Abstract

Groundwater plays an important role in water supply and economic development for Yantai city, China. However, the groundwater quality has degraded due to the increase and expansion of agricultural and industrial development. It is urgent to acquire groundwater characteristics and distinguish impacts of natural factors and anthropogenic activities on the groundwater quality. Forty-six groundwater samples collected from different wells showed a great variation of chemical components across the study area. Most wells with higher total dissolved solids, total hardness, K+, Na+, Ca2+, Mg2+, Cl and SO42− concentrations were located relatively close to the coastal zone. The factor analysis (FA) and hierarchical cluster analysis results displayed that seawater intrusion was the primary mechanism controlling the groundwater quality in the coastal areas. A three-factor model was proposed based on the FA and explained over 85% of the total groundwater quality variation: Factor 1, the seawater intrusion; Factor 2, the water–rock interaction and Factor 3 (NO3), the human activities. Furthermore, the geographical maps of the factor scores clearly described the spatial distributions of wells affected by natural processes or human activities. The study indicated that both natural processes and human activities are the major factors affecting the chemical compositions of groundwater.

INTRODUCTION

Groundwater is a vital provision resource for agriculture, industry, public consumption and rural communities (Ansa-Asare et al. 2009). The quality and quantity of groundwater are greatly dependent on environmental and anthropogenic factors, such as regional geological setting, precipitation and human activities (Devic et al. 2013; Hamzah et al. 2017). Some anthropogenic activities, such as urban development, agricultural production, industrial application, mining, power generation and forestry practices, have caused the deterioration in groundwater quality and quantity (Jiang et al. 2009; Khatri & Tyagi 2015; Khatri et al. 2017; Habib-ur-Rehman et al. 2018). Especially, high abstraction rate or overpumping for the coastal aquifers might lead to seawater intrusion, which could increase the levels of Na, Cl and total dissolved solids (TDS) in the groundwater (Rao et al. 2013). The high ionic concentration could limit the groundwater usage. Therefore, it is very important to understand the influencing/controlling factors of groundwater quality for the groundwater management and utilization (Nishikawa 2016).

As known, the groundwater quality largely depends on the natural processes (such as lithology, hydrogeological conditions, the interaction of water with rock, soil and other types of aquifers, and seawater intrusion), anthropogenic factors (such as agriculture, industry and urban development) and atmospheric input (Jeong 2001; Jiang et al. 2009; Huang et al. 2013). Many previous studies have reported the influences of natural and anthropogenic processes on the groundwater quality in the coastal areas (Kim et al. 2003; Xing et al. 2013), arid/semi-arid areas (Jianhua et al. 2012; Wu et al. 2017; Xiao et al. 2018; Yang et al. 2018) and karst area (Jiang et al. 2009). The relative results suggested that anthropogenic processes played an important role in determining the groundwater chemical components. Generally, the application of water indices analysis, such as the hierarchy method based on the index system (Zhu et al. 2018) and the criticality index method (Pimparkar et al. 2016), and multivariate statistical techniques, such as hierarchical cluster analysis (HCA) and factor analysis (FA) (Alberto et al. 2001; Reghunath et al. 2002; Simeonov et al. 2004; Omo-Irabor et al. 2008; Hynds et al. 2014), allows us to better understand the groundwater quality, identify the possible factors and finally offer a valuable tool for managing water resources and rapid solution to pollution problems. For example, the FA method has been widely used to assess and characterize surface water and groundwater, which is useful in identifying the variations induced by natural and anthropogenic factors (Liu et al. 2003; Shrestha & Kazama 2007; Jiang et al. 2009).

As a typical coastal area, the Dagujia River Basin is located in the middle-north of Yantai city along the Dagujia River, which is one of the most important freshwater resources. Several studies on the evolution of groundwater quality with different technical methods have focused on the Dagujia River Basin (Guo 2005; Li & Liu 2007). However, the previous studies mainly focused on the basic characteristics of groundwater quality, lacking of the analysis of factors affecting the groundwater quality and the relevant affecting mechanisms.

On the basis of analyzing concentrations of the conventional pollutants and minor metal elements, the objectives of this study are to (1) describe the physico-chemical characteristics of the groundwater in the study area and (2) distinguish the impact of natural factors and human activities on the groundwater quality with FA and HCA methods. This study would provide important data and a theoretical basis for groundwater management and utilization and provide references for other coastal areas in China.

STUDY AREA

The study area, named the Dagujia River Basin, is located in the middle-north of Yantai city in China and covers an area of about 2,435 km2 (Figure 1). It lies between latitudes 37°2′31.478″–37°37′51.592″ N and longitudes 120°44′1.857″–121°26′27.636″ E. The Dagujia River is the second largest river of Yantai and occupies an important position in the water supply and economic development of Yantai city. Geomorphologically, the area is composed of about 40% low mountains, 40% hills and 20% valley plain. The annual mean temperature of the area is about 11.5 °C (Liu et al. 2007). The main way of groundwater recharge is atmospheric precipitation, and the annual average precipitation is 677.95 mm (Zeng et al. 2017).

Figure 1

Location of the study area and groundwater sampling sites.

Figure 1

Location of the study area and groundwater sampling sites.

The Quaternary strata are the main characteristics of the Dagujia River Basin, and the groundwater source is mainly alluvial sand and sand–gravel pore water with the total area of 63.3 km2 (Tao et al. 2018). Up to now, five groundwater resources have been exploited. The hydrogeological map in the previous study showed that from bottom to top, the three-layer strata are in the following order: confined aquifer (mainly, sand, gravel and pebble), relative water-blocking layer (silty sand and silt) and porous aquifer (fine silt, silt and a part of medium sand) (Tao et al. 2018). Due to the difference of topography, stratigraphic lithology, runoff condition and distance from the sea, the hydrochemical type of the groundwater changes from HCO3–Ca·Mg to Cl–Na from south to north in the Dagujia River Basin (Kou 2013).

