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Maritime shipping is a cornerstone of South Korea’s economy, yet emissions from port operations remain a major contributor to urban air pollution. However, few studies have quantitatively isolated the specific contribution of ship traffic to local PM 2.5 levels within port-city environments, especially at fine spatial scales. The current study examines the dispersion of PM 2.5 around Busan Port, with a focus on buffer zones of 3 and 10 km from the emission control area (ECA) boundary. We integrate high-resolution automatic identification system (AIS) vessel movement data with ground-based air quality measurements and meteorological observations to capture both emission sources and atmospheric transport dynamics. Two advanced gradient boosting models, LightGBM and XGBoost, were developed and evaluated using K-fold cross-validation; LightGBM achieved strong predictive performance (R² = 0.882), demonstrating the value of data-driven modeling for port air-quality assessment. The results indicate that ship-derived PM 2.5 emissions are the main predictor in the 3-km zone, while wind speed is the most critical factor driving dispersion in both zones. SHAP and PDP analyses provide a transparent interpretation, confirming the relative influence of ship emissions versus meteorological controls. The novelty of this work lies in the spatially explicit integration of AIS-based emissions with meteorological variables within multiple buffer zones.

Impact of maritime PM 2.5 emissions on air quality at Busan Port: an analysis using AIS data and advanced predictive models