Occurrence and Distribution Profiles of Perfluoroalkyl and Polyfluoroalkyl Substances in Water and Sediment of the Lower Reaches of the Yellow River

ABSTRACT

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are known for their potential for long‐range transport and significant bioaccumulation, and long‐term exposure poses risks to both ecosystem integrity and public health. Globally, an increasing number of aquatic systems domestically and internationally have reported PFAS contamination, highlighting the growing severity of this issue. This research focuses on the lower section of the Yellow River, a vital water body in China, presenting a detailed analysis of PFAS pollution characteristics, source identification, and associated health risks. Surface water and sediment samples were collected seasonally from seven locations along the Pingyin–Jinan reach. Concentrations of 21 target PFASs were measured using ultrahigh‐performance liquid chromatography coupled with tandem mass spectrometry. The findings indicate that no polyfluoroalkyl substances were detected in the study area, and the main perfluorinated pollutants in both water and sediment were perfluorooctanoic acid and perfluorobutanoic acid (PFBA), suggesting a transition toward shorter chain compounds in this stretch of the river. Through principal component analysis–multiple linear regression (PCA‐MLR) and Spearman correlation analysis, industrial discharges and atmospheric deposition were identified as the two principal sources of PFASs. Ecological risk evaluation indicated that PFBA, PFPeA, PFBS, PFHxA, and PFOS pose low to negligible risks to the aquatic environment, whereas PFOA presented a moderate risk, particularly at site S2 near a tributary confluence. Human health risk assessment indicated that current PFAS levels in the lower Yellow River pose negligible risks to residents.

Summary

• PFOA and PFBA dominated PFAS profiles in the lower Yellow River, indicating a shift toward short‐chain compounds.

• Industrial discharges and atmospheric deposition as the two primary sources of PFASs.

• A moderate risk from PFOA at sites receiving tributary input, whereas human health risks were negligible.

The contamination profile in the lower Yellow River is dominated by PFOA and PFBA, indicating a progressive shift toward short‐chain alternatives. Industrial emissions and atmospheric deposition were the primary contamination pathways. Ecological risk assessment indicates moderate concern for PFOA‐particularly near tributary inputs–human health risks remains negligible across all demographic groups.

1. Introduction

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) represent a group of anthropogenic persistent organic pollutants characterized by the substitution of all hydrogen atoms in at least one alkyl group with fluorine atoms (Grgas et al. 2023). When all hydrogen atoms on the carbon chain are replaced, the compounds are referred to as perfluoroalkyl substances; those with partial substitution are termed polyfluoroalkyl substances. The remarkable strength of the carbon–fluorine bond (485 kJ/mol) imparts high thermal and chemical stability to PFASs (Zweigle et al. 2024). Furthermore, their molecular structure—combining hydrophobic carbon chains with hydrophilic functional groups—confers oil‐ and water‐repellent characteristics. These properties have led to their extensive application in industrial processes and consumer goods, such as electroplating, paper manufacturing, textiles, leather processing, firefighting foams, and interior decoration materials (Zhang et al. 2020; Hongkachok et al. 2018).

The expansion of PFAS‐related manufacturing has resulted in substantial release of these compounds into various environmental compartments (Huerta et al. 2022; Na et al. 2021; Barbo et al. 2023; Shimizu et al. 2022). Owing to their strong bioaccumulation ability, PFASs can enter organisms through the food chain, where they accumulate and undergo biomagnification, eventually threatening human health. Studies have demonstrated that PFASs can induce severe adverse effects on human reproduction and development, along with carcinogenic, immunotoxic, and neurotoxic properties (Davidsen et al. 2021), thereby representing a considerable public health hazard. With growing scientific insight into the contamination and toxicology of PFASs, regulatory actions have been implemented in some countries and regions, such as the United States, Canada, and the European Union (Gao et al. 2020). The Stockholm Convention on Persistent Organic Pollutants listed perfluorooctane sulfonic acid (PFOS) under its control framework in 2009 (Zhao et al. 2015), and perfluorooctanoic acid (PFOA) and its salts were listed during the Ninth Conference of the Parties in 2019 (Xie et al. 2013). As regulations on longer chain perfluoroalkyl substances tighten, polyfluoroalkyl compounds are increasingly employed as industrial alternatives (Kim et al. 2021).

