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Article

Research on Ecological–Environmental Geological Survey and Evaluation Methods for the Kundulun River Basin in Baotou City

by
Yi Hao
1,2,
Junwei Wan
3,*,
Yihui Xin
4,*,
Wenhui Zhou
1,2,
Yongli Li
1,2,
Lei Mao
1,2,
Xiaomeng Li
1,2,
Limei Mo
1,2 and
Ruijia Li
1,2
1
Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 010010, China
2
Innovation Base for Water Resource Exploration and EcoEnvironmental Effects in the Daheihe Basin of the Yellow River, China Geological Survey, Hohhot 010010, China
3
School of Resources, China University of Geosciences (Wuhan), Wuhan 430074, China
4
School of Architecture and Art, Hebei University of Architecturez, Zhangjiakou 075000, China
*
Authors to whom correspondence should be addressed.
Water 2025, 17(13), 1926; https://doi.org/10.3390/w17131926 (registering DOI)
Submission received: 16 May 2025 / Revised: 24 June 2025 / Accepted: 26 June 2025 / Published: 27 June 2025
(This article belongs to the Special Issue Soil and Groundwater Quality and Resources Assessment, 2nd Edition)
Browse Figures
Versions Notes

Abstract

The Kundulun River Basin is the most prominent branch of the Yellow River system within the jurisdiction of Baotou City. As an important water source and ecological barrier, its ecological quality is directly related to the ecological security and sustainable development of the surrounding areas. This study selected the Kundulun River Basin in Baotou City as the research area. On the basis of collecting relevant information, a field investigation was conducted on the ecological and geological conditions of the atmospheric surface subsurface Earth system, using the watershed as the survey scope and water as the carrier for the transfer and conversion of materials and energy in the watershed. This study selected the main factors that affect the ecological geological quality of the watershed and established an evaluation model using the analytic hierarchy process, the coefficient of variation method, and the comprehensive analysis method. We have established an ecological geological quality evaluation index system for the Kundulun River Basin. We conducted quantitative evaluation and comprehensive analysis of regional ecological and geological environment quality. The results indicate that ecological environment indicators contribute the most to the ecological quality of the study area, while the impact of human activities on ecological quality is relatively small. From the perspective of evaluation indicators, grassland has the highest weight, followed by precipitation, groundwater depth, forest land, and cultivated land. Approximately 30.26% of the land in the research area is in a state of high or relatively high ecological and geological–environmental quality risk. It can be seen that the overall quality of the ecological geological environment is not optimistic and needs further protection.

1. Introduction

Under the dual pressures of global climate change and intensified human activities, the ecological environment quality in arid and semi-arid regions is facing unprecedented major challenges [1,2]. Due to their inherently fragile ecosystems, these areas are particularly sensitive to environmental changes, and issues such as the frequent occurrence of extreme weather events, water resource shortages, and land degradation further exacerbate ecosystem instability [3]. The state of the ecological environment not only affects the conservation of biodiversity and the sustainable use of natural resources but also directly affects the quality of life for local residents and the stable development of regional socioeconomics [4,5]. Against this background, it has become especially important to adopt scientific and effective measures to assess and enhance ecological conditions in arid and semi-arid regions [6,7]. Therefore, conducting systematic evaluations of the ecological–environmental quality in these areas can not only provide a solid theoretical foundation for local ecological civilization construction but also promote harmonious coexistence between humans and the natural environment, laying the groundwork for achieving long-term sustainable development goals [8,9]. Thus, carrying out geological assessments of ecological and environmental quality in arid and semi-arid regions can offer scientific theoretical support for the construction of ecological civilization and the harmonious development of the relationship between humans and the natural environment.
In recent years, with the continuous advancement and integration of GIS (geographic information system), GPS (global positioning system), and RS (remote sensing) technologies, research on ecological environment quality assessment has experienced significant development both domestically and internationally [10,11,12,13]. The analytic hierarchy process (AHP), proposed by Saaty in the 1970s, is a practical and effective method for determining weights [14], which effectively divides complex problems into hierarchical levels, making them more organized. This method remains mainstream today; however, some scholars have explored alternative approaches to enhance the scientific rigor of assessments by introducing objective evaluation methods such as the coefficient of variation, neural network method, and principal component analysis, aiming to achieve more objective determination of factor weights [15,16,17]. However, completely abandoning subjective experience may lead to the neglect of actual information specific to the study area. Therefore, evaluation models that combine both subjective and objective approaches have, to some extent, balanced the contradictions between subjective judgment and objective reality [18,19].
The natural environment of the Kundulun River Basin exhibits typical characteristics of arid and semi-arid regions, with scarce and unevenly distributed precipitation and low vegetation coverage, resulting in a relatively fragile ecological environment [20]. Water scarcity limits vegetation growth, leading to severe land degradation and desertification. In addition, the soil in the basin is mostly infertile chestnut calcic soil, which has poor water retention capacity and is easily affected by weathering and erosion [21]. Recent studies on the basin have mainly focused on climatic conditions [22,23], hydrogeological features [24], and the distribution characteristics of soil pollution [25], while research specifically targeting its ecological and geological–environmental conditions is still limited. This study aims to address the lack of research on ecogeological–environmental assessments in arid and semi-arid regions, particularly within the specific context of the Kundulun River Basin in Baotou City. We propose the following hypotheses: grassland coverage is significantly positively correlated with ecological quality; precipitation plays a crucial role in vegetation growth and groundwater recharge. Therefore, this study selects the Kundulun River Basin in Baotou City as the research area, conducting an in-depth investigation into its ecological environment, natural conditions, geological features, and humanistic factors. Based on GIS technology and using methods such as analytic hierarchy process, coefficient of variation, and comprehensive analysis, this study comprehensively evaluates the quality of the ecological and geological environment within the basin through resampling at 50 × 50 m grid intervals, reclassification, and raster calculation with weighted overlay techniques, providing scientific and rational support for the protection, sustainable development, and utilization of the basin’s ecological environment.

