Authors: Linwei Lv1,a , Qingqing Meng2 , Yufeng Zhao2 , Yingli Dong2 , Qi Wang1 , Qingbin Wei1, 3, 4 b,*
Affiliations:
1 College of Geographical Sciences, Harbin Normal University, Harbin 150025, Heilongjiang, P. R. China
2 Monitoring Center of ecology and environment of Heilongjiang province, Harbin 150096, Heilongjiang, P. R. China.
3 Heilongjiang Academy of Forestry, Harbin 150081, Heilongjiang, P. R. China
4Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, P. R. China.
All authors’ email:
aEmail: lvlinwei0922@163.com
bEmail: wqb.043106@aliyun.com
Funding:
1.Harbin Normal University Graduate Innovation Project for Master’s Students➕ HSDSSCX2024-07, Harbin Normal University;
2.Open Grant for Key Laboratory of Sustainable Forest Ecosystem Management(Northeast Forestry University), Ministry of Education➕KFJJ2021YB04;
3.Heilongjiang Provincial Ecological Environment Protection Scientific Research Project➕HST2024TR005, Department of ecology and environment of heilongjiang province;
All authors’ bio:
Linwei Lv
Qingbin Wei
Abstract: Perfluorinated compounds (PFAS), as a class of artificially synthesized organic pollutants with strong persistence and bioaccumulation, pose a potential threat to ecosystems through their occurrence and diffusion in agricultural soil, and have become a core issue in global environmental governance. This study takes Taicang City, Jiangsu Province, the core agricultural area of the Yangtze River Delta, as the research area. Through 1km × 1km grid stratified random sampling and combined with ultra-high performance liquid chromatography triple quadrupole mass spectrometry technology, the concentration levels of typical PFAS were analyzed. Kriging interpolation, coefficient of variation, and Moran’s I index were used to reveal their spatial distribution characteristics. Positive definite matrix factor analysis was used to analyze pollution sources, and ecological risks were evaluated through risk quotient and potential ecological risk index. The results showed that the spatial distribution of PFAS exhibited significant gradient differences, with a median total amount of 126.8 μ g/kg in the high-intensity zone, showing strong spatial clustering. The concentration in the low-intensity zone significantly decreased with increasing distance, while the low-intensity zone showed a random distribution; Source analysis shows that industrial emission sources contribute the most, with characteristic compounds being PFOS, PFOA, and PFNA, followed by municipal sludge agricultural sources, manure application sources, and atmospheric deposition sources; The ecological risk assessment found that 100% of the high-intensity zone is at a high-risk level, with RQ values of 1.56 and 2.7 for earthworms and rice, respectively. 71.4% of the medium intensity zone is at high risk, and the RQ value of vegetables for PFBA has increased to 1.5. The risk in the low-intensity zone has significantly decreased, but there are still local hotspots. This study provides a scientific basis for risk management of PFAS pollution in agricultural land.
Keywords: Perfluorinated compounds; Agricultural land; Spatial distribution; Source analysis; Ecological risk assessment
I. Introduction
The artificial synthesis of organic pollutants, as a byproduct of industrial civilization development, has become one of the major challenges facing the global ecological environment (Bernhardt et al., 2017). These organic compounds synthesized by human activities have been widely used in the past century due to their excellent properties in materials, medicine, agriculture, and other fields. However, due to their difficult degradation and easy migration, they have caused systematic pollution to environmental media such as the atmosphere, water bodies, and soil (Chaukura et al., 2016). As shown in Figure 1, from early organochlorine pesticides such as DDT to later polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polytetrafluoroethylene (PTFE), perfluoropolyethers (PFPE), and in recent years to highly concerned perfluorinated compounds (PFAS), pharmaceuticals, and personal care products (PPCPs) (Jarman & Ballschmitter, 2012; Yang et al., 2018), there are over ten million types of artificially synthesized organic pollutants, including tens of thousands of substances with environmental risks.
Figure 1 Schematic diagram of representative artificially synthesized organic pollutants
Among them, PFAS, as a type of artificially synthesized compound with unique chemical stability, has been widely used in various fields such as textiles, electroplating, firefighting, food packaging, etc. since its industrial production in the mid-20th century (Chaudhary et al., 2025). However, the strong stability of its carbon fluorine bond makes it difficult to degrade in the environment, exhibiting significant persistence characteristics (Li et al., 2023), and the potential threat to ecosystems and human health through food chain enrichment amplification has become one of the core issues in the field of global environmental governance (Sanchez Lievanos et al., 2024). As shown in Figure 2, in authoritative literature databases such as CNKI, Web of Science, and Scopus, searching for relevant literature using keywords such as “artificially synthesized organic pollutants,” “PFAS,” “perfluorinated compounds,” “agricultural land chemical pollution,” and “organic chemistry and ecological environment,” it was found that there has been a surge in research in recent years.
