Particulate matter concentration from open-cut coal mines: A hybrid machine learning estimation

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM₂.₅ (particles with an aerodynamic diameter smaller than 2.5 μm), PM₁₀ (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF_PSO was presented and the predictive performance was analyzed. The results show that the RF_PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM₂.₅, PM₁₀ and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques.