Shallow Groundwater Quality Assessment and Pollution Source Apportionment: Case Study in Wujiang District, Suzhou City

Abstract

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I ($28\%$), $N{H}_4\text{-}N$ ($18\%$), and Mn ($14\%$); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry ($10.63\%$) > urban domestic sewage discharge ($8.69\%$) > agricultural non-point source pollution ($6.26\%$).

1. Introduction

Groundwater stands as one of the most invaluable natural resources found on our planet. It not only maintains ecological balance but also serves as an indispensable source of water for residential life, industrial production, and agricultural irrigation [1,2]. Hence, the preservation of water resources from quality deterioration is a fundamental and historical concern for human societies and is necessary for life, ecological diversity, and economic stability [3,4]. The quality of groundwater can be influenced by a multitude of factors and processes encompassing both natural and anthropogenic factors [5,6,7,8]. Certain natural factors, including climatic conditions and aquifer geological composition, may lead to the accumulation of specific ions in groundwater; for instance, arsenic, magnesium, and iodine [9,10,11]. Moreover, anthropogenic factors can have substantial detrimental effects on groundwater quality [12,13]. For instance, domestic sewage can contribute to elevated concentrations of ammonia in groundwater [14]; agricultural behaviors such as fertilization and livestock breeding serve as primary contributors to the excessive accumulation of nitrogen, phosphorus, and potassium in the natural environment [15,16,17]; and industrial activities have facilitated the ingress of substantial quantities of noxious chemicals, which include heavy metals and organic pollutants, into the groundwater system via wastewater effluents, inadvertent leakage, or accidental spills, thereby significantly compromising the purity and integrity of water sources [18,19,20]. For effective pollution control, accurately pinpointing the primary sources of groundwater pollution and quantitatively assessing their respective contributions have emerged as a pivotal concern in recent research endeavors [21,22].

To achieve this goal, numerous methods have been conceptualized and refined, including the utilization of stable isotope methodologies, numerical inversion procedures, and sophisticated multivariate statistical analyses [23,24,25,26,27]. The use of stable isotope approaches enables precise allocation of sources; however, their application is limited to instances involving isotope-related contaminants. Numerical inversion procedures demonstrate exceptional capabilities in precisely tracing the origins and pathways of groundwater pollutants at contaminated sites, thereby rendering them an ideal tool for conducting research at a localized scale [28,29,30]. Meanwhile, despite overlooking intricate details like meteorological and topographical factors that influence the emission process and the migration paths or transformations of pollutants, multivariate statistical methods provide a convenient and efficient way to interpret complex data matrices and tackle regional-scale challenges; they have been extensively employed for evaluating groundwater quality through pinpointing the origins of pollution and their controlling elements [31,32,33,34,35,36]. This group of methodologies encompasses various techniques such as factor analysis (FA), principal component analysis (PCA), cluster analysis (CA), and absolute principal component score-multiple linear regression (APCS-MLR), among others. FA and PCA are particularly useful in analyzing groundwater pollution sources as they allow for the efficient reduction in data dimensionality without requiring a deep understanding of the intricate evolution of hydrochemical constituents or the detailed composition of the pollutants [37,38]. Cluster analysis serves as a valuable tool for examining water quality parameters by identifying patterns and relationships related to their sources and contributing factors. However, relying solely on the aforementioned methods may present limitations in achieving an accurate and quantitative evaluation of the degree to which different pollution factors affect specific water quality indicators.

To tackle this problem, Thurston and Spengler [39] devised the APCS-MLR approach in 1985. This innovative method initially utilizes PCA to distill the essential components from the data, making it easier to pinpoint the source. Following this, the APCS method steps in to calculate the absolute scores for each of these components. Ultimately, the MLR model is leveraged to forge a quantitative link between pollution sources and pollutants, enabling the precise quantification of the individual contributions made by each pollution source. Through these procedures, the APCS-MLR method combines the strengths of PCA with the ability to quantify contributions from contaminant sources. Meanwhile, since its inception, this method has been effectively applied to a range of pollution source apportionment challenges across atmospheric, surface water, and sedimentary environments [40,41,42,43,44]. Its successful utilization in these contexts underscores its versatility and robustness in environmental research and assessment. More recently, the APCS-MLR approach has gained traction in groundwater quality research, demonstrating its effectiveness in pinpointing the influence of both natural and anthropogenic factors on groundwater quality [45,46,47]. The authors in Zhang et al. [48] utilized the PCA and APCS-MLR methods to pinpoint the sources of groundwater contamination and their respective contributions to the Hutuo River alluvial–pluvial fan. The authors in Meng et al. [49] employed the APCS-MLR receptor model to evaluate potential contamination origins of groundwater and quantify the relative contributions of various pollution sources within the Limin Groundwater Source Area in Harbin, which span the period from 2006 to 2016. The authors in Yu et al. [50] utilized the APCS-MLR technique for apportioning nitrate pollution sources and subsequently contrasted their findings with those obtained from a Bayesian isotope mixing model. The authors in Sheng et al. [51] adopted the APCS-MLR model as a sophisticated analytical tool for assessing the proportional contributions of diverse sources to heavy metal pollution within an arid oasis region situated in Northwest China. The authors in Haji Gholizadeh et al. [52] evaluated the water quality and quantified the potential pollution sources that affect the water quality of three major rivers in South Florida via the APCS-MLR receptor model. The authors in Cheng et al. [53] employed correlation analysis and the APCS-MLR model to explore the potential pollution sources impacting the Jinsha River Basin from 2016 to 2018, highlighting that the concentration of water pollutants in the Jinsha River mainstream basin is primarily influenced by environmental, agricultural, and demographic variables. The authors in Zhang et al. [54] assessed the suitability of both the PMF and APCS-MLR models for evaluating groundwater pollution sources by comparing their performance using groundwater sample data from typical mixed land use areas in Southwest China. The authors in Chen et al. [55] discovered that both the APCS-MLR and PMF models produced comparable estimates of the proportions of contamination source contributions when applied to evaluate groundwater pollution in Northeastern China. In conclusion, APCS-MLR presents a quantitative framework to assess the specific impacts of various pollution sources on water quality metrics. This versatile approach has the potential to be broadly implemented in quantitatively assessing regional groundwater pollution origins.

