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Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution
Author(s) -
Shaddick Gavin,
Thomas Matthew L.,
Green Amelia,
Brauer Michael,
Donkelaar Aaron,
Burnett Rick,
Chang Howard H.,
Cohen Aaron,
Dingenen Rita Van,
Dora Carlos,
Gumy Sophie,
Liu Yang,
Martin Randall,
Waller Lance A.,
West Jason,
Zidek James V.,
PrüssUstün Annette
Publication year - 2018
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12227
Subject(s) - air quality index , environmental science , air pollution , particulates , aerosol , pollution , meteorology , population , satellite , grid , estimation , geography , environmental health , engineering , medicine , ecology , chemistry , organic chemistry , systems engineering , biology , geodesy , aerospace engineering
Summary Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM 2.5 ). 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.