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

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

Journal of the Royal Statistical Society Series C, 2018, vol. 67, issue 1, 231-253

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://doi.org/10.1111/rssc.12227

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:67:y:2018:i:1:p:231-253

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().