Zoning Method for Groundwater Pollution Risk Control in Typical Industrial–Urban Integration Areas in the Middle Reaches of the Yangtze River

Abstract

With increasing urban economic development, some industrial parks and residential areas are being situated adjacent to each other, creating a potential risk of soil and groundwater contamination from the wastewater and solid waste produced by enterprises. This contamination poses a threat to the health of nearby residents. Currently, groundwater pollution prevention and control zoning in China primarily targets groundwater environmental pollution risks and does not consider the health risks associated with groundwater exposure in industry–city integration areas. Therefore, a scientific assessment of environmental risks in industry–city integration areas is essential for effectively managing groundwater pollution. This study focuses on the high frequency and rapid pace of human activities in industry–city integration areas. It combines health risk assessment and groundwater pollution simulation results with traditional groundwater pollution control classification outcomes to develop a groundwater pollution risk zoning framework specifically suited to these integrated areas. Using this framework, we systematically assessed groundwater pollution risks in a representative industry–city integration area in the middle reaches of the Yangtze River in China and delineated groundwater pollution risk zones to provide a scientific basis for local groundwater environmental management. The assessment results indicate that the total area of groundwater pollution risk control zones is 30.37 km², accounting for 19.06% of the total study area. The first-level control zone covers 5.38 km² (3.38% of the total area), while the secondary control zone spans 24.99 km² (15.68% of the total area). The first-level control zone is concentrated within industrial clusters, whereas the secondary control zone is widely distributed throughout the region. In comparison to traditional assessment methods, the zoning results derived from this study are more suitable for industry–city integration areas. This study also provides groundwater management recommendations for such areas, offering valuable insights for groundwater control in integrated industrial–residential zones.

1. Introduction

Groundwater is a vital resource for irrigation, industrial activities, and drinking [1,2]. However, with rapid economic development, groundwater resources in some areas have been impacted by human activity, resulting in groundwater pollution [3,4]. In industry–city integration areas, where industrial enterprises and residential neighborhoods are adjacent, the wastewater and solid waste generated by industrial enterprises pose health risks to residents [5,6,7]. Scientifically delineating groundwater pollution risk control zones can effectively support local groundwater environmental management [8,9].

Groundwater pollution risk assessment methods primarily include overlay index methods, process-based simulation methods, and statistical approaches [10,11,12,13,14,15,16,17]. Groundwater pollution risk assessments mainly involve evaluating groundwater vulnerability and pollution loads [18]. Among groundwater vulnerability assessment methods, the DRASTIC method, proposed by Aller in 1987, is currently the most widely used [19]. This method primarily considers the natural conditions of the study area, but has certain limitations. Many researchers have optimized the model by adjusting the evaluation parameters [20,21], applying the analytic hierarchy process [22,23], integrating with GIS software (ArcGIS 10.4.1) [24,25,26], and utilizing deep learning techniques [27,28] to develop groundwater vulnerability assessments suitable for various regions.

For groundwater pollution load assessments, mainstream methods mainly involve scoring various pollution sources to calculate pollution loads [29,30], with some researchers proposing new pollution load assessment methods based on MODFLOW and GIS [31]. Chemical industries and landfills can introduce heavy metals, inorganic substances, and emerging pollutants to the environment, which threaten surrounding groundwater resources [32,33,34]. Numerous studies have conducted human health risk assessments and pollution prediction simulations for groundwater in the vicinity of such areas [35,36,37].

China’s Ministry of Ecology and Environment issued groundwater pollution prevention and control zoning guidelines in 2014, 2019, and 2023 [38,39,40], all of which emphasize the importance of advancing groundwater pollution prevention to ensure groundwater safety. The 2014 and 2019 guidelines combined pollution source load assessment (PI) and groundwater vulnerability assessment (DI) to derive groundwater pollution control values, which were then integrated with groundwater value assessment (VI) to define the boundaries of groundwater pollution prevention zones. The 2023 guideline introduced an updated approach, first combining groundwater value assessment (VI) with groundwater vulnerability assessment (DI) to identify control zones, and then integrating it with groundwater pollution load assessment (PI) to delineate the boundaries of groundwater pollution prevention zones. This study employs the 2023 guideline methodology. However, the 2014, 2019, and 2023 guidelines, as well as most current academic studies, focus primarily on groundwater environmental pollution risk, without addressing the health risks associated with groundwater exposure in industry–city integration areas or the impact of future industrial development on groundwater pollution control. S Holding assessed groundwater vulnerability on islands by predicting changes in aquifer recharge under climate change scenarios [41]; Zhang conducted a groundwater pollution risk assessment in the Guanzhong Basin of China by integrating vulnerability with pollution load [30]. Considering the high frequency and rapid growth of human activity in industry–city integration areas, this study innovatively combines three dimensions: key groundwater pollution prevention zones, human health risk assessments, and groundwater pollution simulation predictions. This approach constructs a groundwater pollution risk zoning framework specifically tailored to industry–city integration areas.

2. Study Area

The study area is located in a city in the middle reaches of the Yangtze River in China, bordered by Poyang Lake to the north and the Gan River to the east. It spans approximately 15 km from north to south and 30 km from east to west, covering an area of about 158 km², with an overall topography that slopes from high elevations in the northwest to lower elevations in the southeast. The area experiences a subtropical monsoon climate, with an average annual rainfall of approximately 1651.2 mm and an average annual evaporation of 1141.5 mm. The rainfall distribution is uneven throughout the year, with the majority occurring from February to August.

With advances in economic development, the area has gradually formed seven major industrial clusters. The BS area is primarily characterized by papermaking and chemical manufacturing industries, while the NC, HX, and XB regions are dominated by metal smelting and processing sectors. The LQ and LK zones specialize in electronics manufacturing, whereas the MY area focuses on waste treatment operations. The wastewater and solid waste generated from these industries are the primary sources of pollution in the area. A preliminary groundwater contamination survey has identified that groundwater in certain plots has been affected by industrial activities, with contaminants including ammonia nitrogen, manganese, petroleum hydrocarbons (C₁₀–C₄₀), and carbon tetrachloride. Additionally, the area has a high concentration of residential neighborhoods and universities, with some residential areas adjacent to industrial enterprises, reflecting an industry–city integration status. This wastewater and solid waste from industrial parks pose health risks to nearby residents. The geographical location of the study area is shown in Figure 1.

The study area exhibits a topographic gradient descending from the northwest to the southeast, with a gentle northwest–southeast inclination. Geomorphologically, it comprises three primary units: tectonically eroded hilly terrain, denudation–accumulation platform terrain and fluvial–alluvial plain terrain. Industrial clusters are predominantly distributed within the denudation–accumulation platform zone, with limited presence in the fluvial–alluvial plain areas. The study area is characterized by a predominance of sandy loam soils, with subordinate occurrences of loam and clay loam. In the study area, Quaternary and Paleogene strata exhibit extensive development, whereas Cambrian and Cretaceous formations are only sporadically exposed in localized outcrops. Groundwater is classified into three main types: pore water in loose rock formations, pore–fracture water in red beds, and fracture water in bedrock. The pore water in loose rock formations comprises alluvial deposits of the Holocene Series, Upper Pleistocene Series, and Middle Pleistocene Series, characterized by stratified sand and gravel layers. The pore–fracture water in red beds is all situated beneath the Quaternary pore aquifer. Groundwater is primarily recharged by atmospheric precipitation and exhibits a strong recharge–discharge relationship with surface water, resulting in frequent, cyclical exchange. This flow system ultimately discharges towards the east, exhibiting a distal discharge characteristic.

3. Materials and Methods

3.1. Data Sources

The water yield property and net recharge rates of aquifers were obtained from hydrogeological maps compiled by the Jiangxi Provincial Geological Bureau. The groundwater quality assessments, groundwater usage zones, groundwater table depth, aquifer thickness, soil medium, unsaturated zone medium type, and permeability coefficients were determined from field measurements. Land use types were derived from the 2020 fine classification of land cover by the Aerospace Information Research Institute, Chinese Academy of Sciences (https://www.cas.cn), and terrain slope data was sourced from NASA’s ASTER GDEM V3 global digital elevation model data (https://www.earthdata.nasa.gov). Data related to groundwater pollution loads was provided by the Nanchang Ecological and Environmental Bureau in Jiangxi Province.

In this study, a total of 339 groundwater monitoring wells were deployed, primarily monitoring unconfined aquifers. Samples were collected as follows: 138 samples between August and September 2023, 121 samples between November 2023 and January 2024, and 201 samples between April and May 2024. The sampling points in December 2023 matched those in August 2023, while the sampling points in 2024 differed from those in 2023. Before sample collection, the sampling containers were rinsed three times with well water. A portable multi-parameter water quality analyzer was used for in situ measurement of pH, with an accuracy of ±0.01 units. Total hardness was determined using the disodium EDTA titration method; total dissolved solids were measured by the gravimetric method; and sulfate and chloride were analyzed using ion chromatography. Manganese, aluminum, sodium, potassium, calcium, and magnesium were determined using inductively coupled plasma optical emission spectrometry (ICP-OES). Chemical oxygen demand was measured by the acidic potassium permanganate titration method. Ammonium nitrogen and nitrite nitrogen were analyzed using spectrophotometry, while nitrate nitrogen and fluoride were measured using ion chromatography. Bicarbonate was determined using the acid–base indicator titration method. The sampling locations are shown in Figure 1.

3.2. Analytical Methods

Groundwater in industry–city integration areas is primarily affected by industrial pollution. In this study, the groundwater risk control zones are described through three aspects: the division of key groundwater pollution prevention zones, human health risk assessment of groundwater, and groundwater pollution prediction simulations. The key groundwater pollution prevention zones are divided based on groundwater value assessment, groundwater vulnerability assessment, and groundwater pollution load assessment. The groundwater value assessment indicators include the water yield property, groundwater quality assessment, and groundwater usage zones. The groundwater vulnerability assessment includes factors such as the groundwater table depth (D), aquifer net recharge rate (R), aquifer thickness (A), soil medium (S), terrain slope (T), unsaturated zone medium type (I), permeability coefficient (C), and land use types (L). The groundwater pollution load assessment includes pollution source types, the likelihood of pollutant release, and the amount of pollutant released. The human health risk assessment of groundwater is conducted based on the USEPA human health risk assessment methodology. Groundwater pollution prediction simulations are performed on confirmed polluted sites within the study area to delineate groundwater pollution control zones. Based on the abovementioned classification process, the results from each component are overlaid to determine the first-level and secondary control zones. A flowchart of the control methods is shown in Figure 2.

3.2.1. Key Groundwater Pollution Prevention Zones

Groundwater Value Assessment

This study comprehensively evaluates the groundwater value in the research area by analyzing the water yield property, current groundwater quality, and groundwater usage zones. The water yield property is divided into three levels: areas with a single-well discharge greater than 1000 m³/d are classified as having strong yield properties, those with a discharge between 100 and 1000 m³/d are classified as having moderate yield properties, and those with a discharge less than 100 m³/d are classified as having weak yield properties. The study uses the Environmental Water Quality Index (EWQI) to assess the groundwater quality in the research area. The basic idea of the EWQI is to determine the weight of the evaluation indicators through entropy values, converting a large amount of water quality data into representative values that reflect the water quality status. The entropy-weight method has strong objectivity and effectively eliminates human influence in weighting calculations [2]. Areas with residential use of groundwater are classified as groundwater usage zones, while areas without residential use of groundwater are classified as non-usage zones.

Groundwater Vulnerability Assessment

The traditional method for assessing groundwater vulnerability is the DRASTIC model. However, considering that the study area is an industry–city integration zone, where land development has a negative impact on groundwater quality [42], the land use type (L) is introduced [30,43]. The DRASTIC-L model evaluation indicators include the groundwater table depth (D), aquifer net recharge rate (R), aquifer thickness (A), soil medium (S), terrain slope (T), unsaturated zone medium type (I), permeability coefficient (C), and land use type (L). The modified groundwater vulnerability index (DI) formula is as follows:

$$DI=D_w{D}_R+R_w{R}_R+A_w{A}_S+S_w{S}_R+T_w{T}_R+I_w{I}_R+C_w{C}_R+L_w{L}_R$$

(1)

Due to the fixed values used in traditional evaluation models, this method has certain limitations for different study areas. To address this, the analytical hierarchy process (AHP) is used to replace the traditional expert scoring method for reassigning weights [9], making the results more reasonable. The groundwater vulnerability index (DI) of the DRASTIC-L model is obtained through weighted summation. The AHP method mainly involves constructing a judgment matrix, determining weights, and conducting a one-time consistency check. The importance ranking table is shown in

Table 1. Importance ranking table.

Value	Meaning
1	Equally important
3	Slightly important
5	Moderately important
7	Very important
9	Extremely important
2/4/6/8	Intermediate values between the above cases
Reciprocal	Inverse importance level

.

The final results must satisfy the consistency check, as shown in the following formula:

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(2)

$$CR={\displaystyle \frac{CI}{RI}}$$

(3)

where CI is the consistency index that measures the deviation of the judgment matrix, λ_max is the maximum eigenvalue of the matrix, n is the order of the matrix, RI is the average random consistency index, and CR is the consistency ratio. The results pass the test if CR is less than 0.1. The classification results of the DRASTIC-L model were based on the “Technical Guidelines for Defining Key Areas for Groundwater Pollution Prevention and Control (Trial)” [40] and relevant literature [44,45]. On this basis, the final classification was comprehensively determined according to the hydrogeological characteristics of the study area. The classification table is shown in

Table 2. Groundwater vulnerability risk assessment level classification and scoring.

D	R	A	S	T	I	C	L	Score
Groundwater Depth	Aquifer Recharge Rate	Aquifer Thickness	Soil Medium	Slope	Unsaturated Zone Medium	Permeability	Land Use Type
m	mm/a	m	/	/	/	m/d	/
>30	0	>11.74	Rock	>10	Clay	0–4		1
25–30	0–51	11.74–9.641	Clay Loam	9–10	Silty Clay	4–12	Grassland	2
20–25	51–71	8.241–9.641	Sandy Loam	8–9	Sandy Clay	12–20	Shrub Land	3
15–20	71–92	7.133–8.241	Loam	7–8	Silt Sand	20–30	Forest	4
10–15	92–117	6.142–7.133	Sandy Loam	6–7	Silt Sand–Fine Sand	30–35	Water Bodies	5
8–10	117–147	5.267–6.142	Cohesive Clay	5–6	Fine Sand	35–40	Agricultural Land	6
6–8	147–178	4.451–5.267	Silt	4–5	Medium	40–60		7
4–8	178–216	3.693–4.451	Gravel	3–4	Coarse Sand	60–80		8
2–4	216–235	2.935–4.451	Gravel–Cobble Mixture	2–3	Sandstone	80–100	Built-up Land	9
0–2	>235	<2.935	Thin or Missing	<2	Gravel–Cobble Mixture	<100		10

.

