High-resolution mapping of air quality across Europe: an ensemble machine and deep learning framework integrating multi-scale spatial predictors (CHROMAP v1.0)

Abstract. This article presents a model for mapping air quality at high-resolution (called CHROMAP) based on the fusion of data from deterministic models, in-situ and satellite observations, and spatial proxies using an ensemble of ML and DL algorithms. Annual estimates of the SOMO35 indicator and the average concentrations of NO₂, PM2.5, PM10, and O₃ are produced and evaluated for the 2013–2023 period at a spatial resolution of 500 meters over the European domain. The methodology maintains consistency across all pollutant indicators while ensuring flexibility and transferability.

By including interpretable AI diagnostics, CHROMAP provides a quantitative assessment of the importance of the 26 features over 11 years for each air quality indicator. Integrating all types of stations into the regressions, the evaluation carried out reveals that the performance scores have been significantly improved compared to CAMS reanalyses (~10 km resolution) used for downscaling; with a reduction in RRMSE on average over the period of about -33 % for NO₂, -21 % for O₃, -10 % for SOMO35, -22 % for PM2.5 and -37 % for PM10, and an increase in R² of 28 %, 34 %, 18 %, 14 % and 36 %, respectively. In addition, a sensitivity analysis carried out on the static exposure of the population shows that significant differences can be found with values at high resolution, especially for NO₂, thus impacting the calculation of the health impact.

By ensuring sufficient availability of in-situ observations and concentration fields from CTMs for downscaling, this methodology could be extended to additional air quality indicators and applied at higher temporal frequency, opening new opportunities for comprehensive air quality assessment.