https://orcid.org/0000-0003-2140-0081
ABSTRACT
Nitrate pollution is a major threat caused by intensive agricultural activities in semi-arid regions. This study focuses on the Da Niudi Gas Field in the Mu Us Sandy Land. Groundwater quality dynamics were assessed through analysis of 100 sampling points from 2012, 2015, and 2022. The maximum concentrations recorded were 476 mg/L for NO3−, 0.26 mg/L for NO2−, and 1.61 mg/L for NH4+. Principal component analysis (PCA) and correlation analysis reveal that groundwater chemistry is influenced by mineral dissolution, agricultural activities, and wastewater discharge. The Entropy Water Quality Index (EWQI) was used to evaluate groundwater quality, indicating an overall decline with nitrate pollution showing spatial variability. A health risk assessment model was developed to evaluate health risks for local residents. Results show significant health risks from elevated concentrations of NO3−, NO2−, and NO4+. In 2012, health risks were within acceptable limits, but by 2015, 50% of children and 12.5% of adults exceeded these limits. In 2022, these percentages decreased to 5.36% for children and 1.79% for adults. These findings emphasize the urgent need for measures to reduce nitrate pollution in the area.
HIGHLIGHTS
NO3− is the primary nitrogen source pollutant in the study area.
Nitrate sources in groundwater are influenced by sewage, soil nitrogen, and mixing, with pollution sources increasing.
Health risk assessment indicates groundwater pollution has mobility, with higher risks for children than adults.
INTRODUCTION
Water resources are closely intertwined with human life (Gocic & Arab Amiri 2023). Groundwater is an important source of drinking water, irrigation, and industrial usage (Cartwright et al. 2020; Su et al. 2020). The factors influencing the quality of groundwater can generally be summarized as natural factors and human activities (Li et al. 2017; Wang et al. 2018). Examples of natural factors include climate, landforms, hydrogeology, aquifer characteristics, recharge processes, rock types, weathering, and other related aspects (Yang et al. 2016; Duan et al. 2022; Barbieri et al. 2023). Human activities encompass factors such as agriculture, industry, urbanization, and others (He et al. 2021; Liu et al. 2022). Furthermore, human activities can alter groundwater quality based on the influence of natural factors (Guo et al. 2022).
Water resources are essential for the sustainable development of the environment, agriculture, and energy production, necessitating effective water resource planning and management (Arab Amiri & Gocić 2021). In semi-arid regions characterized by infrequent rainfall and a scarcity of surface water resources, groundwater serves as the primary source of water supply (Mukherjee & Singh 2020; Rahman et al. 2021). The investigation of the composition and distribution of groundwater chemistry in such areas is of particular significance. The Mu Us sandy land is situated in the semi-arid region of northwest China. Groundwater pollution resulting from agriculture, animal husbandry, and various human activities will inevitably affect the quality of groundwater in the MU Us Sandy Land (Qian et al. 2016; Han et al. 2020). The study area is the Daniudi Gas Field, located in the heart of the Mu Us sandy land, accompanied by villages, cultivated land, and areas of gas field exploitation. The importance of this area is underscored by its reliance on groundwater as a primary water source and the potential impact of pollution on local communities and ecosystems. Previous research in this region has primarily focused on the impact of fossil fuel extraction on groundwater, with relatively limited studies on nitrogen pollution caused by human activities (Li et al. 2013). The concentration of nitrate resulting from human activities has significantly increased worldwide, particularly in agricultural and pastoral areas (Jokam Nenkam et al. 2022; Lu et al. 2024). Nitrate concentrations in groundwater originate from various sources, including precipitation, soil nitrogen, sewage input, manure input, and agricultural activities (Spalding et al. 2019; Krimsky et al. 2021). The factors influencing the groundwater quality can generally be categorized into primary natural factors such as climate (Zhang et al. 2022; Rajkumar et al. 2023), landforms (Maleki et al. 2021), and hydrogeology (Liang et al. 2022), and secondary human-induced factors such as agricultural activities (Wang et al. 2021a, b), industrial pollution (Karmakar et al. 2023), and urbanization (Dixit et al. 2024). The primary factors are fundamental processes like aquifer characteristics and rock weathering, which set the baseline water quality (Abanyie et al. 2023). Secondary factors arise from human activities that modify this baseline through pollution or overuse, such as excessive use of fertilizers (Alam et al. 2024), improper wastewater management (Tampo et al. 2022), and industrial runoff (Mukate et al. 2020). Therefore, identifying the primary factors and sources of nitrate is of crucial significance for controlling nitrate pollution in the region. Without understanding these sources and their impact, effective management and mitigation strategies cannot be developed, leading to worsening water quality and increased health risks for local populations.
Multivariate statistical methods such as principal component analysis (PCA) and correlation analysis have been widely employed in research across various domains including groundwater, surface water, and hydro-climatology (Arab Amiri & Mesgari 2019; Amiri & Gocic 2023). These methods are used to analyze and interpret complex datasets, helping to identify the main factors influencing water chemistry, understand groundwater chemical processes, and explore the interactions between various components affecting groundwater composition (Zhang et al. 2021). Krishan et al. (2023) combined statistical methods with hydrochemical analysis, employing PCA to elucidate the primary factors affecting groundwater contamination. Ren et al. (2021) utilized multivariate techniques including correlation analysis and PCA to reveal the key hydrogeochemical processes regulating groundwater quality in urbanized areas. Water quality assessment is an essential task in water resources development and utilization, aimed at determining the degree of contamination in water bodies (Zhang et al. 2022). The primary objective of health risk assessment is to evaluate the overall risk level by establishing quantitative relationships between water pollution and human health (Fida et al. 2023). The combination of these three methods enables a more systematic investigation of nitrate pollution.
The current study focuses on investigating the spatiotemporal variations of nitrate in the typical region of the Mu Us Desert in northwest China and its implications for human health risks. The sub-objectives are as follows: (1) determine the variations in major ion concentrations and their influencing factors in groundwater chemistry; (2) assess spatial and temporal variations in groundwater quality and identify pollution levels; and (3) conduct a health risk assessment. This research can provide valuable insights for local groundwater protection and management, guide water resource utilization, and safeguard human health.
STUDY AREA
Location and climate
The spatial distribution of the study area and sampling sites.
Geological and hydrological settings
Hydrogeological map and cross-section profiles.
MATERIALS AND METHODS
Sampling and sample analysis
This study utilized groundwater data spanning 3 years, with the main type of groundwater being phreatic. In November 2012, October 2015, and November 2022, a total of 28, 13, and 59 groundwater samples were, respectively, collected. Due to differences in field conditions, accessibility, and logistical constraints, no repetitive samples were collected from the same locations in 2012, 2015, and 2022. Despite the variations in sample numbers, the sampling points were selected to ensure comprehensive coverage of the study area, allowing for a thorough assessment of groundwater quality dynamics over time. These samples were stored in clean polyethylene bottles, and the sampling, processing, and storage procedures followed the standard protocols recommended by the Chinese Ministry of Water Resources (Li et al. 2018). Prior to sampling, the containers were rinsed three times with the water to be sampled to prevent contamination. After sampling, the samples were transferred to a refrigerator at 4 degrees Celsius and promptly delivered to the laboratory for analysis. The testing methods for the same parameters in the collected water samples are consistent. Ten parameters were selected for testing, including pH, chemical oxygen demand (CODMn), total hardness (TH), total dissolved solids (TDS), 
, Cl−, 
, 
, 
, and F−. The pH was measured on-site using a portable instrument, while the monitoring of the other parameters followed the recommended methods outlined in the groundwater quality standards, as indicated in Table 1. The accuracy and precision of the analyses were verified by analyzing certified reference materials (CRMs) under the same conditions, thereby guaranteeing the reliability of the data. The selected indicators for each sample were repeatedly tested under the same conditions, ensuring the validity and accuracy of the data.
Detection methods of water quality samples
| The order | Parameter | Detection methods |
|---|---|---|
| 1 | pH | Glass electrode method |
| 2 | CODMn | Acid potassium permanganate titration |
| 3 | TH | EDTA volumetric method |
| 4 | TDS | Drying and weighing |
| 5 |  | Ion chromatography |
| 6 | Cl− | Atomic fluorescence spectrophotometry |
| 7 |  | Spectrophotometry |
| 8 |  | Spectrophotometry |
| 9 |  | Spectrophotometry |
| 10 | F− | Ion selective electrode method |
| The order | Parameter | Detection methods |
|---|---|---|
| 1 | pH | Glass electrode method |
| 2 | CODMn | Acid potassium permanganate titration |
| 3 | TH | EDTA volumetric method |
| 4 | TDS | Drying and weighing |
| 5 | | Ion chromatography |
| 6 | Cl− | Atomic fluorescence spectrophotometry |
| 7 | | Spectrophotometry |
| 8 | | Spectrophotometry |
| 9 | | Spectrophotometry |
| 10 | F− | Ion selective electrode method |
Principal component analysis
PCA is based on a data transformation approach to reduce complicated multivariable datasets to simple datasets and reveal simple underlying patterns in multivariable datasets (Singh et al. 2020a; Rajkumar et al. 2020). PCA is used to transform correlated variables into a smaller number of uncorrelated parameters, thus reducing the dimensionality of the data matrix. Therefore, PCA is commonly used to understand the sources and influencing factors of groundwater chemistry (Rajkumar et al. 2019; Haghnazar et al. 2022). Before statistical analysis, each chemical parameter is standardized (z-scale), eliminating the influence of different dimensions, allowing variables with different units to be compared and analyzed on the same scale (Herojeet et al. 2016). In PCA, eigenvalues measure the importance of each principal component, with eigenvalues >1 being statistically significant for explaining the results (Abdi & Williams 2010). Principal components are generated in an ordered sequence, with their contribution to variance decreasing sequentially. The first principal component explains the largest variance in the data, the second principal component explains the largest portion of the remaining variance, and so on (Vieira et al. 2012; Herojeet et al. 2017). By retaining principal components with larger eigenvalues, the dataset can be simplified, and the main features of the data can be highlighted while discarding minor information that contributes less to the total variance.
Entropy Water Quality Index
The Entropy Water Quality Index (EWQI) serves as a valuable tool for the comprehensive assessment of water quality (He & Wu. 2019; Dashora et al. 2022). EWQI can be used to analyze groundwater quality, and by utilizing information entropy, it determines the weight of each parameter, thereby reducing errors caused by weight assignment (Li et al. 2010). The simplicity of the EWQI calculation process, allowing for a direct reflection of the magnitude of individual groundwater indicators exceeding acceptable levels, has contributed to its widespread adoption. EWQI mitigates the subjectivity inherent in the establishment of artificial classification standards. Consequently, this study utilizes EWQI for the comprehensive assessment of groundwater quality. The specific procedures involved in the calculation of the EWQI index are as follows:
Based on the results of the EWQI, different water quality grades are represented as shown in Table 2.
Groundwater quality classification based on the EWQI
| EWQI | < 25 | 25–50 | 50–100 | 100–150 | >150 |
|---|---|---|---|---|---|
| Rank | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
| Water quality | Excellent quality | Good quality | Medium quality | Poor qualtiy | Extremely poor quality |
| EWQI | < 25 | 25–50 | 50–100 | 100–150 | >150 |
|---|---|---|---|---|---|
| Rank | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
| Water quality | Excellent quality | Good quality | Medium quality | Poor qualtiy | Extremely poor quality |
Heath risk assessment
, 
, and 
. The calculation process for human health risk assessment is depicted in Figure 3 (Guo et al. 2022). 
The process diagram for health risk calculation.
In this process, C represents the concentration of ions in the water (mg/L), while IR stands for the ingestion rate of drinking water (L/day), EF for the exposure frequency (days/year), ED for the exposure duration (years), BW for the body weight (kg), and AT for the average time of non-carcinogenic exposure (day) (Table 3) (Yin et al. 2021). Additionally, ADDoral represents the average daily dose of contaminants ingested from drinking water. For skin exposure, AF is the skin permeability coefficient (cm/h), ET is exposure time (h/d), SA is the skin area in contact with water (cm2), and CF is conversion factor of the unit. ADDdermal signifies the average daily dose of contaminants absorbed through the skin(mg/(kg·day)), RfDoral denotes the reference dosage for the intake of contaminants through drinking water, set at 0.1, 1.6 and 0.97 mg/(kg·day) for 
, 
and 
, respectively (Zhou et al. 2021). RfDdermal represents the reference dosage for contaminant exposure through skin contact. For 
, 
, and 
, the values of ‘RfDdermal’ are equivalent to those of ‘RfDoral’ (Ali et al. 2021; Rajkumar et al. 2023).
Parameter values for health risk assessment
| Parameters | Units | Children | Adult males | Adult females |
|---|---|---|---|---|
| IR | L/d | 0.7 | 1.5 | 1.5 |
| EF | day/year | 365 | 365 | 365 |
| ED | yr | 12 | 30 | 30 |
| BW | kg | 15 | 70 | 55 |
| AT | d | 4,380 | 10,950 | 10,950 |
| AF | cm/h | 0.001 | 0.001 | 0.001 |
| ET | h/day | 0.4 | 0.4 | 0.4 |
| SA | cm2 | 6,600 | 18,000 | 15,500 |
| Parameters | Units | Children | Adult males | Adult females |
|---|---|---|---|---|
| IR | L/d | 0.7 | 1.5 | 1.5 |
| EF | day/year | 365 | 365 | 365 |
| ED | yr | 12 | 30 | 30 |
| BW | kg | 15 | 70 | 55 |
| AT | d | 4,380 | 10,950 | 10,950 |
| AF | cm/h | 0.001 | 0.001 | 0.001 |
| ET | h/day | 0.4 | 0.4 | 0.4 |
| SA | cm2 | 6,600 | 18,000 | 15,500 |
RESULTS AND DISCUSSION
Hydrochemical parameters statistics
. The average TH in the study area for 2012, 2015, and 2022 was 175.27, 250.00, and 246.74 mg/L, respectively, indicating the dominance of water-rock interactions, particularly carbonate mineral dissolution, in shaping groundwater quality. The average 
concentrations for 2012, 2015, and 2022 were 25.53, 70.77, and 44.91 mg/L, respectively. 
