Co-Pollutant and Meteorological Predictors of Urban Benzene Variability Across Environmental Settings: An Explainable Artificial Intelligence Analysis

Benzene is a carcinogenic urban volatile organic compound whose variability reflects interactions among emissions, atmospheric chemistry, and meteorology. We quantified relationships between hourly benzene concentrations and O3, CO, SO2, NO2, and more than twenty meteorological predictors using regulatory air-quality observations from the Ksaverska cesta urban background station in Zagreb, Croatia, complemented by NOAA GDAS meteorological data (2017–2023; n = 39,124). Metaheuristically optimized tree-ensemble models were interpreted using iterative Shapley Additive Global Importance (iSAGE), SHapley Additive exPlanations (SHAP), and an environmental settings framework. The novelty of the study lies in integrating global predictor importance, signed local SHAP attributions, and environmental-setting classification to interpret benzene variability within recurring emission–chemistry–meteorology regimes. The best-performing Extra Trees model achieved R2 = 0.87, RMSE = 0.46 µg m−3, and MAE = 0.24 µg m−3. iSAGE identified CO (1.47), O3 (0.55), and near-surface air temperature (0.35) as the leading predictors of modeled benzene concentrations, whereas SO2 (0.18) and NO2 (0.07) had substantially lower global importance. SHAP showed a positive CO-benzene association, with relative contributions shifting from negative to positive at approximately 0.3–0.6 mg m−3; O3 contributions became predominantly negative above 30–40 µg m−3; and temperature contributions shifted from positive below 7 °C to negative across 7–12 °C. Environmental setting analysis associated the strongest benzene accumulation with stable winter conditions and the weakest accumulation with well-ventilated, photochemically active regimes. These site- and model-specific results reveal nonlinear co-pollutant and meteorological influences on urban benzene variability. Theoretically, the study advances the interpretation of urban VOC variability by linking model-based predictor attributions with physically interpretable atmospheric regimes; practically, it supports the identification of conditions favoring benzene accumulation and can inform regime-sensitive air-quality assessment and management.