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

Author

Listed:
  • 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
  • Annette Prüss‐Ustün

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.

Suggested Citation

  • 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 & Ran, 2018. "Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 231-253, January.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:1:p:231-253
    DOI: 10.1111/rssc.12227
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    Cited by:

    1. C. Forlani & S. Bhatt & M. Cameletti & E. Krainski & M. Blangiardo, 2020. "A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    2. Giovanni Vinti & Mentore Vaccari, 2022. "A Simplified Model for Estimating Household Air Pollution in Challenging Contexts: A Case Study from Ghana," Clean Technol., MDPI, vol. 4(3), pages 1-11, July.
    3. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    4. Oliver Stoner & Gavin Shaddick & Theo Economou & Sophie Gumy & Jessica Lewis & Itzel Lucio & Giulia Ruggeri & Heather Adair‐Rohani, 2020. "Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 815-839, August.
    5. Eric S. Coker & Laura Cavalli & Enrico Fabrizi & Gianni Guastella & Enrico Lippo & Maria Laura Parisi & Nicola Pontarollo & Massimiliano Rizzati & Alessandro Varacca & Sergio Vergalli, 2020. "The Effects of Air Pollution on COVID-19 Related Mortality in Northern Italy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 611-634, August.
    6. Niru Senthilkumar & Mark Gilfether & Francesca Metcalf & Armistead G. Russell & James A. Mulholland & Howard H. Chang, 2019. "Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005–2014," IJERPH, MDPI, vol. 16(18), pages 1-15, September.
    7. Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    8. Alexander Kreuzer & Luciana Dalla Valle & Claudia Czado, 2022. "A Bayesian non‐linear state space copula model for air pollution in Beijing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 613-638, June.
    9. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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