is one of the common anions in groundwater, typically originating from the dissolution of minerals, such as gypsum (He et al. 2022a, b). The average Cl− concentrations for 2012, 2015, and 2022 were 16.49, 45.01, and 12.43 mg/L, respectively. Cl− can originate from mineral dissolution and human activities (Zhou et al. 2020). The exceedance rate of Cl− in 2015 was the highest, indicating the possibility of local pollution. The average concentrations of F− in 2012, 2015, and 2022 were 0.27, 0.82, and 0.34 mg/L, respectively. F− is generally considered to originate from the natural geological environment (Wang et al. 2021a, b). There is a wide range of ion concentration variations in the groundwater of the study area. The concentrations of 
and 
in the groundwater samples exceed the WHO guidelines (WHO 2011). In Figure 4, the concentration box plots for 
, 
, and 
are illustrated. The grey rectangles represent the interquartile range (IQR), i.e., the data between the upper quartile and lower quartile. The line within the rectangle denotes the median, while the height of the rectangle represents the IQR, the distance between the upper and lower quartiles. Data points falling outside 1.5 times the IQR range from the rectangle are considered outliers. From 2012 to 2022, the median concentration of 
is significantly higher than that of 
, and 
. Over this period, the median concentration of 
gradually increased, while that of 
decreased. The increase in 
concentrations indicates that nitrification is occurring, converting 
to 
. However, the observed decrease in 
and the concurrent rise in 
might point to a partial inhibition of complete nitrification (i.e., the conversion of 
to 
). This inhibition could be associated with rising groundwater pH levels, which affect microbial activity and the efficiency of nitrification processes (Soldatova et al. 2021). In 2022, the highest number of outliers for 
, 
, and 
was observed, indicating severe nitrogen pollution in that year. Statistical analysis of main physicochemical indices (units: mg/L except pH)
| Parameter | WHO GL | 2012 | 2015 | 2022 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | ESR (%) | min | max | mean | SD | ESR (%) | min | max | mean | SD | ESR (%) | ||
| pH | 6.5–8.5 | 7.30 | 8.88 | 7.78 | 0.33 | 7.14 | 6.17 | 9.16 | 7.81 | 0.74 | 25.00 | 6.90 | 9.10 | 7.79 | 0.33 | 1.79 |
| COD | – | 0.24 | 3.76 | 1.28 | 0.96 | – | 0.08 | 2.15 | 0.95 | 0.56 | – | 0.42 | 3.80 | 1.63 | 0.83 | – |
| TDS | 1,000 | 153.00 | 452.00 | 256.75 | 77.42 | 0.00 | 182.00 | 1,224.00 | 458.12 | 298.84 | 12.50 | 130.00 | 1,190.00 | 407.75 | 252.48 | 5.36 |
| TH | 500 | 22.20 | 362.00 | 175.27 | 66.71 | 0.00 | 66.10 | 699.60 | 250.00 | 158.24 | 25.00 | 13.00 | 673.00 | 246.74 | 111.08 | 5.36 |
 | 250 | 1.16 | 69.80 | 25.53 | 20.64 | 0.00 | 9.61 | 297.20 | 70.77 | 76.27 | 6.25 | 0.09 | 221.00 | 44.91 | 36.96 | 0.00 |
| Cl− | 250 | 3.76 | 66.20 | 16.49 | 18.19 | 0.00 | 1.78 | 281.80 | 45.01 | 67.35 | 6.25 | 0.50 | 146.00 | 29.92 | 32.57 | 0.00 |
 | 50 | 0.04 | 16.50 | 2.89 | 4.64 | 0.00 | 0.20 | 312.50 | 46.27 | 77.29 | 25.00 | 0.08 | 476.00 | 12.43 | 63.99 | 3.57 |
 | 3 | 0.00 | 0.09 | 0.01 | 0.03 | 0.00 | 0.00 | 0.02 | 0.01 | 0.01 | 0.00 | 0.08 | 0.26 | 0.08 | 0.02 | 0.00 |
 | 0.5 | 0.02 | 1.61 | 0.16 | 0.32 | 7.14 | 0.02 | 0.14 | 0.04 | 0.03 | 0.00 | 0.01 | 0.38 | 0.05 | 0.10 | 0.00 |
| F− | 1.5 | 0.09 | 0.61 | 0.27 | 0.11 | 0.00 | 0.10 | 3.90 | 0.82 | 1.07 | 18.75 | 0.07 | 1.30 | 0.34 | 0.26 | 0.00 |
| Parameter | WHO GL | 2012 | 2015 | 2022 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | ESR (%) | min | max | mean | SD | ESR (%) | min | max | mean | SD | ESR (%) | ||
| pH | 6.5–8.5 | 7.30 | 8.88 | 7.78 | 0.33 | 7.14 | 6.17 | 9.16 | 7.81 | 0.74 | 25.00 | 6.90 | 9.10 | 7.79 | 0.33 | 1.79 |
| COD | – | 0.24 | 3.76 | 1.28 | 0.96 | – | 0.08 | 2.15 | 0.95 | 0.56 | – | 0.42 | 3.80 | 1.63 | 0.83 | – |
| TDS | 1,000 | 153.00 | 452.00 | 256.75 | 77.42 | 0.00 | 182.00 | 1,224.00 | 458.12 | 298.84 | 12.50 | 130.00 | 1,190.00 | 407.75 | 252.48 | 5.36 |
| TH | 500 | 22.20 | 362.00 | 175.27 | 66.71 | 0.00 | 66.10 | 699.60 | 250.00 | 158.24 | 25.00 | 13.00 | 673.00 | 246.74 | 111.08 | 5.36 |
| | 250 | 1.16 | 69.80 | 25.53 | 20.64 | 0.00 | 9.61 | 297.20 | 70.77 | 76.27 | 6.25 | 0.09 | 221.00 | 44.91 | 36.96 | 0.00 |
| Cl− | 250 | 3.76 | 66.20 | 16.49 | 18.19 | 0.00 | 1.78 | 281.80 | 45.01 | 67.35 | 6.25 | 0.50 | 146.00 | 29.92 | 32.57 | 0.00 |
| | 50 | 0.04 | 16.50 | 2.89 | 4.64 | 0.00 | 0.20 | 312.50 | 46.27 | 77.29 | 25.00 | 0.08 | 476.00 | 12.43 | 63.99 | 3.57 |
| | 3 | 0.00 | 0.09 | 0.01 | 0.03 | 0.00 | 0.00 | 0.02 | 0.01 | 0.01 | 0.00 | 0.08 | 0.26 | 0.08 | 0.02 | 0.00 |
| | 0.5 | 0.02 | 1.61 | 0.16 | 0.32 | 7.14 | 0.02 | 0.14 | 0.04 | 0.03 | 0.00 | 0.01 | 0.38 | 0.05 | 0.10 | 0.00 |
| F− | 1.5 | 0.09 | 0.61 | 0.27 | 0.11 | 0.00 | 0.10 | 3.90 | 0.82 | 1.07 | 18.75 | 0.07 | 1.30 | 0.34 | 0.26 | 0.00 |
Notes: WHO GL, WHO guideline; SD, standard deviation; ESR, exceeding standards rate.
Box and Violin plots of 
, 
, and 
.
Hydrogeochemical factors controlling nitrogen nitrate
PCA-based results
To understand the variations in latent factors influencing groundwater parameters, the PCA results for groundwater samples from 2012, 2015, and 2022 are presented in Table 5. Principal components were extracted using the eigenvalue greater than 1, and the corresponding component loadings, percentages of variance, and cumulative percentages were calculated.
