Next Article in Journal
Application of Machine Learning to Assess the Quality of Food Products—Case Study: Coffee Bean
Next Article in Special Issue
Development and Application of an Integrated Site Remediation Technology Mix Method Based on Site Contaminant Distribution Characteristics
Previous Article in Journal
Special Issue on Dental Materials: Latest Advances and Prospects—Volume II
Previous Article in Special Issue
Research on Suitability Evaluation of Urban Engineering Construction Based on Entropy Weight Hierarchy-Cloud Model: A Case Study in Xiongan New Area, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 13
Issue 19
10.3390/app131910781
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Groundwater Vulnerability Assessment and Protection Strategy in the Coastal Area of China: A GIS-Based DRASTIC Model Approach

by
Qian Zhang
1,2,*,
Qiang Shan
3,4,*,
Feiwu Chen
1,2,
Junqiu Liu
1,2 and
Yingwei Yuan
1,2
1
College of Hydraulic Engineering, Tianjin Agricultural University, Tianjin 300384, China
2
Joint Tianjin Agricultural University-China Agricultural University Smart Water Conservancy Research Center, Tianjin 300384, China
3
Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection, Shijiazhuang 050021, China
4
Hebei Geo-Environment Monitoring Centre, Shijiazhuang 050021, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2023, 13(19), 10781; https://doi.org/10.3390/app131910781
Submission received: 29 August 2023 / Revised: 24 September 2023 / Accepted: 25 September 2023 / Published: 28 September 2023
(This article belongs to the Special Issue The Mobilization, Speciation and Transformation of Organic and Inorganic Contaminants in Soil-Groundwater Ecosystems)
Browse Figures
Versions Notes

Abstract

:
Groundwater vulnerability reflects the risk level of groundwater contamination and its self-repairing ability, as well as its sustainability for use. Therefore, it provides significant scientific support for implementing measures to prevent groundwater contamination, especially in coastal areas. In this study, considering the lithology of vadose in valley plains and the extent of karst subsidence areas, a GIS-based DRASTIC model was employed to assess groundwater vulnerability in Tangshan City, a coastal area in China. The assessment results were presented and mapped using GIS, based on a comprehensive evaluation of seven parameters, including “Depth of groundwater, Vertical net recharge, Aquifer thickness, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity”. The identified groundwater vulnerability zones included the highest, higher, moderate, low vulnerability those four zones, which accounted for 4%, 53%, 25%, and 18%, respectively. In addition, according to the results of field investigation, the karst subsidence area and the mined-out coastal area were directly classified as the highest vulnerable areas and covered 1.463 km2; more attention is required here in subsequent groundwater protection processes and strategies. Finally, the groundwater pollution index was used to validate the groundwater vulnerability distribution results, and these two were in high agreement, with an R2 coefficient of 0.961. The study is crucial for the rational utilization and protection of water resources in Tangshan City.

1. Introduction

Water resources are a vital and irreplaceable natural asset for human survival and development. With the rapid progress of national economic construction and the improvement of living standards, the demand for groundwater resources continues to increase, leading to an increasingly prominent imbalance between supply and demand. Simultaneously, the issue of water pollution has become more pronounced due to the continuous development of industry and agriculture, posing a serious threat to human development. Shallow groundwater systems are highly susceptible to changes in precipitation levels since their recharge primarily comes from surface sources [1]. Groundwater, as a crucial water source, has garnered significant attention.
Under recent climate change, groundwater resources have been increasingly exploited and utilized worldwide, resulting in pollutant issues, particularly in highly vulnerable areas [2]. Groundwater extraction, which serves as a widespread and accessible method for obtaining high-quality freshwater, accounts for up to 30% of total global freshwater utilization [3,4]. However, excessive groundwater exploitation often leads to a continuous decline in the water table [5,6,7], posing risks of land subsidence and collapse, especially in densely populated urban areas such as Tianjin, Jakarta, Konya, Mashhad, and Hanoi [8,9,10,11,12]. Moreover, groundwater exploitation significantly contributes to vulnerability associated with high levels of human activity [13,14]. The exploitation of groundwater and its impact on ecological systems pose challenges to global economic and social development [15,16].
Groundwater vulnerability reflects both the extent of contamination and the ability of groundwater to self-repair and maintain sustainability. As a complex black box system, groundwater vulnerability factors are diverse, and various research methods are available. For example, Chaves et al. [17], Van Dijck et al. [18], and Cui et al. [19] employed Mann–Kendall’s Test, Poisson Distribution, End-member mixing analysis, and other relevant models based on land use change and soil infiltration. These studies revealed that groundwater responses to precipitation weaken or even disappear due to the thickened vadose zone and reduced permeability caused by declining groundwater levels and the conversion of natural grasslands to artificial farmland [20]. In a recent study in Nigeria, GLSI and LC models were used to conduct a case study of the Ijero mining site, examining the comparative effect of lateritic shield on groundwater vulnerability [21,22]. Groundwater vulnerability assessment provides significant scientific support for implementing measures to prevent groundwater pollution [23,24]. Vaezihir and Tabarmayeh [25] and Wen et al. [26] selected environmental parameters such as vadose zone impact, hydraulic conductivity, population density, and river recharge, utilizing the DRASTIC model to evaluate aquifer vulnerability.
The DRASTIC model aims to use seven factors such as “Depth to water, Net recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic Conductivity”, and allocates the ratings to all factors according to the range of factors [27], which is simple and widely used in evaluating groundwater contamination vulnerability. However, previous studies tend to assess groundwater vulnerability subjectively and refer only to a small number of factors, or have reported only a weak correlation between water pollutant concentrations (commonly nitrate) and the calculated DRASTIC index [28,29], which provided difficulties in obtaining conclusion accurately. This research integrated DRASTIC with additional methods like the GIS techniques, PCA (Principal Component Analysis), and other statistical approaches, which improve the evaluation systems to be modified to obtain more accurate results based on local hydrogeological conditions [30,31,32]. Furthermore, regression analysis is also applied in the DRASTIC model which has developed a new system for evaluating groundwater vulnerability, thereby enhancing scientific rigor and accuracy in utilization.
Groundwater vulnerability studies are crucial foundational work for the rational development, utilization, and protection of groundwater resources. To establish timely zoning systems for groundwater pollution prevention and control, it is necessary to evaluate the pollution resistance of groundwater systems. In this study, a GIS-based DRASTIC model was employed to assess groundwater vulnerability, with emphasis on the lithology of the vadose in the valley plain and the extent of karst subsidence areas. Comprehensive evaluation results based on seven parameters were presented and mapped using GIS to reflect the typical characteristics of coastal areas, enabling the efficient formulation of protection strategies. The aim of this study is to construct a set of groundwater vulnerability evaluation index systems and methods in the coastal area, to characterize the spatial distribution of vulnerability, and to propose corresponding protection measures and environmental protection strategies for highly vulnerable areas.

