A multiple isotope (S, H, O and C) approach to estimate sulfate increasing mechanism of groundwater in coal mine area

Self-Organizing Map provides new insights into the MixSIAR model for calculating the source contributions of sulfate contamination in groundwater

The concentration of sulfate in global groundwater has been observed a significant upward trend in recent years. Excessive sulfate levels contribute to increased groundwater salinity and acidification, thereby posing a threat to human health and ecological balance. For effective groundwater pollution management and control, accurately quantifying the sources of sulfate pollution remains a challenge. This research integrates the Self-Organizing Map (SOM) clustering method to enhance the accuracy of the Bayesian isotope mixing model (MixSIAR) in quantifying the contribution rate of groundwater sulfate. During the dry season, sulfate (SO42-) primarily originates from the oxidation of pyrite, whereas SO42- sources include both pyrite oxidation and the co-dissolution of carbonate rocks and gypsum during the normal and wet seasons. Incorporating SOM, the MixSIAR model demonstrates reduced values of Leave-One-Out Information Criterion (LOOIC), and Widely Applicable Information Criterion (WAIC) (LOOIC = 82.5, and WAIC = 82.3). Overall, in the study area, coal mines (accounting for 34.3% - 48.4%) are identified as the primary pollution sources, particularly in Clusters 3, 4 and 5. Clusters 1, 2, and 5 are more significantly affected by other pollution sources, with fertilizers contributing 32.7%, evaporite dissolution contributing 24.1% and 24.2%, respectively. This study supports the development of regional pollution control strategies.

Enhanced carbonate weathering and CO2 release in a typical karst watershed (Southwest China): Evidence from hydrochemical and multi-isotopic data

Carbon sinks formed by carbonate weathering are a major component of the carbon sink deficit and play a significant role in the carbon cycle. However, exogenous acids (HNO3 and H2SO4) are also involved in carbonate weathering, complicating the overestimation of the formation of carbon sinks and fluxes. In this study, we analyzed the Hongjiadu karst underground river basin in Guizhou using isotopes (δ13C-DIC, δ34S-SO4, δ18O-SO4, δ18O-H2O, and δ2H-H2O) and hydrochemical methods to investigate carbon cycling characteristics and its influencing factors, and quantitatively evaluate the effects of HNO3 + H2SO4 on CO2 sink fluxes in the basin. The following results were obtained: (1) The nitrate in the watershed mainly comes from fertilizers, and the sulfate mainly comes from local coal seams. (2) The relationship between isotopes (δ13C-DIC, δ34S-SO4, δ18O-SO4, δ18O-H2O, and δ2H-H2O) and hydrochemicals (Ca2+, Mg2+, NO3-, HCO3-, and SO42-) indicates that H2CO3, HNO3, and H2SO4 are involved in carbonate rock dissolution. Most of the samples in the watershed, particularly the acid mine drainage water, were affected by acid rain and sulfide oxidation, resulting in increased δ13C-DIC values. Another portion of the springs was affected by soil CO2, resulting in decreased δ13C-DIC values. (3) Quantitative calculations showed that exogenous acids enhanced the weathering rate of carbonate rocks in groundwater by 67.24 % (dry season) and 76.31 % (wet season) while decreasing the carbon sink flux in groundwater by 55.31 % (dry season) and 66.64 % (wet season). Rainfall enhances the weathering of carbonate rocks. In the process, CO2 is released, increasing the carbon source. Our study highlights the influence of anthropogenic, natural, and hydrological changes on karst carbon sinks in mixed-pollution karst basins. This information is important for future studies on the carbonate weathering processes in karst regions.

Regional characteristics of groundwater sulfate source and evolution in the multi-layer aquifer system of the northern Shaanxi coal mine base, northwestern China: Evidence from geochemical and isotopic fingerprints

Groundwater sulfate contamination in mining areas has attracted widespread attention. However, deciphering the source and evolution of sulfate in large-scale mining areas remains a challenge due to intense anthropogenic influences and complex hydrogeological conditions. In this study, 94 groundwater samples were analyzed by a combination of self-organizing maps, MixSiar model, multi-isotope analyses (δ34S, δ18OSO4, δD and δ18Owater) and hydrogeochemical methods to investigate the regional characteristics of groundwater sulfate source and evolution in China's largest coalfield (the Shenfuyu Coalfield). The results showed that the source and evolution of groundwater sulfate were controlled by human activities (mining and agricultural activities) and hydrogeological conditions. The groundwater sulfate primarily originated from pyrite oxidation, gypsum dissolution and human inputs. For the mining districts with shallow mining depths, pyrite oxidation and fertilizer contributed to groundwater sulfate. In addition, the ground cracks and abandoned mines controlled the BSR and pyrite oxidation processes. In contrast, the gypsum dissolution and cation exchange dominated the sulfate evolution in the mining districts with deep mining depths due to slow groundwater circulation. This study provided new insights into the source and evolution of groundwater sulfate in large coalfields, as well as references for regional water resource utilization and protection.