2. The Study Area

The study area is located in the central-western region of Inner Mongolia, primarily encompassing the administrative districts of Jiuyuan, Qingshan, Kundulun, Shiguai, Guyang, Damu Banner, and parts of Wulateqian Banner in Baotou City [26], with its specific location shown in Figure 1. The Kundulun River has a total length of 134 km and is accompanied by 23 tributaries of varying sizes. The basin features a complex topography with mountains and hills crisscrossing the region: to the east lies the towering Chun Kun Mountain, to the north is the Ata Mountain, to the west is the Mingan Mountain, and to the south and southwest are the Wula Mountain and Daqing Mountain, respectively. The climate is classified as a temperate continental arid to semi-arid monsoon climate, characterized by significant continental features, with windy and dry springs, hot summers, and cold winters. The annual average precipitation is 254.9 mm, with 80% occurring between July and September [27], and the annual evaporation rate ranges from 2200 to 2800 mm, which is 8 to 17 times the precipitation.
The study area is situated on the northern margin of the North China Platform, with the central and southern parts dominated by extensive Proterozoic metamorphic rock formations. Relatively complete Paleozoic, Mesozoic, and Cenozoic strata are also exposed within the region [28]. The plains belong to the Hetao geological sub-region, where Holocene deposits are widely distributed. These deposits are especially common in the Yellow River alluvial plain and the foothills of the Daqing Mountains, where alluvial and fluvioglacial sediments dominate. Additionally, large areas of Holocene deposits are found on the second terraces of rivers and lakes, as well as on the surfaces of the Wula Mountains and Daqing Mountains, primarily characterized by aeolian sand accumulation [29]. The study area spans two groundwater subsystems: the inland water system of the Yinshan Mountains’ northern hills and the Yellow River water system of the Yinshan Mountains’ southern hills [30]. Based on the aquifer medium and storage conditions, the regional groundwater can be categorized into three main types: Quaternary unconsolidated porous water, fractured porous water in clastic rocks, and fractured water in bedrock [31].
In the southern part of the Kundulun River Basin, the topography is mainly characterized by mid-mountain terrain, with elevations mostly exceeding 1600 m. The annual average precipitation is 346 mm, and the dominant rock types include Archean to Proterozoic gneiss, plagioclase amphibolite, schist, and granulite. The soil types are mainly stony soil and grey-cinnamon soils, with vegetation dominated by trees and shrubs, while herbaceous cover is relatively sparse. The primary tree species are poplar, elm, and pine, and the dominant shrub is small-leafed chickenweed. The main herbaceous species are dogtail grass, foxtail grass, Salsola collina, and Artemisia, with low vegetation coverage [32].
In the northern part of the Kundulun River Basin, the topography is mainly characterized by low mountains, hills, and high plains [33], with elevations mostly below 1600 m. The annual average precipitation ranges from 170.2 to 248.5 mm, and the dominant rock types include Silurian to Cretaceous sedimentary rocks, metamorphic rocks, and intrusive rocks, with relatively complex lithological combinations. The soil types are primarily calcic cambisols and brown calcic soils, dominated by herbaceous plants, with extensive distribution of shrub vegetation and scattered tree distributions. Trees are mainly distributed around towns, farmlands, and roadsides, primarily consisting of poplar, elm, and pine, with scattered jujube trees. The dominant shrub is small-leafed chicken weed, and the herbaceous vegetation is diverse, including common species such as Stipa krylovii, Stipa bungeana, Salsola collina, Artemisia, Potentilla anserina, and Leymus secalinus.

3. Materials and Methods

3.1. Establishment of the Evaluation Indicator System

To address the specific ecological and geological issues of the Kundulun River Basin in Baotou City, this study integrated multi-dimensional data from geological background, meteorological records, and field surveys. A systematic analysis was conducted to identify the core factors influencing the environmental quality of the region. The key elements affecting the ecological and geological environment were categorized into four main classes: natural geographic background, climatic conditions, geological environment background, and ecological environment background. Further, geological environment conditions and human activities were further identified as critical factors impacting the ecological environment.
To construct a comprehensive evaluation system, this study selected five primary evaluation criteria: natural geography, climatic conditions, geological environment, ecological environment background, and human activities. Under these primary criteria, more specific secondary evaluation indicators were established, totaling 15 sub-indicators. To verify the stability of the model, sensitivity analysis was conducted using the single-factor variation method. These include, but are not limited to, vegetation cover, precipitation, groundwater quality, arable land status, and others. The detailed evaluation indicator system is illustrated in Figure 2.

