Comprehensive Hydrochemical Analysis, Controlling Mechanisms, and Water Quality Assessment of Surface and Groundwater in a Typical Intensive Agricultural Area, Northern China

Abstract

Groundwater is a significant source of water, and evaluating its hydrochemical attributes, quality, and associated health risks holds paramount importance in guaranteeing safe water access for the population and fostering sustainable socio-economic progress. Situated within a semi-arid region, the Dianbu area (DBA) features numerous greenhouses interspersed amongst open farmlands. An examination revealed a discernible decline in the overall water chemistry environment in this area. This study extensively examined the fundamental water chemistry characteristics of groundwater and surface water samples through a statistical analysis, Piper’s trilinear diagram, ion ratios, and other analytical methods. The assessment of irrigation water quality was conducted using the entropy weight water quality index (EWQI), sodium adsorption ratio (SAR), percentage of soluble sodium (Na%), among other relevant indicators. The findings demonstrate multiple key aspects: 1. Water cations are chiefly composed of Ca²⁺ and Na⁺, while groundwater anions are notably NO₃⁻ and SO₄²⁻ dominant, defining the water type as NO₃-SO₄-Ca. Conversely, surface water primarily displays HCO₃⁻ and SO₄²⁻ anions, aligning it with an HCO₃-SO₄-Ca water type. 2. The extensive agricultural activities in the region, coupled with the excessive utilization of pesticides, chemical fertilizers, as well as the discharge of domestic sewage, contribute to heightened NO₃⁻ concentrations in groundwater. 3. The water quality assessments indicate that approximately 53% of agricultural water quality meets irrigation standards based on EWQI, with SAR results suggesting around 65.52% suitability for irrigation and Na% findings indicating approximately 55.88% viability for this purpose. Proper water selection tailored to specific conditions is advised to mitigate potential soil salinization risks associated with long-term irrational irrigation practices.

1. Introduction

Groundwater is of utmost importance in the daily routines of inhabitants and promoting agricultural progress [1]. According to Siebert et al. [2], global consumptive groundwater use for irrigation totals 545 km³ per year, representing 43% of the total consumptive irrigation water use of 1277 km³ per year. China stands out as one of the largest countries in terms of groundwater irrigated area, with 19 million hectares under irrigation. Meanwhile, particularly in arid and semi-arid regions, where surface water is scarce [3], groundwater plays a vital role in supporting rural, industrial, agricultural, and urban water needs [4]. However, numerous regional aquifers worldwide, particularly in intensively cultivated areas, are presently experiencing severe anthropogenic contamination resulting from the rapid modernization of agriculture, urbanization, and industrial development [5,6]. Therefore, conducting hydrochemical analyses and water quality assessments in agricultural regions is important for the conservation of water resources and the promotion of sustainable development in farming areas [7,8].

Shandong’s Laixi Dianbu (DBA), situated in the semi-arid region of the Jiaodong Peninsula, stands out as a typical agricultural area. Therefore, investigating the hydrochemical characteristics and water quality status of groundwater in this region holds significant practical importance. Zhang et al. [9] conducted an evaluation of the hydrochemical characteristics, ion sources, and water quality in the arid regions using correlation analysis, Piper trilinear diagram, Gibbs diagram, and ion ratios. Nadjai et al. [10] evaluated the groundwater quality for irrigation purposes in the semi-arid Middle Cheliff region using methods such as SAR and Na%. Therefore, this study utilizes DBA hydrochemical data to delineate the hydrochemical characteristics of DBA surface and groundwater. The assessment encompasses both irrigation water quality of surface and groundwater, offering scientific groundwork and a reference for the sustainable development of groundwater resources in the region.

2. Materials and Methods

2.1. Study Area

The study site is situated in the DBA of Laixi City, Shandong Province (Figure 1). Laixi City lies at the heart of the Shandong Peninsula, characterized by a topography where northern areas are elevated, while southern regions are lower, featuring low hills to the north, gradually sloping plains in the central area, and basin-like depressions in the south. Laixi City is situated within North China’s warm temperate monsoon continental climate, influenced by geographic conditions and monsoon winds. This results in significant inter-annual variations in precipitation with uneven spatial and temporal distribution. The area primarily falls within the Dagu River system, with the river flowing southwards through the central part of the region. Groundwater primarily resides in the pore spaces of sand layers, weathered fissures in bedrock, and tectonic fissures. Groundwater in the DBA predominantly comprises pore water from loose rock and pore fissure water from clastic rock [11]. The upper part of the aquifer consists of a loose sand layer, while the lower part comprises sedimentary and metamorphic rock. Groundwater recharge primarily stems from atmospheric precipitation infiltration, with evaporation and artificial pumping representing the main methods of groundwater discharge. Groundwater movement aligns with the topographic slope and surface water system, gently sloping from north to south.

