Considering groundwater from the aquifers overlying the bedrock is an important water source for drinking purposes. As such, the investigation of its property is essential. Based on the spatial structure of aquifers in the study area, the aquifers in the Cenozoic strata are divided into three groups. The multivariate statistical approaches are employed to identify the hydrogeochemical processes and hydro-chemical types, and hydrogeochemical inverse modeling is applied to further validate and elucidate the hydrogeochemical process and water–rock interaction. The results are as follows: (1) The hydro-chemical type of the upper aquifer is dominated by the K + Na-HCO3 type, while others have similar water quality types, which are dominated by the K + Na-Cl type and the K + Na-SO4 type. (2) The saturation index of anhydrite, gypsum, halite, and CO2(g) is below zero in three aquifers, indicating that they are unsaturated. While aragonite, calcite, and dolomite in the middle aquifer remain in the unsaturated–saturated state. (3) The cation exchange process accelerating the reduction of Ca2+ concentration and the increase of SO42− concentration occurs in three aquifers, and the dissolution of calcite and dolomite minerals occurs in most cases. This study supports the fundamental evidence for the hydrogeochemical processes and water resource utilization and has a certain practical significance.

  • Hydrogeochemical characteristics of the Cenozoic aquifers are investigated.

  • Relationship between ions reveals the hydrogeochemical processes and water–rock interaction.

  • Cation exchange process accelerates the reduction of Ca2+ concentration and the increase of SO42+ concentration.

  • A hydro-chemical boundary is identified.

  • Reverse simulation and cluster analysis further validate the water–rock interaction processes.

It is a worrying fact that drinking freshwater accounts for only 3% of the world's water resources, and the water demand for the development of industry and agriculture, and residents' lives cannot be satisfied fully (Hoekstra 2014; Holland et al. 2015; Bonsal et al. 2020; Stewart-Harawira 2020). Thus, the reasonable utilization and assessment of freshwater resources have raised concern in the past few decades (Ferng 2007; Vollmer et al. 2018; Nurtazin et al. 2020). In terms of the water in the mining areas, underground aquifers and surface water systems will be affected after the extraction of mineral resources. Once wastewater generated by mining is poured into the ground surface, the quality of the surface water and below will be polluted (Zhang et al. 2019; Ju et al. 2022). Besides, the water table of aquifers influenced by water-flowing fissures resulting from the excavation of tunnels and working faces is decreasing year by year (Arkoc et al. 2016; Sahoo & Khaoash 2020; Sun et al. 2021). As such, the current situation of water resource management in mining areas is particularly prominent (Prathap & Chakraborty 2019; Xu et al. 2020). Considering the importance of groundwater to human health and industrial production, the hydro-chemical characteristics and groundwater assessment of mining areas have raised more attention in the scientific community.

Researchers have proposed some practical methods and techniques in qualitative analysis and a quantitative evaluation for the hydrogeochemical processes and evolution characteristics of groundwater, such as multivariate statistical analysis, stable isotopes and tracer elements, laboratory tests, and geochemical modeling (Liu et al. 2019; Roy et al. 2020; Qiu et al. 2021; Wijewardhana et al. 2022). Besides, the hydrogeochemical characteristics and the spatial distribution of groundwater in Bangladesh were investigated using a geographic information system (GIS) with geochemical modeling. Areas with the availability of the best-quality drinking water were identified (Das et al. 2021, 2022). Principle component analysis and hydrogeochemical modeling were utilized to investigate the hydrogeochemical evolution in groundwater of the target area (Hu et al. 2023; Jiang et al. 2023). The chemical quality of groundwater in Bushehr, southwest of Iran, was assessed for its suitability for drinking purposes through analysis of geochemical processes and their relation to water quality (Sarikhani et al. 2015). To investigate the comparative performance of two cover systems to reclaim acid mine drainage in Quebec, Canada, coupling with hydrogeological and geochemical numerical simulations was selected to evaluate the reclaimed performance of acid-generating tailling areas (Pabst et al. 2018). The hydrochemistry and water–rock interaction of ground surface water at the Mandi Baha-Ud-Din district in Pakistan was analyzed through GIS and bivariate Gibbs, and it was confirmed that most of the area was affected by water–rock interaction. The water quality in most of the area met drinking standards (Abbas et al. 2021). Water–rock interaction and chemical compositions of deep Taiyuan Formation limestone aquifer were discussed in coal mines, and extensive groundwater drainage has a great influence on water quality, accelerating the water–rock interaction (Chen et al. 2023). Based on previous relevant studies, the complicated rock–water interaction processes between different strata and aquifers have been analyzed and revealed (Morán-Ramírez et al. 2016; Yousif & El-Aassar 2018). Due to the intensive and large-scale mining, some toxic and harmful wastewater and suspended material (rock dust, coal cinder, and mechanical oil) as well as other contaminants that influence the groundwater environment are produced and may flow into groundwater through runoff and infiltration.

Scholars have made achievements in the study of hydrogeochemical processes and water–rock interaction. However, more attention is paid to the Mesozoic aquifers or surface water bodies. As a kind of important water source, the research on the whole Cenozoic loose sandy aquifer water of the extra thickness loose layer is relatively few. In this study, hydrogeochemical processes and evolution among the three aquifers distributed in the huge thick loose strata of the Gubei coal mine, Huainan mining area, are assessed by using the multivariate statistical analysis, ArcGIS 10.4, and inverse geochemical numerical simulation method. It is intended to provide insights into water–rock interaction controlling hydrogeochemical processes by evaluating the general groundwater chemistry characteristics and establishing geochemical evolution inverse modeling.

The study area is located in the Huainan mining area of Anhui province, a typical Northern coal mine, with a longitude ranging from 116°29′20″E to 116°33′14″E, and a latitude from 32°46′30″N to 32°51′29″N as shown in Figure 1. Since 2008, it has been in production with an annual capacity of four million tons. The cover of the study area is 34.014 km2, and the mining depth ranges from −400 to −1,000 m in elevation. The coal mine is located in the alluvial plain, the terrain is flat on the whole, and the ground elevation is generally 21–24 m, making the elevation of the northwestern region higher than that of the southeastern area. The study area is influenced by a semi-humid climate with evident seasonal monsoon patterns and an annual average temperature of 15.1 °C. The monthly rainfall is mostly concentrated in June–August, accounting for about 40% of the total amount of the whole year.
Figure 1

Location of the study area and water sampling sites of three aquifers.

Figure 1

Location of the study area and water sampling sites of three aquifers.

Close modal

More than 20 coal-bearing layers are developed in the study area, most of which are distributed in Permian strata overlaid by the extra-thick Cenozoic strata. Main aquifers include the Cenozoic loose sandy aquifers, the Permian coal strata fractured sandstone aquifers, the Carboniferous Taiyuan Formation fractured limestone aquifers, and the Ordovician fractured limestone aquifers according to the sedimentary period of the stratum. The Cenozoic loose sandy aquifers consisting of the upper aquifer, middle aquifer, and lower aquifer are the main discussed targets in the present study.

As shown in Figure 2, the total thickness of the quaternary sandy aquifer (i.e., upper aquifer) is generally 116 m, the upper layer with an average thickness of 26.39 m is dominated by fine silt, and the middle layer serves as aquiclude layer (thickness of 9.97 m) composed of sandy clay, and the lithology of the lower layer is dominated by medium-fine sand, lenticular sandy clay locally, which is the main domestic and industrial water sources in the mining area. The middle aquifer distributed in the upper and middle of the Neogene strata is commonly developed with a large thickness ranging from 156.56 to 362.84 m. The middle aquifer is divided into two segments by the aquiclude. The aforementioned segment is mainly composed of coarse-medium sand where a thin layer of clay develops and local calcareous cementation forms into a ‘sand disc’. The lithology of the below segment strata is consolidated clay, medium-fine sand, gypsum crystal blocks, and calcium locally. The buried depth of the lower aquifer, which lacks stratigraphic deposition in the paleotopographic uplift area, is greater than 400 m. The lithology of the lower aquifer consists of coarse-medium sand and the partial existence of gravel.
Figure 2

Characteristics of the structure of the Cenozoic aquifers in the study area.

Figure 2

Characteristics of the structure of the Cenozoic aquifers in the study area.

Close modal

Water sampling and testing

Water samples were obtained from ground supply water wells, surface pumping wells, and auxiliary shaft and ventilation shaft including 8 samples from the upper aquifer, 20 samples from the middle aquifer, and 13 samples from the lower aquifer. The sampling location is shown in Figure 1(c). Before sampling, the collectors were preprocessed to prevent the original water quality from being polluted during sampling. Namely, collectors were treated with the diluted HCI solution with a concentration of about 2.5% to remove impurities and washed several times with pure water, and then they are dried and tightly sealed with clean caps. Each time while onsite sampling, we labeled the surface of the sample collectors in time. The information contents of the label mainly included collection location, depth, time, and sample number. Generally, some key physiochemical parameters such as temperature, pH value, electric conductivity, and dissolved inorganic carbon can be measured during the sampling (Liu et al. 2019). To prevent changes in water quality at higher outdoor temperatures, all the samples were stored in the refrigerator at the laboratory.

After that, the water samples were tested in the laboratory by using inductively coupled plasma-optical emission spectrometry for cations including calcium, magnesium, sodium, and potassium, and ion chromatography for anions including chloride and sulfate, whereas bicarbonate was analyzed through the titration method. Besides, the water quality test was required to be completed within 24 h after the water samples were collected. The data relating to the main ions have been acquired to further be analyzed. The statistical description of the test results of three groups water samples is shown in Table 1.