3.2. Selection of Evaluation Indicators

(1) Vegetation Coverage
When estimating vegetation coverage using remote sensing, it is common to use vegetation indices as a proxy to reflect the condition of plant growth. In the southern part of the Kundulun River Basin, vegetation is mainly composed of trees and shrubs, with relatively sparse herbaceous vegetation. The primary tree species include poplar, elm, and pine, while the main shrub species is Caragana microphylla. Herbaceous vegetation primarily consists of Setaria viridis, Chloris virgata, Salsola collina, and Artemisia scoparia. The basin features predominantly herbaceous plants alongside extensive shrubbery, with scattered distribution of trees. Among these, Astragalus membranaceus is widely distributed in Guyang County. By comparing vegetation coverage across different regions, we can clearly observe the close relationship between vegetation coverage and ecological–environmental quality. Therefore, vegetation coverage should be considered a key reference indicator when constructing an ecological environment quality evaluation system.
(2) Terrain Slope
Terrain slope is a crucial indicator for measuring the inclination of surface units, significantly influencing the redistribution process of water resources. Specifically, slope controls the flow and accumulation of organic matter and water within soil, thereby profoundly impacting vegetation growth and its spatial distribution. Terrain slope determines the speed and direction of surface runoff, affecting water resource allocation and flood routing. Steep slopes may lead to rapid surface runoff, increasing the risk of soil erosion, whereas gentle slopes may facilitate water accumulation and infiltration. The study area exhibits significant terrain characteristics, especially with strong topographic dissection, forming complex and varied landscapes. Notably, some areas in the south have steep slopes exceeding 50° (as shown in Figure 3(C2)). This pronounced variation in terrain slope not only affects surface hydrological processes but also deeply reflects the migration of beneficial elements in the soil and local spatial aggregation of heavy metals. Thus, terrain slope is not only an important parameter in geomorphology but also a critical factor to consider when assessing ecological safety and environmental quality in the study area.
(3) Precipitation
Precipitation influences natural processes such as surface water cycles, vegetation growth, soil erosion, and deposition, as well as human activities like agriculture, water conservancy, and urban planning. Appropriate rainfall is beneficial for vegetation growth and recovery, while insufficient or excessive precipitation can negatively impact vegetation. Heavy rainfall may cause soil erosion and threaten soil quality. Therefore, precipitation is a necessary consideration when evaluating ecological–environmental quality, predicting environmental changes, and formulating ecological protection strategies. The Kundulun River Basin has scarce precipitation and abundant sunlight, with an annual average precipitation of 291.1 mm, mostly concentrated from June to August, accounting for 64% of the annual total. The historical maximum daily precipitation was 175.3 mm on 19 July 2018 (previously, the highest daily precipitation was 114.9 mm on 7 August 1958), with an annual average evaporation of 1941.4 mm. Precipitation decreases from east to west in the Kundulun River Basin area of Baotou City (Figure 3(C3)).
(4) Temperature
Temperature is one of the key factors affecting vegetation growth and distribution. Suitable temperature conditions are conducive to vegetation growth and reproduction, while excessively high or low temperatures may stress or limit vegetation. Changes in temperature also affect soil microbial activity, thus influencing the decomposition of soil organic matter and nutrient transformation. Therefore, temperature is an important factor in ecological environment evaluation. The Kundulun River Basin has a typical arid and semi-arid continental climate characterized by low temperatures and large temperature differences. The frost-free period lasts 69–177 days annually. The annual average temperature is 5.5 °C, with the extreme maximum temperature reaching 38.6 °C on 22 June 2005, and the extreme minimum temperature dropping to −36.1 °C on 28 December 1967. Annual average temperatures increase from east to west in the Kundulun River Basin area of Baotou City (Figure 3(C4)).
(5) Groundwater Depth
The level of groundwater can influence the moisture conditions of surface water and soil, thereby affecting vegetation growth and distribution. Excessively high groundwater levels may lead to soil salinization, adversely affecting vegetation growth, while excessively low levels may result in soil drought, hindering normal vegetation growth.
(6) Groundwater Quality
Groundwater quality assessment typically involves multiple aspects, including chemical composition (such as pH, total dissolved solids, ion concentrations), physical properties (such as water temperature, color, turbidity), and biological characteristics (such as microbial content). Variations in these indicators may directly or indirectly affect the usability value of groundwater and ecological–environmental health.
(7) Soil Quality
Soil geochemical properties are core elements in assessing land quality in the study area, directly related to nutrients, heavy metals, organic pollutants, and the physicochemical properties of the soil. Phenomena such as the activation and migration of certain soil elements, like the enrichment of heavy metals and the loss of nutrients, are closely related to soil geochemical factors. These changes not only reveal the health status of the land but also serve as important criteria for assessing ecological safety and stability.
The quality of soil geochemical properties directly impacts the ecological functions and resource value of the land. Healthy soil ensures crop growth and yield in agricultural production, affecting productivity. Additionally, soil quality is a decisive factor in optimizing land resource allocation, influencing land development methods and sustainability. Lastly, research on soil geochemical properties is crucial for ecological–environmental protection efforts, aiding in identifying and controlling potential pollution sources.
(8) Geological Hazards
Geological hazards are an essential factor in ecological environment evaluation, involving geological structure stability, changes in topography, and risks associated with natural disasters. The occurrence of geological hazards can severely damage and impact the ecological environment, such as land destruction, water source pollution, and loss of biodiversity habitats. Geological hazards commonly include earthquakes, landslides, debris flows, and ground subsidence. These events may be influenced by various factors such as geological structures, topography, climatic conditions, and human activities. Monitoring and assessing geological hazards can help understand their risk levels, frequency, and potential impacts on the ecological environment.
(9) Land Use Types
Land resources are fundamental for human survival, and their utilization directly reflects the degree of human intervention in the natural environment, impacting ecosystem structure and function. In the Kundulun River Basin area of Baotou City, land use types are categorized into six classes: cultivated land, grassland, forest land, wetlands, construction land, and unused land.
(10) Population Density
Population density directly affects land resource utilization and cover. High-population-density areas often experience more urbanization and industrial activities, leading to over-exploitation and utilization of land resources, resulting in issues such as land degradation, water shortages, and biodiversity loss. Population density is also closely related to energy consumption and environmental pollution. High-density areas generally have higher energy consumption and pollutant emissions, such as vehicle exhaust and industrial wastewater, all of which negatively impact the ecological environment. Moreover, population density influences biodiversity conservation and restoration. High-density areas face greater pressures on biodiversity loss, necessitating increased attention to biodiversity protection and restoration efforts. In the Kundulun River Basin area of Baotou City, population clusters are mainly concentrated in the Kunqu District of Baotou City, Guyang County, and surrounding villages.

3.3. Data Collection and Processing

The data primarily include vegetation, topography, meteorology, land use, field measurements, socioeconomic, and population data for the Kundulun River Basin in Baotou City in 2020. Among these, vegetation data mainly consist of vegetation cover; slope gradient data mainly include slope and elevation; meteorological data primarily comprise precipitation and temperature; land use data encompass categories such as cultivated land, grassland, forest land, water bodies, construction land, and unused land; field measurement data comprise groundwater depth, groundwater quality, soil quality, and types and scales of geological hazards; and socioeconomic and population data are primarily based on population density. The sources of the data used are listed in Table 1.
The collection of soil samples followed the surface soil sampling methods and requirements specified in DZ/T 0295-2016 Specifications for Geochemical Survey of Land Quality [34] and DZ/T 0167-2012 Specifications for Regional Geochemical Exploration [35]. The soil quality standard refers to DZ/T 0295-2016 Specification for Geochemical Survey of Land Quality. The unified groundwater measurement work was conducted in accordance with the standards outlined in Specifications for 1:50,000 Hydrogeological Survey (DD2019-03) [36] and Technical Specifications for Unified Groundwater Measurement (Trial Version 20200831) [37]. Groundwater quality sampling and analysis were carried out in reference to the Specifications for Multi-Purpose Geochemical Survey (1:250,000) [38]. The groundwater quality standard refers to GB/T 14848-2017 Groundwater Quality Standard [39]. All samples were collected within the Kundulun River Basin area (109°30′–110°45′ E, 40°40′–41°40′ N).

3.4. Calculation of Indicator Weights

3.4.1. Determination of Indicator Weights Based on the Analytic Hierarchy Process (AHP)

The analytic hierarchy process (AHP) is a systematic analysis method that combines qualitative and quantitative analysis. By clearly defining the problem, establishing a hierarchical structure model, constructing a fuzzy consistency matrix, and calculating the weights of each indicator, AHP can quantify uncertain problems that are difficult to solve quantitatively using the theory and methods of fuzzy mathematics. In the evaluation process, quantified weight values are used to obtain final quantified evaluation results [40].
According to the principles of AHP, a judgment matrix is established, and consistency is evaluated using Formulas (1) and (2). When the CR value is less than 0.1 [14], the judgment matrix is considered to have reasonable consistency.
C I = ( λ m a x − n ) / ( n − 1 )
CR = CI/RI
CR is the consistency ratio, CI is the consistency index, RI is the random index, λmax is the maximum eigenvalue of the judgment matrix, and n is the number of indicators.
In this study, the consistency ratio (CR) values of the judgment matrices for each criterion layer relative to the goal layer were all less than 0.1 [14]. The results of the consistency check for the overall hierarchy ranking were as follows: consistency index (CI) = 0.015; random index (RI) = 0.756; and consistency ratio (CR) = 0.020. Since CR < 0.1, this indicates that the measurement is reasonable and passes the consistency test. After passing the consistency check, the components of the eigenvector corresponding to the maximum eigenvalue (λmax) represent the weights of the indicators at that level.

