Abstract
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.
Original language | English |
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Pages (from-to) | 231–253 |
Number of pages | 23 |
Journal | Royal Statistical Society Series C (Applied Statistics) |
Volume | 67 |
Issue number | 1 |
Early online date | 13 Jun 2017 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Keywords
- Air pollution
- Bayesian hierarchical modelling
- Data fusion
- Environmental health effects
- Global burden of disease
- Integrated nested Laplace approximations
- Spatial modelling
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty