Evaluation of disturbance by coal mining to groundwater and surface ecosystem

Evaluation of disturbance by coal mining to groundwater and surface ecosystem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of disturbance by coal mining to groundwater and surface ecosystem Kai Zhang, Jiangang Zhao, Xiaonan Li, Xinhui Zheng, Yajing Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2218281/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coal mining disturbs surface ecosystems in coal mining subsidence areas. Based on the groundwater-surface composite ecosystem analysis, we constructed an ecological disturbance evaluation index system (18 indices) in a coal mining subsidence area using the analytic hierarchy process (AHP). Taking the Nalinhe mining area in Wushen Banner, China, in 2018-2020 as an example, the ecological disturbance degree and the weight and correlation of different indicators were determined by implementing fuzzy mathematics, weighting method, and correlation analysis method. After two years of mining, ecological disturbance was the highest in the study area (Grade III) and the lowest in the non-mining area (Grade I). The ecological disturbance in the coal mining subsidence area continued increasing over two years due to coal mining. The ecological disturbance by coal mining cannot be completely mitigated by relying on the self-repair capability of the environment. Coal mining not only directly interfered with the environment, but also strengthened the connection of different ecological indicators, forming multiple ecological disturbance chains such as "coal mining–surface subsidence–soil chemical factors," "natural climate–soil physical factors–soil chemical factors," and "mining intensity–mining thickness–burial depth and mining thickness ratio”; the last disturbance chain increased the ecological disturbance caused by resource mining. The disturbance chain "coal mining–surface subsidence–soil chemical factors" plays a leading role in controlling the ecological disturbance of soil chemical factors. The disturbance chain that controls the ecological component factors in the region remains unknown; however, the analysis of the results reveals that ecological component factors is the most important factor that hinders the restoration of the ecological state in the coal mining subsidence area. This study is of great significance for ecological restoration and governance of coal mining subsidence areas. Coal mining subsidence area Ecological disturbance assessment Analytic hierarchy process Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction According to the International Energy Agency (IEA), coal remains as a major component of the global fuel supply, accounting for 27% of global energy use. China is the world's largest producer and consumer of coal, and the coal industry has played a supporting role in the social and economic development of the country (Dong et al. 2021 ) The western regions represented by Xinjiang and Inner Mongolia are the main coal mining territories in China (Wang et al. 2021a ) However, coal mining has brought about serious ecological and environmental problems in these areas (Ren et al. 2020 , Tyulenev et al. 2019 ), with surface subsidence being the most prominent issue (Zhu et al. 2021 ). Surface subsidence is a ground deformation caused by the roof subsidence of underground goaf. This subsidence is accompanied by vegetation destruction (Hou et al. 2021 ), river drying (Lv et al. 2020 ), and soil fertility reduction (Ma et al. 2019 ), which are major problems in reclamation and reconstruction of the western region. In multiscale studies on coal mining subsidence areas (in terms of fertility (Yang et al. 2013 ), sensitivity (Tai et al. 2020 ), vulnerability (Li et al. 2018b ), recovery (Yang et al. 2018a ), etc.), ecological disturbance has been the current research focus (Li et al. 2021 , Yan et al. 2022 ). The poor ecological stability and resilience of the western region hinder the recovery and rebuilding of the region after being disturbed by subsidence (Wang et al. 2016 ). Studying the ecological disturbances in coal mining subsidence areas is therefore of great significance. Ecological disturbance in coal mining subsidence areas is usually measured by ecological disturbance degree, which is the degree to which surface deformation caused by mining interferes with the ecosystem (Wang et al. 2016 ). Current methods for measuring ecological disturbance include satellite monitoring(Chowdhury et al. 2021 ), LandTrendr model calculation(Yang et al. 2018b ), 3S technology (Wu et al. 2011 ), and others, the research areas include vegetation (Liu et al. 2020 ), atmosphere (Hughes et al. 2019 ), and soil (Wang et al. 2021b ). However, the reported results have often been controversial. For example, some scholars believe that coal mining is the main factor that disturbs the soil physical and chemical properties in arid and semi-arid regions in the west of China (Ma et al. 2019 ). Others believe that the annual evaporation in this area is much greater than the precipitation, and severe climate is the main factor causing the disturbance of soil physical properties (Wang et al. 2018 ). The main reason for these controversies is that the existing research usually focuses only on the ecological indicators with poor performance in the evaluation system, and fails to consider the interaction and chain reaction between indicators. In fact, the harsh climatic environment and mining activities in the northwestern region impact the environment in nearby areas by disturbing the vegetation growth and causing soil water stress (Ni 2020 ). The interaction between these ecological indicators further exacerbates the degree of ecological disturbance in the coal mining subsidence area. Consequently, the existing research cannot quantify the ecological disturbance in a coal mining subsidence area (Yang et al.). Therefore, it is very important to build an evaluation method of ecological disturbance that comprehensively considers natural and human factors and reveals the relationship between different ecological indicators. Such a method will help to realize the reclamation of coal mining subsidence areas in western China. The analytic hierarchy process (AHP) is a commonly used ecological evaluation method that is compatible with qualitative and quantitative analyses. This analytical method combines qualitative and quantitative analyses to decompose the problem into factors at different levels and uses the low-level factors to explain and supplement the high-level factors to which they belong in order to systematize the problem (Song et al. 2007 ). Theoretically, AHP can be applied to evaluate ecological disturbances in coal mining subsidence areas. However, the method relies on the subjective judgment of decision-makers regarding the problem, and the results generally lack objectivity (Lyu et al. 2020 ). Fuzzy mathematics transforms qualitative evaluation problems into quantitative evaluation problems by operating fuzzy sets and fuzzy relationships, thereby avoiding one-sidedness in the results (Zhang et al. 2021 ). The weighting method reduces the influence of human factors on the results by substituting numerical values for subjective human judgments (Hou 2015 ). Complementing the AHP with these two methods increases the objectivity and accuracy of the evaluation results (Chen 2020 , He et al. 2019 , Li et al. 2018a ). The present study aimed to construct an AHP hierarchical index system to evaluate groundwater and surface ecological environments. We used the weighting method to determine the impact weight and cumulative effect of evaluation indicators on the ecological environment, and employed the fuzzy mathematical method to evaluate the ecological disturbance of a subsystem. The Nalinhe mining area in the desert area in northwestern China was used as an example. Based on the relationship between different ecological indicators in different years, the ecological disturbance mechanism of a coal mining subsidence area was explored. The results of this study enrich the methods for evaluating ecosystem disturbance and provide a theoretical basis for the reclamation of coal mining subsidence areas in the Nalinhe mining area. 2 Materials And Methods 2.1 Study area and sample collection The study site was located in a mining area in Wushen Banner, Ordos City, Inner Mongolia Autonomous Region, China. It is an important coal production base in Western China. The region has an arid and semi-arid continental climate with an average temperature of 20.4°C. The average yearly rainfall is approximately 322.5 mm, mostly concentrated in autumn, and the annual evaporation is approximately 4–10 times the rainfall. Soil freezes once per year, generally from October to April of the following year, and the maximum depth of frozen soil can reach 1.71 m. The study area is distributed mainly on fixed dune lands and semi-fixed and semi-mobile sand dune lands. The surface material composition is loose, vegetation is sparse, the soil type is aeolian sandy soil with poor ability to retain water and fertilizer, and the ecosystem is relatively fragile and sensitive to external stresses. The mine in the study area has recoverable reserves of 4.49 million t of coal. The average burial depth of the main coal seam is approximately 602 m, the average thickness is approximately 4.65 m, and the working face is approximately 241 m. The coal mining method involves fully mechanized longwall mining. The mining conditions of the working face is described in "Modern Analysis Methods of Soil Elements" (edited by China Environmental Monitoring Station) and "Technical Guidelines for Soil Sampling for Soil Quality" ( GB/T36197-2018 ). Combining the characteristics of the tensile, extrusion, and central zones. respectively determine the mining area and the non-mining area (Fig. 1 ), Soil samples were collected from 0–60 cm depth using the plum blossom sampling method according to the checkerboard method[补1], with a total of 60 sampling points. The collected soil samples were packaged in sealed polyethylene bags, labeled, and sent to the laboratory. After air-drying and removing impurities, the dried samples were ground in an agate mortar(From Lichen Company) and passed through a 2-mm nylon sieve for physical and chemical index testing. 2.2 Data sources Soil moisture was measured using the method of rapid moisture meter drying correction. Soil unit weight and soil porosity were based on the agricultural industry standard of the People's Republic of China, Part IV: Determination of Soil Unit Weight(Anonymous 2006a ). The potentiometric method (Koleva et al. 2021 ) was used to measure soil pH. The potassium dichromate volumetric method (Anonymous 2006b ) was used to measure soil organic matter, the alkaline hydrolysis diffusion method (College 1980 ) was used to measure soil alkaline hydrolysis nitrogen, a UV/Vis spectrophotometer was used to measure soil available phosphorus (Anonymous 2014 ), and soil available potassium was determined using leaching-flame photometry (Anonymous 2005 ). All tests were carried out according to the standard operating procedures. Each sample was measured three times, and after removing the outliers with large differences, the average value was calculated to ensure the accuracy of the data. The annual average temperature and precipitation data from the Hengshan Observation Station, which is closest to the mining area, were downloaded from the China Meteorological Data Network portal ( http://data.cma.cn ). Vegetation coverage was measured using the pixel binary model method(Yang et al. 2018a ). Surface subsidence data in the study area were provided by the surface deformation observation stations of the three adjacent working faces of the Nalinhe No. 2 Mine. A water-level observation station was used to observe the changes in regional water levels and calculate groundwater loss. Geological mining data, that is, data on mining intensity, mining depth, mining thickness, burial depth, and mining thickness ratio of the 60 sites along six survey lines, were provided by the mining unit. 2.3 Establishment of groundwater and surface ecological environment evaluation system 2.3.1 Selection of evaluation index criteria The grading standards for each index used in the study are shown in Table 1 . Table 1 Evaluation criteria and their sources 2.3.2 The establishment of AHP hierarchical model The AHP hierarchy model includes a target layer, criterion layer, subcriterion layer, and indicator layer, which correspond to the ecosystem, ecosystem subsystem, ecological factors, and ecological indicators of the study area, respectively (Yang et al. 2018a ). In this study, we defined the ecosystem as a unified whole composed of mining, nature, groundwater-surface. Exploitation, natural climate, groundwater-surface were defined as ecological subsystems, which respectively represented the subjective driving factors, objective driving factors, and objective response factors that affect ecological disturbance in the region. The disturbance of groundwater and surface ecosystem is extensive and complex, and it is jointly determined by soil physicochemical properties, and geo-ecological properties. Those factors are considered ecological factors affecting regional ecological disturbance. Finally, the key indicators driving the change in ecological factors are named ecological indicators. The AHP hierarchical structural model established in this study is shown in Fig. 2 . 