Data integration for high-resolution, continental-scale estimation of air pollution concentrations
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Air pollution constitutes the highest environmental risk factor in relation to heath. In order to provide the evidence required for health impact analyses, to inform policy and to develop potential mitigation strategies comprehensive information is required on the state of air pollution. Traditionally, information on air pollution has come from ground monitoring (GM) networks but these may not be able to provide sufficient coverage and may need to be supplemented with information from other sources (e.g. chemical transport models; CTMs). However, these other sources may only be available on grids and may not capture micro-scale features that may be important in assessing air quality in areas of high population. We develop a model that allows calibration between data sources available at different levels of support, for example, GMs at point locations and estimates from CTMs on grid-cells by allowing the coefficients of calibration equations to vary over space and time. Using a Bayesian hierarchical framework, we address the computational issues that may arise when fitting varying coefficient models in larger scale problems, especially those using MCMC by using INLA. We assess the efficacy of the proposed model, through simulation and out-of-sample evaluation using data from Western Europe. The model is used to produce a comprehensive set of high-resolution (1km x 1km) estimates of NO_2 and PM_2.5 across Western Europe for 2010-2016. The estimates offer a wealth of information, including the ability to calculate exceedance probabilities and, by aligning to estimates of population, country-level population-weighted concentrations. We find that levels of both pollutants are decreasing in the majority of Western Europe, however there remain large populations exposed to levels that exceed the WHO Air Quality Guidelines and as such air pollution remains a serious threat to health.
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