Investigations have documented the frequent occurrence of PFASs in water and sediment of rivers and lakes across China (Chen et al. 2018; Li et al. 2011), posing considerable concerns to the ecological environment and human health. A review of existing literature indicates that studies on PFASs in the lower Yellow River's aquatic and sedimentary environments remain limited. This study was designed to examine the contamination levels and spatiotemporal distribution patterns of PFASs in the study area. By establishing a high‐throughput detection method using liquid chromatography–tandem mass spectrometry (LC‐MS/MS), the presence of PFASs in water and sediment was systematically evaluated. The temporal and spatial variation characteristics and pollution sources of PFASs in water and sediments were preliminarily elucidated. On this basis, the potential ecological risks posed by PFASs in the mainstream of the lower Yellow River and their cumulative health risks to humans were assessed. This research offers a scientific foundation for enhancing PFAS pollution management and implementing targeted control strategies, thereby supporting the security of urban drinking water supplies.

The objectives of this study were (1) to determine the concentration levels and seasonal variation of PFASs in the lower reaches of the Yellow River; (2) to identify major PFAS sources and quantify their contributions using PCA‐MLR and correlation analysis; and (3) to assess the potential ecological and human health risk. This work provides fundamental data for risk assessment and regulation of both legacy and emerging polyfluoroalkyl and perfluoroalkyl substances and aims to raise awareness regarding water environment security in the Yellow River Basin and other comparable regions worldwide.

2. Materials and Methods

2.1. Reagents and Standards

The standard mixtures of 17 PFASs were acquired from Wellington Laboratories Inc. (Ontario, Canada) with a concentration of 2 μg·mL⁻¹. The polyfluoroalkyl substances, including 1H,1H,2H,2H‐perfluorohexanesulfonate sodium, 1H,1H,2H,2H‐perfluorooctanesulfonic acid, N‐Methyl perfluorooctanesulfonamidoacetic acid, and N‐Ethyl perfluorooctanesulfonamidoacetic acid, were obtained from AccuStandard at a concentration of 50 μg·mL⁻¹. HPLC‐grade methanol, acetonitrile, ammonium acetate, formic acid, and ammonia were used for sample pretreatments and analysis. Ultrapure water (18.2 mΩ·cm⁻¹) was prepared by a Millipore‐A10 ultrapure system (Bedford, MA, United States).

2.2. Study Areas and Sampling

A total of 20 sampling stations (Table S1) were established along the river (Figure S1). At each site, surface water (SW) and sediment (SD) samples were collected simultaneously.

Sampling was carried out in each of the four seasons within one hydrological year. River water and sediment samples were collected at a distance of 3 m from the riverbank within the same cross‐section. Before sampling, the sampling containers were rinsed with ultrapure water. Water samples were collected manually with a sampling bucket at a depth of 30 cm–40 cm and transferred into 1‐L brown glass bottles. The collection was repeated three times. Sediment samples were collected using a grab sampler at the same location as water sampling, placed in aluminum containers, and also collected in triplicate. To eliminate cross‐contamination between different sampling sites, the grab sediment sampler was sequentially rinsed with onsite river water and ultrapure water after each sampling; the sampler was air‐dried before being transported to the next cross‐section. Sediment samples were air‐dried in a greenhouse maintained at 30°C. Water samples were stored at 4°C in a refrigerator until processing.

2.3. Sample Analysis

Each water sample (1000 mL) was filtered through a 0.7‐μm glass fiber filter within 24 h. The filtrates were then concentrated using solid‐phase extraction (SPE). For each liter of water, 4.625 g of ammonium acetate was added and the pH adjusted to 6.8–7.0. A WAX solid phase extraction column was activated with 5‐mL ammonia‐methanol (NH₃·H₂O = 0.1%), 7‐mL methanol, and 10‐mL ultrapure water sequentially. After sample loading, elution was performed with 5‐mL methanol followed by 7‐mL ammonia‐methanol (NH₃·H₂O = 0.1%). The eluents were evaporated to dryness under a gentle nitrogen stream and the residue was dissolved in a 0.5‐mL methanol–water solution (V:V = 1:1). All extracts were stored at 4°C and analyzed within 1 week.