2. Study Area and Data

2.1. Location and Meteorology

Tangshan City is situated in the northeast of Hebei Province, with geographical coordinates ranging from 117°31′ to 119°19′ east longitude and 38°55′ to 40°28′ north latitude. The total land area of Tangshan City is 13,472 km2, comprising 5131 km2 (38.09%) of mountainous terrain and 8341 km2 (61.91%) of plains. Additionally, it has a sea area spanning 4472 km2, with a coastline stretching 229.72 km in length. Tangshan experiences a typical warm temperate sub-humid continental monsoon climate characterized by cold and dry winters with northerly winds, and hot and rainy summers with southerly winds. The annual average temperature is 10.6 °C, while the average precipitation from 1956 to 2020 amounts to 608.7 mm.

2.2. Hydrogeology

Based on the occurrence conditions of groundwater and the characteristics of aqueous media in Tangshan, three types of groundwater can be identified: pore water in loose rocks, carbonate karst water, and bedrock rock fissure water. Pore water is primarily found in the piedmont plain, coastal plain, inter-mountain basin, and valley zones of the Yan Mountain region (Figure 1). The main source of recharge for this groundwater type is atmospheric precipitation, and its dynamics closely follow the annual distribution of rainfall. Karst water is predominantly present in the southern foothills of the Yan Mountain, concealed within sloping plains, piedmont areas, and intermountain basins. The degree of karst development varies and is closely associated with the local geological structure and geomorphology. Individual well yields typically range from 40 to 25 m3/h·m, while spring water flow rates vary between 0.3 and 3.0 L/s. Fissure water is located in the northern hilly area, characterized by a recorded fracture rate of 1.3%. The weathered zone has an approximate depth of 50 m, with some localized fractures exceeding 100 m due to significant geological influences [33].

2.3. Data Availability

This study is a significant component of the zoning and technical research for groundwater pollution prevention and control in Tangshan City, encompassing two main aspects: the analysis of groundwater dynamics and the evaluation of groundwater vulnerability.
In the investigation of groundwater dynamics, a total of 75 government-monitored wells with long-term observation data were carefully selected to ensure completeness, control, and representativeness. For each district or county level, 1–2 monitoring wells were chosen for analysis, ensuring a uniform distribution across the study area. Specifically, this study focused on analyzing the response of unconfined groundwater, directly recharged by precipitation. Based on these principles, data from 24 monitoring wells were ultimately determined. To complement this analysis, three precipitation stations in Zunhua, Tangshan, and Leting were selected to match similar monitoring wells based on their proximity.
In the evaluation of groundwater vulnerability, data acquisition was conducted through indoor data collection and gathering relevant information about the study area’s geology, hydrogeology, and drilling. This included parameters such as groundwater depth, vertical net recharge, aquifer thickness, soil medium, terrain slope, vadose medium type, and permeability coefficient. A total of 1599 boreholes and associated information were gathered from various sources, including survey reports on land pollution from key industries in Hebei Province, China (974 cases), hydrogeology and engineering geology borehole construction and in-situ tests (28 cases), comprehensive geological surveys of major cities and towns (19 cases), the national important geological borehole database service platform (55 cases, https://zk.cgsi.cn/ (accessed on 28 August 2023)), groundwater level surveys (164 cases), and geotechnical investigation reports (359 cases). Aquifer permeability data were obtained by combining the aquifer media type with empirical values from the Hydrogeological Manual [34]. All data collected are accurate, providing strong support for the reliability of this study.

3. Methodology

3.1. Description of the GIS-Based DRASTIC Model Framework

In this study, a GIS-based DRASTIC model was employed to assess the vulnerability of groundwater in Tangshan City, a coastal area in China. The DRASTIC model, initially developed by the US Environmental Protection Agency (EPA) to evaluate groundwater pollution potential across the United States [35], considers intrinsic vulnerability based on hydrogeological characteristics while disregarding the specific contaminants. The use of a GIS-model interface facilitates data input, and the GIS can also serve as a common platform for transferring information between different examples of software. By combining various data sets within the GIS framework, thematic maps can be generated for utilization by the DRASTIC model. Furthermore, the GIS enables the conversion of these maps into raster mode, dividing them into pixels with dimensions of 300 m × 300 m. Each pixel is assigned a numerical value corresponding to the resulting grid, which serves as a basis for the seven parameters of the DRASTIC model, as detailed in the following equation:
DRASTICindex = WDRD + WRRR + WARA + WSRS + WTRT + WIRI + WCRC
where, the parameters in Equation (1) include the following: depth of groundwater (D), vertical net recharge of groundwater (R), aquifer thickness of media (A), soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C).
Consequently, vulnerability is calculated at a high resolution for each individual pixel. The GIS is further utilized to overlay the seven thematic maps, thereby generating a groundwater vulnerability map. The DRASTIC index computation involves assigning weights (W) to each parameter and using the assigned values (R).

3.2. Description of the GIS-Based DRASTIC Model Parameters

The seven parameters in Equation (1) that affect and control groundwater flow and pollutant transport are adopted in DRASTIC to constitute the factor system for vulnerability assessment. Based on the “Delineation of Priority Areas for Groundwater Pollution Prevention and Control Technical Guideline” published by the China Geological Survey in 2022 [36], different parameter weights have been assigned by investigation experience and field experiment. According to the influence of each parameter on groundwater vulnerability, different weight values are assigned to them in Table 1.
Based on the aforementioned seven parameters, an evaluation level is assigned to each parameter. As the local groundwater depth increases, vertical net recharge decreases, aquifer thickness increases, soil and vadose zone cutoff particles become finer, topographic slope increases, and aquifer permeability coefficient decreases, the scores for each parameter will decrease. A lower score indicates a smaller contribution of that parameter to the groundwater vulnerability assessment index, suggesting a reduced susceptibility to pollution. Conversely, higher scores indicate a greater contribution of the parameter to the groundwater vulnerability assessment index, indicating a higher vulnerability to pollution. The scores for each indicator range from 1 to 10, with specific classifications provided in Table 2.
A step wise flow diagram representing the data processing, preparations of layers, and assigning of weights has been demonstrated below in Figure 2.

4. Results and Discussion

4.1. Results of Single Parameter Evaluation

4.1.1. Depth of Groundwater

A total of 1867 data points were collected for groundwater depth. The depth at which groundwater is located primarily determines the depth of the transmission medium through which pollutants migrate to the aquifer [37]. Generally, a greater groundwater depth requires more time for pollution to reach the aquifer. This results in a smaller amount of pollution entering the aquifer and weaker levels of contamination within the aquifer, with the opposite being true as well.
The method for analyzing the zoning map of buried depths differs between mountainous and plain areas. In the plain and valley regions, water level data collected and supplementary surveys are interpolated and adjusted based on hydrogeological and river conditions. In mountainous areas, characterized by significant topographic variations and complex geological conditions, underground water depths are mainly classified according to aquifer types. The same type of water-bearing medium within an area is assigned an average value based on measured groundwater levels. Additionally, for valleys within mountainous regions where groundwater exists within loose rock formations, score values are determined by interpolating the buried depths observed in plain areas.
Once the above analysis is completed, the mountainous and plain areas are superimposed using GIS, ensuring uniform layer attributes. Gradation mapping (Figure 3a) is then assigned based on Table 2.

4.1.2. Vertical Net Recharge

Net recharge refers to the amount of water per unit area that infiltrates the surface and reaches the aquifer. Contaminants can enter the aquifer vertically through this recharge water [38]. In the DRASTIC model, net recharge represents the total amount of water applied to the surface and infiltrated into the aquifer. In Tangshan City, atmospheric precipitation serves as the primary source of regional recharge, and therefore, the vertical net recharge can be estimated using precipitation infiltration [39]. Generally, the equation R = P × α is used, where R denotes the vertical net recharge, P represents the precipitation infiltration coefficient, and α represents the annual precipitation.
The annual precipitation data for the region were obtained from the 2020 precipitation records of four gauging stations. In 2020, Tangshan City experienced rainfall ranging from 480–720 mm, with higher precipitation observed in the northern mountainous area compared to the southern plain area. Notably, Luannan County in the southern plain area received relatively higher precipitation. In the study area, the values of α range from 0.15–0.2 in the plain area, 0.09 in the igneous and metamorphic rock area, and 0.08 in the carbonate rock area. The empirical values of the precipitation infiltration coefficient were adjusted based on field seepage test results (30 groups) to ensure accuracy. The resulting vertical net recharge is illustrated in Figure 3b.

4.1.3. Aquifer Thickness

Aquifer thickness (Figure 3c) is determined by combining groundwater depth measurements with borehole data [40]. The data sources include the national important geological borehole database service platform (55 points), Geo-technical Investigation Reports (359 points), and borehole data collected during field investigations (30 holes), totaling 444 points.
In the bedrock mountain areas, the thickness of the weathered fissure layer is primarily considered, and it is obtained by subtracting the groundwater depth. The average value is then calculated for different regions. In the metamorphic rock and igneous rock mountain areas of Tangshan City, the thickness of the weathering layer ranges from 40–50 m, with some areas reaching up to 60 m. For the bare leaky karst mountains, the thickness of the strong water-bearing zone identified through borehole data is used to determine the aquifer thickness. Again, the average value is calculated for different regions. For example, in the northern part of Yutian County, Tangshan City, the thickness of the Jixian dolomite strong water-bearing zone is 40–45 m. In the plain area, the shallow aquifer thickness in the coastal region is relatively thin, mostly less than 10 m. This is determined based on calculations using borehole data and groundwater depth measurements.

4.1.4. Soil Media

Soil refers to the biologically active upper layer of the seepage zone. The influence of soil on groundwater vulnerability primarily relies on the physicochemical properties of different soil types [41]. In the study area, there are various soil media, with sandy loam and swelling or condensing clay being widely distributed. In the northern mountainous area of Tangshan City, the surface soil layer is very thin in some areas, while the lower part consists of weathered bedrock crust. This results in predominantly thin or deficient soil media. In the middle region, sandy loam, silty sand, and fine sand are widely distributed. In the coastal area, clay loam (clay), and silty loam are prevalent. The soil media were classified into different grades as shown in Figure 3d.

4.1.5. Topography

Topographic slope plays a significant role in groundwater migration and subsequently affects groundwater vulnerability [42]. In this study, a digital elevation model (DEM) of the research area was acquired through a measurement system. The DEM had an accuracy of 12.5 m, which met the guidelines’ requirements. Using the GIS’s slope tool, the slope was calculated based on the projected DEM map. Then, according to the scoring standard, each zone’s attributes were determined through reclassification (Figure 3e). Due to the high resolution of the data, fragmentation zones were formed considering the impact of rivers and dams in the plain area on slope classification. This evaluation utilized the geomorphic zones of the study area as a reference, and the average terrain slope within each zone was considered the representative slope value.
The study area exhibits significant variations in topographic slope, generally decreasing from north to south. High topographic slope values are concentrated in the northern mountainous area, while the intermountain basin, piedmont plain, and coastal plain exhibit relatively low slopes. The highest topographic slope values are found along the northern edge of Yutian County, the southern part of Zunhua City, the northern part of Fengrun District, the southern part of Qianxi County, the western part of Qian’an City, and the northwestern part of Luanzhou City. Conversely, the lowest slope values are primarily distributed in Caofeidian District along the southern coast.

4.1.6. Impact of Vadose Zone

The unsaturated zone, also known as the vadose zone, refers to the area between the Earth’s surface and the water table. The type of vadose zone medium plays a crucial role in controlling water exchange between the soil layer and aquifer, the migration and transformation of pollutants, as well as various physicochemical and biological processes [43]. Additionally, the vadose zone medium determines the length and path of percolation. Therefore, it serves as an important indicator for evaluating groundwater vulnerability.
In the plain area of the study region, the vadose zone mainly consists of Quaternary sediments, which can be categorized into nine types. When assessing the self-purification capacity of the vadose zone, factors such as particle size and permeability of the Quaternary sediments are primarily considered. The assigned values range from 1 to 10. In the mountain aquifers, the vadose zone comprises regolith with varying degrees of metamorphic rocks and igneous rocks, as well as dissolution layers of dolomites and limestone in karst areas. Based on vulnerability assessments conducted in different mountainous regions in China, the medium score for the vadose zone in bedrock regolith is determined to be 4 points.
This determination takes into account the extent of fracturing and dissolution observed in the surface regolith of the Tangshan mountain area, finally showed in Figure 3f. For dolomite or limestone vadose zones, the medium score is set at 8 points.

4.1.7. Hydraulic Conductivity

The permeability of an aquifer is influenced by the type of aquifer medium, and the permeability coefficient partially reflects the lithological composition of the aquifer [44]. Generally, larger particle sizes or more voids in the aquifer medium indicate greater permeability, lower dilution capacity, and higher potential for pollution [45]. The aquifer’s permeability coefficient is determined based on the type of aquifer medium, combined with empirical values, and adjusted using data obtained from field exploration and pumping tests (30 groups). The classification of aquifer medium types refers to the data source of aquifer thickness.
In the plain area, the permeability coefficient was obtained by interpolating the collected drilling data using Kriging interpolation with software such as Golden Surfer. For mountain aquifers, the permeability coefficient is classified based on weathered fissure aquifers in metamorphic rocks, weathered fissure aquifers in igneous rocks, and karst fissure aquifers. The assignment of permeability coefficients is performed according to the aquifer types derived from pumping test results from hydrologic boreholes. GIS technology is utilized to overlay the mountain and plain areas, harmonize layer attributes, and assign values to each partition following evaluation guidelines.
In the northern mountainous area of Tangshan City, the permeability coefficient mostly ranges from 0.5–5 m/d, with some areas reaching up to 10 m/d. In the southern coastal area, where silt is the predominant aquifer type, the permeability coefficient is relatively low, mostly below 12 m/d. The Shanqian area in central Tangshan exhibits higher permeability coefficients, with the lowest values observed in the north of Zunhua City, Qianxi County, and the northwest of Qian’an City. Conversely, the highest values are found in the east of Fengrun District, the south of Kaiping District, the south of Guye District, and the south of Luanzhou City. The assignment result for hydraulic conductivity in the DRASTIC parameter is presented in Figure 3g.

4.2. Results of Comprehensive Vulnerability Assessment and Its Validation

4.2.1. Results of Groundwater Vulnerability

Vulnerability mapping was conducted using the Geographic Information System (GIS) based on hydrogeological data of the study area and with reference to the DRASTIC model, as shown in Figure 4. The groundwater vulnerability index values for Tangshan City range from 85 to 173, classified into four levels according to the DRASTIC classification principle (refer to Table 3). Higher vulnerability assessment index values indicate a higher susceptibility to pollution, while lower values suggest a lower vulnerability and reduced susceptibility to pollution. Additionally, karst collapse areas and regions affected by mining subsidence are directly classified as the highest vulnerability areas.
The vulnerability assessment map of shallow groundwater in Tangshan City reveals the following distribution results. Firstly, the shallow groundwater in Tangshan City exhibits high vulnerability, with the highest vulnerability area accounting for 4% of the total study area, the higher vulnerability area accounting for 53%, and the moderate vulnerability area accounting for 25%. Together, these three categories account for 82% of the area, while the low vulnerability area only accounts for 18%. This highlights the weak protection performance of shallow groundwater in the region, making it susceptible to pollution.
Secondly, the spatial variability and causes of groundwater vulnerability in the study area are complex. The area around Luannan County has relatively shallow groundwater depth, significant vertical recharge, and a thick aquifer, leading to its classification as an area with high underground vulnerability. The valley area in the study region is also classified as a high vulnerability area due to its homogeneous lithology, simple rock structure, and strong permeability. These areas experience frequent agricultural activities and have well-developed aquaculture, resulting in a high pollution load and easy pollution of groundwater [46]. Similarly, highly vulnerable areas are also found in the central and southeastern regions of Tangshan, characterized by high vertical net recharge, relatively high permeability coefficients, good water permeability, and limited self-purification capacity. In addition, the karst subsidence area and the mined-out coastal area were directly classified as the highest vulnerable areas, covering 1.463 km2. These factors contribute to weak anti-pollution capabilities of groundwater, making it prone to pollution. Additionally, these areas are marked by intense human activities, including urban industrial and domestic sewage discharge from Tangshan City, as well as excessive fertilization and sewage irrigation in agricultural regions. Therefore, they should be prioritized as key areas for groundwater protection.
Thirdly, low-vulnerability areas are scarce and mainly concentrated in the northern part of Tangshan, such as the northern edge of Zunhua City, Qianxi County, and the northwest of Qian’an City. In these areas, groundwater depth is significant, the vadose zone exhibits fine lithology, and recharge amounts are limited, reducing vulnerability to external pollutants.

4.2.2. Validation for the Mapping of Groundwater Vulnerability

Groundwater with high vulnerability is more easily contaminated and thus more likely to have poorer water quality conditions. Currently, there are fewer methods to validate the vulnerability of groundwater, and the pollution distribution of “NO3−, NO2−, NH4+” is generally used to compare with the vulnerability distribution of groundwater [47,48]. Xu et al. utilized single and combined factors to evaluate the water quality of groundwater, and then examined the correlation between the evaluation level and the vulnerability index [49]. Chao et al. chose typical water quality indicators characterizing groundwater taste, color, and scaling, which were selected to calculate the value of the groundwater pollution index according to the “Groundwater Quality Evaluation Standards” [50], and then compared them with the spatial distribution of groundwater vulnerability. Since this study also addresses the vulnerability of shallow groundwater, and there are similarities with the above studies, similar validation methods were selected.
We selected 11 water quality indicators (in Table 4) from nearly 30 well sites to calculate the Groundwater Pollution Index (GPI), which summed every index up after assigned number based on different categorization criteria in Table 4. The GPIs, ranging from 26 to 72, were used to validate the groundwater vulnerability and mapped in Figure 5, which demonstrates that high groundwater vulnerability corresponded to a high groundwater pollution index. These two were in high agreement, with a R2 coefficient of 0.961, as shown in Figure 6. This certificated that the GIS-based DRASTIC model for groundwater vulnerability assessment was scientific and reliable, and could provide reliable experience for groundwater pollution prevention and management.

4.3. Groundwater Protection Strategy in Coastal Area

4.3.1. Groundwater Protection Objectives Based on Groundwater Vulnerability

According to the results of the vulnerability assessment of shallow groundwater in Tangshan City, the following medium- and long-term objectives for groundwater protection have been formulated in view of the current water quality and water environment.
  • To guarantee water safety and prevent groundwater pollution. Groundwater is one of the important sources of drinking water, and protecting groundwater means guaranteeing people’s drinking water safety and basic usage of water.
  • To maintain ecological balance on this basis. Protecting groundwater can maintain the water supply of wetlands, keep the stability of the ecological environment, and promote the maintenance of species diversity and the balance of the natural ecosystem.
  • To pay attention to the protection of water sources for agricultural irrigation to ensure the sustainability of agricultural irrigation and maintain the normal growth of crops, paying particular attention to reducing the adverse effects on soil salinization in the coastal area.
  • To prevent the decline in groundwater level and ground subsidence. Over-exploitation of groundwater will lead to the decline in groundwater level and ground subsidence, which will in turn lead to geological disasters and ground subsidence. One of the objectives of groundwater protection is to avoid these problems and maintain the stability of the groundwater system.
To realize the sustainable management and utilization of groundwater and to support the sound economic development of Tangshan City, groundwater resources are non-renewable resources, and the protection and rational utilization of groundwater is an important part of realizing the sustainable development of the region, which is also an important goal of this study.

4.3.2. Groundwater Protect Measures in Study Area

Based on the results of vulnerability assessment of groundwater in Tangshan City and the above protection objectives, we have clarified that the protection performance of shallow groundwater is weak, and the current status of shallow groundwater pollution is serious, so we propose the following protection measures for the current situation characteristics.
  • Reasonable land use and control of new highly polluting enterprises to reduce groundwater pollution: Urban planning, especially industrial zoning, should consider vulnerable areas and restrict the establishment of industrial zones in areas vulnerable to groundwater pollution, such as the center of Tangshan City, Kaiping District, Guye District, and Luannan County. In addition, wastewater discharge from industrial zones must be strictly managed to ensure that standards are met and groundwater pollution is controlled [51]. By controlling the sources of pollution, restricting the discharge of hazardous substances, and rationally managing groundwater extraction, it is possible to ensure that the quality of groundwater meets safety standards.
  • Balanced control of groundwater exploitation: Over-exploitation of groundwater will lead to increased water hardness, groundwater pollution, and even ground subsidence. At this stage, deep wells with serious over-exploitation should be closed, the amount of groundwater extraction should be limited, and the number of new wells should be controlled.
  • Regulate sewage irrigation and fertilizer use: It is important to control the amount of fertilizers used in agricultural areas and promote their effective use in order to minimize the amount that enters groundwater. Additionally, effluent used for irrigation should be analyzed and tested to ensure that it does not adversely affect soil and groundwater quality [52].
  • The establishment of a comprehensive groundwater monitoring network and early warning system throughout the region is essential. Regular water quality monitoring should be carried out to detect early warning signs in time for timely interventions to mitigate serious consequences and to report data to ecological and environmental authorities.

4.3.3. Rigorous Implementation Plans for Groundwater Exploitation in High Vulnerability Area

Based on the above groundwater protection objectives and implementation methods, this study suggests that the Tangshan area should follow the following most stringent groundwater exploitation plan to ensure the sustainable development of groundwater resources.
(1).
Key groundwater pollution sources such as chemical and metal products industrial clusters, landfills, hazardous waste disposal sites, and other key groundwater pollution sources should be investigated and evaluated as soon as possible to map out the status of groundwater pollution, establish a system of seepage and leakage prevention for key groundwater pollution sources, carry out seepage and leakage prevention inspections every year, formulate seepage prevention and renovation plans for seepage prevention measures that do not satisfy the corresponding seepage prevention specification requirements, and take technical and management measures in a timely manner to eliminate hidden dangers.
(2).
For areas where groundwater pollution exists, detailed investigation and assessment of groundwater shall be further carried out, and where groundwater treatment and remediation or risk control is required after detailed investigation and assessment, control shall be strengthened and appropriate management shall be carried out in a timely manner. In areas where the health risk of groundwater pollution is unacceptable, the use of groundwater should be prohibited, and drainage methods such as pits and ponds should be restricted to reduce the disturbance of the polluted area; if drainage is really needed, it should be discharged in compliance with the standard after treatment.
(3).
Penalties should be established for enterprises and individuals who do not comply with the rules on groundwater protection, and they should be ordered to rectify the situation thoroughly before continuing production.
(4).
In areas where karst is strongly developed and where there are many fallout holes and karst funnels, construction projects that may cause groundwater pollution shall not be newly built, altered, or expanded.

5. Conclusions

Being a typical coastal city, Tangshan City exhibits a high level of vulnerability in its shallow groundwater system. The highly vulnerable area accounts for 4% of the total study area, while the areas classified as moderately and highly vulnerable account for 25% and 53%, respectively. Collectively, these three categories encompass 82% of the study area, leaving only 18% categorized as having low or minimal vulnerability. The groundwater pollution index was used to validate the groundwater vulnerability distribution results, and these two were in high agreement, with an R2 coefficient of 0.961.
The study area, characterized by complex hydrogeological conditions and high vulnerability, is situated along the coast. The valley regions, with their homogeneous lithology, simple rock structure, and high permeability, are particularly vulnerable according to the assessment results. Additionally, the karst subsidence area and the mined-out coastal area were directly classified as the highest vulnerable areas, covering 1.463 km2, where more attention is required in subsequent groundwater protection processes and strategies.
Based on the vulnerability assessment outcomes for shallow groundwater in Tangshan City, it is evident that the current state of shallow groundwater pollution is severe, indicating a weak performance in safeguarding the quality of shallow groundwater resources. Therefore, it is recommended that relevant departments address these concerns by taking appropriate actions informed by the evaluation results. These actions should focus on improving and developing groundwater resources in a rational manner, implementing effective measures to control groundwater pollution, and undertaking remediation efforts to restore polluted groundwater.