Wujiang District, Suzhou City, situated in the southeastern region of Jiangsu Province, stands as one of the pilot areas for the ecological green integrated development of the Yangtze River Delta. Endowed with a distinct geographical location and abundant natural resources, the district has outlined explicit plans and policies for green development and has an ambition to establish a world-class paradigm of livable waterfront communities. In recent years, the relentless advancements in modern industrialization and urbanization have inevitably impacted the groundwater environment within the region. The shallow groundwater, primarily replenished by precipitation and surface river networks, is highly susceptible to contamination from surface pollution sources such as industrial sewage canals and drains. Past research has shown that multiple contaminants, such as heavy-metal ions, nitrogen-based compounds, and phosphorus-based compounds, have been identified in the groundwater of this region [56,57,58,59]. Although extensive targeted research was conducted on several specific pollutants, the overarching issue of the holistic distribution of groundwater pollution sources remains unresolved.

This research endeavors to conduct a quantitative evaluation of groundwater pollution origins within a regional area characterized by diverse land-use types. The findings offer an impartial assessment of the pollution intensity associated with various groundwater pollution sources specific to each administrative region. Specifically, the present study, based on an in-depth analysis of the spatial distribution of inferior groundwater components in Wujiang District, employs Principal Component Analysis (PCA) to discern the natural and anthropogenic factors influencing groundwater quality. Furthermore, the Absolute Principal Component Scores–Multiple Linear Regression (APCS-MLR) model is adopted to quantitatively assess the contributions of potential water sources to various groundwater quality parameters. This approach offers an advanced methodology for regional groundwater pollution research and provides valuable technical support for administrative management efforts.

2. Materials and Methods

2.1. Study Area

Wujiang District, a jurisdictional entity of Suzhou City within Jiangsu Province, occupies a strategic position in the southeastern region of the province. It is bordered by Shanghai to the east, Taihu Lake to the west, Zhejiang Province to the south, and the primary urban hub of Suzhou to the north. Geographically, it is situated at latitudes ranging from $30°{46}^{\prime }$ to $31°{14}^{\prime }$ north and longitudes spanning from $120°{21}^{\prime }$ to $120°{54}^{\prime }$ east, encompassing a total area of 1176 square kilometers. The climatic profile of Wujiang District is characterized as a North subtropical monsoon marine climate, exhibiting distinct seasonal variations throughout the year. Summer is typically associated with southeasterly winds, whereas winter brings northwesterly winds. The climate is generally temperate, with ample precipitation. The average annual temperature hovers around 16 °C, and the mean annual rainfall stands at approximately 1121 mm. The district is devoid of mountainous terrain, instead exhibiting a predominantly low and flat landscape. The terrain gradually slopes from northeast to southwest, with a minimal height difference of approximately 2 m between the northern and southern extremities.

Wujiang District is situated within the expanse of the Taihu Lake Plain, a geological region characterized by a pronounced Quaternary stratigraphy. The Quaternary loose layer within this district exhibits a remarkable thickness, which attains a peak depth of over 300 m in specific regions and consistently exceeds 200 m in other regions. The phreatic aquifer, featuring a thickness ranging from 4 to 10 m, is composed of various layers, including alluvial and lacustrine silty clay, silt, and silty soil. Meanwhile, the groundwater within this aquifer is typically found buried at a depth of 1 to 1.5 m. Based on comprehensive remote sensing surveys, the land use types in this region are cultivated farmlands, residential settlements, and surface water bodies (Figure 1). Notably, 267 square kilometers of this territory is composed of water bodies, constituting approximately $22.70\%$ of the overall district area. There exist over 300 lakes, each boasting an area exceeding 3000 square meters. As a result, the interplay between surface water and groundwater is exceedingly intricate. What’s more, multiple types of human activities, including factory pollution, municipal sewage, and agricultural contamination, etc., jeopardize the safety of groundwater.

