Machine Learning Downscaling of CAMS Regional Air Quality Reanalyses: High-Resolution Urban Concentrations of PM2.5 and NO2 Across Europe

High-resolution modelling of air pollutants such as NO₂ and PM_2.5 is an essential step in the quantification of the impacts on human health, especially in urban areas. Often, such modelling uses relatively coarse-resolution chemistry transport models (CTMs), which exhibit biases when compared to measurements and cannot consider the heterogenity of urban pollutant concentrations.

This study develops a machine learning (ML) framework to downscale CAMS regional air quality reanalyses for PM_2.5 and NO₂ from approximately 10×10 km² (0.1 degrees) to 1×1 km² resolution, enabling more detailed urban air quality assessments across Europe.

The downscaling methodology integrates meteorological, land-use, and spatial predictors to bridge the resolution gap. Key steps include: (1) interpolating CAMS outputs to a 1×1 km² grid, (2) constructing a training dataset by pairing interpolated CAMS data with ground-based measurements, (3) applying XGBoost (a gradient-boosted decision tree algorithm) and Gaussian Processes to model pollutant concentrations at 1×1 km² resolution, and (4) validating model performance using independent measurement data and FAIRMODE evaluation principles (e.g. Model Quality Objective, MQO). Predictor variables encompass meteorological inputs (e.g., daily temperature extremes, surface pressure, boundary layer height), geographical features (e.g., terrain height, proximity to roads, and coastlines), temporal indicators (e.g., year, month, date), and land-use data (e.g., Corine Land Cover and urban bounding boxes).

Preliminary results demonstrate the ability of the downscaling approach to capture fine-scale spatial patterns in urban air quality for a range of cities in Europe, with improved alignment to ground-based measurements compared to CAMS reanalyses. The high-resolution (1×1 km²) predictions reveal urban-level detail, enabling better inference on pollutant distribution in urban environments. Adherence to FAIRMODE principles ensures transparency and quality of results.

Future work will refine the ML framework, extend its application to other pollutants, and explore spatial and temporal scalability, ultimately aiming to deliver a transferable tool for high-resolution air quality modeling in any urban area across Europe.