Spatially Continuous PM10 Exposure Mapping in the Campania Region Using a Land Use Random Forest Model: Integration of Monitoring Data, Geographic Predictors, ERA5 Reanalysis, and CHIMERE Model Output

In this study, we present a machine-learning approach—a land use random forest (LURF) model—to produce daily PM10 concentration maps at a 1 km resolution across the Campania region for the year 2022. The model combines daily measurements from 13 ARPA Campania monitoring stations with a wide set of spatial and atmospheric information. The predictors include population, land cover, road network, ERA5 meteorological data, satellite aerosol observations from MODIS, output from the CHIMERE chemistry transport model, and a flag identifying days affected by Saharan dust transport. The model is trained and validated using a station-based cross-validation scheme that accounts for spatial correlation between sites. Under this scheme, the LURF reproduces observed concentrations with substantially smaller errors than the raw CHIMERE output (RMSE of 11.0 vs. 23.6 (Formula presented.) g m−3). CHIMERE concentrations and ERA5 meteorology emerge as the most informative predictors, while the dust flag specifically improves the representation of episodic high-PM10 events. The resulting 1-km maps reveal clear urban–rural contrasts. They identify pollution hotspots in the Naples metropolitan area and along major motorways that are not visible in coarser model outputs. Probabilistic exceedance maps further show that meeting the future 2030 EU limit value of 20 (Formula presented.) g m−3 will be challenging across much of the metropolitan area. Overall, the proposed framework provides a low-cost, practical tool for high-resolution PM10 exposure assessment, supporting epidemiological studies, environmental justice analyses, and air quality management in regions with complex terrain and limited monitoring coverage.