Water demand has increased in metropolitan cities due to fast population growth and industrial development. Jilin City, located in the middle of Jilin province, northeast China, has a population of approximately 4.30 million. The main water source for Jilin City is the Songhua River which has a lot of problems. This study aims to determine the appropriate groundwater resource field for Jilin City by groundwater quantity analysis, groundwater quality analysis and multivariate statistical analysis. Results showed that the groundwater quantity in the study area was separated into four main areas. Most of the study areas had good groundwater supplies. The dominant ions were Ca2+, Mg2+, Na+, NH4+, NO3, HCO3 and SO42−. The groundwater types can be classified as Ca–HCO3, Ca + Mg–HCO3, Ca + Na–HCO3, Ca + HCO3–SO4, Ca + Mg–HCO3 + SO4 and Ca + Na–HCO3 + SO4. The regions of groundwater satisfying the groundwater quality standard can be separated into five main areas. Using principal component analysis (PCA), five principal factors were extracted from the dataset. Factor 1 had high positive loadings for most of the major ions (Na+, Ca2+, Mg2+, HCO3 and SO42−) as well as for total hardness (TH) and electrical conductivity (EC), which represents the natural hydro-geochemical evolution of groundwater by groundwater-geological interaction. Factor 2 was dominated by F, pH and PO43−, which may be due to anthropogenic pollution sources. Factor 3 was dominated by NO2, NH4+ and NO3, which may also be due to anthropogenic pollution sources. Factor 4 had a higher loading of chemical oxygen demand (COD), which again may be due to anthropogenic pollution sources. Factor 5 was dominated by Fe and Mn, which are produced by weathering-leaching-dissolution processes. The score values of the principal factors were influenced by urban land use. Chuanying region is the area in which the quality of groundwater is relatively good. It was determined that No. 6 region in Chuanying region is the groundwater resource field which has sufficient good quality water.

INTRODUCTION

Water supply in metropolitan cities has been a major concern especially in recent decades (Navarro & Carbonell 2007). A continuous good quality water supply is a key factor for good quality of life, environmental sustainability and economic growth in urban centers (Ranjan et al. 2012). Jilin City, located in the middle of Jilin province, northeast China, has a population of approximately 4.30 million in a 27,120 km2 area. During 1980–2010, because of fast population growth and industrial development, the demand for water also increased rapidly. Currently, the main water source for Jilin City is the Songhua River instead of groundwater. Although water from the Songhua River provides a sufficient and clean household water resource, the availability of water is frequently limited due to the variable climatic conditions and emerging factors. During drought, water levels in the Songhua River fall significantly, resulting in limitation of water supply. The occurrence of extreme hydrological events, such as drought and floods, has been ascribed to global warming (e.g. Sen 2009). Meanwhile, emerging factors are also impacting the quality of the Songhua River. An explosion in the Jilin petrochemical company polluted the Songhua River in 2005, which resulted in water shortages and caused residents to panic. Thus, an appraisal of alternative water sources is urgently needed.

Groundwater constitutes the largest fresh water source in many parts of the world, providing a buffer to sustain critical water demands during prolonged dry cyclical periods and emergencies (Assaf & Saadeh 2009). Jilin City is located on the banks of the Songhua River, and has abundant undeveloped groundwater resources (Cao et al. 2002). In comparison to surface water, groundwater is harder to pollute, and is often of better quality (Mosley et al. 2014). So the local government considered establishing a groundwater resource field to provide water resources during dry periods and emergencies. The city's groundwater resource field can be divided into two types. One provides water resources in a sudden emergency, and the other provides backup water resources. (Nag & Ghosh 2013). Both types need sufficient and good quality water. The quality of the groundwater in Jilin City has become worse according to the monitoring data from the groundwater monitoring network built during the 1980s. Total nitrogen in the groundwater has gradually increased (Liang et al. 2007), and groundwater contamination presents a regional character (Wei et al. 2014). So to choose the appropriate site and evaluate the hydrogeochemistry of the urban groundwater source field is of crucial importance.

This paper attempts to evaluate groundwater quantity and quality, through geology, hydrogeology and hydrogeochemical analysis, leading to the determination of appropriate groundwater resource fields. The improved Nemerow integrated pollution index method, Kriging method and multivariate statistical methods are used to assist the analysis.

