Fluoride Enrichment and Health Risks in the Aksu River Basin Oasis: Implications for Soil–Groundwater Systems

Abstract

The Aksu River Basin oasis, a typical arid ecological environment, faces considerable ecological and public health risks from fluoride accumulation in soil and groundwater. However, systematic investigations integrating soil–groundwater co-enrichment mechanisms with multi-pathway health risk assessments under environmentally relevant conditions remain scarce. We examined spatial fluoride distribution in the soil–groundwater system, associated health risks, and key driving mechanisms. Based on 2009 soil and 264 groundwater samples, we applied radial basis function (RBF) interpolation, Getis-Ord G_i* hotspot analysis, the geo-accumulation index (I_geo), the ecological risk index (ER), and the U.S. EPA health risk assessment model to evaluate pollution levels, ecological risks, and health impacts on adults and children. Spearman’s correlation analysis revealed relationships with 12 environmental factors, including topography, climate, soil properties, and vegetation. Key results are as follows: (1) High-fluoride soils (>700 mg·kg⁻¹) clustered in the eastern basin, while groundwater fluoride increased along a west–east gradient, with RBF interpolation yielding the highest accuracy; (2) soil fluoride was generally “unpolluted–moderate risk” (mean I_geo = −0.14, ER = 1.40), whereas groundwater posed the primary health risk, with a mean hazard quotient of 1.83 for children via drinking water, indicating non-carcinogenic risk; (3) soil enrichment was driven by evaporation–concentration–alkaline activation, while groundwater enrichment followed a convergence–concentration–evaporation mechanism, being negatively correlated with elevation. Groundwater fluoride presents a clear health risk, particularly to children, arising from high geological background levels and intense evaporation. Managing fluoride pollution and safeguarding drinking water quality in arid oasis regions is consequential. These findings provide a scientific basis for sustainable groundwater management and public health protection in arid oases.

1. Introduction

Fluoride is a prevalent chemical element in nature, exhibiting a typical “dual-threshold” effect on human health: Trace intake prevents dental caries, whereas long-term excessive exposure causes various health issues, including dental and skeletal fluorosis, and impairs the nervous and immune systems [1,2]. In arid and semiarid regions worldwide, high-fluoride groundwater poses a major public health risk that affects hundreds of millions of people [3,4]. For instance, an assessment incorporating over 400,000 fluoride measurements identified high-risk hotspots in central Australia, western North America, eastern Brazil, and extensive areas across Africa and Asia. Approximately 180 million people are potentially affected worldwide, with the majority residing in Asia and Africa. In China, high-fluoride groundwater is widely distributed in arid and semi-arid regions such as the Tarim Basin, Hetao Plain, and the North China Plain, where fluoride concentrations frequently exceed the WHO guideline of 1.5 mg/L [5].

Oasis ecosystems in the arid inland northwest region of China, characterized by environmental fragility and intensive human activity, exemplify classic regions where fluoride-related ecological and health issues converge [6]. Intense evaporation and concentration processes, slightly alkaline geochemical environments, and specific hydrogeological conditions collectively govern fluoride accumulation in surface soils and groundwater [7]. Residents are exposed to fluoride risks both directly, through consumption of high-fluoride groundwater, and indirectly, through the “soil–crop–food chain” via groundwater-irrigated agriculture [8,9]. The Aksu River Basin is a typical inland river system in an arid region, characterized by an extremely dry climate. Groundwater is primarily replenished by snowmelt and glacial meltwater from mountainous areas. The overall flow direction is from west to east, with the water table depth gradually decreasing from the foothills toward the basin center. Strong evaporation and concentration processes dominate the chemical evolution of shallow groundwater. Recent studies specific to the Aksu River Basin have highlighted the region’s vulnerability to salinization and trace element accumulation under intensive agricultural development. However, systematic assessments of fluoride in both soil and groundwater within this basin remain limited.

Despite serving as a vital ecological barrier and agricultural production base in southern Xinjiang, the distribution characteristics, ecological risks, and health effects of fluoride in the soil and water environment of the Aksu River Basin oasis have yet to be systematically elucidated. Current research in this region primarily focuses on single media (either soil or groundwater), lacking a holistic understanding of the synergistic enrichment patterns of fluoride within the “soil–groundwater” system, its multi-pathway health risks, and the integrated driving mechanisms [10]. Specifically, research on the coupling mechanisms between climate characteristics, hydrogeological conditions, and fluoride enrichment remains unexplored. How different environmental factors jointly control fluoride migration and transformation within the soil–groundwater system remains unclear [11]. To address the aforementioned research gap, in this study, we focus on the oasis region of the Aksu River Basin. We systematically integrate multi-medium data from soil and groundwater to achieve the following core objectives: (1) precisely characterize the spatial distribution patterns of fluoride in soil and groundwater using comparative spatial interpolation methods; (2) quantitatively assess the pollution levels, ecological risks, and health impacts of fluoride on adults and children through the geo-accumulation index, potential ecological risk index, and health risk evaluation model; and (3) identify key drivers affecting the spatial distribution of soil and groundwater fluoride using statistical methods, thereby revealing the dominant enrichment mechanisms. We hypothesize that fluoride enrichment in the Aksu River Basin oasis is primarily controlled by natural hydrogeochemical processes—specifically evapotranspiration and alkaline activation—rather than by direct anthropogenic inputs. This study contributes to the sustainability goals of safe drinking water provision (UN SDG 6) and human health protection (UN SDG 3) by identifying high-risk areas and informing targeted management strategies for arid regions.

2. Materials and Methods

2.1. Study Area and Environmental Setting

The Aksu River Basin oasis is situated in the western part of the central southern foothills of the Tianshan Mountains, at the northwestern edge of the Tarim Basin (75°35′–82°00′ E, 40°00′–42°27′ N), covering approximately 4.8 × 10⁴ km² (Figure 1). The terrain slopes gradually from north to south and west to east, comprising both mountainous and plain landscapes. The region exhibits a typical warm temperate continental arid climate, with an average annual temperature of approximately 10 °C and precipitation of 137.7 mm. Annual evaporation substantially exceeds precipitation, producing pronounced aridity.

Water resources are primarily derived from glaciers and snowmelt in the Tianshan Mountains, with additional contributions from precipitation. These sources are critical for sustaining oasis agriculture, urban and rural water supply, and ecological security [12]. Vegetation is dominated by desert, oasis farmland, and meadows. The soil includes brown desert, podzolic, saline–alkali, and alluvial–fluvial types, with parent materials mainly consisting of Quaternary alluvial and fluvial deposits. The regional ecological environment is fragile owing to long-term intensive agricultural development and water resource utilization, leading to various issues (e.g., wetland shrinkage, grassland degradation, and soil salinization), typical of sensitive arid ecosystems [13].

2.2. Sampling and Laboratory Analysis

Data were derived from the “Basic Geological Survey Project of the China Geological Survey (1:250,000 Land Quality Geochemical Survey of Cultivated Areas in Five Northwestern Provinces).” In total, 2009 surface soil and 264 groundwater samples were collected. The spatial distribution of the samples is shown in Figure 1. Soil sampling followed the “Soil Quality Geochemical Evaluation Specification” (DZ/T 0295-2016) [14], with samples collected at depths of 0–20 cm using a diagonal grid sampling method. After collection, soil samples were sealed in polyethylene bags, labeled, and transported to the laboratory in insulated coolers with ice packs within 24 h to preserve integrity. Upon arrival, samples were stored at 4 °C prior to air-drying and processing. Soil samples were naturally air-dried, plant roots and gravel were removed, and samples were sieved through a 100-mesh nylon sieve prior to use.

Soil fluoride contents were determined using the sodium hydroxide alkali fusion–ion-selective electrode method, while groundwater fluoride levels were measured with the ion-selective electrode method. All analyses were performed by accredited professional institutions in accordance with the quality control requirements specified in the Technical Specifications for Soil Environmental Monitoring (HJ/T 166-2004) [15] and Technical Specifications for Groundwater Environmental Monitoring (HJ 164-2020) [16]. Quality assurance and quality control (QA/QC) included the use of certified reference materials (CRMs), method blanks, and duplicate analyses. For soil fluoride, the relative standard deviation (RSD) of duplicate samples was <5%, and the recovery rate for CRMs ranged from 95% to 105%. For groundwater fluoride, the RSD was <3%, and the recovery rate ranged from 97% to 103%. Instrument specifications: fluoride ion-selective electrode (Orion 9609BNWP, Thermo Fisher Scientific, Waltham, MA, USA) with a precision of ±0.01 mg·L⁻¹. Data processing and analysis were performed using SPSS 28.0 and ArcGIS Pro 3.5 for spatial interpolation, statistical analysis, and graphical representation.

2.3. Spatial Interpolation and Hotspot Analysis

We comprehensively employed spatial interpolation and hotspot analysis techniques to reveal the spatial distribution patterns of fluoride in soils and groundwater within the Aksu River Basin oasis.

First, three classical spatial interpolation methods, namely, radial basis function (RBF), local polynomial interpolation (LPI), and global polynomial interpolation (GPI), were systematically evaluated to generate continuous spatial distribution maps. RBF provides precise interpolation for variables that produce smooth surfaces; LPI captures short-term variations by fitting multiple local polynomial equations; and GPI fits the entire dataset with a single polynomial function, suitable for describing macro-level spatial trends [17,18,19]. Kriging methods (ordinary or universal) are widely regarded as geostatistical benchmarks for environmental data interpolation. However, preliminary tests on our dataset revealed that the fluoride concentration distributions in both soil and groundwater deviated substantially from normality even after transformations, and the spatial autocorrelation structures (semivariograms) exhibited poor model fits. Under such conditions, RBF has been shown to outperform Kriging due to its flexibility in handling non-stationary and non-normal data. Therefore, RBF was selected as the primary interpolation method, and the comparative analysis was focused on RBF, LPI, and GPI, which are computationally efficient and commonly used in regional-scale environmental mapping. Cross-validation was used to compare statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R²), and identify the interpolation method with the optimal prediction accuracy for spatial soil and groundwater fluoride distributions. Final spatial distribution maps were generated using the optimal method.

Second, the Getis-Ord G_i* hotspot analysis was applied to identify significant clusters of high (hotspots) and low (cold spots) fluoride concentrations. This method calculates the Getis-Ord G_i* statistic for each feature in its neighborhood, thereby identifying non-random spatial clustering patterns [20]. The calculation formula is as follows:

$${\mathrm{G}}_{\mathrm{i}}^{\ast }={\displaystyle \frac{{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}}{\mathrm{w}}_{\mathrm{i},\mathrm{j}}{\mathrm{x}}_{\mathrm{j}}-\stackrel{-}{\mathrm{X}}{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}}{\mathrm{w}}_{\mathrm{i},\mathrm{j}}}{\mathrm{S}\sqrt{\frac{\mathrm{n}{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}}{\mathrm{w}}_{\mathrm{i},\mathrm{j}}^2-{\left({\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}}{\mathrm{w}}_{\mathrm{i},\mathrm{j}}\right)}^2}{\mathrm{n}-1}}}}$$

(1)

where x_j is the attribute value of element j (i.e., fluoride content); w_i,j is the spatial weight between elements i and j; n is the total number of elements; and $\stackrel{-}{X}$ and S are the mean and standard deviation of all sample attributes, respectively. The resulting Z-scores identified hotspots (areas with high-value clustering and significantly positive Z-scores) and cold spots (areas with low-value clustering and significantly negative Z-scores).

This analysis was performed in ArcGIS using the “Fixed Distance Method” to conceptualize spatial relationships, with the distance threshold determined by the mean proximity of the sample distribution.

Through these spatial analyses, we aimed to precisely characterize the spatial heterogeneity of fluoride pollution in the Aksu River Basin, thereby providing intuitive spatial evidence for future risk assessment and pollution control efforts.

2.4. Health and Ecological Risk Assessment

We comprehensively assessed the environmental risks posed by fluoride in soils and groundwater in the Aksu River Basin oasis and their potential impacts on human health (particularly on adults and children). Pollution levels and ecological risks were evaluated using the geo-accumulation index (I_geo) [21] and potential ecological risk index (ER) [22]. The associated health risks to humans were quantified using the Health Risk Assessment (HRA) model endorsed by the U.S. Environmental Protection Agency (US EPA) [23].

2.4.1. Geo-Accumulation Index Evaluation

The I_geo comprehensively accounts for both natural geological backgrounds and anthropogenic influences on element enrichment. This method is widely applied to assess heavy metal and elemental pollution in soils and sediments [24,25]. The calculation formula is as follows:

$${\mathrm{I}}_{\mathrm{geo}}={\log }_2\left[{\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{\left(\mathrm{k} \times {\mathrm{B}}_{\mathrm{i}}\right)}}\right]$$

(2)

where C_i is the measured fluoride concentration in the sample (soil unit: mg·kg⁻¹; groundwater unit: mg·L⁻¹); B_i is the corresponding background reference value; and k is a correction factor (set to 1.5). For soil fluoride, we adopted the Xinjiang surface soil fluoride background value of 488 mg·kg⁻¹ as B_i. For groundwater fluoride, the Class III water limit of 1.0 mg·L⁻¹ specified in the “Groundwater Quality Standard” (GB/T 14848-2017) [26] was used. k denotes a correction factor that accounts for potential background value fluctuations resulting from diagenesis, which is typically set to 1.5. Pollution levels are categorized into seven grades according to I_geo values, ranging from unpolluted (I_geo < 0) to severely polluted (I_geo ≥ 5) (

Table 1. Classification standards for geo-accumulation and potential ecological risk indices.

Grade	Geo-Accumulation Index (Igeo)	Pollution Level	Potential Ecological Risk Index	Risk Level
1	Igeo < 0	No pollution	ER < 1	Low
2	0 < Igeo < 1	No to moderate pollution	1 < ER < 2	Medium
3	1 < Igeo < 2	Moderate pollution	2 < ER < 4	High
4	2 < Igeo < 3	Moderate to heavy pollution	4 < ER < 8	Very High
5	3 < Igeo < 4	Heavy pollution	ER > 8	Extremely High
6	4 < Igeo < 5	Heavy to severe pollution
7	Igeo > 5	Severe pollution

).

2.4.2. Potential Ecological Risk Index Evaluation

The ER incorporates elemental toxicity response coefficients to better reflect potential ecological effects [27]. The calculation formula is as follows:

$$\mathrm{ER} = {\mathrm{T}}_{\mathrm{r}} \times {\mathrm{C}}_{\mathrm{f}} = {\mathrm{T}}_{\mathrm{r}} \times {\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{C}}_0}}$$

(3)

where T_r is the toxicity response coefficient for fluoride (set to 1 [28]); C_i is the measured concentration; and C₀ is the reference value for assessment, consistent with B_i in the geo-accumulation index. Ecological risk levels are categorized into five tiers based on ER values, ranging from low risk (ER < 1) to incredibly high risk (ER ≥ 8) (Table 1).

