A novel approach to forecast dust concentration in open pit mines by integrating meteorological parameters and production intensity.

Mine dust pollution poses a hindrance to achieving green and climate-smart mining. This paper uses weather forecast data and mine production intensity data as model inputs to develop a novel model for forecasting daily dust concentration values in open pit mines by employing and integrating multiple machine learning techniques. The results show that the forecast model exhibits high accuracy, with a Pearson correlation coefficient exceeding 0.87. The PM2.5 forecast model performs best, followed by the total suspended particle and PM10 models. The inclusion of production intensity significantly enhances model performance. Total column water vapor exerts the most significant impact on the model's predictive performance, while the impacts of rock production and coal production are also notable. The proposed daily forecast model leverages production intensity data to predict future dust concentrations accurately. This tool offers valuable insights for optimizing mine design parameters, enabling informed decisions based on real-time forecasts. It effectively prevents severe pollution in the mining area while maximizing the use of natural meteorological conditions for effective dust removal and diffusion.

Annual dust pollution characteristics and its prevention and control for environmental protection in surface mines.

Dust pollution is a critical challenge in achieving green mining of open-pit coal mines. The scientific basis for dust prevention and management hinges on a thorough understanding of the long-term characteristics of dust pollution. However, analyzing the characteristics of long-term dust pollution in open-pit coal mines has always been a void in research due to the effect of the mines' geographical location and operating conditions. This research investigated the dust pollution and delved into its key production and meteorological influencing elements in a cold-region open pit coal mining. The real-time data was monitored on-site during the four seasons of the year. The characteristics of dust pollution were determined by statistical analysis. The main factors affecting the dust concentration in different seasons were calculated using the comprehensive grey correlation degree. Finally, dust pollution from the mine to the surrounding area was simulated using the Hybrid Single Particle Lagrangian Integrated Trajectory model. The results revealed that dust pollution was most serious in winter, followed by autumn, spring, and summer. The concentrations of PM10 and PM2.5 exceed the national limit. Meteorological elements that substantially impact dust concentration vary season by season. The dew point temperature in spring, the solar radiation in summer and autumn, and the boundary layer height in winter were the most important elements. Mining activities pollute the surrounding areas more in winter, followed by autumn and spring. During the winter, the pollution is concentrated in Shanxi, while in the autumn and spring, it is concentrated in Inner Mongolia. Based on the research findings, optimal mine design strategies can be devised to avoid and regulate dust in mining and neighboring areas, especially during winter.

Dust pollution in cold region Surface Mines and its prevention and control.

The application of traditional dust reduction methods in surface mines is limited, particularly during winter due to long-term drought and a rainless environment. Therefore, it is essential to investigate dust pollution in cold region mines and get insights into its scientific prevention and control. This research analyzed dust pollution (concentration of TSP, PM10, PM2.5) from a combined perspective of production and metrological conditions in the Haerwusu open pit coal mine located in northwest China to provide the basis for prevention and control. The main findings indicate that the dust concentration in the pit exceeds the national regulatory limit of 50 µg/m for PM10 and 35 µg/m for PM2.5. According to the air quality index, PM10 was the primary pollutant at the bottom of the pit where coal mining was occurring. The order of the factors influencing dust concentration was as follows: coal production > boundary layer height > wind speed > temperature difference > temperature > humidity. Our study revealed that mining activity polluted the surrounding areas, mostly in December and January. The southeastern and eastern regions of the mine site were found to be the most polluted areas. The implications of this study could be used to optimize mining operations and develop dust prevention and control strategies.

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.