2.2. Sampling and Measurement

Considering the local farming practices, this research collected 34 water samples in November, which marks the dry season (Figure 1). Of these, 29 groundwater sampling points were located around vegetable greenhouses and cultivated land, drawing from shallow groundwater through mechanized wells with an average depth of 7 m. Additionally, there were five surface water-sampling points, specifically from the Dagu River and drainage canal water.

Table 1. Test methods and detection limit of groundwater samples.

Testing Indicators	Detection Methods/Instruments	Detection Iimit/(5 mg·L−1)
Cl−	Volumetric silver nitrate method	0.05
Ca2+, Mg2+, TH (CaCO3)	Disodium EDTA titration method	0.05
K+, Na+	Flame atomic absorption spectrophotometry	0.05
SO42−	Barium sulfate turbidimetric method	0.05
NO3−	Ultraviolet spectrophotometric method	0.20
pH	Glass electrode method	0.01
TDS	American HACH hash portable pH meter HQ40D	0.01

provides details on the testing methods for each indicator.

2.3. Methods

The hydrochemical attributes and genesis mechanisms of surface and groundwater in the DBA were examined through conventional techniques, including Piper’s plot, Gibbs plot, correlation analysis, ion ratio analysis, etc. Additionally, the water quality assessment in the area employed parameters such as EWQI, SAR, and Na%. By utilizing ArcGIS (10.8) software, the spatial arrangement of sampling sites within the study area was delineated to elucidate the primary chemical components and water quality of both groundwater and surface water in this locale.

2.3.1. The Entropy Water Quality Index (EWQI)

The entropy weighting method for assessing water quality involves computing sub-index data, assigning weights, and subsequently calculating a composite index to evaluate water quality. The calculation method outlined by Liu et al. [12] is as follows:

Construct the initial judgment matrix R

$$\mathrm{R}=\left[\begin{array}{ccc}\begin{array}{cc}{C}_{11}& C_{12}\\ C_{21}& C_{22}\end{array}& \cdots & \begin{array}{c}{C}_{1n}\\ C_{2n}\end{array}\\ ⋮& \ddots & ⋮\\ \begin{array}{cc}{C}_{m1}& C_{m2}\end{array}& \cdots & C_{mn}\end{array}\right]=\mathrm{R}{\left({\mathrm{C}}_{\mathrm{ij}}\right)}_{\mathrm{mn}}$$

(1)

where: m—number of sampling points; n—number of evaluation indicators.

(1)

Calculate the index

Each test value is converted to a multiple of the standard value based on the agricultural irrigation water quality standard, as depicted in Equation (2):

$$Q_{ij}=C_{ij}/S_i$$

(2)

In the Equation, Q_ij signifies the sub-index of the ith for monitoring point j; C_ij is the actual value of the ith for monitoring point j; S_i denotes the standardized value of the i-th indicator.

(2)

Calculation of weights

Weights represent the impact of evaluation elements on the degree of water contamination. Firstly, the entropy value is utilized to standardize the detection value (Equation (3)) and derive the corresponding entropy value, referenced as e_i (Equation (4)). Subsequently, the initial weights are calculated, and finally, the individual indicator weights are normalized (Equations (5) and (6)).

$$P_{ij}=C_{ij}/(C_{i1}+C_{i2}+\cdots +C_{im})$$

(3)

$$e_i=-{\displaystyle \frac{1}{lnm}}{\sum }_1^m\left(P_{ij}·{lnP}_{ij}\right)(0\le e_i\le 1)$$

(4)

$$w_i^{\prime }=1-e_i$$

(5)

$$w_i=w_i^{\prime }/{\sum }_1^n{w}_i^{\prime }$$

(6)

In the Equation, P_ij represents the normalized detection value, e_i signifies the entropy value of the ith index, mm denotes the number of sampling points, $w_i^{\prime }$ stands for the initial weight of the ith index, $w_i$ indicates the normalized weight of the ith index, and n is the number of evaluation indices.