For sediment analysis, 1 g of dried sample was mixed with 5 mL of methanol, sonicated for 20 min, and then shaken at 200 rpm for 3 h at room temperature. After centrifuging at 3000 r/min for 10 min, the supernatant was transferred to a 15‐mL tube. This extraction was repeated three times. The combined supernatant was concentrated under nitrogen and redissolved in 1 mL of 1:1 (v/v) methanol–water.

Analysis of perfluoroalkyl acids (PFASs) was performed using a UPLC‐MS/MS system (Xevo TQd, Waters, United States) equipped with an ACQUITY UPLC HSS T3 column (50 mm × 2.1 mm, 1.7 μm). The mobile phase consisted of (A) 10 mmol L⁻¹ ammonium acetate in ultrapure water and (B) a mixture of methanol and acetonitrile (8:2, v/v). Detection was conducted in negative electrospray ionization mode.

2.4. Data Analysis

2.4.1. Source Apportionment

PCA‐MLR is a powerful approach for identifying major components of target pollutants, tracing their origins, and estimating quantitative contributions from different pollution sources. This two‐step model first conducts principal component analysis to extract orthogonal latent pollution factors representing distinct source categories from the multidimensional PFAS concentration dataset; multiple linear regression is then performed between PCA factor scores and total PFAS concentrations to calculate the quantitative contribution proportion of each independent pollution source. Leng et al. (2021) and Yu et al. (2022) have successfully applied PCA‐MLR to identify PFAA sources in aquatic systems.

Leng et al. (2021) and Yu et al. (2022) have successfully applied PCA‐MLR to identify PFAA sources in aquatic systems. In this work, PFASs levels in surface water samples in the wet season and the dry season were determined and analyzed using PCAMLR method. When the level of an individual PFAS was below the method quantification limit, half of the detection limit was used for calculation. The weight contribution of each source was determined by MLR between PCA factor scores and PFAS concentrations. The contribution of each factor i to the PFASs at a given sampling site was computed using Equation (1).

$$S_i=\text{mean}\sum \text{PFASs}\times \frac{B_i}{\sum B_i}+B_i\times \delta \times \mathrm{F}{S}_i$$ (1)

Predicted concentration of ΣPFASs at each site was calculated as follows:

$$\text{Predicted}\sum \text{PFASs}=\text{mean}\sum \text{PFASs}+B_i\times \delta \times \mathrm{F}{S}_i$$ (2)

where S _i  = contribution of factor i; B _i  = regression coefficient, the contribution of each factor from MLR; $\frac{B_i}{\sum B_i}$ = mean contribution for each factor; FS _i  = PCA factor score for factor i; mean ΣPFASs = mean value of the total PFASs concentration; and δ = standard deviation of the total concentrations.

2.4.2. Ecological Risk Assessment of PFASs

In this work, the ecological risk of PFASs in Yellow River surface water was evaluated using the risk quotient (RQ) method (Lv et al. 2019). The RQ is calculated as the ratio of the measured environmental concentration (MEC, ng L⁻¹) to the predicted no‐effect concentration (PNEC, ng L⁻¹). The RQ values were determined only for six pollutants for which environmental quality standards (EQS) are established under Italian Decree Law 172/2015: PFHxA (1000 ng L⁻¹), PFPeA and PFBS (3000 ng L⁻¹), PFBA (7000 ng L⁻¹), PFOS (0.65 ng L⁻¹), and PFOA (100 ng L⁻¹). The risk levels were categorized as insignificant risk (RQ < 0.01), low risk (0.01 < RQ < 0.1), medium risk (0.1 < RQ < 1), and high risk (RQ > 1) (Li et al. 2020).