Author Contributions

Q.Z.: Writing—Original Draft; Q.S.: Writing—Review and Editing; F.C.: Drawing Pictures; J.L.: Writing—Review and Editing; Y.Y.: Data. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by [the National Natural Science Foundation of China] (NO. 41907149), [Open Funding from Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection] (NO. JCYKT202210), [the China Postdoctoral Science Foundation] (NO. 2018M631732), and [Tianjin Graduate Research Innovation Project (No. 2022SKY195)].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data in this manuscript are derived from the field surveys, reports and references.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Singh, S.K.; Taylor, R.W.; Rahman, M.M.; Pradhan, B. Developing robust arsenic awareness prediction models using machine learning algorithms. J. Environ. Manag. 2018, 211, 125–137. [Google Scholar] [CrossRef]
  2. Houria, B.; Mahdi, K.; Zohra, T.F. Hydrochemical characterisation of groundwater quality: Merdja plain (Tebessa town, Algeria). Civ. Eng. J. 2020, 6, 318–325. [Google Scholar] [CrossRef]
  3. Döll, P.; Hoffmann-Dobrev, H.; Portmann, F.; Siebert, S.; Eicker, A.; Rodell, M.; Strassberg, G.; Scanlon, B. Impact of water withdrawals from groundwater and surface water on continental water storage variations. J. Geodyn. 2012, 59, 143–156. [Google Scholar] [CrossRef]
  4. Giordano, M. Global Groundwater? Issues and solutions. Annu. Rev. Environ. Resour. 2009, 34, 153–178. [Google Scholar] [CrossRef]
  5. Castellazzi, P.; Martel, R.; Galloway, D.L.; Longuevergne, L.; Rivera, A. Assessing groundwater depletion and dynamics using GRACE and InSAR: Potential and limitations. Groundwater 2016, 54, 768–780. [Google Scholar] [CrossRef] [PubMed]
  6. Çelik, R. Temporal changes in the groundwater level in the Upper Tigris Basin, Turkey, determined by a GIS technique. J. Afr. Earth Sci. 2015, 107, 134–143. [Google Scholar] [CrossRef]
  7. Narany, T.S.; Aris, A.Z.; Sefie, A.; Keesstra, S. Detecting and predicting the impact of land use changes on groundwater quality, a case study in Northern Kelantan, Malaysia. Sci. Total Environ. 2017, 599, 844–853. [Google Scholar] [CrossRef]
  8. Bui, L.K.; Le, P.V.V.; Dao, P.D.; Long, N.Q.; Pham, H.V.; Tran, H.H.; Xie, L. Recent land deformation detected by Sentinel-1A InSAR data (2016–2020) over Hanoi, Vietnam, and the relationship with groundwater level change. GISci. Remote Sens. 2021, 58, 161–179. [Google Scholar] [CrossRef]
  9. Chaussard, E.; Amelung, F.; Abidin, H.; Hong, S.-H. Sinking cities in Indonesia: ALOS PALSAR detects rapid subsidence due to groundwater and gas extraction. Remote Sens. Environ. 2013, 128, 150–161. [Google Scholar] [CrossRef]
  10. Khorrami, M.; Abrishami, S.; Maghsoudi, Y.; Alizadeh, B.; Perissin, D. Extreme subsidence in a populated city (Mashhad) detected by PSInSAR considering groundwater withdrawal and geotechnical properties. Sci. Rep. 2020, 10, 11357. [Google Scholar] [CrossRef]
  11. Orhan, O. Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey. Environ. Monit. Assess. 2021, 193, 174. [Google Scholar] [CrossRef] [PubMed]
  12. Tang, W.; Zhan, W.; Jin, B.; Motagh, M.; Xu, Y. Spatial Variability of Relative Sea-Level Rise in Tianjin, China: Insight from InSAR, GPS, and Tide-Gauge Observations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 2621–2633. [Google Scholar] [CrossRef]
  13. Putranto, T.T. Determining the groundwater vulnerability using the aquifer vulnerability index (AVI) in the Salatiga groundwater basin in Indonesia. AIP Conf. Proc. 2018, 2021, 030016. [Google Scholar] [CrossRef]
  14. Sarkar, M.; Pal, S.C. Application of DRASTIC and modified DRASTIC models for modeling groundwater vulnerability of Malda District in West Bengal. J. Indian Soc. Remote Sens. 2021, 49, 1201–1219. [Google Scholar] [CrossRef]
  15. Konikow, L.F.; Kendy, E. Groundwater depletion: A global problem. Hydrogeol. J. 2005, 13, 317–320. [Google Scholar] [CrossRef]
  16. Kumar, V.; Setia, R.; Pandita, S.; Singh, S.; Mitran, T. Assessment of U and As in groundwater of India: A meta-analysis. Chemosphere 2022, 303, 135199. [Google Scholar] [CrossRef]
  17. Chaves, J.; Neill, C.; Germer, S.; Neto, S.G.; Krusche, A.; Elsenbeer, H. Land management impacts on runoff sources in small Amazon watersheds. Hydrol. Process. 2008, 22, 1766–1775. [Google Scholar] [CrossRef]
  18. Van Dijck, S.J.; Laouina, A.; Carvalho, A.V.; Loos, S.; Schipper, A.M.; Van der Kwast, H.; Nafaa, R.; Antari, M.; Rocha, A.; Borrego, C. Desertification in northern Morocco due to effects of climate change on groundwater recharge. In Desertification in the Mediterranean Region. A Security Issue; Springer: Berlin/Heidelberg, Germany, 2006; pp. 549–577. [Google Scholar] [CrossRef]
  19. Cui, Y.; Liao, Z.; Wei, Y.; Xu, X.; Song, Y.; Liu, H. The Response of Groundwater Level to Climate Change and Human Activities in Baotou City, China. Water 2020, 12, 1078. [Google Scholar] [CrossRef]
  20. Mohammed, O.A.; Sayl, K.N. A GIS-based multicriteria decision for groundwater potential zone in the west desert of Iraq. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2021; Volume 856, p. 012049. [Google Scholar] [CrossRef]
  21. Mohammed, O.A.; Sayl, K.N. Determination of groundwater potential zone in arid and semi-arid regions: A review. In Proceedings of the 2020 13th International Conference on Developments in eSystems Engineering (DeSE), Virtual, 14–17 December 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 76–81. [Google Scholar] [CrossRef]
  22. Falade, A.O.; Oni, T.E.; Oyeneyin, A. Comparative effect of lateritic shield in groundwater vulnerability assessment using GLSI and LC models: A case study of Ijero mining site, Ijero-Ekiti. Model. Earth Syst. Environ. 2023, 9, 3253–3262. [Google Scholar] [CrossRef]
  23. Pavlis, M.; Cummins, E. Assessing the vulnerability of groundwater to pollution in Ireland based on the COST-620 Pan-European approach. J. Environ. Manag. 2014, 133, 162–173. [Google Scholar] [CrossRef]