3.4.2. Determination of Indicator Weights Based on the Coefficient of Variation Method

In the evaluation of ecological and environmental quality, the coefficient of variation (CV) method can be used to determine the weights of different evaluation indicators. Its core principle lies in quantifying the relative variability among data to determine the weight of each indicator [41]. Compared to the entropy weight method, the CV method is more suitable for scenarios with significant data dispersion. For example, the standard deviation of precipitation is σ = 120 mm, with a mean value of x = 380 mm, resulting in a coefficient of variation (vi) of σ/x = 0.316. This indicates a high sensitivity of precipitation to the ecogeological environment; thus, it is assigned a higher weight (0.107).
Given that the units and scales of various indicators differ, this directly affects the feasibility of establishing a unified evaluation model. To accurately assess the ecological and environmental quality of the Kundulun River Basin in Baotou City, this study employed the natural breaks classification method as an analytical tool, with the classification results presented in Table 2. The ecological quality status of the study area was divided into four risk levels, as detailed in Table 2.
To achieve a more precise evaluation of ecological and environmental quality, 15 industry experts were convened to independently score multiple key evaluation indicators within this evaluation unit, constructing a weight table for each indicator. The 15 experts who participated in the AHP weight scoring are all from the fields of ecology, hydrogeology, and remote sensing applications, including 7 with senior professional titles and 8 who have experience in ecological restoration projects in arid areas. The consistency ratio of the judgment matrix was calculated using Formulas (1) and (2), resulting in a CR value of 0.02, which is less than 0.1, indicating a high level of consistency in the expert scoring. Using Excel software (2019), the average scores and standard deviations for each evaluation indicator were calculated, and the coefficient of variation for each indicator was further computed using Formula (3). Finally, through normalization processing (Formula (4)). We normalized the coefficients of variation into weight values for each evaluation indicator.
v i = σ i x i
W i = V i ∑ i = 1 n V i
Vi is the coefficient of variation for the i-th indicator, σ i is the standard deviation of the i-th indicator, xi is the mean of the i-th indicator, and Wi is the weight of the i-th indicator.

3.4.3. Comprehensive Analysis

An equal-weighted average (AHP weight × 0.5 + CV weight × 0.5) was used to integrate the subjective and objective weighting results, balancing expert experience with data characteristics. The stability of the weight allocation was verified using the single-factor variation method (Table 3). The results show that adjustments to the weights of highly sensitive indicators such as grassland coverage (C10) and precipitation (C3) significantly affect the comprehensive score (change rate > 8%), proving that the current weight allocation is reasonable.

3.4.4. Sensitivity Analysis

To verify the model’s sensitivity to weight allocation and ensure the reliability of the results, a sensitivity analysis of the weight parameters was conducted using the single-factor variation method. Particular attention was given to the sensitivity of five indicators: grassland (C10), precipitation (C3), groundwater depth (C5), forest land (C11), and cultivated land (C9).
(1) Single-Factor Variation Method
Based on the comprehensive weights listed in Table 4, each of the five indicators was unilaterally adjusted by ±10%, while the other weights remained unchanged. The comprehensive scores were then recalculated.
(2) Analysis Results:
Highly Sensitive Indicators: Grassland (C10), precipitation (C3), and forest land (C11) showed the greatest impact on the comprehensive score (score change rate > 8%). For example, a 10% increase in grassland weight led to a 7.2% improvement in the comprehensive score, indicating that vegetation coverage plays a significant role in regulating ecogeological–environmental quality.
Moderately Sensitive Indicator: Groundwater depth (C5) exhibited a score change rate of 3.1–3.4%, suggesting that weight adjustments have some influence on the model results but are not dominant factors.
Low-Sensitivity Indicator: Cultivated land (C9) had a score change rate between 2.5% and 2.8%, indicating that weight adjustments have minimal impact on the comprehensive score.

3.5. Comprehensive Evaluation of Ecological and Geological–Environmental Quality

We used the comprehensive analysis method to perform a weighted overlay of 15 indicators—vegetation cover, slope gradient, precipitation, temperature, groundwater depth, groundwater quality, soil quality, geological hazards, cultivated land, grassland, forest land, wetlands, construction land, unused land, and population density—was performed using the ArcGIS (10.8) Raster Calculator. The weight values of these indicators were applied in the weighted overlay process. The ecological and environmental quality of each indicator is shown in Figure 3. Based on the evaluation grade table (Table 5), the evaluation results for the study area were classified into different levels.

4. Results and Discussion

4.1. Analysis of Indicator Weights

Based on the weights obtained from the analytic hierarchy process (AHP) and the coefficient of variation (CV) method, the final weights for the criteria and indicators in this evaluation are summarized in Table 6. From the perspective of the criterion layer, it is evident that the land use type evaluation indicator has the highest weight (0.368), while the human activity evaluation indicator has a relatively lower weight of 0.100. This result indicates that ecological indicators play a dominant role in assessing the ecological quality of the study area, with their contribution significantly outweighing that of human activities. Although some areas within the study region (e.g., the eastern part) have experienced significant local ecological degradation due to activities such as mineral extraction, these high-intensity human activities are relatively concentrated in spatial distribution and have limited impact coverage, accounting for only a small proportion of the total study area. Therefore, from the perspective of the entire watershed, the influence of human activities on the comprehensive evaluation results of ecogeological environment quality is relatively weak. Based on this, the weight assigned to human activities in this study, 0.100, is considered reasonable. In addition, land use changes (e.g., expansion of construction land) and irrigation practices (e.g., excessive groundwater extraction) also have significant impacts on ecological quality.
From the perspective of the indicator layer, the grassland evaluation indicator has the highest weight (0.112), followed by precipitation (0.107), groundwater depth (0.093), forest land (0.081), and cultivated land (0.076). This indicates that grasslands contribute most significantly to the ecological quality of the study area. Grasslands are the largest land use type in the study area, covering 81.15% of the total area, and their health status directly affects soil conservation, water retention, climate regulation [42], and other critical aspects. Healthy grasslands help stabilize soil, reduce surface runoff, and minimize erosion caused by rainfall and surface water flow. Additionally, grasslands prevent wind erosion, enhance the ability to block wind-blown sand, and protect soil from being carried away by wind [43]. Moreover, grasslands play a crucial role in climate regulation, improving the regional environment and contributing significantly to soil formation and conservation [44]. The weight of forest land is 0.081, which is relatively low, mainly due to two limiting factors: first, its coverage accounts for only 0.20% of the study area (much lower than grassland at 81.15%), with a scattered spatial distribution and low dispersion (CV weight of 0.093), leading to a dilution of its overall contribution; second, in the AHP scoring, experts in arid regions prioritized high-coverage grassland (weight of 0.112) and key climatic factors (such as precipitation with a weight of 0.107), while forests, due to their small area and significant local effects but limited overall impact, were assigned a relatively lower weight.
Precipitation is also a vital factor influencing the ecological quality of the study area. First, adequate precipitation supports plant photosynthesis and nutrient absorption, promoting lush vegetation growth [45]. Second, precipitation is essential for replenishing surface water and groundwater, which is crucial for maintaining the water quantity and quality of wetlands, rivers, and lakes [46]. Third, soil moisture, which is influenced by precipitation, helps maintain soil structure and fertility, providing favorable conditions for plant growth [47].
Groundwater is an essential component of the water cycle. In arid and semi-arid regions, a decline in groundwater levels can lead to vegetation degradation, soil exposure, and increased susceptibility to wind erosion, ultimately causing land desertification [48]. Reduced or vanished river flows supplied by groundwater can result in diminished surface runoff, leading to comprehensive ecological degradation [49]. When groundwater extraction causes a drop in water levels, pore water pressure decreases while total stress remains constant, resulting in increased effective stress. This can lead to consolidation and dewatering of the geotechnical skeleton, potentially triggering geological issues such as land subsidence [50].
Forests and cultivated land cover 0.20% and 13.98% of the study area, respectively. Forests, as a crucial part of ecosystems, play a significant ecological role. They help retain soil and water, prevent soil erosion, and maintain land health and productivity [51]. Additionally, forests regulate climate by absorbing carbon dioxide and releasing oxygen, mitigating global warming trends. Cultivated land has a dual impact on ecological quality: improper use and management can degrade ecological quality, while scientific management and protection can enhance its positive ecological functions. Unsustainable agricultural practices can expose soil surfaces, making them more vulnerable to erosion by water, wind, and gravity, leading to soil loss and reduced fertility and productivity [52]. Furthermore, agricultural activities can cause the decomposition and oxidation of soil organic matter, reducing its content and leading to soil impoverishment and quality decline [53]. Long-term application of chemical fertilizers and specific crop choices can also cause soil acidification, negatively impacting soil ecology [54]. However, cultivated land also possesses many positive ecological functions. Agricultural ecosystems can absorb waste, maintaining ecological and economic balance, protecting soil, and preventing pollution from being amplified through crops, food chains, and production chains [55]. Crops also provide functions such as windbreaks, soil stabilization, and the regulation of soil pH [56]. Therefore, the role of cultivated land in ecological quality is also significant.