2.2. Sampling Collection and Analysis

Shallow groundwater samples were systematically collected from 117 wells at depths ranging from 0 to 30 cm in May 2022 and May 2023, with sampling sites carefully selected considering diverse land-use types, including cultivated land and residential areas, along with their spatial distribution. In each well, the total dissolved solids (TDS), pH, and dissolved oxygen (DO) were meticulously determined on-site using high-precision instruments, namely the Hanna DiST, SX-620 pH Testor and SX-630 ORP Testor, respectively. For the preservation and transportation of the groundwater samples, polyethylene containers with a capacity of 1.5 L were utilized. Subsequently, these samples were transported to the laboratory for a comprehensive analysis that encompassed the determination of calcium ($C{a}^{2+}$), magnesium ($M{g}^{2+}$), sodium ($N{a}^{+}$), potassium ($K^{+}$), ammonium nitrogen ($N{H}_4\text{-}N$), nitrate nitrogen ($N{O}_3\text{-}N$), nitrite nitrogen ($N{O}_2\text{-}N$), chloride ($C{l}^{-}$), sulfate ($S{O}_4^{2-}$), manganese ($Mn$), iodine (I), antimony ($Sb$), and total phosphorus (TP). The analysis was performed utilizing a diverse array of specialized instruments, which guaranteed the precision and dependability of the obtained results (

Table 1. Overview of quality metrics, units, and analytical equipment used for groundwater samples.

Parameter	Abbreviation	Unit	Analytical Equipment	Manufacturers
Calcium	$C{a}^{2+}$	mg/L	ICAP 6300Duo	Thermo Fisher Scientific, USA
Magnesium	$M{g}^{2+}$	mg/L	ICAP 6300Duo	/
Sodium	$N{a}^{+}$	mg/L	ICAP 6300Duo	/
Potassium	$K^{+}$	mg/L	ICAP 6300Duo	/
Ammonical nitrogen	$N{H}_4\text{-}N$	mg/L	AutoAnalyzer3	Seal Analytical, USA
Nitrate nitrogen	$N{O}_3\text{-}N$	mg/L	Dionex-2500	Thermo Fisher Scientific, USA
Nitrite nitrogen	$N{O}_2\text{-}N$	mg/L	TU-1950	PERSEE, China
Chloride	$C{l}^{-}$	mg/L	Dionex-2500	/
Sulfate	$S{O}_4^{2-}$	mg/L	Dionex-2500	/
Manganese	$Mn$	$\mathsf{\mu }$g/L	ICAP Q	Thermo Fisher Scientific, USA
Iodine	I	$\mathsf{\mu }$g/L	ICAP Q	/
Antimony	$Sb$	$\mathsf{\mu }$g/L	ICAP Q	/
Total phosphorus	$TP$	$\mathsf{\mu }$g/L	ICAP 6300Duo	/
Dissolved oxygen	DO	mg/L	SX-630 ORP Testor	Apera Instruments, German
Pondus Hydrogenii	pH	pH unit	SX-620 pH Testor	Apera Instruments, German
Total dissolved solids	TDS	mg/L	Hanna DiST	Hanna Instruments, USA

). These results are subsequently employed to assess the quality of the groundwater.

2.3. Fuzzy Synthetic Evaluation

As the single-factor water quality assessment mainly depends on the worst single pollution indicator in water quality to determine the overall water quality level, it sometimes imposes overly strict requirements on water quality, resulting in a lower evaluation result. Moreover, it is difficult to consider the interaction and comprehensive impact of various factors, which makes it impossible to comprehensively and scientifically reflect the water quality. Fuzzy sets and fuzzy optimization methods offer a valuable framework for managing the inherent uncertainty in groundwater quality assessment, enabling more precise and nuanced evaluations [60,61]. For instance, the fuzzy synthetic evaluation method effectively incorporates diverse parameters into the assessment process by quantifying and integrating the vague information from different evaluation indicators [62], which enables a more accurate portrayal of the primary sources of uncertainty that arise in large-scale, intricate decision-making scenarios [63]. In this research, the fuzzy membership function is adopted to evaluate the quality of groundwater. Based on the Chinese Groundwater Quality Standard [64], the fuzzy membership function is articulated across 5 distinct levels of groundwater quality for water quality I (j = 1):

$$r_{ij}=\left\{\begin{array}{cc}1,\hfill & C_i\le S_{i,j}\hfill \\ {\displaystyle \frac{C_i-S_{i,j+1}}{S_{i,j}-S_{i,j+1}}},\hfill & S_{i,j}<C_i<S_{i,j+1}\hfill \\ 0,\hfill & C_i\ge S_{i,j+1}\hfill \end{array}\right.$$

(1)

for water quality II to IV (j = 2–4):

$$r_{ij}=\left\{\begin{array}{cc}0,\hfill & C_i\le S_{i,j-1}\hfill \\ {\displaystyle \frac{C_i-S_{i,j-1}}{S_{i,j}-S_{i,j-1}}},\hfill & S_{i,j-1}<C_i<S_{i,j}\hfill \\ {\displaystyle \frac{C_i-S_{i,j+1}}{S_{i,j}-S_{i,j+1}}},\hfill & S_{i,j}<C_i<S_{i,j+1}\hfill \end{array}\right.$$

