Coal Mining Area Research Articles

Multisource remote sensing monitoring and analyzing for land subsidence and crop growth in a coal mining area under different rainfall conditions

Coal has been crucial in driving economic development and production construction. However, the mining-induced subsidence may cause irreversible damage to the surrounding environment of vegetation growth. Meanwhile, with the worsening of global warming, the frequency and intensity of extreme water-related weather events, such as droughts and excessive rainfall, are on the rise, which leads to heightened impacts on ecosystems and agricultural production. Consequently, extreme water-related weather, the distribution of land subsidence, and its effect on vegetation have attracted significant attention. Based on the Sentinel-1 radar data and Sentinel-2 multispectral data from 2017 to 2022, the SBAS-InSAR technology, the object-oriented classification, and the Normalized Difference Vegetation Index (NDVI) were employed respectively in the study to obtain the spatial-temporal evolution of land subsidence, subsidence-induced water, and crop growth in Tiefa mining area, a representative coal mining area in Northeast China. Moreover, the relationship between land subsidence, subsidence-induced water, and vegetation change was analyzed combined with summer precipitation data. The results showed that: (1) The average cumulative subsidence of the mining area was 256.8 mm, and the subsidence area was 42.525 km2 for the six years. Among them, the heaviest subsidence reached a maximum of 380.5 mm in 2022, and the largest subsidence area was 20.109 km2 in 2017. (2) When the rainfall was excessive, the area of subsidence-induced water would increase sharply, with a proportion jumping to 9.71% from 5.37%, which indicated the subsidence would further amplify the destructive effect of excessive rainfall and waterlogging on land resources. (3) In addition to the existing water pits, ground cracks and shallow subsidence pits appeared under the influence of underground coal mining. The direct impact of ground cracks on crops was not apparent, while the effect of subsidence pits on crops under different rainfall conditions was dual character. In dry years, crops in the subsidence pits could grow better due to higher soil moisture. In wet years, crops in the subsidence pits would suffer the more severe waterlogging. The research results are of great significance for further understanding the influence of coal mining on surface vegetation in mining areas in Northeast China.

Groundwater in Katowice - center of a large urban and deep coal mining area in Poland

The paper presents the groundwater resources of Katowice and the results of studies conducted to identify prospective areas in aquifers with sufficient water resources for the location of public water intakes. The city of Katowice is the capital of Upper Silesia, the largest urban-industrial area in Poland, the region with a highly developed hard coal mining and many other different industries covering almost the entire scope of the economy. Due to long term drainage of mines causing the depletion and deterioration of groundwater and surface water resources, the main municipal water intakes have been located outside Katowice. Most of the pumped mine water is discharged into surface waters, causing significant salinity of water and disallow its use for municipal or industrial purposes in Katowice. The water collection and supply system is very extensive and covers a significant part of the Silesian agglomeration which is why Katowice is supplied with water from surface water intakes located outside the city. According to older published materials, such as the Hydrogeological map of Poland, there were no aquifers in the city that could have been used to supply the city with drinking water, however recent studies have shown that groundwater in the Quaternary aquifers in the western part of the city can be used to supply the city with public water. Less productive, Quaternary aquifers in north-eastern parts of the city may constitute the basis for drinking water supply in emergency situations, when surface intakes cannot be used, such as toxic, radiological or viral contamination or damage to the water supply network eg. as a result of an earthquake or warfare.

Effects of groundwater level changes on soil characteristics and vegetation response in arid and semiarid coal mining areas.

