Natural and anthropogenic factors affecting the groundwater quality

Journal of Contaminant Hydrology 109 (2009) 49–61

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Journal of Contaminant Hydrology

j ourna l homepage: www.e lsev ie r.com/ locate / jconhyd

Natural and anthropogenic factors affecting the groundwater quality in theNandong karst underground river system in Yunan, China

Yongjun Jiang a,b,c,d,⁎, Yuexia Wu e, Chris Groves c,d, Daoxian Yuan a,b,f, Pat Kambesis c,d

a School of Geographical Sciences, Southwest University, Chongqing 400715, Chinab Institute of Karst Environment and Rock Desertification Rehabilitation, Chongqing 400715, Chinac Department of Geography and Geology, Western Kentucky University, Bowling Green, 42101, USAd Hoffman Environmental Research Institute, Western Kentucky University, Bowling Green, 42101, USAe Centre of Hydrogeology (CHYN), University of Neuchâtel, 11 Rue Emile-Argand, CH-2000 Neuchâtel, Switzerlandf Institute of Karst Geology, CAGS, Karst Dynamics Laboratory, M L R Guilin 541004, China

a r t i c l e i n f o

⁎ Corresponding author. School of GeographicalUniversity, 400715 Chongqing, China. Tel.: +86 23 6868252425.

E-mail address: [email protected] (Y. Jiang).

0169-7722/$ – see front matter © 2009 Elsevier B.V.doi:10.1016/j.jconhyd.2009.08.001

a b s t r a c t

Article history:Received 11 November 2008Received in revised form 31 July 2009Accepted 6 August 2009Available online 11 August 2009

The Nandong Underground River System (NURS) is located in a typical karst agriculturedominated area in the southeast Yunnan Province, China. Groundwater plays an important rolefor social and economical development in the area. However, with the rapid increase inpopulation and expansion of farm land, groundwater quality has degraded. 42 groundwatersamples collected from springs in the NURS showed great variation of chemical compositionsacross the study basin. With increased anthropogenic contamination in the area, thegroundwater chemistry has changed from the typical Ca–HCO3 or Ca (Mg)–HCO3 type inkarst groundwater to the Ca–Cl (+NO3) or Ca (Mg)–Cl (+NO3), and Ca–Cl (+NO3+SO4) or Ca(Mg)–Cl (+NO3+SO4) type, indicating increases in NO3

−, Cl− and SO42− concentrations that

were caused most likely by human activities in the region. This study implemented the R-modefactor analysis to investigate the chemical characteristics of groundwater and to distinguish thenatural and anthropogenic processes affecting groundwater quality in the system. The R-modefactor analysis together with geology and land uses revealed that: (a) contamination fromhuman activities such as sewage effluents and agricultural fertilizers; (b) water–rockinteraction in the limestone-dominated system; and (c) water–rock interaction in thedolomite-dominated system were the three major factors contributing to groundwaterquality. Natural dissolution of carbonate rock (water–rock interaction) was the primary sourceof Ca2+ and HCO3

− in groundwater, water–rock interaction in dolomite-dominated systemresulted in higher Mg2+ in the groundwater, and human activities were likely others sources.Sewage effluents and fertilizers could be the main contributor of Cl−, NO3

−, SO42−, Na+ and K+

to the groundwater system in the area. This study suggested that both natural andanthropogenic processes contributed to chemical composition of groundwater in the NURS,human activities played the most important role, however.

© 2009 Elsevier B.V. All rights reserved.

Keywords:Groundwater qualityAnthropogenic processesNatural processesFactor analysisNandongChina

1. Introduction

Globally about 15% of the land is karst area and karst wateris the drinking water supply for about ¼ of the population on

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the earth. Ecosystems in the karst regions have been badlydisturbed by increasing pollution of our environment asdeliberate or unintentional consequences of human activities.Groundwater quality in a region is largely determined by thenatural processes (lithology, groundwater velocity, quality ofrecharge waters, interaction of water with soil and rock, andinteraction with other types of aquifers), anthropogenicactivities (agriculture, industry, urban development, andincreasing exploitation of water resources) and atmospheric

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50 Y. Jiang et al. / Journal of Contaminant Hydrology 109 (2009) 49–61

input (Helena et al., 2000; Chan, 2001). Groundwater may becontaminated upon leaching of chemicals in the soil surfacetowards the aquifer.

Karst features are often developed in areas with highlysoluble rocks, particularly carbonate rocks. Karst groundwa-ter systems are particularly liable to contamination becauseof the uniqueness of the system. A karst system often haslittle or no soil cover, which means poor filtration, poor pre-purification, rapid infiltration (this is not explicitly a karstwater factor but an incidental aggravating factor). The flow ina karst system is often conduit dominated and the residencetime in a karst system is often short which provides limitedability for self-purification and for microorganisms to die off.The large number of interconnected fissures in a karst systemmake pollution inputs spread quickly and widely (LeGrand,1984; Prohic, 1989; Ford, 1993).

With the rapid economic growth and fast populationincrease, land use has become one of the most importantfactors responsible for groundwater quality in karst areas.The agricultural irrigation effluent, industrial wastewaterdischarge, and domestic effluents have largely contributedto the contamination of groundwater. Mineralization ofthe karst groundwater increases because of the expansionof farming land and the development of intensive agri-culture lead to salinization and alkalization of groundwa-ter (Chambel and Duque, 1999). Concentrations of nitrateand sulphate increase notably as a result of large amountof chemical fertilizers used in agriculture (Aravena et al.,1999; Lahermo and Backman, 1999; Fialho et al., 1999;Witkowski, 1999; Compton and Boone, 2000; Jiang et al.,2008). Waste gas, waste water and waste residuesproduced in the processes of industrialization and urban-ization cause an increase of pollutants such as nitrogen,phosphate, chloride, sulphate, heavy metals and organicsolvents (André et al., 1999; Wakida and Lerner, 2006).The impact of agricultural activities on water quality inkarst areas is gaining increasing attention in China (Yuan,2003; Jia and Yuan, 2003; Zhang and Yuan, 2004; Jianget al., 2008). However, it is not easy to distinguish thecontributions from natural weathering and anthropogenicinputs based on the chemical composition of groundwateralone.

The multidimensional data analysis methods becamevery popular in environmental studies for dealing withmeasurements and monitoring data. The R-mode factoranalysis is a most widely used statistical method in geo-chemistry. It is very useful for interpreting groundwaterquality data to understand specific hydrogeochemical pro-cesses. This method has several advantages over thehistograms and trilinear diagrams (Dalton and Upchurch,1978) such as reduction of data to dimensionless quantities,consideration of greater and lesser concentration ions withequal emphasis (i.e. their relative importance), and descrip-tion of groups of variables in order of chemical activity.Factor analysis has been successfully applied to clarify thecontributing hydrogeological and hydrogeochemical pro-cesses to groundwater quality, and to identify the ecologicalaspects of pollutants on environmental systems (Ruiz et al.,1990; Chan, 2001; Ganfopadhyay et al., 2001; Raghunathet al., 2002; Liu et al., 2003; Kim et al., 2003, 2005; Pandaet al., 2006).

The purpose of this study was to use the factor analysis(FA) method to distinguish the impact of natural processesand anthropogenic input influencing the composition ofgroundwater in a karst underground river system inNandong,China.

2. Description of study area

Nandong Underground River System (NURS) is located inthe southeast of the Yun-Gui Plateau in Yunnan Province,China (Fig. 1). The underground drainage area of the system isabout 1618 km2. The climate condition is primarily subtrop-ical monsoon with a mean annual precipitation of 830 mmand a mean air temperature of 19.8 °C. In 2007, the totalpopulation in the area was about 0.4 million, about 60% ofwhich or 0.24 million lived in rural areas. The gross domesticproduct (GDP) in 2006 was about 9.1×108 USD, about 1/3 ofwhich or 3.1×108 USD was from agriculture production.

2.1. Geology and hydrogeology

The geologic layers of the study area are shown in Fig. 2. Itis mainly underlain by Mesozoic Triassic strata. Carbonaterocks cover about 950 km2, or 58.7% of the total area.Limestone and dolomite each is about 50% in the carbonaterock area (Fig. 2). The central basin is covered by theQuaternary Mal lateritic clay which is underlain by TriassicGejiu formation (T2g).The length of the main undergroundriver from the Mingjiu cave to Nandong is about 75 km, andthe other two branch underground rivers are about 40 kmlong. The average discharge of the Nandong UndergroundRiver was 9.4 m3/s.

2.2. Land use pattern

There were 5 land use categories, i.e., forested land,grass land, cultivated land, water bodies, and constructionland (towns and other urbanized areas) in the NURS.Cultivated land included dry and paddy land. Land usedata was obtained by interpreting 2007 Thematic Mapper(TM) images. Fig. 3 shows the land use patterns in NURS.The percentages of land use for each type were 29.5 forcultivated land, 27.5 for forested land, 37.7 for grass land,2.8 for construction land, and 2.5for water bodies in 2006,respectively.

3. Materials and methods

3.1. Studied sites

42 groundwater samples were collected from all 42springs from the system (Figs. 2 and 3). 2 samples werecollected from the dolomite and forested land, 7 fromdolomite and grass land, 3 from dolomite and cultivatedland, 4 from dolomite and construction land, 4 fromlimestone and forested land, 7 from limestone and grassland, 5 from limestone and construction land, and 5 fromquaternary and cultivated land, and 5 from quaternary andconstruction land, respectively. It is worth to emphasize thatwater at the monitored points was the sole drinking andirrigation source for local residents.

Fig.

1.Lo

cation

oftheNURS

.

51Y. Jiang et al. / Journal of Contaminant Hydrology 109 (2009) 49–61

Fig. 2. Geological map and the distribution of groundwater samples in the NURS.


3.2. Sampling and analytical procedures

Groundwater samples were collected in January of 2008.The water temperature, pH and electrical conductance (EC, at25 °C) were measured by a hand-held water quality meter(WTW MultiLine P3 pH/LF-SET), and Ca2+ and HCO3

− weretested by a test kit with a titration pipette Aquamerck in thefield. In addition, 1.5 L samples were collected in plasticcontainers and stored at 4 °C before filtration. Samples werefiltered in the water environmental laboratory at theSouthwest University by using a 0.45 micro-meter filter formajor elements. Major cations (Mg2+, K+, Na+) wereanalyzed by inductively coupled plasma-mass spectrometry,and anion concentrations (SO4

2−, NO3−, and Cl−) were

measured using ion chromatography, following the US

Environmental Protection Agency (EPA) standard methods.The estimated analytical errors were within 5% (Table 1).

3.3. Statistical procedures and factor analysis (FA)

Statistical analysis, box plot analysis and factor analysis ofthe water quality data were performed using the SPSSsoftware version 13.0. Factor analysis of chemical data wasused to distinguish the contributions of natural processes andanthropogenic inputs to the chemical composition of ground-water in the NURS. FA can provide information on the mostmeaningful parameters that can describe the whole data setswith minimal loss of original information (Wunderlin et al.,2001). The variables for factor analysis were pH, EC, HCO3

−,Ca2+, Mg2+, Na+, K+, SO4

2−, NO3− and Cl−.

Fig. 3. The land use patterns and the distribution of groundwater samples in the NURS.


Factor analysis was done as follows. The correlationmatrix, i.e. the array of correlation coefficients for all possiblepairs of variables, was calculated by R-mode. Then, the matrixwas diagonalized and its principal components (eigenvec-tors) were obtained. Although the factor matrix obtained inthe extraction phase indicates the relationship between thefactors and the individual variables, it is usually difficult toidentify meaningful factors based on this matrix. However,the interpretation of the matrix can be a little bit easier byusing the rotation procedure. Rotation does not affectgoodness of fit of a factor solution. That is, although thefactor matrix changes, the communalities and the percentageof total variance explained do not change. The rotation

process in FA allows flexibility by presenting a multiplicity ofviews of the same data set. Varimax rotation, which isfrequently applied to increase the participation of thevariables with higher contribution and reduce those withlesser contributions, was adopted in this study. In this way,Principal Component Analysis (PCA) was subjected to rawVarimax rotation. The so-called factor 1 is related to thelargest eigenvalue and is able to explain the greatest amountof variance in the data set. The second factor (orthogonal anduncorrelated with the first one) explains most of theremaining variance, and so forth.

Spatial distribution of factor scores were interpolated byordinary kriging and accomplished using ArcGIS software.

Table 1Chemical analyses of the groundwater in NURS.

Samplepoint

pH EC Ca2+ Mg2+ Na+ K+ HCO3− Cl− NO3

− SO42−

(µs/cm) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l) (mg/l)

1 8.44 291.0 80.00 14.40 1.20 1.59 189.10 1.800 1.24 7.002 7.42 467.0 106.00 13.63 2.25 1.84 286.70 5.600 4.33 14.003 7.21 498.0 116.00 8.28 2.23 1.74 286.70 1.59 4.35 25.424 7.30 493.0 88.00 13.99 2.22 1.76 280.60 1.50 4.25 25.295 7.41 520.0 102.00 13.21 2.24 1.82 274.00 1.60 4.27 25.636 8.13 337.0 92.00 13.56 12.30 11.56 292.50 25.36 25.56 46.827 8.17 869.0 121.00 19.55 23.00 18.94 366.00 45.72 59.45 91.038 7.86 352.0 82.93 31.05 1.52 1.20 337.49 3.90 1.24 13.409 7.20 659.0 158.00 2.23 12.26 11.05 427.00 42.36 25.63 16.3010 7.53 546.0 110.00 7.03 11.77 16.73 283.00 23.98 26.25 94.0211 8.23 495.0 85.63 18.07 33.80 5.60 269.56 38.68 63.56 108.8312 7.90 325.0 90.00 15.96 40.81 11.44 140.00 78.25 75.36 65.2013 7.28 321.0 83.30 4.40 3.23 1.75 168.30 0.96 1.36 1.8014 9.30 311.0 64.00 15.35 40.86 10.56 170.80 78.21 74.53 65.3015 8.05 654.0 79.66 24.05 17.69 15.69 216.26 44.11 65.26 78.4016 7.47 318.0 74.71 13.73 5.38 1.33 301.15 1.42 1.34 2.6017 7.07 296.0 78.15 29.74 20.70 13.28 312.98 24.34 31.56 22.6918 7.50 386.0 78.60 30.17 1.82 1.08 383.36 3.71 1.56 2.4019 7.28 348.0 100.96 26.68 5.56 1.95 439.07 3.54 2.43 3.0020 7.52 498.0 60.00 7.39 1.52 1.75 250.00 5.27 1.56 15.1621 7.59 245.0 77.78 38.84 2.52 1.70 386.64 3.19 2.89 15.8022 7.32 325.0 66.34 22.56 1.60 2.53 311.28 3.60 2.86 14.4023 7.78 352.0 77.21 32.80 1.86 2.85 373.53 2.35 3.21 14.6024 8.35 383.0 90.14 59.47 24.85 22.25 439.07 37.58 64.56 104.5025 7.84 368.0 63.64 4.37 3.39 1.79 186.77 27.90 46.54 14.2026 8.56 528.0 125.68 10.58 24.56 19.85 348.95 68.59 35.89 142.5827 7.45 418.0 74.90 21.90 16.34 7.55 231.11 23.40 76.80 56.3028 8.41 425.0 73.87 28.31 36.78 31.55 295.63 71.61 32.50 69.5029 7.65 344.0 89.42 12.68 66.03 66.42 317.83 103.52 57.98 79.5030 7.29 264.0 96.63 5.74 2.26 1.76 373.53 2.50 26.30 14.5031 7.75 256.0 85.09 17.32 2.30 1.66 349.29 2.60 6.30 18.9632 7.32 245.0 88.70 6.68 2.11 1.79 396.47 1.35 5.62 26.6033 8.33 215.0 81.49 34.55 11.34 12.87 412.25 1.54 5.56 43.4034 8.02 378.0 83.25 6.12 31.08 18.89 268.39 46.44 15.26 27.6035 7.54 382.0 102.26 9.05 1.56 0.12 373.53 1.56 2.52 2.3036 7.56 789.0 156.01 13.58 92.00 58.94 373.53 131.88 104.07 174.0037 8.27 369.0 85.84 19.01 9.57 3.36 281.79 11.70 27.57 58.0038 7.99 482.0 99.26 13.58 27.08 49.31 273.66 58.14 25.24 67.5039 8.04 468.0 97.93 21.27 15.19 12.33 249.02 15.89 14.38 63.5040 7.87 475.0 100.02 14.94 16.88 11.79 268.68 49.99 28.15 74.0041 7.76 502.0 119.43 18.56 20.84 19.72 311.28 53.18 29.96 126.0042 7.96 424.0 98.22 15.84 2.150 1.83 262.13 3.35 4.07 9.40


4. Results and discussion

4.1. Chemical characteristics

Table 2 showed some descriptive statistics for the 10physico-chemical parameters monitored in 42 groundwater

Table 2Descriptive statistics of chemical composition of groundwater in NURS(N=42).

Mean Min. Max. SD CV% Skewness

pH 7.78 7.07 9.30 0.46 5.91 0.866EC (µs/cm) 419.55 215 869 138.29 32.96 1.228Ca2+ (mg/l) 92.48 60.00 158.00 21.02 22.73 1.291Mg2+ (mg/l) 17.86 2.23 59.47 10.99 61.53 1.407Na+ (mg/l) 15.59 1.20 92.00 18.68 119.82 2.128K+ (mg/l) 11.51 0.12 66.42 15.08 131.02 2.203HCO3

− (mg/l) 303.78 140.00 439.07 74.37 24.48 −0.128Cl− (mg/l) 27.47 0.96 131.88 31.54 114.82 1.336NO3

− (mg/l) 26.03 1.24 104.07 27.09 104.07 1.028SO4

2− (mg/l) 46.22 1.80 174.00 41.84 90.52 1.098

samples. Also, Table 2 reflected a moderate to high variability(standard deviation and coefficient of variation) amongsamples of the variables. The highest variability was for K+,followed by Na+, Cl−, and NO3

−, with coefficient of variationvalues above 1.0, reflecting spatial variation of groundwaterquality in the NURS.

Piper diagrams (Fig. 4a, b) showed the dispersion of thecation (Ca2+, Mg2+ and K++Na+) and anion (HCO3

−, Cl−+NO3

− and SO42−) content in groundwater samples from

different lithologies and land uses, which indicated thosevariables could be associated either to lithological hetero-geneities or human activities. Hydrochemical facies ofgroundwater in the study area vary broadly from Ca–HCO3

or Ca (Mg)–HCO3 type to Ca–Cl (+NO3) or Ca (Mg)–Cl(+NO3), and Ca–Cl (+NO3+SO4) or Ca (Mg)–Cl (+NO3+SO4) type, suggesting that the hydrochemistry of groundwa-ter was controlled by water–rock interaction and anthropo-genic pollution. The distribution of cations tended to beclustered in the ternary diagram and was dominated by Ca2+

and Mg2+ (mostly N50% and N20%, respectively). Generally,

Fig. 4. Piper diagrams of ionic compositions in groundwater in different lithology (a) and land use (b) in the NURS.


Fig. 5. Box plots of chemical analysis in the NURS.


the Ca2+, Mg2+ and HCO3− showed higher concentrations in

the groundwater samples collected from the forested andgrass lands than those from the construction and cultivatedlands (Fig. 4b), suggesting that human interference in theforested and grassland areas was minimal and the naturalprocess (water–rock interaction) was the primary sources ofCa2+, Mg2+ and HCO3

− in the groundwater. The Mg2+

concentration was clearly high in the groundwater samplescollected from dolomite aquifers than that from limestoneand Quaternary aquifers (Fig. 4a), which was due to primarilyto thewater–rock interaction in thedolomite-dominated system.On the other hand, the Cl−, NO3

−, SO42−, K+ and Na+

concentration appeared to be relatively high in samples collectedfrom the construction and cultivated lands but typically low insamples from the forested and grass lands, suggesting ananthropogenic cause of high Cl−, NO3

−, SO42−, K+ and Na+ in

the groundwater in the construction and cultivated areas.Box plots of the chemical concentrations showed that

HCO3−, SO4

2−, Cl− and NO3− had the largest variability (Fig. 5).

The Cl−, NO3−, SO4

2−, Na+ and K+ content also showed anobvious difference between the medium and maximumvalues, and medium values were near a quarter of themaximum values, suggesting local contamination inputs tothe groundwater system. The wide range of values in theHCO3

− content, from 140.00 to 439.07 mg/l, is the result of thegeological variation of aquifers and different land covers.

4.2. Assessment of contributions by factor analysis

Variables for factor analysis in this study were pH, EC,HCO3

−, Ca2+, Mg2+, Na+, K+, SO42−, NO3

−, and Cl−. The

correlation matrix for the 10 variables is shown in Table 3.The maximum number of factors to be extracted was fixed bythe Kaiser Criterion, which takes into account only factorshaving eigenvalues higher than 1. Following this procedure,three factors were obtained and rotated according to theVarimax with Kaiser Normalization method, their loading,communalities for each variable, percentage of the variance ofeach factor, and cumulative percentage of variance werelisted in Table 4. The communalities of all ions except pH andEC were greater than 0.70. Those factors accounted for 79.4%of the total variance. These factors with different factorloadings indicate that different contributions are involved indetermining the chemical compositions of groundwater inthe NURS.

Factor 1, accounting for about 47.1% of the total varianceand was primarily composed of Cl−, SO4

2−, NO3−, Na+, K+, pH,

and EC. Because there was no known lithologic source forNO3

−, and generally atmospheric deposition was not consid-ered to be a major source of the NO3

− concentrations in thegroundwater, the substantial contribution of NO3

− in thestudy area was likely resulted from the excessive applicationof agricultural fertilizers and sewage effluents. Na+ ingroundwater derives from the incongruent dissolution ofplagioclase in granite, chemical fertilizer, domestic effluentsand atmospheric input (Edmunds et al., 2003; Aiuppa et al.,2003; Valdes et al., 2007; Brenot et al., 2008). K+ ingroundwater often comes from orthoclase and muscoviteminerals present in granite, and from pollution sources suchas chemical fertilizer and domestic effluents. Cl− may bederived from the pollution sources such as effluents ofindustrial and domestic, fertilizers and septic tanks (Bohlke

Table 3The correlation matrix among the chemical constituents for the groundwater samples.

pH EC Ca2+ HCO3− Cl− NO3

− SO42− Mg2+ Na+ K+

pH 1.000EC −0.004 ⁎ 1.000Ca2+ −0.182 ⁎⁎ 0.653 ⁎ 1.000HCO3

− −0.247 ⁎⁎ 0.028 ⁎⁎ 0.380 ⁎ 1.000Cl− 0.378 ⁎⁎ 0.428 ⁎ 0.360 ⁎ −0.126 ⁎⁎ 1.000NO3

− 0.363 ⁎⁎ 0.413 ⁎ 0.202 ⁎ −0.197 ⁎ 0.804 ⁎⁎ 1.000SO4

2− 0.426 ⁎⁎ 0.544 ⁎ 0.451 ⁎ 0.015 ⁎ 0.776 ⁎⁎ 0.742 ⁎⁎ 1.000Mg2+ 0.236 ⁎⁎ −0.198 ⁎ −0.270 ⁎ 0.387 ⁎⁎ −0.073 ⁎ 0.073 ⁎⁎ 0.100 ⁎⁎ 1.000Na+ 0.310 ⁎⁎ 0.356 ⁎ 0.319 ⁎ −0.058 ⁎ 0.942 ⁎⁎ 0.791 ⁎⁎ 0.748 ⁎⁎ 0.010 ⁎ 1.000K+ 0.198 ⁎ 0.358 ⁎ 0.351 ⁎ 0.082 ⁎ 0.838 ⁎⁎ 0.569 ⁎⁎ 0.671 ⁎⁎ 0.023 ⁎ 0.855 ⁎⁎ 1.000

⁎ Significant at 0.05 level.⁎⁎ Significant at 0.01 level.


and Horan, 2000; Petelet-Giraud et al., 2003; Negrel andPauwels, 2003; Edmunds et al., 2003; Widory et al., 2004;Valdes et al., 2007), and from natural sources such as rainfall,the dissolution of fluid inclusions and Cl− bearing minerals(Negrel and Roy, 1998; Negrel, 1999). Sources of SO4

2−

include rainfall, fertilizers (Edmunds et al., 2003; Negrel andPetelet-Giraud, 2005; Valdes et al., 2007), sewage effluentsand dissolution of sulfide minerals present in granite.However, in the study area, it is underlain by limestone anddolomite rather than by granite. Also, as shown in Table 3,there are significant positive correlations between NO3

− andCl−, NO3

− and SO42−, NO3

− and Na+, NO3− and K+, Cl− and Na+,

Cl− and K+, Cl− and SO42−, Na+ and K+, Na+ and SO4

2−, K+

and SO42−, which indicate that such ions in the groundwater

were likely from the same sources. As mentioned in theprevious section, NO3

− has no known lithologic source andatmospheric deposition, the significant high positive loadingsand correlation of NO3

−, Cl−, SO42−, Na+ and K+ suggest that

those variables are associated with agricultural fertilizers andsewage effluents. Therefore, factor 1, is assumed to beindicative of the contamination source related to humanactivities.

Factor 2, accounting for about 17.9% of the total variance,consisting of Ca2+, pH and EC. The significantly high positivecontent of Ca2+ indicated the variables association with

Table 4Factor loadings and communality of the variables after Varimax rotation.

Variables Factor 1 Factor 2 Factor 3 Communalities

pH 0.532 −0.556 −0.006 0.592EC 0.442 0.657 −0.123 0.642Ca2+ 0.287 0.871 0.099 0.851Mg2+ 0.110 0.437 0.809 0.857HCO3

− −0.125 0.402 0.849 0.898Cl− 0.945 0.130 −0.106 0.921NO3

− 0.871 −0.0171 −0.096 0.768SO4

2− 0.878 0.186 0.090 0.813Na+ 0.928 0.107 − .013 0.872K+ 0.816 0.212 0.101 0.722% of variance 47.122 17.920 14.330Cumulative %of variance

47.122 65.042 79.372

Extraction method: Principal Component Analysis. Rotation method:Varimax with Kaiser Normalization.Kaiser–Meyer–Olkin measure of sampling: adequacy=0.736.Bartlett's Test of Sphericity: approx. chi-square=323.278,df=45, Sig.=0.000.

water–rock interaction, because Ca2+ in groundwater pri-marily comes from the dissolution of carbonate in karst area.Also, the significantly high positive loading of EC and strongnegative loading of pH supported the hypothesis of water–rock interaction. The electrical conductance reflects theamount of material dissolved in groundwater, and the pHvalue of groundwater reflects the concentration of H+ ingroundwater (pH=− logH+), but H+ was absorbed by theprocesses of water–rock interaction (Pacheco and Van derWeijden, 1996). Therefore, factor 2 was assumed to beindicative of water–rock interaction, i.e. calcium-rich rocksuch as carbonate rocks. Note that Ca2+, EC and pH alsoshowed a high loading in factor 1, which indicated bothwater–rock interaction and human activities were responsi-ble for the Ca2+, EC and pH in groundwater.

Factor 3, accounting for about 14.3% of the total variance,consisting of HCO3

− and Mg2+. The significantly high positiveloadings of Mg2+ and HCO3

− indicated that those variableswere also associated with water–rock interaction, however,Mg2+-rich rock such as dolomite, because Mg2+ in ground-water often comes from the input of the dissolution ofdolomite in karst areas (Aiuppa et al., 2003; Negrel andPetelet-Giraud, 2005; Brenot et al., 2008). Therefore, factor 3,is assumed to be indicative of water–rock interaction indolomite-dominated bedrocks. Note that theMg2+ andHCO3

−

also showed a significant loading in factor 2, which indicatedthat both Mg2+ and HCO3

− were from the water–rockinteractions in dolomite-dominated and limestone-dominat-ed bedrocks. However, Ca2+ content was higher in thecalcium-rich bedrocks and Mg2+ content was higher indolomite area.

4.3. Effect of landuse on the chemical compositions of groundwater

In order to highlight the relative importance of the above-described effect of human activities and geochemical pro-cesses on the chemical compositions of groundwater, thefactor scores from factor analysis were mapped in Fig. 6. Byplotting the factor scores together with land uses, this mapcan show estimations of relative statistical weight in eachcase or water sample.