2.3.3 Evaluation methods for groundwater and surface ecological environment 2.3.3.1 Using 3δ principle to classify pH At present, the intermediate type of the basic fuzzy ecological disturbance degree function, commonly used for pH value, has not been classified using an accurate standard. The overall pH value in the study area was close to 7, which should be classified as Grade I. However, due to the limitations imposed by field operations, the accuracy of pH measurements is usually lower than that of other indicators. Therefore, it is unreasonable to rate all of pH as Grade I; accordingly, we graded the soil pH values from the perspective of data confidence. First, the measured data were tested for normality; the pH values in the study area assumed normal distribution (Fig. 2 and Table 2 ). Reuse statistics 3δ of the division results for pH (Qin &Xiong 2009 ) are shown in Table 3 (see Note). Table 2 Normality test of soil pH values Inspection type Kolmogorov–Smirnov Shapiro–Wilke Statistics Freedom Significance Statistics Freedom Significance pH 0.081 48 0.200 * 0.964 48 0.148 *: This is the lower limit of true significance Table 3 Soil pH grade table pH range Level 7.13–7.41 Ⅰ 7.41–7.56 Ⅱ 6.99–7.13 7.56–7.70 Ⅲ 6.84–6.99 > 7.70 Ⅳ < 6.84 Note: According to the 3δ principle, under normal distribution, the probability of numerical values distributed in the range (µ - σ, µ + σ) is 0.6826; in ranges (µ − 2σ, µ - σ) and (µ + σ, µ + 2σ) it is 0.2719; and in the ranges (µ − 3σ, µ − 2σ) and (µ + 2σ, µ + 3σ) it is 0.0428; the probability of values distributed in other ranges is only 0.0027. In other words, the higher the probability, the better the accuracy and the higher the level of the data. 2.3.3.2 Weight calculation by weighting method The evaluation factor weight employed in this study adopts the weighting method (Pei 2016 ): x i = \(\frac{{a}_{i}}{{p}_{i}}\) (1) \({p}_{i}\) = \(\frac{1}{n}\) ( d i1 + d i2 +…… d in ) \(\) (2) normalized x i : b i = x i / \({\sum }_{k=1}^{n}{x}_{i}\) (3) where x i is the weight factor, p i is the average of the standard values at all levels of the i th evaluation index, b i is the weight value of the i th evaluation index, and d in is the standard value of level n of the i th evaluation index. The weight value of each evaluation factor was used to form the fuzzy matrix A: B = { b 1 , b 2 , ….. , b n }. 2.3.3.3 Calculation of ecological disturbance degree by fuzzy mathematical model The fuzzy membership matrix was introduced to reflect the evaluation index level of the ecological disturbance degree in the study area(Zhen et al. 2009 ); \({a}_{i}\) is the measured value of the i th evaluation factor. If the standard value of grade j was \({d}_{ij}\) ( i = 1, 2, ..., n ; j = 1, 2, ..., n ), the following ecological disturbance degree function of the evaluation factor was used: Ecological disturbance degree function of the first level evaluation standard: j = 1 Positive index Negative indicators \({r}_{i1}\) = \(\left\{\begin{array}{c}1\\ \frac{{d}_{i2}-{a}_{i}}{{d}_{i2}-{C}_{i1}}\\ 0\end{array}\right.\) \(\left\{\begin{array}{c}{a}_{i}\ge {d}_{i1}\\ {d}_{i2}<{a}_{i}\le {d}_{i1}\\ {a}_{i}\le {d}_{i2}\end{array}\right.\) \(\left\{\begin{array}{c}{a}_{i}\le {d}_{i1}\\ {d}_{i1}{d}_{i2}\end{array}\right.\) \(\) (4) Ecological disturbance degree function of evaluation criteria from level j to level n -1: j = 2, 3, 4, …, n -1. Positive index Negative indicators \({r}_{ij}\) = \(\left\{\begin{array}{c}\frac{{a}_{i}-{d}_{ij-1}}{{d}_{ij}-{d}_{ij-1}}\\ \frac{{d}_{ij+1}-{a}_{i}}{{d}_{ij+1}-{d}_{ij}}\\ 0\end{array}\right.\) \(\left\{\begin{array}{c}{d}_{ij-1}>{a}_{i}\ge {d}_{ij}\\ {d}_{ij}\ge {a}_{i}>{d}_{ij+1}\\ {d}_{ij-1}<{a}_{i}\le {d}_{ij+1}\end{array}\right.\) \(\left\{\begin{array}{c}{d}_{ij-1}<{a}_{i}\le {d}_{ij}\\ {d}_{ij}\le {a}_{i}{d}_{ij+1}\end{array} \right.\) (5) Ecological disturbance degree function for the n th level evaluation standard: j = n Positive index Negative indicators \({r}_{in}\) = \(\left\{\begin{array}{c}1\\ \frac{{d}_{ij}-{a}_{i}}{{d}_{ij}-{d}_{ij-1}}\\ 0\end{array}\right.\) \(\left\{\begin{array}{c}{a}_{i}\le {d}_{ij}\\ {d}_{ij-1}>{a}_{i}>{d}_{ij}\\ {a}_{i}>{d}_{ij-1}\end{array}\right.\) \(\left\{\begin{array}{c}{a}_{i}\ge {d}_{ij}\\ {d}_{ij-1}<{a}_{i}<{d}_{ij}\\ {a}_{i}\le {d}_{ij-1}\end{array}\right.\) (6) According to the ecological disturbance degree function and the measured values, the ecological disturbance degree of each single factor was calculated for the evaluation standard, and the following fuzzy relationship matrix R was obtained: $$R=\left[\begin{array}{ccc}{r}_{11}& \cdots & {r}_{1j}\\ ⋮& \ddots & ⋮\\ {r}_{i1}& \cdots & {r}_{ij}\end{array}\right]$$ 7 The weight of each element and the fuzzy matrix established by the fuzzy mathematical method were obtained according to the weighting method. According to ecological disturbance evaluation matrix C = BR , the ecological disturbance degree of each sub-criterion layer, criterion layer, and target layer index was further calculated. 2.3.4. Research on regional disturbance correlation based on Origin 2021 In this study, four regional ecological indicators, namely annual average temperature, annual precipitation, surface subsidence, and groundwater loss, were excluded, and Origin 2021(OriginLab Corporation) was used to conduct a correlation analysis on the remaining 14 ecological indicators. 3 Results 3.1 The weight of each layer in the AHP evaluation index system The weighted results of the 18 indicators in the AHP evaluation index system constructed in this study are shown in Fig. 4 . 3.2 Evaluation results of ecological disturbance in coal mining subsidence area 3.2.1 Grade and distribution of ecological disturbance in resource development The ecological disturbance of different regions in the resource development subsystem were in the following order: unmined area (excellent) > one-year mining area (good) ≈ two-year mining area (good) (Fig. 5 ). The ecological disturbance level of the resource development subsystem in the unmined area was of Grade I, the ecological disturbance degree was 1, and all the ecological indicators included were of Grade I. The one-year and two-year mining areas belonged to Grade II, with ecological disturbances of 0.491 and 0.459, respectively. The resource development subsystem includes four ecological indicators: two are of Grade II (good), and two are Grade III (medium). The mining intensity and mining thickness in the mining area experienced the most obvious changes. The difference in ecological disturbance of resource development between one-year and two-year mining areas was not significant. 3.2.2 Level and distribution of natural ecological disturbance The ecological disturbance evaluation results of different areas in the natural ecological subsystem are: unmined area (poor) = one-year mining area (poor) = two-year mining area (poor) (Fig. 6 ). The ecological disturbance level of the natural subsystems in all regions was Grade IV (poor), with a degree of ecological disturbance of 0.619. The natural ecological subsystem includes two ecological indicators: annual mean temperature and mean annual precipitation. The annual average temperature was in Grade IV (ecological disturbance degree = 1), and the average annual precipitation was in Grade III (ecological disturbance degree = 0.61). There were no significant differences in natural ecological disturbances between the regions. 3.2.3 Grade and distribution of groundwater and surface ecological disturbance The evaluation results of the ecological disturbance on groundwater and surface subsystems in different regions were as follows: unmined area (excellent) > one-year mining area (middle) ≈ two-year mining area (middle) (Fig. 7 ). The ecological disturbance level of the groundwater and surface subsystems in the unexploited area belonged to Grade I (degree of ecological disturbance = 0.455). These subsystems included three ecological factors: physical, (Grade IV, ecological disturbance degree = 0.673); chemical (Grade I, ecological disturbance degree = 0.473); and ecological (Grade I, ecological disturbance degree = 1). These ecological factors included six indexes with Grade I, two indexes with Grade III, and four indexes with Grade IV. Level I indicators were concentrated on ecological component factors and soil chemical factors, and level IV indicators were concentrated on soil physical factors. The non-uniform subsidence area in the one-year mining area was in Grade III (ecological disturbance degree = 0.367). The three ecological factors included were in Grade IV (ecological disturbance degree = 0.663), Grade II (ecological disturbance degree = 0.409), and Grade III (ecological disturbance degree = 0.851). Under these ecological factors, three indices were with Grade I, one index with Grade II, four indices with Grade III, and four indices with Grade IV. Level I indices were concentrated in soil chemical factors, and level IV indices were concentrated in soil physical factors. The uniform subsidence area in the one-year mining area belonged to Grade III (ecological disturbance degree = 0.376). The three ecological factors included were Grade IV (ecological disturbance degree 0.680), Grade II (ecological disturbance degree = 0.590), and Grade III (ecological disturbance degree = 0.866). Under these ecological factors, two indices were with Grade I, two indices with Grade II, four indices with Grade III, and four indices wre with Grade IV. Level I indicators were concentrated in soil chemical and ecological factors, and level IV indicators were concentrated in soil physical factors. The non-uniform subsidence area after two years of mining belonged to Grade III (ecological disturbance degree = 0.359). The three ecological factors included were with Grade IV (ecological disturbance degree = 0.666), Grade I (ecological disturbance degree = 0.446), and Grade III (ecological disturbance degree = 0.781). Under these ecological factors, two indicators were with Grade I, one indicator with Grade II, four indicators with Grade III, and five indicators with Grade IV. Level I indices were concentrated in soil chemical factors, and level IV indices were concentrated in soil physical factors. The average subsidence area after two years of mining was classified in Grade III (ecological disturbance degree = 0.344). The three ecological factors included were with Grade IV (ecological disturbance degree = 0.636), Grade I (ecological disturbance degree = 0.476), and Grade III (ecological disturbance degree = 0.781). Under these ecological factors, three indices were assigned Grade I, one index was Grade II, four indices were Grade III, and four indices were Grade IV. Level I indices were distributed in soil chemical factors, and level IV indices were concentrated in soil physical factors. In summary, the groundwater and surface ecological disturbances in the unmined area were much lower than those in one-year and two-year mining areas. The soil chemical factors in each region were less disturbed, whereas the soil physical factors were more disturbed. 