(2)

for water quality V (j = 5):

$$r_{ij}=\left\{\begin{array}{cc}0,\hfill & C_i\le S_{i,j-1}\hfill \\ {\displaystyle \frac{C_i-S_{i,j-1}}{S_{i,j}-S_{i,j-1}}},\hfill & S_{i,j-1}<C_i<S_{i,j}\hfill \\ 1,\hfill & C_i\ge S_{i,j}\hfill \end{array}\right.$$

(3)

where $r_{ij}$ represents the degree of fuzzy membership of the water quality indicator i belonging to the class j, $C_i$ indicates the analytically determined value of the water quality parameter i, $S_{i,j}$ stands for the threshold value of the water quality parameter i in class j. The water quality indicators and classes $r_{ij}$ are then employed to generate the fuzzy membership matrix R.

The fuzzy synthetic evaluation results for each sample can be determined by matrix B through the following equation:

$$\mathbf{B}=\mathbf{A}·\mathbf{R}$$

(4)

where A represents the weight matrix, which is composed of the normalized weight of each indication derived using Equation (5),

$$a_i={\displaystyle \frac{C_i}{S_i}}/\sum _{i=1}^n{\displaystyle \frac{C_i}{S_i}}$$

(5)

where $a_i$ is the normalized weight of indicator i, $S_i$ stands for the average value of indicator i across 5 thresholds.

2.4. Principal Component Analysis

Initially, the Kaiser–Meyer–Olkin (KMO) criterion and Bartlett’s test of sphericity were executed to evaluate the appropriateness of the dataset for PCA [65]. For PCA to be considered valid, it is necessary that the KMO value surpasses 0.5 and the significance level of Bartlett’s test of sphericity falls below 0.05. After implementing the PCA process, the key principal components corresponding to relevant groundwater quality indicators were successfully extracted.

$${(A_z)}_{ij}=a_{i1}{C}_{1j}+a_{i2}{C}_{2j}+\dots +a_{im}{C}_{mj},$$

(6)

where $A_z$ denotes the component score, i represents the index of components number, j stands the index of samples, a represents the component loading, C is the measured concentration of each groundwater quality parameter and m indicates the total number of groundwater quality parameters.

The principal components that exhibit eigenvalues exceeding 1.0 in the variance computation are deemed capable of providing qualitative insights about the probable contamination [66]. To facilitate a more straightforward understanding, it is typically necessary to rotate the original loadings principal component loadings are reallocated and polarized. Typically, this process is referred to as varimax rotation, resulting in the creation of new variables known as varifactors (VFs). For each of these VFs, the loadings of their components reflect the proportional contribution of groundwater quality metrics. Specifically, absolute loading values surpassing $0.75$ are classed as high, those between $0.75$ and $0.5$ are termed medium, and those ranging from $0.5$ to $0.3$ are defined as weak [67,68].

2.5. APCS-MLR Model

Utilizing the principal components derived from PCA, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) methodology was employed to quantify the individual contributions of each component. Specifically, the methodology involves transforming the standardized principal component scores into absolute principal component scores, followed by the integration of a multivariate linear regression analysis to comprehensively evaluate and model the concentrations of the measured contaminants [69,70]. The quantity of each groundwater quality parameter at each sampling location is assessed using a multiple linear regression analysis, as outlined in Equation (7):

$$C_{kj}=r_{k0}+\sum _{i=1}^p{r}_{ki}\times APC{S}_{ij},$$

(7)

where $r_{k0}$ and $r_{ki}$ denotes the intercept term and the coefficient in the multiple linear regression model for pollutant k, respectively; $APC{S}_{ij}$ represents the absolute principal component scores, which can be derived by following the computational steps outlined in Equations (8)–(10).

$${(Z_0)}_k={\displaystyle \frac{-\overline{C_k}}{{\sigma }_k}},$$

(8)

$${(A_0)}_i=\sum _{k=1}^m{S}_{ki}\times {(Z_0)}_k,$$

(9)

$$APC{S}_{ij}={(A_z)}_{ij}-{(A_0)}_i,$$

(10)

where ${(Z_0)}_k$ denotes the normalized concentration of contaminant k found in an area that is not subject to pollution, $\overline{C_k}$ and ${\sigma }_k$ stands for the mean and standard deviation concentration of contaminant k respectively, ${(A_0)}_i$ represents the principal component score in the non-pollution site, $S_{ki}$ denotes the score coefficient of component i for pollutant k and ${(A_z)}_{ij}$ is the principal component score of sample j in principal component i. It is noteworthy that during the calculation process, negative values may arise, potentially resulting in a cumulative contribution of all pollutants exceeding 100%. Consequently, to address this issue, Haji Gholizadeh et al. [52] introduced a method utilizing absolute values to compute the contribution of contaminant sources to groundwater quality parameters. This approach is outlined in Equations (11) and (12), ensuring a more accurate and meaningful assessment of the contaminant sources’ influence on groundwater quality.

$$P{C}_{ki}={\displaystyle \frac{\left|r_{ki}\times \overline{APC{S}_i}\right|}{\left|r_{k0}\right|+{\sum }_{i=1}^p\left|r_{ki}\times \overline{APC{S}_i}\right|}}\times 100\%,$$

(11)

$$P{C}_k={\displaystyle \frac{\left|r_{k0}\right|}{\left|r_{k0}\right|+{\sum }_{i=1}^p\left|r_{ki}\times \overline{APC{S}_i}\right|}}\times 100\%,$$

(12)

where $P{C}_{ki}$ denotes the relative contribution rate of contaminant source i to the pollutant k, $P{C}_k$ stands for the relative contribution rate of unidentified source to the pollutant k, $\overline{APC{S}_i}$ indicates the mean value of absolute principal component scores across all samples.