Table 1

Descriptive statistics for three aquifers water samples

ItemUpper aquifer (n = 8)
Middle aquifer (n = 20)
Lower aquifer (n = 13)
Min.Max.MeanSDMin.Max.MeanSDMin.Max.MeanSD
Ca2+ 24.05 65.73 41.92 16.91 16.43 151.44 51.96 30.02 16.04 61.86 42.25 12.48 
Mg2+ 9.72 24.31 16.06 5.52 2.52 33.36 18.66 9.49 12.40 40.31 23.11 7.54 
K+ + Na+ 42.52 296.13 178.86 86.96 335.42 2,170.26 765.43 369.93 27.82 1,040.38 714.99 253.50 
 0.00 0.00 0.00 0.00 0.00 73.81 12.42 19.26 0.00 18.45 5.95 6.88 
 323.41 506.47 446.06 61.14 56.29 443.30 212.52 89.92 176.10 347.20 282.60 52.38 
Cl 35.00 177.25 72.29 47.77 3,35.71 2,987.73 912.15 547.48 13.83 1,047.55 797.39 298.32 
 19.21 164.70 63.09 46.69 30.35 979.81 366.93 218.25 2.68 795.57 377.51 222.69 
PH 7.90 8.45 8.23 0.22 8.18 10.60 8.70 0.61 7.62 8.70 8.29 0.32 
TDS 354.00 831.00 597.38 155.69 924.00 6,017.00 2,288.95 1,074.73 290.00 3,112.00 2,122.92 698.84 
ItemUpper aquifer (n = 8)
Middle aquifer (n = 20)
Lower aquifer (n = 13)
Min.Max.MeanSDMin.Max.MeanSDMin.Max.MeanSD
Ca2+ 24.05 65.73 41.92 16.91 16.43 151.44 51.96 30.02 16.04 61.86 42.25 12.48 
Mg2+ 9.72 24.31 16.06 5.52 2.52 33.36 18.66 9.49 12.40 40.31 23.11 7.54 
K+ + Na+ 42.52 296.13 178.86 86.96 335.42 2,170.26 765.43 369.93 27.82 1,040.38 714.99 253.50 
 0.00 0.00 0.00 0.00 0.00 73.81 12.42 19.26 0.00 18.45 5.95 6.88 
 323.41 506.47 446.06 61.14 56.29 443.30 212.52 89.92 176.10 347.20 282.60 52.38 
Cl 35.00 177.25 72.29 47.77 3,35.71 2,987.73 912.15 547.48 13.83 1,047.55 797.39 298.32 
 19.21 164.70 63.09 46.69 30.35 979.81 366.93 218.25 2.68 795.57 377.51 222.69 
PH 7.90 8.45 8.23 0.22 8.18 10.60 8.70 0.61 7.62 8.70 8.29 0.32 
TDS 354.00 831.00 597.38 155.69 924.00 6,017.00 2,288.95 1,074.73 290.00 3,112.00 2,122.92 698.84 

Note: The units of ions concentration and TDS-mg/l; SD, standard deviation.

Analytical methods

In this study, we conducted a preliminary analysis using the hydro-chemical type analysis method on the basic hydro-chemical data from the laboratory test. It is commonly accepted that a Piper trilinear diagram is applied to identify the hydrogeochemical characteristics (Piper 1944; Hill 1940). The ratio of major cations and anions can be visualized on the graph, where the diamond-shaped and triangle-shaped areas are classified into several subareas with individual hydro-chemical characteristics. Besides, we used a GIS with a powerful spatial interpolation function to investigate the distribution characteristics of the total dissolved solids (TDS) for three aquifers. Multivariate statistical approaches including correlation analysis and cluster analysis were taken to analyze the chemical characteristics of groundwater and its formation mechanism. The correlation between the main ions can reflect the groundwater chemical evolution processes and water–rock interaction. Another multivariate statistical method-cluster analysis is widely used in various fields, such as geochemistry and bioengineering. The process of categorizing the distance proximity of conventional ionic components allows for the assessment of water chemistry.

Finally, we adopted the reverse hydrogeochemical simulation to reproduce the chemical evolution characteristics of groundwater and water–rock interaction, and to further verify the accuracy of the conclusions obtained by multivariate statistical approaches. PHREEQC software used for geochemical calculation and inverse simulation is a powerful and widely applied program in modeling, and hydrogeochemical processes have been confirmed in the previous study (Triantafyllidis & Psarraki 2020; Islam & Mostafa 2022; Mosai et al. 2022). It is based on the investigated runoff condition, namely, the determination of recharge area, discharge area, and ‘possible mineral phases’, and the study of the differences in water chemical composition between the two areas, to speculate the possible mineral dissolution and precipitation along the specific path (Zhang et al. 2020, 2022; Jiang et al. 2022a; Lu et al. 2023).

Hydro-chemical characteristics

Based on the test results of the previous groundwater samples, it was observed that the groundwater samples were all slightly alkaline, showing a minimum value of pH above 7.60. The hydro-chemical types of three aquifers were determined and analyzed by using the hydro-chemical analysis software (Qian et al. 2018; Guo et al. 2022). The Piper trilinear diagram of groundwater quality shows the distinct differences between the upper aquifer and the other two aquifers (Figure 3). The hydro-chemical type of the upper aquifer samples is dominated by the K + Na-HCO3 type, showing a lower concentration of Cl, while the other two groundwater samples have similar water quality type, namely, they are dominated by the K + Na-Cl type and the K + Na-Cl + SO4 type.
Figure 3

Piper trilinear diagram of different aquifers' water samples.

Figure 3

Piper trilinear diagram of different aquifers' water samples.

Close modal
Meanwhile, the analysis was conducted on the TDS of three aquifers. The results show that the TDS of the middle aquifer and lower aquifer samples is higher than that of upper aquifer samples in the study area from Table 1. Utilizing ArcGIS 10.4 software with the powerful data difference analysis function, the distribution of TDS in the study area was summarized. The average TDS values of the upper aquifer, middle aquifer, and lower aquifer are 597.38, 2,288.95, and 2,122.92 mg/l, respectively. In Figure 4, the TDS of the upper aquifer is larger in the north than in the south, and the distribution characteristics of TDS in the middle aquifer and lower aquifer are not significant. The spatial distribution characteristics of TDS may be related to massive mining activities (Zhang et al. 2021). The current mining of the mine is mainly concentrated in the southern and central 1# coal mining areas. Since anthropogenic activities accelerate the physical and chemical processes in groundwater, water–rock interaction, evaporation, and concentration, dissolution of the halite and sulfate minerals, and cation exchange are the primary causes of high TDS value in the study area.
Figure 4

TDS spatial distribution of different aquifers: (a) upper aquifer, (b) middle aquifer, and (c) low aquifer.