METHODOLOGY

Sampling

Forty-six groundwater samples, including 7 confined samples and 39 phreatic samples, were collected from different wells in 22–27 May 2014, and the location of the wells is illustrated in Figure 1. The sampling depth ranges from 1.7 to 100 m. Before the samples were collected, the wells were pumped out for about 30 min to remove stagnant or polluted water. The collected samples were stored at 4 °C. The selected groundwater parameters include pH, electrical conductivity (EC), turbidity, alkalinity (Alk), TDS, total hardness (TH), Ca2+, K+, Na+, Mg2+, Cl, SO42−, HCO3, F, NO3, As, Pb, Cr, Hg, Zn, Cd, Se, Mn, Fe and Al. The indices pH, EC and turbidity were measured in the field, and other indices were analyzed in the laboratory. The physico-chemical analyses of all samples were performed according to the National Sanitary Method for Determination of Drinking Water (GBT 5750-2006), and the related analysis methods are listed in Table S1 in Supplementary Materials.

As shown in Table S2, the groundwater type of wells 16, 17, 38, 39, 40, 42 and 44 is confined water, while that of other wells is phreatic water. The three wells 2, 17 and 44, five wells 16, 29, 33, 38 and 41 and the other wells are screened in the carbonate, igneous and metamorphic, and loose rock aquifers, respectively. It also can be seen that from Table S2, the utilization of well water is mainly for drinking, agricultural irrigation, industry and domestic water.

Multivariate statistical methods

Factor analysis

FA, one of the multivariate statistical methods, is used to classify the groundwater chemical compositions with multivariate patterns by obtaining the general relationship for the measured chemical variables. The first step was to standardize the raw groundwater data. Let xi, …, xP denote P variables, each with N observations. The jth observation of the ith variable is named Xi,j, where i = 1, …, P and j = 1,…, N. The mean value and standard deviation were denoted as xm and Si, respectively. The jth observation of the ith variable is computed in the following equation: 
formula
(1)
where Zij is the jth value of the standardized variable Zi. The average and variance of Zi are 0 and 1, respectively, for all i values.
Then, the correlation coefficient (rx,y) is computed in the following equation, and the correlation matrix is obtained as follows: 
formula
(2)

Finally, the data were transformed into factors, and the eigenvalue and the percent of variance associated with each factor were obtained. The number of factors extracted is determined in a way that reduces the data by using the inherent interdependences. Therefore, a small number of factors could be remained and account for about the same amount of all original data.

Furthermore, to emphasize the relation of the effects of the factors, the contribution of each factor at each well (factor scores) is computed by SPSS. The comprehensive factor score (F) can be calculated based on the factor scores, which is shown in the following equation: 
formula
(3)
Here, F is the comprehensive factor score, denotes the factor score of the ith factor, m is the number of the extracted factors, and is the weight of the ith factor, which is expressed as follows: 
formula
(4)
where is the corresponding eigenvalue of the ith factor.

Hierarchical cluster analysis

HCA, one of the most commonly used multivariate statistical methods, could group the factors into clusters on the basis of similarities within a cluster and dissimilarities between different clusters (Zhang et al. 2012; Huang et al. 2013; Abu-Alnaeem et al. 2018). The results of HCA can help in interpreting the hydrochemical data and patterns.

The selected variables for FA and HCA were pH, EC, TDS, Ca2+, K+, Na+, Mg2+, Cl, SO42−, HCO3 and NO3.

Interpolation analysis

The spatial distribution of factors (Figure 2) is derived from space interpolation analysis with the inverse distance weighting method.

Figure 2

Maps of the geographical distribution for (a) Factor 1, (b) Factor 2, (c) Factor 3 and (d) the comprehensive factor (F). Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/aqua.2019.113.

Figure 2

Maps of the geographical distribution for (a) Factor 1, (b) Factor 2, (c) Factor 3 and (d) the comprehensive factor (F). Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/aqua.2019.113.

RESULTS AND DISCUSSION

Physico-chemical characteristics

Fifteen physico-chemical parameters were detected and are shown in Tables 1 and 2. The pH of the groundwater samples varies from 6.84 to 8.07. The majority of the groundwater samples is moderately mineralized, suggested by its moderate to high EC values from 549 to 4,000 μS/cm. The highest EC value is found at both wells 5 and 9, which is probably ascribed to their short distances to the coast. A wide range in TDS concentrations from 396.92 to 19,436.22 mg/L with an average of 1,141.45 mg/L is recorded for all the groundwater samples. Notably, the maximum concentrations of TDS (19,436.22 mg/L), TH (3,676.21 mg/L), K+ (172.5 mg/L), Na+ (5,875 mg/L), Ca2+ (306.69 mg/L), Mg2+ (706.75 mg/L), Cl (1,0684.75) and SO42− (1,575.19 mg/L) are all found in the well 5, which is closest to the coastal line in the north, suggesting that these values are probably related to seawater intrusion.

Table 1

Physico-chemical characteristics of the 46 groundwater samples in the study area (all values are in mg/L except for pH, turbidity (NTU) and EC (μS/cm))

