Explainable Machine Learning for Next-Hour Prediction Using Three Meteorological Variables and Historical PM2.5 Data

Ambient fine particulate matter (PM2.5) remains one of the most consequential urban air-quality hazards, yet forecasting its next-hour concentration accurately and transparently remains challenging owing to the non-linear, multivariate nature of pollutant-meteorology interactions and the black-box character of high-performing ensemble models. This study proposes an explainable machine learning framework for next-hour PM2.5 concentration prediction that integrates Extreme Gradient Boosting (XGBoost) with a parsimonious set of predictors comprising three meteorological variables (air temperature, relative humidity, and wind speed) together with historical PM2.5 concentrations. Using 43,824 hourly observations from the Beijing PM2.5 dataset (2010–2014), lag, rolling-statistic, and cyclical calendar features were engineered, and model hyperparameters were systematically tuned through Bayesian optimization using the Optuna framework with the Tree-structured Parzen Estimator over 30 trials under a strictly chronological training validation testing split. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis. The tuned XGBoost model achieved a root mean squared error of 16.50 µg/m³, a mean absolute error of 9.89 µg/m³, a mean absolute percentage error of 21.75%, and a coefficient of determination of 0.9478 on the test set, corresponding to reductions of approximately 4.4%, 10.5%, and 12.4% in RMSE, MAE, and MAPE, respectively, relative to a non-optimized baseline model. SHAP analysis revealed that the one-hour PM2.5 lag feature overwhelmingly dominated the model's predictions, far surpassing the combined contribution of all meteorological variables, while wind speed and temperature retained secondary but physically meaningful influence. These findings demonstrate that a minimal, readily obtainable feature set can support highly accurate and interpretable next-hour PM2.5 forecasting, offering a practical and scientifically transparent tool to support early-warning air-quality systems and evidence-based environmental health policy.