Analysis of Mining-Related Injuries in Chinese Coal Mines and Related Risk Factors: A Statistical Research Study Based on a Meta-Analysis

Background and Objectives: Coal mine injuries commonly occur, affecting both the safety and health of miners, and the normal operation of the coal mine. Accordingly, this study aimed to explore the regularity of injury and injury-related risk factors in coal mines in China so as to establish a scientific basis for reducing the incidence and promoting the prevention and control of injuries. Methods: A meta-analysis of casualty cases and injury-related risk factors from 1956 to 2017 in China was conducted utilizing data from six databases, including CNKI, Web of Science, PubMed, Medline, Embase, and Wanfang data. Summary estimates were obtained using random effects models. Results: There were statistically significant variations in coal mine accident types, types of work, injury sites, age, experience, months, and shifts (p < 0.001). Eight types of accidents were susceptible to the risk of injury, and the greatest risk was presented by roof-related accidents (odds ratio (OR) = 0.46, 95% confidence interval (CI) = 0.32−0.6). Coal miners and drillers were at a greater risk of injury (OR = 0.39, 95% CI = 0.35−0.44; OR = 0.22, 95% CI = 0.17−0.26, respectively). The extremities and the soft tissues of the skin were at the greatest risk of injury (OR = 0.44, 95% CI = 0.3−0.58; OR = 0.23, 95% CI = 0.1−0.48, respectively). Compared with other ages, miners aged 21−30 were at a greater risk of injury (21−30 years, OR = 0.45, 95% CI = 0.42−0.47; 31−40 years, OR = 0.29, 95% CI = 0.25−0.32; <20 years, OR = 0.13, 95% CI = 0.03−0.23; >40 years, OR = 0.17, 95% CI = 0.09−0.25). Compared with other miners, those with 6−10 years of experience were at a greater risk of injury (6−10 years, OR = 0.29, 95% CI = 0.25−0.32; 2−5 years, OR = 0.33, 95% CI = 0.25−0.41; <1 year, OR = 0.22, 95% CI = 0.08−0.33; >11 years, OR = 0.22, 95% CI = 0.17−0.27). During the months of July to September, the risk of injury was elevated (7−9th months, OR = 0.32, 95% CI = 0.25−0.39; 10−12th months, OR = 0.24, 95% CI = 0.16−0.31; 1st−3rd months, OR = 0.22, 95% CI = 0.16−0.28; 4−6th months, OR = 0.21, 95% CI = 0.16−0.27). In the three-shift work system, the risk of injury was higher during night shifts (22:00−06:00, OR = 0.43, 95% CI = 0.3−0.56; 14:00−22:00, OR = 0.3, 95% CI = 0.23−0.27; 06:00−14:00, OR = 0.27, 95% CI = 0.18−0.35). Conclusions: The results of this research study reveal that coal mine injuries are prevalent among coal miners. These injuries are often related to the age, experience, months of work, and the three-shift work system of miners.

Heavy metal contamination assessment and probabilistic health risks in soil and maize near coal mines

Objective

Coal mining activities have continuously introduced heavy metals into the soil-crop system, causing increasing damage to crops. This study integrated the analysis of the heavy metal contamination status and human health risk in soil and maize near coal mines to help formulate control strategies for soil quality, maize production, and safe consumption.

Method

This study was carried out on maize agricultural land near a coal mining plant. Heavy metal contamination was assessed by the geo-accumulation index (I_geo), enrichment factor (EF), and bioaccumulation factor (BCF). The Monte Carlo simulation was used to estimate the probabilistic health risk of heavy metals exposure in soil and maize. The relationship between the concentration of heavy metal in the soil and that in maize was further visualized by correlation analysis and random forest analysis.

