Development of a predictive model for PM2.5 over the greater Athens metropolitan area, Greece, at a 1 km by 1 km grid...

Ambient PM2.5 exposure is strongly associated with adverse health effects, including all-cause mortality. However, the lack of monitoring networks globally necessitates a better understanding of the spatiotemporal distribution of near-surface PM2.5 pollution. While ground-level pollutants are traditionally measured at fixed stations, integrating land use and atmospheric reanalysis data captures the broad geospatial trends and specific aerosol compositions necessary for high-resolution exposure assessment. This study aims to demonstrate the accuracy and reliability of ensemble modeling for estimating near-surface PM concentrations at a high spatiotemporal resolution in Athens, Greece.

Daily PM2.5 concentrations were estimated using a stacked ensemble machine learning model to incorporate distributed random forest, gradient boosting, and feedforward neural network algorithms to minimize predictive error compared to individual models. The input set for all base learners consisted of daily observations from air quality monitors between 2007-2019 combined with satellite-derived estimates, providing a total of 61 variables describing regional aerosol, weather, and land use characteristics.

We observed strong predictive performance in our ensemble model, with a mean R2 of 0.85 and an average error of 4.18 μg/m3. The annual average concentration of PM2.5 (22.30 μg/m3) exceeded current WHO and EU guidelines, with considerable spatiotemporal variation across greater Athens. The highest annual mean PM2.5 concentrations were in 2007 (33.18 μg/m3) and on average year-to-year, PM2.5 concentrations were highest during the mid-winter months, in agreement with the expected seasonal maximum for the region, likely driven by increased residential heating alongside winter meteorological conditions, such as temperature inversions and a shallow, stable planetary boundary layer.

This is among the first studies to estimate PM2.5 exposures in the greater Athens region at a high spatiotemporal resolution using diverse satellite and land use data. This framework enables the investigation of cumulative exposures, particularly in regions with limited ground-level monitoring.