Coal mining in arid and semiarid regions often leads to numerous ecological and environmental problems, such as aquifer depletion, lake shrinkage, vegetation degradation, and surface desertification. The drainage from coal mining activities is a major driving force in the evolution of the groundwater-soil-vegetation system. In order to explore the effect of groundwater level fluctuation on soil properties and the response mechanism of surface vegetation in coal mining areas, this study is based on hydrogeological and ecological vegetation investigations in the Bojianghaizi Basin, and soil and vegetation samples are collected in the areas with different groundwater levels, and soil and vegetation indexes are analyzed with the aid of methods such as numerical statistics, linear regression, and correlation analysis with the aid of the Origin software. The results show that there is a significant negative correlation between groundwater table (GWT) and soil water content (SWC), soil conductivity, soil organic matter (SOM), soil available nitrogen (SAN), and soil available potassium (SAK). Mining activities have led to the destruction of the soil structure, greatly reducing its ability to retain water and fertilizer. The contents of SWC, SOM, and SAN in the mining area are significantly reduced, which are at least 49.73%, 47.56% and 59.90% lower than those around the mining area. On the northern and southern sides of the lake, serious soil salinization exists in the lakeshore zone where the depth to the water table is <0.5m, and the water required for the growth of vegetation here mainly comes from the groundwater, so there are only a few water-loving and saline-resistant plants; when the depth to the water table is 0.5-7m, the growth of surface vegetation is influenced by the double impacts of the water table and atmospheric precipitation with a high degree of species richness; when the depth to the water table is >7m, the surface vegetation is only dependent on the limited atmospheric precipitation for water. When the depth of groundwater is >7m, the surface vegetation only relies on limited atmospheric precipitation for water, and drought-tolerant plants mainly grow in these areas. This study not only provides a scientific basis for the sustainable development and environmental protection of similar mines in the world, but also has important significance in guiding the ecological management and rational utilization of water resources in coal mine areas. What is more, This study provides valuable insights into sustainable water resource management in arid and semi-arid regions, crucial for mitigating the ecological impacts of coal mining activities.

High prevalence of alcohol use disorder & psychiatric comorbidity among coal mine workers: Observations from a cross-sectional study in West Bengal.

Background & objectives Tobacco (30%), alcohol (21.4%) and cannabis (3%) are the three most commonly abused substances in India. Tobacco and alcohol abuse have higher prevalence rates among different occupational groups as compared to that in general population in the country. Both tobacco and alcohol lead to significant occupational harm in terms of absenteeism, injuries, sickness and lost productivity. This study estimated the patterns of substance use and associated common psychiatric comorbidity in a sample of coal mine workers. Methods Coal mine workers at the age of 18 yr or above engaged in mining activities (skilled/semi-skilled/unskilled) were recruited from the Raniganj - Asansol coal mining areas of Eastern Coalfields Limited (ECL). Participants were screened to identify patterns of substance use and other common mental health problems. All participants were administered ASSIST (The Alcohol, Smoking and Substance Involvement Screening Test); GENACIS (Gender, Alcohol and Culture: An International Study) modified questionnaire; and Hospital Anxiety and Depression Scale (HADS) for primary screening for anxiety and depression. Results Out of 202 participants 69 per cent (n=140) were found with either 'hazardous and harmful' (moderate risk); or 'dependent' (high risk) patterns of use of either tobacco or alcohol or cannabis or of more than one substance. Only 31 per cent (n=62) were 'low-risk' for all substances. Almost 65 per cent (n=132) and 49 per cent (n=99) participants out of the whole sample (n=202) belonged to 'hazardous and harmful' (moderate risk) and 'dependent' (high risk) pattern of tobacco and alcohol consumption, respectively. About 28.8 per cent (n=38) and 23.7 per cent (n=31) of all participants had anxiety and depression, respectively. Combined moderate to high use of both alcohol and tobacco use was significantly associated with the risk of having anxiety [adjusted odds ratio (AOR)=4.896, P<0.015] and depression (AOR=5.335, P<0.012). Interpretation & conclusions Alcohol and tobacco are major substance abuse problems among coal mine workers. This population requires early community and primary care based brief intervention as well as additional community-based pharmacotherapy for substance dependence problems as well as intervention for common mental health issues.

Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Abstract. Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.

Source Apportionment and Human Health Risks of Potentially Toxic Elements in the Surface Water of Coal Mining Areas.