3.2.4 Evaluation results of ecological disturbance in coal mining subsidence area The evaluation results for each area are as follows: unmined area (excellent) > one-year mining area (good) > two-year mining area (middle) (Fig. 8 ). Based on the ecological disturbance evaluation results, the unmined area belonged to Grade I, the non-uniform settlement area after one year of mining to Grade II, the non-uniform settlement area of mining after two years to Grade III, the uniform settlement area after mining for one year to Grade II, and the uniform settlement area of mining after two years to Grade II.Different regions exhibited different ecological disturbances. 3.2.5 Correlation analysis results of various ecological indicators The correlation analysis of 14 indicators of non-regional changes is shown in Fig. 9 . There were significant differences in the number and types of significant relationships between indicators in different regions. Ten pairs of significant relationships were detected in the non-mining area, comprising six pairs of significant positive relationships and four pairs of significant negative relationships. Significant positive relationships existed between soil chemical indicators, and significant negative relationships were detected between pH and other soil chemical indicators, as well as between soil physical indicators. There were 28 pairs of significant relationships in the one-year mining area, and they included 17 pairs of significant positive relationships and 11 pairs of significant negative relationships. These significant positive relationships existed between soil physical and chemical indexes, mining depth and soil chemical indexes, and mining indexes. In contrast, the significant negative relationships existed between pH and other soil physical and chemical indicators, soil bulk density and other soil physical and chemical indicators, and mining indicators. There were 11 pairs of significant relationships in the two-year mining area, namely seven pairs of significant positive relationships and four pairs of significant negative relationships. The significant positive relationships existed between soil chemical indexes, and significant negative relationships existed between mining indexes, soil bulk density and soil porosity, and mining depth and organic matter. In summary, the complexity of significant relationships between the ecological indicators in the region first increased and then decreased over time; the significant relationships between ecological indicators in the region within one year of mining were the most complex. 4 Discussion 4.1 Reasons for ecological disturbance in natural and resource development Natural ecological disturbance is mainly affected by the average annual temperature and annual precipitation. Precipitation indirectly affects vegetation cover and soil physical and chemical properties in the study area (Grothmann et al. 2017 ), whereas temperature can affect soil physical and chemical properties in the study area by changing the growth pattern of plants in the study area (Hao et al. 2020 ). Less precipitation and unsuitable temperatures can exacerbate natural ecological disturbance. In this study, the ecological disturbance of all natural subsystems was classified as Grade IV, the annual average temperature to Grade IV, and the average annual precipitation to Grade III (Fig. 6 ). The mining area is small, and the overall climate is uniform. There were no significant climatic differences among the regions. While the annual precipitation in the area is 194.7-531.6 mm (average precipitation of 322.5 mm), the annual evaporation of 2297.4–2833 mm.This severe natural climate causes relatively strong disturbances in the natural ecological subsystem. In the ecological subsystem of resource development, mining thickness is the most important ecological disturbance index. It is the driving factor of ecological disturbance caused by the resource development subsystem. The thickness of coal mines increases the surface displacement and deformation value (Wu et al. 2021 ), thereby aggravating the ecological disturbance of resource development. In addition to the independent effects of different ecological indicators, the resource development subsystem is also affected by the synergistic disturbance of mining intensity, mining thickness, burial depth, and mining thickness ratio. The three ecological indicators were significantly correlated (Fig. 9 ). This indicates that coal mining is affected by the actual occurrence of coal in the study area. The ecological indicators in the ecological subsystem of resource development are easily influenced and restricted by each other, forming a disturbance chain of "mining intensity–mining thickness–burial depth and mining thickness ratio” and leading to more complex ecological disturbances than any single indicator would cause. Measures such as layered driving, height-limited mining, and infill mining to interrupt the connection between these ecological indicators may reduce the ecological disturbance of the resource development subsystem. 4.2 Reasons for groundwater and surface ecological disturbance The evaluation of groundwater and surface ecological disturbance (Fig. 6 b) identified the chemical factor as the ecological factor with the largest weight, followed by physical and ecological component factors. Chemical factors are the disturbance center in the region, with available potassium and available phosphorus being the top two ecological indicators. Potassium and phosphorus are essential mineral nutrients for crop growth and development. Available potassium and available phosphorus reflect the ecological quality of surface soil to a certain extent—the less potassium and phosphorus, the lower the ecological diversity in the region, and the higher the ecological disturbance of groundwater and surface. The prominence of potassium and phosphorus in an assessment portrays these two elements as the driving indicators affecting the ecological disturbance of chemical ecological factors in the region. However, given that the ecological indicators of soil chemical factors were correlated before and after mining (Fig. 9 ), the soil chemical indicators in the study area can be regarded as the driving factors affecting the ecological disturbance of chemical ecological factors in the region. Consequently, Grade I was assigned to the non-mining and two-year mining areas and Grade II to the one-year mining area (Fig. 7 ). The reasons for such classification are multiple and complex: (1) Soil chemical factors may be positively regulated by the mining depth. The mining depth in the study area was Grade I, and the one-year and two-year mining areas were Grade II (Fig. 5 ). In the one-year mining area, the mining depth had a strong positive correlation with chemical ecological indicators except for pH (Fig. 9 ), which is contrary to the traditional view that coal mining will directly cause soil nutrient loss (Ma et al. 2019 ). This has been attributed to surface deformation, which arises with increasing mining depth, and the available fertility of soil surface, which is enriched in low stand areas with changing terrain (Fig. 10 ). However, this correlation disappears with the change in mining stop (Fig. 9 ). Therefore, the increase in mining depth reduces the disturbance to soil chemical ecological factors, strongly correlates the soil physical factors and chemical factors, and produces a series of indirect ecological disturbances under the interaction of different ecological subsystems. (2) The soil physical and chemical factors are correlated (Fig. 9 ). The mining process indirectly disrupts the chemical factors by disturbing the soil physical factors, thereby affecting the natural ecosystem. (3) Coal mining leads to surface subsidence and ground fissures (Liu et al. 2019 ), which leads to soil nutrient loss along ground fissures. However, ground fissures produced by coal mining can be self-repaired and closed, thus recovering available nutrients (Fu et al. 2021 ) (Fig. 10 ). In conclusion, the ecological disturbance caused by chemical ecological factors in the study area is determined by three ecological disturbance chains, namely "coal mining–soil chemical factor," "natural climate–soil physical factor–soil chemical factor," and "coal mining–surface subsidence–soil chemical factor." The assessment results of soil chemical factor disturbance are consistent with the generation and restoration process of ground fissures, indicating the leading role of "coal mining–surface subsidence–soil chemical factor" in the disturbance chain group. Physical factors may be indirectly affected by soil chemical factors while being affected by natural ecological subsystems. This impact may be triggered by mining. Soil bulk density is the most important ecological index, followed by soil porosity. Soil bulk density and soil porosity are indicators of soil permeability and water retention capacity (Dang et al. 2021 ) and have a significant impact on the overall quality of soil. The lower the soil bulk density and porosity, the greater the possibility of intensified ecological disturbance of groundwater and surface. In the past, it was believed that coal mining caused surface deformation, which led to local soil compaction or looseness, as well as changes in soil bulk density and porosity in the subsidence area (Wang et al. 2015 ). This is the main reason for the increased disturbance of groundwater and surface ecosystem. However, the assessment result of surface settlement in the non-mining area was Grade I (Fig. 7 ), and the assessment of soil physical indicators assigned Grade IV to soil bulk density and Grade III to soil porosity (Fig. 7 ). Our study could not establish a correlation between soil physical factors and surface subsidence. The causes of soil physical factors disturbance are the following: (1) During mining, soil physical and chemical factors are correlated (Fig. 9 ), generating a disturbance chain of "coal mining–soil chemical factors–soil physical factors," increasing the ecological disturbance of soil physical factors in the study area, and establishing the coexistence between positive and negative ecological disturbance. (2) The average annual temperature and average annual precipitation in the region were assigned Grade IV (Fig. 6 ), consistent with the grade classification of soil physical factors. The presence of the disturbance chain "natural climate–soil physical factors,” which plays a leading role in the region, is thus confirmed. The ecological component factor is mainly disturbed by mining, but it remains unverified whether this factor is affected by other ecological factors or subsystems. Groundwater loss and surface subsidence rate are the two most important ecological weight indicators. Groundwater is water located in the formation voids below the vadose zone, which is sensitive to arid and semi-arid environmental changes and is an important environmental energy parameter (Zhao et al. 2018 ). Surface subsidence is a geological hazard phenomenon that occurs when the upper rock layer is caving, fractured, squeezed, bent, moved, and otherwise deformed under the action of gravity after the mineral is mined and spreads to the ground. Surface subsidence directly destroys the natural state of surface land and reduces its use value (Cui et al. 2020 ). Both groundwater loss and the surface subsidence rate are important indicators for measuring the contribution of coal mining to geological damage. The greater the groundwater loss, the greater the surface subsidence rate and the greater the possibility of increased groundwater and surface ecological disturbance. In this study, the unmined area was assigned Grade I, whereas the one-year and two-year mining areas were both reduced to Grade III (Fig. 7 ). The resource development subsystem in the unmined area was downgraded to level III in the one-year and two-year mining areas (Fig. 6 ). This is because coal seam mining leads to changes in the surface and topography, especially when the thick coal seam and mining face in the study area are large; severe deformation of the overlying strata will cause ground fissures and deformation on the surface, and the subsidence rate will also increase (Cui et al. 2020 ). Coal mining leads to drying of coal-measure aquifers, which may have varying degrees of impact on bedrock and phreatic aquifers (Wu et al. 2021 ). As a result, the loss of groundwater increases, and the ecological components are seriously disturbed. Because most of the ecological component factors are with regional influences, the sites in the same area are subject to the same disturbance in the evaluation; therefore, it cannot be confirmed whether they will be affected by ecological subsystems or ecological factors other than resource development. However, the evaluation results suggest that the ecological component factor is the main barrier to the reduced ecological disturbance in the coal mining subsidence area. 4.4 Reasons for increased ecological disturbance in coal mining subsidence areas The ecological disturbance in the coal mining subsidence area is negatively correlated with the degree of mining; that is, the ecological disturbance in the coal mining subsidence area decreased in the order two-year mining area > one-year mining area > non-mining area (Fig. 8 ). The number of ecological disturbance chains decreased as follows: one-year mining area > two-year mining area > non-mining area (Fig. 9 ). This reveals that, in the first year of mining, the generation of ecological disturbance chains was the main reason for the increase in regional ecological disturbance. After two years of mining, although the number of disturbance chains decreased (Fig. 9 ), the disturbance of the ecological component factors did not end (Fig. 7 ), which further increased the ecological disturbance in the study area. In conclusion, coal mining deepens the connection between different ecological subsystems and factors. Multiple ecological disturbance chains are formed in the region, resulting in ecological disturbances that are difficult to resolve. 