Principal components of groundwater samples for 2012, 2015, and 2022
| Parameter | 2012 | 2015 | 2022 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 1 | 2 | 3 | 1 | 2 | 3 | 4 | |
| TDS | 0.33 | 0.66 | −0.39 | −0.36 | 0.95 | 0.22 | 0.01 | 0.97 | 0.00 | 0.08 | 0.09 |
 | 0.74 | 0.17 | 0.21 | −0.42 | 0.91 | 0.11 | −0.26 | 0.77 | −0.20 | 0.12 | 0.02 |
| TH | −0.31 | 0.44 | 0.68 | −0.17 | 0.79 | −0.52 | 0.02 | 0.91 | −0.18 | 0.19 | −0.13 |
| Cl− | 0.54 | 0.74 | −0.10 | −0.16 | 0.56 | 0.71 | −0.08 | 0.81 | 0.23 | −0.09 | 0.16 |
 | 0.41 | 0.39 | 0.56 | 0.54 | 0.83 | −0.21 | −0.20 | 0.39 | −0.44 | 0.58 | 0.25 |
| pH | 0.61 | 0.16 | −0.22 | 0.63 | −0.41 | 0.80 | −0.14 | −0.21 | 0.32 | 0.20 | 0.87 |
| F− | −0.37 | 0.64 | −0.24 | 0.34 | 0.09 | 0.80 | 0.32 | 0.50 | 0.68 | −0.30 | 0.10 |
| COD | − 0.66 | 0.54 | 0.04 | −0.11 | 0.75 | −0.05 | 0.39 | 0.69 | 0.24 | −0.15 | −0.23 |
 | − 0.75 | 0.33 | −0.29 | 0.12 | 0.20 | 0.22 | 0.85 | −0.21 | 0.32 | 0.66 | −0.25 |
 | −0.29 | 0.09 | 0.48 | 0.00 | 0.25 | 0.50 | − 0.52 | −0.12 | 0.51 | 0.59 | −0.26 |
| Eigen value | 2.80 | 2.21 | 1.41 | 1.20 | 4.18 | 2.46 | 1.37 | 3.97 | 1.39 | 1.25 | 1.06 |
| Variance (%) | 27.98 | 22.07 | 14.11 | 12.04 | 41.81 | 24.56 | 13.74 | 39.70 | 13.89 | 12.46 | 10.65 |
| Cumulative (%) | 27.98 | 50.05 | 64.16 | 76.20 | 41.81 | 66.37 | 80.11 | 39.70 | 53.59 | 66.05 | 76.70 |
| Parameter | 2012 | 2015 | 2022 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 1 | 2 | 3 | 1 | 2 | 3 | 4 | |
| TDS | 0.33 | 0.66 | −0.39 | −0.36 | 0.95 | 0.22 | 0.01 | 0.97 | 0.00 | 0.08 | 0.09 |
| | 0.74 | 0.17 | 0.21 | −0.42 | 0.91 | 0.11 | −0.26 | 0.77 | −0.20 | 0.12 | 0.02 |
| TH | −0.31 | 0.44 | 0.68 | −0.17 | 0.79 | −0.52 | 0.02 | 0.91 | −0.18 | 0.19 | −0.13 |
| Cl− | 0.54 | 0.74 | −0.10 | −0.16 | 0.56 | 0.71 | −0.08 | 0.81 | 0.23 | −0.09 | 0.16 |
| | 0.41 | 0.39 | 0.56 | 0.54 | 0.83 | −0.21 | −0.20 | 0.39 | −0.44 | 0.58 | 0.25 |
| pH | 0.61 | 0.16 | −0.22 | 0.63 | −0.41 | 0.80 | −0.14 | −0.21 | 0.32 | 0.20 | 0.87 |
| F− | −0.37 | 0.64 | −0.24 | 0.34 | 0.09 | 0.80 | 0.32 | 0.50 | 0.68 | −0.30 | 0.10 |
| COD | − 0.66 | 0.54 | 0.04 | −0.11 | 0.75 | −0.05 | 0.39 | 0.69 | 0.24 | −0.15 | −0.23 |
| | − 0.75 | 0.33 | −0.29 | 0.12 | 0.20 | 0.22 | 0.85 | −0.21 | 0.32 | 0.66 | −0.25 |
| | −0.29 | 0.09 | 0.48 | 0.00 | 0.25 | 0.50 | − 0.52 | −0.12 | 0.51 | 0.59 | −0.26 |
| Eigen value | 2.80 | 2.21 | 1.41 | 1.20 | 4.18 | 2.46 | 1.37 | 3.97 | 1.39 | 1.25 | 1.06 |
| Variance (%) | 27.98 | 22.07 | 14.11 | 12.04 | 41.81 | 24.56 | 13.74 | 39.70 | 13.89 | 12.46 | 10.65 |
| Cumulative (%) | 27.98 | 50.05 | 64.16 | 76.20 | 41.81 | 66.37 | 80.11 | 39.70 | 53.59 | 66.05 | 76.70 |
Note: Bold indicates significance at 0.01 level (two-tailed).
In 2012, PCA identified four principal components, cumulatively explaining 76.20% of the total variance. PC1, which explains 27.98% of the total variance, has strong positive loadings on 
and negative loadings on 
and COD. 
is often associated with salinity, COD reflects organic matter content, indicating pollution levels in the water, and 
may be related to agricultural fertilization or sewage discharge (Liu et al. 2023a, b; Sewak et al. 2023). These high loadings suggest that PC1 likely represents a combination of pollution levels and salinity. PC2 explains 22.07% of the total variance and has a high negative loading on F⁻ (0.64), indicating that PC2 is likely related to the dissolution of fluoride minerals. PC3 explains 14.11% of the variance, with strong positive loadings on TH, 
, and 
, suggesting that PC3 is related to water hardness and nitrate pollution, potentially influenced by agricultural or anthropogenic activities (Zhu et al. 2023). PC4, which explains 12.04% of the total variance and has strong positive loadings on pH, reflects changes in water alkalinity.
In 2015, PCA identified three principal components, cumulatively explaining 80.11% of the total variance. PC1 explains 41.81% of the total variance and shows a strong positive correlation with TDS, 
, TH, 
, and COD, indicating the influence of mineral weathering on groundwater chemistry (Wu et al. 2020). High loadings of TDS (0.95), 
(0.91), and TH (0.79) suggest that PC1 primarily represents the impact of groundwater salinity and hardness, possibly influenced by agricultural irrigation and geological processes. The presence of 
may stimulate an increase in COD levels, likely due to agricultural activities (Ouhakki et al. 2024). Meanwhile, this also indicates that groundwater quality is strongly affected by hardness. PC2 accounts for 24.56% of the total variance, with moderate positive loadings on F⁻, pH, and Cl⁻. F⁻ shows a significant increase in loading (0.80), suggesting that the dissolution of fluoride minerals had a more prominent influence on groundwater chemistry during this period. PC3, responsible for 12.46% of the variance, shows strong positive loadings on 
and negative loadings on 
. The positive loading of 
reflects nitrogen input, likely from agricultural fertilization, while the negative loading of 
could indicate inhibition of certain nitrogen cycling processes (Mushinski et al. 2021).
In 2022, PCA identified four principal components, cumulatively explaining 76.70% of the total variance. PC1 explains 39.70% of the variance and shows strong positive loadings on TDS, 
, TH, Cl⁻, and COD, which are controlled by lithogenic factors, possibly associated with the weathering of silicate minerals and halite dissolution (Ekwere et al. 2012; Li et al. 2016). TH shows strong loadings in PC1 (0.91), alongside Cl− and 
, which reflect mineral weathering processes. Although salinity (Cl and 
) is present, the high loadings of TH suggest that groundwater hardness plays a more prominent role in influencing water chemistry compared to salinity. PC2 accounts for 13.89% of the variance and shows a positive loading on F⁻, continuing to indicate the dissolution of fluoride minerals. PC3 explains 12.46% of the variance, with positive loadings on 
, 
, and 
, likely related to agricultural fertilization, wastewater discharge, or organic matter degradation (Wang et al. 2022; Oliveira et al. 2023). Additionally, PC4 explains 10.65% of the total variance, showing positive loadings with pH, which reflects changes in water alkalinity.
Correlation analysis results
Correlation analysis results between major indices for (a) 2012, (b) 2015, and (c) 2022. * p ≤ 0.05. Correlation is significant at the 0.05 level (two-tailed). ** p ≤ 0.01. Correlation is significant at the 0.01 level (two-tailed).
Correlation analysis results between major indices for (a) 2012, (b) 2015, and (c) 2022. * p ≤ 0.05. Correlation is significant at the 0.05 level (two-tailed). ** p ≤ 0.01. Correlation is significant at the 0.01 level (two-tailed).
As shown in Figure 5(a), which represents the correlation analysis results for 2012, pH exhibited a weak negative correlation with TH (−0.45), suggesting that lower pH enhances the solubility of certain minerals, contributing to higher TH (Mohamed et al. 2023). TDS was significantly positively correlated with Cl⁻, an important component of TDS, with possible sources including agricultural activities and the dissolution of halite (Ki et al. 2015; Duan et al. 2024). 
had a weak negative correlation with 
(−0.46), indicating strong nitrification, where 
is converted to 
, leading to a decrease in ammonium as nitrate levels increase (Li et al. 2015). 
exhibited a positive correlation with F⁻ (0.52), suggesting that industrial or agricultural activities may be linked to elevated levels of fluoride and ammonium (Wu et al. 2019). The strong positive correlation between 
and COD (0.7) indicates that organic pollution sources could contribute to higher 
levels, suggesting that untreated sewage or agricultural activities may be responsible for contamination (Hong & Gao 2022).
In Figure 5(b), which presents the correlation analysis results for 2015, pH showed a strong negative correlation with TH (−0.76), indicating that as pH decreases, TH increases significantly. This could be due to the enhanced dissolution of carbonate and related minerals under lower pH conditions, resulting in greater hardness. TDS had correlation coefficients of 0.86 with Cl⁻ and 0.65 with 
, indicating that the dissolution of minerals such as halite and gypsum significantly contributes to TDS. The correlation coefficient between 
and 
was 0.54, suggesting nitrification, where 
is oxidized to nitrite and then to nitrate in the nitrogen cycle (Zaryab et al. 2022). 
exhibited a correlation coefficient of 0.7 with Cl⁻, indicating a strong positive correlation between ammonium and chloride, suggesting potential pollution from sources such as domestic sewage or agricultural runoff. Cl⁻ was positively correlated with COD (0.61), indicating that chloride and COD may originate from the same pollution sources, such as industrial wastewater or agricultural runoff, where both chloride ions and organic matter are elevated (Javahershenas et al. 2022).
In Figure 5(c), which presents the correlation analysis results for 2022, TDS was positively correlated with TH, Cl⁻, F⁻, and COD. The correlation coefficient between TDS and TH was 0.79, indicating a high content of hardness ions in TDS. The positive correlations of TDS with Cl⁻ (0.76) and F⁻ (0.59) suggest that dissolved chlorides and fluorides may be important components of TDS. The correlation coefficient between TDS and COD was 0.66, indicating that human activities leading to organic pollution have contributed to increases in both TDS and COD (Maiti et al. 2016). The correlation coefficient between F⁻ and COD was 0.50, suggesting that these components may co-occur in certain pollution sources. Industrial wastewater or domestic sewage may contain both organic pollutants and fluorides, leading to simultaneous increases in both in groundwater (Guo et al. 2018). The strong correlation between F⁻ and Cl⁻ indicates that these ions in groundwater may be influenced by similar pollution sources, such as agricultural fertilization or industrial activities (Currell et al. 2011). Cl⁻ showed a positive correlation with COD (0.46), suggesting that domestic sewage and industrial wastewater, which are typically rich in Cl⁻ and organic matter, contribute to elevated COD levels.
Source of nitrogen nitrate
/Cl− molar concentration ratio and Cl− is widely used to determine the source of nitrate in groundwater (Alex et al. 2021; Naidu et al. 2021). The potential sources of Cl− include natural sources (such as mineral dissolution), agricultural chemicals (potash or potassium chloride), animal manure, and sewage. The majority of Cl− is primarily from human activities, and Cl− serves as a good indicator of sewage influence because it is not affected by the physical, chemical, and microbial processes occurring in groundwater (Manikandan et al. 2015). The concentrations of Cl− are higher in sewage and manure, while the lowest Cl− concentrations are found in atmospheric precipitation. Nitrate inputs from precipitation often exhibit lower Cl− concentrations. The use of fertilizers in agricultural activities is a major factor contributing to the increase in 
concentrations in groundwater. Therefore, Figure 6 illustrates five inputs influencing nitrate sources in groundwater: precipitation, soil nitrogen input, sewage input, manure input, and agricultural input. It can be observed that in 2012, nitrate contamination in groundwater originated from soil nitrogen, sewage, and mixing action. In 2015, the primary source of nitrate pollution in groundwater was sewage and mixing action. By 2022, nitrate contamination in groundwater primarily arose from soil nitrogen, sewage, precipitation, and mixing action. This indicates an expanding range of nitrate sources in the region. Agricultural input makes a minor contribution to nitrate pollution in the study area, whereas sewage input and mixing action are identified as the primary sources of nitrate pollution. Additionally, the study area is a traditional agricultural and pastoral region where extensive grazing, cultivation, fertilization, and irrigation over the years have resulted in the accumulation of nitrates in soil and shallow groundwater, making soil nitrogen another significant source of nitrate pollution. 
Nitrate source identification by a 
/Cl− molar ratio versus Cl−.
Water quality evaluation
Summary of groundwater quality (EWQI) and water quality grade distribution
| Year | EWQI | Water Quality Grade Count | Water Quality Grade Proportion(%) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | R1 | R2 | R3 | R4 | R5 | E | G | M | P | EP | |
| 2012 | 13.93 | 43.11 | 23.40 | 7.63 | 18 | 9 | 0 | 0 | 0 | 66.67 | 33.33 | 0.00 | 0.00 | 0.00 |
| 2015 | 24.53 | 183.94 | 55.91 | 40.19 | 1 | 10 | 4 | 0 | 1 | 6.25 | 62.50 | 25.00 | 0.00 | 6.25 |
| 2022 | 13.43 | 146.50 | 35.82 | 19.00 | 12 | 38 | 5 | 1 | 0 | 21.43 | 67.86 | 8.93 | 1.79 | 0.00 |
| Year | EWQI | Water Quality Grade Count | Water Quality Grade Proportion(%) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | R1 | R2 | R3 | R4 | R5 | E | G | M | P | EP | |
| 2012 | 13.93 | 43.11 | 23.40 | 7.63 | 18 | 9 | 0 | 0 | 0 | 66.67 | 33.33 | 0.00 | 0.00 | 0.00 |
| 2015 | 24.53 | 183.94 | 55.91 | 40.19 | 1 | 10 | 4 | 0 | 1 | 6.25 | 62.50 | 25.00 | 0.00 | 6.25 |
| 2022 | 13.43 | 146.50 | 35.82 | 19.00 | 12 | 38 | 5 | 1 | 0 | 21.43 | 67.86 | 8.93 | 1.79 | 0.00 |
Notes: R1 = Rank Ⅰ, R2 = Rank Ⅱ, R3 = Rank Ⅲ, R4 = Rank Ⅳ; R5 = Rank Ⅴ; while E, G, M, P, represent the count of samples with excellent, good, medium, poor, and extremely poor water quality, respectively.
Groundwater quality assessment results for (a) 2012, (b) 2015, and (c) 2022.
Health risks assessment
Tables 7 and 8, respectively, show estimates of the non-carcinogenic health rish with respect to the hazard quotient (HQ) and hazard index (HI) parameters. As shown in Table 7, there were no potential non-carcinogenic health risks through ingestion and dermal contact in 2012. In 2015 and 2022, the HQ caused by 
through the ingestion pathway posed varying degrees of non-carcinogenic health risks across different populations. In contrast, the health risks posed by 
and 
through the ingestion pathway were minimal, with all values below 1 in all years.
