Introduction

Groundwater is usually the main source of water in arid and semi-arid areas1,2. In the past few decades, with the development of economy and society, water environment problems have attracted more and more attention. Although China has a large total water resource volume, its per capita water resources are less than one-quarter of the global average3. Under these circumstances, karst groundwater resources have become particularly important, often serving as the primary water supply for many cities. As a result, the study of karst groundwater environment has become very urgent, and the assessment of the groundwater environment in a region requires the study of its formation mechanism and hydrochemical characteristics, in this process, the analysis of runoff conditions plays a crucial role4.

Currently, studies on groundwater runoff belts are mostly conducted from the perspectives of water quantity, water level, and geologic structure5,6. Commonly used methods include: statistical methods7, hydrochemical and isotopic characterization8, remote sensing interpretation9, tracer methods10, hydrological time series analysis11, numerical modeling12, and the multitaper method13.

To date, research on karst water in the Fengfeng mining area has mostly focused on hydrochemical evolution14,15,16,17, with little analysis of karst groundwater runoff belts. In other regions, studies on runoff belts have generally been approached from the perspective of groundwater dynamics, without identifying karst groundwater runoff belts from the perspective of the groundwater chemical field18,19. Although the isotope dating method20,21 can judge the age of the water year and analyze the flow conditions of groundwater, its cost is high, and it usually consumes a lot of manpower, material resources and financial resources. Therefore, this study focuses on applying hydrogeochemical methods to analyze the hydrogeochemical reactions occurring during the flow of karst groundwater. This approach helps address the shortcomings in hydrogeochemical modeling of karst groundwater in the Fengfeng mining area. It also provides a deeper understanding of karst groundwater runoff conditions. The findings can offer recommendations and references for water resource management in karst regions, thereby supporting economic development and policymaking for public welfare in these areas. The article will primarily focus on the following aspects of the study: (1) clarifying the spatial distribution pattern of the hydrochemical types of karst groundwater in the study area; (2) quantitatively evaluating the changes that occur in karst groundwater during its flow process; (3) identifying the runoff intensity in the three runoff belts and comparing it with existing data.

Study area

Geographical location

Fig. 1
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Map of the geographical location of the Fengfeng Mining Area (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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The Fengfeng mining area, located in Handan City, Hebei Province, China, lies at the intersection of Hebei, Shanxi, and Henan provinces, the total area is 353 square kilometers. It extends from the Taihang Mountains in the west to the North China Plain in the east, covering latitudes from 36°20′N to 36°34′N and longitudes from 114°03′E to 114°16′E. The geographical location of the Fengfeng mining area22 is illustrated in Fig. 1 .

Topography and landforms

The Fengfeng mining area is situated in the transitional zone between the Taihang Mountains and the North China Plain, located on the eastern foothills of the southern edge of the Taihang Mountains. The western part consists of intermountain basins, while the eastern side features sloping plains. The highest elevation in the area is 891 m. Gu Mountain extends north to south through the study area. The western region features shallowly eroded and sedimentary terrain formed by the Hecun and Wuan fault basins, while the eastern part is the slope-proluvial piedmont slope, which continues to enter the North China Plain eastward. The topographic map of the study area is shown in Fig. 2.

Fig. 2
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Topographic map (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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Geological conditions

The aquifer in the Fengfeng mining area from top to bottom is the Quaternary gravel pore aquifer; the sandstone aquifer of the Upper and Lower Shihezi Formation; Shanxi Formation sandstone fissure aquifer; Taiyuan Formation wild bluish limestone fissure karst aquifer; Taiyuan Formation mountain and underlying limestone fissure karst aquifer; Small bluish limestone fissure karst aquifer of Taiyuan Formation; Taiyuan Formation fissure karst aquifer of Great bluish limestone; and Middle Ordovician limestone karst fissure aquifer. The study area is located on the eastern side of the third uplift zone of the neo-cathaysian tectonic system. The geological strata are well-exposed in the area, with formations ranging from the Middle Ordovician, Carboniferous, Permian, Triassic, to Quaternary, progressing from west to east. The Middle Ordovician is primarily composed of thick layer dolomite, dolomitic breccia, and tight limestone, while the Lower Ordovician consists mainly of gray dolomite and yellow-green calcareous shale. The comprehensive stratigraphic column of the Fengfeng mining area23 is shown in Fig. 3.