STUDY AREA

The study area is located in the floodplain of the Songhua River in Jilin City, northeast China, and is approximately 138 km2 in area. The study area is divided into five regions as follows: Jiangbei region, Zhongxin region, Hada region, Jiangnan region and Chuanying region (Figure 1(a)). The geomorphic types are classified into floodplain, Songhua River first terrace and Songhua River second terrace. The climate is semi-humid and subject to continental monsoons. The annual average precipitation from 1954 to 2006 was approximately 655.5 mm and approximately 75% of the annual rainfall falls between July and September. The mean annual potential evapotranspiration ranges from 1,300 to 1,500 mm. The mean monthly air temperature ranges between a minimum of −17.5 °C in December–February and a maximum of 36.6 °C in June–August, with a mean annual temperature of 5.1 °C. The main surface water body in the study area is the Songhua River and its tributaries.
Figure 1

(a) Geographical location of the study area; (b) hydrogeological cross section.

Figure 1

(a) Geographical location of the study area; (b) hydrogeological cross section.

The Jilin City groundwater monitoring network was built during the 1980s, and includes 177 monitored wells (Figure 1(a)). Inorganic chemical compounds including K+, Na+, Ca2+, Mg2+, HCO3, SO42−, Cl, F, NH4+, NO2, NO3, PO43−, Fe2+ and Fe3+; metallic elements including Fe, Mn, Cr, Pb, As, Cu and Cd; and other parameters including pH, dissolved oxygen, electrical conductivity (EC), total dissolved solids (TDS), volatile phenol, total hardness (TH) and total alkalinity were monitored.

Geological groups of the study area are mainly Permian, Jurassic and Quaternary formations. The Permian formation consists of granite, granodiorite and tuffaceous sandstone. The Jurassic formation consists of sandstone and gravel. Based on lithology and field relationships, the Quaternary formations have been divided into Holocene alluvium, Upper Pleistocene alluvium, Middle Pleistocene alluvium and Lower Pleistocene alluvium. Holocene alluvium is distributed in the floodplain and Songhua River first terrace and Upper Pleistocene alluvium is distributed in Songhua River second terrace. Lower Pleistocene alluvium is continuously overlaid by Holocene alluvium and Upper Pleistocene alluvium (Wei et al. 2014). The vadose zone is mainly dominated by loamy sand, sandy loam, sandy clay, loam, silt clay and silt (Figure 1(b)). Based on groundwater storage conditions, hydrological and hydraulic characteristics, the groundwater in the study area was a quaternary unconsolidated pore unconfined aquifer.

The land uses of the urban areas were divided into conventional industrial areas, high-tech industrial areas, public facilities areas, transportation facilities areas, residential and commercial areas, urban infrastructure facilities areas, grassland and managed green areas and river areas (Figure 2).
Figure 2

Land uses of urban areas in Jilin City.

Figure 2

Land uses of urban areas in Jilin City.

METHODOLOGY

Groundwater quantity analysis

The lithology, thickness and degree of porosity of the aquifer were analyzed to evaluate the groundwater quantity in the study area. Additionally, pumping tests were carried out at 138 wells. Figure 3 shows the spatial distribution of the groundwater storage capacity.
Figure 3

The spatial distribution of the groundwater storage capacity in the study area.

Figure 3

The spatial distribution of the groundwater storage capacity in the study area.

Groundwater sampling and analytical methods

A total of 177 groundwater samples were collected from the study area in May 2013 and analyzed for inorganic chemical composition, metallic elements and other parameters. All sampling bottles were acid washed before the sampling campaign and were rinsed thoroughly with the groundwater at each site prior to sampling. To ensure that samples were not contaminated by air bubbles and entrapped organic particulate matter, the bottles were completely filled with sampled water.

All samples were analyzed for inorganic chemical compounds including K+, Na+, Ca2+, Mg2+, NH4+, Fe2+, Fe3+, HCO3, SO42−, Cl, F, NO2, NO3 and PO43−; metallic elements including Fe, Mn, Cr, Pb, As, Cu and Cd, and other parameters including pH, chemical oxygen demand (COD), EC, TDS, volatile phenol, TH and total alkalinity. All samples were filtered through 0.45-μm membrane filters before analysis.

