Explainable Machine Learning Reveals Distinct Air Pollution Profiles in Two Geographically Adjacent Cities

Air pollution is one of the fundamental environmental problems that directly threaten public health, ecosystems, and sustainable urban life in regions with high industrialization and urbanization density. This study aims to investigate whether the air pollution dynamics in Gaziantep and Kilis, two neighboring cities in Turkey, exhibit distinctive city-specific characteristics in their time series. In this context, Dynamic Time Warping (DTW) distance matrix and hierarchical clustering approaches were applied to compare the temporal behavior of pollutants from daily time series of PM10, SO2, CO, and O3 measurements across provinces between 2021 and 2025. Random Forest (RF), XGBoost, and Support Vector Machines (SVM) models were then developed to examine the separability of cities based solely on pollutant concentrations. The results revealed that the RF and XGBoost models successfully classified the two cities with over 93% accuracy. Additionally, SHAP analysis was used to interpret the contribution of each pollutant within the classification models, indicating that PM10 and SO2 have relatively higher importance in distinguishing between the two cities. It should be noted that SHAP provides model-based interpretability rather than a direct representation of physical or atmospheric mechanisms. The findings suggest that pollutant time series may exhibit statistically distinguishable structures even between neighboring cities.