3. Results

3.1. Hydrochemical Characteristics

A comprehensive summary of the descriptive statistics pertaining to the physicochemical parameters of all groundwater samples is presented in

Table 2. Overview of groundwater physicochemical characteristics in Wujiang District.

Parameter	Min	Max	Mean	Standard Deviation	Coefficients of Variation (%)	National Standard, Class III	Exceeding Standard Rate (%)
$C{a}^{2+}$	13.60	158.50	70.08	27.56	39	/	/
$M{g}^{2+}$	0.83	75.20	17.75	12.41	70	/	/
$N{a}^{+}$	2.57	139.40	49.55	24.31	49	200	0
$K^{+}$	0.42	90.30	17.71	18.05	102	/	/
$N{H}_4\text{-}N$	/	6.62	0.50	1.14	228	0.50	18
$N{O}_3\text{-}N$	/	23.26	4.89	5.44	111	20	3
$N{O}_2\text{-}N$	/	2.98	0.25	0.52	207	1	8
$C{l}^{-}$	2.14	138.60	46.29	29.44	64	250	0
$S{O}_4^{2-}$	3.00	171.10	46.28	32.31	70	250	0
$Mn$	0.10	2050.00	117.21	368.12	314	100	14
I	1.81	605.00	82.42	110.55	134	80	28
$Sb$	/	5.89	2.31	1.40	61	5	4
P	6.37	3740.00	530.38	625.94	118	/	/
$DO$	0.2	21.0	3.1	2.3	75	/	/
$pH$	6.57	8.67	7.19	0.33	5	6.5–8.5	1
$TDS$	40	801	406	153	38	1000	0

. According to the Level III of Chinese Groundwater Quality Standard, the main exceeding indicators of groundwater in Wujiang District are I ($28\%$), $N{H}_4\text{-}N$ ($18\%$), and $Mn$ ($14\%$). For the hydrochemical test data of these three types of excessive ions, the inverse distance method is used for regional interpolation to generate a fishing net map with a grid size of 1 square kilometer (Figure 2). The groundwater exhibits a noteworthy maximum I content of $605$μg/L, with a mean value of $82.4\mathsf{\mu }$g/L. The overall exceedance rate is conspicuously high, leading to the general classification of this region’s water quality as Class IV. Additionally, the groundwater with elevated levels of I is predominantly found in the middle part of the investigated region, specifically Pingwang Town, as well as in the eastern region encompassing Lili Town. The groundwater in Wujiang District exhibits a maximum $N{H}_4\text{-}N$ content of $8.51$ mg/L, accompanied by an average concentration of $0.64$ mg/L. Furthermore, the coefficient of variation stands at a remarkable $228\%$, indicating significant variability in $N{H}_4\text{-}N$ levels. Notably, limited regions within the district have been classified as Class V water, a category denoting substandard water quality. Analysis of the concentration distribution map reveals that these Class V water areas are primarily concentrated in the northern section of Tongli Town, the southern portion of Qidu Town, and the eastern segment of Lili Town. The maximum concentration of $Mn$ in groundwater is recorded at $2050\mathsf{\mu }$g/L, whereas the mean concentration averages out to $117.2\mathsf{\mu }$g/L. The coefficient of variation stands at a considerable value of $314\%$, indicating significant variability in $Mn$ content. In comparison to other elements present in the region, the $Mn$ content exceeds the established standards at a relatively lower frequency, with approximately $14\%$ of groundwater samples surpassing the permissible limit. Nevertheless, the occurrence of numerous high-value instances is noteworthy, and the region encompassing Class V water exhibits a comparatively vast extent, primarily concentrated in the southwestern section of the study area.

To perform a more logical and comprehensive quality assessment of groundwater, a Fuzzy Synthetic Evaluation method is employed, and the outcome is presented in Figure 3. The groundwater quality in Wujiang District appears to be generally favorable. The Class I-III water accounts for $21.7\%$, $30.1\%$, $35.1\%$, respectively, of the total area, and is widely distributed throughout the region. Among them, the water quality in Tongli Town is the best as a whole, with almost all the water in the town belonging to Class I. Water of Class IV and V accounts for $4.7\%$ and $8.4\%$, respectively, of the total area and is mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town in the east.