EC and pH were measured in situ using a low-range pH/EC tester (Hanna Combo Model HI98129, Beijing Hengtairuibo Technology Development Co., Ltd, Beijing, China). Inorganic chemical compounds (K+, Na+, Ca2+, Mg2+, NH4+, Fe2+, Fe3+, HCO3, SO42−, Cl, F, NO2, NO3 and PO43−) were determined with an atomic absorption spectrometer (Shimadzu AA-6300CF, Shimadzu International Trading Ltd, Shimane, Japan) and ion chromatography (ICS-2100). Metallic elements (Fe, Mn, Cr, Pb, As, Cu and Cd) were determined by ICP-MS (7500a, Agilent Technologies Inc., NY, USA). COD was determined using titration against KMnO4. Volatile phenol was determined using a spectrophotometer (V-1300, Macy China Instruments Inc., Beijing, China). TH and total alkalinity were determined using titration against EDTA and HCl. The analytical precision for ion measurements was determined by calculating the ionic balance error, which is generally within ±10%. The statistical summaries of the measured parameters in the groundwater are shown in Table 1 and the dominant groundwater anions and cations were also evaluated using a piper diagram (Figure 4).
Table 1

Statistical summaries of the measured parameters in the groundwater

ParameterRangeMeanMedianStandard deviationParameterRangeMeanMedianStandard deviation
pH 6.50–7.20 6.85 6.90 0.16 NO2 0–4.55 0.14 0.03 0.45 
EC 135.00–2,750.00 590.36 520.00 348.40 F 0–1.50 0.16 0.00 0.29 
K+ 0.40–16.30 4.89 4.30 2.91 PO43− 0–1.29 0.06 0.03 0.16 
Na+ 5.40–85.50 37.06 33.10 15.88 Fe 0–4.26 0.66 0.43 0.61 
Ca2+ 17.00–186.10 65.33 60.48 28.94 Mn 0–10.10 1.23 0.15 3.24 
Mg2+ 1.30–66.00 24.09 23.20 13.77 Cd 0.00 0.00 0.00 0.00 
Fe3+ 0–2.73 0.31 0.15 0.43 Pb 0.00 0.00 0.00 0.00 
Fe2+ 0–1.76 0.35 0.08 0.53 Cr 0.00 0.00 0.00 0.00 
Cl 0–175.00 51.69 43.10 36.19 As 0.00 0.00 0.00 0.00 
SO42− 0–172.20 74.79 76.40 38.71 TDS 155.00–1,375.00 510.16 475.00 196.04 
HCO3 53.70–643.00 205.17 207.40 78.08 COD 0.40–105.80 2.89 1.40 8.82 
CO32− 0.00 0.00 0.00 0.00 Total hardness 19.00–739.00 250.96 229.00 138.66 
NH4+ 0–0.34 0.01 0.00 0.03 Total alkalinity 44.00–527.00 166.07 156.00 79.98 
NO3 0–340.30 31.27 7.30 53.38 Volatile phenol 0–2.52 0.02 0.00 0.19 
ParameterRangeMeanMedianStandard deviationParameterRangeMeanMedianStandard deviation
pH 6.50–7.20 6.85 6.90 0.16 NO2 0–4.55 0.14 0.03 0.45 
EC 135.00–2,750.00 590.36 520.00 348.40 F 0–1.50 0.16 0.00 0.29 
K+ 0.40–16.30 4.89 4.30 2.91 PO43− 0–1.29 0.06 0.03 0.16 
Na+ 5.40–85.50 37.06 33.10 15.88 Fe 0–4.26 0.66 0.43 0.61 
Ca2+ 17.00–186.10 65.33 60.48 28.94 Mn 0–10.10 1.23 0.15 3.24 
Mg2+ 1.30–66.00 24.09 23.20 13.77 Cd 0.00 0.00 0.00 0.00 
Fe3+ 0–2.73 0.31 0.15 0.43 Pb 0.00 0.00 0.00 0.00 
Fe2+ 0–1.76 0.35 0.08 0.53 Cr 0.00 0.00 0.00 0.00 
Cl 0–175.00 51.69 43.10 36.19 As 0.00 0.00 0.00 0.00 
SO42− 0–172.20 74.79 76.40 38.71 TDS 155.00–1,375.00 510.16 475.00 196.04 
HCO3 53.70–643.00 205.17 207.40 78.08 COD 0.40–105.80 2.89 1.40 8.82 
CO32− 0.00 0.00 0.00 0.00 Total hardness 19.00–739.00 250.96 229.00 138.66 
NH4+ 0–0.34 0.01 0.00 0.03 Total alkalinity 44.00–527.00 166.07 156.00 79.98 
NO3 0–340.30 31.27 7.30 53.38 Volatile phenol 0–2.52 0.02 0.00 0.19 

