Effects of an abandoned Pb-Zn mine on a karstic groundwater reservoir

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Effects of an abandoned Pb-Zn mine on a karstic groundwater reservoir ⁎

Wenjing Qina,b, Dongmei Hana,c, , Xianfang Songa,c, Peter Engesgaardd a

Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China c College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China d Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen (UCPH), Copenhagen DK-1350, Denmark b

A R T I C LE I N FO

A B S T R A C T

Keywords: Abandoned Pb-Zn mine Karst water Trace elements Arsenic

The release and transport of metals in active mines pose a threat to water quality when mine sites become abandoned. Karst aquifer systems are especially vulnerable due to their ability to quick transport polluted water over large distances through surface and subsurface routes. Surface and groundwater samples collected from the Guilin-Yangshuo Basin in southwestern China were analyzed for δ18O and δ2H of water, major and trace elements, including Sr, pH, temperature, and electrical conductivity to gain an understanding of how an abandoned Pb-Zn mine and its tailing deposits influence the groundwater quality of a downgradient karst carbonate aquifer. The δ18O and δ2H values of the water samples indicate that the karst aquifer is dominantly recharged by local precipitation during the wet season and additionally by water from upstream mountainous areas during the dry period. Severe heavy metal contamination (9154, 1081, 37 and 55 μg/L of Zn, Mn, Ni and Cd, respectively) was detected in the mine drainage water. Ninety-one percent of the downstream surface samples and 67% of the groundwater samples collected during the dry season exceeded the limit value for As (10 μg/L) according to the China Standards for Drinking Water Quality. Factor analysis was applied, and showed the river and groundwater were affected by the Pb-Zn mine and its tailing deposits and by carbonate dissolution. The Sidi River plays an important role in trace element transport, which supported by a strong correlation between sulfate and strontium (with r2 = 0.85 in the dry season; r2 = 0.94 in the wet season). The strong acid generated during sulfide oxidation of the mine tailings is neutralized by carbonate rock dissolution, resulting in neutral to alkaline water which hinders the mobility of trace elements. However, the increasing sulfate contents and the elevated total trace element concentrations (from undetectable to 1503 μg/L except for the mine drainage with 10,350 μg/L) confirm the release of toxic metals into the water system. In addition, adsorbed arsenate anions may exchange with dissolved carbonate anions and be released into the groundwater.

1. Introduction As one of the world's largest lead and zinc mining countries, tens of billions tons of tailing deposits and waste products have been produced in China (Huang et al., 2015). Leaching of these tailing deposits and waste products results in the release of trace elements into the natural environment. Heavy metal pollution in China, such as cadmium and arsenic on crops, causes a direct economic loss of more than $30 million (US dollars) every year (Zhou et al., 2014). In addition, historical mining, tailing deposits, and trace elements (such as lead, zinc and arsenic) stored in soil and stream (or cave streams) sediments have become potential second contaminants sources to groundwater contaminants (Vesper et al., 2001; Gutiérrez et al., 2016). The study area is in Yangshuo County, Guilin City, southwestern China. The climate of ⁎

Yangshuo is subtropical with significant rainfall, even during the driest (Climate-DaData.ORG). The average annual precipitation is 1698 mm. Karst aquifers supply drinking water to 20–25% of the global population (Ford and Williams, 2013). Southwestern China is one of the world's largest karst areas, with an area of 540,000 km2 and a population of > 100 million (Guo and Jiang, 2011). Groundwater flow in karst aquifers is characterized by flow through dual porosity medium, with fracture flow occurring in fractures and conduits, and diffuses flow occurring in the parent rock matrix (Garrels and Christ, 1965; Shuster and White, 1971, 1972; Lopez-Chicano et al., 2001). Rapid groundwater flow (often turbulent) may take place within dissolution-generated conduits and within the aquifer medium, resulting in a hierarchical permeability structure in the flow paths (White, 2002; Gutiérrez et al., 2014). Highly developed karst features, such as sinkholes, swallow

Corresponding author. E-mail address: [email protected] (D. Han).

https://doi.org/10.1016/j.gexplo.2018.09.007 Received 10 May 2018; Received in revised form 26 August 2018; Accepted 18 September 2018 0375-6742/ © 2018 Elsevier B.V. All rights reserved.

Please cite this article as: Qin, W., Journal of Geochemical Exploration, https://doi.org/10.1016/j.gexplo.2018.09.007

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1.2. Geology and hydrogeology