WellpHECTDSAlkTurbidityTHK+Na+Ca2+Mg2+ClSO42−HCO3FNO3
7.62 766 703.97 182.01 4.9 373.19 18 77 93.68 33.82 93.04 199.52 221.96 0.33 60.27 
7.88 1,357 855.985 264.79 13.7 470.62 1.7 115 107.06 49.37 183.02 123.39 322.85 0.15 95.24 
7.98 750 515.645 193.87 0.6 314.68 1.15 49.5 82.53 26.38 66.46 78.76 236.37 0.19 70.06 
8.07 1,022 690.3 184.42 0.2 337 36 80 86.99 29.08 133.94 73.51 224.84 0.31 119.37 
7.74 4,000 19,436.22 141.83 6.4 3,676.21 172.5 5,875 306.69 706.75 10,684.75 1,575.19 172.96 0.12 22.7 
7.77 2,445 1,546.225 243.52 0.2 442.8 8.1 400 94.79 50.05 576.67 231.03 296.91 0.23 21.6 
7.89 1,432 1,031.82 89.83 0.9 453.96 0.85 139.25 150.56 18.94 186.09 189.02 109.54 <0.10 266.45 
7.41 1,370 888.31 271.89 0.1 467.87 18.25 115 107.06 48.69 167.68 144.39 331.5 0.42 101.66 
7.58 4,000 4,082.47 293.16 0.9 1,320.09 7.3 1,012.5 295.53 141.35 2,096.05 321.86 357.44 0.7 5.7 
10 7.8 1,904 1,144.45 292.06 N.D. 370.38 14.5 261 63.57 51.4 367.96 184.3 356.16 0.49 7.5 
11 7.85 807 555.69 148.93 N.D. 331.4 4.5 46 88.1 27.05 59.3 148.19 181.6 0.48 77.38 
12 7.48 967 644.295 226.95 0.2 356.47 5.3 77.6 98.14 27.05 135.99 118.14 276.73 0.37 20.4 
13 7.64 940 839.94 368.78 0.7 428.89 65.5 71 91.45 48.69 87.93 120.76 449.68 0.13 72.48 
14 1,579 1,250.78 94.59 7.4 607.15 182.5 121.56 73.72 342.53 412.16 115.3 0.18 7.25 
15 7.61 1,320 793.595 208.04 0.7 462.32 1.9 67 117.1 41.26 96.11 90.31 253.67 0.21 233.72 
16 6.84 763 491.01 160.74 N.D. 269.04 21.25 41 73.16 20.97 54.19 85.67 196.02 0.14 78.7 
17 6.88 1,199 664.1 179.66 N.D. 424.43 1.85 55.2 97.47 43.96 103.27 152.82 219.08 0.2 87.69 
18 7.84 770 527.665 222.7 N.D. 325.89 4.4 47.5 78.07 31.79 63.38 124.97 271.53 0.3 23.25 
19 7.92 640 403.19 144.63 N.D. 250.63 3.35 32.5 62.45 22.99 44.9 98.71 176.32 0.43 40.55 
20 7.67 986 656.37 146.58 3.6 314.68 3.5 102.5 72.49 32.46 155.41 170.65 178.72 0.2 9.36 
21 7.82 733 486.405 248.72 N.D. 295.22 3.8 54.6 71.37 28.41 59.86 90.84 303.27 0.37 17.8 
22 7.52 747 587.58 160.74 2.7 350.92 3.85 54 90.33 30.43 93.04 123.39 196.02 0.23 79.3 
23 7.6 864 661.925 174.96 N.D. 354.82 1.5 84 94.79 28.68 130.88 100.96 213.31 0.19 94.06 
24 7.56 1,034 651.325 193.87 N.D. 392.65 55 103.72 32.46 69.53 125.04 236.37 0.3 127.25 
25 7.85 1,570 1,122.03 163.15 0.3 682.31 5.5 58 185.13 53.43 93.04 201.45 198.9 <0.10 408.25 
26 7.37 932 586.44 241.12 0.3 387.1 7.4 48 95.91 35.84 71.57 129.67 294.02 0.26 34.09 
27 7.89 549 429.55 127.66 0.1 261.79 3.3 36.5 65.8 23.67 48.06 108.83 155.66 0.34 49.7 
28 7.52 1,072 697.895 208.04 0.5 350.92 0.5 100 82.53 35.17 113.49 112.89 253.67 0.33 105.1 
29 7.82 1,361 816.14 163.15 0.3 476.23 2.7 70 117.1 44.64 145.19 99.57 198.9 0.18 217.62 
30 6.93 926 562.475 193.87 0.4 384.3 2.25 31 94.79 35.84 53.17 81.04 236.37 0.16 133.94 
31 7.59 733 469.495 191.47 5.6 286.86 6.1 54 71.37 26.38 95.09 78.76 233.49 0.32 
32 7.65 670 463.455 151.29 0.1 292.41 2.6 38.3 75.84 25.02 61.35 102.39 184.49 0.27 52.1 
33 7.96 675 498.705 85.13 0.7 239.52 48.5 73.6 13.53 44.99 81.38 103.77 0.11 158.25 
34 7.56 824 515.05 167.85 N.D. 317.49 1.7 48 80.3 28.41 72.59 127.35 204.66 0.21 44.78 
35 7.8 603 396.925 99.29 0.2 233.91 25 59.11 20.97 34.76 67.15 121.07 0.19 109.88 
36 7.76 603 495.705 174.96 0.2 334.2 3.8 24 81.41 31.79 36.81 85.67 213.31 0.18 107 
37 7.58 596 434.19 108.75 1.1 247.87 1.6 40 65.8 20.29 55.21 102.39 132.6 0.14 67.35 
38 7.52 580 488.87 113.5 1.5 250.68 1.35 46.2 78.07 13.53 49.08 53.26 138.36 <0.10 156 
39 7.38 1,294 1,025.935 224.6 590.43 12.4 80 149.44 52.75 125.76 134.3 273.85 0.21 305 
40 7.16 1,648 815.45 250.33 402.21 2.55 125 91.57 42.15 145.57 61.59 305.22 0.15 180.25 
41 7.98 611 504.595 177.31 0.3 320.24 4.7 30.5 84.76 26.38 51.12 68.26 216.19 0.16 109.16 
42 7.75 788 613.335 208.04 0.8 362.08 51.5 115.98 17.58 66.46 70.88 253.67 0.25 146.07 
43 7.37 839 539.78 220.3 333.35 15.75 39.4 90.46 26.09 47.49 87.06 268.6 0.28 78.9 
44 7.81 920 533.16 295.37 0.4 107.45 3.35 162.5 23.17 12.04 45.43 80.12 329.64 2.57 14.05 
45 7.6 698 521.73 179.66 323.09 2.4 45.5 80.3 29.76 67.48 110.26 219.08 0.22 64.04 
46 7.75 686 611.01 146.58 395.46 2.4 22.5 107.06 31.11 40.9 91.89 178.72 0.21 211.5 
WHO limit (WHO 2009– – 1,000 – – – 200 – – 250 500 – 1.5 50 
WellpHECTDSAlkTurbidityTHK+Na+Ca2+Mg2+ClSO42−HCO3FNO3
7.62 766 703.97 182.01 4.9 373.19 18 77 93.68 33.82 93.04 199.52 221.96 0.33 60.27 
7.88 1,357 855.985 264.79 13.7 470.62 1.7 115 107.06 49.37 183.02 123.39 322.85 0.15 95.24 
7.98 750 515.645 193.87 0.6 314.68 1.15 49.5 82.53 26.38 66.46 78.76 236.37 0.19 70.06 
8.07 1,022 690.3 184.42 0.2 337 36 80 86.99 29.08 133.94 73.51 224.84 0.31 119.37 
7.74 4,000 19,436.22 141.83 6.4 3,676.21 172.5 5,875 306.69 706.75 10,684.75 1,575.19 172.96 0.12 22.7 
7.77 2,445 1,546.225 243.52 0.2 442.8 8.1 400 94.79 50.05 576.67 231.03 296.91 0.23 21.6 
7.89 1,432 1,031.82 89.83 0.9 453.96 0.85 139.25 150.56 18.94 186.09 189.02 109.54 <0.10 266.45 
7.41 1,370 888.31 271.89 0.1 467.87 18.25 115 107.06 48.69 167.68 144.39 331.5 0.42 101.66 
7.58 4,000 4,082.47 293.16 0.9 1,320.09 7.3 1,012.5 295.53 141.35 2,096.05 321.86 357.44 0.7 5.7 
10 7.8 1,904 1,144.45 292.06 N.D. 370.38 14.5 261 63.57 51.4 367.96 184.3 356.16 0.49 7.5 
11 7.85 807 555.69 148.93 N.D. 331.4 4.5 46 88.1 27.05 59.3 148.19 181.6 0.48 77.38 
12 7.48 967 644.295 226.95 0.2 356.47 5.3 77.6 98.14 27.05 135.99 118.14 276.73 0.37 20.4 
13 7.64 940 839.94 368.78 0.7 428.89 65.5 71 91.45 48.69 87.93 120.76 449.68 0.13 72.48 
14 1,579 1,250.78 94.59 7.4 607.15 182.5 121.56 73.72 342.53 412.16 115.3 0.18 7.25 
15 7.61 1,320 793.595 208.04 0.7 462.32 1.9 67 117.1 41.26 96.11 90.31 253.67 0.21 233.72 
16 6.84 763 491.01 160.74 N.D. 269.04 21.25 41 73.16 20.97 54.19 85.67 196.02 0.14 78.7 
17 6.88 1,199 664.1 179.66 N.D. 424.43 1.85 55.2 97.47 43.96 103.27 152.82 219.08 0.2 87.69 
18 7.84 770 527.665 222.7 N.D. 325.89 4.4 47.5 78.07 31.79 63.38 124.97 271.53 0.3 23.25 
19 7.92 640 403.19 144.63 N.D. 250.63 3.35 32.5 62.45 22.99 44.9 98.71 176.32 0.43 40.55 
20 7.67 986 656.37 146.58 3.6 314.68 3.5 102.5 72.49 32.46 155.41 170.65 178.72 0.2 9.36 
21 7.82 733 486.405 248.72 N.D. 295.22 3.8 54.6 71.37 28.41 59.86 90.84 303.27 0.37 17.8 
22 7.52 747 587.58 160.74 2.7 350.92 3.85 54 90.33 30.43 93.04 123.39 196.02 0.23 79.3 
23 7.6 864 661.925 174.96 N.D. 354.82 1.5 84 94.79 28.68 130.88 100.96 213.31 0.19 94.06 
24 7.56 1,034 651.325 193.87 N.D. 392.65 55 103.72 32.46 69.53 125.04 236.37 0.3 127.25 
25 7.85 1,570 1,122.03 163.15 0.3 682.31 5.5 58 185.13 53.43 93.04 201.45 198.9 <0.10 408.25 
26 7.37 932 586.44 241.12 0.3 387.1 7.4 48 95.91 35.84 71.57 129.67 294.02 0.26 34.09 
27 7.89 549 429.55 127.66 0.1 261.79 3.3 36.5 65.8 23.67 48.06 108.83 155.66 0.34 49.7 
28 7.52 1,072 697.895 208.04 0.5 350.92 0.5 100 82.53 35.17 113.49 112.89 253.67 0.33 105.1 
29 7.82 1,361 816.14 163.15 0.3 476.23 2.7 70 117.1 44.64 145.19 99.57 198.9 0.18 217.62 
30 6.93 926 562.475 193.87 0.4 384.3 2.25 31 94.79 35.84 53.17 81.04 236.37 0.16 133.94 
31 7.59 733 469.495 191.47 5.6 286.86 6.1 54 71.37 26.38 95.09 78.76 233.49 0.32 
32 7.65 670 463.455 151.29 0.1 292.41 2.6 38.3 75.84 25.02 61.35 102.39 184.49 0.27 52.1 
33 7.96 675 498.705 85.13 0.7 239.52 48.5 73.6 13.53 44.99 81.38 103.77 0.11 158.25 
34 7.56 824 515.05 167.85 N.D. 317.49 1.7 48 80.3 28.41 72.59 127.35 204.66 0.21 44.78 
35 7.8 603 396.925 99.29 0.2 233.91 25 59.11 20.97 34.76 67.15 121.07 0.19 109.88 
36 7.76 603 495.705 174.96 0.2 334.2 3.8 24 81.41 31.79 36.81 85.67 213.31 0.18 107 
37 7.58 596 434.19 108.75 1.1 247.87 1.6 40 65.8 20.29 55.21 102.39 132.6 0.14 67.35 
38 7.52 580 488.87 113.5 1.5 250.68 1.35 46.2 78.07 13.53 49.08 53.26 138.36 <0.10 156 
39 7.38 1,294 1,025.935 224.6 590.43 12.4 80 149.44 52.75 125.76 134.3 273.85 0.21 305 
40 7.16 1,648 815.45 250.33 402.21 2.55 125 91.57 42.15 145.57 61.59 305.22 0.15 180.25 
41 7.98 611 504.595 177.31 0.3 320.24 4.7 30.5 84.76 26.38 51.12 68.26 216.19 0.16 109.16 
42 7.75 788 613.335 208.04 0.8 362.08 51.5 115.98 17.58 66.46 70.88 253.67 0.25 146.07 
43 7.37 839 539.78 220.3 333.35 15.75 39.4 90.46 26.09 47.49 87.06 268.6 0.28 78.9 
44 7.81 920 533.16 295.37 0.4 107.45 3.35 162.5 23.17 12.04 45.43 80.12 329.64 2.57 14.05 
45 7.6 698 521.73 179.66 323.09 2.4 45.5 80.3 29.76 67.48 110.26 219.08 0.22 64.04 
46 7.75 686 611.01 146.58 395.46 2.4 22.5 107.06 31.11 40.9 91.89 178.72 0.21 211.5 
WHO limit (WHO 2009– – 1,000 – – – 200 – – 250 500 – 1.5 50 

N.D., not detected.

Table 2

Statistics of physico-chemical parameters of 46 groundwater samples in the study area (all values are in mg/L except for pH, turbidity (NTU) and EC (μS/cm))

ParameterMinimumMaximumMeanStandard deviation
pH 6.84 8.07 7.60 0.29 
EC 549 4,000 980.49 402.12 
TDS 396.92 19,436.22 1,141.45 2,810.86 
Alk 85.13 368.78 187.53 60.58 
Turbidity 13.7 1.49 2.77 
TH 107.45 3,676.21 448.48 515.71 
K+ 0.5 172.5 10.59 26.77 
Na+ 22.5 5,875 220.03 865.30 
Ca2+ 23.17 306.69 98.75 50.64 
Mg2+ 12.04 706.75 49.02 101.06 
Cl 34.76 10,684.75 375.56 1,583.08 
SO42− 53.26 1,575.19 154.74 223.83 
HCO3 103.77 449.68 228.00 72.82 
F 0.11 2.57 0.31 0.37 
NO3 5.7 408.25 95.84 85.80 
ParameterMinimumMaximumMeanStandard deviation
pH 6.84 8.07 7.60 0.29 
EC 549 4,000 980.49 402.12 
TDS 396.92 19,436.22 1,141.45 2,810.86 
Alk 85.13 368.78 187.53 60.58 
Turbidity 13.7 1.49 2.77 
TH 107.45 3,676.21 448.48 515.71 
K+ 0.5 172.5 10.59 26.77 
Na+ 22.5 5,875 220.03 865.30 
Ca2+ 23.17 306.69 98.75 50.64 
Mg2+ 12.04 706.75 49.02 101.06 
Cl 34.76 10,684.75 375.56 1,583.08 
SO42− 53.26 1,575.19 154.74 223.83 
HCO3 103.77 449.68 228.00 72.82 
F 0.11 2.57 0.31 0.37 
NO3 5.7 408.25 95.84 85.80 

Concentrations of minor elements (As, Pb, Cr, Hg, Zn, Cd, Se, Mn, Fe and Al) were also detected and are displayed in Table S3. As expected, the concentrations of minor elements in most groundwater wells are low. The relatively high pH (>6.5) in the majority of the groundwater samples reduces the solubility of most minor elements, whose solubility in water strongly relies on pH (Appelo & Postma 2005; Lin et al. 2011). However, Mn and Al in several samples showed the elevated concentrations. The concentrations of Mn in wells 8, 14 and 20 are 0.58, 1.26 and 0.61 mg/L, respectively, which all exceed the WHO limits (0.4 mg/L) in Guidelines for Drinking-Water Quality (WHO 2009), and Mn values in four well samples (wells 6, 8, 14 and 20) are higher than the limit value (0.3 mg/L) in the Drinking Water Sanitary Standard (GB 5749-2006). Additionally, the elevated concentration of Al (0.26 mg/L) is found in only one well sample (well 2), which indicates that the well 2 is not suitable for drinking.

Assessment of contributions by FA

Variables for FA in the study were pH, EC, TDS, K+, Na+, Ca2+, Mg2+, Cl, SO42−, HCO3 and NO3. The correlation matrix for the 11 parameters is displayed in Table 3. As seen from Table 3, high correlation coefficients are found between most of the corresponding two variables, indicating that the selected variables could be classified by FA. The eigenvalues, the percent of variance, the cumulative eigenvalue and the cumulative percentage of variance associated with each other are shown in Table 4. Generally, the maximum number of factors is extracted based on the Kaiser criterion (Jiang et al. 2009), taking factors only into account with eigenvalues higher than 1. Accordingly, three factors were selected and then rotated based on the Varimax with the Kaiser Normalization method. The corresponding factor loadings and communality of the variables for the three factors are listed in Table 5.

Table 3

Matrix of correlation coefficients for the groundwater data of the Dagujia River Basin

pHECTDSK+Na+Ca2+Mg2+ClSO42−HO3NO3
pH 1.000           
EC −0.048 1.000          
TDS 0.050 0.730 1.000         
K+ 0.038 0.556 0.899 1.000        
Na+ 0.059 0.712 0.998 0.899 1.000       
Ca2+ −0.031 0.848 0.734 0.561 0.701 1.000      
Mg2+ 0.020 0.721 0.997 0.905 0.994 0.729 1.000     
Cl 0.054 0.720 0.999 0.895 0.999 0.719 0.995 1.000    
SO42− 0.001 0.724 0.976 0.877 0.972 0.719 0.979 0.972 1.000   
HO3 −0.063 −0.264 −0.048 0.089 −0.052 0.056 −0.033 −0.056 −0.104 1.000  
NO3 0.056 −0.070 −0.136 −0.156 −0.170 0.205 −0.138 −0.169 −0.173 −0.208 1.000 
pHECTDSK+Na+Ca2+Mg2+ClSO42−HO3NO3
pH 1.000           
EC −0.048 1.000          
TDS 0.050 0.730 1.000         
K+ 0.038 0.556 0.899 1.000        
Na+ 0.059 0.712 0.998 0.899 1.000       
Ca2+ −0.031 0.848 0.734 0.561 0.701 1.000      
Mg2+ 0.020 0.721 0.997 0.905 0.994 0.729 1.000     
Cl 0.054 0.720 0.999 0.895 0.999 0.719 0.995 1.000    
SO42− 0.001 0.724 0.976 0.877 0.972 0.719 0.979 0.972 1.000   
HO3 −0.063 −0.264 −0.048 0.089 −0.052 0.056 −0.033 −0.056 −0.104 1.000  
NO3 0.056 −0.070 −0.136 −0.156 −0.170 0.205 −0.138 −0.169 −0.173 −0.208 1.000 

Kaiser–Meyer–Olkin measure of sampling: adequacy = 0.557.

Bartlett's test of sphericity: approximate χ2 = 1,467.686, df = 55, Sig. = 0.000.

Bold values are the greater values.

Table 4

Eigenvalues, the percent of variance, cumulative eigenvalue, the cumulative percentage of variance for the FA in the Dagujia River Basin

FactorEigenvaluePercent of varianceCumulative eigenvalueCumulative percentage of variance
6.947 63.12 6.949 63.12 
1.288 11.71 8.235 74.86 
1.125 10.23 9.36 85.09 
0.959 8.72 10.319 93.82 
0.472 4.29 10.791 98.10 
0.102 0.93 10.893 99.03 
0.073 0.66 10.966 99.70 
0.030 0.27 10.996 99.97 
0.003 0.03 10.999 100.00 
10 0.00001554 0.00 10.999 100.00 
11 0.000003342 0.00 10.999 100.00 
FactorEigenvaluePercent of varianceCumulative eigenvalueCumulative percentage of variance
6.947 63.12 6.949 63.12 
1.288 11.71 8.235 74.86 
1.125 10.23 9.36 85.09 
0.959 8.72 10.319 93.82 
0.472 4.29 10.791 98.10 
0.102 0.93 10.893 99.03 
0.073 0.66 10.966 99.70 
0.030 0.27 10.996 99.97 
0.003 0.03 10.999 100.00 
10 0.00001554 0.00 10.999 100.00 
11 0.000003342 0.00 10.999 100.00 
Table 5

Factor loadings and communality of the variables after Varimax rotation

VariableFactor 1Factor 2Factor 3Communalities
pH 0.039 − 0.505 −0.057 0.260 
EC 0.786 0.425 0.171 0.828 
TDS 0.994 −0.060 −0.037 0.992 
K+ 0.895 −0.026 −0.162 0.828 
Na+ 0.988 −0.078 −0.080 0.988 
Ca2+ 0.788 0.246 0.449 0.883 
Mg2+ 0.991 −0.041 −0.043 0.986 
Cl 0.990 −0.072 −0.069 0.990 
SO42− 0.979 −0.074 −0.054 0.967 
HCO3 −0.015 0.829 −0.247 0.748 
NO3 −0.124 −0.122 0.927 0.889 
VariableFactor 1Factor 2Factor 3Communalities
pH 0.039 − 0.505 −0.057 0.260 
EC 0.786 0.425 0.171 0.828 
TDS 0.994 −0.060 −0.037 0.992 
K+ 0.895 −0.026 −0.162 0.828 
Na+ 0.988 −0.078 −0.080 0.988 
Ca2+ 0.788 0.246 0.449 0.883 
Mg2+ 0.991 −0.041 −0.043 0.986 
Cl 0.990 −0.072 −0.069 0.990 
SO42− 0.979 −0.074 −0.054 0.967 
HCO3 −0.015 0.829 −0.247 0.748 
NO3 −0.124 −0.122 0.927 0.889 

Extraction method: principal component analysis.

Rotation method: Varimax with Kaiser Normalization.

Bold values are the greater absolute values.

From Table 4, it can be noted that the first three factors account for about 85.09% of the total variance, which indicates that it is reasonable and suitable to extract the first three factors as the main factors. As listed in Table 5, the communalities of all variables except pH and HCO3 were higher than 0.80. The different factor loadings in Table 5 suggest different contributions in identifying the chemical compositions of the groundwater samples.

The terms ‘strong’, ‘moderate’ and ‘weak’ were used to denote the absolute factor loading values of >0.75, 0.75–0.50 and 0.50–0.30, respectively (Liu et al. 2003). Factor 1 consists of EC, TDS, K+, Na+, Ca2+, Mg2+, Cl and SO42−, accounting for about 63.12% of the total variance. As known, EC, TDS, K+, Na+, Ca2+, Mg2+, Cl and SO42− are the primary parameters in seawater (Liu et al. 2003). The previous study by Zeng et al. (2017) showed that the increase in the Cl concentration was in positive correlation with the seawater intrusion in the Dagujia River Basin. Moreover, as seen from Table 3, EC is positively connected with TDS, K+, Na+, Ca2+, Mg2+, Cl and SO42−. Therefore, EC can be noted as a water salinization index. The association of EC, TDS, K+, Na+, Ca2+, Mg2+, Cl and SO42− could reflect the influence of seawater intrusion on groundwater pollution (Liu et al. 2003). Factor 1 can be termed as ‘the seawater intrusion factor’.

The previous studies showed that overpumping of groundwater was the main cause of seawater intrusion (Lambrakis et al. 1997; Liu et al. 2003). In this study, it can be seen from Figure 2(a) that the wells with high scores, shown as the dark colors, are likely distributed in the north of the study area, which is closer to the coast. Coincidently, the areas with dark colors are the main residential and agricultural lands (shown in Figure S1). The finding confirms that overpumping of groundwater for domestic water and agricultural water is the major cause of seawater salinization in the study area. Especially, the highest score at well 5 suggests heavy seawater salinization. The spatial distribution of Factor 1 (Figure 2(a)) indicates that the well locations are corresponding to the seawater intrusion.

Factor 2, explaining 11.71% of the total variance, is composed of pH, EC and HCO3. The ‘strong’ positive loading of HCO3 indicates that the variable is related to the water–rock interaction. The negative loading of pH and positive loading of EC also support the water–rock interaction hypothesis (Jiang et al. 2009). The EC value reflects the amounts of dissolved ions, and the pH of groundwater denotes the H+ concentration in groundwater. However, H+ could be absorbed by the water–rock interaction process (Pacheco & Weijden 1996). Therefore, Factor 2 is assumed to be termed as ‘the water–rock interaction factor’.

Factor 3, accounting for about 10.23% of the total variance, mainly includes NO3. As known, no lithologic source was found for NO3, and atmospheric deposition was not generally regarded as a main source of NO3 in the groundwater (Jiang et al. 2009). Generally, NO3 could be introduced into the groundwater by urea fertilization in farmlands. The extra amount of fertilizer could not be absorbed and quickly degraded in the soil and then could migrate down to the groundwater (Jiang et al. 2009). Moreover, NO3 could be introduced by the sewage generated from the residential areas. Hence, the high concentration of NO3 in the study area was probably derived from the anthropogenic processes. Noticeably, the land-use type (Figure S1) around the wells with high NO3 concentrations is mainly agricultural land and urban land, and the surroundings possibly affecting the groundwater quality (Table S2) are some livestock farms and farmlands, which could provide an evidence for that anthropogenic processes could cause high NO3 concentration in groundwater. Therefore, the Factor 3 is assumed to be the indicative of human activities.

To emphasize the relation of the mentioned influences of seawater intrusion, geochemical processes and human activities on the chemical compositions of groundwater, the contribution of each factor at each well (factor scores) is analyzed and listed in Table 6, and the corresponding maps are illustrated in Figure 2.

Table 6

Factor scores for the three-factor model

WellFactor 1Factor 2Factor 3FOrder
−0.09417 −0.23217 −0.46461 −0.15768 25 
−0.08001 0.67341 −0.11655 0.01927 15 
−0.29290 −0.59027 −0.47749 −0.35601 38 
−0.05362 −0.78002 −0.11499 −0.16095 26 
6.33892 −1.19655 −0.61280 4.46644 
0.28033 0.97414 −0.63096 0.26627 
0.06948 −1.19306 2.24328 0.15702 10 
−0.01325 1.39651 −0.03843 0.17772 
1.47693 3.10084 0.89296 1.63020 
10 0.06129 1.13877 −1.26592 0.05004 13 
11 −0.20330 −0.89327 −0.23071 −0.30154 31 
12 −0.19392 0.66513 −0.74472 −0.14191 23 
13 0.07025 1.83413 −1.06761 0.17621 
14 0.30027 0.08799 −0.16525 0.21511 
15 −0.12766 0.40309 1.43569 0.13328 11 
16 −0.27147 0.61144 −0.18097 −0.13910 22 
17 −0.16258 1.05732 0.19991 0.04886 14 
18 −0.25757 −0.07445 −0.99610 −0.32114 33 
19 −0.32822 −1.18051 −0.78636 −0.50056 45 
20 −0.17095 −0.62743 −0.87801 −0.31875 32 
21 −0.31719 0.22020 −1.15982 −0.34452 36 
22 −0.23646 −0.29902 −0.15081 −0.23477 28 
23 −0.22798 −0.18289 −0.00319 −0.19476 27 
24 −0.18899 0.18284 0.36429 −0.07132 17 
25 0.18307 −0.20055 3.60184 0.54119 
26 −0.19978 0.94470 −0.66729 −0.09848 18 
27 −0.32175 −1.36638 −0.64848 −0.50477 46 
28 −0.22630 0.33655 −0.02001 −0.12405 20 
29 −0.07334 −0.38638 1.35775 0.05559 12 
30 −0.28518 1.01777 0.49923 −0.01160 16 
31 −0.30623 −0.08389 −1.06068 −0.36631 39 
32 −0.30503 −0.65567 −0.51829 −0.37891 40 
33 −0.32075 −1.89493 0.53140 −0.43495 41 
34 −0.26041 −0.25739 −0.53788 −0.29334 29 
35 −0.35892 −1.57380 −0.04516 −0.48838 44 
36 −0.30978 −0.58468 −0.09341 −0.32160 34 
37 −0.34288 −1.11239 −0.33671 −0.44803 42 
38 −0.37330 −0.95609 0.56640 −0.34055 35 
39 0.02486 0.95541 2.26728 0.42243 
40 −0.16524 1.60291 0.83260 0.19800 
41 −0.30247 −0.85275 −0.11027 −0.35509 37 
42 −0.25333 0.02347 0.50006 −0.12469 21 
43 −0.23102 0.62914 −0.30065 −0.12102 19 
44 −0.41641 0.36714 −1.57932 −0.44836 43 
45 −0.28904 −0.24732 −0.43687 −0.30107 30 
46 −0.24399 −0.80101 1.14760 −0.15338 24 
WellFactor 1Factor 2Factor 3FOrder
−0.09417 −0.23217 −0.46461 −0.15768 25 
−0.08001 0.67341 −0.11655 0.01927 15 
−0.29290 −0.59027 −0.47749 −0.35601 38 
−0.05362 −0.78002 −0.11499 −0.16095 26 
6.33892 −1.19655 −0.61280 4.46644 
0.28033 0.97414 −0.63096 0.26627 
0.06948 −1.19306 2.24328 0.15702 10 
−0.01325 1.39651 −0.03843 0.17772 
1.47693 3.10084 0.89296 1.63020 
10 0.06129 1.13877 −1.26592 0.05004 13 
11 −0.20330 −0.89327 −0.23071 −0.30154 31 
12 −0.19392 0.66513 −0.74472 −0.14191 23 
13 0.07025 1.83413 −1.06761 0.17621 
14 0.30027 0.08799 −0.16525 0.21511 
15 −0.12766 0.40309 1.43569 0.13328 11 
16 −0.27147 0.61144 −0.18097 −0.13910 22 
17 −0.16258 1.05732 0.19991 0.04886 14 
18 −0.25757 −0.07445 −0.99610 −0.32114 33 
19 −0.32822 −1.18051 −0.78636 −0.50056 45 
20 −0.17095 −0.62743 −0.87801 −0.31875 32 
21 −0.31719 0.22020 −1.15982 −0.34452 36 
22 −0.23646 −0.29902 −0.15081 −0.23477 28 
23 −0.22798 −0.18289 −0.00319 −0.19476 27 
24 −0.18899 0.18284 0.36429 −0.07132 17 
25 0.18307 −0.20055 3.60184 0.54119 
26 −0.19978 0.94470 −0.66729 −0.09848 18 
27 −0.32175 −1.36638 −0.64848 −0.50477 46 
28 −0.22630 0.33655 −0.02001 −0.12405 20 
29 −0.07334 −0.38638 1.35775 0.05559 12 
30 −0.28518 1.01777 0.49923 −0.01160 16 
31 −0.30623 −0.08389 −1.06068 −0.36631 39 
32 −0.30503 −0.65567 −0.51829 −0.37891 40 
33 −0.32075 −1.89493 0.53140 −0.43495 41 
34 −0.26041 −0.25739 −0.53788 −0.29334 29 
35 −0.35892 −1.57380 −0.04516 −0.48838 44 
36 −0.30978 −0.58468 −0.09341 −0.32160 34 
37 −0.34288 −1.11239 −0.33671 −0.44803 42 
38 −0.37330 −0.95609 0.56640 −0.34055 35 
39 0.02486 0.95541 2.26728 0.42243 
40 −0.16524 1.60291 0.83260 0.19800 
41 −0.30247 −0.85275 −0.11027 −0.35509 37 
42 −0.25333 0.02347 0.50006 −0.12469 21 
43 −0.23102 0.62914 −0.30065 −0.12102 19 
44 −0.41641 0.36714 −1.57932 −0.44836 43 
45 −0.28904 −0.24732 −0.43687 −0.30107 30 
46 −0.24399 −0.80101 1.14760 −0.15338 24 

As illustrated in Figure 2(a), the dark degree denotes for the score of Factor 1 for different wells, and the area with darker color represents a higher pollutant concentration included in Factor 1. The wells with high scores, shown as the dark colors, are likely distributed in the north of the study area, which is closer to the coast. Especially, the highest score at well 5 suggests heavy seawater salinization. It can be obtained that the distance to the sea probably affects the score of the wells, and thus the spatial distribution of Factor 1 indicates that the well locations are corresponding to the seawater intrusion. Figure 2(b) gives the geographical distribution of Factor 2 for all the 46 wells. The high scores at wells 8, 13 and 40, which are the darkly shaded areas, indicate the strong water–rock interaction. Figure 2(c) shows three wells 7, 25 and 39 with high scores, indicating that high concentrations of NO3 are detected in these groundwater samples. It can be seen from the land-use map (Figure S1) that the land types around these wells are mainly agricultural and residential lands, which are in good accordance with the spatial distribution of Factor 3. The distribution of the comprehensive factor (F) (Figure 2(d)) displays that the highest score with the darkest color is found at well 5, suggesting that the groundwater quality of the well 5 is most affected by the natural and anthropogenic processes. The distribution of different factors is in good accordance with the groundwater physico-chemical quality of different wells.

Assessment of contributions by HCA

Except the FA method, the HCA method was also proved to be effective to cluster the variances based on the chemical similarity. The wells with similar hydrochemical characteristics were classified into the same group. The results using HCA are shown in Figure 3.

Figure 3

Rescaled distance cluster combine result.

Figure 3

Rescaled distance cluster combine result.

As illustrated in Figure 3, three clusters were obtained by selecting the phenon line with a linkage distance of 13. The selection of the phenon value is on the basis of yielding the lowest number of groups which could elucidate most satisfactory results of the difference in chemical characteristics (Cloutier et al. 2008; Monjerezi et al. 2011). Cluster 1 includes parameters of Na+, Cl, TDS, Mg2+, SO42−, K+, EC and Ca2+, which are exactly as the terms contained in Factor 1. Therefore, Cluster 1 could be the indicator of the seawater intrusion. The parameters pH and HCO3 are classified as Cluster 2. The difference between Cluster 2 and Factor 2 is that EC is not included in Cluster 2. As mentioned, EC has a ‘weak’ positive loading for Factor 2, which indicates that parameters pH and HCO3 are the main factors affecting the Factor 2. Since factors HCO3 and pH are concerned in the water–rock interaction (Lin et al. 2011; Huang et al. 2013), the Cluster 2 could be termed as ‘the water–rock interaction factor’. As Factor 3, Cluster 3 is comprised of NO3, which is mainly generated from anthropogenic processes (fertilizer application and domestic sewage discharge) (Huang et al. 2013). Accordingly, Cluster 3 could be assumed to be the indicative of human activities. It can be noted that the results from HCA are generally consistent with those from FA, suggesting that both of the two methods are valuable tools to understand the possible pollution sources of groundwater.

CONCLUSIONS

The physico-chemical characteristics of groundwater in the Dagujia River Basin were studied to clarify the situation of groundwater quality and speculate the possible factors affecting the groundwater quality. A mass of 46 groundwater samples were collected and analyzed, and the results showed that several groundwater quality indices in some wells (such as wells 2, 6, 8, 14 and 20) are out of WHO limits and the standards of GB 5749-2006, indicating that these wells are not suitable for drinking. A great variation of chemical components was found across the study area, where the wells with high TDS, TH, K+, Na+, Ca2+, Mg2+, Cl and SO42− concentrations were inclined to be located close to the coastal zone. The FA and HCA results showed that seawater intrusion was the primary mechanism controlling the groundwater quality in the coastal areas. A three-factor model was proposed based on FA and explained over 85% of the total groundwater quality variation. Factor 1 was denoted as ‘the seawater intrusion factor’, consisting of EC, TDS, K+, Na+, Ca2+, Mg2+, Cl and SO42− and accounting for 63.16% of the total variance. Factor 2 mainly included pH, EC and HCO3, explaining about 11.71% of the total variance, which was thought to be related to the water–rock interaction. Factor 3 (NO3) was assumed to be the indicative of human activities. Additionally, the geographical maps of the factor scores clearly illustrated the spatial distributions of wells affected by natural processes or anthropogenic processes. Three clusters were also generated from HCA, which were generally consistent with the results from FA. The results showed that both natural processes and human activities are the major factors affecting the groundwater quality. The results could provide references for the local government to protect groundwater ecology and allocate water resources.

ACKNOWLEDGEMENT

This work is supported by the Shandong Province Geological exploration projects (No. 201345).

SUPPLEMENTARY MATERIAL

The Supplementary Material for this paper is available online at http://dx.doi.org/10.2166/aqua.2019.113.

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Supplementary data