Fig. 6a showed that water samples with high F1 scoreswere located in the middle section and the north-easternsection of the NURS, both areas with the most concentratedpopulation and extensive agricultural activities. The land use

Fig. 6. Distribution maps of factor scores of the 3 factors obtained by ordinary kriging in the NURS.


pattern was the cultivated and construction land. This mapvalidated the hypothesis of an anthropogenic derivation forthese chemicals. The spatial distribution of F2 (Fig. 6b)revealed that groundwater with high Ca2+ loadings generallyoccurred on the downstream site of the underground riverwhere geologic layers are mainly underlain by limestone andquaternary deposits. This finding further supported thehypothesis of the effect of water–rock interaction on thesechemicals. Finally, the spatial distribution of F3 scores(Fig. 6c) revealed that groundwater with high Mg2+ loadingswere from dolomite, which indicated the dolomite was the

primary source of Mg2+. This information supported thehypothesis of origin of Mg2+ in groundwater from water–rock interaction in the dolomite area.

Also, as shown in Table 5, the Cl−, SO42− and NO3

−

concentration in groundwater showed a systematic control ofland-use characteristics whichwere heavily affected by humanactivities and as indicated by FA tended to increase in thefollowing order: forested land, grass land, cultivated land andconstruction land, whereas Na+ and K+ tended to increasefrom the grass land, to the forested land, to the cultivated land,and to the construction land, which indicated the influence of

Table 5Chemical data for groundwater according to land use and strata in NURS.

Land use Strata pH EC Ca2+ HCO3− Cl− NO3

− SO42− Mg2+ Na+ K+

µs/cm mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L

Forested land (6) Dolomite Mean 7.90 266.50 78.10 356.70 1.48 3.45 23.00 24.14 8.36 7.12 SD 0.4 51.5 3.4 55.5 0.1 2.1 20.4 10.4 2.9 5.7Limestone Mean 7.65 316.50 93.00 349.50 2.06 2.95 9.72 14.20 2.60 1.404 SD 0.5 52.5 9.2 95.5 0.8 1.6 9.9 7.7 1.7 0.7Total Mean 7.73 299.83 88.02 351.93 1.87 3.12 14.15 17.52 4.52 3.27

SD 0.5 57.3 10.5 84.4 0.8 1.8 15.6 9.9 3.5 4.3Grass land (14) Dolomite Mean 7.68 349.71 80.65 303.90 3.09 2.95 12.73 21.09 2.16 1.82

7 SD 0.2 82.9 10.6 73.6 1.2 1.7 5.2 12.3 0.6 0.5Limestone Mean 7.35 421.86 93.37 313.74 2.87 6.85 17.38 15.37 2.09 1.797 SD 0.1 91.1 15.7 42.4 1.4 8.0 8.0 7.8 0.2 0.4Total Mean 7.51 385.79 87.00 308.83 2.98 4.89 15.05 18.23 2.12 1.80

SD 0.2 94.3 14.8 60.3 1.3 6.1 7.1 10.7 0.4 0.4Cultivated land (13) Dolomite Mean 8.12 446.33 90.50 273.34 33.46 39.76 80.28 17.34 20.08 6.92

3 SD 0.2 55.3 6.7 6.0 16.1 16.8 21.2 1.7 10.1 3.6Limestone Mean 7.81 575.80 115.79 323.50 30.66 30.25 62.33 12.73 14.90 14.125 SD 0.4 180.2 23.4 64.3 11.4 15.3 28.9 7.3 4.2 3.1Quaternary Mean 7.82 376.60 88.04 322.39 54.00 38.92 60.36 24.32 33.95 34.035 SD 0.4 61.2 7.1 61.7 27.1 19.1 31.2 19.2 16.4 20.4Total Mean 7.88 469.31 99.28 311.50 40.28 35.78 65.71 18.25 23.42 20.12

SD 0.4 149.8 20.3 59.1 22.6 17.7 29.4 13.8 14.4 17.2Construction land (9) Dolomite Mean 8.36 357.25 72.88 198.30 64.00 57.23 53.55 16.00 30.46 13.84

4 SD 0.6 44.4 10.7 58.6 21.0 18.4 22.8 8.5 15.7 10.9Quaternary Mean 7.88 578.20 111.14 296.23 64.23 62.40 115.46 17.73 34.29 24.355 SD 0.4 129.7 30.3 62.6 36.8 27.2 42.8 5.0 29.0 17.8Total Mean 8.09 480.00 94.13 252.70 64.13 60.10 87.94 16.96 32.59 19.68

SD 0.5 149.3 30.4 77.9 30.8 23.8 46.8 6.8 24.0 16.0


anthropogenic contamination from fertilizer applications in theagricultural areas and sewage discharges in the residentialareas. In general, NO3

− and SO42− could be introduced with urea

(CO(NH2)2) and ammonium-sulfate ((NH4)2SO4) fertilizationin croplands. The excessive amount of fertilizer could not beabsorbedby thenegatively charged soil colloids and thuswouldmigrate downward to the groundwater table through karstconduits and fissures. NO3

− and SO42− could also be introduced

into the groundwater by the effluent from the septic systemand livestockwaste in the residential areas. Sources for Cl− andK+ could be the applicationof KCl and compound fertilizers andforNa+ could be theuseofNaNO3 in agricultural areas. Anothersource of Cl−, K+ and Na+ in the study area could be thedetergent in the wastewater.