holes, and shafts, can result in the intense interaction of surface water and groundwater, and that can affect water quality through mixing and a series of chemical reactions (Lopez-Chicano et al., 2001). The chemical composition of the groundwater is modified by minerals through water-rock interaction (Díaz-Puga et al., 2016; Moral et al., 2008; Pu et al., 2013; Wu et al., 2009). Remediation after a contamination event is difficult and time-consuming in karst water system. Insufficient filtration of both point and diffuse recharge sources can cause surface contaminants to be rapidly carried underground. (Vesper et al., 2001; Ford and Williams, 2013). These specific hydraulic and hydrogeologic characteristics make karst aquifers extremely vulnerable to contaminants resulting from human activities of even a short duration (Gutiérrez et al., 2014; Kačaroğlu, 1999; Lopez-Chicano et al., 2001; Zhang et al., 2016). Therefore, there have been many studies on the adverse effects of contaminants on karst groundwater quality (Padilla and Vesper, 2018). More than half of the metal mines and deposits are located in south China, which is characterized by large areas with carbonate rock aquifers (Chen, 2013). The existence of numerous tailing and mine waste deposits has already led to a degradation of karst water quality, greatly limiting the local water supply and economic development which has also occurred in other countries (Bakalowicz, 2005; LopezChicano et al., 2001; Zhang et al., 2016). Moreover, the groundwater in the study area is at serious risk of contamination due to severe heavy metal pollution of the soil, which was caused by the historical mining activity that began in the 1950s and leaching of the tailing deposits (Gong, 2011; Lin, 1997). The monitoring of groundwater chemistry and identification of possible factors/sources of pollutants to karst aquifers is imperative. Studying the hydrogeochemical characteristics and behavior of trace elements affected by an abandoned Pb-Zn mine can be helpful for water management, water quality assessment and remediation of karst groundwater. The objectives of this study are as follows: (1) to investigate the distribution of major ions and trace elements in surface and groundwater flowing from the upstream non-karst area to the downstream karst area, (2) to identify the most abundant trace element pollutants based on the China Standards for Drinking Water Quality (GB 14848—2017); (3) to determine the recharge sources of karst aquifers in the downstream area during the dry and wet seasons by comparing the δ18O and δ2H of precipitation, surface water, and groundwater samples; (4) to gain an understanding of the possible controlling processes (water flow and physical-chemical reactions) that affect the most abundant trace element pollutants released and their migration in the karst water system.

The study area includes unconfined non-karstic aquifers in Cambrian-Devonian (Є-D) sandstones in the mountainous area to the east, shallow (< 10 m) unconfined Devonian to Carboniferous (C) karst aquifers to the west, and unconfined karst aquifers in Devonian carbonates underlying discontinuity and Quaternary (Q) clay and clay loam. The stratum is generally northwest-trending, which also controls the direction of karst development. The Laochang Pb-Zn mine and its tailing deposits are in the non-karst mountainous area to the west (Fig. 1 c). The explored Pb-Zn ore dominantly contains sphalerite (ZnS) with Zn/Pb > 2, galena-sphalerite (PbS-ZnS) with Pb > Zn, and a small amount of pyrite (Ning, 1992). The downstream soil around Sidi Village is hydromorphic paddy soil which developed from sandy shale and limestone (Jin et al., 2015). Hydrogeologic surveys denoted that the unconfined karst aquifers to the west have runoff modulus numbers of 5 to 6 L/s. The Silver Spring, located in Sidi Village, has a discharge rate of 0.8 L/s. The major direction of groundwater flow is controlled by a south-north karstic cave extending from Dabiao Village to Dayuan Village. This cave is approximately 2 m high and 1 km long (Institute of Karst Geology, 2015). The Sidi River originates in the mountains to the east where the Laochang Pd-Zn mine is located, runs westwards to Sidi Village and then turns north through the karstic cave, eventually draining into the Dayuan River. The Dayuan River flows into the Guijiang River to the west (Figs. 1, 2). In this study, the upstream area/mining area/nonkarst area/eastern mountainous area will be used to represent the eastern part of the study area and the downstream area/karst area/ catchment area is used to represent the western part of the study area. 1.3. Mining history and environmental issues The abandoned Pb-Zn mine is in the northeastern part of the study area (Fig. 1). Mining began in the 1950s; and in 1958, a tailing site was set up near the mining pits (Fig. 1; Qi et al., 2005). Due to poor environmental awareness at that time, no protective measures e.g. seepage control, leakage water collection, or treatment, were used. Polluted mining water drained directly into downstream irrigation canals and into the Sidi River. This caused contamination of the crops around the villages of Sidi, Jiaotian and Dabiao (Lin, 1997). The tailing dam collapsed in the 1970s due to a heavy rainfall and the tailings and polluted dam water were transported downstream to the Sidi River. Subsequently, local farmers flattened silt channels into farmland, planted crops (rice, canola, and citrus; Li et al., 2014). Mining activity was suspended from 1979 to 1985 while the polluted soil was removed, and the area was planted with non-food crop. The total cadmium content of the paddy soil in 1975 was 48.4 mg/kg on average with a maximum of 136 mg/kg. In 1986 it decreased to 7.8 mg/ kg on average with a maximum of 9.4 mg/kg, which is still much higher than 0.3 mg/kg, the Level II of soil environmental quality standard of China (GB15618—1995; Lin, 1995, 1997). Mining activity continued intermittently for the next twenty years until it was completely abandoned in 2012. The long history of mining without effective waste water treatment especially in the early days of mining activity, in addition to the dam collapse caused high contents of Zn, Pb, Cd, and Cu to accumulate in soils around Sidi Village (Li et al., 2014; Jin et al., 2015; Mo et al., 2016). Between the early stage of mining in 1986 (Lin, 1997) and sample collection in 2015 (Li et al., 2015), the average level of Zn has significantly decreased from 3936 mg/kg to 1442 mg/kg; the average level of Pb has decreased from 2007 mg/kg to 923 mg/kg; and the average level of copper has decreased from 239 mg/kg to 117 mg/ kg. However, the total Cd content of the soil has risen from 14 to 40 mg/kg accompanied by a 26% of reduction in the soil's organic carbon (SOC) (see Table S2). The release of trace elements from waste rock, tailings, and drainage continued long after the cessation of mining. The Concentration of