5 Conclusion In this study, an ecological disturbance evaluation index system of 18 indicators was constructed for coal mining subsidence areas from three subsystems: nature, resource development, and groundwater and surface. The driving factors of the ecological disturbance in the coal mining subsidence area were revealed, and the following conclusions were drawn: (1) Ecological disturbance in the coal mining subsidence area decreased in the order two-year mining area > one-year mining area > non-mining area. The AHP is suitable for ecological disturbance evaluation of coal mining subsidence areas in northwest China. (2) Coal mining not only directly increases the ecological disturbance in the region, but also strengthens the relationship between different ecological subsystems, ecological factors, and ecological indicators. Among them, the "mining intensity–mining thickness–burial depth and mining thickness ratio" disturbance chain increases the ecological disturbance caused by resource mining. The disturbance chain "coal mining–surface subsidence–soil chemical factors" plays a leading role in controlling the ecological disturbance of soil chemical factors, and the disturbance chain "natural climate–soil physical factors" is the main factor controlling the ecological disturbance of soil physical factors. Notably, the disturbance chain that controls the ecological component factors in the region remains to be determined. However, the ecological component factor is the most important factor that hinders the restoration of the ecology in a coal mining subsidence area. (3) The number of significant relationships of ecological disturbance in the region decreases with time, but the ecological disturbance continues to increase. The ecological disturbance by coal mining cannot be completely eliminated by relying only on the self-repair capabilities of natural systems. To reduce regional ecological disturbance, artificial intervention measures such as vegetation planting, artificial irrigation, and backfilling subsidence areas should be incorporated. In this study, considering the uphole and downhole factors, an ecological disturbance evaluation system integrating "space, sky and earth" is constructed. The correlation of different ecological indicators in the region was studied, and the possible ecological disturbance chain in the study area was discussed. The results can provide a reference for the decision of reclamation and ecological management of destroyed coal mining subsidence areas. Some of the selected ecological indicators are homogenized(show the same level in different areas) in the region, and these indicators failed to participate in the study of the formation of an ecological disturbance chain in the study area. We did not consider the impact of time on ecological disturbance in each area. Future research should include additional indicators to establish an intelligent monitoring system for long term observation of groundwater and surface ecology in an area. Declarations Author Contributions: Conceptualization, J.Z and X.L.; methodology, X.Z.; writing—original draft preparation, J.Z.; writing—review and editing, K.Z.,J.Z,Y.W and G.K. All authors have read and agreed to the published version of the manuscript. Funding This study was supported by the Science and Technology Innovation Project of the Shendong Group (Grant No.202016000041), the Talent Introduction Plan for Xinjiang in 2020, the National Natural Science Foundation of China (Grant No.42177037), and Research on Ecological Restoration and Protection of Coal Base in Arid Region (Grant No. GJNY2030XDXM-19-03.2). Availability of data and materials The data presented in this study are available on request from the corresponding author. Ethics approval and consent to participate: Not applicable. 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3","display":"","copyAsset":false,"role":"figure","size":178833,"visible":true,"origin":"","legend":"\u003cp\u003eNormal P-P, Q-Q test chart of soil pH values\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2218281/v1/aa17367cb9e7604dce1c3781.jpeg"},{"id":31026203,"identity":"c62ca963-8b60-43eb-9b2d-91b072ea88f2","added_by":"auto","created_at":"2023-01-03 14:38:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":781522,"visible":true,"origin":"","legend":"\u003cp\u003eWeight of ecological evaluation indexes of the index layer (4a), sub-criterion layer (4b), and criterion layer (4c) (unit: %) (from inside to outside: undisturbed area, one-year non-uniform settlement area, two-year non-uniform settlement area, one-year uniform settlement area, and two-year uniform settlement 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years).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2218281/v1/78188240c52a3b0f4f8a16b9.png"},{"id":31028085,"identity":"04778db9-9f07-4ea2-98fc-e0af5ec8762f","added_by":"auto","created_at":"2023-01-03 14:46:03","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":119655,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical chart of ecological evaluation membership degree of the target layer in each study area\u003c/p\u003e\n\u003cp\u003e(UA is the non-mining area, 1-UA is the uniform settlement area after mining for one year, 1-NUA is the non-uniform settlement area after mining for one year, 2-UA is the uniform settlement area after mining for two years, and 2-NUA is the non-uniform settlement area after mining for two years).\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2218281/v1/02cda9ef2d5c634cc939ac38.jpeg"},{"id":31026200,"identity":"4eaf6f1d-b3c9-4736-8150-c71cddeb45ff","added_by":"auto","created_at":"2023-01-03 14:38:03","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":813577,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heat map of changes in non-regional ecological indicators in non-mining area, one-year mining area, and two-year mining area\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2218281/v1/78c455f1eb37e4bd7101ba17.jpeg"},{"id":31026209,"identity":"b431354e-7074-4232-96fe-915259e71feb","added_by":"auto","created_at":"2023-01-03 14:38:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1295235,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact mechanism of coal mining on groundwater and surface ecosystem\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-2218281/v1/91fb2718772cf1c77e4e541e.png"},{"id":31596119,"identity":"3b0cf415-11ba-4854-a80b-71b93518dd2c","added_by":"auto","created_at":"2023-01-15 20:10:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2186042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2218281/v1/3bfc1477-615e-4dec-8a02-cc15a5dfcec7.pdf"}],"financialInterests":"","formattedTitle":"Evaluation of disturbance by coal mining to groundwater and surface ecosystem","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAccording to the International Energy Agency (IEA), coal remains as a major component of the global fuel supply, accounting for 27% of global energy use. China is the world's largest producer and consumer of coal, and the coal industry has played a supporting role in the social and economic development of the country (Dong et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) The western regions represented by Xinjiang and Inner Mongolia are the main coal mining territories in China (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) However, coal mining has brought about serious ecological and environmental problems in these areas (Ren et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Tyulenev et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with surface subsidence being the most prominent issue (Zhu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Surface subsidence is a ground deformation caused by the roof subsidence of underground goaf. This subsidence is accompanied by vegetation destruction (Hou et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), river drying (Lv et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and soil fertility reduction (Ma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which are major problems in reclamation and reconstruction of the western region.\u003c/p\u003e \u003cp\u003eIn multiscale studies on coal mining subsidence areas (in terms of fertility (Yang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), sensitivity (Tai et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), vulnerability (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e), recovery (Yang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), etc.), ecological disturbance has been the current research focus (Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Yan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The poor ecological stability and resilience of the western region hinder the recovery and rebuilding of the region after being disturbed by subsidence (Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Studying the ecological disturbances in coal mining subsidence areas is therefore of great significance.\u003c/p\u003e \u003cp\u003eEcological disturbance in coal mining subsidence areas is usually measured by ecological disturbance degree, which is the degree to which surface deformation caused by mining interferes with the ecosystem (Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Current methods for measuring ecological disturbance include satellite monitoring(Chowdhury et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), LandTrendr model calculation(Yang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e), 3S technology (Wu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and others, the research areas include vegetation (Liu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), atmosphere (Hughes et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and soil (Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). However, the reported results have often been controversial. For example, some scholars believe that coal mining is the main factor that disturbs the soil physical and chemical properties in arid and semi-arid regions in the west of China (Ma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Others believe that the annual evaporation in this area is much greater than the precipitation, and severe climate is the main factor causing the disturbance of soil physical properties (Wang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The main reason for these controversies is that the existing research usually focuses only on the ecological indicators with poor performance in the evaluation system, and fails to consider the interaction and chain reaction between indicators. In fact, the harsh climatic environment and mining activities in the northwestern region impact the environment in nearby areas by disturbing the vegetation growth and causing soil water stress (Ni \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The interaction between these ecological indicators further exacerbates the degree of ecological disturbance in the coal mining subsidence area. Consequently, the existing research cannot quantify the ecological disturbance in a coal mining subsidence area (Yang et al.). Therefore, it is very important to build an evaluation method of ecological disturbance that comprehensively considers natural and human factors and reveals the relationship between different ecological indicators. Such a method will help to realize the reclamation of coal mining subsidence areas in western China.