Data integration for high-resolution, continental-scale estimation of air pollution concentrations

TL;DR

This paper presents a novel data integration model that combines multiple sources to produce high-resolution estimates of air pollution across Western Europe, revealing persistent health risks despite overall declines.

Contribution

The study introduces a calibration-based model that dynamically integrates diverse data sources for detailed, high-resolution air pollution mapping at a continental scale.

Findings

Pollution levels decreased from 2010 to 2016.

Large populations are still exposed to harmful pollution levels.

High-resolution estimates reveal micro-scale pollution hotspots.

Abstract

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. Information on air pollution traditionally comes 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 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 multiple data sources available at different levels of support by allowing the coefficients of calibration equations to vary over space and time, enabling downscaling where the data is sufficient to support it. The model is used to produce high-resolution (1km $\times$ 1km) estimates of NO$_2$ and PM$_{2.5}$ across Western Europe for 2010-2016. Concentrations of both pollutants are decreasing during this period, however there remain large populations exposed to levels exceeding the WHO Air Quality Guidelines and thus air pollution remains a serious threat to health.