2.4.3. Human Health Risk Assessment of PFASs

In this study, the health risk of PFASs in drinking water was assessed using the hazard quotient mixture (HQ _mix) approach (Tang et al. 2022), considering that the Yellow River serves as a major source of drinking water in the region. HQ _mix was conducted for seven different age groups: 3~6, 7~11, 12~16, 17~19, 20~24, 25~59, and 60+ years. RQ ≥ 1.0 suggests a significant potential health effect, 0.2 ≤ RQ < 1.0 indicates uncertain risk, and RQ < 0.2 represents negligible risk. The RQ value was calculated as follows:

$$\mathit{\text{DWEL}}=\frac{p\times \mathit{AD}I\times \mathit{BW}}{\mathit{DWI}\times \mathit{AB}\times \mathit{FOE}}$$ (3)

$${\mathit{HQ}}_{\mathit{mix}}=\sum _{i=1}^n{\mathit{HQ}}_i={\sum }_{i=1}^n\frac{{\mathit{MEC}}_i}{{\mathit{\text{DWEL}}}_i}$$ (4)

where DWEL is the drinking water equivalent level; p is the relative source contribution factor (default = 0.2) (Post et al. 2017); ADI is the allowable daily intake (μg kg⁻¹ day⁻¹); values for PFBS, PFHxA, PFHpA, PFOA, and PFOS are 500, 100, 100, 0.2, and 0.15 μg/(kg·day), respectively (Schwanz et al. 2016); DWI is the daily drinking water intake (L·day⁻¹); DWI for different age/gender groups is shown in Table S3; AB is the gastrointestinal absorption factor (assumed = 1) (Riva et al. 2018); FOE is the frequency of exposure (350/365 days = 0.96 (Yang et al. 2017)); BW is the average body weight (kg), values by age/sex in Table S3; and MEC is the measured PFAS concentration in water (ng·L⁻¹).

3. Results and Discussion

3.1. Occurrence and Spatial–Temporal Distribution of PFASs

Comprehensive analysis of water and sediment samples from the lower Yellow River revealed the presence of 17 target PFASs and four emerging fluorinated alternatives. Notably, only perfluoroalkyl compounds were detected across all sampling seasons, with no polyfluoroalkyl substances identified above detection limits. The spatial and temporal distribution patterns of these compounds are summarized in Figure 1 and Table 1.

FIGURE 1.

Concentrations of PFASs in water and sediments of the Yellow River across different seasons (A: aqueous phase; B: sediment; from left to right for each sampling site: spring, summer, autumn, and winter).

TABLE 1.

Concentrations of PFASs in water and sediments of the Yellow River across seasons (water and mixed: ng/L; sediment: ng/g dry weight; ND: not detected).

Target compounds	Spring			Summer			Autumn			Winter
	Water	Sediment	Mixed	Water	Sediment	Mixed	Water	Sediment	Mixed	Water	Sediment	Mixed
PFBA	4.0–33.3	5.9–53.7	4.1–31.9	3.4–11.0	ND	3.3–10.7	8.6–15.4	30.5–62.7	9.7–16.5	2.9–19.7	1.7 ~ 4.7	2.8 ~ 18.5
PFBS	1.1–56.8	ND	0.9–56.9	0.7–1.4	ND	0.7–1.4	0.4–1.3	ND	0.4–1.1	0.7–1.3	ND	0.7–1.2
PFPeA	1.6–9.1	ND	1.5–9.4	2.2–3.7	ND	2.0–3.5	0.4–2.2	ND	0.5–2.0	0.7–3.1	ND	0.6–3.0
PFHxA	2.0–4.3	0.8–9.2	2.0–4.1	1.2–2.6	ND	1.2–2.4	0.7–1.3	2.2–11.5	0.6–1.5	0.6–2.8	ND	0.5–2.7
PFHpA	5.2–7.5	0.9–9.3	5.0–7.7	2.3–3.2	ND	2.1–3.1	0.5–1.2	2.0–11.0	0.6–1.37	ND	ND	ND
PFOA	6.3–36.1	2.1–26.7	6.5–34.2	4.6–16.3	1.3–3.0	4.5–15.7	1.4–18.4	6.6–44.8	3.0–19.4	2.7–18.7	0.8–1.3	2.6–18.1
PFOS	ND	ND	ND	0.7–1.0	ND	0.7–1.0	0.8–1.5	ND	0.7–1.3	ND	ND	ND
PFNA	ND	ND	ND	0.6–0.9	ND	0.5–0.9	0.5–0.8	ND	0.4–0.77	ND	ND	ND

In aqueous matrices, eight PFAS congeners were quantified, with total concentrations (∑PFASs) ranging from 9.2 to 90.7 ng/L. A pronounced contamination hotspot was identified at station SW2 (36.9–90.7 ng/L), where PFOA concentrations reached 34.2 ng·L⁻¹, representing the highest ecological risk among all monitored compounds. This elevation likely reflects contributions from upstream municipal and domestic wastewater discharges through the tributary system. Downstream from this confluence point, PFAS concentrations exhibited substantial homogenization, suggesting efficient mixing and dilution by the main river flow.

Sediment analysis identified four predominant PFASs: PFOA, PFHxA, PFHpA, and PFBA, with concentrations ranging from 0.8 to 62.7 ng/g dry weight (Figure 1B). Notably, PFOA demonstrated the highest ecological risk potential in sediments, particularly at S2 station where its concentration reached 44.8 ng·g⁻¹, significantly exceeding levels of other PFASs. Compositional analysis revealed that PFBA and PFOA collectively accounted for 84.6% of total PFASs in spring and autumn sediments, demonstrating a clear transition toward shorter chain compounds in sedimentary compartments. This pattern aligns with previous observations by Wu et al. (2020) and Ding et al. (2018) in other Chinese aquatic systems.

Among all detected compounds, PFOA emerged as the most environmentally significant contaminant due to its persistence, bioaccumulation potential, and elevated concentrations in both water and sediment phases. A marked predominance of perfluorocarboxylic acids (PFCAs) over perfluorosulfonic acids (PFSAs) was observed, consistent with the higher aqueous solubility and mobility associated with carboxylate functional groups. Whereas PFOS was detected in summer and autumn water samples, its concentrations were substantially lower than those of PFPeA at corresponding stations. This compositional shift may reflect industrial substitution of PFOS with 6:2 fluorotelomer sulfonate (6:2FTS) in metal plating operations, given that PFPeA is a recognized degradation product of 6:2FTS (Dasu et al. 2022; Wang et al. 2011).

PFOA and PFBA emerged as the dominant pollutants in this study, with concentration ranges of 2.6–34.2 and 2.8–31.9 ng·L⁻¹, respectively. The observed PFOA levels are comparable to those reported in previous studies of the Yellow River and other freshwater systems in China (Li et al. 2022; Liu et al. 2018). The significant abundance of short‐chain PFBA among the predominant compounds indicates an ongoing industrial transition from long‐chain to short‐chain PFASs within the basin, mirroring trends documented by Wu et al. (2020) in the Guanlan River system.

3.2. Spatiotemporal Variation Characteristics

Distinct seasonal variations in PFAS concentrations were observed in both aqueous and sedimentary phases (Figure 2). In water, mean ∑PFASs followed the order: spring (47.6 ng·L⁻¹) > summer (23.0 ng·L⁻¹) > autumn (22.1 ng·L⁻¹) > winter (15.9 ng·L⁻¹). Sediment concentrations displayed different temporal patterns, with maximum levels in autumn (77.25 ng·g⁻¹) and minimum values in winter (1.52 ng·g⁻¹). These seasonal dynamics can be attributed to several hydrological and biogeochemical processes. During wet seasons, increased precipitation and runoff likely facilitate PFAS remobilization from sediment compartments to the water column. Conversely, reduced flow conditions during dry periods enhance particle‐associated sedimentation and sorption processes, thereby reducing aqueous‐phase concentrations (Zhong et al. 2021).

FIGURE 2.

Average concentrations of PFASs at eight sampling points in different seasons (a: water; b: sediment).

Seasonal variations in PFAS composition were also evident. In summer and autumn (wet seasons), water samples contained eight detectable PFASs (five short‐chain and three long‐chain compounds), whereas winter and spring samples contained only six (five short‐chain and PFOA). The appearance of PFOS and PFNA exclusively during wet seasons suggests potential mobilization through sediment resuspension or upstream flushing operations. In autumn, sediments showed the highest PFAS accumulation, whereas winter samples contained only trace PFOA levels, possibly due to scouring effects from water‐sediment regulation measures that remove contaminated surface layers.