  24. Prasad, R.K.; Singh, V.S.; Krishnamacharyulu, S.K.G.; Banerjee, P. Application of drastic model and GIS: For assessing vulnerability in hard rock granitic aquifer. Environ. Monit. Assess. 2011, 176, 143–155. [Google Scholar] [CrossRef] [PubMed]
  25. Vaezihir, A.; Tabarmayeh, M. Total vulnerability estimation for the Tabriz aquifer (Iran) by combining a new model with DRASTIC. Environ. Earth Sci. 2015, 74, 2949–2965. [Google Scholar] [CrossRef]
  26. Wen, X.; Wu, J.; Si, J. A GIS-based DRASTIC model for assessing shallow groundwater vulnerability in the Zhangye Basin, northwestern China. Environ. Geol. 2009, 57, 1435–1442. [Google Scholar] [CrossRef]
  27. Aller, L.; Thornhill, J. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings; Robert, S., Ed.; Kerr Environmental Research Laboratory, Office of Research and Development, US Environmental Protection Agency: Washington, DC, USA, 1987. [Google Scholar]
  28. Panagopoulos, G.P.; Antonakos, A.K.; Lambrakis, N.J. Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS. Hydrogeol. J. 2006, 14, 894–911. [Google Scholar] [CrossRef]
  29. Kwon, E.; Park, J.; Park, W.-B.; Kang, B.-R.; Hyeon, B.-S.; Woo, N.C. Nitrate vulnerability of groundwater in Jeju Volcanic Island, Korea. Sci. Total Environ. 2022, 807, 151399. [Google Scholar] [CrossRef]
  30. Rama, F.; Busico, G.; Arumi, J.L.; Kazakis, N.; Colombani, N.; Marfella, L.; Hirata, R.; Kruse, E.E.; Sweeney, P.; Mastrocicco, M. Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the drastic framework: The case of South America. Sci. Total Environ. 2022, 823, 153748. [Google Scholar] [CrossRef]
  31. An, Y.; Lu, W. Assessment of groundwater quality and groundwater vulnerability in the northern Ordos Cretaceous Basin, China. Arab. J. Geosci. 2018, 11, 118. [Google Scholar] [CrossRef]
  32. Bai, L.; Wang, Y.; Meng, F. Application of DRASTIC and extension theory in the groundwater vulnerability evaluation. Water Environ. J. 2012, 26, 381–391. [Google Scholar] [CrossRef]
  33. Chen, S.M.; Liu, F.T.; Zhang, Z.; Zhang, Q.; Wang, W. Changes of groundwater flow field of Luanhe River Delta under the human activities and its impact on the ecological environment in the past 30 years. China Geol. 2021, 4, 455–462. [Google Scholar] [CrossRef]
  34. Hydrogeological Manual, China Geological Survey. 2023. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=SNAD&filename=SNAD000001542056 (accessed on 28 August 2023).
  35. US EPA (Environmental Protection Agency). DRASTIC: A Standard System for Evaluating Groundwater Potential Using Hydrogeological Settings; Oklahoma WA/EPA Series, Ada; US EPA (Environmental Protection Agency): Washington, DC, USA, 1985; p. 163.
  36. China Geological Survey. Delineation of Priority Areas for Groundwater Pollution Prevention and Control Technical Guideline; China Geological Survey: Beijing, China, 2022.
  37. Yu, H.; Wu, Q.; Zeng, Y.; Zheng, L.; Xu, L.; Liu, S.; Wang, D. Integrated variable weight model and improved DRASTIC model for groundwater vulnerability assessment in a shallow porous aquifer. J. Hydrol. 2022, 608, 127538. [Google Scholar] [CrossRef]
  38. Abu-Bakr, H.A.E.-A. Groundwater vulnerability assessment in different types of aquifers. Agric. Water Manag. 2020, 240, 106275. [Google Scholar] [CrossRef]
  39. Hayashi, M.; Farrow, C.R. Watershed-scale response of groundwater recharge to inter-annual and inter-decadal variability in precipitation (Alberta, Canada). Hydrogeol. J. 2014, 22, 1825–1839. [Google Scholar] [CrossRef]
  40. Nair, A.M.; Prasad, K.R.; Srinivas, R. Groundwater vulnerability assessment of an urban coastal phreatic aquifer in India using GIS-based DRASTIC model. Groundw. Sustain. Dev. 2022, 19, 100810. [Google Scholar] [CrossRef]
  41. Taghavi, N.; Niven, R.K.; Kramer, M.; Paull, D.J. Comparison of DRASTIC and DRASTICL groundwater vulnerability assessments of the Burdekin Basin, Queensland, Australia. Sci. Total Environ. 2023, 858, 159945. [Google Scholar] [CrossRef]
  42. Rodriguez-Galiano, V.; Mendes, M.P.; Garcia-Soldado, M.J.; Chica-Olmo, M.; Ribeiro, L. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Sci. Total Environ. 2014, 476, 189–206. [Google Scholar] [CrossRef]
  43. Goyal, D.; Haritash, A.; Singh, S. A comprehensive review of groundwater vulnerability assessment using index-based, modelling, and coupling methods. J. Environ. Manag. 2021, 296, 113161. [Google Scholar] [CrossRef]
  44. Khosravi, K.; Sartaj, M.; Tsai, F.T.-C.; Singh, V.P.; Kazakis, N.; Melesse, A.M.; Prakash, I.; Bui, D.T.; Pham, B.T. A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci. Total Environ. 2018, 642, 1032–1049. [Google Scholar] [CrossRef]
  45. Jiang, W.; Sheng, Y.; Wang, G.; Shi, Z.; Liu, F.; Zhang, J.; Chen, D. Cl, Br, B, Li, and noble gases isotopes to study the origin and evolution of deep groundwater in sedimentary basins: A review. Environ. Chem. Lett. 2022, 20, 1497–1528. [Google Scholar] [CrossRef]
  46. Jin, G.; Shimizu, Y.; Onodera, S.; Saito, M.; Matsumori, K. Evaluation of drought impact on groundwater recharge rate using SWAT and Hydrus models on an agricultural island in western Japan. Proc. Int. Assoc. Hydrol. Sci. 2015, 371, 143–148. [Google Scholar] [CrossRef]
  47. Zhuang, Y.; Tao, W.; Jun, L. Evaluation of special vulnerability of groundwater in Guangzhou based on fuzzy comprehensive judgment. Mod. Geol. 2011, 25, 796–801. (In Chinese) [Google Scholar]
  48. Hua, J.; Ke, W.; Ying, Q. Special vulnerability of groundwater in the Guanzhong Basin and its evaluation. J. Jilin Univ. 2009, 39, 1106–1116. (In Chinese) [Google Scholar]
  49. Yuan, X.; Yang, Y.; Lu, L. Modeling and validation of groundwater pollution prevention performance zoning in reclaimed water irrigation areas. J. Agric. Eng. 2010, 26, 57–63. (In Chinese) [Google Scholar]
  50. GB14848-2017; China Environmental Quality Standards for Groundwater. PRC State Administration of Quality Supervision and Quarantine: Beijing, China, 2017.
