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

A comprehensive evaluation method for dust pollution: Digital image processing and deep learning approach

Dust pollution poses a grave threat to both the environment and human health, especially in mining operations. To combat this issue, a novel evaluation method is proposed, integrating grayscale average (GA) analysis and deep learning (DL) in image classification. By utilizing a self-designed dust diffusion simulation system, 300 sample images were generated for analysis. The GA method establishes a correlation between grayscale average and dust mass, while incorporating fractal dimension (FD) enhances classification criteria. Both GA and DL methods were trained and compared, yielding promising results with a testing accuracy of 92.2 % and high precision, recall, and F1-score values. This approach not only demonstrates efficacy in classifying dust pollution but also presents a versatile solution applicable beyond mining to diverse dust-contaminated work environments. By combining image processing and deep learning, it offers an automated and reliable system for environmental monitoring, thereby enhancing safety standards and health outcomes in affected industries. Ultimately, this innovative method signifies a significant advancement towards mitigating dust pollution and ensuring sustainable industrial practices.

Evaluating the surface water pollution risk of mineral resource exploitation via an improved approach: a case study in Liaoning Province, Northeastern China

Pollution from mineral exploitation is an important risk factor affecting surface water environment in mineral regions. It is urgent to construct a simple and accurate model to assess the surface water pollution risk from mineral exploitation in the regional scale. Thus, taking a mining province namely Liaoning in northeastern China as the study area, we proposed a framework to simulate the transport process of pollutants from mineral exploitation points to the surrounding surface water based on the "source-sink" theory. In our framework, we adopted the regional growth method (RGM) to extract the potential polluted water area as the certain "sink" considering the influence of the topography, and then applied Minimum Cumulative Resistance (MCR) model to assess the surface water pollution risk from mineral exploitation. The results revealed that: (1) 9.5% of the water areas were located at the potential impact area of MEPs. (2) The total value of resistance surface in Liaoning is relatively low, and gradually decreased from west to east. (3) MEPs in Liaoning had a high risk and seriously threatened the surface water environment, among 2125 MEPs, 733 MEPs (32.99%) were assessed as extremely high risk level, and about 35% of the MEPs were distributed within 10KM buffer zone of surface water. (4) Water pollution risk of MEPs in Dalian, Tieling, Fuxin and Dandong need to be emphasized. (5) Compared to previous studies, we considered the topographical influence before applying MCR model directly, so the results of water pollution risk were more reliable. This study provides a methodological support and scientific reference for the water environment protection and regional sustainable development.

Similar experimental study on diffusion and distribution of diesel exhaust in confined space of a coal mine

In the confined space of the underground coal mine, which is dominated by transportation lanes, explosion-proof diesel-powered trackless rubber-wheeled vehicles are becoming the main transportation equipment, and the exhaust gas produced by them is hazardous to the health of workers and pollutes the underground environment. In this experiment, a similar test platform is built to study the effects of wind speed, vehicle speed, and different wind directions on the diffusion characteristics of exhaust gas. In this paper, CO and SO2 are mainly studied. The results show that the diffusion of CO and SO2 gas is similar and the maximum SO2 concentration only accounts for 11.4% of the CO concentration. Exhaust gas is better diluted by increasing the wind speed and vehicle speed, respectively. Downwind is affected by the reverse wind flow and diffuses to the driver's position, which is easy to cause occupational diseases. When the wind is a headwind, the exhaust gases spread upwards and make a circumvention movement, gathering at the top. When the wind speed and vehicle speed are both 0.6 m/s, the CO concentration corresponds to the change trend of the Lorentz function when the wind is downwind and the CO concentration corresponds to the change trend of the BiDoseResp function when the wind is headwind. The study of exhaust gas diffusion characteristics is of great significance for the subsequent purification of the air in the restricted mine space and the protection of the workers' occupational health.

Study of spatiotemporal evolution of coupled airflow-gas-dust multi-field diffusion at low-gas tunnel

The increasing level of mechanization in coal mining means more dust and gas are generated during excavation operations in tunnels. The high concentrations of dust and gas severely affect production efficiency and the physical and mental health of workers. Here, Ansys Fluent simulations were performed to derive the spatiotemporal evolution of coupled airflow-dust-gas diffusion in a low-gas excavation face. The aim was to optimize pollution control by determining the optimal duct distance, L, from the working face in the excavation tunnel. Our results showed that the airflow field affects the coupled diffusion and transport of dust and gas. According to a comparison of the effects of different duct distances from the working face, when L = 6 m, the average dust concentration in the tunnel is low (257.6 mg/m3), and the average gas concentration in the tunnel is 0.28 %, which does not exceed the safety limit. Accordingly, the optimal distance of the duct for pollution control is 6 m. The results of field measurements supported the validity of the simulation. Our findings can be used to improve the air quality in tunnels, thereby keeping miners safe and the working area clean.

Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.