The potential ecological risk index (ER) was originally developed for heavy metals, but its application to fluoride has precedent in the recent literature [28,29]. Although fluoride is a non-metal, its environmental toxicity—particularly in terms of soil ecosystem functioning and groundwater-dependent ecosystems—warrants ecological risk assessment. The toxicity response coefficient for fluoride (T_r = 1) has been adopted following established studies [28,30] to maintain consistency with heavy-metal-based ER frameworks while acknowledging fluoride’s lower relative toxicity compared to metals such as Cd (T_r = 30) or Hg (T_r = 40). Thus, ER serves as a semi-quantitative indicator of potential ecological stress rather than an absolute measure of toxicity. It is important to note that the I_geo and ER indices address different aspects of contamination: I_geo measures the degree of anthropogenic enrichment relative to a background value, while ER incorporates a toxicity coefficient to assess potential ecological harm. Consequently, a site may exhibit “unpolluted” status by I_geo (low enrichment) but still show “moderate” ecological risk by ER (high toxicity response), especially in naturally high-background regions.

2.4.3. Human Health Risk Assessment

The US EPA HRA model was employed to quantify health risks associated with fluoride exposure through various pathways in adults and children [29]. Considering that fluoride primarily enters the human body through drinking water intake as well as via oral ingestion, dermal contact, and inhalation from soil [30], we calculated the Hazard Quotient (HQ) separately for drinking water intake and the three soil exposure pathways. The calculation formulae are as follows:

$$\mathrm{Oral} \mathrm{ingestion}: {\mathrm{HQ}}_{\mathrm{ois} }={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}} \times {\mathrm{IR}}_{\mathrm{soil} }\times \mathrm{EF} \times \mathrm{ED} \times { \mathrm{ABS}}_{\mathrm{o}}}{\mathrm{BW} \times \mathrm{AT} \times { \mathrm{RfD}}_{\mathrm{o}} \times \mathrm{SAF}}}\times 10^{-6}$$

(4)

$$\mathrm{Skin} \mathrm{contact}: {\mathrm{HQ}}_{\mathrm{dcs}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}} \times \mathrm{SL} \times \mathrm{SAE} \times \mathrm{EF} \times \mathrm{ED} \times {\mathrm{ABS}}_{\mathrm{d}}}{\mathrm{BW} \times \mathrm{AT} \times {\mathrm{RfD}}_{\mathrm{d}} \times \mathrm{SAF}}}\times 10^{-6}$$

(5)

$$\mathrm{Inhalation}: {\mathrm{HQ}}_{\mathrm{pis} }={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}} \times { \mathrm{PM}}_{10} \times {\mathrm{IR}}_{\mathrm{air} }\times \mathrm{ED} \times \mathrm{PIAF} \times \left({\mathrm{f}}_{\mathrm{spo} }\times \mathrm{EFO}+{\mathrm{f}}_{\mathrm{spi} }\times \mathrm{EFI}\right)}{\mathrm{BW} \times \mathrm{AT} \times { \mathrm{RfD}}_{\mathrm{i}} \times \mathrm{SAF}}}$$

(6)

$$\mathrm{Water} \mathrm{ingestion}: {\mathrm{HQ}}_{\mathrm{wis} }={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}} \times {\mathrm{IR}}_{\mathrm{water}} \times \mathrm{EF} \times \mathrm{ED}}{\mathrm{BW} \times \mathrm{AT} \times {\mathrm{RfD}}_{\mathrm{w}} \times \mathrm{SAF}}}$$

(7)

An HQ value of “1” serves as the risk control standard for non-carcinogenic chronic toxic effects. HQ ≥ 1.0 indicates that the non-carcinogenic substance poses a significant health hazard to humans, exceeds the acceptable threshold for residents, and warrants attention. HQ < 1.0 is deemed acceptable. The model exposure parameters (e.g., intake rate, exposure frequency, exposure duration, body weight, and skin surface area) were derived from US EPA-recommended values and the Chinese Population Exposure Parameters Handbook. The SAF value of 0.5 was adopted following the US EPA’s default assumption for residential exposure to account for the fraction of the reference dose allocated to soil exposure pathways when multiple exposure routes exist. For single-pollutant assessments, SAF was retained as a conservative measure to maintain consistency with standard HRA protocols. The PM₁₀ concentration was derived from the annual mean PM₁₀ level in the Aksu River Basin area, based on local monitoring data from the Xinjiang Environmental Monitoring Centre. Calculations were performed separately for adults and children to identify sensitive populations. All parameters are listed in

Table 2. Health risk exposure parameters.

Symbol	Parameter Name	Unit	Adult Reference Value	Child Reference Value
Ci	Fluoride content	mg·kg−1	Actual measured	Actual measured
IRsoil	Daily soil ingestion rate	mg·d−1	100	200
IRair	Daily air respiration volume	m3·d−1	14.5	7.5
IRwater	Daily water intake	L·d−1	2	1
ED	Exposure duration	a	24	6
EF	Exposure frequency	d·a−1	350	350
BW	Average body weight	kg	61.8	19.2
AT	Average exposure time	d	2190	2190
SAE	Exposed skin surface area	cm2	5700	2800
SL	Soil adhesion coefficient on skin surface	mg·(cm2·d)−1	0.07	0.2
PM10	Airborne respirable particulate matter concentration	mg·m−3	0.1197	0.1197
fspi	Proportion of soil-derived particulate matter in indoor air	-	0.8	0.8
fspo	Proportion of soil-derived particulate matter in outdoor air	-	0.5	0.5
EFO	Outdoor exposure frequency (respiratory inhalation)	d·a−1	87.5	87.5
EFI	Indoor exposure frequency (inhalation)	d·a−1	262.5	262.5
PIAF	Retention fraction of inhaled soil particles in the body	-	0.75	0.75
SAF	Reference dose allocation factor for soil exposure	-	0.5	0.5
ABSO	Absorption efficiency factor for oral intake	-	1	1
ABSd	Skin absorption factor	-	0.01	0.01
RfDo	Reference dose for oral intake	mg·(kg·d)−1	0.04	0.04
RfDd	Reference dose for skin contact	mg·(kg·d)−1	0.04	0.04
RfDi	Reference dose for inhalation	mg·(kg·d)−1	0.003	0.005
RfDw	Reference dose for drinking water intake	mg·(L·d)−1	0.06	0.06