1.1. Study area The study area is in the Guilin-Yangshuo Basin, Southwestern China, which is one of the world's best-known karst areas (Fig. 1 a, b). The eastern section of the study area, where the Pb-Zn mine is located, is the southwestern end of the Haiyang Mountains, which is also the eastern side of the Guilin-Yangshuo Basin. This area ranges in elevation from 900 to 1500 m a.s.l. (meters above sea level) (Fig. 1 c). The central and western parts of the study area are mature tropical karst landscapes dominated by the Fengcong type of karst (altitude of hills ranges from 500 to 600 m a.s.l.) in the western part, and a valley in the central part of the study area (around 400 m a.s.l. on average in the lowlands). The Fengcong landform is identified by clustered conical limestone hills (also known as “peak cluster”) (Waltham, 2009). The climate of the study area is humid and tropical, with an average annual rainfall of 1698 mm. Almost 70% of the total precipitation occurs in the rainy season, from April to August. The mean annual temperature is around 19 °C (People's Government of Yangshuo County, 2017).

2

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Fig. 1. Situation sketch showing (a) The Guangxi Zhuang Autonomous Region in China, (b) study area location in Guangxi Province, (c) the topography and (d) distribution of sampling points in the study area.

cadmium in pit water ranges from 1270 to 8120 μg/L in pit #1 and from 610 to 9730 μg/L in pit #2 in mineral processing (Lin, 1995). An exceedingly high concentration of cadmium in wastewater after treatment (1460 μg/L) and before drainage into the Sidi River (1440 μg/L) have been reported by Lin (1995). The observed decrease in Cd concentrations in the Sidi River from the upstream to the downstream is a combined result of river water dilution and the sedimentation of suspended solids. As an important trace element source, the Cd content of the river bed sediment is 3 to 18 times higher than that of the water (Lin, 1995). In February 2011, an elderly villager was reported to be suffering from symptoms of cadmium poisoning, a typical disease caused by long-term consumption of cadmium-containing rice (Gong, 2011). This alarming incident suggests that the heavy metal pollution caused by the Pd-Zn mine in Yangshuo is still affecting the health of

local villagers. 2. Materials and methods 2.1. Data collection and sample analysis Water samples were collected in December 2015 and June 2016. A total of 29 samples (GL01-GL29) were collected in December 2015, including 10 groundwater samples, 11 stream water samples, and 8 spring samples. In June 2016, 25 samples (GL30-GL54) were collected, including 12 groundwater samples, 9 river water samples and 4 spring water samples. The sample locations are shown in Fig. 1 (d). All of the groundwater samples were taken from shallow domestic wells drilled in the karst aquifer with well depths ranging from 2 to 20 m. Samples for 3

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Fig. 2. Hydrogeology sketch of study area (simplified from the Hydrogeological Investigation Report of the Guilin Region, Institute of Karst Geology, Chinese Academy of Geological Sciences). 1. Villages, 2. Springs, 3. Groundwater flow direction, 4. Rivers, 5. Cave streams, 6. Watershed boundary, 7. High elevation area, 8. Spring water sampling point, 9. Groundwater sampling point, 10. River water sampling point, 11. Pb-Zn drainage sampling point, 12. Non-karst water in detrital rock, (sandstone of Cambrian-Devonian), 13. Karst water in exposed carboniferous carbonate fissures, caves of Devonian, 14. Karst aquifers in covered carbonate fissures, caves of Devonian underlying clay and clay loam of Quaternary.

principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) have all been successfully applied to groundwater quality assessment of hydrogeochemical processes and the identification of their governing factors (Ezekwe et al., 2012; Lu et al., 2016; Masoud, 2014; Muñoz-Carpena et al., 2005; Subbarao et al., 1996). Factor analysis is one type of multivariate statistical analysis. It sums up variables with intricate relationships and is a generalization and extension of principal component analysis (Yin et al., 2013). This method is effectively simplifies data by extracting eigenvalues and eigenvectors from the correlation coefficient matrix (Davis and Sampson, 1986). The factor analysis used in this study was performed using IBM SPSS Statistics 21. The Kaiser-Meyer-Olkin (KMO) and Bartlett's test of sphericity are often used to examine the appropriateness of data for factor analysis. Our computed value of the Bartlett test of sphericity (0.74) is larger than 0.5 and the KMO results are significant at the < 0.001 level, suggesting that the data set is suitable for FA. Principle component analysis was used for factor extraction. In total, 13 parameters (Ca2+, Mg2+, Na+, K+, HCO3−, SO42−, Cl−, NO3−, Al, Cd, Fe, Mn, and Zn) were used in the factor analysis of the data for 54 water samples collected in 2015 and 2016.

water chemistry analysis were collected in 500 mL, tightly capped HDPE bottles and were stored at 4 °C. All of the samples were immediately transported to the laboratory for further analysis. The sample locations were recorded using Global Positioning System (GPS). The DEM (Digital Elevation Model) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image shown in Fig. 1, was obtained from the Geospatial Data Cloud. A HACH 40D portable water quality meter was used to measure the oxidation-reduction potential (ORP, mv), conductivity (EC, μS/cm), pH, and water temperature in situ after stable values were reached. The total alkalinity was measured on the sampling day using the Gran titration method. Major anions (Cl−, SO42−, and NO3−) were measured using ion chromatography (IC) (ICS-2100, Dionex); while major cations (Ca2+, Na2+, K+, and Mg2+) and trace elements (Al, As, B, Ba, Cd, Cr, Gu, Fe, Li, Mn, Ni, Pb, Se, Sr, V, and Zn) were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) (Perkin-Elmer Optima 5300 DV), at the Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, China Academy of Sciences. Charge balance errors in all analyses were < 8%. The δ18O and δ2H values were measured using mass spectrometry, with a liquid water isotope analyzer (LGR, USA) at the Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, China Academy of Sciences. The δ18O and δ2H values were measured relative to internal standards that were calibrated using IAEA standards, using equilibration with CO2-He for δ18O and H-He for 2H. The δ18O and δ2H values reported in d-units calculated with respect to VSMOW (Vienna Standard Mean Ocean Water) expressed in ‰. The analytical precision of the long-term standard measurements was ± 2‰ for δ2H and ± 0.5‰ for δ18O. Analytical error bars of isotopic composition for all of the water samples are shown in Fig. 2.

