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

By Gavin Shaddick, Matthew L. Thomas, A. Green, M. Brauer, A. Donkelaar, R. Burnett, Howard H. Chang, A. Cohen, R. Dingenen, C. Dora and 7 more. 2016

188 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/9ae9d3249dcfbce188c8ef394b7d22cace38a7a6

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Citations in the corpus

Visualization in Bayesian workflow (2017, background): “Exposure to PM2:5 is linked to a number of poor health outcomes, and a recent report estimated that PM2:5 is responsible for 3 million deaths world wide each year (Shaddick et al., 2018).”
Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter (2018, methodology): “5 exposures (34) based on the GEMM and the IER model for each country and globally, a burden analysis.”
Spatial modelling with R-INLA: A review (2018, methodology): “R-INLA’s spatial capabilities were also a key tool used by Shaddick et al. (2018) to produce the global estimates of ambient exposure to ultra-ﬁne particulate matter of less than 2.5 µ m in diameter, known as PM 2 .”
Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem. (2021, unknown): “7) 34 (20-47) 28 (22-35) Coarse South America 2,389 8.”