  51. Balacco, G.; Alfio, M.R.; Fidelibus, M.D. Groundwater Drought Analysis under Data Scarcity: The Case of the Salento Aquifer (Italy). Sustainability 2022, 14, 707. [Google Scholar] [CrossRef]
  52. Kruseman, G.P.; De Ridder, N.A.; Verweij, J.M. Analysis and Evaluation of Pumping Test Data; International Institute for land Reclamation and Improvement: Wageningen, The Netherlands, 1983; Volume 11, p. 200. Available online: https://www.researchgate.net/publication/284969758 (accessed on 28 August 2023).
Applsci 13 10781 g001
Figure 1. The location of study area and its hydrogeological condition.
Figure 1. The location of study area and its hydrogeological condition.
Applsci 13 10781 g001
Applsci 13 10781 g002
Figure 2. Flowchart of groundwater vulnerability assessment.
Figure 2. Flowchart of groundwater vulnerability assessment.
Applsci 13 10781 g002
Applsci 13 10781 g003
Figure 3. Mapping of DRASTIC parameters ((a) Depth of groundwater; (b) Vertical net recharge; (c) Aquifer thickness; (d) Soil media; (e) Topography; (f) Impact of vadose zone; (g) Hydraulic conductivity).
Figure 3. Mapping of DRASTIC parameters ((a) Depth of groundwater; (b) Vertical net recharge; (c) Aquifer thickness; (d) Soil media; (e) Topography; (f) Impact of vadose zone; (g) Hydraulic conductivity).
Applsci 13 10781 g003
Applsci 13 10781 g004
Figure 4. Comprehensive evaluation of vulnerability of groundwater in Tangshan.
Figure 4. Comprehensive evaluation of vulnerability of groundwater in Tangshan.
Applsci 13 10781 g004
Applsci 13 10781 g005
Figure 5. The mapping of groundwater vulnerability index and groundwater pollution index.
Figure 5. The mapping of groundwater vulnerability index and groundwater pollution index.
Applsci 13 10781 g005
Applsci 13 10781 g006
Figure 6. Groundwater vulnerability assessment and verification index correlation.
Figure 6. Groundwater vulnerability assessment and verification index correlation.
Applsci 13 10781 g006
Table 1. Description of DRASTIC model parameters and its weights.
Table 1. Description of DRASTIC model parameters and its weights.
ParameterDescriptionRelative Weight
Depth of groundwater
(D)		Depth to groundwater table is the distance from the surface to the submerged surface; unit is m.5
Vertical net recharge
(R)		Approximate using precipitation infiltration recharge instead of vertical net recharge; unit is mm/a.4
Aquifer thickness
(A)		Aquifer thickness can be analyzed from borehole data; refers to the saturated zone material properties, which
controls the pollutant attenuation processes; unit is m.		3
Soil media
(S)		The soil media is a weathered layer with a thickness of 2 m or less at the surface, which controls the amount of recharge that can infiltrate downward.2
Topography
(T)		Slope values can be automatically generated in GIS after DEM extraction from 1:50,000 or 1:10,000 topographic maps.1
Impact of Vadose Zone
(I)		The unsaturated zone material: it controls the passage and attenuation of the contaminated material to the saturated zone.5
Hydraulic conductivity
(C)		Indicates the ability of the aquifer to transmit water, and hence determines the rate of flow of contaminant material within the groundwater system.3
Table 2. The parameter Ranking and Assignment in DRASTIC.
Table 2. The parameter Ranking and Assignment in DRASTIC.
ParameterGrade
12345678910
D (m)>30(25, 30](20, 25](15, 20](10, 15](8, 10](6, 8](4, 6](2, 4]≤2
R (mm/a)0(0, 51](51, 71](71, 92](92, 117](117, 147](147, 178](178, 216](216, 235]>235
A (m)>50(45, 50](40, 45](35, 40](30, 35](25, 30](20, 25](15, 20](10, 15]≤10
Srockclay loamsilt loamloamsandy loamswelling or condensing claysilt-sand/fine sandmedium sand/coarse sandgravel-cobblethin layer or missing
T(%)>10(9, 10](8, 9](7, 8](6, 7](5, 6](4, 5](3, 4](2, 3]≤2
Iclayloamsandy loam soilsilt-sandsilty, fine sandfine sandmedium sandcoarse sandsand gravelgravel-cobble
C (m/d)[0, ≤4](4, 12](12, 20](20, 30](30, 35](35, 40](40, 60](60, 80](80, 100]>100
Table 3. Classification of vulnerability of shallow groundwater in Tangshan City.
Table 3. Classification of vulnerability of shallow groundwater in Tangshan City.
Groundwater Vulnerability Composite Index ValueVulnerabilityLevel
(70, 100]Potentially contaminatedLow
(100, 120]Easily contaminatedModerate
(120, 150]highly prone to be contaminatedHigher
>150Particularly vulnerable to be contaminatedHighest
Table 4. Assignment criteria of groundwater pollution index.
Table 4. Assignment criteria of groundwater pollution index.
Assigned IndexpHTH mg/LTDS mg/LF− mg/LCl− mg/LSO42− mg/LNO3− mg/LNO2− mg/LFe mg/LCu mg/LMn mg/L
16.5–81503000.2505020.0050.10.010.01
36–6.5; 8–8.53005000.515015050.010.20.050.05
55.5–6.0; 8.5–9450100012502502010.310.1
71–5.5; 9–1365020002350350304.821.51.5
100–1; 13–14>650>2000>2>350>350>30>4.8>2>1.5>1.5
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, Q.; Shan, Q.; Chen, F.; Liu, J.; Yuan, Y. Groundwater Vulnerability Assessment and Protection Strategy in the Coastal Area of China: A GIS-Based DRASTIC Model Approach. Appl. Sci. 2023, 13, 10781. https://doi.org/10.3390/app131910781

AMA Style

Zhang Q, Shan Q, Chen F, Liu J, Yuan Y. Groundwater Vulnerability Assessment and Protection Strategy in the Coastal Area of China: A GIS-Based DRASTIC Model Approach. Applied Sciences. 2023; 13(19):10781. https://doi.org/10.3390/app131910781

Chicago/Turabian Style

Zhang, Qian, Qiang Shan, Feiwu Chen, Junqiu Liu, and Yingwei Yuan. 2023. "Groundwater Vulnerability Assessment and Protection Strategy in the Coastal Area of China: A GIS-Based DRASTIC Model Approach" Applied Sciences 13, no. 19: 10781. https://doi.org/10.3390/app131910781

APA Style

Zhang, Q., Shan, Q., Chen, F., Liu, J., & Yuan, Y. (2023). Groundwater Vulnerability Assessment and Protection Strategy in the Coastal Area of China: A GIS-Based DRASTIC Model Approach. Applied Sciences, 13(19), 10781. https://doi.org/10.3390/app131910781

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop