干旱区生态安全与可持续发展服务平台

Modeling of urban air quality dynamics using hyperparameter-tuned boosted regression

Rahman, Mohammad Shahedur ， Mitra, Bijoy ， Mahmud, Khaled ， Rahman, Mohammod Hafizur ， Rahman, Muhammad Muhitur ， Rahman, Syed Masiur

2026-12-31 GEOCARTO INTERNATIONAL 2026   41(卷), 1(期), (null页)

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Rapid urbanization in the Gulf Cooperation Council (GCC) region has intensified air quality challenges, particularly elevated concentrations of fine particulate matter (PM2.(5)). While meteorological drivers have been studied, the combined influence of land cover and socioeconomic factors remain underexplored in arid environments. This study employs a Bayesian-optimized XGBoost model to predict PM2.(5) levels across ten major GCC cities (2012-2024). The framework integrates satellite-derived meteorological variables, land surface temperature and vegetation indices, and nighttime light radiance as a proxy for anthropogenic activity. Results show high PM2.5 concentrations (120-140 mu gm(-3)) along the Arabian Gulf coast, with Kuwait City most affected. SHAP (SHapley Additive exPlanations) analysis identifies surface pressure and wind speed as key predictors. With strong performance (Coefficient of variation, R-2 > 0.75, and Root Mean Square Error, RMSE < 15 gm(-3)), the study substantiates the value of interpretable machine learning for evidence-based air quality management in desert urban regions.

关键词

Gulf cooperation council particulate matter landcover nighttime light extreme gradient boosting

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