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Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

by Green, Amelia , Chang, Howard H. , Gumy, Sophie , Waller, Lance A. , Liu, Yang , Prüss-Ustün, Annette , Martin, Randall , Brauer, Michael , Burnett, Rick , Dora, Carlos , Cohen, Aaron , Zidek, James V. , Thomas, Matthew L. , van Donkelaar, Aaron , Van Dingenen, Rita , Shaddick, Gavin , West, Jason

in Air / Air monitoring / Air pollution / Air quality / Bayesian analysis / Bayesian hierarchical modelling / Data fusion / Data integration / Data quality / Deaths / Environmental health effects / Environmental monitoring / Exposure / Global burden of disease / Hierarchies / Integrated nested Laplace approximations / Outdoor air quality / Pollution monitoring / Population statistics / Public health / Risk factors / Spatial modelling / Transportation / Uncertainty / World population

2018

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Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

2018

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Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

2018

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Journal Article

Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

Green, Amelia,

Chang, Howard H.,

Gumy, Sophie,

Waller, Lance A.,

Liu, Yang,

Prüss-Ustün, Annette,

Martin, Randall,

Brauer, Michael,

Burnett, Rick,

Dora, Carlos,

Cohen, Aaron,

Zidek, James V.,

Thomas, Matthew L.,

van Donkelaar, Aaron,

Van Dingenen, Rita,

Shaddick, Gavin,

West, Jason

2018

Overview

Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM2.5).The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks but, although coverage is increasing, regions remain in which monitoring is limited. The data integration model for air quality supplements ground monitoring data with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. Set within a Bayesian hierarchical modelling framework, the model allows spatially varying relationships between ground measurements and other factors that estimate air quality. The model is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world from which it is estimated that 92% of the world's population reside in areas exceeding the World Health Organization's air quality guidelines.

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Publisher

John Wiley & Sons Ltd,Oxford University Press

Subject

/ Bayesian hierarchical modelling

/ Data fusion