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.

An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines.

Dust is a severe environmental issue in open-pit mines, and accurate estimation of its concentration allows for viable solutions for its control and management. This research proposes a machine learning-based solution for accurately estimating dust concentrations. The proposed approach, tested using real data from the Haerwusu open-pit coal mine in China, is based upon the integrated random forest-Markov chain (RF-MC) model. The random forest method is used for estimation, while the Markov chain is used for estimation correction. The wind speed, temperature, humidity, and atmospheric pressure are used as inputs, while PM2.5, PM10, and TSP are taken as estimated outputs. A detailed procedure for implementing the RF-MC is presented, and the estimated performance is analyzed. The results show that after correction, the root mean squared error significantly decreased from 7.40 to 2.56 µg/m3 for PM2.5, from 15.73 to 5.28 µg/m3 for PM10, and from 18.99 to 6.27 µg/m3 for TSP, and the Pearson correlation coefficient and the mean absolute error also improved considerably. This work provides an improved machine learning approach for dust concentration estimation in open-pit coal mines, with a greater emphasis on simplicity and rapid model updates, which is more applicable to ensure the prudent use of water resources and overall environmental conservation, both of which are advantageous to green mining.

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.