\u003c/p\u003e \u003cp\u003eThe analytic hierarchy process (AHP) is a commonly used ecological evaluation method that is compatible with qualitative and quantitative analyses. This analytical method combines qualitative and quantitative analyses to decompose the problem into factors at different levels and uses the low-level factors to explain and supplement the high-level factors to which they belong in order to systematize the problem (Song et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Theoretically, AHP can be applied to evaluate ecological disturbances in coal mining subsidence areas. However, the method relies on the subjective judgment of decision-makers regarding the problem, and the results generally lack objectivity (Lyu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fuzzy mathematics transforms qualitative evaluation problems into quantitative evaluation problems by operating fuzzy sets and fuzzy relationships, thereby avoiding one-sidedness in the results (Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The weighting method reduces the influence of human factors on the results by substituting numerical values for subjective human judgments (Hou \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Complementing the AHP with these two methods increases the objectivity and accuracy of the evaluation results (Chen \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, He et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study aimed to construct an AHP hierarchical index system to evaluate groundwater and surface ecological environments. We used the weighting method to determine the impact weight and cumulative effect of evaluation indicators on the ecological environment, and employed the fuzzy mathematical method to evaluate the ecological disturbance of a subsystem. The Nalinhe mining area in the desert area in northwestern China was used as an example. Based on the relationship between different ecological indicators in different years, the ecological disturbance mechanism of a coal mining subsidence area was explored. The results of this study enrich the methods for evaluating ecosystem disturbance and provide a theoretical basis for the reclamation of coal mining subsidence areas in the Nalinhe mining area.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Study area and sample collection\u003c/h2\u003e\n \u003cp\u003eThe study site was located in a mining area in Wushen Banner, Ordos City, Inner Mongolia Autonomous Region, China. It is an important coal production base in Western China. The region has an arid and semi-arid continental climate with an average temperature of 20.4\u0026deg;C. The average yearly rainfall is approximately 322.5 mm, mostly concentrated in autumn, and the annual evaporation is approximately 4\u0026ndash;10 times the rainfall. Soil freezes once per year, generally from October to April of the following year, and the maximum depth of frozen soil can reach 1.71 m. The study area is distributed mainly on fixed dune lands and semi-fixed and semi-mobile sand dune lands. The surface material composition is loose, vegetation is sparse, the soil type is aeolian sandy soil with poor ability to retain water and fertilizer, and the ecosystem is relatively fragile and sensitive to external stresses. The mine in the study area has recoverable reserves of 4.49\u0026nbsp;million t of coal. The average burial depth of the main coal seam is approximately 602 m, the average thickness is approximately 4.65 m, and the working face is approximately 241 m. The coal mining method involves fully mechanized longwall mining.\u003c/p\u003e\n \u003cp\u003eThe mining conditions of the working face is described in \u0026quot;Modern Analysis Methods of Soil Elements\u0026quot; (edited by China Environmental Monitoring Station) and \u0026quot;Technical Guidelines for Soil Sampling for Soil Quality\u0026quot; (\u003cem\u003eGB/T36197-2018\u003c/em\u003e). Combining the characteristics of the tensile, extrusion, and central zones. respectively determine the mining area and the non-mining area (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), Soil samples were collected from 0\u0026ndash;60 cm depth using the plum blossom sampling method according to the checkerboard method[补1], with a total of 60 sampling points. The collected soil samples were packaged in sealed polyethylene bags, labeled, and sent to the laboratory. After air-drying and removing impurities, the dried samples were ground in an agate mortar(From Lichen Company) and passed through a 2-mm nylon sieve for physical and chemical index testing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Data sources\u003c/h2\u003e\n \u003cp\u003eSoil moisture was measured using the method of rapid moisture meter drying correction. Soil unit weight and soil porosity were based on the agricultural industry standard of the People\u0026apos;s Republic of China, Part IV: Determination of Soil Unit Weight(Anonymous \u003cspan class=\"CitationRef\"\u003e2006a\u003c/span\u003e). The potentiometric method (Koleva et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) was used to measure soil pH. The potassium dichromate volumetric method (Anonymous \u003cspan class=\"CitationRef\"\u003e2006b\u003c/span\u003e) was used to measure soil organic matter, the alkaline hydrolysis diffusion method (College \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) was used to measure soil alkaline hydrolysis nitrogen, a UV/Vis spectrophotometer was used to measure soil available phosphorus (Anonymous \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), and soil available potassium was determined using leaching-flame photometry (Anonymous \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). All tests were carried out according to the standard operating procedures. Each sample was measured three times, and after removing the outliers with large differences, the average value was calculated to ensure the accuracy of the data. The annual average temperature and precipitation data from the Hengshan Observation Station, which is closest to the mining area, were downloaded from the China Meteorological Data Network portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.cn\u003c/span\u003e\u003c/span\u003e). Vegetation coverage was measured using the pixel binary model method(Yang et al. \u003cspan class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Surface subsidence data in the study area were provided by the surface deformation observation stations of the three adjacent working faces of the Nalinhe No. 2 Mine. A water-level observation station was used to observe the changes in regional water levels and calculate groundwater loss.\u003c/p\u003e\n \u003cp\u003eGeological mining data, that is, data on mining intensity, mining depth, mining thickness, burial depth, and mining thickness ratio of the 60 sites along six survey lines, were provided by the mining unit.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Establishment of groundwater and surface ecological environment evaluation system\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.3.1 Selection of evaluation index criteria\u003c/h2\u003e\n \u003cp\u003eThe grading standards for each index used in the study are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Evaluation criteria and their sources\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"1540\" height=\"612\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.3.2 The establishment of AHP hierarchical model\u003c/h2\u003e\n \u003cp\u003eThe AHP hierarchy model includes a target layer, criterion layer, subcriterion layer, and indicator layer, which correspond to the ecosystem, ecosystem subsystem, ecological factors, and ecological indicators of the study area, respectively (Yang et al. \u003cspan class=\"CitationRef\"\u003e2018a\u003c/span\u003e). In this study, we defined the ecosystem as a unified whole composed of mining, nature, groundwater-surface. Exploitation, natural climate, groundwater-surface were defined as ecological subsystems, which respectively represented the subjective driving factors, objective driving factors, and objective response factors that affect ecological disturbance in the region. The disturbance of groundwater and surface ecosystem is extensive and complex, and it is jointly determined by soil physicochemical properties, and geo-ecological properties. Those factors are considered ecological factors affecting regional ecological disturbance. Finally, the key indicators driving the change in ecological factors are named ecological indicators. The AHP hierarchical structural model established in this study is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec8\"\u003e\n \u003ch2\u003e2.3.3 Evaluation methods for groundwater and surface ecological environment\u003c/h2\u003e\n \u003cdiv class=\"Section4\" id=\"Sec9\"\u003e\n \u003ch2\u003e2.3.3.1 Using 3\u0026delta; principle to classify pH\u003c/h2\u003e\n \u003cp\u003eAt present, the intermediate type of the basic fuzzy ecological disturbance degree function, commonly used for pH value, has not been classified using an accurate standard. The overall pH value in the study area was close to 7, which should be classified as Grade I. However, due to the limitations imposed by field operations, the accuracy of pH measurements is usually lower than that of other indicators. Therefore, it is unreasonable to rate all of pH as Grade I; accordingly, we graded the soil pH values from the perspective of data confidence. First, the measured data were tested for normality; the pH values in the study area assumed normal distribution (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Reuse statistics 3\u0026delta; of the division results for pH (Qin \u0026amp;Xiong \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (see Note).\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNormality test of soil pH values\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInspection type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eKolmogorov\u0026ndash;Smirnov\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eShapiro\u0026ndash;Wilke\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreedom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreedom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.200\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*: This is the lower limit of true significance\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSoil pH grade table\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13\u0026ndash;7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41\u0026ndash;7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.99\u0026ndash;7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.56\u0026ndash;7.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.84\u0026ndash;6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;7.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eNote: According to the 3\u0026delta; principle, under normal distribution, the probability of numerical values distributed in the range (\u0026micro; - \u0026sigma;, \u0026micro;\u0026thinsp;+\u0026thinsp;\u0026sigma;) is 0.6826; in ranges (\u0026micro; \u0026minus;\u0026thinsp;2\u0026sigma;, \u0026micro; - \u0026sigma;) and (\u0026micro;\u0026thinsp;+\u0026thinsp;\u0026sigma;, \u0026micro;\u0026thinsp;+\u0026thinsp;2\u0026sigma;) it is 0.2719; and in the ranges (\u0026micro; \u0026minus;\u0026thinsp;3\u0026sigma;, \u0026micro; \u0026minus;\u0026thinsp;2\u0026sigma;) and (\u0026micro;\u0026thinsp;+\u0026thinsp;2\u0026sigma;, \u0026micro;\u0026thinsp;+\u0026thinsp;3\u0026sigma;) it is 0.0428; the probability of values distributed in other ranges is only 0.0027. In other words, the higher the probability, the better the accuracy and the higher the level of the data.