Interpretable Machine Learning for Predicting Blast-Induced Particulate Matter Emissions in Surface Mines

Blasting is an essential operation in surface mining, but it can generate high particulate matter concentrations within a short period. Accurate prediction of blast-induced dust concentration is useful for air quality management, worker protection, and dust control planning. In this study, an interpretable machine learning framework was developed to predict particulate matter with an aerodynamic diameter less than 10 μm (PM10) and total suspended particulate matter (TSP) concentrations induced by blasting in a large surface coal mine. The dataset was derived from published field monitoring records and included blasting design parameters, monitoring distance, material-related variables, and measured dust concentrations. A total of 148 valid samples were used for model development and evaluation. Six tree-based ensemble models, including Extra Trees Regression, Random Forest, Gradient Boosting Regression Trees, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were established and compared. Bayesian optimization was used for hyperparameter tuning, and model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Under the adopted 80:20 hold-out validation scheme, CatBoost achieved the best test performance for PM10 prediction, with a test R2 of 0.9555, RMSE of 444.87 μg/m3, and MAE of 343.48 μg/m3. Extra Trees Regression performed best for TSP prediction, with a test R2 of 0.8803, RMSE of 3899.04 μg/m3, and MAE of 3036.27 μg/m3. Residual analysis further indicated that the optimal models had no obvious systematic bias. Shapley additive explanations (SHAP) analysis, supported by within-model feature importance rankings, showed that explosive quantity, number of blastholes, and monitoring distance were the dominant variables affecting PM10 and TSP predictions. The explosive quantity and number of blastholes mainly increased the predicted dust concentration, whereas the monitoring distance generally reduced it. The proposed framework may provide useful support for blast parameter optimization, monitoring point arrangement, and dust control decision-making in surface mines, but further validation using larger multi-site datasets is still needed.