Abstract
Spatio–temporal models of ambient air pollution can be used to predict pollutant levels across a geographical region. These predictions may then be used as estimates of exposure for individuals in analyses of the health effects of air pollution. Integrated nested Laplace approximations is a method for Bayesian inference, and a fast alternative to Markov chain Monte Carlo methods. It also facilitates the SPDE approach to spatial modelling, which has been used for modelling of air pollutant levels, and is available in the R-INLA package for the R statistics software. Covariates such as meteorological variables may be useful predictors in such models, but covariate misalignment must be dealt with. This paper describes a flexible method used to estimate pollutant levels for six pollutants in Suzhou, a city in China with dispersed air pollutant monitors and weather stations. A two-stage approach is used to address misalignment of weather covariate data.
Highlights
Research into the health effects of ambient air pollution requires long-term measurements of pollutant exposure at the individual level
The application of the SPDE approach for modelling pollutant levels has previously been reported by Cameletti et al (2013) and Blangiardo et al (2016). These analyses used covariates measured at or aligned to the same locations as the observed pollutant measure ments. We extended this approach using a two-stage method to address misalignment of covariates
After using spatio–temporal models to produce predictions for four meteorological variables at all relevant locations, we used error models to add these as predictors in the models for pollutants
Summary
Research into the health effects of ambient air pollution requires long-term measurements of pollutant exposure at the individual level. Integrated nested Laplace approximations (INLA) (Rue et al, 2009) allow fast computa tion for Bayesian inference and enable the use of the SPDE approach for spatial modelling (Lindgren et al, 2011). These methods have been used in modelling of air pollutant levels in Italy (Cameletti et al, 2013; Fioravanti et al, 2021) and England (Blangiardo et al, 2016). Meteorological variables can be useful predictors in models of ambient air pollution, but weather station locations may not coincide with the pollutant monitor locations or locations where predictions are sought. One clinic located outside the urban area of Suzhou was excluded from this analysis
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