. The food chain uptake pathway (soil–crop–human) was not quantitatively assessed in this study owing to the absence of crop fluoride concentration data and crop-specific bioconcentration factors. While acknowledged in the introduction as a potential indirect exposure route, its quantitative evaluation requires dedicated agricultural sampling and is thus beyond the scope of this investigation. Future studies should incorporate food chain exposure to provide a more complete risk profile.

2.5. Driving Factor Analysis

We investigated the spatial variation in fluoride in the Aksu River Basin oasis and identified key environmental factors influencing its distribution in soil and groundwater by systematically selecting 12 potential drivers across four categories (

Table 3. Potential driving factors and their anticipated mechanisms affecting fluoride enrichment.

Factor Category	Specific Factor	Variable Description	Expected Mechanism of Influence
Topography	DEM	Elevation (m)	Influences material transport and energy allocation, indirectly controlling hydrological processes and chemical weathering intensity.
	Slope	Slope gradient (°)	Affects surface runoff and solute transport; gentle slopes favor fluoride accumulation.
	Aspect	Slope aspect	Indirectly regulates evapotranspiration and soil moisture conditions via light exposure and temperature.
Climate	Temperature	Air temperature (°C)	Influences the intensity of evaporative concentration, with high temperatures typically promoting fluoride enrichment.
	Precipitation	Precipitation (mm)	Affects fluoride concentration through leaching and dilution; expected negative correlation.
	Evapotranspiration	Evapotranspiration (mm)	Intense transpiration concentrates fluoride in shallow soil water and groundwater, promoting fluoride enrichment (positively correlated).
Soil Properties	Available soil moisture	Soil available water content	Influences water movement and ion solubility, with complex relationships.
	Volume weight of soil	Soil bulk density (g/cm3)	Reflects soil compaction and porosity, affecting water movement and solute migration.
	Soil pH index	Soil pH	Alkaline conditions (high pH) typically promote fluoride desorption from minerals, increasing its reactivity (positive correlation).
Vegetation Index	NPP	Net primary productivity (g C/m2)	Characterizes vegetation growth status; biological activity may influence local fluoride biogeochemical cycling.
	NDVI	Normalized difference vegetation index	Reflects vegetation cover, potentially linked to processes, such as groundwater depth and soil salinization.
	FVC	Vegetation cover (%)	Indirectly indicates ecological environment conditions.

and Figure 2) and conducting correlation analyses via statistical software.

All driver data were processed in raster formats aligned with sampling points. In R 4.6.0, we initially standardized the soil fluoride content, groundwater fluoride content, and driver data to eliminate dimensional effects. Subsequently, we employed Spearman’s rank correlation analysis to calculate the correlation coefficients (ρ) and significance levels (p-values) between soil fluoride, groundwater fluoride, and each driver. This method is appropriate for non-normally distributed data and effectively captures nonlinear monotonic relationships between variables [31].

To visualize complex relationships among multiple variables more intuitively, we generated correlation matrix heatmaps using R packages, such as corrplot [32]. Additionally, we employed SPSS 28.0 for dual verification of the analytical results, thereby ensuring the robustness of the statistical conclusions. This approach allowed us to quantitatively identify dominant drivers of fluoride enrichment, determine their influence direction (promoting or suppressing), and assess their intensity, thereby clarifying the formation mechanisms underlying spatial fluoride distribution patterns in the Aksu River Basin from multiple dimensions.

It should be noted that while Spearman’s correlation analysis effectively captures monotonic relationships between variables, it does not imply causation. Therefore, the identified correlations should be interpreted as associations rather than definitive causal links. Future studies incorporating machine learning models (e.g., random forest or XGBoost) are recommended to quantify the relative importance of individual drivers and to validate the causal pathways suggested here.

3. Results

3.1. Accuracy Evaluation and Spatial Distribution Patterns

The spatial fluoride distribution patterns in soil and groundwater within the Aksu River Basin oasis, along with the interpolation accuracy verification results, are presented in Figure 3 and Figure 4. Cross-validation of the three spatial interpolation models, RBF, LPI, and GPI, demonstrated that the RBF model exhibited optimal predictive performance in simulating fluoride spatial distribution within the study area. For soil fluoride data, the RMSEs for RBF, LPI, and GPI were 100.94, 103.23, and 140.37, respectively. For groundwater fluoride data, the corresponding RMSEs were 0.73, 0.75, and 1.00, respectively. For soil fluoride, the R² values for RBF, LPI, and GPI were 0.87, 0.85, and 0.76, respectively; for groundwater fluoride, the R² values were 0.92, 0.91, and 0.84, respectively. The cross-validation was performed using a 10-fold random subsampling scheme. Given that the RBF model exhibited the lowest prediction error and good spatial smoothness in both soil and groundwater media, RBF interpolation was adopted for the subsequent spatial distribution characteristic analyses in this study.