The hydrochemical evolution of groundwater is influenced by numerous factors, and the analysis of the correlation among dissolved constituents within groundwater serves as a powerful tool for elucidating the origins of dissolved substances as well as the hydrochemical processes occurring during transportation. To achieve a thorough comprehension of the origins and contributions of diverse hydrochemical constituents found in the groundwater of Wujiang District, a correlation coefficient graph was constructed, focusing on the primary hydrochemical indicators specific to this region (Figure 4). A notable positive correlation exists between TDS and the ionic concentrations of $C{a}^{2+}$, $M{g}^{2+}$, $N{a}^{+}$ and $C{l}^{-}$ with correlation coefficients ranging from 0.55 to 0.77. This correlation suggests a common material source or formation mechanism underlying the observed variations in these parameters. Meanwhile, a positive correlation with a coefficient of 0.54 is observed between $N{H}_4\text{-}N$ and $N{O}_2\text{-}N$ in groundwater samples collected from Wujiang District. The strength of this correlation strongly suggests that the primary source of $N{H}_4\text{-}N$ and $N{O}_2\text{-}N$ in these groundwater samples is the discharge of domestic sewage. There exists a substantial positive correlation (0.57) between P and $N{O}_3\text{-}N$ concentrations in groundwater. Given that $N{O}_3\text{-}N$ typically originates from anthropogenic activities, including the discharge of domestic sewage and the contamination from agricultural fertilizers, the strong correlation observed between P and $N{O}_3\text{-}N$ suggests a common source. Based on these findings, it is hypothesized that the primary source of P and $N{O}_3\text{-}N$ in groundwater is agricultural fertilizers.

3.2. Source Identification

3.2.1. PCA

To evaluate the appropriateness of the dataset for conducting principal component analysis, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were utilized in the present academic investigation. The resulting KMO value stands at 0.75, exceeding the commonly accepted threshold of 0.5, thereby indicating a sufficient level of common variance among the variables. Furthermore, Bartlett’s test of sphericity yielded a probability value of 0.0001, which is significantly below the critical threshold of 0.001. This finding satisfies the prerequisites for the application of principal component analysis, thus validating the subsequent results derived from this analytical technique. Five principal components with eigenvalues greater than 1 were selected as the focus of this study, with a cumulative variance contribution rate of $67.88\%$ (

Table 3. Varimax rotated varifactor loadings of 16 groundwater quality parameters. Values that are highlighted in bold represent parameters with strong loadings.

Parameters	VF1	VF2	VF3	VF4	VF5
$C{a}^{2+}$	0.79	−0.03	0.24	0.15	−0.24
$M{g}^{2+}$	0.80	−0.38	0.07	−0.10	0.01
$N{a}^{+}$	0.76	−0.19	0.15	−0.25	0.12
$K^{+}$	0.08	0.34	0.51	0.28	−0.47
$N{H}_4\text{-}N$	0.25	−0.02	0.67	−0.24	0.08
$N{O}_3\text{-}N$	0.07	0.82	0.03	−0.09	0.19
$N{O}_2\text{-}N$	0.18	0.14	0.84	0.05	0.06
$C{l}^{-}$	0.67	−0.09	0.19	−0.40	0.14
$S{O}_4^{2-}$	0.63	0.32	−0.23	0.01	0.14
$Mn$	0.18	−0.33	0.06	−0.44	−0.19
I	0.29	−0.61	0.10	−0.27	0.05
$Sb$	0.09	0.39	−0.49	0.53	−0.04
P	−0.13	0.78	0.26	−0.05	−0.14
$DO$	0.04	0.10	0.14	0.24	0.84
$pH$	−0.08	−0.17	0.01	0.81	0.12
$TDS$	0.89	−0.02	0.20	0.01	−0.09
Eigenvalues	4.40	2.43	1.69	1.21	1.12
% of Variance	27.47	15.23	10.57	7.59	7.02
Cumulative %	27.47	42.70	53.27	60.86	67.88

and Figure 5).

The variance contribution rate of VF1, which stands at $27.47\%$, constitutes the primary determinant of the groundwater chemical characteristics observed in Wujiang District. In VF1, the elements $C{a}^{2+}$, $M{g}^{2+}$, $N{a}^{+}$, $C{l}^{-}$, $S{O}_4^{2-}$, and TDS exhibit significant loadings ranging from 0.63 to 0.89. This combination of ion characteristics, when analyzed collectively, suggests that VF1 reflects hydrochemical processes of natural origin, particularly those attributed to water–rock interactions.

The variance contribution rate of VF2 stands at $15.23\%$, with primary loading ions comprising P at a loading of 0.78 and $N{O}_3\text{-}N$ at 0.82. It is widely acknowledged that $N{O}_3\text{-}N$ in groundwater often originates from anthropogenic activities, including the disposal of domestic sewage and the contamination stemming from agricultural fertilizer application. Given the abundance of farmland and the dispersed rural population in Wujiang District, coupled with the intensive use of pesticides, fertilizers, and livestock feed, it is plausible to hypothesize that VF2 serves as an indicator of agricultural non-point source pollution.

The variance contribution rate of VF3 is $10.57\%$, and the main loading ions are $N{H}_4\text{-}N$(0.67) and $N{O}_2\text{-}N$(0.84). The most common sources of $N{H}_4\text{-}N$ and $N{O}_2\text{-}N$ in groundwater are domestic sewage and livestock manure. In addition, studies have shown that the artificial paving of asphalt pavements and the coverage of poorly porous fine clay in urban areas can cause a lack of oxygen in groundwater, promote the reduction conditions of aquifers, and thus prevent the transformation of $N{H}_4\text{-}N$(such as nitrification), leading to the accumulation of $N{H}_4\text{-}N$ and $N{O}_2\text{-}N$ in groundwater. In this study area, the main coverage area of VF3 is mostly urban areas, so VF3 should refer to urban domestic sewage discharge.

The variance contribution rate of VF4 has been determined to be $7.59\%$, with Sb (0.53) and pH (0.81) identified as the primary loading ions. The enrichment of Sb in groundwater is predominantly attributed to primary minerals or the discharge of wastewater from textile factories. Similarly, the observed abnormal increase in pH, indicating an alkaline environment, is associated with wastewater from printing and dyeing factories. Given the presence of significant textile factories in Zhenze Town and Songling Town within the research area, it is reasonable to deduce that VF4 represents the pollution discharge stemming from the textile industry in Wujiang District.

The variance contribution rate of VF5 is $7.02\%$, and the main loading ion is DO (0.84). High DO concentrations in groundwater are typically associated with industrial discharge or interactions with surface water. Given the observation that the loading of other hydrochemical indicators in VF5 is relatively low, and considering the well-developed surface water system within the study area, a logical inference is that VF5 serves as an indicator of interactions with surface water.

3.2.2. APCS-MLR

On the basis of principal component analysis, this study also used the APCS-MLR receptor model to conduct a quantitative assessment of the respective contributions of diverse pollution sources to the groundwater quality indicators in Wujiang District. The derived principal component coefficients and regression equation $R^2$ values, presented in

Table 4. Mean contribution of pollution sources to each groundwater quality metrics based on APCS-MLR model.

Parameters	Measured Mean	Predicted Mean	${\mathit{R}}^2$	Source Contribution
				VF1	VF2	VF3	VF4	VF5	UIS
$C{a}^{2+}$	70.08	70.07	0.76	0.33	0.00	0.06	0.06	0.30	0.24
$M{g}^{2+}$	17.75	17.75	0.79	0.64	0.11	0.03	0.07	0.03	0.12
$N{a}^{+}$	49.55	49.55	0.71	0.45	0.04	0.05	0.14	0.22	0.11
$K^{+}$	17.71	17.71	0.67	0.03	0.04	0.10	0.08	0.44	0.31
$N{H}_4\text{-}N$	0.50	0.50	0.58	0.16	0.00	0.24	0.14	0.15	0.31
$N{O}_3\text{-}N$	4.89	4.89	0.72	0.04	0.18	0.01	0.05	0.37	0.35
$N{O}_2\text{-}N$	0.25	0.25	0.76	0.10	0.03	0.29	0.03	0.10	0.45
$C{l}^{-}$	46.29	46.29	0.66	0.37	0.02	0.06	0.20	0.22	0.12
$S{O}_4^{2-}$	46.28	46.28	0.57	0.34	0.06	0.07	0.00	0.23	0.29
$Mn$	117.21	117.21	0.37	0.08	0.05	0.01	0.18	0.25	0.43
I	82.42	82.42	0.54	0.24	0.18	0.05	0.21	0.11	0.21
$Sb$	2.31	2.25	0.67	0.06	0.10	0.21	0.36	0.08	0.20
P	530.38	530.37	0.71	0.09	0.18	0.10	0.03	0.28	0.32
$DO$	3.1	3.10	0.79	0.01	0.01	0.02	0.04	0.47	0.45
$pH$	7.19	7.19	0.71	0.01	0.01	0.00	0.09	0.04	0.84
$TDS$	406	406.00	0.84	0.59	0.00	0.08	0.01	0.18	0.14

, exhibit a commendable correlation between the predicted APCS-MLR concentrations and their corresponding measured values. Specifically, for all water quality indicators, except for Mn (0.37), I (0.54), $S{O}_4^{2-}$ (0.57), and $N{H}_4\text{-}N$ (0.58), the $R^2$ values exceed 0.6, averaging at 0.68. This observation signifies a substantial agreement between the modeled and observed data, thus validating the appropriateness and acceptability of the simulation results.

Regarding the analysis, Figure 6 provides a detailed visualization of the spatial distribution of pollution sources within three distinct categories: VF2, representing agricultural non-point source pollution; VF3, pertaining to domestic sewage; and VF4, associated with pollution from the textile industry. Upon scrutiny of the figure, it becomes apparent that Qidu Town, Zhenze Town, Taoyuan Town, and Shengze Town are the regions most severely impacted by agricultural non-point source pollution. Conversely, Lili Town, Songling Town, Tongli Town, and Qidu Town exhibit significant pollution stemming from domestic sources. Furthermore, Zhenze Town, Taoyuan Town, Shengze Town, Songling Town, and Tongli Town are identified as the areas heavily contaminated by the textile industry. These insights offer a comprehensive understanding of the spatial patterns and impact of pollution sources within the study area.