Notably, PFOA concentrations at several monitoring stations exceeded the United States Environmental Protection Agency (USEPA) lifetime health advisory level (4 ng L⁻¹). At present, China has not yet issued unified national standard limit values for PFOA and short‐chain PFASs in surface drinking water sources; however, the Technical Guidelines for Risk Control of New Pollutants (2023) issued by the Ministry of Ecology and Environment of China clearly lists PFOA and PFOS as priority controlled new persistent organic pollutants in river basins and requires strengthened regular monitoring for water supply source sections of major urban rivers including the lower Yellow River. Based on the elevated PFOA levels near tributary junctions, local water management departments should strengthen regular PFAS monitoring across river tributaries and advance upgrading of drinking water treatment processes to reduce potential exposure risks for residents. As these samples represent baseflow conditions, additional research is needed to evaluate the removal efficiency of conventional water treatment processes for these contaminants.

3.3. Pollution Source Analysis

Given the limited external hydrological inputs to the Pingyin‐Jinan reach, source apportionment analysis provides valuable insights into local contamination origins. Principal component analysis–multiple linear regression (PCA‐MLR) and Spearman correlation analysis were employed to identify potential PFAS sources (Gao et al. 2014). The resolved pollution factors and their variance contributions are presented in Figure 3 and Table 2.

FIGURE 3.

PCA‐MLR source identification in the (a) dry season and (b) wet season.

TABLE 2.

Total variances in PCA of PFASs in dry season and wet season.

Components		Initial eigenvalue			Quadratic sum of extracted loading			Quadratic sum of rotated loading
		Aggregate	Variance percent (%)	Cumulative (%)	Aggregate	Variance percent (%)	Cumulative (%)	Aggregate	Variance percent (%)	Cumulative (%)
Dry season	Component 1	3.896	64.937	64.937	3.896	64.937	64.937	3.896	64.937	64.937
	Component 2	1.229	20.475	85.412	1.229	20.475	85.412	1.229	20.475	85.412
	Component 3	0.747	12.442	97.855
	Component 4	0.107	1.777	99.632
	Component 5	0.021	0.349	99.981
	Component 6	0.001	0.019	100.000
Wet season	Component 1	3.831	47.888	47.888	3.831	47.888	47.888	3.269	40.865	40.865
	Component 2	1.555	33.920	81.808	1.555	33.920	81.808	1.976	24.701	65.566
	Component 3	1.425	19.433	101.240	1.425	19.433	101.240	1.566	19.572	85.138
	Component 4	0.633	17.818	119.058
	Component 5	0.526	7.906	126.964
	Component 6	0.027	6.573	133.538
	Component 7	0.004	0.336	133.874
	Component 8	0.000	0.047	133.920

In the dry seasons, two principal factors (eigenvalues > 1) were determined, which accounted for 85.412% of the total variance. Factor 1 (64.9% of variance) exhibited high loadings for PFOA, PFPeA, PFHxA, and PFBA. Possible sources of PFOA, PFPeA, PFHxA, and PFBA are shown in Table 3. PFOA is commonly associated with production of fluoropolymers as well as atmospheric dry and wet deposition. PFHxA was hypothesized to originate from the fabric and textile industries and the degradation of FTOH in the atmosphere. PFPeA was associated with PFBA (R = 0.67) in this study. Thus, PFPeA and PFBA were hypothesized to originate from the chrome‐plating industry and fast‐food packaging. PFBA was strongly associated with PFOA (R = 0.95) in this study (as shown in Figure 4). The ratios of PFBA/PFOA in water bodies in dry seasons are less than 2 (Table S2), indicating that livestock manure and domestic sewage contribute less to the source of PFASs in water in dry seasons. Thus, the possible sources of PFBA included production of fluoropolymers as well as long‐chain degradation. Factor 2 (20.5% of variance) represented PFBS and PFHpA associated with food packaging materials. Overall, the contribution percentage of Factor 1 was far more than that of Factor 2 in each sampling site, proclaiming that Factor 1 was the main contribution source in the dry season.

TABLE 3.

Potential sources of PFASs.

