The coal resources in the northwest region of China are abundant and make up a substantial proportion of the total coal resources in the country, accounting for approximately 80% [1
]. However, the water resources in the same region are quite limited, making up only 9.4% of the total water resources in the country [5
]. With rich coal and poor water in northwest China, a large amount of mine water will be produced in the process of coal mining, which leads to water loss. Many coal mines in arid areas are faced with the problem of unclear carrying capacity of groundwater resources, which makes it impossible to escape the dilemma of groundwater loss and water shortage. The dry and arid climate, with low precipitation and high evaporation levels, has made water resources scarcity a key challenge to the development of the coal industry in this region. At present, the research on water resources carrying capacity has been widely carried out, but mainly concentrated on surface water resources. The study on the carrying capacity of groundwater resources is still insufficient, especially the research on coal mines in arid areas is rarely carried out. Groundwater, as a crucial water source, directly influences the mining prospects and construction directions in a region due to its carrying capacity. An analysis of groundwater carrying capacity can provide insight into the relationship between limited groundwater resources, population, environment, and economic development, thereby identifying the factors and conditions that restrict the sustainable development of the mining area. Conducting an evaluation of groundwater carrying capacity is of great significance for comprehending the endowment of groundwater resources in the mining area and other arid regions. The coal mine in Lingwu City selected in this paper is the first to carry out an analysis of groundwater resource carrying capacity. As a case study, this study opens a window to understanding the characteristics of groundwater endowment in northwest China arid region.
The fuzzy comprehensive evaluation method (FCE) is a systematic evaluation approach that leverages the principles of fuzzy mathematics to address complex and non-quantifiable issues [8
]. Its comprehensiveness and ability to produce clear outcomes have led to its widespread application in various domains [11
]. Zhu et al. [12
] utilized the FCE in conjunction with the Analytic Hierarchy Process (AHP) to develop a coal burst prediction model for a coal mine in Shanxi Province, resulting in a successful evaluation of the coal burst risk level. Chen et al. [13
] proposed a framework for the evaluation of teaching performance that incorporates both the AHP and FCE, demonstrating the effectiveness, reasonableness, and accuracy of the evaluation results. Guo et al. [14
] employed the FCE in the evaluation of road pavement performance for the Xinglin section of Taihang Mountain Expressway reconstruction and expansion project, providing a quantitative assessment of the old pavement’s performance level through fuzzy mathematical iteration calculations. Wang et al. [15
] developed an improved version of the FCE for the evaluation of desulfurization technology based on the principle of maximum membership degree, and obtained an effective solution. Fuzzy theory is based on the fuzzy set, which is a class of objects with continuous membership levels [16
]. Such a set is represented by a membership (feature) function that assigns each object a membership rank between 0 and 1 [17
]. Fuzzy sets and theory have been applied in many ways. Yang et al. [18
] evaluated the geological environment quality of heavy metal mines in Zhaotong City based on the fuzzy set theory, and divided the evaluation into four grades: excellent, good, common, and inferior, which provided a reference for decision-making of current geological environment protection. In this paper, the groundwater carrying capacity of the mining area was calculated based on the FCE, and the changes in the groundwater carrying capacity and evaluation index membership in the mining area from 2011 to 2020 were analyzed, providing support for the follow-up exploitation and construction of the coal mine.