The study area is located in the western part of the North China Plain subsidence zone of the Neocathaysian system. In the northern part of the study area, there are magmatic rocks intruded in a long strip in the NNE direction and intruded along the main fault structure in the NNW direction(The location of the fault is shown in Fig. 5).

Fig. 3
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Comprehensive Bar chart (This image is cited from a paper I previously published. The figure was created using CorelDRAW software, version CorelDRAW 2020.)

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Regional hydrogeological conditions

The dynamic changes of groundwater depend on the periodic variation in recharge. The fluctuation of the groundwater level in the area shows a periodic time lag with atmospheric precipitation, rising during the rainy season and declining during the dry season.

The overall trend of groundwater levels in the area is a gradual decrease from west to east. The western mountainous area has a large recharge area, good runoff conditions, strong water productivity, high groundwater levels, and unstable dynamic water levels with large annual fluctuations. In contrast, the eastern plain has limited recharge range, poor runoff conditions, weak water productivity, and significantly lower groundwater levels compared to the western mountainous area. The groundwater levels are stabilized by lateral recharge from fracture-karst groundwater, with stable dynamic water levels and smaller annual fluctuations. The spatial distribution map of the aquifers is shown in Fig. 4.

Fig. 4
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Spatial distribution of aquifer in Fengfeng Mining area(This image is cited from a paper I previously published. The figure was created using CorelDRAW software, version CorelDRAW 2020.)

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Due to the influence of terrain, structure, and drainage conditions, groundwater flows into the Hecun Basin from the west, north, and south sides of the limestone expose leakage area, forming three runoff belts in the north, south, and east of Gu Mountain. The northern strong runoff belt is located in the northern part of the Hecun Basin, with the Cuilu to Dagogang to Yangjiaopu line as the center and lateral recharge as the main supply source, with good supply conditions. The southern strong runoff belt is located in the south of the Hexun Basin, centered on the line of the Gangziyao to Heilongdong spring group with strong productivity. Atmospheric precipitation infiltration is the main supply source of the runoff belt on the east side of Gu Mountain, and the center line of the Niuerzhuang to the Heilongdongquan Group is poor in water productivity, the terrain in this area is flat, so the groundwater flow velocity is relatively slower compared to the other two runoff belts in the study area. The hydrogeological map of the study area is shown in Fig. 5.

Fig. 5
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Regional hydrogeological plan (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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Materials and methods

Sample collection and analysis

In this study, 20 karst groundwater samples were collected between June and August 2022, all the water samples are from Ordovician karst water. The field data collection was carried out in strict accordance with the “Technical specifications for environmental monitoring of groundwater” issued in 2021. The collected samples were initially stored in a refrigerated container and then transported to the laboratory for chemical analysis. As shown in Fig. 6, the locations of the karst water sampling points are evenly distributed along the three main groundwater runoff belts within the study area.

Fig. 6
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Distribution of sampling points (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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This study primarily analyzes data for K+, Na2+, Ca2+, Mg2+, Cl⁻, SO42⁻, HCO3⁻, pH value, Total Dissolved Solids (TDS), and Total Hardness (TH). TH is measured as the sum of alkaline earth metal ions in water, including Ca2+, Mg2+, Ba2+, and Sr2+. However, in most cases, the concentrations of other metal ions in water are minimal compared to Ca2+ and Mg2+, so TH is generally measured based on the levels of Ca2+ and Mg2+. The TH calculation formula used in this study is as follows (with units in mg/L):

$$TH=2.497{\text{C}}{{\text{a}}^{2+}}(\frac{{mg}}{L})+4.115{\text{M}}{{\text{g}}^{2+}}(\frac{{mg}}{L})$$
(1)

Data analysis methods

In addition to statistical analysis, this study combines the Shukarev classification with ArcGIS (Geographic Information System. The version we are using is ArcGIS 10.2. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html.) software to conduct a spatial analysis of hydrochemical types. Furthermore, the study employs the inverse modeling software PHREEQC (PHREEQC software is fully known as Phreactor Equilibrium Code. The version we are using is PHREEQC Version 3, and its URL link is: https://www.usgs.gov/software/phreeqc-version-3.) alongside ArcGIS to quantitatively evaluate the reactions occurring in groundwater during its flow, from a spatial perspective. By integrating the aforementioned hydrogeochemical methods, this study aims to assess the runoff intensity of different karst groundwater zones within the study area. This approach provides a deeper understanding of karst groundwater dynamics and offers new methods and perspectives for determining groundwater runoff conditions.