Based on the “Geochemical Evaluation Specifications for Land Quality (DZ/T 0295-2016)” and “Sanitary Standards for Drinking Water (GB 5749-2022),” [33] fluoride concentrations in soil and groundwater were classified into five levels. The five classification levels for soil fluoride are as follows: Level I (≤400 mg·kg⁻¹), Level II (400–550 mg·kg⁻¹), Level III (550–700 mg·kg⁻¹), Level IV (700–1000 mg·kg⁻¹), and Level V (>1000 mg·kg⁻¹). For groundwater fluoride: Level I (≤0.5 mg·L⁻¹), Level II (0.5–1.0 mg·L⁻¹), Level III (1.0–1.5 mg·L⁻¹), Level IV (1.5–2.0 mg·L⁻¹), and Level V (>2.0 mg·L⁻¹), according to the respective standards. The spatial distribution results (Figure 3) demonstrate that soil fluoride levels in the study area were generally high, with areas with fluoride concentrations >550 mg·kg⁻¹ (Level III) comprising more than 85% of the total area. Spatially, high-fluoride soil zones (Level IV) were distinctly clustered and primarily concentrated in the eastern oasis, which is attributed to the combined effects of topographic depression, shallow groundwater table, and intense evapoconcentration in this lower-lying region. By contrast, groundwater fluoride levels were generally low, with >68.56% of areas containing <1.0 mg·L⁻¹. Spatially, groundwater fluoride concentrations increased gradually from west to east, resulting in a continuous and distinct east–west concentration gradient. The spatial discrepancy between soil and groundwater fluoride distributions is primarily controlled by their distinct geochemical environments. Soil fluoride enrichment is strongly influenced by local factors such as soil pH and evapotranspiration, leading to heterogeneous surface accumulation. By contrast, groundwater fluoride follows regional hydrogeological gradients, with flow convergence in the eastern basin causing a more gradual west-to-east increase. Compared with other high-fluoride regions globally, in the Aksu River Basin, the average groundwater fluoride concentration is lower than that of the East African Rift Valley but comparable to values reported for the Hetao Plain and Tarim Basin oasis. The soil fluoride average exceeds the Chinese national background and is similar to levels found in the Yuncheng Basin but lower than those in fluorosis-endemic areas of Guizhou Province.

Getis-Ord G_i* hotspot analysis revealed considerable spatial fluoride clusters (Figure 5). Soil fluoride hotspots (i.e., high-value clusters) were predominantly distributed in the eastern basin, whereas cold spots (i.e., low-value clusters) were mainly located in the northwest and southwest regions. Groundwater fluoride hotspots were the most pronounced in the southeastern part of the study area, with cold spots concentrated in the western region, thereby reflecting a transitional west–east pattern. Notably, the high- and low-value fluoride clusters identified through hotspot analysis exhibited strong consistency with the RBF spatial interpolation results. This not only confirms the inherent structural nature of the spatial distribution of fluoride but also cross-validates the reliability of the spatial interpolation method and robustness of the results employed in this study.

3.2. Ecological and Health Risk Assessment

We employed the I_geo, ER, and HRA models to systematically evaluate the ecological and environmental health risks associated with fluoride in the Aksu River Basin oasis. The results are presented in

Table 4. Ecological risk assessment of fluoride.

Indicator	Fluoride in Soil			Fluoride in Groundwater
	Concentration	Geo-Accumulation Index (Igeo)	Potential Ecological Risk Index	Concentration	Geo-Accumulation Index (Igeo)	Potential Ecological Risk Index
Maximum	1474 mg/kg	1.01	3.02	8.90 mg/L	2.57	8.90
Minimum	218 mg/kg	−1.75	0.45	0.19 mg/L	−2.98	0.19
Average	683 mg/kg	−0.13	1.40	1.60 mg/L	−0.33	1.60

and

Table 5. Health risk assessment of fluoride.

Indicator	Exposure Routes	Children			Adults
		Maximum	Minimum	Average	Maximum	Minimum	Average
Fluoride in soil	Oral ingestion	0.74	0.11	0.34	0.46	0.07	0.21
	Dermal contact	0.02	0.00	0.01	0.02	0.00	0.01
	Inhalation	0.01	0.00	0.01	0.06	0.01	0.03
	Hazard Index (sum)	0.77	0.11	0.36	0.54	0.08	0.25
Fluoride in groundwater	Oral ingestion	10.16	0.21	1.83	4.74	0.10	0.85

.

The pollution and ecological risk assessments revealed clear differences in fluoride conditions between soil and groundwater in the study area. The mean I_geo value for soil fluoride was −0.13, indicating an overall “unpolluted” status. However, its average ER value was 1.40, categorizing it within the “moderate” potential ecological risk category. This suggests that although the anthropogenic pollution overlay is not pronounced, environmental stress driven by high regional background values is evident. By contrast, the mean I_geo and ER values for groundwater fluoride (−0.33 and 1.60, respectively) similarly indicated a “non-polluted” state with “moderate” ecological risk. Notably, groundwater fluoride reached maximum I_geo and ER values of 2.57 (moderately polluted) and 8.90 (extremely high risk), respectively, thereby highlighting intense fluoride enrichment and exceptionally high ecotoxicity effects at specific local sites.