The accuracy of evaluation results in the FCE critically depends on the proper determination of the weights of the evaluation indicators [19
]. AHP is a decision analysis method which is widely used because of its simplicity and systematism. Compared with other mathematical-based approaches, it combines qualitative and quantitative methods to solve complex multi-objective problems, especially in the allocation of weights [21
]. In addition, AHP can determine the weight by layers and combine subjective and objective factors to make the decision more scientific and reasonable [21
]. Wang et al. [25
] introduced a variable weight factor into the traditional AHP process and presented a novel and improved weight distribution technique. The finding indicates that the AHP method is effective in managing the dynamic interactions between indicators. Guo et al. [26
] proposed a social vulnerability assessment model based on the rough AHP method, where the standard weights were established using the rough AHP process and the social vulnerability of different regions was evaluated. Zhu et al. [27
] utilized the AHP and life cycle assessment methods to rank the evaluation indicators and examine the assessment methods of public and some rural toilets from design to decommissioning, thereby enhancing the comprehensiveness of toilet evaluation under varying conditions. This study adopts the AHP method to determine the weights of the indicators for evaluating groundwater carrying capacity, thereby ensuring the accuracy of carrying capacity computation.
One of the preconditions for the calculation of FCE is to determine the weight of each evaluation index, that is the weight vector, which is generally directly specified by the decision-maker. However, for complex problems, such as many evaluation indexes and mutual influence relationships, it is difficult to directly give the weight of each evaluation index, which is exactly what AHP is good at. FCE and AHP have successful practice cases in the situation of combined use, and have achieved the expected effect. In this study, the combined method of FCE and AHP is used to analyze the groundwater resources carrying capacity of coal mines in arid areas. The present study first constructed an evaluation index system for the carrying capacity of groundwater resources based on the actual situation of the coal mine area and the collection of measured and statistical data from the coal mine. Subsequently, the weights of the evaluation indicators were assigned using the AHP. Next, the evaluation level grading criteria were determined based on the actual situation of the study mining area, and a fuzzy relationship matrix was established based on the membership function. Finally, the comprehensive value for the carrying capacity of groundwater resources was calculated, and the changes in the carrying capacity of groundwater from 2011 to 2020 and in the membership of important indicators were analyzed. This study provides a basis for strengthening the protection of coal mine groundwater resources. In addition, different from other similar studies, the highlight of this paper lies in the specificity and certainty of the research object, which can provide ideas for the development of coal mining industry in arid areas and provide references for coal mines to improve the carrying capacity of groundwater resources.
3. Results and Discussion
3.1. Indicator Weight Assignment
Using the AHP to assign weights to the evaluation indicators of the groundwater resources carrying capacity in the coal mine. The paired comparison matrix for the objective layer (A) and the criteria layer (B) can be seen in Table 6
The comparison matrices for the criterion layer and each indicator layer can be seen in Table 7
, Table 8
and Table 9
The summary of the weights of evaluation indicators can be seen in Table 10
3.2. Fuzzy Relationship Matrix
Based on the membership degree functions 1–3 and based on the data and evaluation index system of the mining area, the fuzzy relationship matrix of indicator from 2011 to 2020 is calculated as shown below.
As shown by the above data, the three indices of total groundwater resources, the degree of groundwater development and utilization, and per unit GDP of water consumption have relatively high weights in the evaluation index system. The changes in the membership degree of these three indices over time can be seen in Figure 2
The magnitude of the groundwater resources in a mining area has a direct impact on its carrying capacity. As depicted in Figure 2
a, the membership degree of the total groundwater resources index to V3 gradually increased during 2011–2018. Combined with the field data, the decrease in the groundwater carrying capacity in the mining area is related to the decrease in the total groundwater resources from 2011 to 2018. With the increase in total groundwater resources from 2018 to 2020, the carrying capacity of groundwater resources has a recovery trend. The analysis of total groundwater resource index shows that groundwater carrying capacity is closer to V3 level. The degree of groundwater development and utilization also underwent a transformation, with 74% belonging to the V1 level in 2011, transitioning to 91% belonging to the V2 level in 2014, and eventually evolving to 59% belonging to the V3 level in 2017 (Figure 2
b). This demonstrates that the utilization of groundwater resources has continuously intensified from 2011 to 2017, reaching its limit.