Hazard quotient (HQ) for oral and dermal pathways in children, adult males, and adult females
| Year | Non-carcinogenic risk | Child | Adult male | Adult female | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | %ER | min | max | mean | SD | %ER | min | max | mean | SD | %ER | ||
| 2012 | HQoral_ | 1.2E-03 | 4.8E-01 | 8.4E-02 | 1.4E-01 | 0 | 5.4E-04 | 2.2E-01 | 3.9E-02 | 6.2E-02 | 0 | 6.8E-04 | 2.8E-01 | 4.9E-02 | 7.9E-02 | 0 |
HQoral_ | 4.7E-04 | 4.2E-02 | 6.1E-03 | 1.2E-02 | 0 | 2.1E-04 | 1.9E-02 | 2.8E-03 | 5.6E-03 | 0 | 2.7E-04 | 2.5E-02 | 3.6E-03 | 7.1E-03 | 0 | |
HQoral_ | 9.6E-04 | 7.7E-02 | 7.8E-03 | 1.5E-02 | 0 | 4.4E-04 | 3.6E-02 | 3.6E-03 | 7.1E-03 | 0 | 5.6E-04 | 4.5E-02 | 4.6E-03 | 9.0E-03 | 0 | |
HQdermal_ | 4.4E-06 | 1.8E-03 | 3.2E-04 | 5.1E-04 | 0 | 2.6E-06 | 1.1E-03 | 1.9E-04 | 3.0E-04 | 0 | 2.8E-06 | 1.2E-03 | 2.0E-04 | 3.3E-04 | 0 | |
HQdermal_ | 1.8E-06 | 1.6E-04 | 2.3E-05 | 4.6E-05 | 0 | 1.0E-06 | 9.3E-05 | 1.4E-05 | 2.7E-05 | 0 | 1.1E-06 | 1.0E-04 | 1.5E-05 | 2.9E-05 | 0 | |
HQdermal_ | 3.6E-06 | 2.9E-04 | 3.0E-05 | 5.8E-05 | 0 | 2.1E-06 | 1.7E-04 | 1.7E-05 | 3.4E-05 | 0 | 2.3E-06 | 1.9E-04 | 1.9E-05 | 3.7E-05 | 0 | |
| 2015 | HQoral_ | 5.8E-03 | 9.1E + 00 | 1.3E + 00 | 2.3E + 00 | 50.00 | 2.7E-03 | 4.2E + 00 | 6.2E-01 | 1.0E + 00 | 12.50 | 3.4E-03 | 5.3E + 00 | 7.9E-01 | 1.3E + 00 | 12.50 |
HQoral_ | 9.3E-04 | 7.9E-03 | 3.0E-03 | 2.0E-03 | 0 | 4.3E-04 | 3.6E-03 | 1.4E-03 | 9.2E-04 | 0 | 5.5E-04 | 4.6E-03 | 1.8E-03 | 1.2E-03 | 0 | |
HQoral_ | 9.6E-04 | 6.7E-03 | 1.8E-03 | 1.4E-03 | 0 | 4.4E-04 | 3.1E-03 | 8.1E-04 | 6.4E-04 | 0 | 5.6E-04 | 3.9E-03 | 1.0E-03 | 8.2E-04 | 0 | |
HQdermal_ | 2.2E-05 | 3.4E-02 | 5.1E-03 | 8.5E-03 | 0 | 1.3E-05 | 2.0E-02 | 3.0E-03 | 5.0E-03 | 0 | 1.4E-05 | 2.2E-02 | 3.3E-03 | 5.4E-03 | 0 | |
HQdermal_ | 3.5E-06 | 3.0E-05 | 1.1E-05 | 7.5E-06 | 0 | 2.1E-06 | 1.8E-05 | 6.7E-06 | 4.4E-06 | 0 | 2.3E-06 | 1.9E-05 | 7.2E-06 | 4.8E-06 | 0 | |
HQdermal_ | 3.6E-06 | 2.5E-05 | 6.7E-06 | 5.3E-06 | 0 | 2.1E-06 | 1.5E-05 | 3.9E-06 | 3.1E-06 | 0 | 2.3E-06 | 1.6E-05 | 4.3E-06 | 3.4E-06 | 0 | |
| 2022 | HQoral_ | 2.3E-03 | 1.4E + 01 | 3.6E-01 | 1.9E + 00 | 5.36 | 1.1E-03 | 6.4E + 00 | 1.7E-01 | 8.6E-01 | 1.79 | 1.4E-03 | 8.1E + 00 | 2.1E-01 | 1.1E + 00 | 1.79 |
HQoral_ | 3.7E-02 | 1.2E-01 | 3.9E-02 | 1.1E-02 | 0 | 1.7E-02 | 5.6E-02 | 1.8E-02 | 5.2E-03 | 0 | 2.2E-02 | 7.1E-02 | 2.3E-02 | 6.6E-03 | 0 | |
HQoral_ | 4.8E-04 | 1.8E-02 | 2.4E-03 | 4.9E-03 | 0 | 2.2E-04 | 8.4E-03 | 1.1E-03 | 2.2E-03 | 0 | 2.8E-04 | 1.1E-02 | 1.4E-03 | 2.8E-03 | 0 | |
HQdermal_ | 8.8E-06 | 5.2E-02 | 1.4E-03 | 7.0E-03 | 0 | 5.2E-06 | 3.1E-02 | 8.0E-04 | 4.1E-03 | 0 | 5.6E-06 | 3.3E-02 | 8.7E-04 | 4.5E-03 | 0 | |
HQdermal_ | 1.4E-04 | 4.6E-04 | 1.5E-04 | 4.2E-05 | 0 | 8.3E-05 | 2.7E-04 | 8.6E-05 | 2.5E-05 | 0 | 9.0E-05 | 2.9E-04 | 9.4E-05 | 2.7E-05 | 0 | |
HQdermal_ | 1.8E-06 | 6.9E-05 | 9.2E-06 | 1.8E-05 | 0 | 1.1E-06 | 4.0E-05 | 5.4E-06 | 1.1E-05 | 0 | 1.2E-06 | 4.4E-05 | 5.9E-06 | 1.2E-05 | 0 | |
| Year | Non-carcinogenic risk | Child | Adult male | Adult female | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | %ER | min | max | mean | SD | %ER | min | max | mean | SD | %ER | ||
| 2012 | HQoral_ | 1.2E-03 | 4.8E-01 | 8.4E-02 | 1.4E-01 | 0 | 5.4E-04 | 2.2E-01 | 3.9E-02 | 6.2E-02 | 0 | 6.8E-04 | 2.8E-01 | 4.9E-02 | 7.9E-02 | 0 |
| HQoral_ | 4.7E-04 | 4.2E-02 | 6.1E-03 | 1.2E-02 | 0 | 2.1E-04 | 1.9E-02 | 2.8E-03 | 5.6E-03 | 0 | 2.7E-04 | 2.5E-02 | 3.6E-03 | 7.1E-03 | 0 | |
| HQoral_ | 9.6E-04 | 7.7E-02 | 7.8E-03 | 1.5E-02 | 0 | 4.4E-04 | 3.6E-02 | 3.6E-03 | 7.1E-03 | 0 | 5.6E-04 | 4.5E-02 | 4.6E-03 | 9.0E-03 | 0 | |
| HQdermal_ | 4.4E-06 | 1.8E-03 | 3.2E-04 | 5.1E-04 | 0 | 2.6E-06 | 1.1E-03 | 1.9E-04 | 3.0E-04 | 0 | 2.8E-06 | 1.2E-03 | 2.0E-04 | 3.3E-04 | 0 | |
| HQdermal_ | 1.8E-06 | 1.6E-04 | 2.3E-05 | 4.6E-05 | 0 | 1.0E-06 | 9.3E-05 | 1.4E-05 | 2.7E-05 | 0 | 1.1E-06 | 1.0E-04 | 1.5E-05 | 2.9E-05 | 0 | |
| HQdermal_ | 3.6E-06 | 2.9E-04 | 3.0E-05 | 5.8E-05 | 0 | 2.1E-06 | 1.7E-04 | 1.7E-05 | 3.4E-05 | 0 | 2.3E-06 | 1.9E-04 | 1.9E-05 | 3.7E-05 | 0 | |
| 2015 | HQoral_ | 5.8E-03 | 9.1E + 00 | 1.3E + 00 | 2.3E + 00 | 50.00 | 2.7E-03 | 4.2E + 00 | 6.2E-01 | 1.0E + 00 | 12.50 | 3.4E-03 | 5.3E + 00 | 7.9E-01 | 1.3E + 00 | 12.50 |
| HQoral_ | 9.3E-04 | 7.9E-03 | 3.0E-03 | 2.0E-03 | 0 | 4.3E-04 | 3.6E-03 | 1.4E-03 | 9.2E-04 | 0 | 5.5E-04 | 4.6E-03 | 1.8E-03 | 1.2E-03 | 0 | |
| HQoral_ | 9.6E-04 | 6.7E-03 | 1.8E-03 | 1.4E-03 | 0 | 4.4E-04 | 3.1E-03 | 8.1E-04 | 6.4E-04 | 0 | 5.6E-04 | 3.9E-03 | 1.0E-03 | 8.2E-04 | 0 | |
| HQdermal_ | 2.2E-05 | 3.4E-02 | 5.1E-03 | 8.5E-03 | 0 | 1.3E-05 | 2.0E-02 | 3.0E-03 | 5.0E-03 | 0 | 1.4E-05 | 2.2E-02 | 3.3E-03 | 5.4E-03 | 0 | |
| HQdermal_ | 3.5E-06 | 3.0E-05 | 1.1E-05 | 7.5E-06 | 0 | 2.1E-06 | 1.8E-05 | 6.7E-06 | 4.4E-06 | 0 | 2.3E-06 | 1.9E-05 | 7.2E-06 | 4.8E-06 | 0 | |