(3)

Calculate the composite index EWQI:

$$Q_{\mathrm{E}\mathrm{W}\mathrm{Q}\mathrm{I}}={\sum }_1^n{w}_i·Q_{ij}$$

(7)

In the Equation, $Q_{\mathrm{E}\mathrm{W}\mathrm{Q}\mathrm{I}}$ represents the comprehensive index value for the jth sampling point; $Q_{ij}$ denotes the sub-index of the ith indicator at the jth monitoring point; n signifies the number of assessment indicators.

2.3.2. Sodium Adsorption Ratio (SAR) and Soluble Sodium Percentage (Na%)

There is a correlation between the sodium adsorption ratio of irrigation water and the soil’s sodium adsorption, making it a useful indicator of the risk of sodium or alkali in water affecting the soil. The ratio is commonly calculated as shown in Equation (8) [13]:

$$Q_{SAR}={Na}^{+}/\sqrt{({Ca}^{2+}+{Mg}^{2+})/2}$$

(8)

The qualifying equations in the water quality classification chart are as follows (Equations (9)–(11)):

High: S = 43.75 − 8.87(logC)

(9)

Medium: S = 31.35 − 6.66(logC)

(10)

Low: S = 18.87 − 4.44(logC)

(11)

where: S—Sodium Adsorption Ratio (SAR); C—Conductivity, us/cm; log—Logarithmic with base 10.

Na% serves as a crucial indicator for assessing the potential alkali-related harm from irrigation water; a higher Na% a value signifies an increased risk of alkali damage. Utilizing such groundwater for irrigation can trigger cation exchange on the soil surface, with Na⁺ being adsorbed by the soil and displacing Ca²⁺ and Mg²⁺, thereby diminishing soil permeability and causing inadequate drainage. The corresponding formula is presented below [14]:

$$\mathrm{N}\mathrm{a}\mathrm{\%}={\displaystyle \frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+K^{+}}}\times 100\%$$

(12)

The proportion of sodium ions in irrigation water relative to the total cation concentration is commonly denoted as the soluble sodium percentage (Na%). Regarding sodium hazards, water with a soluble sodium percentage below 60% is suitable for irrigation, while levels exceeding 80% are unsuitable for this purpose. Water samples are categorized based on conductivity into four groups: water with a conductivity below 250 μs/cm is highly suitable for irrigation, water in the 250–750 μs/cm range is suitable, water ranging from 750 to 2250 μs/cm is classified for reserved irrigation, and water exceeding 2250 μs/cm is unsuitable for irrigation.

Wilcox graph (1955) [15] and USSL graph (1954) [16] technique are employed to demonstrate the accuracy and classification of water utilized for irrigation consistently [17]. The USSL graph features a logarithmic scale of EC in µS/cm on the X-axis and SAR on the Y-axis, delineating different water quality classes: higher, high, mid, and low. In contrast, the Wilcox plot uses the standard EC scale on the X-axis and Na% on the Y-axis [18], illustrating five water quality divisions: Unsuitable, Retained, Suitable, Very Suitable, and Over Irrigation. These graphs were constructed utilizing Diagramme software (6.60) [19].

3. Results and Discussion

3.1. Descriptive Statistics

As shown in

Table 2. Statistical results of water chemical indicators of water samples in the DBA.

		pH	TDS	TH	Na+	SO42−	Cl−	HCO3−	NO3−
	Standard Value	6.5–8.5	≤1000	≤450	≤200	≤250	≤250	≤250	≤88.6
Groundwater	Minimum	6.86	358.26	235.13	24.7	51.65	27.69	88.62	29.48
	Maximum	7.99	4154.21	1606.10	678.00	700.00	1030.91	480.61	1820.0
	Mean value	7.51	1340.79	759.91	115.68	246.81	231.15	200.74	369.67
	Standard deviation	0.25	737.96	324.80	128.84	126.06	234.12	101.89	362.056
	Exceedance rate (%)	0	68.97	82.76	17.24	34.48	24.14	20.69	79.31
	Coefficient of variation (%)	3.3	55.04	42.74	111.38	51.08	101.28	50.76	97.94
Surface water	Minimum	7.48	543.64	393.36	45.26	132.94	84.61	187.47	11.90
	Maximum	7.98	1203.28	639.14	150	256.83	291.63	565.82	68.27
	Mean value	7.81	875.33	528.84	90.16	191.54	172.98	361.39	43.58
	Standard deviation	0.20	302.63	129.96	44.48	54.04	88.75	187.73	21.79
	Exceedance rate (%)	0	40.00	60.00	0	20.00	20.00	60.00	0
	Coefficient of variation (%)	3.0	35.0	25.0	49.0	28.0	51.0	52.0	50.0

Note: pH is dimensionless, and the rest of the indicators are in mg·L⁻¹.

, surface water predominantly contains Ca²⁺ cations along with SO₄²⁻ and HCO₃⁻ anions. Conversely, groundwater is characterized by NO₃⁻ and SO₄²⁻ anions alongside Ca²⁺ and Na⁺ cations. In the study area, groundwater exhibited a pH range of 6.86 to 7.99, averaging 7.51, while surface water showed a pH range of 7.48 to 7.98, with an average of 7.81, demonstrating predominantly weak alkalinity. The groundwater TDS ranged from 358.26 mg/L to 4154.21 mg/L, with a mean concentration of 1340.79 mg/L. In the study area, freshwater (TDS < 1 g·L⁻¹), brackish water (1 g·L⁻¹ < TDS < 3 g·L⁻¹), and salty water (3 g·L⁻¹ < TDS < 10 g·L⁻¹) constituted 31.03%, 65.52%, and 3.45% of the groundwater samples, respectively. Brackish water predominantly dominates the shallow groundwater in this area. The TDS in surface water ranged from 543.61 mg·L⁻¹ to 1203.28 mg·L⁻¹, averaging 875.33 mg·L⁻¹. In surface water samples, freshwater constituted 60%, while brackish water made up 40%. The total hardness (TH) of water bodies in the region exhibited elevated levels. Groundwater TH varied between 235.13 mg·L⁻¹ and 1606.10 mg·L⁻¹, averaging 759.91 mg·L⁻¹, while surface water exhibited TH levels ranging from 393.36 mg·L⁻¹ to 639.14 mg·L⁻¹, with a mean value of 528.84 mg·L⁻¹. The coefficients of variation analysis revealed that concentrations of Na⁺, Cl⁻, and NO₃⁻ in groundwater surpassed 100%, indicating significant fluctuations in its quality. In contrast, surface water exhibited coefficients of variation below 100%, signifying greater stability in its quality compared to groundwater.

3.2. Spatial Distribution of Major Ions

Sampling points for surface water were arranged at three locations along the Dagu River and two locations along the southeast drainage channel. The order of cation concentration content is Ca²⁺ > Na⁺ > Mg²⁺ > K⁺. Water samples from the upstream river and two drainage canals exhibited comparable Ca²⁺ and Mg²⁺ levels, with Ca²⁺ concentrations at 161.80 mg·L⁻¹, 180.30 mg·L⁻¹, and 164.30 mg·L⁻¹, and Mg²⁺ concentrations at 51.60 mg·L⁻¹, 45.94 mg·L⁻¹, and 49.86 mg·L⁻¹, in that order. The Mg²⁺ concentrations measured were 51.60 mg·L⁻¹, 45.94 mg·L⁻¹, and 49.86 mg·L⁻¹, significantly surpassing standard limits. Na⁺ concentrations at the drainage-canal-sampling sites notably exceeded those at the river sampling sites, measuring 112.80 mg·L⁻¹ and 150.00 mg·L⁻¹. In terms of anions, the overall distribution followed the trend HCO₃⁻ > SO₄²⁻ > Cl⁻ > NO₃⁻, decreasing towards the river. The analysis revealed a slightly basic aquatic setting particularly in the southeastern area. Particularly, the drainage canal on the southeast side exhibits notably higher HCO₃⁻ concentrations compared to other surface water sampling points. Moreover, higher mass concentrations of HCO₃⁻ in surface water correspond to lower levels of NO₃⁻ in the water. Chloride (Cl⁻) and sulfate (SO₄²⁻) exhibited a positive correlation, with Cl⁻ concentrations exceeding those of SO₄²⁻. This trend aligned with the distribution of the cation Na⁺, confirming the incidence of secondary soil salinization.

Figure 2a,b demonstrate consistently elevated Ca²⁺ and Mg²⁺ concentrations across the study area, with Ca²⁺ levels notably higher in the central residential and farm shed zones compared to the river and southern farm shed areas. Meanwhile, Mg²⁺ concentrations exhibit a more uniform spatial distribution. In Figure 2c, higher Na⁺ concentrations in the eastern region echo the distribution of Cl⁻ concentrations, possibly linked to human activities. Figure 2d highlights regions with elevated K⁺ concentrations, likely attributable to algal blooms within the water, indicating signs of water body eutrophication and resulting in increased K⁺ levels.

According to Figure 2e, the chloride (Cl⁻) concentration is notably elevated in the eastern region, specifically measuring 1030.91 mg·L⁻¹ at point LX07, indicating significant anthropogenic influences. Being a semi-arid region known for agriculture, this area extensively utilizes open-air and greenhouse cultivation. Excessive use of chemical fertilizers and pesticides, coupled with improper irrigation practices, leads to soil salinization due to increased salt content. Figure 2f illustrates the interpolation of groundwater nitrate (NO₃⁻) concentrations. The average NO₃⁻ concentration is 369.67 mg·L⁻¹, with a peak of 1820.0 mg·L⁻¹. In the west–central region, The concentration of NO₃⁻ ranges from 171.86 mg·L⁻¹ to 825.03 mg·L⁻¹, while the majority of the area shows concentrations above 50 mg·L⁻¹. These levels significantly surpass the groundwater NO₃⁻ standard of 88.6 mg·L⁻¹, impeding crop growth and posing a threat to the groundwater environment.

In Figure 2g, the distribution of SO₄²⁻ concentration mirrors that of the NO₃⁻ concentration, displaying a high mean value of 246.81 mg·L⁻¹ and peaking at 700.00 mg·L⁻¹ in the west–central region. Because groundwater contaminated by domestic wastewater and natural fertilizers (manure, a source of organic nitrogen) is typically connected with an enrichment of SO₄²⁻ [20,21,22], a hypothesis arises linking nitrate pollution to the pesticides and fertilizers washed away during farmland cultivation in the research area. Figure 2h illustrates the distribution of HCO₃⁻ concentrations in groundwater, highlighting a peak value near the drainage canal in the southeast.

3.3. Hydrochemical Type

3.3.1. Kurlovian and Shukarevian Type

The analysis of water chemistry reveals a notable rise in the nitrate (NO₃⁻) concentration with increased chemical pesticide usage. This increase exposes limitations in traditional water chemistry classifications. To resolve this issue, a novel water chemistry classification method is introduced based on the principles of Kurllov and Schlumberger classifications [23]. When the milliequivalents of NO₃⁻ in groundwater exceed 25% of the total ion content, surpassing that of other ions, this water type can be denoted as “NO₃·SO₄-Ca”, visually indicating the highest nitrate concentration. For samples with NO₃⁻ milliequivalents below 25%, categorization can be “Cl-Ca·Na” or “HCO₃·SO₄-Ca”, according to this classification. Surface water can be classified as “HCO₃·SO₄-Ca”. Cast the sampling point test data onto a Piper trilinear chart

3.3.2. The Piper Diagram

A Piper trilinear diagram is employed to delineate the principal ionic compositions and hydrochemistry of groundwater [24]. The sampled data points were cast on a Piper diagram (Figure 3). The analysis reveals that most water samples are positioned within the I region, with cation triangle sampling points located in regions A and B. In the anion triangle area, most sampling points are in region B, although regions E and G also show some distribution. Consequently, the predominant hydrochemical type in the DBA is identified as SO₄·Cl-Ca·Mg.

Within the region, NO₃⁻ concentrations are notably elevated, surpassing the capabilities of the Piper trilinear diagram to comprehensively depict the local water chemistry. To address this, distinct color coding was utilized to differentiate between high and low NO₃⁻ levels, facilitating a thorough examination of the region’s water chemistry profiles. Of the samples, 38.24% of groundwater and all surface water samples displayed NO₃⁻ concentrations below 100 mg/L; meanwhile, 14.71% of groundwater samples exhibited concentrations exceeding 600 mg/L. Notably, a discernible relationship emerged between the proximity of sampling points to the summits of Zone I and Zone A and elevated NO₃⁻ levels, highlighting a correlation between nitrate concentration and calcium–magnesium levels.

3.4. The Interrelationships Among Various Chemical Indicators

There is a correlation between the chemical composition of groundwater and its sources. Researchers often employ the interrelationships among chemical components to unveil the origins of ions [25]. The water chemistry data were analyzed for correlations using SPSS software (28.0), resulting in a correlation coefficient matrix for the chemical components (Figure 4). Nitrate (NO₃⁻) shows significant correlations at the 0.01 level (two-tailed) with TDS, Ca²⁺, Mg²⁺, and HCO₃⁻, with respective coefficients of 0.69, 0.87, 0.67, and −0.45. This suggests that NO₃⁻ is highly correlated with these ions. The correlation between CO₃⁻ and Na⁺, K⁺, and Cl⁻ at the 0.01 significance level is notably strong, with respective correlation coefficients of 0.62 and 0.52, indicating homogeneity with HCO₃⁻ for these ions. Similarly, SO₄²⁻ exhibits significant correlations with Ca²⁺ and Mg²⁺ at the 0.01 significance level, with correlation coefficients of 0.85 and 0.81, indicating that the primary ions in the study area’s groundwater likely stem from the dissolution of evaporite rocks (gypsum) and silicate rocks.

3.5. Factors Controlling the Hydrochemical Characteristics

3.5.1. Rock Weathering

Evaporative processes, atmospheric precipitation, and rock weathering are the key factors that impact the chemical composition of water [12]. The Gibbs diagram model established by Gibbs (1970) [26] was employed to pinpoint the primary factors impacting groundwater chemistry [27]. Figure 5 shows the distribution of water samples in the Gibbs diagram within the study area, predominantly located in the rock-weathering control zone. This indicates a significant influence of water–rock interactions on the hydrochemical characteristics of the study area. However, some water samples are also affected by evaporation crystallization processes, potentially linked to human activities and agricultural irrigation.

3.5.2. Ion Ratios

Ion ratios play a vital role in discerning the rock-weathering mechanisms that influence the hydrochemical properties of groundwater [28]. Water samples in the DBA are primarily distributed within the control ranges of silicate and carbonate rocks (Figure 6). The distribution of water-sampling points, as illustrated in Figure 6a, demonstrates a skew towards silicate rocks based on cation ratios. From the perspective of anion ratios (Figure 6b), the water samples in the study area fall within the spectrum between evaporitic salt rocks and silicate rocks. This observation suggests that evapotranspiration plays a role in water chemistry, with all surface and groundwater components primarily originating from silicate rocks [29].

3.5.3. Main Ion Sources

The dissolution of Na⁺ and Cl⁻ is primarily associated with salt rock dissolution. When sampling points are predominantly positioned along the 1:1 line, it indicates that Na⁺ and Cl⁻ in surface water primarily originate from rock salt dissolution. In Figure 7a, most samples are concentrated below the 1:1 line, indicating that the dissolution of salt minerals is not the primary source of Na⁺ and Cl⁻ ions. Furthermore, the clustering of samples at lower positions below the 1:1 line indicates potential influences of cation exchange and silicate weathering [30]. In the study area, the γ (Cl⁻) content is slightly higher than the γ (Na⁺) content, indicating that besides the dissolution of salt rocks, chloride (Cl⁻) may have other sources influenced by human activities, for example, the application of pesticides and fertilizers and wastewater discharge.

