Arabian Journal of Geosciences

, Volume 7, Issue 10, pp 3973–3982

Using correlation and multivariate statistical analysis to identify hydrogeochemical processes affecting the major ion chemistry of waters: a case study in Laoheba phosphorite mine in Sichuan, China

Authors

  • Jianhua Wu
    • School of Environmental Science and EngineeringChang’an University
    • Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid RegionMinistry of Education
    • School of Environmental Science and EngineeringChang’an University
    • Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid RegionMinistry of Education
  • Hui Qian
    • School of Environmental Science and EngineeringChang’an University
    • Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid RegionMinistry of Education
  • Zhao Duan
    • College of Geology and EnvironmentXi’an University of Science and Technology
  • Xuedi Zhang
    • School of Environmental Science and EngineeringChang’an University
    • Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid RegionMinistry of Education
Original Paper

DOI: 10.1007/s12517-013-1057-4

Cite this article as:
Wu, J., Li, P., Qian, H. et al. Arab J Geosci (2014) 7: 3973. doi:10.1007/s12517-013-1057-4

Abstract

Prior to mining, the water in and around the mine is rarely influenced by human activities, and hydrogeochemical processes are the major factors influencing and controlling water chemistry. To identify these natural hydrogeochemical processes in Laoheba phosphorite mine (Sichuan Province, China), correlation and multivariate statistical techniques were used. Results show that water quality in the area is generally good before the Laoheba phosphorite mine goes into construction and production. The cluster analysis classified water samples into 4 clusters (C1–C4). Samples from C1 and C2 are of HCO3−Ca·Mg and HCO3−Ca type, while those from C3 and C4 are of HCO3−Ca·Mg type. Most parameters except Cl and pH show an increasing trend in the order of C1 to C4. Three principal components were extracted, and PC1 represents the ion exchange and the weathering of calcite, dolomite, and silicate minerals. PC2 and PC3 indicate the process of water recharge from upstream waters and the process of evaporation, respectively. The hydrochemistry of waters in the area is a result of multiple factors, and natural mineral weathering and ion exchange are the most important ones.

Keywords

HydrogeochemistryGroundwater resourcesMajor ion chemistryWater–rock interactionMineral weathering

Introduction

In mining areas, water resources are important for human consumption, plant growth, and mine exploitation and their rational development has always been an important issue worthy of discussion. In mining areas, heavy metal pollution in water, soil, and sediments is popular and many cases of this kind have been reported (Prasad and Bose 2001; Razo et al. 2004; Nikolaidis et al. 2010). In fact, in addition to heavy metal pollution, various mining activities can also influence the concentrations of major ions in surface and groundwater in and around a mine (Singh et al. 2011). Therefore, it is quite important and useful to investigate and understand the major ion concentration variations prior to and after the mine production.

Generally, prior to mining, major ions in water are usually controlled by natural processes, such as mineral weathering, ion exchange, water–rock interactions, etc. (Li et al. 2013a). These natural processes are slow (Li et al. 2013a; Qian et al. 2012). However, after the mine goes into construction and production, human interference may increase the speed of these processes, resulting in the noticeable variation of these major ions. Technically, water resources protection in mining areas requires the understanding of water quality status and processes that control the major ion chemistry before it is seriously polluted. As a result, major ion chemistry investigation before water pollution is an important and necessary task for water resources protection in mining areas.

Research on water quality and hydrochemistry has been widely conducted over the world due to the increase of awareness of water quality protection. Arnous and El-Rayes (2012) developed a GIS-based model to assess groundwater contamination in the West Ismailia area. They produced a groundwater potential risk map and reported that the groundwater pollution may result from agricultural, domestic, and industrial activities. Similarly, Alfy (2012) assessed groundwater contamination integrating geostatistics and GIS techniques in Al Arish area, Sinai, Egypt. Dar et al. (2012) found nitrate and total hardness pollution in some villages in Tamil Nadu after an investigation on environmental chemistry of groundwater in this area. Hydrochemical investigations have also widely been carried out in various mining areas. Rozkowski et al. (1993) have realized the negative effects of mining on groundwater chemistry and environment in the early 1990s. Mine water usually contains high concentrations of chemical constituents such as sulfate, fluoride, and heavy metals released from surrounding rocks because of natural processes and human interferences (Makni et al. 2013; Aleksander-Kwaterczak and Ciszewski 2012). Janson et al. (2009) studied the quality of mine water in a coal mine, and Aleksander-Kwaterczak and Ciszewski (2012) investigated groundwater and soil pollution near an abandoned lead–zinc mine. Favas et al. (2012) and Byrne et al. (2013) reported the effects of mining activities on the quality and hydrochemistry of surface water. These studies have shown that mining activities will have significant influences on surface water and groundwater as well as soils surrounding the mine.

The Laoheba phosphorite mine, located in Mabian County, Sichuan Province, belongs to Zhongyi Mining Development Co. Ltd. After the mine goes into production, mining activities will probably change the hydrochemical characteristics and quality of waters in and around the mining area. Nevertheless, before anthropogenic disturbance, natural hydrogeochemical processes such as mineral weathering and ion exchange probably are the most important factors affecting the water chemistry. Therefore, the present study aimed at investigating the main hydrogeochemical processes controlling and influencing the water chemistry in and around the Laoheba phosphorite mine using correlation and multivariate statistical techniques.

Materials and methods

Study area

The Laoheba phosphate mine, 42 km southwest of Mabian County, covers an area of 5.12 km2 (Fig. 1). Located at the southwest edge of the Sichuan Basin, far away from the urban area, the phosphate mining region is characterized by local high mountains and deep valleys and undulating terrain. Controlled by geological structures, the west is covered by the Woziboluo Mountain, and the east has erosion valleys formed by faults. The topography of ground surface generally declines from west to east.
https://static-content.springer.com/image/art%3A10.1007%2Fs12517-013-1057-4/MediaObjects/12517_2013_1057_Fig1_HTML.gif
Fig. 1

Study area and sampling locations

The study area belongs to the tropical monsoon climate zone. Warm and moist air from the Sichuan Basin is blocked by the high mountain at the western edge, producing a foggy and rainy climate in the area. According to the data from Mabian weather station, the local annual average temperature is 16.9 °C, the highest average temperature is 25.2 °C, and the lowest average is –2.3 °C (Xiang and Zhang 2011). Rainfall is abundant and the annual rainfall is 1,052.3 mm. August has the highest precipitation (256.4 mm) and January the lowest (4.2 mm). The area is part of the Yangtze River drainage area, and the surface water resources are well developed. There are mainly three rivers running from west to east (Fig. 1). The three rivers are all tributaries of the Mabian River. The central river near Shanshuigou has the highest flow rate with a maximum value of 3,200 L/s and an average of 950 L/s. The river near Kucaiwan is the smallest with the maximum and average flow rates of 50 and 17.5 L/s, respectively. In many places of the area, groundwater is usually discharged as springs, accounting for a great proportion of river flow. Therefore, close physiochemical relationship between surface water and groundwater can be expected.

Aquifer media in the area is mainly composed of carbonate minerals formed in Cambrian, Sinian, and Quaternary periods. Therefore, karst water, pore water, and fissure water are popular here. Groundwater generally flows from northwest to southeast and the water chemistry types are HCO3−Ca·Mg and HCO3−Ca. Groundwater gets recharge from precipitation through fissures.

Sample collection and analysis

Twenty water samples were collected during August to November 2008 in and around the mining area. Six were collected from surface water (labeled as S), 11 were collected from spring outcrops (labeled as G), and three were mine water (labeled as K). The sampling locations are shown in Fig. 1. Samples were collected in pre-cleaned 1 L plastic polyethylene bottles for physicochemical analysis. Prior to sampling, all the bottles were washed and rinsed thoroughly with the water to be taken for analysis. All these samples were analyzed in the laboratory for major irons (K++Na+, Ca2+, Mg2+, HCO3, SO42−, and Cl), total dissolved solid (TDS), total hardness (TH), and free carbon dioxide (CO2). During sample collection, handling, and preservation, standard procedures recommended by the Standard Examination Methods for Drinking Water (Ministry of Health of the People's Republic of China 2006) were followed to ensure data quality and consistency. Among the analyzed chemical parameters, Na+ and K+ were determined by flame photometer (GFU2202); SO42− and Cl were analyzed by ion chromatography (HLC2601); Ca2+, Mg2+, HCO3, and CO32− were analyzed by titrimetric method; TDS was determined by the method of drying and weighing; and TH was the sum of Ca2+ and Mg2+. The pH, easily influenced by external environment, was measured with a portable pH meter on site. The analysis results of the chemical indices were listed in Table 1. Furthermore, the saturation indices (SI) of calcite, dolomite, and gypsum were also listed in Table 1 for later use.
Table 1

Physicochemical index of water samples

Sample

Ca2+

Mg2+

K++Na+

Cl

SO42−

HCO3

CO32−

TH

TDS

pH

CO2

SIcalcite

SIdolomite

SIgypsum

S1

44.89

8.51

0.78

3.00

16.67

150.11

1.20

147.12

150.15

7.86

5.28

0.18

−1.10

−2.23

S2

42.96

16.66

1.63

2.04

11.93

200.14

0.00

175.90

175.29

7.84

10.38

0.26

−0.60

−2.40

S3

33.67

11.67

2.05

3.00

14.60

142.79

0.00

132.11

136.39

7.89

5.46

0.07

−0.77

−2.40

S4

31.98

7.73

0.57

3.00

12.10

117.16

0.00

111.69

113.96

7.83

3.70

−0.09

−1.62

−2.49

S5

38.72

10.06

3.56

2.14

7.95

164.14

0.00

138.10

144.50

7.81

5.28

0.10

−0.52

−2.62

S6

43.69

11.07

2.67

1.84

35.79

128.75

3.00

154.68

168.64

8.03

0.00

0.26

−0.38

−1.92

G1

43.33

21.97

0.76

2.04

27.17

206.25

0.00

198.65

198.40

7.62

14.43

0.05

−1.35

−2.05

G2

36.07

15.53

2.60

3.20

7.88

173.91

0.60

158.13

153.84

7.71

7.04

0.00

−0.84

−2.65

G3

32.72

2.52

0.07

2.04

13.26

92.14

0.00

92.07

96.68

7.47

10.56

−0.54

−3.44

−2.43

G4

34.47

15.07

2.88

4.00

12.30

147.06

1.80

148.72

144.05

7.89

5.28

0.09

−0.59

−2.47

G5

38.72

17.07

9.29

2.04

23.42

176.35

0.00

175.17

189.95

7.83

7.04

0.14

−0.03

−2.17

G6

31.66

7.05

1.59

3.00

11.72

115.94

0.00

108.09

112.99

7.70

6.86

−0.23

−1.45

−2.51

G7

36.32

11.46

2.14

1.02

30.71

133.02

0.00

137.88

148.16

7.52

8.80

−0.30

−1.53

−2.05

G8

28.78

10.26

1.59

3.00

17.48

115.94

0.00

114.09

119.08

7.76

5.98

−0.21

−1.37

−2.38

G9

61.02

17.33

5.47

2.55

40.21

231.88

0.00

223.74

242.52

7.54

20.94

0.15

−0.45

−1.77

G10

39.64

17.33

1.36

2.04

24.30

176.96

0.00

170.34

173.15

7.74

8.45

0.07

−1.01

−2.13

K1

48.03

17.84

4.92

2.55

20.99

217.84

0.00

193.40

203.25

7.40

17.60

−0.11

−0.90

−2.13

K2

35.27

11.67

0.83

2.20

19.02

140.35

0.00

136.11

139.17

7.61

14.43

−0.20

−1.72

−2.27

K3

41.48

16.38

5.15

2.04

8.39

208.08

0.00

171.00

177.48

7.60

10.56

0.02

−0.57

−2.58

Min

28.78

2.52

0.07

1.02

7.88

92.14

0.00

92.07

96.68

7.40

0.00

−0.54

−3.44

−2.65

Max

61.02

21.97

9.29

4.00

40.21

231.88

3.00

223.74

242.52

8.03

20.94

0.26

−0.03

−1.77

Mean

39.13

13.01

2.63

2.46

18.73

159.94

0.35

151.95

157.24

7.72

8.85

−0.01

−1.07

−2.30

SD

7.41

4.79

2.25

0.67

9.43

39.66

0.81

33.78

36.11

0.17

5.09

0.21

0.75

0.24

Skewness

1.39

−0.28

1.62

0.23

0.92

0.23

2.56

0.24

0.50

−0.22

0.83

−0.91

−1.79

0.49

Units for all chemical indices are mg/L except pH, SD standard derivation

Each analysis was checked for accuracy by calculating their percent charge balance errors (%CBE) using formula (1).
$$ \%\mathrm{CBE}=\frac{{\displaystyle \sum \mathrm{cations}-{\displaystyle \sum \mathrm{anions}}}}{{\displaystyle \sum \mathrm{cations}+{\displaystyle \sum \mathrm{anions}}}}\times 100\% $$
(1)

Where all cations and anions are expressed in milliequivalent per liter. The results show that most samples have a %CBE smaller than ±5 % except sample G11 which possesses a %CBE of 10.038 %. Therefore, sample G11 was not used in the study.

Multivariate statistical techniques

Multivariate statistical techniques such as principal component analysis (PCA), cluster analysis (CA), and discriminant analysis are popular methods for solving multivariate problems (Tang 2010). They have been widely used in geology, hydrology, meteorology, medical science, industry, agriculture, and environmental science (for example, Baeza and Corominas 2001; Cloutier et al. 2008; Koklu et al. 2010; Yidana et al. 2010). In the present study, PCA and CA were selected and applied in hydrogeochemical data analysis. CA is a data classification technique which groups samples or indices with similar characteristics, while PCA is a data transformation technique that reveals a simple underlying structure within a multivariate dataset (Cloutier et al. 2008).

For CA, several cluster techniques are available (for example, furthest neighbor, nearest neighbor, centroid clustering, Ward's method, etc.). In the present study, furthest neighbor was applied since it produced the most reasonable clusters. Wishart (1969) has provided a uniform equation expressing the distance between different clusters as follows:
$$ {d}_{kr}^2={\alpha}_p{d}_{kp}^2+{\alpha}_q{d}_{kq}^2+\beta {d}_{pq}^2+\gamma \left|{d}_{kp}^2-{d}_{kq}^2\right| $$
(2)

Where cluster r is clustered by sub-clusters p and q, d represents the distance between different clusters, αp, αq, β, and γ are coefficients. In furthest neighbor method, αp = αq = γ = 1/2, β = 0.

Generally, if p indices (x1, x2, …, xp) are included in each of the N samples; after PCA, p integrated variables will be formed as expressed by the following linear combinations (Tang 2010):
$$ \left\{\begin{array}{c}\hfill {y}_1={c}_{11}{x}_1+{c}_{12}{x}_2+\cdots +{c}_{1p}{x}_p\hfill \\ {}\hfill {y}_2={c}_{21}{x}_1+{c}_{22}{x}_2+\cdots +{c}_{2p}{x}_p\hfill \\ {}\hfill \cdots \cdots \hfill \\ {}\hfill {y}_p={c}_{p1}{x}_1+{c}_{p2}{x}_2+\cdots +{c}_{pp}{x}_p\hfill \end{array}\right. $$
(3)

Where y1, y2, …, yn are independent variables subject to the condition that ck12 + ck22 + … + ckp2 = 1(k = 1, 2, …, p).

Results and discussion

Hydrochemical characteristics

TDS and TH are in the range of 96.68 to 242.52 mg/L and 92.07 to 223.74 mg/L, respectively (Table 1), indicating fresh and soft water. These values generally show a gradual increasing trend toward southeast, which implies that more minerals have dissolved during water flow. Accordingly, the saturation indices of major minerals also show a rough increasing trend. Saturation index of calcite is in the range of −0.54 to 0.26, suggesting a general equilibrium state, while those of dolomite and gypsum range from −3.44 to −0.03 and from −2.65 to −1.77, respectively, implying unsaturated state of the two minerals. With water flowing, more dolomite and gypsum can dissolve in the water (Li et al. 2013b). The pH values of groundwater and surface water are in the range of 7.40 to 8.03, indicating alkalescent water.

A Durov diagram (Durov 1948) was plotted to examine the hydrochemical types of waters (Fig. 2). The Durov diagram shows that the chemical composition of groundwater and surface water in the study area is composed of two main types: HCO3−Ca and HCO3−Ca·Mg as per the equal 25 % increments. Mine water has higher concentration of HCO3 in general but lower pH than the surface water and spring water, which may be attributed to its occurrence environment in which more CO2 can be detected. The hydrochemical types of water have suggested that water in the area is low mineralized, and rare human activity intervention has been imposed on water quality.
https://static-content.springer.com/image/art%3A10.1007%2Fs12517-013-1057-4/MediaObjects/12517_2013_1057_Fig2_HTML.gif
Fig. 2

Durov diagrams of water samples. Red triangle spring water, blue square mine water, black circle surface water

Correlation of parameters

Correlation reveals a statistical relation between two or more variables. It can help in analyzing the primary reactions that have formed current water chemistry (Li et al. 2011). Pearson correlation coefficients were calculated using SPSS 13.0 for Windows, and the results were listed in Table 2.
Table 2

Pearson correlation of physiochemical parameters

 

Ca2+

Mg2+

K++Na+

Cl

SO42−

HCO3

CO32−

TH

TDS

pH

CO2

Ca2+

1

0.535a

0.408

−0.216

0.592b

0.782b

0.096

0.856b

0.888b

−0.227

0.598b

Mg2+

 

1

0.462a

−0.103

0.313

0.868b

−0.086

0.892b

0.837b

−0.108

0.467a

K++Na+

  

1

−0.107

0.211

0.527a

−0.050

0.533a

0.600b

−0.045

0.196

Cl

   

1

−0.440

−0.132

0.206

−0.177

−0.247

0.317

−0.201

SO42−

    

1

0.238

0.223

0.506a

0.589b

−0.133

0.327

HCO3

     

1

−0.208

0.943b

0.913b

−0.269

0.640b

CO32−

      

1

0.001

0.000

0.583b

0.518a

TH

       

1

0.987b

−0.178

0.592b

TDS

        

1

−0.198

0.595b

pH

         

1

−0.799b

CO2

          

1

aCorrelation is significant at the 0.05 level (two-tailed)

bCorrelation is significant at the 0.01 level (two-tailed)

Revealed by Table 2, TDS is correlated with Ca2+, Mg2+, K++Na+, SO42−, and HCO3, indicating the continuous addition of these ions along groundwater flow path. Generally speaking, TDS is likely to show an increasing trend from upstream to downstream in a given hydrogeological unit due to the dissolution of minerals. Na+ is not significantly correlated with Cl, indicating that halite dissolution may not be the major reaction influencing the water chemistry. This is also confirmed by the plots shown in Fig. 3a. With the increase of Na+, the concentration of Cl does not vary a lot. Thus, the increase of Na+ can be explained by the weathering of silicate minerals. On the contrary, HCO3 is highly correlated with Ca2+ and Mg2+ with correlation coefficients more than 0.75, which suggests the possibility of dissolution and/or precipitation of calcite and dolomite. Theoretically speaking, if calcite and dolomite dissolution is the only source of Ca2+ and Mg2+, the ratio between HCO3 and Ca2+, Mg2+ should be within the range of 1:1 to 2:1, depending on the amount of CO2 involved in the reactions (Li et al 2013a) because:
https://static-content.springer.com/image/art%3A10.1007%2Fs12517-013-1057-4/MediaObjects/12517_2013_1057_Fig3_HTML.gif
Fig. 3

Plots of a Na versus Cl, b Ca2+ against HCO3, c Ca2++Mg2+ against HCO3, d Ca2+ versus SO42−

$$ {\mathrm{Ca}\mathrm{CO}}_3+{\mathrm{H}}^{+}\rightleftharpoons {\mathrm{H}\mathrm{CO}}_3^{-}+{\mathrm{Ca}}^{2+} $$
(R1)
$$ {\mathrm{Ca}\mathrm{CO}}_3+{\mathrm{H}}_2\mathrm{O}+{\mathrm{CO}}_2\rightleftharpoons 2{\mathrm{H}\mathrm{CO}}_3^{-}+{\mathrm{Ca}}^{2+} $$
(R2)
$$ \mathrm{CaMg}{\left({\mathrm{CO}}_3\right)}_2+2{\mathrm{H}}^{+}\rightleftharpoons 2{\mathrm{H}\mathrm{CO}}_3^{-}+{\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+} $$
(R3)
$$ \mathrm{CaMg}{\left(\mathrm{C}{\mathrm{O}}_3\right)}_2+2{\mathrm{H}}_2\mathrm{O}+{\mathrm{CO}}_2\rightleftharpoons 4{\mathrm{H}\mathrm{CO}}_3^{-}+{\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+} $$
(R4)
Figure 3c shows that dolomite dissolution is probably the source of Mg2+ and Ca2+ since it is within the theoretical range (HCO3 versus (Ca2++Mg2+) = 1:1 to 2:1 expressed in mmol/L). However, the ratio of HCO3 and Ca2+ is larger than two (Fig. 3b), indicating the effect of calcite dissolution on water chemistry is not obvious. Considering the increase of Na+, it can be judged that ion exchange between Ca2+ in solution and Na+ from the solid surface may have taken place, resulting in the deviation of Ca2+ out of the theoretical range and the surplus of Na+. The ion exchange can be expressed as follows:
$$ {\mathrm{Ca}}^{2+}+2\mathrm{NaX}\rightleftharpoons {\mathrm{Ca}\mathrm{X}}_2+2{\mathrm{Na}}^{+} $$
(R5)
Ca2+ is also correlated with SO42− with a correlation coefficient of 0.592, suggesting the possible effect of gypsum dissolution on water chemistry.
$$ {\mathrm{Ca}\mathrm{SO}}_4\cdotp \kern2pt 2{\mathrm{H}}_2\mathrm{O}\rightleftharpoons {\mathrm{Ca}}^{2+}+{\mathrm{SO}}_4^{2-}+2{\mathrm{H}}_2\mathrm{O} $$
(R6)

However, the ratio between Ca2+ and SO42− is not theoretical 1:1. Taking into account the dissolution of dolomite which will introduce Ca2+ into water, it is logical to conclude that the deviation of plots away from the theoretical 1:1 line is caused by calcium-containing mineral weathering such as calcite and dolomite. The three different water bodies (surface, spring, and mine water) have similar variation trend and/or relations between ions, which indicates similar processes have occurred in these waters. However, in addition to natural processes of mineral weathering and water–rock interactions, water chemistry may also be influenced by other factors such as recharge water chemistry, precipitation, or evaporation.

CA

Q mode CA is usually used to highlight spatial relationships among the sample points, while R mode CA is done to classify the parameters into groups or facies based on their similarity with each other (Banoeng-Yakubo et al. 2009). In the study, Q mode CA was performed with SPSS 13.0 for Windows. A combination of the Euclidean as a similarity measure and the furthest neighbor agglomeration scheme to link clusters has been determined to yield optimal results in the CA. The samples with the larger similarity were first grouped, and then groups of samples were joined with a linkage rule. The steps were repeated until all observations had been classified.

The dendrogram of Q mode cluster is shown in Fig. 4. In the study, a phenon line was drawn across the dendrogram at a linkage distance of about seven. This position of the phenon line allows a division of the dendrogram into four clusters of water samples, i.e., C1–C4. The Stiff diagrams of the four clusters based on median concentrations were also included in Fig. 4.
https://static-content.springer.com/image/art%3A10.1007%2Fs12517-013-1057-4/MediaObjects/12517_2013_1057_Fig4_HTML.gif
Fig. 4

Dendrogram for the water samples (Q mode) showing the division into four clusters and the median concentration Stiff diagram of each cluster

The dendrogram reveals some indications of the level of similarity between the four clusters (Fig. 4). C1 contains four samples, and C2 contains eight samples. C1 and C2 are linked with a lower linkage distance, indicating that they have a greater similarity than with C3 or C4. It can thus be expected that the geochemistry of C1 samples would have similarities with the ones of C2. Similarities between the geochemistry of C3 and C4 samples are also expected, as both C3 and C4 are also linked at a low distance. To describe the characteristics of each cluster of samples, the median values of geochemical and physical data were presented in Table 3.
Table 3

Median concentrations of parameters of different clusters

Index

C1

C2

C3

C4

Ca2+

31.82

36.20

42.22

61.02

Mg2+

7.39

11.57

17.20

17.33

K++Na+

1.08

2.37

3.28

5.47

Cl

3.00

2.60

2.04

2.55

SO42−

12.68

15.64

22.21

40.21

HCO3

115.94

144.93

203.20

231.88

CO32−

0.00

0.30

0.00

0.00

TH

109.89

142.61

175.54

223.74

TDS

113.48

146.33

183.71

242.52

pH

7.73

7.84

7.68

7.54

CO2

6.42

5.37

10.47

20.94

The stiff diagrams (Fig. 4) and the data (Table 3) indicate that the four clusters are geochemically independent groups. Samples from C1 and C2 are of HCO3−Ca·Mg or HCO3−Ca type and the concentrations of all ions except Cl are lower than those of samples from C3 and C4. Besides, TH and TDS are also lower in C1 and C2 than in C3 and C4. Samples from C3 and C4 are of HCO3−Ca·Mg type. They have more HCO3 and Ca2+ indicated by the Stiff diagrams. Most parameters except Cl and pH show an increasing trend in the order of C1 to C4. The Cl concentration and pH do not have a noticeable variation trend, which is probably caused by its small variation in values. Samples of C1 have the lowest ion concentrations, which may indicate that they have not undergone long residence within the geosphere, and their interactions with rocks are not sufficient. This is confirmed by the saturation index listed in Table 1. Samples from C1 usually have small saturation index values which suggest that they are unsaturated. On the contrary, samples from C4 have higher values of most ions, which may be explained by longer residence time and sufficient interactions with rocks. The saturation indices of minerals in C4 are usually higher than those of samples in C1.

PCA

The number of components to keep in PCA was determined based on the Kaiser criterion for which only the components with eigenvalues greater than one are retained (Cloutier et al. 2008). According to the calculation, the first three principal components (PCs) extracted have eigenvalues greater than 1 and represent 81.46 % of the total variance in the hydrochemistry (Table 4). Table 4 also shows the component loadings which represent the importance of the variables for the components (values greater than 0.57 are in bold and underlined).
Table 4

Total variance explained by each PC and the loading matrix of PCs

 

Components

PC1

PC2

PC3

TDS

0.978

0.205

−0.005

TH

0.965

0.211

0.091

HCO3

0.926

0.033

0.308

Ca2+

0.866

0.172

−0.162

Mg2+

0.826

0.178

0.282

CO2

0.735

-0.573

0.027

K++Na+

0.572

0.217

0.208

CO32−

−0.163

0.838

−0.268

pH

−0.385

0.829

0.106

SO42−

0.555

0.197

−0.704

Cl

−0.307

0.283

0.689

Eigenvalues

5.62

2.03

1.31

% of Variance

51.10

18.48

11.87

Cumulative %

51.10

69.58

81.46

Bold indicates significance at 0.01 level (two-tailed)

PC1 explains the greatest of the variance (51.10 %) and is characterized by highly positive loadings in TDS, TH, HCO3, Ca2+, Mg2+, CO2, and Na++K+ (Table 4), which represents the ion exchange and the weathering of calcite, dolomite, and silicate minerals as suggested by correlation analysis. It is a major factor influencing the water chemistry. PC2 and PC3 explain 18.48 and 11.87 % of the variance, respectively, indicating that these components are related to more local effects than PC1, and they are secondary factors. PC2 is highly correlated with CO32− and pH, which may represent the process of water recharge from upstream waters which usually have relative higher pH values, while PC3 is positively correlated with Cl and negatively correlated with SO42−, which may be explained by the process of evaporation since evaporation is great in this area.

Figure 5 shows the relation of the total scores for each component of each sample within different clusters. If the score is bigger than 0, the processes represented by the component have significant influences on water chemistry. On the contrary, if the score is less than 0, the processes represented by the component probably do not have any significant influence on the hydrochemistry at the location (Banoeng-Yakubo et al. 2009). Samples of C1 are influenced by PC3, namely the evaporation. It is obvious that the hydrochemistry of most samples of C2 and C3 are influenced by the three PCs, namely it is a comprehensive result of the multiple processes such as natural mineral weathering, water–rock interactions, recharge water, and evaporation, while C4 is influenced merely by PC1. Overall, water in the study area is controlled by natural weathering processes and influenced by recharge water quality and/or evaporation.
https://static-content.springer.com/image/art%3A10.1007%2Fs12517-013-1057-4/MediaObjects/12517_2013_1057_Fig5_HTML.gif
Fig. 5

Plots of scores of different PCs. a PC1 versus PC2, b PC1 versus PC3

Conclusions

Well-proven correlation and multivariate statistical techniques (CA and PCA) were used to identify the processes controlling the major ion chemistry in and around Laoheba phosphorite mine. The following conclusions can be summarized:
  • Before the Laoheba phosphorite mine goes into construction and production, the water quality is good and fit for drinking with regard to the major physiochemical parameters. It is controlled by natural weathering processes and influenced by recharge water quality and evaporation indicated by correlation analysis.

  • Four geochemically distinct clusters can be classified by CA (C1–C4). Samples from C1 and C2 are of HCO3−Ca·Mg and HCO3−Ca type, and those from C3 and C4 are of HCO3−Ca·Mg type. Most parameters except Cl and pH show an increasing trend in the order of C1 to C4.

  • The first three PCs explain 81.46 % of the total variance in the dataset. PC1, characterized by great positive loadings in TDS, TH, HCO3, Ca2+, Mg2+, CO2, and Na++K+, represents the ion exchange and the weathering of calcite, dolomite, and silicate minerals. PC2 and PC3 indicate the process of water recharge from upstream waters and the process of evaporation, respectively. The hydrochemistry of waters in the area is influenced by multiple factors, and mineral weathering and ion exchange are the most important ones, but the influences of recharge water quality and evaporation cannot be ignored.

Acknowledgments

The research was supported by the Doctor Postgraduate Technical Project of Chang'an University (2013G5290002 and CHD2011ZY022), the Special Fund for Basic Scientific Research of Central Colleges (CHD2011ZY020 and CHD2012TD003), and the National Natural Science Foundation of China (41172212). We are grateful to the editor and reviewers for their valuable comments. Engineer Xiang G was highly appreciated for providing the basic physiochemical and hydrogeological data of the study.

Copyright information

© Saudi Society for Geosciences 2013