combination with sodium selenite |
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Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA |
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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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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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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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