Research on blasting mechanism and blasting effect of aqueous media in open pit coal mines

Surface coal mining procedures include piercing-blasting-mining and loading-transportation-discharging, blasting link exists due to the poor blasting effect leads to low loading efficiency, blasting dust caused by environmental pollution and other problems. In this paper, from the mechanical characteristics of the water medium, we analyze in detail the transferring effect, transducing effect and bubble pulsation phenomenon of the water medium in the blasting process. The results show that when the blasting medium is water medium, the maximum principal stress is 1.53 times that of air medium; the peak energy transfer can be up to 2.73 times that of air medium. With the help of TrueGrid/LS-DYNA finite element analysis software to simulate the dynamic process of blasting, the study of the maximum principal stresses around the hole, the top of the slope, the foot of the slope on the maximum principal stress changes, the results show that the maximum principal stresses around the hole, the top of the slope, the foot of the slope unit with the increase in the water content is gradually increasing trend. Finally, combined with the actual mine production conditions for blasting field test, water-mediated blasting dust reduction rate of 75%, the use of AHP-fuzzy comprehensive evaluation method of two groups of traditional dry hole blasting and three groups of water-mediated blasting comprehensive evaluation, the results show that the water-mediated blasting scores are higher than the traditional dry hole blasting, proving that the water-mediated blasting has a certain prospect of engineering applications.

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 integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils

Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.

Spatial and temporal evolution of mine dust research: visual knowledge mapping analysis in Web of Science from 2001 to 2021

Dust pollution control is the basic guarantee of mine safety production, which has been widely concerned by scholars. Based on a total of 1786 publications collected by the Web of Science Core Collection (WOSCC) from 2001 to 2021, this paper analyzes the spatial-temporal distribution characteristics, hot topics, and frontier trends of the international mine dust field during the past 20 years by using Citespace and VOSviewer knowledge graph technology. The research shows that the study of mine dust can be divided into three stages: initial period (2001 ~ 2008), stable transition period (2009 ~ 2016), and boom period (2017 ~ 2021). The journals and disciplines which belong to mine dust research mainly focus on environmental science and engineering technology. A stable core group of authors and institutions have been preliminarily formed in the dust research field. The main themes of the study contained the whole process of mine dust generation, transport, prevention, and control, as well as the consequences of disaster. At present, the hot research fields mainly focus on mine dust particle pollution, multi-stage dust prevention, and emission reduction technologies, and mine occupational protection, monitoring, and early warning. In the future, the research should focus on the mechanism of dust production and transportation, the theory of efficient prevention and control, the technology and equipment of precise prevention and control of dust, and the high-precision monitoring and early warning of dust concentration. Future research should be concerned with dust control in underground mines and deep concave open-pit mines with complicated and treacherous environments, and strengthen research institutions, interdisciplinary cooperation, and interaction so as to promote the integration and application of mine dust and automation, information, and intelligent technology.

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.

Study on Crust-Shaped Dust Suppressant in Non-Disturbance Area of Open-Pit Coal Mine-A Case Study

Dust pollution in open-pit coal mines severely restricts the green development of mines. Therefore, dust control has become an important requirement for the sustainable development of the mining industry. With the goal of dust pollution prevention and control in open-pit coal mines, this paper puts forward the concept of a non-disturbance area of an open-pit coal mine. It clarifies the characteristics of dust generation, the coverage area, and the dust particle size distribution characteristics of the non-disturbance area. Taking the dust control at the dump site as an example, the study comprehensively utilizes indoor tests and field tests to develop a dust suppressant for the dump site and determine its dust suppression efficiency and effective service cycle. The results show that the D10, D50, and D90 particle sizes of dust in the non-disturbance area are smaller than those in the disturbance area, and the difference in particle size of D90 is the most obvious. Gelatinized starch and non-ionic polyacrylamide, as the main components of the dust suppressant, can effectively reduce dust pollution in the dump; the optimal concentration is 1.0%, and the dust suppression service cycle is more than one month. The developed dust suppressant does not contain corrosive, toxic, or heavy metal elements. Although the application of a dust suppressant will cause plant growth to lag, it does not affect plant health. The research findings serve as a reference for the zoning treatment of dust in open-pit mines.

Green Mining Strategy Selection via an Integrated SWOT-PEST Analysis and Fuzzy AHP-MARCOS Approach

Deciding on an appropriate development strategy is one of the most crucial aspects of the mining industry’s green transition. This research introduces a novel integrated decision support model that can be applied to analyze various environmental factors and determine development strategies. In this study, a strengths, weaknesses, opportunities, and threats (SWOT) analysis is employed from multiple perspectives, including political, economic, social, and technological (PEST), to assess the internal and external factors that influence green mining. The fuzzy analytic hierarchy process (AHP) is used to analyze the factor weights quantitatively, and the fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is used to rank and select development strategies. According to the results, “grasp the trend of green development and improve the protection and exploitation level of mineral resources” is found to be the final optimal strategy. Comparative analysis and sensitivity analysis confirmed the accuracy of the model and the case study results.