3. Results 3.1. Stable isotopes of water The stable isotope results for the stream water and groundwater are shown in Fig. 3. Stream water samples in the dry (cold) season have δ18O values of −7.0‰ to −5.8‰ with an average of −6.5‰ and δ2H values of −43‰ to −33‰ with an average of −38‰. In the wet (warm) season, the δ18O values ranged from −7.0‰ to −4.6‰ with an average of −5.6‰ and the δ2H values ranged from −38‰ to −24‰ with an average of −31‰. The isotope composition of the groundwater exhibits slightly less variation than that of the stream water. The groundwater has δ18O values range of −7.1‰ to −5.6‰ with an average of −6.2‰ and δ2H values of −45‰ to −33‰ with an average of −38‰ in the dry season; while in the wet season, it has

2.2. Factor analysis Multivariate statistical analysis is becoming increasingly important in data analysis in the field of environmental science because it makes interpreting the data easy (Narváez et al., 2007). Factor analysis (FA), 4

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Fig. 3. Deuterium and oxygen-18 isotopes composition of precipitation (upper left panel) and spring water, groundwater and river water of the dry season (a) and the wet season (b).

δ18O values of −6.6‰ to −4.3‰ with an average of −5.3‰ and δ2H values of −40‰ to −20‰ with an average of −29‰. Given the similar water vapor source, climate condition, and altitude (the Guilin and downstream karst area are both in the GuilinYangshuo Basin), the isotopic composition of the precipitation in Guilin can be used to represent the precipitation in study area (downstream karst area). Therefore, the local meteoric water line (LMWL) is defined as δ2H = 8.75 δ18O + 17.6 (r2 = 0.97), which was calculated based on 126 precipitation samples collected at Guilin station over the entire year of the study (Wu et al., 2014). The δ18O and δ2H values of the stream water, spring water and groundwater samples plot close to the Global Meteoric Water Line (GMWL: δ2H = 8δ18O + 10; Craig, 1961), and very close to the LMWL in Fig. 3. Compared with the GMWL, the steeper slope and higher yintercept of the LMWL is due to the warm and rainy climate of the Guilin area and less secondary evaporation (Wu et al., 2014; Fig. 3). All of the groundwater samples fall along the LMWL line, which suggests relatively direct recharge of the groundwater by local precipitation through sinkholes, shafts and fractures. The stream water samples reflect a similarly intense interaction between stream water and groundwater. The water samples can be divided into two different groups according to their δ2H and δ18O compositions. The samples collected around the Pb-Zn mine in the upstream area are plotted as group A (dry season) and have δ18O of −7.1‰ to −6.6‰ and δ2H of −45‰ to −39‰. The rest of the data falls in group B (δ18O: −6.4‰ to −5.6‰; δ2H: −39‰ to −33‰) (Fig. 3 a). Unfortunately, no samples were taken from the mining site during the wet season. However, the samples can still be dividing into two groups: group A′ with δ18O of −7.0‰ to −6.5‰ and δ2H of −38‰ to −36‰; group B′ with δ18O of −5.9‰ to −4.3‰ and δ2H of −33‰ to −20‰ (Fig. 3 b). The analytically error bars for each sample are also presented in Fig. 3 a and b. These significantly depleted isotopic values are caused by an altitude difference approximately 1000 m in elevation between the upstream

and downstream areas, as shown in Fig. 1. Overall, there are two distinguishable water sources for the downstream karst water. One is the rainfall in the downstream area, and the other is water from upstream mountainous area, which is characterized by depleted isotope, transferred to the downstream area by the Sidi River or underground pathways. 3.2. Hydrochemical characteristics The physical and chemical characteristics of the surface and groundwater samples are presented in Table 1. The surface and groundwater in the downstream karst area are dominated by Ca-HCO3 and Ca·Mg-HCO3 as is indicated by the piper plot (Fig. S1). Mine drainage sample GL02 is dominated by Mg·Ca-SO4·HCO3. The river water samples collected from the upstream around the abandoned PbZn mine and tailing dam (GL13, GL14, GL15) are dominate by Ca·MgHCO3·SO4. The distribution of the chemical and physical parameters along the Sidi River is showing in Fig. 4. There is a ~20 °C difference in the temperature of both the surface water and groundwater between the wet season and the dry season. This is likely caused by the differences in the air temperature during the summer (wet season) and winter (dry season) seasons. Most of the groundwater and the spring water have EC values higher than 300 μs/cm, while the river water mostly has values of < 300 μs/cm, reflecting the fact that groundwater has more contact with the aquifers, and thus, it contains more dissolved solids. The pH values of the water samples were all above 7 and the river water were more alkaline (Table 1, Fig. 4). The surface and groundwater samples were generally oxidizing, except for one groundwater sample with a negative ORP value. The river water containing more oxygen than the spring water and groundwater. The variation of the major ion concentrations from the upstream to the downstream area is shown in Fig. 5. Fig. S1 shows that the Ca2+ and 5

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Table 1 Summary of major hydrochemical parameters for river, spring and groundwater. Type

River water (n = 20)

Spring water (n = 12)

Groundwater (n = 22)

Parameter

Min

Max

Mean

St dev

Min

Max

Mean

St dev

Min

Max

Mean

St dev

EC (mg/L) DO (mg/L) pH T (°C) ORP (mv) NO3− (mg/L) Na+ (mg/L) K+ (mg/L) Mg2+ (mg/L) Ca2+ (mg/L) Cl− (mg/L) SO42− (mg/L) HCO3− (mg/L)

97.3 4.7 7.4 13.3 44.2 2.1 0.6 0.3 2.6 10.8 0.7 2.0 18.9

466.0 12.1 9.2 30.0 147.3 18.2 2.0 2.7 16.0 69.8 3.4 31.7 263.4

182.2 8.7 8.3 20.0 97.8 6.8 1.2 0.9 5.5 24.8 1.6 12.6 83.9

91.2 1.9 0.4 6.4 33.5 4.9 0.4 0.6 2.9 15.2 0.8 9.2 58.1

97.9 2.9 7.4 13.8 70.5 0.9 0.2 0.0 2.5 12.3 0.6 2.7 46.8

563.0 8.9 8.5 25.3 169.1 103.0 2.9 6.1 30.5 94.7 5.5 164.3 325.8

395.2 6.4 7.7 19.2 116.3 19.8 1.4 0.9 10.6 60.3 2.4 21.4 190.3

153.0 2.0 0.3 2.9 29.3 28.6 0.9 1.7 9.0 27.3 1.7 45.3 92.5

20.7 2.4 7.2 13.1 −32.2 0.8 0.3 0.1 0.9 1.0 0.7 1.6 6.1

757.0 9.7 8.3 30.6 1032 54.9 17.4 12.7 40.9 92.3 12.2 46.9 381.8

387.6 5.4 7.6 20.9 131.5 17.1 3.7 3.7 8.9 57.4 4.5 16.4 188.8

188.5 2.2 0.2 4.6 205.3 15.4 3.6 4.0 8.5 27.2 3.4 10.2 94.8

“-” not available.

HCO3− concentrations of the karst groundwater are remarkably higher than those of non-karst water. As the most dominate cation and anion in most of the water samples, the variations in the Ca2+ and HCO3−, respectively, are similar to the variation in EC from the upstream nonkarst area to the downstream karst area. The relative proportion of sulfate (percentage equivalent) decreases from the mountainous area to the downstream area although there is no clear increasing trend in concentration (Figs. 4, 5).

downstream is shown in Fig. S3. Correlation analysis was carried out as the first step of the factor analysis (Table S1). Trace elements contents are significantly correlated with the sulfate contents, indicating a similar source for these components. EC and the Ca2+ and HCO3− contents are significantly correlated (larger than 0.94) since calcium and bicarbonate comprise most of the TDS in the samples. In addition, the positive correlation between chloride and nitrate (0.83), and chloride and boron (0.64) demonstrate the potential effects of anthropogenic activities such as fertilizer utilization and domestic sewage. Eighty-nine point seven percent of the total variance is accounted for after Varimax rotation by four factors (Table 3). Thirty-nine point five percent of the total variance is accounted for Factor 1, with high loading of Cd (0.98), Fe (0.96), Cu (0.92), Zn (0.98), Sr (0.90), and SO42− (0.96) (Table 3, Fig. S2), reflecting the effect of the Pb-Zn mine and the tailing deposits. Factor 1 has an extremely high score (up to 7) at the mining site and is quite flat throughout the rest of the area (Fig. S3), which indicates that the high loading parameters for Cd, Fe, Cu, Zn, Sr, and SO42− are derived from the Pb-Zn mine. Within the study area, the aquifers are dominantly composed of limestone, dolomite, and dolomitic limestone. Therefore, dissolution of carbonate minerals largely determines the chemical composition of the aquifer. Consequently, 21.9% of the total variance can be explained by Factor 2, with high loading of Ca2+ (0.92), HCO3− (0.94), and EC (0.91), which results from carbonates mineralization and comprises the bulk of the total dissolved solids. Thus, Factor 2 is significantly higher in the downstream karst area water (especially the spring water and groundwater) than in the upstream non-karst area waters (Fig. S3). Rice, corn and citrus are the main crops grown in the study area. According to field investigations, different kinds of fertilizers e.g., manure and urea, and herbicide/pesticide e.g., boracic acid, have been applied to farms and gardens. These agriculture and/or domestic activities likely caused the relatively higher concentrations of nitrate, chloride, and boron in the downstream groundwater (Fig. 5, Fig. S3). Therefore, 16.8% of the total variance can be explained by Factor 3, with loading of NO3− (0.88), B (0.64), and Cl (0.87). The lower scores of Factor 3 reflect its less significant influence on the water's chemistry (Fig. S3). Eleven point five percent of the total variance can be explained by Factor 4 after rotation, with opposite loading of As (−0.92) and Se (0.65) due to their different geochemical behaviors (selenium mobility is favored by oxidizing conditions while arsenic is mobile under reducing conditions) (Fig. S3, Table 3). Samples collected during the dry period have more negative values (0 to −3), while samples collected during the wet period have more positive values (0 to 2). This seasonal variation is caused by the relatively higher selenium concentrations during the wet season and higher arsenic concentrations during the dry season (Fig. S3).

3.3. Trace elements Concentrations of trace elements including lead (Pb), strontium (Sr), arsenic (As), selenium (Se), cadmium (Cd), barium (Ba), zinc (Zn), boron (B), and major metals including iron (Fe), manganese (Mn), and aluminum (Al) in each water sample from the upstream to downstream areas are presented in Table 2 and Fig. 6. The limit values of each element according to the China Standards for Drinking Water Quality (GB 14848—2017) are shown as red dash lines in Fig. 6. Ninety-one percent of the river water samples and 67% of the groundwater samples collected during the dry season exceeds the limit value for As (10 μg/L). In the 2016 wet season, 78% of the surface water samples and 94% of the groundwater samples exceeded the limit value for Se. In the 2015 dry season, 61% of the groundwater samples and 36% of the surface water samples exceeded the limit value for Se. Generally, the trace element concentrations of samples collected in the 2016 wet season (summer) are significantly lower than those collected in the 2015 dry season (winter). This is likely due to the dilution by heavy rainfall and the fast circulation of the water system. Some of the water samples e.g. sample GL01 (surface water in tailing pond), sample GL14 (groundwater in tailing pond) and sample GL02 (mine drainage) collected around the Pb-Zn mine and tailing deposits have significantly higher Cadmium (Cd) contents (5, 9, and 54.5 μg/L, respectively). Sample GL02 also has high concentrations of other heavy metals such as As (17.7 μg/L, with limit value of 10 μg/L), Zn (9154 μg/L, with limit value of 1000 μg/L), Mn (1081 μg/L, with limit value of 100 μg/L) and Ni (37.2 μg/L, with limit value of 20 μg/L; Fig. 6). The Ni was only detected in the mine drainage water (sample GL02), and thus, it is not included in Fig. 6. 3.4. Factor analysis Table 3 shows the Varimax rotated factor matrix consisting of the component factors, the loadings of the variables on each factor, and the percentage of the data variance accounted for each factor. The covariance of Factor 1 and Factor 2, as well as Factor 3 and Factor 4 are shown in Fig. S2. The variation in each factor from upstream to 6

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surface water and groundwater samples collected at higher altitudes are distinct from those collected in the downstream catchment area. This is also observed in the wet season except for a lack of water collected from the upstream area. However, several water samples collected from the downstream karst area have similar isotopic signatures to those of the water in the upstream area (river water samples GL06, GL08, and GL24, and groundwater samples GL21, GL41, and GL42). This is because of the connectivity (via the Sidi River and/or groundwater flow path) of the upstream are near the Pb-Zn mine and the downstream area polluted by arsenic in groundwater. The isotopic composition of precipitation in Guilin-Yangshuo Basin shows a remarkable seasonal variation as reported by Wu et al. (2014). However, the surface water and groundwater do not vary in the same way. As shown in Fig. 7, neither the surface water nor the groundwater has δ2H values that overlap the precipitation values during the sampling period. During the dry season, this incongruent isotopic variation between the local precipitation and the surface water and the groundwater could cause by the recharge of isotopically depleted water (water in group A/A′, Fig. 3) from upstream either via the Sidi River or a groundwater flow path (Fig. 7 a). During the wet season, the isotopic compositions of the surface water and groundwater are heavier than those of the local precipitation because the water is released from different karst structures, which stored water from the previous season. Water from the previous season has heavier isotopic composition because of the heavier isotopic composition of the precipitation during the previous season (Fig. 7 a). The release of this store water could occur when a storm flushes during the wet season due to the dual-porosity of karst systems. In some karst aquifers, the stored water may contribute most of the spring discharge at the onset of the storm flow (Lakey and Krothe, 1996). Mixing recharge of the downstream precipitation with the Sidi River water from upstream has been observed during the dry period. In the wet period, the water stored from the previous season in karst structures is flushed out due to storm perturbation and mixes with the river water, that discharge into carbonate aquifer. 4.2. Neutralization effect caused by carbonates dissolution Acid generation due to the oxidation of pyrite and other metalsulfide minerals causes a large environmental impact, especially in lowland areas due to drainage transport (Appelo and Postma, 2004). The environmental impact is specifically caused by extremely low pH values and the release of heavy metals. The process is described by the following reaction:

FeS2 (s ) + 15/4O2 (aq) + 7/2H2 O → Fe(OH)3 + 2SO24− + 4H+

(1)

Nevertheless, the calcium-rich soil formed from the carbonate rocks of the karst aquifer has the ability to neutralize this acid (Li et al., 2015; Appelo and Postma, 2004; Banks et al., 1997; Favas et al., 2016). Acid mine drainage was not generated in the present study (surface water and groundwater) since all of the water samples had pH values between 8 and 9 (Figs. 4, 8 c). Carbonate rock can react with both H2CO3 and H2SO4 as described in Eqs. S1, S2 and S3. As shown in Fig. 8 (a), most of the hydrochemistry of the water samples obeys the reaction Eq. S1 which results from carbonates reacting with H2CO3, since the studied aquifer is dominated by calcite and dolomitic calcite. However, several of the water samples (mainly stream water) plot near the cross in Fig. 8 (a), indicating that the carbonates have reacted with both carbonic acid and sulfuric acid. In addition, several of the water samples (GL13, GL15 and GL02) collected near the mining site obey the reaction Eq. S2, which is the result of carbonate reacting with sulfuric acid. This buffering capacity is common when the mine is hosted by carbonate, which is the most prolific alkaline mineral (Banks et al., 1997; Dogramaci et al., 2017; Pavoni et al., 2018). The neutral pH of water makes it difficult for

Fig. 4. Variations in temperature (T), electrical conductivity (EC), oxidationreduction potential (ORP), and dissolved oxygen (DO) of water samples from the upstream non-karst area to the downstream karst area.