Figure 4

TDS spatial distribution of different aquifers: (a) upper aquifer, (b) middle aquifer, and (c) low aquifer.

Close modal
In this study, the calculation function in PHREEQC software was initially applied to calculate various primary hydrogeochemical parameters, such as ion speciation, minerals saturation index (SI), and partial pressures of carbon dioxide. Figure 5 shows the calculation results of each mineral's SI value in three aquifers. The SI values of anhydrite, gypsum, halite, and CO2(g) of three aquifers are all below zero, indicating that these minerals and gas are unsaturated in groundwater. In the middle aquifer, aragonite, calcite, and dolomite minerals exist in a saturated-unsaturated state, while they are saturated in the upper and lower aquifers. The fact that some minerals are unsaturated indicates that their dissolution in groundwater acts as a dominant role in ion exchange, which is also the reason why the higher TDS occurs in the study area.
Figure 5

SI of the mineral phases.

Figure 5

SI of the mineral phases.

Close modal

Correlation analysis

On the foundation of the previous research, the hydro-chemical composition of water samples varied with the burial depth (Qian et al. 2018; Jiang et al. 2022b). There is a change in chemical composition at the strata thickness of about 200 m in the study area (dashed line in Figure 6). Thus, for water samples collected at the buried depth of less than 120 m, the concentrations of K+ + Na+, Ca2+, Mg2+, , Cl, , , and other two indicators, TDS and PH, are relatively lower and stable, comparing with water samples collected at the buried depth of more than 120 m. The changing pattern indicated a hydro-chemical boundary around 120 m below the ground surface.
Figure 6

Correlation diagrams between the main ions, TDS, PH, and buried depth.

Figure 6

Correlation diagrams between the main ions, TDS, PH, and buried depth.

Close modal

The results provide confirmations to the conceptual sight that two flow compartments exist in the study area, and the hydro-chemical boundary probably lies between the upper aquifer (Quaternary aquifer) and middle aquifer (Neogene aquifer), indicating hydraulic connectivity is poor. Relative to the middle and lower aquifers, the shallow upper aquifer is influenced by natural climate and human activities, which result in a lower extent of leaching and concentrations of major ions in groundwater.

The correlation analysis of main anions and cations in each aquifer shows different correlations between ions as follows. In Figure 7, the larger the ratio between the major axis and the minor axis of the ellipse, the greater the correlation between ions. When the slope of the ellipse major axis is greater than 0, there is a positive correlation between ions. * presents p ≤ 0.05, ** presents p ≤ 0.01, and *** presents p ≤ 0.001, where p is a significance indicator, and the smaller the p-value is, the stronger the significance of ions is (Qian et al. 2021).
Figure 7

Correlation coefficients among the main ions: (a) upper aquifer, (b) middle aquifer, and (c) low aquifer.

Figure 7

Correlation coefficients among the main ions: (a) upper aquifer, (b) middle aquifer, and (c) low aquifer.

Close modal

In the upper aquifer, Ca2+ and Mg2+, K+ + Na+, and /Cl are characterized by a positive correlation. Ca2+, K+ + Na+, , and Ca2+/Mg2+ have a negative correlation. Ca2+ and , K+ + Na+, and are characterized by the positive correlation in the middle aquifer. Mg2+ and , K+ + Na+, and Cl are characterized by a positive correlation in the lower aquifer. The correlation of ions implied complex chemical processes involving cation exchange and adsorption, dissolution and filtration of carbonate minerals and halite minerals, and desulfurization have occurred in groundwater.

Hydrogeochemical processes

It is well-known that a higher concentration of K+ + Na+ in groundwater is closely related to evaporite and silicate mineral weathering (Chen et al. 2019; Raja et al. 2021). Scholars adopted scatter plots to illustrate the cations' imbalance and exchange processes. If Ca2+, Mg2+, and Na+ arise from mineral dissolution, the ratio of Na+ and Cl, as well as (Ca2+ + Mg2+) and will be a constant at 1:1 (Voutsis et al. 2015). Therefore, the correlation of K+ + Na+ and Cl can support to confirm the results about the initial sources of ions. As seen from Figure 8, the positive correlation of Cl and K+ + Na+ for all samples is observed (R2 = 0.9622), indicating that the dissolution of evaporate minerals such as halite and sylvite was the primary source of K+ + Na+. The plotted water sample points fell below the line of y=x, that is, sodium concentration exceeds chloride to some extent, indicating silicate mineral weathering influenced the dissolution speed of K+ + Na+. In Figure 9, the ratio of and (Ca2+ + Mg2+) is greater than 1.0, showing that calcium and magnesium are both deficient compared with sulfate and bicarbonate. The plotted scatters may also indicate that ion exchange does not have much influence on the solutions. The direction of the exchange is such that the water is adsorbing calcium/magnesium and desorbing sodium, as described in Equations (1)–(3), where X is the exchange complex.
formula
(1)
formula
(2)
formula
(3)
Figure 8

Correlation of K+ + Na+ and Cl.

Figure 8

Correlation of K+ + Na+ and Cl.

Close modal
Figure 9

Correlation of (Ca2+ + Mg2+) and ().

Figure 9

Correlation of (Ca2+ + Mg2+) and ().

Close modal
If all Ca2+ and are derived from gypsum, the ratio of their concentrations should be constant at 1:1 according to the dissolution Equation (4). However, Figure 10 shows that concentration is much higher than Ca2+ concentration, which implies that cation exchange adsorption accelerated the reduction of calcium concentration and the increase of concentration.
formula
(4)
Figure 10

Correlation of Ca2+ and .

Figure 10

Correlation of Ca2+ and .

Close modal
Meanwhile, the ratio of and Ca2+ can reflect the dissolution of dolomite and calcite minerals according to the dissolution Equations (1) and (2). If the hydro-chemical processes only ran on by the calcite mineral dissolution, the ratio of and Ca2+ should be a constant 2.0. Likewise, if there is only dolomite dissolving, the ratio would be a constant 4.0 according to Equation (2); however, the ratio of and Ca2+ is greater than 2.0 as shown in Figure 11, suggesting that the dissolution of calcite and dolomite occurred simultaneously in most cases.
Figure 11

Correlation of Ca2+ and .

Figure 11

Correlation of Ca2+ and .

Close modal
The comparison of the remaining cations with anions is presented in Figure 12. Na+ concentration is deficient compared with Cl concentration (X-axis), demonstrating that the cation exchange and the dissolution of evaporated minerals are dominated by the reactions in groundwater. In Figure 13, the ratio of and Ca2+ decreases gradually with the increase of calcium concentration, showing that a higher concentration of calcium hindered the dissolution of dolomite.
Figure 12

Scatter plot of (Ca2+ + Mg2+) vs (Na+ − Cl).

Figure 12

Scatter plot of (Ca2+ + Mg2+) vs (Na+ − Cl).

Close modal
Figure 13

Scatter plot of (/Ca2+) vs Ca2+.

Figure 13

Scatter plot of (/Ca2+) vs Ca2+.

Close modal
Generally, along the groundwater flow direction, the content of TDS increases due to the fact that more minerals are dissolved as water flows. The overview of evolution is acquired through a comparison between TDS and the contributions of a single ion. Figure 14 shows the relationship between TDS and each ion. After preliminary analysis, the correlation between them is not a simple linear relationship, but a nonlinear trend. The nonlinear fitting relationship between the primary ions and TDS presents a little difference, and R2 of K+ + Na+ is 0.9883, R2 of Cl is 0.9614, and R2 of is 0.9614. While the R2 values of Ca2+, Mg2+, and are 0.8450, 0.8763, and 0.8671, respectively. Besides, K+ + Na+, Cl, , and contribute more to TDS than Ca2+ and Mg2+. This also explains the characteristics of groundwater hydro-chemical types to a certain extent.
Figure 14

Correlation between the main ions and TDS.

Figure 14

Correlation between the main ions and TDS.

Close modal

Cluster analysis

Cluster analysis is the statistical concept that group data objects according to the information found in the data describing the objects and their relationships (Selmane et al. 2022; Wu et al. 2022). The greater the similarity within the group and the greater the gap between the groups, the better the clustering effect. Cluster analysis consists of q-mode and r-mode cluster analysis, and the q-mode cluster analysis based on sample classification is applied in this study, whose main idea is to use distance to measure the similarity between samples. The advantages have the following three aspects: (1) it can comprehensively use the information of multiple variables to classify samples; (2) the classification results are intuitive, and the clustering pedigree diagram clearly shows the numerical classification results; and (3) the results obtained by the cluster analysis are more detailed, all-round, and rational than those obtained by traditional classification methods.

The results of the cluster analysis are shown in Figure 15. The position of the dashed line of 7.5 divided the samples into five clusters (①–⑤). Cluster ① comprised mostly samples from the Cenozoic middle aquifer and lower aquifer and cluster② consisted of mostly Cenozoic upper aquifer samples and one sample from the Cenozoic middle aquifer. Water chemistry types of cluster ① and cluster ② are dominated by the K + Na-Cl type and the K + Na-HCO3 type, respectively. The concentrations of Na+ + K+ and Cl increased from cluster ② (upper aquifer) to cluster ① (middle aquifer), demonstrating increasing salinity in groundwater due to the dissolution of evaporated minerals. Cluster ③, the lower aquifer sample, is characterized by the K + Na-Cl + SO4 type. Compared with other groups, cluster ④ and ⑤ from the middle aquifer showed an extremely lower concentration of Mg2+ with water chemistry type of K + Na-Cl + SO4. The difference between cluster ④ and cluster ⑤ is that the Na+ concentration in cluster ⑤ is higher, about three times than that in cluster ④.
Figure 15

Dendrogram of cluster analysis based on the q-mode.

Figure 15

Dendrogram of cluster analysis based on the q-mode.

Close modal

Inverse simulation modeling

Inverse modeling of PHREEQC 3.7 was applied in this research as a validation approach to further reveal the hydrogeochemical processes. The partial pressure of CO2, cation exchange phases CaX2, MgX2, and NaX, and minerals including halite, calcite, dolomite, and gypsum are considered as primary reactants. C and S serve as the reaction balance in the inverse simulation. To illustrate the processes, one path in the each of three aquifers was selected. The selected data and location of water samples are shown in Table 2 and Figure 16, respectively.
Table 2

Ion concentration of the selected water samples for inverse modeling (units: mg/l)

AquiferSample no.PositionCa2+Mg2+K+ + Na+ClPHTDS
Upper aquifer S2 Initial 33.67 11.67 195.58 0.00 440.3 53.18 46.11 8.21 584 
S5 Final 25.65 9.72 257.11 0.00 500.36 84.53 84.53 8.45 712 
Middle aquifer S10 Initial 16.43 5.96 335.42 42.91 189.77 335.71 90.76 9.56 924 
S22 Final 151.44 2.52 763.48 73.81 56.29 610.76 979.81 10.60 3,516 
Lower aquifer S32 Initial 35.47 12.4 415.89 0.00 190.99 529.27 135.21 7.91 1,227 
S41 Final 30.59 22.56 762.04 9.00 327.90 653.40 621.10 8.50 2,269 
AquiferSample no.PositionCa2+Mg2+K+ + Na+ClPHTDS
Upper aquifer S2 Initial 33.67 11.67 195.58 0.00 440.3 53.18 46.11 8.21 584 
S5 Final 25.65 9.72 257.11 0.00 500.36 84.53 84.53 8.45 712 
Middle aquifer S10 Initial 16.43 5.96 335.42 42.91 189.77 335.71 90.76 9.56 924 
S22 Final 151.44 2.52 763.48 73.81 56.29 610.76 979.81 10.60 3,516 
Lower aquifer S32 Initial 35.47 12.4 415.89 0.00 190.99 529.27 135.21 7.91 1,227 
S41 Final 30.59 22.56 762.04 9.00 327.90 653.40 621.10 8.50 2,269 
Figure 16

Inverse simulation traces in the aquifers.

Figure 16

Inverse simulation traces in the aquifers.

Close modal

Eight models are generated along trace 1 (S2 → S5) of the Cenozoic upper aquifer, as shown in Table 3. CO2 is in an equilibrium or emission state on the whole, while halite and gypsum minerals are dissolved in the models. Calcite and dolomite minerals remain in the dissolved or precipitation state. Meanwhile, the occurrence of the cation exchange process has been determined by analyzing the molar transfer of NaX, CaX2, and MgX2.

Table 3

Calculated results of inverse modeling along trace 1 (units: mol/l)

PhaseFormulaModel 1
Model 2
Model 3
Model 4
Mole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 — — — — 2.07e − 04 Dissolution 1.17e − 04 Dissolution 
CO2(g) CO2 −1.18e − 04 Emission — — −1.18e − 04 Emission — — 
Gypsum CaSO4·2H24.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 
Halite NaCl 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 
Dolomite CaMg(CO3)2 1.03e − 04 Dissolution 5.83e − 05 Dissolution — — — — 
NaX NaX 1.86e − 03 Desorption Na+ 1.68e − 03 Desorption Na+ 1.86e − 03 Desorption Na+ 1.68e − 03 Desorption Na+ 
MgX2 MgX2 −1.84e − 04 Adsorption Mg2+ −1.39e − 04 Adsorption Mg2+ −8.02e − 05 Adsorption Mg2+ −8.02e − 05 Adsorption Mg2+ 
CaX2 CaX2 −7.46e − 04 Adsorption Ca2+ −7.01e − 04 Adsorption Ca2+ −8.49e − 04 Adsorption Ca2+ −7.59e − 04 Adsorption Ca2+ 
Model 5
Model 6
Model 7
Model 8
PhaseFormulaMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 3.67e − 04 Dissolution 2.77e − 04 Dissolution −1.49e − 03 Precipitation −1.40e − 03 Precipitation 
CO2(g) CO2 −1.18e − 04 Emission — — −1.18e − 04 Emission — — 
Gypsum CaSO4·2H24.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 
Halite NaCl 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 
Dolomite CaMg(CO3)2 −8.02e − 05 Precipitation −8.02e − 05 Precipitation 8.49e − 04 Dissolution 7.59e − 04 Dissolution 
NaX NaX 1.86e − 03 Desorption Na+ 1.68e − 03 Desorption Na+ 1.86e − 04 Desorption Na+ 1.68e − 03 Desorption Na+ 
MgX2 MgX2 — — — — −9.30e − 04 Adsorption Mg2+ −8.39e − 04 Adsorption Mg2+ 
CaX2 CaX2 −9.30e − 04 Adsorption Ca2+ −8.39e − 04 Adsorption Ca2+ — — — — 
PhaseFormulaModel 1
Model 2
Model 3
Model 4
Mole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 — — — — 2.07e − 04 Dissolution 1.17e − 04 Dissolution 
CO2(g) CO2 −1.18e − 04 Emission — — −1.18e − 04 Emission — — 
Gypsum CaSO4·2H24.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 
Halite NaCl 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 
Dolomite CaMg(CO3)2 1.03e − 04 Dissolution 5.83e − 05 Dissolution — — — — 
NaX NaX 1.86e − 03 Desorption Na+ 1.68e − 03 Desorption Na+ 1.86e − 03 Desorption Na+ 1.68e − 03 Desorption Na+ 
MgX2 MgX2 −1.84e − 04 Adsorption Mg2+ −1.39e − 04 Adsorption Mg2+ −8.02e − 05 Adsorption Mg2+ −8.02e − 05 Adsorption Mg2+ 
CaX2 CaX2 −7.46e − 04 Adsorption Ca2+ −7.01e − 04 Adsorption Ca2+ −8.49e − 04 Adsorption Ca2+ −7.59e − 04 Adsorption Ca2+ 
Model 5
Model 6
Model 7
Model 8
PhaseFormulaMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 3.67e − 04 Dissolution 2.77e − 04 Dissolution −1.49e − 03 Precipitation −1.40e − 03 Precipitation 
CO2(g) CO2 −1.18e − 04 Emission — — −1.18e − 04 Emission — — 
Gypsum CaSO4·2H24.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 4.00e − 04 Dissolution 
Halite NaCl 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 9.55e − 04 Dissolution 
Dolomite CaMg(CO3)2 −8.02e − 05 Precipitation −8.02e − 05 Precipitation 8.49e − 04 Dissolution 7.59e − 04 Dissolution 
NaX NaX 1.86e − 03 Desorption Na+ 1.68e − 03 Desorption Na+ 1.86e − 04 Desorption Na+ 1.68e − 03 Desorption Na+ 
MgX2 MgX2 — — — — −9.30e − 04 Adsorption Mg2+ −8.39e − 04 Adsorption Mg2+ 
CaX2 CaX2 −9.30e − 04 Adsorption Ca2+ −8.39e − 04 Adsorption Ca2+ — — — — 

PHREEQC generates four models along trace 2 (S10 → S22) in the middle aquifer. The results are presented in Table 4. Calcite mineral shows the precipitation or equilibrium characteristic probably due to the dissolution of CO2. Halite and gypsum minerals are dissolved in the four models. However, the precipitation of dolomite has been found in most cases. Similarly, the simulated path is also accompanied by the cation exchange process, resulting in a lower concentration of Mg2+.

Table 4

Calculated results of inverse modeling along trace 2 (units: mol/l)

PhaseFormulaModel 1
Model 2
Model 3
Model 4
Mole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 −1.10e − 02 Precipitation — — −1.33e − 03 Precipitation −1.05e − 03 Precipitation 
CO2(g) CO2 1.84e − 03 Dissolution 1.84e − 03 Dissolution 1.84e − 03 Dissolution 1.84e − 03 Dissolution 
Gypsum CaSO4·2H29.56e − 03 Dissolution 9.56e − 03 Dissolution 9.56e − 03 Dissolution 9.56e − 03 Dissolution 
Halite NaCl 7.78e − 03 Dissolution 7.78e − 03 Dissolution 7.78e − 03 Dissolution 7.78e − 03 Dissolution 
Dolomite CaMg(CO3)2 4.85e − 03 Dissolution −6.67e − 04 Precipitation — — −1.41e − 04 Precipitation 
NaX NaX 9.99e − 03 Desorption Na+ 9.99e − 03 Desorption Na+ 9.99e − 03 Desorption Na+ 9.99e − 03 Desorption Na+ 
MgX2 MgX2 −4.99e − 03 Adsorption Mg2+ 5.25e − 04 Desorption Mg2+ −1.41e − 04 Adsorption Mg2+ — — 
CaX2 CaX2 — — −5.52e − 03 Adsorption Ca2+ −4.85e − 03 Adsorption Ca2+ −4.99e − 03 Adsorption Ca2+ 
PhaseFormulaModel 1
Model 2
Model 3
Model 4
Mole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 −1.10e − 02 Precipitation — — −1.33e − 03 Precipitation −1.05e − 03 Precipitation 
CO2(g) CO2 1.84e − 03 Dissolution 1.84e − 03 Dissolution 1.84e − 03 Dissolution 1.84e − 03 Dissolution 
Gypsum CaSO4·2H29.56e − 03 Dissolution 9.56e − 03 Dissolution 9.56e − 03 Dissolution 9.56e − 03 Dissolution 
Halite NaCl 7.78e − 03 Dissolution 7.78e − 03 Dissolution 7.78e − 03 Dissolution 7.78e − 03 Dissolution 
Dolomite CaMg(CO3)2 4.85e − 03 Dissolution −6.67e − 04 Precipitation — — −1.41e − 04 Precipitation 
NaX NaX 9.99e − 03 Desorption Na+ 9.99e − 03 Desorption Na+ 9.99e − 03 Desorption Na+ 9.99e − 03 Desorption Na+ 
MgX2 MgX2 −4.99e − 03 Adsorption Mg2+ 5.25e − 04 Desorption Mg2+ −1.41e − 04 Adsorption Mg2+ — — 
CaX2 CaX2 — — −5.52e − 03 Adsorption Ca2+ −4.85e − 03 Adsorption Ca2+ −4.99e − 03 Adsorption Ca2+ 

Finally, trace 3 (S32 → S41) is introduced to illustrate the hydrogeochemical process of the Cenozoic lower aquifer. As shown in Table 5, a higher concentration of Na+ may be related to the dissolution of halite and NaX, and the cation exchange process. The higher concentration of TDS in the final position is due to the halite and gypsum minerals dissolved in five models.

Table 5

Calculated results of inverse modeling along trace 3 (units: mol/l)

PhaseFormulaModel 1
Model 2
Model 3
Model 4
Model 5
Mole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 — — — — 5.35e − 04 Dissolution 1.37e − 03 Dissolution −1.18e − 02 Precipitation 
CO2(g) CO2 −5.10e − 03 Emission −4.75e − 03 Emission −5.10e − 03 Emission −5.10e − 03 Emission −5.10e − 03 Emission 
Gypsum CaSO4·2H25.07e − 03 Dissolution 5.07e − 03 Dissolution 5.07e − 03 Dissolution 5.07e − 03 Dissolution 5.07e − 03 Dissolution 
Halite NaCl 3.53e − 03 Dissolution 3.53e − 03 Dissolution 3.53e − 03 Dissolution 3.53e − 03 Dissolution 3.53e − 03 Dissolution 
Dolomite CaMg(CO3)2 6.87e − 04 Dissolution 5.13e − 04 Dissolution 4.20e − 04 Dissolution — — 6.57e − 03 Dissolution 
NaX NaX 1.23e − 02 Desorption Na+ 1.14e − 02 Desorption Na+ 1.23e − 02 Desorption Na+ 1.23e − 02 Desorption Na+ 1.23e − 02 Desorption Na+ 
MgX2 MgX2 −2.67e − 04 Adsorption Mg2+ — — — — 4.20e − 04 Desorption Mg2+ −6.15e − 03 Adsorption Mg2+ 
CaX2 CaX2 −5.88e − 03 Adsorption Ca2+ −5.71e − 03 Adsorption Ca2+ −6.15e − 03 Adsorption Ca2+ −6.57e − 03 Adsorption Ca2+ — — 
PhaseFormulaModel 1
Model 2
Model 3
Model 4
Model 5
Mole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse modelMole transfersProcess indicated from the inverse model
Calcite CaCO3 — — — — 5.35e − 04 Dissolution 1.37e − 03 Dissolution −1.18e − 02 Precipitation 
CO2(g) CO2 −5.10e − 03 Emission −4.75e − 03 Emission −5.10e − 03 Emission −5.10e − 03 Emission −5.10e − 03 Emission 
Gypsum CaSO4·2H25.07e − 03 Dissolution 5.07e − 03 Dissolution 5.07e − 03 Dissolution 5.07e − 03 Dissolution 5.07e − 03 Dissolution 
Halite NaCl 3.53e − 03 Dissolution 3.53e − 03 Dissolution 3.53e − 03 Dissolution 3.53e − 03 Dissolution 3.53e − 03 Dissolution 
Dolomite CaMg(CO3)2 6.87e − 04 Dissolution 5.13e − 04 Dissolution 4.20e − 04 Dissolution — — 6.57e − 03 Dissolution 
NaX NaX 1.23e − 02 Desorption Na+ 1.14e − 02 Desorption Na+ 1.23e − 02 Desorption Na+ 1.23e − 02 Desorption Na+ 1.23e − 02 Desorption Na+ 
MgX2 MgX2 −2.67e − 04 Adsorption Mg2+ — — — — 4.20e − 04 Desorption Mg2+ −6.15e − 03 Adsorption Mg2+ 
CaX2 CaX2 −5.88e − 03 Adsorption Ca2+ −5.71e − 03 Adsorption Ca2+ −6.15e − 03 Adsorption Ca2+ −6.57e − 03 Adsorption Ca2+ — — 

Through comprehensive analysis of hydro-chemical data using statistical techniques such as correlation analysis and cluster analysis, as well as inverse geochemical modeling based on PHREEQC, the hydrogeochemical processes were elucidated. The following conclusions have been obtained:

  • (1) The hydro-chemical type of the upper aquifer is dominated by the K + Na-HCO3 type, showing a lower concentration of Cl, while the other two aquifers have similar water quality types dominated by the K + Na-Cl type and the K + Na-Cl + SO4 type. Water samples are divided into five groups by cluster analysis, and the types of water quality are further detailed. The concentrations of Na+ + K+ and Cl increase from the upper aquifer to the middle aquifer, demonstrating increasing salinity in groundwater due to the dissolution of evaporated minerals.

  • (2) Through geochemistry calculation, the SI of anhydrite, gypsum, halite, and CO2(g) of aquifers is less than zero, indicating these minerals and gas are unsaturated. While aragonite, calcite, and dolomite of the middle aquifer remain in the unsaturated–saturated state, these minerals in the upper and lower aquifer are in the saturated state.

  • (3) After correlation analysis and water–rock interaction analysis, the cation exchange process accelerating the reduction of Ca2+ concentration and the increase of concentration widely occurred in three aquifers, and dissolution of calcite and dolomite occurred simultaneously in most cases, except for the middle aquifer.

  • (4) Inverse modeling further elucidates the hydrogeochemical processes and water–rock interaction and verifies the preliminary summaries obtained through statistical methods. The dissolution of gypsum and halite minerals widely occurs in three aquifers. Calcite and dolomite minerals also remain in the dissolved state in most cases, except for the middle aquifer. Besides, the cation exchange process releasing Na+ and absorbing Ca2+/Mg2+ is ubiquitous along the traces in three aquifers.

The authors are grateful for the help from the Gubei coal mine with respect to the basic data provided. Also, thanks go to the editors and anonymous reviewers for your constructive comments on the manuscript which greatly improved this paper.

This research was financially supported by the Key Program of the National Natural Science Foundation of China (42372316), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_2760), the Fundamental Research Funds for the Central Universities (2023XSCX003), and the Graduate Innovation Program of China University of Mining and Technology (2023WLKXJ003).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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