Results

The results revealed that the mean concentrations of soil Ni, Cu, As, Cd, Sn, Zn, Pb, and Hg were all above the local background level. Ni was the most severely polluted heavy metal in maize and had a concentration higher than the risk control standard for corn in China (NY 861-2004). The I_geo values of all heavy metals were low, and EF values showed enrichment in V, Cr, Ti, Ni, and As. The assessment of probabilistic health risk exposed by heavy metals in soil and maize indicated that 1.16 and 1.46% of residents exceeded the carcinogenic risk level due to heavy metal exposure from soil and maize, respectively. Children were the most sensitive to maize and soil heavy metal exposure in the contaminated area. Ingestion of heavy metals was associated with the highest health risk to residents, followed by dermal contact and inhalation. As and Cr in soil and Cr and Ni in maize had the greatest impact on human health risk. Furthermore, maize heavy metals were affected the most by soil Cr, Cd, and V.

Conclusion

These results may provide useful information for human carcinogenic risk associated with soil and maize heavy metal exposure due to coal mining activities.

Analysis of Air and Soil Quality around Thermal Power Plants and Coal Mines of Singrauli Region, India

Singrauli region is known as the energy capital of India, as it generates nearly 21 GW of electricity, supplied to various parts of the northern India. Many coal-based Thermal Power Plants (TPPs) using coal from several nearby coal mines, and numerous industries are set up in this region which has made it as one of the highly polluted regions of India. In the present study, detailed temporal analysis and forecast of carbon dioxide (CO₂), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and methane (CH₄) concentrations retrieved from satellite data have been carried out for the periods 2005-2020. Based on the classical multiplicative model and using linear regression, the maximum concentration of CO₂, NO₂, SO₂, and CH₄ in the year 2025 is found to be 422.59 ppm, 29.28 ppm, 0.23 DU, and 1901.35 ppbv, respectively. Detailed analysis shows that carbon dioxide has a 95% correlation with all other trace gases. We have also carried out the geo-accumulation index for the presence of various contaminants in the soil of this region. The geo-accumulation index shows that soil in and around thermal power plants and coal mines is contaminated by heavy metals. The cumulative index shows that soil around Hindalco industries, Bina coal mines, Khadia coal mines, and coal-based TPPs (Anpara and Vindhayachal) are highly polluted and a threat to human population living in the region.

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.

Mental health in underground coal miners

As mental health goes beyond the simple absence of mental disorders, this paper characterized mental health components in 89 underground coal miners in southern Brazil. This is a cross-sectional study, that detected a low prevalence of depression; light or moderate anxiety in 13% of the workers; self-perception of good health; life quality and good psychological capacity in most of the participants; poor sleep quality in half of them; reverse correlations between anxiety and life quality, and anxiety and psychological capital; positive correlations between psychological capital and life quality; associations between self-perception of health, time, and sleep quality; associations between anxiety and sleep duration and quality; and between alcohol consumption and location at work (front, rear, or variable). The results suggest vulnerabilities regarding anxiety and sleep quality. However, there is a potential coping of determinants that impact on mental health.

Power Allocation Based on Data Classification in Wireless Sensor Networks

Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy.

The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes' placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper.

Mycobacterium marinum Infection After Exposure to Coal Mine Water

Mycobacterium marinum infection has been historically associated with exposure to aquariums, swimming pools, fish, or other marine fauna. We present a case of M marinum left wrist tenosynovitis and elbow bursitis associated with a puncture injury and exposure to coal mine water in Illinois.

Sir Humphry Davy and the coal miners of the world: a commentary on Davy (1816) 'An account of an invention for giving light in explosive mixtures of fire-damp in coal mines'