The human HRA (Table 5) further revealed that fluoride posed substantial health risks to residents in the study area, especially among children. For soil fluoride, the mean single HQ and Hazard Index (HI) values for adults and children via the oral ingestion, dermal contact, and inhalation pathways were all below 1. This indicates that the overall non-carcinogenic risks associated with soil exposure were acceptable. However, the health risks associated with groundwater fluoride via the drinking water pathway were particularly pronounced. The mean HQ for adults was 0.85, whereas that for children was 1.83, which far exceeded the safety threshold (HQ = 1). This confirms that fluoride intake from local groundwater consumption poses a clear non-carcinogenic health risk to children in the study area. Spatially, the proportion of groundwater sampling points where HQ for children exceeded the threshold of 1 was 31.44%, predominantly located in the eastern basin where fluoride concentrations were highest. The spatial distribution of high-risk areas closely matched the groundwater fluoride hotspots identified in Figure 5, thereby reinforcing groundwater as the primary source of fluoride exposure and health risks in this study area.

The Aksu River Basin thus faces a fluoride environmental issue driven by a combination of high geological background levels and localized, intense enrichment. Although the overall health risk associated with soil fluoride is manageable, groundwater fluoride, especially through drinking water, poses a clear health risk to residents, notably affecting children as the primary sensitive population. Targeted measures are urgently required to safeguard drinking water quality and protect public health, with priority given to children.

3.3. Key Drivers of Fluoride Enrichment

We performed Spearman’s correlation analyses to determine the primary factors governing spatial fluoride variation in the Aksu River Basin. The analysis involved soil fluoride concentrations, groundwater fluoride concentrations, and 12 environmental factors across four major categories (Figure 6). Fluoride enrichment across various media was driven by distinct environmental factors, revealing clear differences in the mechanisms underlying these enrichments.

Soil fluoride enrichment was primarily governed by soil physicochemical properties and climatic factors. Correlation analysis revealed a highly significant positive correlation between soil pH and fluoride content (ρ = +0.62, p < 0.01), identifying pH as the most critical driver in this region. This indicates that alkaline environments promote fluoride-bearing mineral dissolution and fluoride ion desorption, thereby significantly increasing fluoride availability in the soil. Concurrently, climatic factors exerted strong influences: Evapotranspiration demonstrated a highly significant positive correlation with soil fluoride (ρ = +0.58, p < 0.01), whereas precipitation exhibited a highly significant negative correlation (ρ = −0.51, p < 0.01). This clearly confirms that intense evaporation–concentration effects in arid regions constitute the key geochemical processes driving fluoride enrichment in surface soils, while precipitation exerts a diluting and leaching effect. Furthermore, soil bulk density showed a significant positive correlation with fluoride content (ρ = +0.45, p < 0.05), indicating that soil texture and structure may influence fluoride retention by affecting water movement.

The combined effects of topography and climatic factors strongly influenced the groundwater fluoride distribution. Elevation (DEM) revealed a significant negative correlation with groundwater fluoride content (ρ = −0.49, p < 0.05), indicating that lower-lying basin centers or alluvial plains facilitate high-fluoride groundwater accumulation and storage. Similar to soil fluoride, evaporation and concentration remained the core mechanisms, as evidenced by significant positive correlations between groundwater fluoride and both evapotranspiration (ρ = +0.53, p < 0.01) and air temperature (ρ = +0.46, p < 0.05). Notably, vegetation ecological indices (normalized difference vegetation index and fractional vegetation coverage) were negatively correlated with groundwater fluoride levels, possibly reflecting vegetation-driven reductions in groundwater levels through transpiration, which may indirectly intensify evaporation concentration in shallow aquifers; however, this process requires further validation.

Spatial fluoride variation in the Aksu River Basin thus results from the coupled effects of multiple environmental factors. Soil enrichment is primarily driven by evaporation–concentration coupled with alkaline activation, whereas groundwater enrichment follows a convergence–concentration pattern coupled with evaporation–enrichment, and is profoundly influenced by hydrogeological conditions and climatic processes. These insights provide a scientific basis for accurately identifying high-fluoride risk zones and formulating targeted (zoned and categorized) prevention and control strategies.

4. Discussion

In this study, we systematically analyzed spatial fluoride distribution, associated health risks, and driving mechanisms in the Aksu River Basin, revealing the multidimensional nature of fluoride-related environmental issues. These findings not only confirm the controlling role of geochemical processes typical of arid zones in fluoride cycling but also provide a crucial scientific basis for regional environmental management and public health interventions.

4.1. Mechanisms Controlling Fluoride Spatial Distribution

Soil and groundwater fluoride distributions exhibit both correlations and distinct characteristics. High-fluoride soil zones are predominantly concentrated in the eastern part of the watershed, whereas groundwater fluoride increases along a west–east gradient. The spatial differentiation pattern is primarily driven by the coupled effects of multiple environmental factors.

Correlation analysis indicates that soil fluoride enrichment primarily adheres to an “evaporation–concentration–alkaline activation” mechanism. Intense evaporation (positively correlated with evapotranspiration) and alkaline soil conditions (positively correlated with pH) jointly promote fluoride activation and surface accumulation [34]. This aligns with the typical geochemical behavior of fluoride in arid regions of Northwest China.