The Ca2+ concentration were typically higher in ground-water samples collected from limestone-dominated aquifersand from agricultural and residential areas (Table 5). The Ca2+

concentration combinedwith the factor 2 and factor 1 from theFA analysis suggests that Ca2+ in groundwater was primarilyfrom natural dissolution of carbonate rocks but anthropogenicinput was clearly an important factor. The application offertilizers such as superphosphate and calcium phosphate,wastewater discharges, and the concrete from constructions,and etc. could be the contributing factors for high Ca2+

concentration in the area.The pH and EC values in groundwater tended to increase

from the forested land, to the grass land, to the cultivatedland, and to the construction land (Table 5) which was due tothe both the water–rock interactions and anthropogeniccause such as domestic effluents in the study area.

This study did not find evidence of any specific land-usecontrol on the Mg2+ concentration. High Mg2+ concentrationwas only observed in groundwater samples collected from

dolomite-dominated aquifers (Table 5), which suggests theorigin of Mg2+ in groundwater was primarily from water–rock interaction in dolomite-dominated aquifers.

The concentration of both CO2 and HCO3− concentration

was higher in the forested and grass lands than those fromthe construction and cultivated lands (Table 5). The high CO2

concentration could have accelerated the water–rock inter-action and thus resulted in higher HCO3

− concentration ofgroundwater in the forested and grass land areas.

5. Summary and conclusions

The chemical characteristics of groundwater in the NURSwere investigated to understand the factors affecting ground-water quality in the Nandong karst underground river systemin Yunnan, China. The study results showed that both naturaland anthropogenic processes were the two major factors forthe chemical compositions of groundwater.

The water quality data from 42 springs showed significantdisparities of water chemistry in the NURS. These spatialheterogeneities were attributed to both the lithological andanthropogenic factors. The Piper's diagram showed thatgroundwater changed widely from Ca–HCO3 or Ca (Mg)–HCO3 type to Ca–Cl (+NO3) or Ca (Mg)–Cl (+NO3) and Ca–Cl(+NO3+SO4) or Ca (Mg)–Cl (+NO3+SO4) type because ofthe increase in NO3

−, Cl− and SO42− concentrations due to

anthropogenic causes such as fertilization, wastewaterdischarge and waste disposal in the area. Results from factoranalysis indicated that Cl−, NO3

−, SO42−, Na+ and K+ in

groundwater were from sources related to human activities;and Ca2+, HCO3

−, pH and EC were primarily from water–rockinteractions in calcium carbonate rock area but slightly fromanthropogenic inputs; and Mg2+ and HCO3

− were also from


water–rock interaction but in the dolomite rich area. Analysisof distribution of factor scores combined with land usepatterns further supported the hypothesis of the natural andanthropogenic processes affecting water quality in the studyarea. Factor 1 indicated that chemical compositions inNandong groundwater system were influenced by anthropo-genic inputs. Factor 2 indicated water–rock interaction incalcium-rich carbonate rock areas, and factor 3 indicatedwater–rock interaction in dolomite-dominated areas.

This study demonstrated that the R-mode factor analysisis an effective tool to characterize the dominant factors ofgroundwater chemical components. The R-mode analysisresults combined with land uses and hydrogeologic conditionof the system helped to distinguish natural and anthropo-genic sources affecting groundwater quality in the karstgroundwater system. The results from this analysis will notonly help to understand the cause of groundwater contam-ination in the Nandong underground karst system but alsoshed light on the methodologies that can be effective to studygroundwater chemical characteristics and identify the dom-inance of different contributing sources.

Acknowledgements

This research was funded by the National Basic ResearchProgram of China (No: 2008CB417208); The China Environ-mental Health Project; Open foundation of Karst DynamicsLaboratory (No: kdl2008-06); Key Laboratory CultivationProject: Guangxi Karst Dynamics Laboratory with the seriesnumber GuiKeNeng (No: 0842008); and the project of M L R(No. 200310400024). Thanks are given to Cheng Zhang, ShiyiHe, Erin Lynch, Duncan Collis, Joel Despain, Cyndie Walck,Shane Fryer, Mike Futrell, Andrea Futrell, Yinggang Li, JieZhang, Junbing Pu, Yinglun Kuang and Qiang Zhang for theirhelp in field work. Thanks are also given to Drs. Zaihua Liu andYanqing Lian, and to the reviewers whose comments andsuggestions have significantly contributed to the improve-ment of this manuscript.

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Natural and anthropogenic factors affecting the groundwater quality