Groundwater Pollution Load Assessment

The groundwater pollution load reflects the impact of human activity on groundwater. The types of pollution sources in the study area include industrial enterprises, landfills, and gas stations. This assessment evaluates the groundwater pollution load by analyzing the pollution source type (T), the likelihood of release (L), and the potential release amount (Q) of pollutants. This scoring is based on the “Technical Guidelines for Defining Key Areas for Groundwater Pollution Prevention and Control (Trial)” [40]. The pollution load for an individual pollution source is calculated as follows:

$$P_i = T_i \times l_i \times Q_i$$

(4)

where P_i represents the pollution load risk index of source i, T_i indicates the toxicity of pollutants from source i, L_i denotes the likelihood of release from source i, and Q_i is the quantity of potential pollutants released.

The comprehensive pollution load assessment formula is as follows:

$$PI = \sum P_i \times W_i$$

(5)

where PI is the comprehensive risk index of pollution sources, and W_i is the weight assigned to pollution source i. Based on hydrogeological data and field investigations, a buffer radius of 600 m was chosen for pollution sources in the study area. The groundwater pollution load assessment is determined by considering the pollutant toxicity, likelihood of release, and the quantity of potential pollutants. Regions with high pollution load (PI ≥ 60) are designated as first-level control zones, while regions with moderate pollution load (40 ≤ PI < 60) are designated as secondary control zones.

(1)

Pollution source types

The pollution source type represents the physicochemical properties of pollutants released from specific pollution sources. In the study area, pollution sources include industrial pollution sources, landfills, and gas stations. The scores for each type are listed in

Table 3. Classification and scoring of pollution source types.

Pollution Source Type	Toxicity Category	Ti Score
Industrial Pollution Sources	Petroleum processing, coking, and nuclear fuel processing	2.5
	Non-ferrous metal smelting and rolling	3
	Ferrous metal smelting and rolling	2
	Chemical raw material and product manufacturing	2.5
	Textile industry	1
	Leather, fur, and feather products	1
	Metal product industry	1.5
	Other industries	0.2
Landfills	Primarily household waste	1.5
Gas Stations	Petroleum hydrocarbons and polycyclic aromatic hydrocarbons	2.5

.

(2)

Likelihood of pollutant release

The likelihood of pollutant release is associated with factors such as the time since the facility’s establishment and the protective measures in place. Generally, older facilities with poorer protective measures have a higher likelihood of groundwater pollution. The scoring criteria are shown in

Table 4. Classification and scoring of pollutant release likelihood.

Pollution Source Type	Release Likelihood	Li Score
Industrial Pollution	Commissioned after 2011	0.2
	Commissioned between 1998 and 2011	0.6
	Commissioned before 1998 or lacking protection	1
Landfills	≤5 years, AAA grade for harmless treatment	0.1
	>5 years, AAA grade for harmless treatment	0.2
	≤5 years, AA grade for harmless treatment	0.2
	>5 years, AA grade for harmless treatment	0.4
	≤5 years, A grade for harmless treatment	0.4
	>5 years, A grade for harmless treatment	0.5
	Basic protection, B grade for harmless treatment	0.6
	No protection, B grade for harmless treatment	1
Gas Stations	≤5 years, double-walled tank or anti-seepage pool	0.1
	(5, 15] years, double-walled tank or anti-seepage pool	0.2
	>15 years, double-walled tank or anti-seepage pool	0.5
	≤5 years, single-walled tank without anti-seepage pool	0.2
	(5, 15] years, single-walled tank without anti-seepage pool	0.6
	>15 years, single-walled tank without anti-seepage pool	1

.

(3)

Pollutant release quantity

The amount of pollutant released is directly proportional to the pollution area and concentration: larger areas and higher concentrations result in greater amounts of pollutants reaching groundwater. The grading and scoring for potential pollutant release quantities are shown in

Table 5. Classification and scoring of pollutant release quantities.

Pollution Source Type	Type	Qi Score
Industrial Pollution (wastewater discharge, units: ×103 t/a)	≤1	1
	(1, 5]	2
	(5, 10]	4
	(10, 50]	6
	(50, 100]	8
	(100, 500]	9
	(500, 1000]	10
	>1000	12
Landfills (landfill volume, units: ×103 m3)	≤1000	4
	(1000, 5000]	7
	>5000	9
Gas Stations (number of 30 m3 tanks)	1	1

.

3.2.2. Human Health Risk Assessment of Groundwater

Inorganic and organic substances in groundwater can impact human health through exposure pathways such as ingestion and dermal contact [46]. This study applies the USEPA human health risk assessment method to investigate groundwater in the study area [47]. The main steps include hazard identification, exposure assessment, toxicity assessment, and risk characterization [48]. Exposure parameters specific to the study area were used in the calculations to improve accuracy. The primary exposure pathways for groundwater in the study area are ingestion and skin contact. Therefore, these two exposure pathways were selected for assessment. To ensure representativeness and comprehensively consider groundwater contamination in the area, heavy metals (Fe, Mn, Cd, Co), inorganic substances (NH₄⁺, NO₃⁻, As, F), and organic substances (CCl₄, petroleum hydrocarbons (C₁₀–C₄₀)) were chosen as evaluation factors. The exposed population is divided into adults and minors.

The formulas for the route of consumption and dermal exposure are as follows [49,50,51]:

$$R_c = ADD \times SF/L$$

(6)

$$R_n = ADD/\left(RfD \times L\right)$$

(7)

The formulas for calculating the average daily exposure (ADD) for children and adults are as follows:

$$AD{D}_i = c \times IR \times ED \times EF/\left(BW \times AT\right)$$

(8)

$$AD{D}_d = c \times SA \times ET \times ED \times EF \times CF \times PC/\left(BW \times AT\right)$$

(9)

Since the health effects of various substances in groundwater are cumulative [52], the total health risk is calculated as

$$RT = \sum R$$

(10)

The meanings and values of the parameters are shown in

Table 6. Summary of parameters for the assessment model.

Parameter	Meaning	Unit	Value
RT	Total carcinogenic risk	1/a	Calculated
Rc	Carcinogenic risk	1/a	Calculated
Rn	Non-carcinogenic risk	1/a	Calculated
L	Average lifespan	a	76 a
ADDi	Average daily exposure by ingestion	mg/(kg·d)	Calculated
SF	Carcinogenic slope factor	(kg·d)/mg	Table 7
RfD	Reference dose	mg/(kg·d)	Table 7
ADDd	Average daily exposure by dermal contact	mg/(kg·d)	Calculated
c	Concentration	mg/L	Measured value
IR	Ingestion rate	L/d	Children: 0.7 L/d, Adults: 1.8 L/d
ED	Exposure duration	a	Children: 6 a, Adults: 24 a
EF	Exposure frequency	d/a	Both children and adults: 350 d/a
BW	Body weight	kg	Children: 19.2 kg, Adults: 61.8 kg
AT	Average exposure time	d	Carcinogenic: 27,740 d, Non-carcinogenic: 2196 d
SA	Skin contact surface area	cm2	Children: 6000, Adults: 18,000
ET	Daily exposure time	h/d	Children: 0.42, Adults: 0.63
CF	Volume conversion factor	ml/cm2	0.001
PC	Permeability coefficient	cm/h	Table 7

, and the intake parameters for pollutants through drinking water are listed in

Table 7. Drinking water pollutant intake parameters.

No	Pollutant Indicator	PC	SFo		RFDi
			Oral	Dermal	Oral	Dermal
1	Fe	0.001	-	-	0.7	0.7
2	Mn	0.001	-	-	0.14	0.14
3	Cd	0.001	-	-	0.001	0.000025
4	Co	0.0004	-	-	0.0003	0.0003
5	NH4+	0.001	-	-	0.97	0.97
6	NO3−	-	-	-	1.6	1.6
7	As	0.001	1.5	1.5	0.0003	0.0003
8	F	0.001	-	-	0.04	0.04
9	CCl4	0.016	0.07	0.07	0.004	0.004
10	Petroleum hydrocarbons (C10-C40)	0.001	-	-	0.04	0.04

Note: “-” indicates missing data.

. The parameter selection for this project is based on the guidelines for health risk assessment of groundwater pollution [53].

In this study, the acceptable non-carcinogenic hazard quotient for pollutants is set to 1, and the acceptable carcinogenic risk is set to 10⁻⁶, which facilitates the determination of risk control values for each pollutant. Groundwater health risk assessment is divided into usage and non-usage zones. In usage zones, if a groundwater indicator exceeds the groundwater risk control value and surpasses the Class III quality standard for Chinese groundwater [54], it is designated as a first-level control zone. In non-usage zones, if a groundwater indicator exceeds the risk control value and the Class IV quality standard for Chinese groundwater [54], it is designated as a secondary control zone.

3.2.3. Groundwater Pollution Prediction and Simulation

Groundwater flow in the study area is simulated using numerical modeling software, and pollution prediction simulations are conducted in areas known to be affected by contamination. Areas within the factory boundaries are designated as first-level control zones. Outside the factory boundaries, areas where pollutant concentrations are projected to exceed the Chinese groundwater Class III quality standard within the next 20 years are designated as secondary control zones.

4. Key Groundwater Pollution Prevention Zones

4.1. Groundwater Value Assessment

This study evaluates the groundwater value in the study area through a comprehensive analysis of the water yield property, groundwater quality, and groundwater use zones.

4.1.1. Water Yield Property

In the study area, regions with strong water yield properties are found only in the southeastern part, covering an area of 0.14 km², or 0.1% of the total area. Areas with moderate water yield properties are primarily located in the western mountainous region and central areas, totaling 103.52 km² or 64.9% of the area. Areas with low water yield properties are mainly in the southwestern part of the region, covering 55.75 km² or 35.0%. Figure 3 covers the distribution details.

4.1.2. Groundwater Quality Assessment

Seventeen indicators were selected for the EWQI water quality assessment, including pH, total hardness, total dissolved solids, sulfate, chloride, manganese, aluminum, oxygen demand, ammonia nitrogen, sodium, nitrite nitrogen, nitrate nitrogen, fluoride, calcium, potassium, magnesium, and total carbonate. As the sampling locations in August and December 2023 were identical, the EWQI values for 2023 were averaged for these months.

According to the 2017 edition of the Groundwater Quality Standard [55], manganese (Mn) exhibits the highest exceedance frequency among all monitored parameters: 263 samples (57.17%) exceed the Class III groundwater standard, and 106 samples (23.04%) exceed the Class IV standard. Elevated exceedance rates are also observed for pH and NH₄⁺-N. Specifically, 202 samples (43.91%) exceed the Class III pH standard, with 25 samples (5.43%) surpassing the Class IV standard; for NH₄⁺-N, 113 samples (24.57%) exceed the Class III limit and 67 samples (14.57%) exceed the Class IV limit. Hydrochemically, groundwater in the study area is dominated by Ca²⁺ as the principal cation and HCO₃⁻ as the principal anion, yielding a prevailing HCO₃–Ca facies. In discharge zones and areas subject to anthropogenic disturbance, Na⁺ and Cl⁻ become the predominant ions.

Based on the EWQI results, the water quality is categorized into five classes. Among 339 samples, 79.1% had EWQI values below 100, indicating good water quality; 7.4% had values between 100 and 150, indicating moderate quality; 4.7% had values between 150 and 200, indicating poor quality; and 8.8% had values above 200, indicating very poor quality. See Figure 4 for groundwater EWQI evaluation results in the study area.

4.1.3. Groundwater Use Zones

The study area has no large centralized groundwater supply sources and no extensive use of groundwater for agriculture, industry, or drinking purposes. However, field investigations revealed that some areas still have groundwater wells. These regions are designated as groundwater use zones, as shown in Figure 5.

4.1.4. Groundwater Function Value Evaluation Results

Based on the results of water yield property and quality assessments, a matrix is created to represent the groundwater function value, as shown in

Table 8. Groundwater function value matrix.

	Quality	Good	Moderate	Poor
Abundance
Strong		High	High	Moderate
Moderate		High	Moderate	Low
Weak		Low	Low	Low

.

After obtaining the evaluation results, groundwater use zones are directly assigned to areas with high groundwater function values. The area of regions with high groundwater function value is 94.14 km², accounting for 59.09%; the area with moderate groundwater function value is 11.31 km², accounting for 7.10%; and the area with low groundwater function value is 53.87 km², accounting for 33.81%. Figure 6 shows a detailed representation of this.

4.2. Groundwater Vulnerability Assessment

In this study, the AHP weight range for the DRASTIC-L model is 1 to 9. Based on the judgment matrix [a_ij], the maximum eigenvalue λ_max is calculated as 7.3287, with a CR of 0.04, which is less than 0.1, meeting the consistency requirement. The subjective weighting results are shown in

Table 9. Pairwise judgment matrix and subjective weighting results.

Indicator	D	R	A	S	T	I	C	L	Subjective Weight
D	1	3	5	7	9	1	5	5	0.298
R	1/3	1	2	6	7	1/3	3	3	0.162
A	1/5	1/3	1	3	5	1/5	1	1	0.066
S	1/7	1/6	1/3	1	5	1/7	1/5	1/3	0.031
T	1/9	1/7	1/5	1/5	1	1/9	1/6	1/6	0.015
I	1	3	5	7	9	1	5	5	0.298
C	1/5	1/3	1	3	5	1/5	1	1	0.066
L	1/5	1/3	1	3	5	1/5	1	1	0.066

, and the DRASTIC-L model formula for this study is given below. Based on the model’s classification principles, the risk assessment results for each indicator were obtained, as shown in Figure 7.

$$DI = 0.298D_R + 0.162R_R + 0.066A_R + 0.031S_R + 0.015T_R + 0.298I_R + 0.066C_R + 0.066L_R$$

(11)

This study developed a groundwater vulnerability assessment method based on the specific conditions in the study area and the characteristics of the industry–city integration zone. Using ArcGIS Pro, we conducted a layer overlay analysis by assigning relative weight values to the scoring maps for each indicator. This analysis yielded the groundwater vulnerability index for the study area, and the natural breaks classification method was then applied to divide the groundwater vulnerability into five levels: low, moderately low, moderate, moderately high, and high. A lower vulnerability index indicates that the aquifer is less susceptible to pollution, with a lower vulnerability level. Conversely, a higher index reflects greater susceptibility to pollution. The comprehensive groundwater vulnerability zoning is shown in Figure 8. The moderately high vulnerability zones are mainly concentrated in the eastern region and along riverbanks, where groundwater depths are relatively shallow and the slopes are gentle, making the groundwater more susceptible to pollution. In contrast, the moderately low vulnerability zones are primarily located in the western mountainous areas, where groundwater depths are deeper and the slopes are steeper, providing more protection from pollution.

4.3. Groundwater Pollution Load Assessment

The groundwater pollution load assessment yielded a pollution load map for the study area, categorizing regions into low, moderate, and high pollution loads. The pollution load is relatively concentrated within the industrial clusters, as shown in Figure 9. In the MY area, the presence of a municipal landfill, coupled with a contaminated site in the western section of the XB area and buffer zone effects, significantly elevates the pollution load in this region. The NC area has a long history of gear and tractor manufacturing, which presents a high pollution potential. Additionally, the BS area has a dense concentration of enterprises in heavily polluting industries such as paper manufacturing, leather, fertilizers, and hard alloy production, with many of these enterprises being large-scale, imposing a substantial pollution load on the groundwater in this area.

4.4. Assessment Results

Based on the results of the groundwater functional value assessment, groundwater vulnerability assessment, and groundwater pollution load assessment, key zones for groundwater pollution prevention and control are delineated. Areas with both high groundwater functional value and high vulnerability are designated as key zones for groundwater pollution prevention and control. Using the groundwater pollution load map, areas with a high pollution load are designated as the first-level control zone, while areas with a moderate pollution load are designated as the secondary control zone. The total area of key zones for groundwater pollution prevention and control is 13.28 km², accounting for 8.33% of the study area in Figure 10. The first-level control zone covers 4.44 km² (2.78% of the study area), and the secondary control zone covers 8.84 km² (5.55% of the study area).

5. Groundwater Human Health Risk Assessment

The calculation results of the groundwater human health risk assessment are shown in

Table 10. Statistical results of groundwater human health risk assessment calculations.

No.	Pollutant	Carcinogenic Risk Control Value	Non-Carcinogenic Risk Control Value	Risk Control Value	Groundwater Class III Standard	Control Value for Groundwater Usage Zones	Groundwater Class IV Standard	Control Value for Groundwater Non-Usage Zones
1 2	Fe	- -	10 2	10 2	0.3 0.10	10 2.00	2.0 1.50	10 2
3	Mn	-	0.0143	0.0143	0.005	0.0143	0.010	0.0143
4	Cd	-	0.00429	0.00429	0.05	0.05	0.10	0.1
5	Co	-	13.9	13.9	0.50	13.9	1.50	13.9
6	NH4+	8.7 × 10−5	0.00429	8.7 × 10−5	0.01	0.01	0.05	0.05
7	NO3−	-	22.9	22.9	20.0	22.9	30.0	30
8	As	-	0.572	0.572	1.0	1.0	2.0	2
9	F	0.00185	0.0376	0.00185	0.0020	0.0020	0.0500	0.05
10	CCl4	-	0.572	0.572	0.6	0.6	1.2	1.2

. Using inverse distance weighting (IDW) interpolation, all indicators exceeding standard limits are spatially analyzed. The range of each ion exceeding the standard is shown in Figure S1–S10 of the supplementary document. In groundwater usage zones, areas exceeding risk control standards are designated as first-level control zones; in non-usage zones, areas exceeding risk control standards are designated as secondary control zones. The total area of groundwater human health risk assessment is 20.69 km², accounting for 12.99% of the study area. The first-level control zone covers an area of 0.16 km², accounting for 0.10% of the total area, while the secondary control zone covers 20.53 km², or 12.89%, of the total area. The risk control scope is shown in Figure 11. Groundwater health risk control zones are widely distributed across the study area, primarily due to the impact of industrial activities on groundwater pollution and the naturally high background value of manganese in the study area [56].

6. Groundwater Pollution Prediction and Simulation

The groundwater levels and shallow groundwater quality in the study area are significantly impacted by human activity [57,58]. The primary groundwater type is HCO₃⁻-Ca²⁺ [56], and the region exhibits high background concentrations of manganese and iron [59], with groundwater in certain areas already being affected by industrial activities. By analyzing the groundwater pollution prevention priority zones and human health risk assessment, the groundwater areas within the study area that require particular attention were identified. However, previous research has focused solely on the current state of a region and lacks future pollution predictions. Therefore, this study introduces groundwater pollution prediction simulations to assess the potential future impacts of current pollution levels.