Contamination with potentially toxic elements (PTEs) frequently occurs in surface water in coal mining areas. This study analyzed 34 surface water samples collected from the Yunnan-Guizhou Plateau for their hydrochemical characteristics, spatial distribution, source apportionment, and human health risks. Our statistical analysis showed that the average concentrations of PTEs in the surface water ranked as follows: Fe > Al > Zn > Mn > Ba > B> Ni > Li > Cd > Mo > Cu > Co > Hg > Se > As > Pb > Sb. The spatial analysis revealed that samples with high concentrations of Fe, Al, and Mn were predominantly distributed in the main stream, Xichong River, and Yangchang River. Positive matrix factorization (PMF) identified four sources of PTEs in the surface water. Hg, As, and Se originated from wastewater discharged by coal preparation plants and coal mines. Mo, Li, and B originated from the dissolution of clay minerals in coal seams. Elevated concentrations of Cu, Fe, Al, Mn, Co, and Ni were attributed to the dissolution of kaolinite, illite, chalcopyrite, pyrite, and minerals associated with Co and Ni in coal seams. Cd, Zn, and Pb were derived from coal melting and traffic release. The deterministic health risks assessment showed that 94.12% of the surface water samples presented non-carcinogenic risks below the health limit of 1. Meanwhile, 73.56% of the surface water samples with elevated As posed level III carcinogenic risk to the local populations. Special attention to drinking water safety for children is warranted due to their lower metabolic capacity for detoxifying PTEs. This study provides insight for PTE management in sustainable water environments.

The Effect of CO on the Exothermic Characteristics of Low-Temperature Oxidation in Coal

ABSTRACT In recent years, abnormally high concentrations of CO gas have often been detected in goaf areas of coal mines in China, which have posed a serious threat to safe production in coal mines and the life and safety of underground personnel. The effect of CO on the exothermic characteristics of low-temperature oxidation in coal was studied by analyzing the exothermic characteristics, heat release, oxidation stages, and activation energy of coal at different CO concentrations using a C600 microcalorimeter. The experimental results indicated that CO promoted the coal oxidation at low temperature, but inhibited it after reaching 144°C. When the CO concentration is 30%, the exothermic characteristics of coal in low-temperature oxidation adsorption stage were significantly affected, with the heat flow value decreased by 27.0% and the initial exothermic temperature point moved forward by 3.9 K. In the later stage of the oxidation reaction, the heat release of coal was linearly negatively correlated with CO concentration. The higher the concentration, the lower the heat release. These findings contribute to understanding the combustion mechanism of coal in high-concentration CO atmosphere and provide valuable insights for designing and optimizing coal utilization technologies.

Occupation exposure to coal dust and vulnerability to Influenza: Scottish miners and the 1918 Influenza Pandemic.

ObjectiveThere has been little work on the high level of mortality, noted that the time, in coal mining areas during the 1918 Influenza pandemic. Increased risk during viral infection from exposure to particulates (eg cigarette smoke, air pollution) has been studied. ApproachWe use the historic administrative data for Scotland, 1900 to 1930, with standardised codes for occupation and cause of death, and a pseudo household design and Quasi Poisson models, where the spouses of miners are their controls, to test whether miners (aged 20 to 50) were more at risk of death during the main waves of the 1918 pandemic than a matched population. Secondly, we repeat the analysis for Labourers to test if [1] Sex or [2] hard labour and for clerks, if [3] Pulmonary Tuberculosis might explain the effect. ResultsWe find that miners compared to their spouses were significantly more likely to die during all waves of the pandemic but considerably more so during the Autumn wave (RR 4.7). This difference was even greater for younger miners (20 to 35) (RR 7.6). In contrast labourers and clerks had a much lower and often non-significant comparative risk of death during all waves than their spouses. This was true even for those aged 20-35. ConclusionsMale coal miners appear to have been at much higher risk of death compared to matched populations. ImplicationsWorkers with exposure to airborne particulates should be a priority for public health planning during periods of high virus infections.