4. Discussion 4.1. Deuterium and oxygen-18 as indicators of groundwater origin The isotopic compositions of surface water and groundwater samples are closely related to precipitation (Fig. 3). Water samples collected from the eastern mountainous area around the Pb-Zn mine and the tailing deposits are plot in group A (Fig. 3 a). Group A demonstrates that precipitation in the upstream mountainous area near the Pb-Zn mine is affected by altitude, which is approximately 1000 m higher in the mountains than in the downstream catchment area. Therefore, the 7

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Fig. 5. Variations in nitrate and the major ions from the upstream non-karst area to the downstream karst area.

Table 2 Summary of trace elements for river, spring and groundwater. Type

River water (n = 20)

Spring water (n = 12)

Parameter

Min

Max

Mean

St dev

Min

Max

Mean

St dev

Min

Max

Mean

St dev

Al (μg/L) As (μg/L) B (μg/L) Ba (μg/L) Cd (μg/L) Co (μg/L) Cr (μg/L) Cu (μg/L) Fe (μg/L) Li (μg/L) Mn (μg/L) Ni (μg/L) Pb (μg/L) Se (μg/L) Sr (μg/L) V (μg/L) Zn (μg/L)

– 0.6 – 7.3 – – – – – – – – – 1.7 17.5 – –

60.2 20.5 7.7 39.8 9.0 0.6 5.5 1.0 41.3 1.0 16.1 1.9 0.1 18.9 85.0 60.1 861.8

9.6 8.5 2.4 21.5 1.4 0.3 1.6 0.1 7.8 0.3 2.1 0.6 – 10.0 44.1 21.8 140.8

16.0 6.0 2.1 8.4 2.1 0.2 2.0 0.3 11.1 0.2 4.4 0.7 – 5.0 20.9 24.3 211.4

– – – 6.3 – – – – – – – – – – 22.3 – –

16.6 18.8 5.2 59.3 54.5 17.4 4.9 3.0 236.0 3.2 1081 37.2 2.1 30.8 223.0 52.5 9154

2.1 8.2 2.5 14.3 4.7 1.6 0.8 0.3 21.3 0.5 90.3 3.7 0.2 14.7 53.0 14.1 770.9

4.9 7.1 1.6 14.6 15.7 5.0 1.5 0.9 67.6 0.9 312.0 10.6 0.6 9.1 54.6 21.0 2640

– – – 2.8 – – – – – – – – – – 13.1 – –

17.3 23.0 14.9 214.2 5.0 0.8 5.4 0.3 15.4 5.8 1502 2.4 2.1 45.1 86.5 54.0 465.5

2.6 6.4 5.7 33.6 0.3 0.3 1.5 – 3.1 0.7 69.1 0.6 0.1 17.0 45.5 24.2 23.9

5.8 7.6 4.2 53.5 1.1 0.3 1.8 0.1 5.0 1.3 320.1 0.7 0.5 11.7 18.3 22.0 98.7

“–” not available. 8

Groundwater (n = 22)

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Fig. 6. Variations in elements from the upstream non-karst area to the downstream karst area.

is due to the oxidation of sulfide minerals, e.g., pyrite, from upstream. Furthermore, Sr is highly correlated with sulfate and several trace elements (Fe, Cd, and Cu, Table S1), indicating a similar source for these elements, i.e., the oxidation of sulfide minerals (galena, sphalerite and pyrite). Therefore, Sr is regarded as an indicator of sulfide mineral oxidation. The significant positive linear relationship between sulfate and Sr from the Sidi River during the dry (y = 1.2x + 0.18, r2 = 0.85) and the wet seasons (y = 1.4x + 0.17, r2 = 0.94; Fig. 9) demonstrates the transport of these species from the Pb-Zn mine to the downstream area by the Sidi River. Thus, the possibility of the Sr being produced

arsenic and other metals to migrate over long distance (Concas et al., 2006; Kwaterczak and Ciszewski, 2012; Lee et al., 2005; Zabowski et al., 2001). However, as indicated by Fig. 8 (b), such “invisible acidification” potentially results in the release of trace elements and sulfate without elevated pH. Therefore, the generation and migration of toxic metals could be masked by a neutral pH (Fig. 8 c).

4.3. Role of the Sidi River in contaminant transport The higher sulfate proportions of the stream water samples (Fig. S1) 9

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and in waters collected near the mine (GL13, GL15). Figs. 6 and 10 illustrate the effects of mine drainage and tailing deposits. Water in the Sidi River is characterized by higher sulfate concentrations and lower carbonate contents (Fig. 10). The displacement of the As adsorbed on iron oxyhydroxide by (bi-)carbonate ions can increase As desorption in both aerobic and anaerobic conditions (Appelo et al., 2002; Kim et al., 2000). Thus, water in a downstream karst environment contains elevated arsenic. The same situation has been observed in another karst area, in Nandan County, Guizhou Province, southwestern China (Zhang and Yang, 2013). However, the relatively higher arsenic in samples GL03, GL05, GL11, and GL12 which are characterized by lower sulfate and carbonate concentrations, may cause by microbial sulfate reduction (Tuttle et al., 1969) in non-karst mountainous area.

Table 3 Results of factor analysis. Parameter −

NO3 SO42− Cd Cu Fe Sr Se B Cl− As Zn EC Ca2+ HCO3− Eigenvalues % of variance explained Cumulative % of variance

F1

F2

F3

F4

Communalities

−0.08 0.96 0.98 0.92 0.96 0.90 −0.03 0.13 −0.08 0.21 0.98 0.11 −0.05 −0.15 5.53 39.53 39.53

0.29 0.07 −0.03 −0.08 −0.04 −0.05 0.44 0.24 0.35 −0.11 −0.01 0.91 0.92 0.94 3.07 21.91 61.45

0.88 0.12 −0.07 −0.02 −0.04 −0.03 0.35 0.64 0.87 0.00 −0.06 0.37 0.27 0.21 2.35 16.79 78.23

−0.05 −0.04 −0.08 −0.04 −0.10 −0.02 0.65 0.48 0.13 −0.92 −0.05 0.11 0.19 0.15 1.61 11.47 89.71

0.87 0.95 0.97 0.86 0.93 0.81 0.74 0.71 0.91 0.91 0.97 0.99 0.97 0.98

5. Conclusions The groundwater system investigated in this study is characterized by a karstic aquifer with a wide range of As concentrations from undetectable to 23 μg/L, which is much higher than the limit value for drinking water in China (10 μg/L). The 67% of the surface water samples and 91% of the groundwater samples collected during the dry period exceeded the limit value. In addition, serious heavy metal contamination (9154, 1081, 37, and 55 μg/L of Zn, Mn, Ni, and Cd, respectively) was detected in the mine drainage water. δ18O, δ2H values, geochemistry, pH, temperature, and electrical conductivity were analyzed to understand the effects of the abandoned Pb-Zn mine and its tailing deposits on the quality of the surrounding groundwater. The karstic aquifers are dominantly recharged by local precipitation in both the dry and wet seasons. In addition, during the dry period the aquifer is also recharged by water from the upstream mountainous area where the Pb-Zn mine and its tailing deposits are located. Therefore, the fact that the concentrations of As and other heavy metals in the surface water and groundwater are higher during the dry period than during the wet period can be explained by the additional recharge from the abandoned mining site located in the upstream area. The major ion chemistry of the groundwater is largely determined by its karstic carbonate background. Under such conditions, the acidification caused by sulfide oxidation resulting from the presence of the lead‑zinc mine may be hindered by the reaction of the acid with the carbonates. However, instead of increasing the pH value, the increasing trace element

Extraction method: principal component analysis. Factor loadings beyond −0.6 to 0.6 are marked by bold font.

from dissolution of carbonate can be excluded since such a relationship would only be significant in river water, not in groundwater. More directly, the increase of the total amounts of metals with increasing sulfate in river water (Fig. 8 b) further reflects the importance of the Sidi River to the transport of metals.

4.4. Major pollutants in groundwater As is shown by the results, arsenic and selenium most frequently exceeded the safety guideline levels by the highest amounts in the study area (Fig. 6). There is no evidence indicating that the high Se concentration of the groundwater is related to the Pb-Zn mine. The background value of Se in Guangxi soil is 0.59 mg/kg, which is 2.7 times the average background value of soil in China (Tang et al., 1984). Therefore, the leaching of soil Se may be the cause of the Se in the groundwater. Nevertheless, it needs to be further investigated. Here we focus on discussing the origin of arsenic because of the high concentrations of arsenic in the Pb-Zn mine drainage (GL02), in the wastewater (CL14)

Fig. 7. Covariance of the δ2H values and electrical conductivity (EC) in the study area. (a). dry season; (b) wet season. Data of δ2H variation in precipitation is from Wu et al. (2014). 10

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Fig. 8. (a) Variations of [SO42−/HCO3−] with [(Mg2++Ca2+)/HCO3−] equivalent ratios of the surface and groundwater of study area; (b) Variations of total trace elements including Zn, Pb, Ni, Mn, Cu, Cd, As with sulfate in surface and groundwater; (c) Variations of total trace elements with sulfate in surface and groundwater.

responsible for the lower HCO3+ (< 100 mg/L) of the river water. Southwestern China is characterized by vulnerable carbonate aquifers and karst ecosystems where non-ferrous metals account for about 40% of China's reserve (Cai, 2007). It is crucial to conduct strictly controlling exploration and close monitoring of mine sites in order to ensure a safe domestic water supply and economic development. For the already polluted carbonate aquifers, future studies should be conducted to develop multi-disciplinary methods to investigate these groundwater systems.

concentrations (Zn, Pb, Ni, Mn, Cu and As) (from undetectable to 1503 μg/L, except for the mine drainage with 10,350 μg/L) and the elevated sulfate indicate the release of toxic metals into the water system. Most importantly, this is only observed in the Sidi River water. Combined with the significant correlation between sulfate and Sr, which is an indicator of most of the trace elements released by sulfide oxidation, this denotes the important role of surface water runoff in the transport of pollutants. Thus, trace elements in the Sidi River should be investigated using a long-term monitoring network. Furthermore, the release of arsenic due to displacement by (bi-)carbonate ions may be

Fig. 9. Positive correlation between strontium and sulfate in river water. 11

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