In the period between 1815 and 1818, Sir Humphry Davy read four papers to the Royal Society and published a monograph dealing with a safety lamp for coal miners, all of which record in detail the experimental work that he carried out, with his assistant Michael Faraday, so as to determine how to prevent catastrophic accidents in coal mines by the explosion of fire-damp (methane) in the presence of a naked flame. This article describes the key experiments that he performed at the Royal Institution and some of the subsequent trials made in the coal mines of the north of England. It begins, however, with an account of Davy's prior achievements in science before he was approached for help by the clergymen and doctors in the Gateshead and Newcastle upon Tyne areas. There is little doubt that the Davy lamp, from the 1820s onwards, transformed the coal industry worldwide. It also profoundly influenced the science of combustion, and in the words of a pioneer in that field, W. A. Bone, FRS, 'There is no better model of logical experimental procedure, accurate reasoning, philosophical outlook and fine literary expression.' It is a remarkable fact that it took Davy essentially only two weeks from the time he was given samples of fire-damp to solve the problem and to devise his renowned miner's safety lamp. A brief account is also given of the contemporaneous invention of a safety lamp by George Stephenson, and of some of Davy's subsequent accomplishments. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.

Application of Foam-gel Technique to Control CO Exposure Generated During Spontaneous Combustion of Coal in Coal Mines

In China, 47.3% of state-owned coal mines are located in coal seams that are prone to spontaneous combustion. The spontaneous combustion of coal is the main cause of the generation of a large amount of carbon monoxide, which can cause serious health issues to miners. A new technique using foam-gel formation was developed to effectively control the spontaneous combustion of coal. The gel can capture more than 90% of the water in the grout and at the same time the foam can cover dangerous areas in the goaf by stacking and cooling of foam in all directions. In this study, a mechanism of foam-gel formation was introduced and the optimal proportions of additives were defined based on experiments of different foaming properties, gelling time and water loss rate as the main index parameters. The results of a field application in a coal mine promise that this new technique would effectively prevent coal oxidation in the goaf and reduce the generation of carbon monoxide.

Roles of benthic algae in the structure, function, and assessment of stream ecosystems affected by acid mine drainage

Tens of thousands of stream kilometers worldwide are degraded by a legacy of acid loads, high metal concentrations, and altered habitat caused by acid mine drainage (AMD) from abandoned underground and surface mines. As the primary production base in streams, the condition of algal-dominated periphyton communities is particularly important to nutrient cycling, energy flow, and higher trophic levels. Here, we synthesize current knowledge regarding how AMD-associated stressors affect (i) algal communities and their use as ecological indicators, (ii) their functional roles in stream ecosystems, and (iii) how these findings inform management decisions and evaluation of restoration effectiveness. A growing body of research has found ecosystem simplification caused by AMD stressors. Species diversity declines, productivity decreases, and less efficient nutrient uptake and retention occur as AMD severity increases. New monitoring approaches, indices of biological condition, and attributes of algal community structure and function effectively assess AMD severity and effectiveness of management practices. Measures of ecosystem processes, such as nutrient uptake rates, extracellular enzyme activities, and metabolism, are increasingly being used as assessment tools, but remain in their infancy relative to traditional community structure-based approaches. The continued development, testing, and implementation of functional measures and their use alongside community structure metrics will further advance assessments, inform management decisions, and foster progress toward restoration goals. Algal assessments will have important roles in making progress toward improving and sustaining the water quality, ecological condition, and ecosystem services of streams in regions affected by the legacy of unregulated coal mining.

Application of zeolites for radium removal from mine water

For removal of radium from saline waters in Upper Silesian mines, several methods of purification have been developed. The most efficient one is based on application of barium chloride, which was implemented in full technical scale in two Polish coal mines several years ago. Very good results of purification have been achieved-the removal efficiency exceeding 95% of the initial activity. Another possibility for the removal of different ions from salty waters and brines is the application of zeolites. We found that technique as a very promising method for removal of not only radium isotopes from mine waters but also other ions (barium, iron, manganese). Treatment of several various water samples has been done to assess the removal efficiency for natural radionuclides. Preliminary results show very good effects for radium isotopes as well as for barium ions. In the paper, a short description of laboratory results of the purification of mine waters with application of synthetic zeolites is presented.

Development of uncertainty-based work injury model using Bayesian structural equation modelling

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.