Multi-isotopes (δD, δ18Owater, 87Sr/86Sr, δ34S and δ18Osulfate) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China

Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δ18Owater, δD, 87Sr/86Sr, δ18Osulfate and δ34S) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO4). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.

Limestone water mixing process and hydrogen and oxygen stable isotope fractionation response under mining action

North China type coalfield are gradually mining deep, and the mixing of groundwater is intensified. Hydrogen and oxygen isotopes are important elements for tracing groundwater movement. The fractionation response mechanism under mining conditions is not clear. In this paper, combined with numerical simulation, MixSIAR isotope mixing model and other methods, according to the δD, δ18O and hydrochemical information of various water bodies, the impact of coal mining on hydrogen and oxygen isotope fractionation is analyzed from multiple perspectives. The results show that summer soil water is the main source of recharge for limestone water, accounting for 30.7%-41.5%, and the Zhan River is the main source of recharge for limestone water. Before groundwater recharge, evaporation leads to the increase of δ18O in surface water by 0.31‰-5.58‰, water loss by 1.81%-28.00%, the increase of δ18O in soil water by 0.47‰-6.33‰, and water loss by 2.74%-35.80%. Compared with the coal mining layer, the degree of hydrogen and oxygen isotope drift and water-rock interaction in the coal mine stopping layer are significantly improved. The results of numerical simulation show that the pumping activity reduces the 18O concentration in the mining layer. The ion ratio is used as a new variable to eliminate the influence of water-rock interaction when calculating the mixing ratio. The results show that the limestone water is in a state of receiving external recharge, and mixing effect increases the δ18O in limestone water by 0.86‰ on average, and the δD increases by 0.72‰ on average. The research results explain the controlled process of hydrogen and oxygen isotope fractionation under mining conditions, which is of great significance to coal mine safety production.

Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method

Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing one-dimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf Optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other areas to provide a basis for the planning and management of groundwater resources.

Tracing sulfate sources and transformations of surface water using multiple isotopes in a mining-rural-urban agglomeration area

Rapid urbanization and mining activities are exacerbating sulfate (SO42-) pollution in surface water, and the information on its sources and transformations is crucial for understanding the sulphur cycle in mining areas. In this study, the SO42- in the surface water of Huaibei mining area were monitored and the main sources of pollution and biogeochemical processes were identified using stable isotopes (δD, δ18O-H2O, δ34S-SO42- and δ18O-SO42-) and water chemistry. The results demonstrated the SO42- content in the Huihe River and Linhuan subsidence water area (SWA) is higher than that in other rivers and SWAs, which exceeded the environmental quality standard of surface water. The SO42- content of different rivers and SWAs showed seasonal differences, and the dry season was higher than the wet season. In addition, the SO42- in Tuohe River and Suihe River is primarily caused by urban sewage and agriculture activities, while in Zhonghu and Shuoxihu SWA is mainly contributed by natural evaporate dissolution. Notably, the input of SO42- in the Huihe River and Linhuan SWA caused by mining activities cannot be disregarded. The aerobic environment and isotopic fractionation of surface water indicate that sulfide oxidation is not the major cause of SO42- formation. This work has revealed the multiple sources and transformation mechanisms of SO42-, and provided a reference for the development of comprehensive management and effective remediation strategies of SO42- contamination in surface water around mining areas.

A review: The formation, prevention, and remediation of acid mine drainage

In abandoned open-pit coal mines, surface water and groundwater form acidic waters with high concentrations of metal ions due to chemical interactions with ores such as pyrite, and the formation of acid mine drainage (AMD) is one of the major sources of pollution of world concern. For this reason, this paper reviews the formation mechanisms and influencing factors of AMD. It also describes the prediction, prevention, and remediation techniques for AMD, identifying key research gaps. It also discusses the current challenges and shortcomings faced globally in the management of AMD. The formation of AMD is mainly caused by the oxidation of pyrite in mines, but it is mainly influenced by history, climate, topography, and hydrogeology, making the formation mechanism of AMD extremely complex. Currently, the remediation technologies for AMD mainly include active treatment and passive treatment, which can effectively neutralize acidic wastewater. However, the prediction technology for AMD is blank, and the source treatment technology such as passivation and microencapsulation only stays in the experimental stage. This leads to the high cost of treatment technologies at this stage and the inability to identify potential risks in mines. Overall, this review provides remediation tools for AMD from predicting root causes to treatment. Geophysical technology is an effective method for predicting the motion path and pollution surface of AMD in the future, and resource recovery for AMD is a key point that must be paid attention to in the future. Finally, integrated treatment technologies that deserve further exploration need to be emphasized.