Compounds	Potential sources	Refs.
PFOA	Processing aid during the production of fluoropolymers Degradation products of 8:2 FTOH (volatile precursor substances) in the atmosphere	Wang et al. (1951 ); Wallington et al. (2006 ); Bach et al. (2016 )
PFHxA	Fluoropolymer processing aids, surface treatment of textile and garment waterproof coatings, and antifouling coatings of textiles and carpets Substitution for PFOA Degradation products of 6:2 FTOH	Klaunig et al. (2015 ); Thackray et al. (2020 )
PFPeA	Mist suppressant in the chrome‐plating industry and fast‐food packaging	Schaider et al. (2017 )
PFBA	Mist suppressant in the chrome‐plating industry and fast‐food packaging Products of long‐chain degradation	Schaider et al. (2017 ); Marius et al. (2025 )
PFHpA	Degradation products of food packaging and stain‐ and grease‐resistant paint on sofas and carpets	Thackray et al. (2020 ); Wang et al. (2015 )
PFBS	Mist suppressant in the chrome‐plating industry and fast‐food packaging	Schaider et al. (2017 )

FIGURE 4.

Pearson correlation analysis among individual PFASs in the (a) dry season and (b) wet season. (*p < 0.05, **p < 0.01, ***p < 0.001).

In wet seasons, three factors accounted for 85.1% of total variance. Factor 1 was remarkably characterized by PFOA, PFPeA, and PFBA, which took about 40.9% of the total loading. Possible sources of PFASs are shown in Table 2. PFPeA was associated with PFBA (R = 0.50) in this study (as shown in Figure 4). Thus, PFPeA and PFBA were hypothesized to originate from the chrome‐plating industry and fast‐food packaging. PFBA was strongly associated with PFOA (R = 0.81) in this study (as shown in Figure 4). The ratios of PFBA/PFOA (0.6–10.8) indicated that livestock wastewater and domestic sewage contribute significantly to the source of PFASs in water (Chen et al. 2016). Thus, the possible sources of PFBA also including production of fluoropolymers, long‐chain degradation, livestock manure, and domestic sewage. Factor 2 (24.7%) represented PFBS from industrial applications, whereas Factor 3 (19.6%) represented PFHpA from degradation of food packaging and stain‐resistant coatings. Collectively, these results indicate that industrial discharges and atmospheric deposition represent the primary PFAS sources in the study area.

3.4. Risk Assessment

The ecological risk assessment revealed distinct risk hierarchies among detected PFASs. Ecological risk assessment of detected PFASs was conducted using the RQ method, with seasonal RQ values for Yellow River water samples shown in Figure 5. The results indicated that PFOA posed moderate risk (RQ > 0.1) at multiple sampling locations, particularly at Station S2 near the tributary input. In contrast, other detected compounds including PFBA, PFPeA, PFBS, PFHxA, and PFOS exhibited RQ values below 0.1, representing low or negligible risk levels. Longitudinal assessment revealed limited spatial variation in ecological risk for non‐PFOA compounds along the river continuum. Notably, the spatial distribution of PFOA risk showed significant variation, with the highest RQ value (0.34) observed at Station S2 near the Jinshui River tributary confluence. This finding suggests that tributary inputs represent critical control points for PFOA risk mitigation. The consistent elevation of PFOA risk compared to other PFASs underscores its priority status in regional monitoring and management programs. The spatial differentiation of PFOA ecological risk is completely controlled by tributary confluence input, whereas short‐chain PFASs show uniform low‐risk distribution along the mainstream without obvious pollution hotspots.

FIGURE 5.

Seasonal RQ values for the eight samples (a: spring; b: summer; c: autumn; d: winter).

Health risk assessment of PFASs was conducted using the hazard quotient (HQ) method. Yellow River water mainly enters the human body through drinking, so the health risk assessment is calculated and evaluated based on the content of perfluoroalkyl substances in the water. Seasonal health risks are presented in Figure 6. The HQ _mix for detected PFASs remained below 0.2 across all seasons, indicating negligible health risks to human populations. Under equivalent water consumption conditions, females exhibited slightly higher HQ _mix values (0.0027–0.0393) compared to males (0.0024–0.0338). Females showed slightly elevated HQ~mix~values compared to males, reflecting body weight differences and consequent higher exposure per unit body weight.

FIGURE 6.