By contrast, groundwater fluoride distribution aligns more closely with a “convergence–concentration” pattern, exhibiting a significant negative correlation with elevation. This indicates that topography-controlled groundwater flow systems transport dissolved fluoride ions to low-lying areas, leading to relative enrichment in the discharge zone of the eastern watershed. Simultaneously, evaporation–concentration processes continue to influence shallow groundwater, thereby increasing fluoride concentrations. This multi-process coupled enrichment mechanism is a defining characteristic of oasis zones along the Tarim Basin periphery [6].

Beyond the aforementioned statistical correlations, the formation of this spatial distribution pattern is closely linked to regional hydrogeological conditions. Groundwater in the study area generally flows from west to east. In the eastern low-lying areas, the water table becomes shallower, and groundwater flow slows, creating favorable hydrodynamic conditions for the long-term accumulation of fluoride. Simultaneously, intense evaporation and concentration continuously enrich fluoride ions in shallow groundwater, resulting in significantly higher fluoride concentrations in eastern groundwater compared to the west. This coupled mechanism of “slowed flow-evaporation concentration” represents a typical pattern for the formation of high-fluoride groundwater in arid basin regions.

4.2. Environmental and Public Health Implications

Groundwater fluoride via the drinking water pathway was identified as the primary health risk in the study area. The non-carcinogenic risk to children (HQ mean = 1.83) exceeds acceptable levels, underscoring the importance of distinguishing between media and exposure pathways in fluoride environmental assessments for arid regions.

Although soils display relatively high background fluoride levels, their bioavailability remains comparatively low. Exposure mainly occurs indirectly through soil particles, rendering the associated health risks relatively manageable. However, groundwater serves as a direct drinking water source, with fluoride ions in groundwater exhibiting high bioavailability and amplifying the associated health risks [35]. Children represent the most sensitive exposure group owing to their higher water intake per unit body weight and vigorous physiological metabolism. These findings are consistent with those of other HRAs conducted in high-fluoride regions globally [5,36].

It is noteworthy that the seemingly contradictory conclusion of soil fluoride being in an “unpolluted” state yet exhibiting “moderate ecological risk” actually reflects the fundamental difference between the two evaluation metrics: I_geo assesses the enrichment level of fluoride relative to background values, while ER evaluates the potential ecological toxicity effects of fluoride. The study area exhibits a naturally elevated soil fluoride background level (488 mg·kg⁻¹), resulting in a relatively low I_geo value. However, this high background level itself constitutes a potential ecological risk, leading to a moderate ER value. This finding suggests that when conducting risk assessments in areas with naturally high background levels, it is essential to comprehensively consider the dual impacts of background values and toxicity effects to avoid the limitations of relying on a single indicator.

4.3. Limitations and Future Research Directions

Although this study systematically revealed the spatial distribution of fluoride, health risks, and natural driving mechanisms in the Aksu River basin, some limitations remain. First, the current study design did not incorporate direct indicators of human activities (e.g., land use type, irrigation intensity, fertilizer application rates) in the driver selection (Table 3). While natural factors were systematically evaluated, the absence of anthropogenic variables constitutes a gap that should be addressed in future research by explicitly including these factors to assess their relative contributions to fluoride enrichment.

Second, while the correlation analysis used in this study reveals the strength of relationships between variables, it cannot fully elucidate causal mechanisms. Subsequent research could employ structural equation modeling and isotope tracing to deepen the understanding of the geochemical behavior of fluoride. Furthermore, the bioavailability of soil fluoride and its migration patterns within the “soil–crop” system remain unclear in the study area. Conducting pot experiments or in situ field studies is recommended to provide more direct evidence for precise risk assessment.

5. Conclusions

We comprehensively assessed the environmental behavior and health risks of fluoride in the oasis area of the Aksu River Basin using multiple methodologies, yielding the following core conclusions:

(1)

Spatially, soil fluoride exhibits significant enrichment in the eastern basin, while groundwater fluoride shows a west-to-east concentration gradient. These patterns are governed by distinct dominant mechanisms: soil fluoride follows an “evaporation–concentration–alkaline activation” pathway, whereas groundwater fluoride is driven by a “convergence–concentration–evaporation-enrichment” mechanism.

(2)

Regarding health risks, groundwater fluoride poses a clear non-carcinogenic health risk to children via drinking water exposure (HQ mean = 1.83, far exceeding the safety threshold of 1), while the risk to adults is lower (HQ mean = 0.85). This makes groundwater the primary health concern in the study area. While overall soil fluoride risks are manageable, ecological risks stemming from regional high background levels warrant attention.

(3)

Spatially, soil fluoride exhibits significant enrichment in the eastern basin, while groundwater fluoride shows a west-to-east concentration gradient. These patterns are governed by distinct dominant mechanisms: soil fluoride follows an “evaporation–concentration–alkaline activation” pathway, as evidenced by significant positive correlations with evapotranspiration and soil pH. Groundwater fluoride is driven by a “convergence–concentration–evaporation–enrichment” mechanism, with significant positive correlations with evapotranspiration and air temperature and a negative correlation with elevation.