This study develops a season-aware machine-learning (ML) framework to predict hourly concentrations of PM ₁₀ , PM _2.5 and O ₃ across İstanbul. A comprehensive 2021–2023 dataset was compiled from three co-located air-quality and meteorological monitoring stations that typify contrasting source regimes, i.e., a traffic-dominated urban site, a rural background site, and a semi-urban coastal site. Seven ML algorithms, namely eXtreme Gradient Boosting (XGBoost), Extra Trees (ETR), Random Forest (RF), Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), k-Nearest Neighbors (KNN) and Support Vector Regression (SVR), were utilized to establish a holistic comparison scheme. Hyperparameters were optimized using five-fold cross-validated Bayesian search, and models were evaluated with various performance indicators on season-withheld test sets. In the winter months, ETR achieved a mean R ² = 0.93 (RMSE ≈ 10 µg/m ³ ) for PM ₁₀ at Bağcılar, while XGBoost yielded R ² = 0.88 for O ₃ at the same site. Summer predictions were more challenging. PM ₁₀ skill in rural Arnavutköy dropped to R ² = 0.61 despite strong training fits, highlighting over-fitting risks under complex, non-stationary chemical conditions. By contrast, MLP maintained robust urban performance for PM _2.5 (summer test R ² = 0.80) and KNN provided the most stable O ₃ prediction in rural areas (R ² = 0.74). To enhance interpretability, SHAP (SHapley Additive exPlanations) analysis was applied to the best-performing models, enabling a transparent assessment of how meteorological and co-pollutant inputs shaped predictions at each site. The proposed framework demonstrates that data-driven models can complement traditional air-quality modeling systems by providing station-level insights and interpretable relationships between pollutants and meteorological drivers, supporting air-quality assessment and policy-relevant analyses in rapidly urbanizing regions.

Chronic obstructive pulmonary disease (COPD) is strongly associated with the inhalation of harmful particulate matter in ambient air. This study examined 786,290 COPD-related hospital admissions among patients aged 45–64 in Istanbul from 2013 to 2015, using a Generalized Linear Model (GLM) with meteorological variables included as covariates and air pollutant effects evaluated across lag days 0–9. Daily mean concentrations of PM₁₀, PM_2.5, and NO₂ were used as air pollution indicators, while average temperature and relative humidity were considered as meteorological variables. Relative risk (RR) and excess relative risk (ERR) estimates were calculated for a 10 μg/m³ increase in pollutant concentrations across the lag period. Significant associations were found between air pollution and COPD-related hospital admissions in overall analyses as well as seasonal assessments, especially for temperature-related effects. A 10 μg/m³ increase in PM_2.5 was associated with an ERR of 1.26% in females and 1.07% in males at lag 1, while NO₂ exposure showed ERRs of 1.31% in males and 1.30% in females. The effects of PM₁₀ were comparatively smaller, peaking at about 1.13% ERR at lag 5. Stronger associations were observed in both summer and winter seasons. PM_2.5 demonstrated the highest overall impact, particularly among females, with an excess risk of 1.7%. Pollutant effects were more pronounced at ambient temperatures around 0 °C and 25 °C.