Seasonal HQ _mix seasonal RQ values for Yellow River water samples (a: spring; b: summer; c: autumn; d: winter).

The cumulative health risk quotient of PFASs (HQ _mix ) with different ages was examined. As shown in Figure 7, all PFASs exhibited low human health threat levels (HQ _mix < 0.2) in different age groups. In the same age group, HQ _mix values of PFASs for females were slightly higher than those for males since the water‐intake‐to‐body‐weight ratio of females was higher compared with males (Goeden et al. 2019). There were significant differences in health risks posed by PFASs among the different age groups. The HQ _mix value of children aged 3~6 was the highest. Children aged 3–6 years showed the highest susceptibility to PFOA exposure, with HQ values approximately 1.8 times higher than adult populations. This age‐dependent vulnerability emphasizes the importance of considering PFOA specifically in drinking water safety assessments, particularly for vulnerable subpopulations. Gender differences in health risk are weaker than age differences, and preschool children are the core sensitive population that needs targeted water environment protection.

FIGURE 7.

HQ _mix values for ∑PFASs related to drinking water intake for male and female with different age groups for all sampling sites.

Although the cumulative health risk of PFASs does not reach the threshold of adverse health damage at present, the HQmix of children aged 3–6 is nearly 1.8 times that of adults, which is mainly driven by the higher daily water intake per kilogram of body weight in preschool children. PFOA dominates the total health risk contribution among all PFAS congeners; with the continuous substitution of short‐chain PFBA in industrial production, long‐term cumulative exposure risks of mixed short/long‐chain PFASs still cannot be ignored for vulnerable groups such as young children.

4. Conclusion and Limitations

4.1. Conclusion

This comprehensive assessment of PFAS contamination in the lower Yellow River reveals several critical findings. First, the contamination profile is dominated by PFOA and PFBA, indicating a progressive shift toward short‐chain alternatives in both aqueous and sedimentary compartments. Second, source apportionment analysis identifies industrial emissions and atmospheric deposition as primary contamination pathways. Third, although ecological risk assessment indicates moderate concern for PFOA—particularly near tributary inputs—human health risks remain negligible across all demographic groups, though young children represent a potentially vulnerable subpopulation. These findings underscore the importance of continued monitoring and source‐directed management strategies to protect water quality in this critically important river system.

4.2. Limitations of This Study

There are several limitations associated with the statistical and risk assessment approaches adopted in this work. First, PCA‐MLR can only quantitatively distinguish pollution sources with obvious concentration correlation characteristics; low‐concentration scattered atmospheric background inputs and irregular emergency wastewater discharge cannot be fully captured by the model. Second, the human health risk assessment only considered drinking water exposure pathways, ignoring potential combined exposure from aquatic food ingestion and atmospheric inhalation, which may underestimate the total human exposure load of PFASs. Third, the ecological risk assessment adopted foreign PNEC benchmarks due to the lack of domestic freshwater biotoxicity reference values, and the interspecific difference of aquatic organisms in the Yellow River basin may lead to slight deviation in RQ risk grading results. Finally, this research only covered one complete hydrological year of seasonal sampling, long‐term interannual variation rules of PFAS pollution cannot be reflected in the current dataset.

Author Contributions

Xin Xiaodong: conceptualization, methodology, writing – original draft, investigation. Gao Ke: writing – original draft, formal analysis, investigation. Mu Lan: validation, data curation, visualization. Liu Hong: formal analysis, investigation. Zhang Hong: software, investigation. Wang Mingquan: resources, writing – review and editing, supervision. Jia Ruibao: funding acquisition, project administration, writing – review and editing, methodology, conceptualization, supervision.

Funding

This work was supported by the National key research and development program in China (2021YFC3200904).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Overview of surface water and sediment sampling sites in the Yellow River Basin.

Table S2: The ratios of PFASs of surface water and sediment sampling sites in the Yellow River Basin.

Table S3: Mean body weight (BW) and the quantity of drinking water intake (DWI) for different age/gender groups in China.

Figure S1: Sample sites of Surface water, Sediments, and Reservoir water in Jinan section of Yellow River.

Data Availability Statement

The data generated and analyzed during this study are available from the corresponding author upon reasonable request. All data are included within the manuscript and in the Supporting Information.