If only gypsum (CaSO₄·2H₂O) is dissolved in groundwater, the milliequivalent ratio of γ(SO₄²⁻) to γ(Ca²⁺) should be 1:1 [31]. As shown in Figure 7b, most water samples in the study area are distributed on both sides of γ(SO₄²⁻): γ (Ca²⁺) = 1:2, indicating that the γ(Ca²⁺) in the study area originates not only from gypsum dissolution but also suggests a significant influence from calcium-rich rock weathering during water–rock interactions. The interrelationship between γ(Ca²⁺+Mg²⁺) and γ(HCO₃⁻+SO₄⁻) can verify the effects of mineral weathering and ion exchange reactions. If calcium and magnesium originate from the weathering of carbonate, sulfate minerals (gypsum, anhydrite), and silicate minerals, both will be balanced by HCO₃⁻+SO₄⁻ [32]. In Figure 7c, approximately 86.1% of samples show a higher concentration of γ (Ca²⁺ + Mg²⁺) than γ (HCO₃⁻ + SO₄⁻), indicating that the dissolution of silicate rocks significantly influences the water’s chemical composition, with silicates dissolving into groundwater through chemical weathering. A small proportion of samples (approximately 13.9%) fall below the 1:1 line, indicating the influence of carbonate rock weathering on the hydrochemical environment [33,34]. Additionally, Figure 7d illustrates that the sampling points predominantly align with the 1:1 line, suggesting potential additional sources of Mg²⁺ and Ca²⁺ in the water, such as dissolution of ion exchange or silicate rocks [35].

The intensive agricultural cultivation and dense human activities in the study area indicate a significant impact of human activities on the hydrochemical environment. Nitrate is a crucial contaminant in groundwater, likely originating from agricultural practices and domestic sewage. The ratio between (SO₄²⁻/Ca²⁺) and (NO₃⁻/Ca²⁺) can serve as an indicator of human activities’ impact on groundwater [36]. As depicted in Figure 6, the increase in NO₃⁻/Ca²+ and SO₄²⁻/Ca²⁺ values indicates the influence of domestic sewage disposal, agricultural practices, and industrial activities on groundwater chemistry. The area under study is notably more influenced by agricultural cultivation and domestic wastewater. Due to anthropogenic influences, the groundwater in this region exhibits elevated levels of NO₃⁻, Cl⁻, and SO₄²⁻ [37,38]. The ratios of NO₃⁻/Na⁺ and Cl⁻/Na⁺ tend to rise. As shown in Figure 7, most samples are located within the agricultural impact end-member, indicating a significant influence of agricultural activities on the groundwater of the DBA.

3.6. Water Quality Assessment

The fundamental concept behind EWQI involves using entropy values to ascertain the weights of assessment indices and converting extensive water quality data into reflective values of water quality status [39]. The entropy value method is noted for its strong objectivity and its capacity to efficiently rectify errors in weight calculations [40]. Therefore, it is more reasonable to use EWQI.

The agricultural cultivation practices in the study area involve extensive use of fertilizers and pesticides during cultivation. The primary sources of irrigation water are the local surface and groundwater. Therefore, evaluating the quality of groundwater for irrigation holds significant importance for agricultural cultivation and provides a scientific basis for guidance.

3.6.1. Evaluation of Irrigation Water Quality Based on Groundwater Standards

The evaluation results of the 34 water sampling points in the study area based on the EWQI criteria fall into five categories: EWQI < 25, 25 < EWQI < 50, 50 < EWQI < 100, 100 < EWQI < 150, and EWQI > 150, representing grades I, II, III, IV, and V, respectively, where grades IV and V are deemed unsuitable for irrigation. The analysis reveals that none of the total sampled water falls into grade I, accounting for 0%; grade II includes six samples (four groundwater samples, two surface water samples), representing 17.65%; grade III comprises eleven samples (nine groundwater samples, two surface water samples), amounting to 32.35%; grade IV consists of eleven samples (all groundwater samples), also at 32.35%; and grade V encompasses six samples (all groundwater samples), making up 17.65%.

The assessment results indicate that most of the study area falls under Class III and Class IV water categories. Class III water is predominant in the upper reaches and southern regions of the rivers, interspersed with isolated Class II water sources. The central areas housing clustered villages and greenhouse farming sites are classified as Class IV water, while the water quality in the Yujiaxiaoli Village to Dianbu Town region is categorized as Class V. Agricultural activities represent a primary source of water system pollution, introducing nutrients (utilized as fertilizers in crop production) and plastics (employed for crop covering), among other contaminants [41]. Consequently, the water quality in surface [42] and groundwater [43,44] is progressively deteriorating.

3.6.2. Evaluation of Irrigation Water Quality Based on Farmland Safety Standards

Agricultural practices in the study area are extensive, involving significant usage of chemical fertilizers and pesticides. The primary sources of irrigation water are derived from the Dagu River and groundwater. Assessing the quality of irrigation water sourced from groundwater is crucial for agricultural cultivation in this context, offering a foundational framework for informed scientific guidance.