4.2. Analysis of the Comprehensive Evaluation Results for Ecological and Geological–Environmental Quality

Based on the weighted overlay of 15 indicators, the weighted formula is as follows: Vegetation Cover × 0.055 + Slope Gradient × 0.041 + Precipitation × 0.109 + Temperature × 0.060 + Groundwater Depth × 0.093 + Groundwater Quality × 0.057 + Soil Quality × 0.041 + Geological Hazards × 0.068 + Cultivated Land × 0.077 + Grassland × 0.083 + Forest Land × 0.114 + Wetlands × 0.073 + Construction Land × 0.030 + Unused Land × 0.038 + Population Density × 0.052. According to the ecological quality evaluation table (Table 4), the ecological and geological–environmental quality grades were determined. The final evaluation map of the ecological and geological–environmental quality for the Kundulun River Basin in Baotou City is shown in Figure 4. Figure 4 shows that most areas of the study region have good or excellent ecological and geological–environmental quality, with the quality improving from the northwest to the southeast.
Areas with poor ecogeological–environmental quality are mainly located in the central, southern, and eastern parts of the study area, covering a total area of 127.86 km2, accounting for 6.01% of the total study area. Areas with relatively poor ecogeological–environmental quality are primarily found in the western part of the study area, covering 836.19 km2, or 39.30%, of the total study area. Areas with better ecogeological–environmental quality are mainly located in the mountainous valleys of the eastern and central regions, with a total area of 1072.72 km2, accounting for 50.41% of the total study area. Areas with good ecogeological–environmental quality are concentrated in the central region where vegetation growth conditions are favorable and groundwater is shallow (with a burial depth of 0.4–3 m), covering an area of 91.07 km2, or 4.28% of the total study area. The specific proportion of the evaluation results is shown in Figure 5.
In summary, the total area of regions with poor and relatively poor ecogeological–environmental quality is 964.05 km2, accounting for 45.31% of the total study area. These areas are mainly ecologically fragile zones characterized by deep and poor-quality groundwater, harsh natural conditions, single vegetation types, and low vegetation coverage. The soil is infertile and has low productivity, and precipitation is relatively lower compared to the eastern part of the study area. A small number of areas are located within mineralization zones, and the overall ecological environment faces certain challenges.
In summary, the total area of regions with poor and relatively poor ecological and geological–environmental quality in the study area is 647.94 km2, accounting for 30.26% of the total area of the study area. These areas are predominantly ecologically fragile zones, characterized by deep groundwater levels, poor water quality, harsh natural conditions, limited vegetation types, and low coverage. The soil is barren, with low productivity, and precipitation is relatively scarce compared to the eastern part of the study area. These regions belong to the agropastoral ecotone, significantly affected by human activities and irrigation. Some areas are located within mineral concentration zones, leading to vegetation destruction, reduction in forest and grassland areas, and deterioration of the ecological environment.

4.3. Zoning of Ecological and Geological–Environmental Quality

According to the characteristics of ecological and geological–environmental quality zoning, the ecological and geological–environmental quality of the county study area is divided into 11 sub-regions (Figure 4) based on specific ecological, environmental, and geological issues and geological–environmental conditions.

4.3.1. Areas with Good Ecogeological–Environmental Quality (Category I)

(1) I-1 Area: Located in the eastern part of the study area, this region has shallow groundwater (0.4–3 m) with excellent water quality, mainly classified as Class I and II. Vegetation coverage is high, primarily consisting of grasslands with a small amount of forest land. The terrain slope is gentle (0–12°), which facilitates water infiltration and retention. Annual precipitation is moderate (370–405 mm), temperatures are suitable, averaging between 1 °C and 3.5 °C, providing favorable conditions for vegetation growth.
(2) I-2 Area: Situated near the Kundulun River in the southern part of the study area, this region also features shallow groundwater with good water quality, mainly Class III. It contains abundant grassland and a small amount of forest land, with overall good soil quality and minimal heavy metal pollution. The terrain slope is moderate, without severe geological hazards. Climate conditions are favorable, with annual precipitation of 370–405 mm and average temperatures ranging from 3.5 °C to 6 °C, contributing to a stable natural ecosystem.

4.3.2. Areas with Better Ecogeological–Environmental Quality (Category II)

(1) II-1 Area: Located in the northeastern part of the study area, this region has relatively high vegetation coverage but slightly lower than Category I areas. The terrain slope is moderate, ranging from 5°to 15°, with no severe geological hazards. Predominantly covered by chestnut calcic soils, the vegetation coverage is 50–60%, with annual precipitation of 405–440 mm and an average temperature of 3.5–6 °C. Groundwater depth is 3–5 m, with Class III water quality, making it suitable for agriculture.
(2) II-2 Area: Located in the mountainous valley areas of the study region, such as Liufa Gully, the area features diverse vegetation types and rich herbaceous plant coverage. Groundwater depth is shallow with good water quality, mainly Class III. Climatic conditions are favorable, with temperatures and precipitation conducive to vegetation growth, annual precipitation of about 405–440 mm, and an average temperature around 5.5 °C. Land use is reasonable, mostly comprising grasslands and cultivated land. This area is predominantly covered by grey-brown soils, which have good water and nutrient retention capabilities. The terrain slope is moderate, ranging from 5°to 15°.

4.3.3. Areas with Relatively Poor Ecogeological–Environmental Quality (Category III)

(1) III-1, III-2, and III-4 Areas: Located in the eastern and northern parts of the study area, Zone III-1 has a small-scale concentrated mining area in its central part, primarily consisting of iron ore, with sporadically distributed construction granite and quartzite mines. The groundwater level is deep, and the water quality is relatively poor, mainly classified as Class IV and V. Vegetation coverage is low, dominated by sparse grasslands. Terrain slopes are steep, exceeding 15° in some areas, posing a risk of geological hazards. Climatic conditions are harsh, with annual precipitation below 365 mm, unfavorable for vegetation growth. These areas are mainly covered by chestnut calcic soils, which are infertile and prone to wind erosion, with lower temperatures.
(2) III-3, III-5 Areas: Located in the western part of the study area, including the northwest and southwest of Guyang County, these regions have low vegetation coverage and steep terrain slopes, with low annual precipitation and average temperatures of 1–3.5 °C. Groundwater depth is significant, and water quality is poor. Geological hazards occur at a moderate frequency, and population density is higher.

4.3.4. Areas with Poor Ecogeological–Environmental Quality (Category IV)

(1) IV-1, IV-2 Areas: Located in Guyang County and its surrounding areas in the central part of the main study area, this region is characterized by deep groundwater levels and poor water quality, mainly classified as Class IV and V. Vegetation types are limited and show low coverage. In addition, frequent geological hazards further deteriorate the ecological environment. Although the area features diverse soil types, soil salinization and wind erosion are commonly observed. Annual precipitation is less than 365 mm, and the climate is cold, with an average annual temperature below 2 °C.

4.4. Discussion and Comparison

Through a systematic assessment of the ecogeological–environmental quality in the Kundulun River Basin of Baotou City, we have reached several important conclusions. To better understand these findings and verify the effectiveness of our research methods, we compared the results from the Kundulun River Basin with those from Li Hang et al. (2022) [57] in Ertaizhen, Zhangbei County. Additionally, we compared them with recent international cutting-edge research on the assessment of ecogeological environments in arid and semi-arid regions.
(1) Comparison with Li Hang et al. (2022) [57] in Ertaizhen, Zhangbei County
Comparing our findings with those of Li Hang et al. (2022) [57] in Ertaizhen, Zhangbei County, it was found that both the Kundulun River Basin and Ertaizhen are geographically close, located in central-western Inner Mongolia, with similar annual precipitation levels (346 mm for the Kundulun River Basin and approximately 400 mm for Ertaizhen). Despite differences in geomorphological features (the Kundulun River Basin consists mainly of medium and low mountains and hills, while Ertaizhen is characterized by plateau hills), both areas exhibit common ecological, geological, and environmental issues. For example, both regions have a significant proportion of land at high risk of desertification, primarily concentrated in the central and eastern areas.
Further comparisons reveal that the evaluation index system of the Kundulun River Basin is more comprehensive, covering key factors such as groundwater depth and water quality, which significantly impact the ecological quality in arid and semi-arid regions. Specifically, the Kundulun River Basin not only considers vegetation coverage and climatic conditions but also deeply analyzes changes in groundwater levels and their impact on ecosystems, making its evaluation results more accurate and comprehensive. In contrast, although Li Hang et al.’s study in Ertaizhen also focuses on soil texture and vegetation coverage, it places less emphasis on geological–environmental aspects. Therefore, the Kundulun River Basin study can better reveal the complexity of the ecogeological environment and provide more targeted management recommendations.
(2) Comparison with International Cutting-Edge Research
1. Comparison with Sun et al. (2020) [17] using the PCA-Disaster Theory Method
Sun et al. (2020) [17] used a combination of principal component analysis (PCA) and disaster theory to assess the overall ecological–environmental quality in the mid-Atlantic region of the United States. This method reduced data complexity through dimensionality reduction techniques and effectively identified major ecological risk factors. In contrast, the Kundulun River Basin study combined the analytic hierarchy process (AHP) with coefficient of variation (CV), considering expert experience while reflecting the importance of each indicator through objective data. This subjective–objective integrated approach provides a more comprehensive assessment of ecogeological–environmental quality, especially in data-scarce arid and semi-arid regions.
2. Comparison with Zhang et al. (2021)’s [18] AHP-GPCA Model
Zhang et al. (2021) [18] proposed an integrated assessment method for the Qinghai–Tibet Plateau’s ecogeological environment based on RS/GIS and the AHP-GPCA model. This method uses geographic information systems (GIS) and remote sensing (RS) technologies to obtain multi-source data and determine weight values through AHP. Although both studies use AHP, the Kundulun River Basin study further incorporates CV and validates the stability of weight allocation through the single-factor variation method. Results show that adjustments in the weights of highly sensitive indicators such as grassland (C10) and precipitation (C3) significantly affect the comprehensive score (change rate > 8%), proving the current weight allocation is reasonable and highly reproducible.
(3) Uniqueness of the Kundulun River Basin Study
Through comparisons with the aforementioned international cutting-edge research and Li Hang et al.’s study in Ertaizhen, we find that the Kundulun River Basin study has several unique aspects and advantages:
Comprehensive Index System: The evaluation index system of the Kundulun River Basin not only includes conventional factors like vegetation coverage and climatic conditions but also delves into groundwater depth and its impact on ecosystems, making its evaluation results more accurate and comprehensive.
Methodological Innovation: By combining AHP with CV, this study considers expert knowledge while objectively reflecting the importance of each indicator, thereby enhancing the rationality and reliability of weight allocation.
Targeted Recommendations: Addressing specific problems in arid and semi-arid regions, such as declining groundwater levels and land degradation, this study provides more targeted management recommendations, offering scientific support for ecological protection in similar regions.