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q &gt; 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Geo-sustainability Challenges and Remediation Strategies: A GIS-based Assessment of Sripur Coalmine Areas, West Bengal, India

This study examines the health, risk, and safety aspects of coal mining in the Sripur area, focusing on the Bhanora West Block, Asansol, West Bengal, India as one of the 14 operational mines of Eastern Coalfields Limited in Asansol. The study addresses the increasing risks of accidents, fires, and subsidence in active, ageing, and closed mines and storage yards. The study’s incorporation of field surveys, satellite imagery, and environmental data ensures that the findings are based on a well-rounded and multi-faceted dataset. The satellite imagery data (GIS and Remote Sensing) are perceived along with available literature and environmental impact compliance reports from time to time, with particular interest in the Bhanora West coal mine, both open cast and underground. The aspect, Hill shade, LULC and stream order maps of the Damodar are generated using Q-GIS, 3.28.7. The environmental clearance data, accident data, subsidence area, and landslide data are analysed. A vast area in Sripur is observed to be under subsidence, with cracks appearing even near residential areas. The flow of the Nunia nallah is nearby, and old closed mines are not adequately backfilled, so the chance of seepage cannot be neglected though regularly attended. Pneumoconiosis (CWP), a lung disease (black lung), chronic obstructive pulmonary disease (COPD), and silicosis are common in Sripur due to dust and substandard water. Effective and riskless mining extractions using proper technologies can avoid mining hazards, such as subsidence, the influx of water in underground mines, health hazards, air/water pollution, mine fire, and intact physical landscape and observing criteria of sustainable Development Goal SDG-1 SDG-3, SDG-6, SDG 12, SDG-13, and SDG 16.

DRA-UNet for Coal Mining Ground Surface Crack Delineation with UAV High-Resolution Images.

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation.

A Study on Historical Big Data Analysis of Surface Ecological Damage in the Coal Mining Area of Lvliang City Based on Two Mining Modes

Coal mining inevitably causes damage to the surface ecological environment. In response to the characteristics of multiple factors, wide scope, and long time series of surface ecological environment damage in coal mining subsidence areas, how to integrate multiple data sources and monitoring methods to achieve monitoring and trend analysis of ecological damage throughout the entire mining cycle still needs to be studied. In addition, the 110 mining method provides an innovative method for underground coal mining to reduce its surface ecological damage, but the differences in surface damage between the two mining modes and the effectiveness of the 110 method in realizing surface ecological damage-reducing mining still need to be studied in depth. Therefore, this study takes the surface ecological damage in the mining area of Lvliang City, a typical resource city in Shanxi Province, China, as the object. It establishes a four-in-one “Satellite–UAV–Ground–Underground” information monitoring method, proposes a historical big data evolution analysis method, constructs three spatial scales of historical big databases, clarifies the current situation and development trend of damage in coal mining areas in Lvliang City and explores the differences in surface ecological environment damage characteristics in coal mining areas based on the 121 and 110 mining methods. The results show that: (1) The existing coal mining subsidence area in Lvliang City is as high as 92,191.47 hectares, and it is expected to continue to increase to 130,739.55 hectares in the future 2035, with a growth rate of 41.812%, which realizes the goals of mapping the current situation, retracing the history and predicting the future of the ecological damage of the surface in Lvliang City. (2) The surface NDVI of the 110 working face is restored to the average level of the mining area faster than that of the 121 working face. The surface crack width, step displacement, length, distribution density, and surface settlement height of the 110 working face are all smaller than those of the 121 working face. It has been verified that the unique top-cutting and swelling filling effect of the 110 methods can effectively reduce the ecological damage caused by coal mining subsidence. And its widespread application can effectively realize the ecological environmental protection of the coal mine area and contribute to the high-quality development of the coal industry in Lvliang City.

Evolution characteristics and attribution analysis of runoff in the coal mining area in northwest China: A case study of the Kuye River Basin

ABSTRACT The Kuye River Basin is the main coal-producing area in northwest China, and it is also a typical arid and semi-arid area. Under the combined influence of climate change and human activities, the runoff of the basin decreases noticeably. The Mann–Kendall trend test, the Hurst index, cumulative anomaly, and wavelet analysis were used to analyze the runoff evolution characteristics of the Kuye River, and the contributions of climate change and human activities to the runoff reduction were quantitatively separated by the slope-changing ratio of the cumulative quantity. The results showed that the runoff decreased significantly from 1970 to 2020, and there was an obvious abrupt change in 1996, while the change trends in precipitation and evaporation were not significant. The contributions of climate change and human activities to runoff reduction were 22.71 and 77.29%, respectively, and human activities have become the dominant factor in runoff variations. Coal mining and the implementation of soil and water conservation measures are considered to be the main human activities contributing to the reduction in runoff. In particular, mining activities reduce groundwater recharge to rivers, which provides a profound understanding of the effect of human activities in mining areas on runoff variation.

Analysis of spatio-temporal development of mining landforms using aerial photographs: Case study from the Ostrava–Karviná mining district

Abstract The anthropogenic relief transformations of the mining landscape are characterised by high dynamics of changes over time that can be effectively mapped on a large scale using aerial images. The Karviná part of the Ostrava-Karviná mining district, which stands for significant hard coal mining area in the Czech Republic, has been selected to analyse the spatio-temporal development of anthropogenic landforms. Anthropogenic landforms were visually identified from the aerial images from 1947, 1966, 1971, 1985, 1994, 2009, and 2018. Specific anthropogenic landforms were analysed along with their spatio-temporal changes based on obtained vector data. The total extent and number of anthropogenic landforms increased the most during the periods 1947–1966 and 1971–1985. The same anthropogenic landforms occurred on circa 24 hectares in the entire period 1947–2018. The most anthropogenic landforms remained preserved in the last observed period 2009–2018. The multi-temporal analysis of aerial photographs and overlay operations in GIS enabled to map the age of specific landforms and to identify changed or unchanged anthropogenic landforms. This method is suitable for the study of landform dynamics at mining sites and is particularly relevant for the planning challenges in post-mining reclamation.

Spatiotemporal Patterns of Vegetation Evolution in a Deep Coal Mining Subsidence Area: A Remote Sensing Study of Liangbei, China

This study aims to provide a comprehensive analysis of the impacts of high-intensity coal mining on vegetation in Liangbei Town, a typical deep coal mining area in central of China. Using Landsat remote sensing data from 2000 to 2023, processed by the Google Earth Engine (GEE) platform, the study calculates the Normalized Difference Vegetation Index (NDVI). Temporal and spatial distribution patterns of vegetation were assessed using LandTrendr algorithm, Sen’s slope estimation, the Mann–Kendall test, the coefficient of variation, and the Hurst index. Vegetation growth dynamics were further analyzed through transfer matrix and intensity analysis frameworks. Driving factors influencing vegetation trends were evaluated using local climate data and surface deformation variables from SAR imagery. Temporal Dimension: From 2000 to 2023, the annual NDVI in Liangbei Township showed an upward trend with a growth rate of 0.0894 (10a)−1, peaking at 0.51 in 2020. Spatial Dimension: The NDVI distribution in Liangbei Township displayed a pattern of being lower in the center and higher around the edges, with values concentrated between 0.4 and 0.51, covering 50.34% of the total area. Trend of Change: Between 2000 and 2023, 83.28% of the area in Liangbei Township experienced significant improvement in the NDVI, with vegetation growth trends shifting primarily from slight to significant improvement, encompassing a total area of 10.98 km². This shift exhibited a marked tendency. Driving Factors: Deep mining in Liangbei Township is concentrated in the eastern part, with SAR imagery indicating a maximum surface subsidence of 0.26 m. As surface subsidence increases, the NDVI significantly decreases. The findings suggest that in the future, 91.13% of the vegetation in Liangbei Township will display an antipersistent change trend. The study offers critical insights into the interaction between mining activities and vegetation cover can serve as a reference for environmental evolution and management in similar mining areas.

