Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques Applied across Hospitals in England

Laure de Preux, Dheeya Rizmie (), Daniela Fecht, John Gulliver and Weiyi Wang
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Dheeya Rizmie: Centre for Health Economics & Policy Innovation, Department of Economics & Public Policy, Imperial College Business School, Imperial College London, London SW7 2AZ, UK
Daniela Fecht: Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
John Gulliver: Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
Weiyi Wang: Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK

IJERPH, 2023, vol. 20, issue 5, 1-26

Abstract: Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.

Keywords: air pollution; inverse distance weighting; land use regression; pollution exposure (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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