5. Conclusions and Recommendations

(1) From the Evaluation Criteria Layer
The evaluation index weight for land use type is the highest at 0.368, while the human activity evaluation index has the lowest weight at 0.100. This indicates that land use type contributes most significantly to the ecological quality of the study area, whereas human activities have a relatively smaller impact. Spatial distribution analysis of human activity evaluation indices shows that population density is sparse overall, resulting in only a minor influence.
(2) From the Evaluation Indicators Layer
The grassland evaluation indicator has the highest weight (0.112), with its coverage directly impacting regional ecological quality. Grasslands enhance soil stability through their extensive root networks, reducing the risk of soil erosion and water loss, which is especially crucial in arid and semi-arid regions. High grassland coverage effectively retains moisture, reduces surface runoff, prevents soil erosion, and maintains ecosystem stability and water conservation functions. Therefore, increasing grassland coverage is one of the key measures to improve the ecological quality of this region.
Precipitation, as an important factor affecting ecological quality (weight: 0.107), not only directly promotes vegetation photosynthesis and nutrient absorption but also indirectly affects the replenishment of surface water and groundwater. Adequate precipitation helps increase vegetation coverage, improving soil structure and fertility. Moreover, precipitation is essential for maintaining water levels and quality in wetlands and rivers. However, in the Kundulun River Basin, low and unevenly distributed annual precipitation limits vegetation growth in some areas, leading to declining groundwater levels and exacerbating–environmental degradation.
Groundwater depth (weight: 0.093) is closely related to ecological quality, particularly in arid and semi-arid regions, where a decline in groundwater levels can lead to vegetation degradation and land desertification. Groundwater extraction can reduce pore water pressure, causing soil compaction, and potentially triggering geological problems like land subsidence. Thus, managing groundwater resources rationally and avoiding over-extraction are crucial for protecting local ecosystems.
(3) Ecological Environment Quality Assessment Results
The assessment results show that the ecological–environmental quality around Guyang County in the central part of the study area is poor, while the western part has relatively poorer ecological conditions. The total area of regions with poor and relatively poor ecological quality is 964.05 km2, accounting for 45.31% of the total study area. The main reasons include low precipitation, deep groundwater levels with poor water quality, harsh natural conditions, single vegetation types, infertile soils, and parts of the area being located in mining zones, leading to vegetation destruction, reduced forest and grassland areas, and environmental degradation.
(4) Sensitivity Analysis Using Single-Factor Variation Method
Vegetation coverage (C1), precipitation (C3), and forest land (C11) are highly sensitive indicators (score change rate > 8%), with precipitation having the most significant impact. Groundwater depth (C5) and cultivated land (C9) are moderately sensitive indicators (change rate: 2.5–3.4%). Population density (C15), geological disasters (C8), etc., are low-sensitivity indicators (change rate: < 1.5%). These findings highlight the critical role of water resource factors in the evaluation of ecogeological–environmental quality.
(5) Overall Status and Recommendations
Approximately 45.31% of the land in the study area is in a high- or relatively high-risk state in terms of ecogeological–environmental quality, and the overall status of the ecogeological–environmental quality is not optimistic, requiring further strengthening of protection measures. It is recommended that in Zone I (areas with good ecogeological environment), strict protection should be the main approach, establishing ecological red lines to prohibit mineral exploitation and expansion of construction land, promoting water-saving agricultural technologies, and setting up grassland health monitoring stations; in Zone II (better areas), grassland coverage should be increased to 70% through measures such as converting farmland back to grassland (e.g., planting drought-resistant forage like Stipa tenacissima) and constructing rainwater harvesting facilities; in Zone III (poorer areas), efforts should focus on mine reclamation (e.g., increasing vegetation coverage from <30% to 60% in mining areas within the Kundulun River Basin) and groundwater management (e.g., constructing artificial wetlands to intercept mine water pollution); in Zone IV (areas with poor conditions), urgent interventions should be implemented, such as improving saline soils through soil replacement and microbial remediation in Zone IV-2 around Baotou City, while implementing grazing bans and constructing forage bases in the agropastoral ecotone (Zone IV-1). Regarding implementation and safeguards, priority should be given to the treatment of Zone IV (within 5 years) and Zone III (within 3–5 years), combined with remote sensing/GIS dynamic monitoring indicators (e.g., vegetation coverage, groundwater depth), and special national funding should be applied for to support restoration projects in Zone IV.
(6) Research Limitations and Future Prospects
This study constructed an ecogeological–environmental quality evaluation model for the Kundulun River Basin based on static data from 2020, with a focus on quantitative assessment of current ecological quality and zoning-based management strategies. Due to limitations in research duration, data availability, and technical methods, the dynamic trends under climate change or human activity disturbances were not thoroughly explored. For example, an ecological response model under drought intensification scenarios (e.g., a 15% reduction in precipitation) was not developed, nor was the long-term impact of grassland restoration projects quantitatively assessed. This limitation restricts the forward-looking application of the research findings in policy-making. Future work will integrate IPCC climate projections and MODFLOW groundwater modeling to conduct multi-scenario simulations (e.g., precipitation changes, land-use transitions) to predict the spatiotemporal evolution of ecological quality indicators. Additionally, the latest remote sensing data (e.g., Landsat 9) and field monitoring data will be used to verify the stability of the current model and supplement dynamic weight allocation methods.

Author Contributions

Y.H., writing—original draft preparation, software, and formal analysis; J.W., methodology and writing—review and editing; Y.X., writing—review and editing; W.Z., validation, investigation, and supervision; Y.L., conceptualization, methodology, and data curation; L.M. (Lei Mao), investigation, validation, and resources; X.L. responsible for data organization; L.M. (Limei Mo) investigation and funding acquisition; R.L. investigation and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Survey of Lakes in the Mengxin Plateau Lake Region (DD20230510). It was further Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China (2024QN04003) and Innovation Base for Water Resource Exploration and Eco-environmental Effects in the Daheihe Basin of the Yellow River.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We appreciate the anonymous reviewers for their invaluable comments and suggestions on this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area location map. (a) Location of Baotou City within Inner Mongolia Autonomous Region; (b) Location of the Kundulun River Basin within Baotou City; (c) Sampling sites in the Kundulun River Basin.
Figure 1. Study area location map. (a) Location of Baotou City within Inner Mongolia Autonomous Region; (b) Location of the Kundulun River Basin within Baotou City; (c) Sampling sites in the Kundulun River Basin.
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Figure 2. Evaluation index system.
Figure 2. Evaluation index system.
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Figure 3. Risk assessment chart for indicators. (C1): Vegetation Coverage Distribution Map; (C2): Topographic Slope Distribution Map; (C3): Rainfall Distribution Map; (C4): Temperature Distribution Map; (C5): Groundwater Level Distribution Map; (C6): Groundwater Quality Distribution Map; (C7): Soil Quality Distribution Map; (C8): Geological Hazard Distribution Map; (C9): Cultivated Land Distribution Map; (C10): Grassland Distribution Map; (C11): Forest Land Distribution Map; (C12): Wetland Distribution Map; (C13): Construction Land Distribution Map; (C14): Future Land Use Distribution Map; (C15): Population Density Distribution Map.
Figure 3. Risk assessment chart for indicators. (C1): Vegetation Coverage Distribution Map; (C2): Topographic Slope Distribution Map; (C3): Rainfall Distribution Map; (C4): Temperature Distribution Map; (C5): Groundwater Level Distribution Map; (C6): Groundwater Quality Distribution Map; (C7): Soil Quality Distribution Map; (C8): Geological Hazard Distribution Map; (C9): Cultivated Land Distribution Map; (C10): Grassland Distribution Map; (C11): Forest Land Distribution Map; (C12): Wetland Distribution Map; (C13): Construction Land Distribution Map; (C14): Future Land Use Distribution Map; (C15): Population Density Distribution Map.
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Figure 4. Ecological environment quality assessment map of Kundulun River basin in Baotou City.
Figure 4. Ecological environment quality assessment map of Kundulun River basin in Baotou City.
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Figure 5. Histogram of ecological quality evaluation results in the study area.
Figure 5. Histogram of ecological quality evaluation results in the study area.
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Table 1. Data source and description.
Table 1. Data source and description.
Data NameData Source
Vegetation CoverMultispectral remote sensing images from Landsat 8 OLI_TIRS satellite digital products available on the Geospatial Data Cloud
Slope Gradient30 m resolution ASTER GDEM (Digital Elevation Model) data from the Geospatial Data Cloud
Precipitation1 km raster data of 2020 precipitation from the National Earth System Science Data Center, part of the National Science and Technology Infrastructure Platform
Temperature1 km raster data of 2020 temperature from the National Earth System Science Data Center, part of the National Science and Technology Infrastructure Platform
Groundwater DepthField measurements conducted in July 2020 under the China Geological Survey project (ZD20220217)
Groundwater QualityField measurements conducted in July 2020 under the China Geological Survey project (ZD20220217)
Soil QualityField measurements conducted from June to August 2020 under the China Geological Survey project (ZD20220217)
Geological HazardsField measurement results from the First Hydrogeological and Engineering Geological Survey Institute of Inner Mongolia Autonomous Region
Cultivated LandChina Land Cover Dataset, CLCD (30 m Fine Land Cover Dataset of Inner Mongolia in 2020)
Grassland
Forest Land
Water Bodies
Construction Land
Unused Land
Population Density1 km resolution population spatial distribution raster data from the LandScan dataset for 2020
Table 2. Ecological quality risk level table.
Table 2. Ecological quality risk level table.
Risk LevelAssigned Value
Low risk9
Medium risk7
Higher risk5
High risk3
Table 3. Research area evaluation index division standard reference table.
Table 3. Research area evaluation index division standard reference table.
Goal LayerCriterion LayerIndicator LayerClassification Standards
Low Risk (9)Medium Risk (7)Higher Risk (5)High Risk (3)
AB1C10.63~10.32~0.630.12~0.32<0.12
C20~12°12~32°32~43°43~75°
B2C3>440 mm405~440 mm370~405 mm<370 mm
C4>6 °C3.5~6 °C1~3.5 °C<1 °C
B3C50.4~3 m3~5 m5~10 m>10 m
C6Class I, Class IIClass IIIClass IVClass V
C7Class I, Class IIClass III
C8RareLow ProbabilityModerate ProbabilityHigh Probability
B4C9<30%30~50%50~80%>80%
C10>80%50~80%30~50%<30%
C11>80%50~80%30~50%<30%
C12>80%50~80%30~50%<30%
C13<30%30~50%50~80%>80%
C14<30%30~50%50~80%>80%
B5C15<1000 people/km21000~3000 people/km23000~5000 people/km2>5000 people/km2
Table 4. Results of sensitivity analysis for five key indicators.
Table 4. Results of sensitivity analysis for five key indicators.
IndicatorOriginal WeightScore Change Rate After +10% WeightScore Change Rate After −10% Weight
Grassland (C10)0.083+7.2%−7.5%
Precipitation (C3)0.109+9.3%−10.1%
Groundwater Depth (C5)0.093+3.1%−3.4%
Forest Land (C11)0.114+8.7%−9.6%
Cultivated Land (C9)0.077+2.5%−2.8%
Table 5. Evaluation table of ecological geological environment quality in the study area.
Table 5. Evaluation table of ecological geological environment quality in the study area.
Evaluation ResultsPoor Ecological and Geological–Environmental QualityFair Ecological and Geological–Environmental QualityGood Ecological and Geological–Environmental QualityExcellent Ecological and Geological–Environmental Quality
Evaluation Grades3.26~4.084.08~4.454.45~4.744.74~5.53
Table 6. Comprehensive weight of evaluation indexes of Kundulun River Basin in Baotou.
Table 6. Comprehensive weight of evaluation indexes of Kundulun River Basin in Baotou.
Goal LayerCriterion LayerAHP WeightCV WeightComprehensive WeightIndicator LayerAHP WeightCV WeightComprehensive Weight
AB10.0970.1410.119C10.0650.0430.054
C20.0320.0470.04
B20.1600.1660.163C30.120.0930.107
C40.040.0750.058
B30.2630.2360.250C50.1230.0630.093
C60.0250.0890.057
C70.0420.040.041
C80.0730.0640.069
B40.4180.3180.368C90.1060.0450.076
C100.1560.0680.112
C110.0680.0930.081
C120.0430.0980.071
C130.027 0.0450.036
C140.018 0.040.029
B50.061 0.1390.100C150.061 0.040.051
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Hao, Y.; Wan, J.; Xin, Y.; Zhou, W.; Li, Y.; Mao, L.; Li, X.; Mo, L.; Li, R. Research on Ecological–Environmental Geological Survey and Evaluation Methods for the Kundulun River Basin in Baotou City. Water 2025, 17, 1926. https://doi.org/10.3390/w17131926

AMA Style

Hao Y, Wan J, Xin Y, Zhou W, Li Y, Mao L, Li X, Mo L, Li R. Research on Ecological–Environmental Geological Survey and Evaluation Methods for the Kundulun River Basin in Baotou City. Water. 2025; 17(13):1926. https://doi.org/10.3390/w17131926

Chicago/Turabian Style

Hao, Yi, Junwei Wan, Yihui Xin, Wenhui Zhou, Yongli Li, Lei Mao, Xiaomeng Li, Limei Mo, and Ruijia Li. 2025. "Research on Ecological–Environmental Geological Survey and Evaluation Methods for the Kundulun River Basin in Baotou City" Water 17, no. 13: 1926. https://doi.org/10.3390/w17131926

APA Style

Hao, Y., Wan, J., Xin, Y., Zhou, W., Li, Y., Mao, L., Li, X., Mo, L., & Li, R. (2025). Research on Ecological–Environmental Geological Survey and Evaluation Methods for the Kundulun River Basin in Baotou City. Water, 17(13), 1926. https://doi.org/10.3390/w17131926

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