As a socioeconomic indicator, per unit GDP of water consumption reflects the production conditions of the mining area. As shown in Figure 2
c, during the period of 2011–2013, the coal mining was relatively rough, with 88% belonging to the V2 level and 12% belonging to the V3 level. From 2014 to 2020, the coal mine continuously improved its production process and technology to increase efficiency, resulting in the indicator gradually shifting from the V2 level to the V1 level.
3.3. Carrying Capacity Calculation Results
According to the Equations (4) and (5), the results of the calculation for the groundwater resources carrying capacity in the mining area from 2011 to 2020 are shown in Table 11
Based on the evaluation results and the principle of maximum membership degree, it can be seen that from 2011 to 2020, the carrying capacity of groundwater resources in the mining area is the highest for V2 level, followed by V3 level, and the lowest for V1 level. This indicates that the carrying capacity of groundwater resources in the mine area will reach the limit and the remaining development potential can be ignored. The subsequent mining construction should focus on the protection of groundwater resources and reduce groundwater exploitation.
The trend of the comprehensive evaluation value of groundwater resources carrying capacity in the mining area from 2011 to 2020 is shown in Figure 3
As shown in Figure 3
, the groundwater resources carrying capacity in the mining area changed from a large decline to a slight increase during the period of 2011–2020. The groundwater resource carrying capacity was low between 2011 and 2018, with a minimum value of 0.3518, and generally declining. The decrease may be due to the increasing scale of production in the mining area, which caused a short-term increase in the groundwater drainage, resulting in a groundwater funnel. In addition, the study mining area is located in the northwest arid region of China, with sparse rainfall and strong evaporation. The combined effect of climate factors and unstable groundwater recharge sources has led to a downward trend in the total amount of groundwater resources in the study period.
The groundwater resources were effectively protected during the period of 2018–2020 and the total groundwater resources increased. The carrying capacity of groundwater resources in mining area was partially recovered to a certain extent, but it was still necessary to ensure that the groundwater resources were within a reasonable range during coal mining. The carrying capacity of groundwater resources increased slightly from 2018 to 2020, with a value of 0.3985 in 2020. This may be due to the continuous use of advanced mining equipment and improved production technology in the research mining area, and the application of water-conserving mining technology has greatly improved the utilization efficiency of groundwater. The continuous improvement of clean production management and system in mining areas also guarantees the multilevel utilization of domestic water. At the same time, new engineering measures such as the construction of an underground concentrated brine reservoir have been taken to improve the production efficiency, bringing in a decrease in the per unit GDP water consumption from 16.2 m3/10,000 yuan to 11.1 m3/10,000 yuan, a decrease of 31.48%. In addition, the extent of groundwater development and utilization in mining areas has also been reduced, from 43.46% to 42.85%. During the period when coal mining capacity increases sharply, the amount of groundwater outflow caused by coal mining remains relatively stable. This can also reveal that the mining area has greatly realized the reuse of groundwater, reduced unnecessary waste, and realized the recovery of groundwater resource carrying capacity. With the enhancement of people’s awareness of environmental protection, the concept of building green mines is gradually applied to the entire mining process. During 2018–2020, the study area implemented large-scale greening actions, greatly improving the ecological environment of the mine and its surrounding areas. The decline in vegetation coverage has been reversed, and the ability to replenish groundwater has been enhanced, leading to an increase in the total groundwater resources from 14.867 × 107 m3 in 2018 to 15.146 × 107 m3 in 2020. This is an important reason for the increase in the carrying capacity of groundwater resources.
This study indicates that the size and temporal variation of the groundwater resources carrying capacity in the mining area can provide direction and basis for the subsequent mining production in coal mining in arid areas. The groundwater resources in the study coal mining area are scarce, the development intensity at a high level, and the carrying capacity at a low level. Therefore, in the subsequent development, efforts should continue to be made to protect groundwater resources, optimize production processes to improve the efficiency of groundwater utilization, strengthen the construction of environmental protection infrastructure in the mining area, and slow down the development and utilization of groundwater.