| HQdermal_ | 3.6E-06 | 2.5E-05 | 6.7E-06 | 5.3E-06 | 0 | 2.1E-06 | 1.5E-05 | 3.9E-06 | 3.1E-06 | 0 | 2.3E-06 | 1.6E-05 | 4.3E-06 | 3.4E-06 | 0 | |
| 2022 | HQoral_ | 2.3E-03 | 1.4E + 01 | 3.6E-01 | 1.9E + 00 | 5.36 | 1.1E-03 | 6.4E + 00 | 1.7E-01 | 8.6E-01 | 1.79 | 1.4E-03 | 8.1E + 00 | 2.1E-01 | 1.1E + 00 | 1.79 |
| HQoral_ | 3.7E-02 | 1.2E-01 | 3.9E-02 | 1.1E-02 | 0 | 1.7E-02 | 5.6E-02 | 1.8E-02 | 5.2E-03 | 0 | 2.2E-02 | 7.1E-02 | 2.3E-02 | 6.6E-03 | 0 | |
| HQoral_ | 4.8E-04 | 1.8E-02 | 2.4E-03 | 4.9E-03 | 0 | 2.2E-04 | 8.4E-03 | 1.1E-03 | 2.2E-03 | 0 | 2.8E-04 | 1.1E-02 | 1.4E-03 | 2.8E-03 | 0 | |
| HQdermal_ | 8.8E-06 | 5.2E-02 | 1.4E-03 | 7.0E-03 | 0 | 5.2E-06 | 3.1E-02 | 8.0E-04 | 4.1E-03 | 0 | 5.6E-06 | 3.3E-02 | 8.7E-04 | 4.5E-03 | 0 | |
| HQdermal_ | 1.4E-04 | 4.6E-04 | 1.5E-04 | 4.2E-05 | 0 | 8.3E-05 | 2.7E-04 | 8.6E-05 | 2.5E-05 | 0 | 9.0E-05 | 2.9E-04 | 9.4E-05 | 2.7E-05 | 0 | |
| HQdermal_ | 1.8E-06 | 6.9E-05 | 9.2E-06 | 1.8E-05 | 0 | 1.1E-06 | 4.0E-05 | 5.4E-06 | 1.1E-05 | 0 | 1.2E-06 | 4.4E-05 | 5.9E-06 | 1.2E-05 | 0 | |
Note: %ER represents %exceeding acceptable risk.
Hazard index (HI) for oral and dermal pathways in children, adult males, and adult females
| Year | Non-carcinogenic risk | child | Adult male | Adult female | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | %ER | min | max | mean | SD | %ER | min | max | mean | SD | %ER | ||
| 2012 | HI_ | 0.0012 | 0.4831 | 0.0845 | 0.1360 | 0 | 0.0005 | 0.2220 | 0.0388 | 0.0625 | 0 | 0.0007 | 0.2824 | 0.0494 | 0.0795 | 0 |
HI_ | 0.0005 | 0.0422 | 0.0061 | 0.0122 | 0 | 0.0002 | 0.0194 | 0.0028 | 0.0056 | 0 | 0.0003 | 0.0246 | 0.0036 | 0.0072 | 0 | |
HI_ | 0.0010 | 0.0777 | 0.0079 | 0.0155 | 0 | 0.0004 | 0.0357 | 0.0036 | 0.0071 | 0 | 0.0006 | 0.0455 | 0.0046 | 0.0091 | 0 | |
| HItotal | 0.0026 | 0.4854 | 0.0985 | 0.1350 | 0 | 0.0012 | 0.2231 | 0.0453 | 0.0621 | 0 | 0.0015 | 0.2838 | 0.0576 | 0.0789 | 0 | |
| 2015 | HI_ | 0.0059 | 9.1489 | 1.3545 | 2.2628 | 50.00 | 0.0027 | 4.2055 | 0.6226 | 1.0401 | 12.5 | 0.0034 | 5.3487 | 0.7919 | 1.3229 | 12.5 |
HI_ | 0.0009 | 0.0080 | 0.0030 | 0.0020 | 0 | 0.0004 | 0.0037 | 0.0014 | 0.0009 | 0 | 0.0005 | 0.0047 | 0.0018 | 0.0012 | 0. | |
HI_ | 0.0010 | 0.0068 | 0.0018 | 0.0014 | 0 | 0.0004 | 0.0031 | 0.0008 | 0.0006 | 0 | 0.0006 | 0.0040 | 0.0010 | 0.0008 | 0 | |
| HItotal | 0.0087 | 9.1532 | 1.3593 | 2.2632 | 50.00 | 0.0040 | 4.2074 | 0.6248 | 1.0403 | 12.50 | 0.0051 | 5.3512 | 0.7947 | 1.3231 | 12.50 | |
| 2022 | HI_ | 0.0023 | 13.9357 | 0.3705 | 1.8902 | 5.00 | 0.0011 | 6.4057 | 0.1673 | 0.8612 | 1.79 | 0.0014 | 8.1471 | 0.2128 | 1.0953 | 1.79 |
HI_ | 0.0375 | 0.1218 | 0.0391 | 0.0113 | 0 | 0.0172 | 0.0560 | 0.0180 | 0.0052 | 0 | 0.0219 | 0.0712 | 0.0229 | 0.0066 | 0 | |
HI_ | 0.0005 | 0.0184 | 0.0024 | 0.0049 | 0 | 0.0002 | 0.0084 | 0.0011 | 0.0022 | 0 | 0.0003 | 0.0107 | 0.0014 | 0.0028 | 0 | |
| HItotal | 0.0403 | 13.9736 | 0.4056 | 1.8732 | 5.36 | 0.0185 | 6.4232 | 0.1864 | 0.8610 | 1.79 | 0.0236 | 8.1693 | 0.2371 | 1.0951 | 1.79 | |
| Year | Non-carcinogenic risk | child | Adult male | Adult female | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| min | max | mean | SD | %ER | min | max | mean | SD | %ER | min | max | mean | SD | %ER | ||
| 2012 | HI_ | 0.0012 | 0.4831 | 0.0845 | 0.1360 | 0 | 0.0005 | 0.2220 | 0.0388 | 0.0625 | 0 | 0.0007 | 0.2824 | 0.0494 | 0.0795 | 0 |
| HI_ | 0.0005 | 0.0422 | 0.0061 | 0.0122 | 0 | 0.0002 | 0.0194 | 0.0028 | 0.0056 | 0 | 0.0003 | 0.0246 | 0.0036 | 0.0072 | 0 | |
| HI_ | 0.0010 | 0.0777 | 0.0079 | 0.0155 | 0 | 0.0004 | 0.0357 | 0.0036 | 0.0071 | 0 | 0.0006 | 0.0455 | 0.0046 | 0.0091 | 0 | |
| HItotal | 0.0026 | 0.4854 | 0.0985 | 0.1350 | 0 | 0.0012 | 0.2231 | 0.0453 | 0.0621 | 0 | 0.0015 | 0.2838 | 0.0576 | 0.0789 | 0 | |
| 2015 | HI_ | 0.0059 | 9.1489 | 1.3545 | 2.2628 | 50.00 | 0.0027 | 4.2055 | 0.6226 | 1.0401 | 12.5 | 0.0034 | 5.3487 | 0.7919 | 1.3229 | 12.5 |
| HI_ | 0.0009 | 0.0080 | 0.0030 | 0.0020 | 0 | 0.0004 | 0.0037 | 0.0014 | 0.0009 | 0 | 0.0005 | 0.0047 | 0.0018 | 0.0012 | 0. | |
| HI_ | 0.0010 | 0.0068 | 0.0018 | 0.0014 | 0 | 0.0004 | 0.0031 | 0.0008 | 0.0006 | 0 | 0.0006 | 0.0040 | 0.0010 | 0.0008 | 0 | |
| HItotal | 0.0087 | 9.1532 | 1.3593 | 2.2632 | 50.00 | 0.0040 | 4.2074 | 0.6248 | 1.0403 | 12.50 | 0.0051 | 5.3512 | 0.7947 | 1.3231 | 12.50 | |
| 2022 | HI_ | 0.0023 | 13.9357 | 0.3705 | 1.8902 | 5.00 | 0.0011 | 6.4057 | 0.1673 | 0.8612 | 1.79 | 0.0014 | 8.1471 | 0.2128 | 1.0953 | 1.79 |
| HI_ | 0.0375 | 0.1218 | 0.0391 | 0.0113 | 0 | 0.0172 | 0.0560 | 0.0180 | 0.0052 | 0 | 0.0219 | 0.0712 | 0.0229 | 0.0066 | 0 | |
| HI_ | 0.0005 | 0.0184 | 0.0024 | 0.0049 | 0 | 0.0002 | 0.0084 | 0.0011 | 0.0022 | 0 | 0.0003 | 0.0107 | 0.0014 | 0.0028 | 0 | |
| HItotal | 0.0403 | 13.9736 | 0.4056 | 1.8732 | 5.36 | 0.0185 | 6.4232 | 0.1864 | 0.8610 | 1.79 | 0.0236 | 8.1693 | 0.2371 | 1.0951 | 1.79 | |
Note: %ER represents %exceeding acceptable risk.
The non-carcinogenic HI represents the combination of non-carcinogenic HQ risk factors from multiple exposure pathways for 
, 
, and 
in groundwater. As shown in Table 8, the average and maximum values of 
in 2012 were both below the safety reference limit of 1. In 2015, the maximum non-carcinogenic health risk values for 
in children, adult males, and adult females were 9.1489, 4.2055, and 5.3487, respectively, with the percentage of exceedance of the acceptable limit being 50, 12.5, and 12.5%, respectively. This indicates that certain areas of the study region face significant non-carcinogenic health risks. Children are at higher non-carcinogenic risk compared to adults, mainly due to their lower body weight, which makes them more susceptible to health impacts. If nitrate is consumed untreated over the long term, it can accumulate in the body, and since children's body weight is lower than that of adults, the cumulative effect is more pronounced. In 2022, the maximum non-carcinogenic health risk values for 
in children, adult males, and adult females were 13.9357, 6.4057, and 5.3512, respectively, with the percentage of exceedance of the acceptable limit being 5.36, 1.79, and 1.79%. This suggests that in certain areas of the study region, unacceptable non-carcinogenic health risks still existed in 2022. In contrast, the non-carcinogenic health risks posed by 
and 
were minimal, with all values below 1 in 2012, 2015 and 2022. Therefore, the health risk assessment of HQ and HI confirms that children are more vulnerable to harm and threats compared to adults, which cannot be ignored.
CONCLUSIONS
This study utilized groundwater data from three years using multivariate statistical techniques, EWQI, and health risk assessment, revealing the relationship between hydrogeochemical factors, groundwater quality, health risks, and nitrogen pollution. The study draws the following conclusions:
1. Groundwater in the study area is generally weakly alkaline, with significant variations in ion concentrations over the years. The PCA results indicate that pollution levels and salinity are key factors influencing groundwater quality, particularly from agricultural activities and wastewater discharge, leading to changes in ion concentrations.
2. Using the EWQI, this study assessed groundwater quality, revealing spatial and temporal variations. In 2015 and 2022, 6.25 and 1.79% of samples exhibited extremely poor and poor water quality, respectively. Nitrate pollution showed spatial variability. Additionally, the sources of nitrates in groundwater have become increasingly complex.
3. Health risk assessments highlighted potential risks associated with consuming contaminated groundwater, particularly for children, who are more susceptible to health risks. In 2012, risks were within safe limits. In 2015, 50% of children, 12.5% of adult males, and 12.5% of adult females exceeded acceptable levels. In 2022, these percentages were 5.36, 1.79, and 1.79%, respectively.
4. Long-term drinking of groundwater with high levels of
can have negative effects on local residents. Considering the high health risks in the area, it is imperative to strengthen groundwater monitoring, control the use of agricultural fertilizers, and regulate sewage discharge.
ACKNOWLEDGEMENTS
All authors express gratitude for the valuable and constructive feedback provided by the editors and reviewers. Numerous colleagues contributed to the field investigation, although they are not listed as coauthors. Special thanks to Bingchao Yang, Rui Long, Ningchao Zhou, and Ying Zhang. Finally, a sincere appreciation to the kind-hearted people of Northern Shaanxi and Inner Mongolia, who extended invaluable assistance during field investigations.
FUNDINGS
Key Research and Development Program of Shaanxi (2022SF-327); Natural Science Basic Research Program of Shaanxi (2022JQ-238); Natural Science Basic Research Program of Shaanxi (2022JQ-271); The National Natural Science Foundation of China (42177346).
AUTHOR CONTRIBUTIONS
R.D. collected and analyzed the data and was involved with the writing of the manuscript. X.G. provided supervision in the presented work, and was involved in the writing and editing of the manuscript. L.C., X.C., X.L., X.Y., and Q.Z. helped in collecting data, methodology selection and visualization, and participated in reading and editing of the earlier versions of the manuscript.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.
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