Spatial mapping of PM2.5 in complex urban and suburban terrains remains challenging for classical geostatistical interpolation. This study evaluates a Random Forest (RF) framework for high-resolution air pollution mapping and compares its performance with ordinary kriging in the Kraków region. The analysis integrates measurements from 51 low-cost air quality sensors with topographic and meteorological predictors, including elevation, temperature, relative humidity, and wind speed. Five representative hours during a relatively windless, inversion dominated day were selected to examine hourly variability in pollution patterns. Model robustness was assessed using leave-one-out (LOO) cross-validation, while interpretability was addressed through permutation-based predictor importance analysis. The RF model achieved high predictive accuracy (R2 = 0.85 to 0.95) and good spatial stability with an LOO standard error below 5%. Elevation consistently emerged as the dominant predictor, confirming the key role of terrain-controlled accumulation, while temperature and humidity gained importance during evening and nighttime hours. The RF approach captured fine-scale transport features along river valleys that were not resolved by ordinary kriging, which produced smoother but less interpretable surfaces. The results demonstrate that RF mapping provides an accurate and explainable support to traditional geostatistical methods for analyzing urban air pollution dynamics in complex terrain.
Węglińska et al. (2026) studied this question.