Descriptive statistical analysis

Descriptive statistics refers to the process s of organizing and calculating a large amount of data included in a survey sample. It provides an effective yet relatively simple method for summarizing and describing the data24. Commonly used measures include: mean, median, and mode, which reflect the central tendency of the data; range, variance, standard deviation, and quartiles, which illustrate the dispersion and variability of the data; skewness and kurtosis, which indicate how the data deviates from a normal distribution25.

Shukalev classification method

Groundwater chemical classification using the Shukalev method is divided into two types. One is the Shukalev numerical naming method, where groundwater is classified based on the six major ions (Na⁺, Ca²⁺, Mg²⁺, HCO₃⁻, SO₄²⁻, Cl⁻) and mineralization. Anions and cations with a content greater than 25% milliequivalents are combined, which can result in 49 types of water. The other method is the Shukalev sequential naming method, where ions with a milliequivalent percentage greater than 25% are named in the order of anions first, followed by cations26.

Kriging interpolation method

The Kriging interpolation method, also known as the best spatial autocovariance interpolation method, is widely used in fields such as groundwater modeling and soil mapping27. It first considers the variation in spatial attributes across different spatial locations, determining the distance range that influences the value of an unsampled point. Then, it uses the sample points within this range to estimate the attribute value of the unsampled point. Mathematically, this method provides an optimal linear unbiased estimation for the studied object. It takes into account the geometric characteristics of the sample data, such as the shape, size, and spatial relationships with the unsampled block, as well as the spatial structure of the grades.

Saturation index

Since the 1970s, the Saturation Index (SI) has been widely adopted and used. It helps determine whether minerals are in a dissolved or precipitated state in water28. The SI can also be used to assess the validity of hydrogeochemical modeling results. The calculation formula is as follows:

$$SI={\text{lg}}\frac{{IAP}}{K}$$
(2)

In this formula, IAP represents the ion activity product of the relevant ions in the reaction, while K is the equilibrium constant for the mineral dissolution reaction at a given temperature.

Inverse hydrogeochemical modeling

Inverse hydrogeochemical modeling is a method that uses observed chemical and isotope data to infer the hydrogeochemical processes occurring between groundwater and rocks29,30. It is primarily used to understand the geochemical evolution paths of water in groundwater flow systems, including water-rock reactions, the dissolution and precipitation of minerals, and their magnitudes. This modeling is based on mass conservation principles. It simulates and predicts changes in groundwater environments by analyzing water sample data and determining the saturation state of minerals, among other steps.

Along the runoff path, starting and ending points are selected. As groundwater flows from the starting point to the endpoint, it interacts chemically with mineral phases, other solutions, or gases. The chemical composition of the groundwater at the endpoint reflects the changes that have occurred during these interactions. Commonly used hydrogeochemical modeling software includes WATEQ31,32, NETPATH33,34, Eq. 3/635,36, PHREEQE37,38, SOLMINEQ.8839, and PHREEQC40,41.

Results and discussion

Descriptive statistical analysis

Table 1 Descriptive statistics analysis.
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Based on the mean concentrations of the various ions (Table 1), the ions are ranked from highest to lowest concentration as follows: Ca²⁺ > K⁺ + Na⁺ > Mg²⁺, HCO₃⁻ > SO₄²⁻ > Cl⁻. Furthermore, the concentrations of Ca²⁺, HCO₃⁻, and SO₄²⁻ are significantly higher than those of the other ions. This indicates that in the karst groundwater of the study area, the primary cation is Ca²⁺, while the predominant anions are HCO₃⁻ and SO₄²⁻.

The content of K+ + Na+ ranges from 12.3 mg/L to 137.1 mg/L, with a coefficient of variation of 86.7%. The content of Cl− ranges from 15.8 mg/L to 254.0 mg/L, with a coefficient of variation of 130%. The median value of Cl− is 24.9 mg/L, and the maximum value is 10.2 times higher. The region with a Cl− concentration of 254 mg/L is located at point D-20 in the eastern plain area. The content of SO42− ranges from 74.9 mg/L to 615.8 mg/L, with a coefficient of variation of 83.9%. The region with a SO42− concentration of 615.8 mg/L is also at point D-20. The concentrations and coefficients of variation of these ions are relatively high, indicating significant spatial variation in their distribution. The notably higher concentrations of Cl− and SO42− at point D-20 compared to other areas may be attributed to the nearby coal mining activities and frequent agricultural practices in the region. The concentration ranges of Ca2+, Mg2+, HCO3−, and NO3− are small, and their coefficients of variation are also small, indicating that their spatial distribution is relatively more uniform compared to other ions. The high concentrations of Ca²⁺, Mg²⁺, HCO₃⁻, and SO₄²⁻ make them the main ions controlling the variation in hydrochemical types in the study area.

Hydrochemical types and Spatial distribution

Table 2 Results of the Shukarev classification.
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The Shukarev sequential naming method42 is used to categorize the karst groundwater in the study area, and the results are shown in Table 2.

Based on the Shukarev classification, six hydrochemical types were identified in the study area: HCO3·SO4-Ca·Mg·Na type (10%), HCO3·SO4-Ca·Mg type (65%), HCO3·SO4-Ca type (10%), HCO3-Ca type (5%), SO4·HCO3-Ca type (5%), and SO4·Cl-Ca type (5%). This indicates that the predominant hydrochemical type in the study area is HCO3·SO4-Ca·Mg, with the primary ions controlling the groundwater chemical types being Ca²⁺, Mg²⁺, HCO₃⁻, and SO₄²⁻, which is consistent with the descriptive statistical analysis results presented earlier.

Since the results from the Shukarev classification are somewhat abstract and do not readily reveal the spatial distribution of hydrochemical types, this study utilizes the Kriging interpolation method in ArcGIS43,44 to visually represent the hydrochemical types of karst groundwater in the study area, as illustrated in Fig. 7.

Fig. 7
figure 7

Spatial distribution of hydrochemical types (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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Figure 7 shows that karst water in the study area exhibits significant spatial variation. In both the northern and southern runoff belts, the predominant hydrochemical type is HCO3·SO4-Ca·Mg·Na, with minimal variation in hydrochemical types. In the eastern region, the predominant hydrochemical types are SO4·Cl-Ca and SO4·HCO3-Ca, with significant variation in hydrochemical types this area features high SO4 and high Cl type retention water. This indicates that, compared to the western region, groundwater in the eastern region has had more time to interact with the surrounding rock, so the runoff conditions are relatively poor.

Spatial distribution of major ion concentration

From the previous analysis, the main ions in the karst groundwater of the study area are Ca²⁺, Mg²⁺, HCO₃⁻, and SO₄²⁻. To visually analyze the spatial distribution trends of these major ions, Kriging interpolation was performed using ArcGIS on the ions with higher concentrations. The spatial distribution of Ca²⁺, Mg²⁺, HCO₃⁻, and SO₄²⁻ ion concentrations is shown in Fig. 8.

Fig. 8
figure 8

Spatial distribution of major ion concentrations (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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Figure 8 reveals that the concentrations of the major ions Ca²⁺, Mg²⁺, HCO₃⁻, and SO₄²⁻ are higher in the eastern region and lower in the western region. Moreover, the ion concentrations in the northern and southern runoff belts are lower than those in the runoff belts on the eastern side of the Gu Mountain.

Combining the spatial distribution of hydrochemical types with the distribution patterns of Ca²⁺, Mg²⁺, and HCO₃⁻ concentrations, it is evident that these ions follow similar distribution trends. Therefore, Ca²⁺, Mg²⁺, and HCO₃⁻ concentrations are the primary factors influencing the variation in hydrochemical types of karst water in the study area. Moreover, ion concentrations show less variation in the western region and greater variation in the eastern plain region. From a hydrological geochemical perspective, this suggests that the northern and southern runoff belts have better runoff conditions, whereas the eastern runoff belt and eastern stagnation area have poorer runoff conditions. Consequently, the more extensive water-rock interactions in the eastern region result in higher ion concentrations compared to the western region.

Saturation index

The ion concentrations were input into the PHREEQC program, and the resulting SI for each mineral are shown in Table 3.

Table 3 The calculation result of mineral SI.
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To more intuitively study the spatial evolution of mineral SI, the data from Table 3 were subjected to Kriging interpolation using ArcGIS. Due to the small variation in the SI of CO₂(g), it was not included in the spatial analysis. The distribution maps of the SI for various mineral phases in the study area are shown in Fig. 9.

Figure 9 shows that in the northern and southern runoff belts, the SI for all three minerals increase along the runoff direction, reaching their maximum values in the discharge area. As groundwater flows, the SI steadily increases, indicating that as the groundwater moves from the runoff belt to the discharge zone, the terrain becomes less steep and the flow velocity decreases. As a result, the runoff conditions in the runoff belt are better compared to those in the discharge area.

In the study area, the SI for anhydrite, gypsum, and halite in the karst groundwater are all below 0, indicating that these minerals are in a dissolved state. Figure 9 shows that in the northern and southern runoff belts, the SI for anhydrite and gypsum increase along the direction of groundwater flow. In the northern runoff belt, the SI for halite increases with the direction of flow. However, in the southern runoff belt, and the eastern runoff belt of Gu Mountain, there is no significant trend. Overall, the index shows a decreasing trend from east to west, indicating that groundwater flow velocity in the eastern region is much lower than in the western region.

Fig. 9
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Spatial distribution of SI of each mineral phase (The figure was created using ArcGIS 10.2 software. The URL link is: https://desktop.arcgis.com/zh-cn/desktop/index.html).

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Inverse hydrogeochemical modeling

This study will employ PHREEQC software for inverse hydrogeochemical modeling, ased on the geological and hydrogeological conditions of the study area, there are three runoff belts in the Fengfeng mining area. The northern runoff belt includes: Cuilu, Bate → Dagougang → Yangjiaopu (Point No. 02 → 10). The southern runoff belt includes: Gangziyao → Heilongdong Spring Group (Point No. 18 → 10). The eastern Gu Mountain runoff belt includes: Niuerzhuang → Wukuang → Yikuang → Heilongdong Spring Group (Point No. 12 → 10).

A necessary condition in hydrogeochemical modeling is that the water sampling points being simulated must lie along the same groundwater flow path. The primary criteria for determining the “possible mineral phases” include: the lithological conditions of the study area, groundwater occurrence conditions, and the analysis results of groundwater hydrochemical components45. Based on the geological, hydrogeological conditions, and hydrochemical analysis results of the study area, this research identifies the “possible mineral phases” as: calcite, CO₂(g), dolomite, gypsum, halite, and cation exchange reactions (CaX, NaX).

The main dissolution reaction equations are as follows:

Halite: \(N{\text{a}}Cl=N{a^+}+C{l^ - }\)

Dolomite: \(CaMg{\left( {C{O_3}} \right)_2}+2{H_2}O=C{a^{2+}}+M{g^{2+}}+2HC{O_3}^{ - }+2O{H^ - }\)

CO2(g): \(C{O_2}+{H_2}O={H_2}C{O_3}\)

Gypsum: \(CaSO_{4} \cdot2H_{2} O = Ca^{{2 + }} + SO_{4} ^{{2 - }} + 2H_{2} O\)

Calcite: \(CaC{O_3}+{H_2}O=C{a^{2+}}+HC{O_3}^{ - }+O{H^ - }\)

Cation exchange reactions: \(2NaX+C{a^{2+}}=2N{a^+}+Ca{X_2}\)

Mass balance modeling is used to calculate the transformation of major minerals along the modeling path in the study area. The specific method involves analyzing the hydrochemical data of the upstream (“initial water composition”) and downstream (“endpoint water composition”) along the groundwater flow path. The relationship is expressed as: “Initial water composition” + “Reactants” = “Endpoint water composition” + “Products”. Since inverse hydrogeochemical modeling results are often “non-unique,” it is necessary to combine the geological and hydrogeological conditions of the study area to determine the possible “mineral phases” and select the solution that best matches the actual conditions46. The initial water samples, mineral phases, and uncertainty coefficients for each runoff path were input into the PHREEQC program to perform inverse hydrogeochemical modeling. This process involved three models: the northern runoff belt model, the southern runoff belt model, and the eastern runoff belt model of Gu Mountain. The modeling results are presented in Tables 4 and 5, and 6.

Table 4 Modeling results for the Northern runoff zone.
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Table 5 The modeling results of the runoff zone on the East side of Gu Mountain.
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Table 6 Modeling results for the Southern runoff zone.
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Northern runoff belt

Along the northern runoff belt (Point No. 02 → 10), the dissolution and precipitation quantities of each mineral phase are shown in Table 4. Three representative modeling results were selected for analysis.

In the three models, Model 1 shows that gypsum and NaX have a tendency to precipitate, while the other minerals tend to dissolve. In Model 2, dolomite, gypsum, and NaX exhibit a tendency to precipitate, with no observable dissolution or precipitation of halite, while the other minerals continue to dissolve. In Model 3, gypsum and NaX show a tendency to precipitate, with no observable dissolution or precipitation of dolomite, while the other minerals continue to dissolve. Based on the previous statistical analysis, ion concentration spatial distribution patterns, and SI analysis, it is observed that in the northern runoff belt, the concentrations of Na⁺ and SO₄²⁻ decrease, while Ca²⁺ and Cl⁻ concentrations increase. Additionally, gypsum precipitation, halite dissolution, and cation exchange occur in this runoff belt. Therefore, Model 1 accurately reflects the actual conditions in the Fengfeng mining aera.

Runoff belt on the east side of Gu mountain

Along the runoff belt on the east side of Gu Mountain (point number 12→10), the amount of dissolution and precipitation of each mineral phase is shown in Table 5, and three representative modeling results were selected for analysis.

In the runoff belt on the east side of Gu Mountain, dissolution of CO2(g) and dolomite occurs in karst groundwater from north to south, and the dissolution of dolomite leads to an increase in the concentration of HCO3⁻, and the precipitation of calcite, gypsum, and halite leads to a relative decrease in the concentration of Ca2+, SO42−, Na+, and Cl⁻. Compared with model 2, model 1 shows Ca-Na exchange, and compared with model 3, model 1 shows calcite precipitation. Thus, Model 1 best describes the hydrogeochemical evolution of karst groundwater along the eastern runoff belt.

Southern runoff belt

Along the southern runoff belt (Point No. 18 → 10), the dissolution and precipitation quantities of each mineral phase are detailed in Table 6. Three modeling results meet the accuracy requirements. In the southern runoff belt, Model 1 best reflects the hydrogeochemical processes of karst groundwater in this area. This is because the geological strata in the study area contain gypsum and dolomite. The SI results show that calcite, CO₂(g), dolomite, and gypsum all dissolve, leading to increased concentrations of Ca²⁺, HCO₃⁻, and SO₄²⁻ in the groundwater at the endpoint. Additionally, the direction of Ca-Na exchange, which is consistent with the increase in Na⁺ and decrease in Ca²⁺, further supports this model.

Although dolomite in the karst groundwater of the study area is in a supersaturated state, dolomite still dissolves in the groundwater. This may be due to the dissolution of CO₂ during the groundwater flow process, which continues to dissolve dolomite, along with the influence of precipitation lag47. Existing research indicates that the SI only reflects the thermodynamic potential for mineral dissolution or precipitation, but it does not indicate the rate at which these processes occur. Whether a mineral actually dissolves or precipitates also depends on the reaction kinetics48.

Comparing the inverse hydrogeochemical modeling results of different runoff belts, it can be seen that the amount of mineral transfer in the runoff belt on the east side of Gu Mountain is higher than that in the northern runoff belt and the southern runoff belt, which verifies that the groundwater runoff conditions in the northern and southern runoff belts are better than that in the east side of Gu Mountain, and therefore the northern and southern runoff belts are strong runoff belts, and the runoff belt on the east side of Gu Mountain is a weak runoff belt.

Conclusions

This study, from a hydrogeochemical perspective and incorporating geological and hydrogeological conditions, analyzes the runoff conditions of karst groundwater in the study area. It also provides a quantitative assessment of the changes occurring during the flow of karst groundwater. The main conclusions are as follows:

  1. (1)

    The primary hydrochemical type of karst groundwater in the Fengfeng mining area is HCO3·SO4-Ca·Mg, while the runoff belt on the east side of Gu Mountain exhibits characteristics of high SO4 and high Cl retention water.

  2. (2)

    In the same area, the hydrogeochemical processes differ across various runoff belts, indicating that the chemical reactions and water-rock interactions occurring during flow are different.

  3. (3)

    The northern and southern runoff belts are identified as strong runoff belts, while the runoff belt on the eastern side of Gu Mountain is classified as a weak runoff belt, consistent with existing geological data. This demonstrates that hydrogeochemical methods can accurately evaluate the runoff conditions of karst groundwater.

Limitations of the study

The present study did not sample or analyze the sediments, and the chemical and media components were not sufficiently characterized. Additionally, the simulation process omitted the effects of redox reactions and human activities. These aspects represent key directions for future optimization.