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section4\" id=\"Sec10\"\u003e\n \u003ch2\u003e2.3.3.2 Weight calculation by weighting method\u003c/h2\u003e\n \u003cp\u003eThe evaluation factor weight employed in this study adopts the weighting method (Pei \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\frac{{a}_{i}}{{p}_{i}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e(1)\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{i}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{n}\\)\u003c/span\u003e\u003c/span\u003e(\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ei1\u003c/em\u003e\u003c/sub\u003e+ \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ei2\u003c/em\u003e\u003c/sub\u003e+\u0026hellip;\u0026hellip;\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ein\u003c/em\u003e\u003c/sub\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003e(2)\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003enormalized \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e=\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e /\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{k=1}^{n}{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e(3)\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the weight factor, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the average of the standard values at all levels of the \u003cem\u003ei\u003c/em\u003eth evaluation index, \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the weight value of the \u003cem\u003ei\u003c/em\u003eth evaluation index, and \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ein\u003c/em\u003e\u003c/sub\u003e is the standard value of level \u003cem\u003en\u003c/em\u003e of the \u003cem\u003ei\u003c/em\u003eth evaluation index.\u003c/p\u003e\n \u003cp\u003eThe weight value of each evaluation factor was used to form the fuzzy matrix A: \u003cem\u003eB\u003c/em\u003e = {\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003e\u0026hellip;..\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e}.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section4\" id=\"Sec11\"\u003e\n \u003ch2\u003e2.3.3.3 Calculation of ecological disturbance degree by fuzzy mathematical model\u003c/h2\u003e\n \u003cp\u003eThe fuzzy membership matrix was introduced to reflect the evaluation index level of the ecological disturbance degree in the study area(Zhen et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the measured value of the \u003cem\u003ei\u003c/em\u003eth evaluation factor. If the standard value of grade \u003cem\u003ej\u003c/em\u003e was \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{ij}\\)\u003c/span\u003e\u003c/span\u003e(\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2, ..., \u003cem\u003en\u003c/em\u003e; \u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2, ..., \u003cem\u003en\u003c/em\u003e), the following ecological disturbance degree function of the evaluation factor was used:\u003c/p\u003e\n \u003cp\u003eEcological disturbance degree function of the first level evaluation standard: \u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePositive index Negative indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({r}_{i1}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}1\\\\ \\frac{{d}_{i2}-{a}_{i}}{{d}_{i2}-{C}_{i1}}\\\\ 0\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}{a}_{i}\\ge {d}_{i1}\\\\ {d}_{i2}\u0026lt;{a}_{i}\\le {d}_{i1}\\\\ {a}_{i}\\le {d}_{i2}\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}{a}_{i}\\le {d}_{i1}\\\\ {d}_{i1}\u0026lt;{a}_{i}\\le {d}_{i2}\\\\ {a}_{i}\u0026gt;{d}_{i2}\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003e(4)\u003c/p\u003e\n \u003cp\u003eEcological disturbance degree function of evaluation criteria from level \u003cem\u003ej\u003c/em\u003e to level \u003cem\u003en\u003c/em\u003e-1: \u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, 3, 4, \u0026hellip;, \u003cem\u003en\u003c/em\u003e-1.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePositive index Negative indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({r}_{ij}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}\\frac{{a}_{i}-{d}_{ij-1}}{{d}_{ij}-{d}_{ij-1}}\\\\ \\frac{{d}_{ij+1}-{a}_{i}}{{d}_{ij+1}-{d}_{ij}}\\\\ 0\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}{d}_{ij-1}\u0026gt;{a}_{i}\\ge {d}_{ij}\\\\ {d}_{ij}\\ge {a}_{i}\u0026gt;{d}_{ij+1}\\\\ {d}_{ij-1}\u0026lt;{a}_{i}\\le {d}_{ij+1}\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}{d}_{ij-1}\u0026lt;{a}_{i}\\le {d}_{ij}\\\\ {d}_{ij}\\le {a}_{i}\u0026lt;{d}_{ij+1}\\\\ {d}_{ij-1}\\ge {a}_{i}\u0026gt;{d}_{ij+1}\\end{array} \\right.\\)\u003c/span\u003e\u003c/span\u003e(5)\u003c/p\u003e\n \u003cp\u003eEcological disturbance degree function for the \u003cem\u003en\u003c/em\u003eth level evaluation standard: \u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePositive index Negative indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({r}_{in}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}1\\\\ \\frac{{d}_{ij}-{a}_{i}}{{d}_{ij}-{d}_{ij-1}}\\\\ 0\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}{a}_{i}\\le {d}_{ij}\\\\ {d}_{ij-1}\u0026gt;{a}_{i}\u0026gt;{d}_{ij}\\\\ {a}_{i}\u0026gt;{d}_{ij-1}\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{c}{a}_{i}\\ge {d}_{ij}\\\\ {d}_{ij-1}\u0026lt;{a}_{i}\u0026lt;{d}_{ij}\\\\ {a}_{i}\\le {d}_{ij-1}\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e(6)\u003c/p\u003e\n \u003cp\u003eAccording to the ecological disturbance degree function and the measured values, the ecological disturbance degree of each single factor was calculated for the evaluation standard, and the following fuzzy relationship matrix \u003cem\u003eR\u003c/em\u003e was obtained:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$R=\\left[\\begin{array}{ccc}{r}_{11}\u0026amp; \\cdots \u0026amp; {r}_{1j}\\\\ ⋮\u0026amp; \\ddots \u0026amp; ⋮\\\\ {r}_{i1}\u0026amp; \\cdots \u0026amp; {r}_{ij}\\end{array}\\right]$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe weight of each element and the fuzzy matrix established by the fuzzy mathematical method were obtained according to the weighting method. According to ecological disturbance evaluation matrix \u003cem\u003eC\u0026thinsp;=\u0026thinsp;BR\u003c/em\u003e, the ecological disturbance degree of each sub-criterion layer, criterion layer, and target layer index was further calculated.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003e2.3.4. Research on regional disturbance correlation based on Origin 2021\u003c/h2\u003e\n \u003cp\u003eIn this study, four regional ecological indicators, namely annual average temperature, annual precipitation, surface subsidence, and groundwater loss, were excluded, and Origin 2021(OriginLab Corporation) was used to conduct a correlation analysis on the remaining 14 ecological indicators.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.1 The weight of each layer in the AHP evaluation index system\u003c/h2\u003e\n \u003cp\u003eThe weighted results of the 18 indicators in the AHP evaluation index system constructed in this study are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003e3.2 Evaluation results of ecological disturbance in coal mining subsidence area\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec16\"\u003e\n \u003ch2\u003e3.2.1 Grade and distribution of ecological disturbance in resource development\u003c/h2\u003e\n \u003cp\u003eThe ecological disturbance of different regions in the resource development subsystem were in the following order: unmined area (excellent)\u0026thinsp;\u0026gt;\u0026thinsp;one-year mining area (good)\u0026thinsp;\u0026asymp;\u0026thinsp;two-year mining area (good) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The ecological disturbance level of the resource development subsystem in the unmined area was of Grade I, the ecological disturbance degree was 1, and all the ecological indicators included were of Grade I. The one-year and two-year mining areas belonged to Grade II, with ecological disturbances of 0.491 and 0.459, respectively. The resource development subsystem includes four ecological indicators: two are of Grade II (good), and two are Grade III (medium). The mining intensity and mining thickness in the mining area experienced the most obvious changes. The difference in ecological disturbance of resource development between one-year and two-year mining areas was not significant.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec17\"\u003e\n \u003ch2\u003e3.2.2 Level and distribution of natural ecological disturbance\u003c/h2\u003e\n \u003cp\u003eThe ecological disturbance evaluation results of different areas in the natural ecological subsystem are: unmined area (poor)\u0026thinsp;=\u0026thinsp;one-year mining area (poor)\u0026thinsp;=\u0026thinsp;two-year mining area (poor) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The ecological disturbance level of the natural subsystems in all regions was Grade IV (poor), with a degree of ecological disturbance of 0.619. The natural ecological subsystem includes two ecological indicators: annual mean temperature and mean annual precipitation. The annual average temperature was in Grade IV (ecological disturbance degree\u0026thinsp;=\u0026thinsp;1), and the average annual precipitation was in Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.61). There were no significant differences in natural ecological disturbances between the regions.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec18\"\u003e\n \u003ch2\u003e3.2.3 Grade and distribution of groundwater and surface ecological disturbance\u003c/h2\u003e\n \u003cp\u003eThe evaluation results of the ecological disturbance on groundwater and surface subsystems in different regions were as follows: unmined area (excellent)\u0026thinsp;\u0026gt;\u0026thinsp;one-year mining area (middle)\u0026thinsp;\u0026asymp;\u0026thinsp;two-year mining area (middle) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The ecological disturbance level of the groundwater and surface subsystems in the unexploited area belonged to Grade I (degree of ecological disturbance\u0026thinsp;=\u0026thinsp;0.455). These subsystems included three ecological factors: physical, (Grade IV, ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.673); chemical (Grade I, ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.473); and ecological (Grade I, ecological disturbance degree\u0026thinsp;=\u0026thinsp;1). These ecological factors included six indexes with Grade I, two indexes with Grade III, and four indexes with Grade IV. Level I indicators were concentrated on ecological component factors and soil chemical factors, and level IV indicators were concentrated on soil physical factors.\u003c/p\u003e\n \u003cp\u003eThe non-uniform subsidence area in the one-year mining area was in Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.367). The three ecological factors included were in Grade IV (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.663), Grade II (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.409), and Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.851). Under these ecological factors, three indices were with Grade I, one index with Grade II, four indices with Grade III, and four indices with Grade IV. Level I indices were concentrated in soil chemical factors, and level IV indices were concentrated in soil physical factors.\u003c/p\u003e\n \u003cp\u003eThe uniform subsidence area in the one-year mining area belonged to Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.376). The three ecological factors included were Grade IV (ecological disturbance degree 0.680), Grade II (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.590), and Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.866). Under these ecological factors, two indices were with Grade I, two indices with Grade II, four indices with Grade III, and four indices wre with Grade IV. Level I indicators were concentrated in soil chemical and ecological factors, and level IV indicators were concentrated in soil physical factors.\u003c/p\u003e\n \u003cp\u003eThe non-uniform subsidence area after two years of mining belonged to Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.359). The three ecological factors included were with Grade IV (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.666), Grade I (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.446), and Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.781). Under these ecological factors, two indicators were with Grade I, one indicator with Grade II, four indicators with Grade III, and five indicators with Grade IV. Level I indices were concentrated in soil chemical factors, and level IV indices were concentrated in soil physical factors.