All values in mg/L except pH (no unit) and EC (μS/cm).

Figure 4

Piper plot of the groundwater chemistry.

Figure 4

Piper plot of the groundwater chemistry.

Groundwater quality assessment method

According to the Chinese quality standard for groundwater (GB/T 14848-93) (CSBTS 1993), pH, NH4+, SO42−, Cl, F, NO2, NO3, Fe, Mn, TDS and TH were chosen to assess the groundwater quality.

The improved Nemerow integrated pollution index was used for groundwater quality assessment. The method considers the weights of different evaluation indices in Equations (1)–(5): 
formula
1
 
formula
2
 
formula
3
 
formula
4
 
formula
5
where is defined as the ratio of the groundwater quality parameter concentration to the standard value (defining = 1) according to the Chinese groundwater quality standard (GB/T 14848-93) (CSBTS 1993). is the improved Nemerow integrated pollution index. When > 1, the groundwater is polluted. is the maximum value of , is the value of with the maximum weight value, and is the average value of and . is the weight value of the groundwater quality parameter, and is the ratio for parameter correlation.
The spatial distribution of the groundwater quality in the study area was interpolated by the Kriging method using the integrated pollution index with the semivariogram model in the form of the exponential model. The distributions of groundwater solutes may present a non-stationary characteristic because of the river and lakes (Lee et al. 2003). Because groundwater flows into the Songhua River, the spatial distribution of groundwater quality was interpolated by the partition interpolation method, and then the partition interpolation results were fused to obtain the overall interpolation (Figure 5).
Figure 5

The improved Nemerow integrated pollution index spatial distribution.

Figure 5

The improved Nemerow integrated pollution index spatial distribution.

Multivariate statistical analysis

Multivariate statistical analysis is a quantitative and independent approach for groundwater classification which allows for the grouping of groundwater samples and correlations between chemical parameters in the samples (Cloutier et al. 2008). In this study, the identification of pollution sources and groundwater quality distribution characteristics were analyzed by principal component analysis (PCA) and factor analysis (FA). Optimal results in multivariate statistical analyses require normal distribution and homoscedasticity. In this respect, the data for each parameter were tested for normal distribution using statistics from the statistical software SPSS 17.0. The parameter is considered to follow normal distribution when the coefficients of skewness and kurtosis are all smaller than 1.96 (at 0.05 confidence level). When the parameters do not follow normal distribution, they need to be log normally transformed. Positively skewed chemical data are commonly log normally transformed and standardized for multivariate statistical analyses (Yidana et al. 2012; Fu et al. 2014). Then, standardization of the data results in new values that have zero mean and are measured in units of standard deviation (s). The standardized data are obtained by subtracting the mean of the distribution from each data point and dividing by the standard deviation of the distribution, . An inter-correlation matrix of the variables is computed from the standardized variables. The correlation coefficient matrix quantifies the linear relationship existing between pairs of variables present therein. The percentages of eigenvalues are computed since the eigenvalues quantify the contribution of a factor to the total variance. The factor extraction is done using minimum acceptable eigenvalues that is >1 (Kaiser 1960). The factor loading matrix is rotated to an orthogonal simple structure by the varimax rotation method (Table 2). This procedure renders a new rotated factor matrix in which each factor is described in terms of only those variables affording easier interpretation. Factor loading is the measure of the degree of closeness between the variables and the factor (Dalton & Upchurch 1978). The factor score can reflect the degree of effect of the components (Purushothaman et al. 2014). Figure 6(a)–(e) show the spatial distribution of factor scores for the principal factors. The appropriate site of the groundwater resource field was determined according to the groundwater quantity analysis, the groundwater quality assessment and multivariate statistical analysis (Figure 6(f)).
Table 2

Varimax rotated factor loadings matrix of the studied hydrological parameters

VariableFactor 1Factor 2Factor 3Factor 4Factor 5
TH 0.98 0.12 0.12 0.00 0.06 
EC 0.97 0.12 0.12 0.06 0.08 
Na+ 0.97 0.12 0.11 −0.08 0.06 
Ca+ 0.91 −0.01 0.18 −0.09 −0.01 
Mg2+ 0.88 0.26 0.04 0.11 0.14 
HCO3 0.62 −0.32 0.11 −0.02 0.52 
SO42− 0.54 −0.14 −0.17 0.49 0.14 
F 0.16 0.72 0.25 0.12 0.15 
pH −0.32 0.70 −0.07 −0.10 −0.03 
PO43− 0.09 0.63 0.09 0.01 0.14 
Cl 0.52 0.56 0.02 −0.12 0.21 
NO2 0.01 −0.05 0.90 −0.12 0.11 
NH4+ 0.27 −0.08 0.80 −0.14 0.29 
NO3 0.12 0.16 0.71 0.14 −0.37 
COD −0.25 0.09 −0.09 0.76 0.07 
Fe −0.13 0.08 −0.02 0.15 0.70 
Mn 0.06 0.42 0.07 −0.03 0.80 
% of variance explained 36.14 12.883 11.305 7.920 6.765 
% cumulative explained 36.14 49.023 60.327 68.247 75.012 
VariableFactor 1Factor 2Factor 3Factor 4Factor 5
TH 0.98 0.12 0.12 0.00 0.06 
EC 0.97 0.12 0.12 0.06 0.08 
Na+ 0.97 0.12 0.11 −0.08 0.06 
Ca+ 0.91 −0.01 0.18 −0.09 −0.01 
Mg2+ 0.88 0.26 0.04 0.11 0.14 
HCO3 0.62 −0.32 0.11 −0.02 0.52 
SO42− 0.54 −0.14 −0.17 0.49 0.14 
F 0.16 0.72 0.25 0.12 0.15 
pH −0.32 0.70 −0.07 −0.10 −0.03 
PO43− 0.09 0.63 0.09 0.01 0.14 
Cl 0.52 0.56 0.02 −0.12 0.21 
NO2 0.01 −0.05 0.90 −0.12 0.11 
NH4+ 0.27 −0.08 0.80 −0.14 0.29 
NO3 0.12 0.16 0.71 0.14 −0.37 
COD −0.25 0.09 −0.09 0.76 0.07 
Fe −0.13 0.08 −0.02 0.15 0.70 
Mn 0.06 0.42 0.07 −0.03 0.80 
% of variance explained 36.14 12.883 11.305 7.920 6.765 
% cumulative explained 36.14 49.023 60.327 68.247 75.012 
Figure 6

(a)–(e) The spatial distribution of factor scores for factor 1 to factor 5. (f) The appropriate site of the groundwater resource field.

Figure 6

(a)–(e) The spatial distribution of factor scores for factor 1 to factor 5. (f) The appropriate site of the groundwater resource field.

RESULTS AND DISCUSSION

Groundwater quantity

The groundwater quantity in the study area was separated into four main areas according to lithology, aquifer thickness, degree of porosity of the aquifer and pumping test results of 138 monitored wells (Figure 3).