Groundwater quality in high-sulfur coal mining region of India: Spatial distribution, source control, and health risk assessment

The groundwater quality in the vicinity of the Makum coalfield, renowned for its high-sulfur coal deposits, was investigated. The oxidation of sulfur in the coal generates acid mine drainage (AMD), a global environmental challenge that contaminates natural resources. The region's high sulfur coal content intensifies AMD formation, necessitating a comprehensive assessment of its impact on human health and the environment. This study analyzes the water quality parameters such as pH, EC, TDS, Na+, Ca+2, Mg+2, K+, HCO3‐, SO4−2, F−, Cl -, and NO3− in groundwater, findings concerning low pH levels (5.8) and fluoride concentration (0.15 mg/L) compared to standards. Groundwater chemistry was analyzed to identify the sources controlling water composition through Gibbs diagrams, Piper diagrams, and saturation indices. The Gibbs diagram shows that rock weathering is the crucial factor controlling groundwater chemistry, while the Piper diagram indicates Ca-Cl as the Principal water type. Additionally, an in-depth analysis of groundwater chemistry reveals that carbonate dissolution primarily occurs due to minerals like calcite, dolomite, and gypsum, findings supported by saturation indices. The present study yielded an average water quality index of 40.19, indicating excellent to good water quality in 51 out of 52 samples analyzed. The average hazard index values for adults and children were 0.60 and 0.58, respectively, indicating that 49 of 52 samples pose negative non-carcinogenic risks associated with nitrate and fluoride contamination. The irrigation indices, graphical representations such as the Wilcox and Doneen classification, and the USSL diagram elucidate the suitability for irrigation purposes. Moreover, the Principal Component Analysis identified the sources of ions as originating from geogenic processes and mining activities. The study stresses environmental assessments, health risk management, and sustainable practices for groundwater in high-sulfur coal mining areas.

A 6-year review status on soil pollution in coal mining areas from Europe.

Coal is an essential component in achieving the goal of fulfilling the energy demands of the world. Nevertheless, the extensive practice of coal mining has resulted in environmental contamination through the release of both organic and inorganic pollutants, including polycyclic aromatic compounds and potentially toxic elements, into various mediums, notably soil. The escalating coal-mining activities across Europe have amplified the concentration of specific elements in the soil. Therefore, a thorough and meticulous assessment of these environmental impacts is imperative to furnish policymakers, industries, and communities with valuable insights, facilitating the formulation and adoption of effective mitigation strategies. Considering the results of studies from 2018 to 2023, this review thoroughly evaluates the current state of soil pollution in the coal mining areas of Europe, focusing on polycyclic aromatic hydrocarbons and potentially toxic elements. By analyzing the acquired data, this study aims to evaluate the levels of contamination by these pollutants in soils. The findings reveal that low molecular weight polycyclic aromatic hydrocarbons dominate the polycyclic aromatic compounds present, while potentially toxic elements including Zn, Pb, Mn, and Cr emerge as major contributors to soil contamination in coal mining areas from Europe.

A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error.

Systematic assessment of source identification and ecological and probabilistic health risks of potentially toxic elements (PTEs) in soils of a typical coal mining area in Guanzhong region

Mining activities may cause the accumulation of potentially toxic elements (PTEs) in surrounding soils, posing ecological threats and health dangers to the local population. Therefore, a comprehensive assessment using multiple indicators was used to quantify the level of risk in the region. The results showed that the mean values of the nine potentially toxic elements in the study area were lower than the background values only for Cr, and the lowest coefficient of variation was 17.1 % for As, and the spatial distribution characteristics of the elements indicate that they are enriched by different factors. The elements Hg and Cd, which have substantial cumulative features, are the key contributors to ecological risk in the study region, which is overall at moderate risk. APCS-MLR model parses out 4 possible sources: mixed industrial, mining and transportation sources (53.98 %), natural sources (24.56 %), atmospheric deposition sources (12.60 %), and agricultural production sources (8.76 %). The probabilistic health risks show that children are more susceptible to health risks than adults; among children, the safety criteria (HI < 1 and CR < 10−4) were surpassed by 29.29 % of THI and 8.58 % of TCR. According to source-orientated health hazards, the element Ni significantly increases the risk of cancer. Mixed sources from industry, mining, and transportation are important sources of health risks. The results of this research provide some scientific references for the management and decrease of regional ecological and health risks.

Mechanical Characteristics of Suspended Buried Pipelines in Coal Mining Areas Affected by Groundwater Loss

Mechanical Characteristics of Suspended Buried Pipelines in Coal Mining Areas Affected by Groundwater Loss