\u003c/p\u003e\n \u003cp\u003eThe average subsidence area after two years of mining was classified in Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.344). The three ecological factors included were with Grade IV (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.636), Grade I (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.476), and Grade III (ecological disturbance degree\u0026thinsp;=\u0026thinsp;0.781). Under these ecological factors, three indices were assigned Grade I, one index was Grade II, four indices were Grade III, and four indices were Grade IV. Level I indices were distributed in soil chemical factors, and level IV indices were concentrated in soil physical factors.\u003c/p\u003e\n \u003cp\u003eIn summary, the groundwater and surface ecological disturbances in the unmined area were much lower than those in one-year and two-year mining areas. The soil chemical factors in each region were less disturbed, whereas the soil physical factors were more disturbed.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec19\"\u003e\n \u003ch2\u003e3.2.4 Evaluation results of ecological disturbance in coal mining subsidence area\u003c/h2\u003e\n \u003cp\u003eThe evaluation results for each area are as follows: unmined area (excellent)\u0026thinsp;\u0026gt;\u0026thinsp;one-year mining area (good)\u0026thinsp;\u0026gt;\u0026thinsp;two-year mining area (middle) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Based on the ecological disturbance evaluation results, the unmined area belonged to Grade I, the non-uniform settlement area after one year of mining to Grade II, the non-uniform settlement area of mining after two years to Grade III, the uniform settlement area after mining for one year to Grade II, and the uniform settlement area of mining after two years to Grade II.Different regions exhibited different ecological disturbances.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec20\"\u003e\n \u003ch2\u003e3.2.5 Correlation analysis results of various ecological indicators\u003c/h2\u003e\n \u003cp\u003eThe correlation analysis of 14 indicators of non-regional changes is shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. There were significant differences in the number and types of significant relationships between indicators in different regions. Ten pairs of significant relationships were detected in the non-mining area, comprising six pairs of significant positive relationships and four pairs of significant negative relationships. Significant positive relationships existed between soil chemical indicators, and significant negative relationships were detected between pH and other soil chemical indicators, as well as between soil physical indicators.\u003c/p\u003e\n \u003cp\u003eThere were 28 pairs of significant relationships in the one-year mining area, and they included 17 pairs of significant positive relationships and 11 pairs of significant negative relationships. These significant positive relationships existed between soil physical and chemical indexes, mining depth and soil chemical indexes, and mining indexes. In contrast, the significant negative relationships existed between pH and other soil physical and chemical indicators, soil bulk density and other soil physical and chemical indicators, and mining indicators.\u003c/p\u003e\n \u003cp\u003eThere were 11 pairs of significant relationships in the two-year mining area, namely seven pairs of significant positive relationships and four pairs of significant negative relationships. The significant positive relationships existed between soil chemical indexes, and significant negative relationships existed between mining indexes, soil bulk density and soil porosity, and mining depth and organic matter. In summary, the complexity of significant relationships between the ecological indicators in the region first increased and then decreased over time; the significant relationships between ecological indicators in the region within one year of mining were the most complex.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Reasons for ecological disturbance in natural and resource development\u003c/h2\u003e \u003cp\u003eNatural ecological disturbance is mainly affected by the average annual temperature and annual precipitation. Precipitation indirectly affects vegetation cover and soil physical and chemical properties in the study area (Grothmann et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas temperature can affect soil physical and chemical properties in the study area by changing the growth pattern of plants in the study area (Hao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Less precipitation and unsuitable temperatures can exacerbate natural ecological disturbance. In this study, the ecological disturbance of all natural subsystems was classified as Grade IV, the annual average temperature to Grade IV, and the average annual precipitation to Grade III (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The mining area is small, and the overall climate is uniform. There were no significant climatic differences among the regions. While the annual precipitation in the area is 194.7-531.6 mm (average precipitation of 322.5 mm), the annual evaporation of 2297.4\u0026ndash;2833 mm.This severe natural climate causes relatively strong disturbances in the natural ecological subsystem.\u003c/p\u003e \u003cp\u003eIn the ecological subsystem of resource development, mining thickness is the most important ecological disturbance index. It is the driving factor of ecological disturbance caused by the resource development subsystem. The thickness of coal mines increases the surface displacement and deformation value (Wu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thereby aggravating the ecological disturbance of resource development. In addition to the independent effects of different ecological indicators, the resource development subsystem is also affected by the synergistic disturbance of mining intensity, mining thickness, burial depth, and mining thickness ratio. The three ecological indicators were significantly correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This indicates that coal mining is affected by the actual occurrence of coal in the study area. The ecological indicators in the ecological subsystem of resource development are easily influenced and restricted by each other, forming a disturbance chain of \"mining intensity\u0026ndash;mining thickness\u0026ndash;burial depth and mining thickness ratio\u0026rdquo; and leading to more complex ecological disturbances than any single indicator would cause. Measures such as layered driving, height-limited mining, and infill mining to interrupt the connection between these ecological indicators may reduce the ecological disturbance of the resource development subsystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Reasons for groundwater and surface ecological disturbance\u003c/h2\u003e \u003cp\u003eThe evaluation of groundwater and surface ecological disturbance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) identified the chemical factor as the ecological factor with the largest weight, followed by physical and ecological component factors. Chemical factors are the disturbance center in the region, with available potassium and available phosphorus being the top two ecological indicators. Potassium and phosphorus are essential mineral nutrients for crop growth and development. Available potassium and available phosphorus reflect the ecological quality of surface soil to a certain extent\u0026mdash;the less potassium and phosphorus, the lower the ecological diversity in the region, and the higher the ecological disturbance of groundwater and surface. The prominence of potassium and phosphorus in an assessment portrays these two elements as the driving indicators affecting the ecological disturbance of chemical ecological factors in the region. However, given that the ecological indicators of soil chemical factors were correlated before and after mining (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), the soil chemical indicators in the study area can be regarded as the driving factors affecting the ecological disturbance of chemical ecological factors in the region. Consequently, Grade I was assigned to the non-mining and two-year mining areas and Grade II to the one-year mining area (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The reasons for such classification are multiple and complex: (1) Soil chemical factors may be positively regulated by the mining depth. The mining depth in the study area was Grade I, and the one-year and two-year mining areas were Grade II (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the one-year mining area, the mining depth had a strong positive correlation with chemical ecological indicators except for pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which is contrary to the traditional view that coal mining will directly cause soil nutrient loss (Ma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This has been attributed to surface deformation, which arises with increasing mining depth, and the available fertility of soil surface, which is enriched in low stand areas with changing terrain (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). However, this correlation disappears with the change in mining stop (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Therefore, the increase in mining depth reduces the disturbance to soil chemical ecological factors, strongly correlates the soil physical factors and chemical factors, and produces a series of indirect ecological disturbances under the interaction of different ecological subsystems. (2) The soil physical and chemical factors are correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The mining process indirectly disrupts the chemical factors by disturbing the soil physical factors, thereby affecting the natural ecosystem. (3) Coal mining leads to surface subsidence and ground fissures (Liu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which leads to soil nutrient loss along ground fissures. However, ground fissures produced by coal mining can be self-repaired and closed, thus recovering available nutrients (Fu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In conclusion, the ecological disturbance caused by chemical ecological factors in the study area is determined by three ecological disturbance chains, namely \"coal mining\u0026ndash;soil chemical factor,\" \"natural climate\u0026ndash;soil physical factor\u0026ndash;soil chemical factor,\" and \"coal mining\u0026ndash;surface subsidence\u0026ndash;soil chemical factor.\" The assessment results of soil chemical factor disturbance are consistent with the generation and restoration process of ground fissures, indicating the leading role of \"coal mining\u0026ndash;surface subsidence\u0026ndash;soil chemical factor\" in the disturbance chain group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePhysical factors may be indirectly affected by soil chemical factors while being affected by natural ecological subsystems. This impact may be triggered by mining. Soil bulk density is the most important ecological index, followed by soil porosity. Soil bulk density and soil porosity are indicators of soil permeability and water retention capacity (Dang et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and have a significant impact on the overall quality of soil. The lower the soil bulk density and porosity, the greater the possibility of intensified ecological disturbance of groundwater and surface. In the past, it was believed that coal mining caused surface deformation, which led to local soil compaction or looseness, as well as changes in soil bulk density and porosity in the subsidence area (Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is the main reason for the increased disturbance of groundwater and surface ecosystem. However, the assessment result of surface settlement in the non-mining area was Grade I (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), and the assessment of soil physical indicators assigned Grade IV to soil bulk density and Grade III to soil porosity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Our study could not establish a correlation between soil physical factors and surface subsidence. The causes of soil physical factors disturbance are the following: (1) During mining, soil physical and chemical factors are correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), generating a disturbance chain of \"coal mining\u0026ndash;soil chemical factors\u0026ndash;soil physical factors,\" increasing the ecological disturbance of soil physical factors in the study area, and establishing the coexistence between positive and negative ecological disturbance. (2) The average annual temperature and average annual precipitation in the region were assigned Grade IV (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), consistent with the grade classification of soil physical factors. The presence of the disturbance chain \"natural climate\u0026ndash;soil physical factors,\u0026rdquo; which plays a leading role in the region, is thus confirmed. The ecological component factor is mainly disturbed by mining, but it remains unverified whether this factor is affected by other ecological factors or subsystems. Groundwater loss and surface subsidence rate are the two most important ecological weight indicators. Groundwater is water located in the formation voids below the vadose zone, which is sensitive to arid and semi-arid environmental changes and is an important environmental energy parameter (Zhao et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Surface subsidence is a geological hazard phenomenon that occurs when the upper rock layer is caving, fractured, squeezed, bent, moved, and otherwise deformed under the action of gravity after the mineral is mined and spreads to the ground. Surface subsidence directly destroys the natural state of surface land and reduces its use value (Cui et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Both groundwater loss and the surface subsidence rate are important indicators for measuring the contribution of coal mining to geological damage. The greater the groundwater loss, the greater the surface subsidence rate and the greater the possibility of increased groundwater and surface ecological disturbance. In this study, the unmined area was assigned Grade I, whereas the one-year and two-year mining areas were both reduced to Grade III (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The resource development subsystem in the unmined area was downgraded to level III in the one-year and two-year mining areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This is because coal seam mining leads to changes in the surface and topography, especially when the thick coal seam and mining face in the study area are large; severe deformation of the overlying strata will cause ground fissures and deformation on the surface, and the subsidence rate will also increase (Cui et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Coal mining leads to drying of coal-measure aquifers, which may have varying degrees of impact on bedrock and phreatic aquifers (Wu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, the loss of groundwater increases, and the ecological components are seriously disturbed. Because most of the ecological component factors are with regional influences, the sites in the same area are subject to the same disturbance in the evaluation; therefore, it cannot be confirmed whether they will be affected by ecological subsystems or ecological factors other than resource development. However, the evaluation results suggest that the ecological component factor is the main barrier to the reduced ecological disturbance in the coal mining subsidence area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Reasons for increased ecological disturbance in coal mining subsidence areas\u003c/h2\u003e \u003cp\u003eThe ecological disturbance in the coal mining subsidence area is negatively correlated with the degree of mining; that is, the ecological disturbance in the coal mining subsidence area decreased in the order two-year mining area\u0026thinsp;\u0026gt;\u0026thinsp;one-year mining area\u0026thinsp;\u0026gt;\u0026thinsp;non-mining area (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The number of ecological disturbance chains decreased as follows: one-year mining area\u0026thinsp;\u0026gt;\u0026thinsp;two-year mining area\u0026thinsp;\u0026gt;\u0026thinsp;non-mining area (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This reveals that, in the first year of mining, the generation of ecological disturbance chains was the main reason for the increase in regional ecological disturbance. After two years of mining, although the number of disturbance chains decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), the disturbance of the ecological component factors did not end (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which further increased the ecological disturbance in the study area. In conclusion, coal mining deepens the connection between different ecological subsystems and factors. Multiple ecological disturbance chains are formed in the region, resulting in ecological disturbances that are difficult to resolve.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this study, an ecological disturbance evaluation index system of 18 indicators was constructed for coal mining subsidence areas from three subsystems: nature, resource development, and groundwater and surface. The driving factors of the ecological disturbance in the coal mining subsidence area were revealed, and the following conclusions were drawn:\u003c/p\u003e \u003cp\u003e(1) Ecological disturbance in the coal mining subsidence area decreased in the order two-year mining area\u0026thinsp;\u0026gt;\u0026thinsp;one-year mining area\u0026thinsp;\u0026gt;\u0026thinsp;non-mining area. The AHP is suitable for ecological disturbance evaluation of coal mining subsidence areas in northwest China.\u003c/p\u003e \u003cp\u003e(2) Coal mining not only directly increases the ecological disturbance in the region, but also strengthens the relationship between different ecological subsystems, ecological factors, and ecological indicators. Among them, the \"mining intensity\u0026ndash;mining thickness\u0026ndash;burial depth and mining thickness ratio\" disturbance chain increases the ecological disturbance caused by resource mining. The disturbance chain \"coal mining\u0026ndash;surface subsidence\u0026ndash;soil chemical factors\" plays a leading role in controlling the ecological disturbance of soil chemical factors, and the disturbance chain \"natural climate\u0026ndash;soil physical factors\" is the main factor controlling the ecological disturbance of soil physical factors. Notably, the disturbance chain that controls the ecological component factors in the region remains to be determined. However, the ecological component factor is the most important factor that hinders the restoration of the ecology in a coal mining subsidence area.\u003c/p\u003e \u003cp\u003e(3) The number of significant relationships of ecological disturbance in the region decreases with time, but the ecological disturbance continues to increase. The ecological disturbance by coal mining cannot be completely eliminated by relying only on the self-repair capabilities of natural systems. To reduce regional ecological disturbance, artificial intervention measures such as vegetation planting, artificial irrigation, and backfilling subsidence areas should be incorporated.\u003c/p\u003e \u003cp\u003eIn this study, considering the uphole and downhole factors, an ecological disturbance evaluation system integrating \"space, sky and earth\" is constructed. The correlation of different ecological indicators in the region was studied, and the possible ecological disturbance chain in the study area was discussed. The results can provide a reference for the decision of reclamation and ecological management of destroyed coal mining subsidence areas. Some of the selected ecological indicators are homogenized(show the same level in different areas) in the region, and these indicators failed to participate in the study of the formation of an ecological disturbance chain in the study area. We did not consider the impact of time on ecological disturbance in each area. Future research should include additional indicators to establish an intelligent monitoring system for long term observation of groundwater and surface ecology in an area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.Z and X.L.; methodology, X.Z.; writing\u0026mdash;original draft preparation, J.Z.; writing\u0026mdash;review and editing, K.Z.,J.Z,Y.W and G.K. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Science and Technology Innovation Project of\u0026nbsp;the\u0026nbsp;Shendong Group (Grant No.202016000041), the Talent Introduction Plan for Xinjiang in 2020, the National Natural Science Foundation of China (Grant No.42177037), and Research on Ecological Restoration and Protection of Coal Base in Arid Region\u0026nbsp;(Grant No.\u0026nbsp;GJNY2030XDXM-19-03.2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnonymous (2005): Determination of soil available potassium and slowly available potassium. 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ADVANCES IN CIVIL ENGINEERING 2021.https://doi.org/10.1155/2021/5177174\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coal mining subsidence area, Ecological disturbance assessment, Analytic hierarchy process","lastPublishedDoi":"10.21203/rs.3.rs-2218281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2218281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoal mining disturbs surface ecosystems in coal mining subsidence areas. Based on the groundwater-surface composite ecosystem analysis, we constructed an ecological disturbance evaluation index system (18 indices) in a coal mining subsidence area using the analytic hierarchy process (AHP). Taking the Nalinhe mining area in Wushen Banner, China, in 2018-2020 as an example, the ecological disturbance degree and the weight and correlation of different indicators were determined by implementing fuzzy mathematics, weighting method, and correlation analysis method. After two years of mining, ecological disturbance was the highest in the study area (Grade III) and the lowest in the non-mining area (Grade I). The ecological disturbance in the coal mining subsidence area continued increasing over two years due to coal mining. The ecological disturbance by coal mining cannot be completely mitigated by relying on the self-repair capability of the environment. Coal mining not only directly interfered with the environment, but also strengthened the connection of different ecological indicators, forming multiple ecological disturbance chains such as \"coal mining–surface subsidence–soil chemical factors,\" \"natural climate–soil physical factors–soil chemical factors,\" and \"mining intensity–mining thickness–burial depth and mining thickness ratio”; the last disturbance chain increased the ecological disturbance caused by resource mining. The disturbance chain \"coal mining–surface subsidence–soil chemical factors\" plays a leading role in controlling the ecological disturbance of soil chemical factors. The disturbance chain that controls the ecological component factors in the region remains unknown; however, the analysis of the results reveals that ecological component factors is the most important factor that hinders the restoration of the ecological state in the coal mining subsidence area. This study is of great significance for ecological restoration and governance of coal mining subsidence areas.\u003c/p\u003e","manuscriptTitle":"Evaluation of disturbance by coal mining to groundwater and surface ecosystem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-03 14:37:57","doi":"10.21203/rs.3.rs-2218281/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18c3b7f1-f89c-492d-9ba6-2ddef9d2bede","owner":[],"postedDate":"January 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-15T20:10:44+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-03 14:37:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2218281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2218281","identity":"rs-2218281","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}