The SAR and the Na% serve as indicators of the harm caused by excessive sodium content in irrigation water. Elevated levels of Na⁺ in irrigation water can detrimentally affect the physicochemical properties of soil, leading to issues like soil compaction and reduced permeability [45], which in turn affect plant growth. Based on statistical analysis of hydrochemical data, a water quality classification diagram for irrigation water was generated using the USSL and Wilcox graphical methods.

The SAR results, as shown in Figure 8a, indicate that in groundwater samples, 55.18% fell within the C3 zone, while 34.48% and 10.34% of samples fell within the C4 and C2 zones, respectively, with no samples in the C1 zone. Among the groundwater samples, distribution in the S1, S2, S3, and S4 zones was 20.69%, 48.28%, 10.34%, and 20.69%, respectively. Surface water samples all fell within the C3 zone, with distributions of 40%, 20%, and 40% in the S1, S2, and S3 zones, respectively, and no samples in the S4 zone. The majority of both groundwater and surface water samples exhibit relatively low alkalinity levels but elevated soluble salt concentrations. Utilizing these waters for irrigation may contribute to soil salinization, potentially impacting crop growth. Overall, groundwater samples from the study area, falling within the C2-S1, C3-S1, and C3-S2 categories, are deemed suitable for irrigation purposes, constituting 65.52% of the total samples, with groundwater accounting for 47.06% and surface water for 8.82%.

The results for Na% are depicted in Figure 8b. The EC values of samples in the DBA varied from 537.39 to 3394.01 μs/cm, with an average of 1768.68 μs/cm. Only one groundwater sample had a sodium content exceeding 50%, while the rest were below this threshold. Samples from areas categorized for suitable irrigation, marginal irrigation, and unsuitable irrigation constituted 8.8%, 61.79%, and 29.41% of all the samples, respectively. Notably, all surface water samples were within the marginal irrigation category. Situated in a semi-arid region, waters with EC values lower than the average were also considered suitable for irrigation. The analysis indicates that approximately 55.88% of the water could be used for irrigation purposes.

4. Conclusions

Groundwater and surface water serve as primary agricultural irrigation sources in the DBA. An investigation of five surface water samples and twenty-nine groundwater samples were conducted to evaluate its hydrochemical attributes and suitability for irrigation purposes, employing methods like EWQI, SAR, and Na% for water quality assessment. The findings are as follows:

(1) In the DBA, water samples exhibit a pH range between 6.86 and 7.99, with an average of 7.55, indicating weak alkalinity. The TDS concentration in the study zone varies from 358.26 mg·L⁻¹ to 4154.21 mg·L⁻¹, with an average of 1340.79 mg·L⁻¹. The underground water shows a cation order of Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ and an anion sequence of NO₃⁻ > SO₄²⁻ > Cl⁻ > HCO₃⁻. The cationic composition of surface water mirrors that of groundwater, although notable discrepancies are found in the anionic content. The descending order of anionic mass concentrations is HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻. Analysis through Piper diagrams reveals a prevailing water chemistry profile in the DBA characterized by a dominance of SO₄·Cl-Ca·Mg type.

(2) Chemical control factors analysis indicates that all natural elements and anthropogenic activities significantly impact the hydrochemical characteristics of surface and groundwater. The chemical environment of the study area is notably influenced by natural processes like evaporite dissolution (gypsum), silicate rock weathering, cation exchange, and carbonate rock dissolution. Human activities, particularly industrial activities, notably influence the hydrochemical characteristics through factors like agricultural cultivation and domestic wastewater discharge.

(3) Calculating the quality of water used for irrigation reveals that 65.52% of the water is suitable for irrigation, with groundwater constituting 47.06% and surface water 8.82%. The Na% analysis indicates that 8.8% of water samples are suitable for irrigation, while 55.88% are usable. Irrigation decisions should align with actual production circumstances, establishing rational upper limits for groundwater levels.

(4) By establishing a monitoring and data analysis system for both groundwater and surface water, real-time observation of water resources fluctuations is enabled. This system integrates agricultural resources, advocates for efficient water-saving irrigation methods like drip and spray irrigation, and introduces quality equipment to reduce water wastage and control soil salinization. Additionally, it aims to prevent excessive water resource exploitation and implement comprehensive measures and planning strategies effectively.