Region I, with a water flow greater than 3000 m3/d, is an excellent zone for groundwater supply located in the Jiangbei region, north of the study area. The area of region I is 8.11 km2 accounting for 6.68% of the study area. The lithology is dominated by sand and gravel and the thickness of the aquifer ranged from 10 to 30 m. Region II, with a water flow from 1000 to 3000 m3/d, is a good zone for groundwater supply. The area of region II is 59.93 km2 covering 49.35% of the study area and located in most parts of Jiangbei region, Hada region, and Chuanying region (Figure 3). The lithology is dominated by sand and gravel and the thickness of the aquifer ranged from 5 to 20 m. Region III, with a water flow from 500 to 1000 m3/d, is a fair zone for groundwater supply. Region III is located in Zhongxin region and Jiangnan region around the edges of Region II. The area of Region III is 34.02 km2 accounting for 28.02% of the study area. The lithology is silt, sand and gravel and the thickness of the aquifer ranged from 5 to 10 m. Region IV, with a water flow from 100 to 500 m3/d, is a poor zone for groundwater supply. The area of Region IV is only 19.39 km2 covering 15.96% of the study area. Region IV is located in the south of the study area. The lithology is silt, sand and gravel and the thickness of the aquifer ranged from 2 to 5 m.

The lithologies of the studied aquifers are silt, sand and gravel, and the thickness and porosity of every region are different.

Major hydrochemistry

A summary of the major hydrochemical variables of the groundwater samples is presented in Table 1. Ca2+, Mg2+, Na+, NH4+, NO3, HCO3 and SO42− were the dominant ions. The groundwater samples were near-neutral with pH values ranging from 6.50 to 7.20. The EC varied widely from 135 to 2750 μS/cm (mean 590.36 μS/cm). TDS ranged from 155 to 1375 mg/L (mean 510.16 mg/L).

The concentration of Cl and SO42− ranged from ND to 175 mg/L (mean 51.69 mg/L) and ND to 172.2 mg/L (mean 74.79 mg/L), respectively. The concentrations of NH4+, NO3 and NO2 ranged from ND to 0.34 mg/L (mean 0.01 mg/L), ND to 340.30 mg/L (mean 31.27 mg/L) and ND to 4.55 mg/L (mean 0.14 mg/L), respectively.

The concentrations of Fe and Mn ranged from ND to 4.26 mg/L (mean 0.66 mg/L) and ND to 10.10 mg/L (mean 1.23 mg/L), respectively. The concentrations of Cd, Pb, Cr and As were not detected. When the measured concentrations of the variables were higher than the values of the groundwater quality standard, the groundwater is considered to be contaminated.

The groundwater types may be classified as Ca–HCO3 in Jiangbei region and Chuanying region, Ca + Mg–HCO3 and Ca + Mg–HCO3 + SO4 in Hada region, Ca + Na–HCO3 and Ca + Na–HCO3 + SO4 in Jiangnan region, Ca–HCO3 + SO4 in Zhongxin region and Chuanying region (Figure 4).

Results of groundwater quality assessment

The groundwater quality has obvious spatial distribution characteristics regarding land uses (Figures 2 and 5). In Jiangbei region and in the south of Hada region, the land use types are mainly conventional industrial areas, which produce industrial wastewater. The maximum integrated pollution index is higher than 100. In Zhongxin region, the land use types are mainly residential and commercial areas, which produce large amounts of sewage runoff. The maximum integrated pollution index is higher than 60. In Chuanying and Jiangnan regions, the land uses mainly comprise high-tech industrial areas and public facilities areas. The integrated pollution indices are smaller relative to other regions. The spatial distribution of groundwater contamination in the study area has a good correlation with the urban land uses (Yan et al. 2015).

According to the results here presented, the groundwater satisfying the Chinese groundwater quality standard (GB/T 14848-93) can be separated into five main regions (Figure 5). No. 1 region is located in the Jiangbei region with an average integrated pollution index of 0.84; No. 2 region is located in the Hada region with an average integrated pollution index of 0.58; No. 3 region is located in the Zhongxin region with an average integrated pollution index of 0.91; No. 4 region is located in the Jiangnan region with an average integrated pollution index of 0.77; No. 5 region is located in the Chuanying region with an average integrated pollution index of 0.87.

Results of multivariate statistical analysis

Principal component analysis (PCA)

The identification of pollution sources was analyzed by PCA. Table 2 presents the factor loadings matrix. A total of five components extracted had eigenvalues >1, and accounted for 75.01% of the total variance in the dataset.

Factor 1 explained 36.14% of the total variance and had high positive loadings for most of the major ions (Na+, Ca2+, Mg2+, HCO3 and SO42−) as well as for TH and EC. Factor 1 represents the natural hydro-geochemical evolution of groundwater by groundwater-geological interaction which can be elucidated by the dissolution of rocks and minerals in sediments by chemical weathering (Nosrati & Eeckhaut 2012; Ranjan et al. 2012). Factor 2 accounted for 12.88% of total variance in groundwater quality and is positively dominated by F, pH and PO43−. This may be due to anthropogenic pollution sources mainly resulting from domestic wastewater in the residential district (Cai et al. 2013). Factor 3 represented 11.31% of variance in groundwater quality with high loadings of NO2, NH4+ and NO3. This is because of anthropogenic pollution sources mainly resulting from fertilizers and pesticides used in the city's green belt, and domestic wastewater and industrial wastewater in the residential district and industrial district (Liang et al. 2007; Meng et al. 2007). Factor 4 accounted for 7.92% of the total variation in hydrochemistry and had a higher loading of COD which indicated the amount of organic matter in the groundwater. This may be due to anthropogenic pollution sources mainly resulting from landfill leachate, domestic wastewater and industrial wastewater. Factor 5 accounted for 6.77% of the total variation in groundwater quality with high loadings of Fe and Mn. There are large amounts of Fe and Mn in the studied geological environment (Liu et al. 2013). The Fe and Mn in the groundwater were produced during weathering–leaching–dissolution processes. So factor 5 also represents natural hydro-geochemical evolution.

Factor score analysis

The groundwater showed a total of five factors derived from the multivariate statistical analysis as follows: factor 1 (TH, EC, Na+, Ca2+, Mg2+, HCO3 and SO42−), factor 2 (F, pH, PO43−, Cl), factor 3 (NO2, NH4+ and NO3), factor 4 (COD) and factor 5 (Fe, Mn) which represented different factors influencing groundwater quality. The degree of effect of the component on the groundwater quality can be well reflected by factor score analysis. Figure 5 (a)–(e) shows the spatial distribution of factor scores for factor 1 to factor 5.

The high score value of factor 1 (TH, EC, Na+, Ca2+, Mg2+, HCO3 and SO42−) is distributed mainly in Jiangbei region, Hada region and the north of Zhongxin region; while the low score value is distributed mainly in the south of Zhongxin region, Jiangnan region and Chuanying region (Figure 6(a)). There are mainly conventional industrial areas, residential areas and commercial areas in Jiangbei region, Hada region and the north of Zhongxin region. The pollutants were produced by human activity which infiltrated into the aquifer. The high score value of factor 2 is distributed mainly in the north and south of Jiangbei region where there are many chemical factories, coal industries and disposal of waste containing phosphorus, organic matter, heavy metals and other contaminants. Therefore, in these areas, factor 2 has a great influence on groundwater quality. The high score value of factor 3 is distributed mainly in the west of Jiangbei region and Zhongxin region which are mainly constituted of residential areas, commercial areas and managed green areas which produce domestic sewage and chemical fertilizer wastewater containing large amounts of nitrogen. Thus, the most seriously polluted areas regarding groundwater were Jiangbei region and Zhongxin region. The higher the scores of factor 2 and factor 3, the worse the groundwater quality. The high score value of factor 4 is distributed mainly in Jiangbei region and Hada region in which there are many chemical factories which produce wastewater containing a lot of organic matter. Factor 5 represents the natural hydro-geochemical evolution of the groundwater which reflects water-rock (or sediment) interaction.

The site of the groundwater resource field

The groundwater quantity in the study area was separated into four main areas. The groundwater quality in the study area satisfying the Chinese groundwater quality standard (GB/T 14848-93) can be separated into five main areas. Using factor analysis, No. 1 region in Jiangbei region is mainly controlled by factor 1, factor 2, factor 4 and factor 5; No. 2 region in Hada region is mainly controlled by factor 1, factor 2, factor 4 and factor 5; No. 3 region in Zhongxin region is mainly controlled by factor 2, factor 3 and factor 5; No. 4 region in Jiangnan region is mainly controlled by factor 1, factor 2, factor 4 and factor 5; No. 5 region in Chuanying region is mainly controlled by factor 1. According to the groundwater quantity and groundwater quality analysis, No. 5 region was the most suitable for a groundwater resource field. Considering groundwater quantity, No. 6 region in Chuanying region is determined as the best groundwater resource field (Figure 6(f)).

In the Chinese water quality standard (GB/T 14848-93), the concentration values of pH, EC, TDS, Cl, SO42−, NH4+, NO3, NO2, Fe and Mn are 6.5 to 8.5, <2,000 μs/cm, <1,000 mg/L, <250 mg/L, <250 mg/L, <0.2 mg/L, <20 mg/L, <0.02 mg/L, <0.3 mg/L and <0.1 mg/L, respectively. In No. 6 region, the water flow is greater than 3,000 m3/d and the pH values ranged from 6.88 to 7.10. The EC varied widely from 417 to 640 μs/cm. The TDS ranged from 462.6 to 713.0 mg/L. The concentration of Cl and SO42− ranged from 41.32 to 75.30 mg/L and 59.46 to 116.50 mg/L, respectively. The concentrations of NH4+, NO3 and NO2 ranged from 0.008 to 0.021 mg/L, 3.06 to 11.95 mg/L and 0.002 to 0.015 mg/L, respectively. The concentrations of Fe and Mn ranged from 0.06 to 0.25 mg/L (mean 0.66 mg/L) and 0.05 to 0.09 mg/L. The concentrations of Cd, Pb, Cr and As were not detected. All the hydrochemical variables satisfy the Chinese groundwater quality standard. The groundwater type may be classified as Ca–HCO3 and Ca–HCO3 + SO4.

CONCLUSIONS

By using geology, hydrogeological analysis, pumping tests, hydrogeochemical analysis and multivariate statistical analysis of groundwater quantity and quality in Jilin City, the following conclusions were obtained.

The volume of groundwater available in the study area was separated into four main areas including areas with a discharge greater than 3,000 m3/d, areas with a discharge from 1,000 to 3,000 m3/d, areas with a discharge from 500 to 1,000 m3/d and areas with a discharge from 100 to 500 m3/d. Most of the study areas had good groundwater supplies.

Chemical analysis shows dominance of Ca2+, Mg2+, Na+, NH4+, NO3, HCO3 and SO42− ions in the groundwater. The groundwater types may be classified as Ca–HCO3, Ca + Mg–HCO3, Ca + Na–HCO3, Ca–HCO3 + SO4, Ca + Mg–HCO3 + SO4 and Ca + Na–HCO3 + SO4. The groundwater quality has obvious spatial distribution characteristics with regard to land use. Severe pollution was found in the Jiangbei and Zhongxin regions. The region of the groundwater satisfying the groundwater quality standard can be separated into five main areas.

Five principal factors were extracted from the multivariate statistical analysis of the dataset. Among them, factor 1 had high positive loadings for most of the major ions (Na+, Ca2+, Mg2+, HCO3 and SO42−) as well as for TH and EC, which represents the natural hydro-geochemical evolution of groundwater by groundwater-geological interaction. Factor 2 was dominated by F, pH and PO43−, which may be due to anthropogenic pollution sources. Factor 3 is dominated by NO2, NH4+ and NO3, which may also be due to anthropogenic pollution sources. Factor 4 has a higher loading of COD, which may again be due to anthropogenic pollution sources. Factor 5 had higher loadings of Fe and Mn, which are produced during weathering-leaching-dissolution processes. The scores of factor 2 and factor 3 were higher in Jiangbei region and Zhongxin region. Jiangnan region was the area in which the quality of groundwater is relatively good.

The score values of the principal factors were influenced by urban land uses. In the Jiangbei region and Zhongxin region, there are mainly traditional industrial areas and residential and business areas, which produce industrial wastewater and sewage runoff. The groundwater resource field (No. 6 region) in Chuanying region was decided on finally because it has sufficient water (the water flow is greater than 3,000 m3/d) and good quality water satisfying the groundwater quality standard.

ACKNOWLEDGEMENTS

This work was financially supported by the ‘11th Five-Year Plan’ science and technology project of China (2006BAB04A09-02, 2007BAB28B04-03) and by the Science and Technology Key Research of Jilin Province (20100452).

Conflict of Interest

The authors declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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