Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast.

Abstract

Fine particulate matter (PM_2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM_2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM_2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM_2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m³) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m³) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m³), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m³ vs. -1.5 μg/m³ for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m³, S-MESH outperforms the reanalysis (MB of -7.3 μg/m³ and -10.3 μg/m³ respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM_2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.