6.1. Model Development

A groundwater model was constructed for the study area using MODFLOW. Given that the western mountainous area has few residents and industries, it was excluded from the simulation area. The aquifer in the study area consists mainly of porous and fractured water and is simplified as a unified aquifer system. The eastern boundary is defined by rivers with constant head boundaries, while the western boundary, set along the foothills, is defined by a constant flow boundary. The northern and southern boundaries align with administrative borders and are represented by general head boundaries. Considering the presence of numerous rivers and lakes within the simulated range, the area is represented using both general head and constant head boundaries. A steady-state groundwater flow simulation was obtained through condition simplification, showing the overall groundwater flow from the western mountains toward the eastern rivers. The simulated area in this study covers 139.32 km². Based on the extent and shape of the simulation area, the model domain was divided into 152 rows and 194 columns, yielding a total of 29,488 grid cells with a horizontal resolution of 100 × 100 m. Among them, there are 13,932 active cells. According to the geological and geomorphological conditions, as well as hydrogeological characteristics, the permeability coefficient of the phreatic aquifer system was divided into several zones, with the horizontal permeability coefficient ranging from 0.3 to 150 m/d. Rainfall recharge coefficient zones were determined through model fitting and lithological classification of the study area, with recharge intensity varying from 0.0001 m/d to 0.0015 m/d, and the evaporation limit depth was set to 4 m. There are numerous rivers and lakes within the study area, and the interaction between surface water and groundwater is significant. To simulate this interaction, five rivers and five lakes were incorporated into the model. Rivers were defined as general head boundaries, while lakes were set as constant head boundaries. The specific parameter selection of the model and the fitting verification of the groundwater level can be found in Section 2 groundwater pollution prediction and simulation of the supplementary document. The groundwater flow simulation map is presented in Figure 12.

6.2. Evaluation Results

For this simulation, seven contaminated sites identified in prior investigations were selected. The groundwater simulation timeframe was determined based on the site’s 20-year industrial operation history, serving as the scientific basis for defining the predictive period [60]. The MT3DMS module was used to simulate contaminant migration over 7300 days. Contaminated sites within plant boundaries were designated as first-level control zones, while areas where contaminants migrated beyond plant boundaries were designated as secondary control zones. The total area of groundwater pollution prediction and simulation is 1.85 km², accounting for 1.16% of the study area. The first-level control zone covers an area of 1.24 km², accounting for 0.78% of the total area, while the secondary control zone covers 0.61 km², or 0.38%, of the total area. The simulation of overall contaminant migration is shown in Figure 13.

7. Groundwater Pollution Risk Control Zone Assessment Results

Groundwater pollution risk control zones were identified by overlaying the results from groundwater pollution prevention priority zone identification, human health risk assessment, and pollution prediction simulations. The first-level control zone was defined by overlaying and taking the highest value from each zone’s first-level control areas, and the secondary control zone was similarly defined based on the first-level control zones. The total area of groundwater pollution risk control zones is 30.37 km², accounting for 19.06% of the study area. The first-level control zone covers 5.38 km² (3.38% of the total area), while the secondary control zone covers 24.99 km² (15.68% of the total area). The first-level control zone is primarily within the industrial clusters, while the secondary control zone is broadly distributed throughout the area, as shown in Figure 14.

Compared with the groundwater pollution prevention priority zoning method [40], the risk control strategy that incorporates groundwater pollution risk prediction simulation delineated a broader control area. Specifically, the first-level control zone increased by 0.94 km², and the second-level control zone expanded by 16.15 km². In some groundwater pollution risk areas, the traditional method failed to identify certain zones, whereas the approach adopted in this study successfully supplemented those omitted areas, effectively reflecting the current status of groundwater pollution and accounting for potential future pollution scenarios. The groundwater pollution risk control zones defined in this study are more suitable for application in industry–city integration areas, with a wider coverage and greater scientific rationale, thereby enhancing the capacity of environmental authorities to effectively manage and protect groundwater resources.

8. Conclusions and Recommendations

(1)

Through groundwater pollution risk control, it is found that the first-level control zone in the study area covers 5.38 km², accounting for 3.37% of the total area, while the secondary control zone covers 24.99 km², or 15.68%, of the total area. The combined control zone area totals 30.37 km², comprising 19.06% of the study area. The first-level control zone is mainly concentrated in industrial clusters, with the secondary control zone distributed broadly across the region.

(2)

In comparison to traditional methods for defining groundwater pollution prevention priority areas, the groundwater pollution risk control strategy identifies more extensive control zones. The first-level control zone area increased by 0.94 km², and the secondary control zone area expanded by 16.15 km². This method provides more suitable outcomes for industry–city integration zones, enabling environmental management agencies to more effectively monitor and protect groundwater environments.

(3)

For active enterprises within the first-level control zones, it is recommended to conduct quarterly groundwater environmental monitoring and perform soil and groundwater pollution risk investigations. For inactive enterprises, risk-based control measures should be implemented, and effective measures should be promptly taken to prevent pollution spread in already contaminated sites.

(4)

The secondary control zones should strengthen regional environmental monitoring and risk investigations. It is recommended that areas with potential human health impacts conduct environmental monitoring twice annually, based on the region’s monitoring network, to track pollution trends. Additionally, since some wells remain in certain control areas, residents should be guided by the government to seal groundwater wells, with unauthorized groundwater extraction being strictly prohibited.