Furthermore, Figure 7 illustrates the results of source assignment obtained by the APCS-MLR model, the relative contributions of each pollution source to various pollutants were calculated, and the average relative contribution of each pollution source was determined for a comprehensive evaluation. The hydrochemical characteristics of groundwater in Wujiang District are most significantly influenced by the natural hydrochemical processes (VF1), accounting for $22.11\%$ of the total sources, with higher contributions to $M{g}^{2+}$ ($64\%$) and TDS ($59\%$). The second most significant influence comes from surface water sources (VF5), comprising $21.73\%$ of the total sources, with the highest contribution to DO ($47\%$). The sewage discharge from the textile industry (VF4) accounts for $10.63\%$ of the total sources, with relatively high contributions to Sb ($36\%$), I ($21\%$), and $C{l}^{-}$ ($20\%$) among the 16 water quality parameters. Additionally, urban domestic sewage discharge (VF3) constitutes $8.69\%$ of all pollution sources, with higher contributions to $N{O}_2\text{-}N$ ($29\%$) and $N{H}_4\text{-}N$ ($24\%$). Agricultural non-point source pollution (VF2) accounts for $6.26\%$ of all pollution sources, with a relatively high contribution to P ($18\%$), $N{O}_3\text{-}N$ ($18\%$), and I ($18\%$). In addition, unknown pollution sources account for $30.59\%$ of the total sources, primarily due to the complexity of pollutant evolution processes. Consequently, the order of contributions to groundwater pollution sources in Wujiang District, Suzhou City, is as follows: natural hydrochemical processes > surface water sources > sewage discharge from the textile industry > urban domestic sewage discharge > agricultural non-point source pollution.

4. Discussion

Through the application of fuzzy synthetic evaluation and source apportionment of groundwater pollution conducted in this study, we successfully identify the primary pollutants and their respective origins of the shallow groundwater in Wujiang District. A comprehensive analysis of the distribution of pollution sources for the main exceeding indicators can be found in Table 4 and Figure 6. The main origins of I, $N{H}_4\text{-}N$, and $Mn$ are natural hydrochemical processes, domestic sewage, and surface water sources, respectively. Furthermore, a comparative analysis of land use patterns alongside the spatial distribution of VF2, VF3, and VF4 reveals a notable consistency. Agricultural pollution is predominantly concentrated in paddy field areas. Pollution associated with the textile industry is primarily concentrated in the southern and central areas of Wujiang District, demonstrating a strong correlation with the geographical distribution of textile factories in the area. As for the domestic sewage discharge, the primary distribution does not concentrate within built-up areas. This phenomenon can be attributed to the highly systematic nature of the drainage infrastructure in such regions. In contrast, urban-rural fringe areas are more vulnerable to contamination from domestic sewage discharge.

In addition to the aforementioned advantages, the results further indicate that for certain indicators (pH and DO), the proportion of unidentified pollution sources remains excessively high. This situation underscores the need to incorporate additional hydrochemical indicators into the APCS-MLR model, or alternatively, employing a combined approach with other methods to enhance the effectiveness of apportioning the contaminant sources.

5. Conclusions

In this research, a fuzzy comprehensive evaluation technique was implemented to evaluate the groundwater quality in Wujiang District, Suzhou City. Subsequently, Principal Component Analysis (PCA) was leveraged to discern the underlying natural and human-induced factors that may be influencing the groundwater quality. Based on these insights, the study adopted the Absolute Principal Component Scores–Multiple Linear Regression (APCS-MLR) model to quantitatively ascertain the contributions of potential pollution sources to 16 distinct groundwater quality parameters.

The findings suggest a generally favorable groundwater quality within the studied region, with Classes I-III comprising $21.7\%$, $30.1\%$, and $35.1\%$ of the total area, respectively, and evenly dispersed across the area. Meanwhile, Class IV and Class V, accounting for $4.7\%$ and $8.4\%$, respectively, are predominantly located in specific areas such as the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Moreover, utilizing the correlation of hydrochemical components and principal component analysis, we have pinpointed five potential origins that impact groundwater quality in Wujiang District. These include natural hydrochemical processes, agricultural non-point source pollution, urban domestic sewage discharge, sewage discharge from the textile industry, and groundwater–surface interaction. Utilizing Principal Component Analysis (PCA) as a foundation, the APCS-MLR receptor model was employed to quantify the individual contributions of potential pollution sources within the study area. The major sources of pollution were identified as follows: sewage discharge from the textile industry ($10.63\%$) > urban domestic sewage discharge ($8.69\%$) > agricultural non-point source pollution ($6.26\%$). The areas severely affected by sewage discharge from the textile industry are mainly distributed in Zhenze Town, Taoyuan Town, Shengze Town, Songling Town, and Tongli Town. The areas severely affected by domestic sewage discharge are primarily located in the northern part of the study area, including Lili Town, Songling Town, and Tongli Town. The areas severely affected by agricultural non-point source pollution are mainly distributed in the southern part of the study area, primarily in Qidu Town, Zhenze Town, Taoyuan Town, and Shengze Town. All in all, this study provides scientific evidence and reasonable recommendations for the pollution management of shallow groundwater resources by evaluating groundwater quality and quantitatively analyzing pollution source contributions in Wujiang District, Suzhou City.