Figure 2 Trends in Related Research Papers
From a global perspective, PFAS environmental pollution presents a complex situation of multi-media diffusion and multi-path exposure (Clarke, 2024). The discharge of wastewater, volatilization of exhaust gas, and stacking of solid waste in industrial production processes constitute the main anthropogenic sources of PFAS (Loganathan et al., 2020). At the same time, the use of fluorine-containing pesticides and sewage irrigation in the agricultural production process further exacerbates the migration and accumulation of PFAS to the soil environment, forming a complex pollution chain of “industrial emissions and agricultural diffusion” (Donley et al., 2024). As shown in Figure 3, by establishing a migration model of PFAS in the soil water system, it can be found that it has a long half-life in soils with high clay content and can continuously pollute groundwater through leaching, demonstrating strong environmental persistence (Rosato et al., 2024).
Figure 3 Schematic diagram of PFAS migration
As a special ecological functional area, agricultural land has distinct system specificity in the occurrence behavior of PFAS in its soil. Soil organic matter strongly adsorbs PFAS through hydrogen bonding, and the adsorption coefficient significantly increases with the increase of carbon chain length, leading to the enrichment effect of long-chain PFAS in cultivated soil (Li et al., 2019). More importantly, crop roots absorb PFAS from soil through active absorption and passive diffusion mechanisms, which means that agricultural products produced from contaminated soil may pose potential risks to human health through dietary pathways. From the perspective of ecosystem service functions, the interference of PFAS on soil microbial community structure may lead to a decrease in the activity of key enzymes in nitrogen cycling, which in turn affects soil fertility maintenance and crop productivity improvement, posing an implicit threat to sustainable agricultural development (Preisendanz et al., 2025).
Although the global academic community has conducted extensive research on PFAS, there are still significant limitations to the current cognitive system. In terms of spatial distribution research, existing data is mostly concentrated in point source pollution areas and lacks large-scale grid monitoring data, making it difficult to reveal the macroscopic distribution patterns of PFAS in agricultural land (Darkwah, 2023); In terms of source apportionment methods, traditional ratio methods and principal component analysis are difficult to distinguish the contribution ratios of industrial and agricultural sources, and there is an urgent need to introduce new technological means such as stable isotope tracing (Giglioli et al., 2023); The ecological risk assessment framework tends to prioritize human health over ecosystems, with insufficient research on toxicity thresholds for soil animals and microorganisms. A comprehensive regional risk classification standard has not yet been established (Jha et al., 2021). These research gaps highlight the urgency of conducting systematic empirical research, especially for the sensitive area of agricultural land. It is urgent to establish an integrated research paradigm of distribution, sources, and risks to provide scientific support for the formulation of pollution prevention and control policies.
Based on this, this study takes Taicang City, Jiangsu Province, the core agricultural sector of the Yangtze River Delta, as the research area, focusing on the spatial distribution, sources, and ecological risk assessment of PFAS in agricultural land soil. The study systematically conducts research on the spatial distribution characteristics of PFAS, clarifies the concentration differences and distribution patterns in different intensity zones; Using the PMF model to analyze pollution sources, quantify the contribution ratios of industrial emission sources, municipal sludge agricultural sources, manure application sources, and atmospheric deposition sources, and identify the characteristic compounds and fingerprint identifiers of each source; By calculating RQ and RI, evaluate the ecological risks of PFAS in different intensity zones on soil microorganisms, soil animals, and crops, and classify the risk levels.
This article fills the research gap in the macro distribution pattern, industrial and agricultural source differentiation, and ecosystem risk assessment of PFAS in agricultural land through large-scale grid monitoring and multidimensional analysis methods, providing systematic scientific data for understanding the environmental behavior of PFAS in agricultural ecosystems. The research results can provide strong support for the formulation of precise prevention and control policies for PFAS pollution in agricultural land, clarify the leading role of industrial emission sources and the secondary contribution of agricultural activities, and provide important theoretical basis and practical guidance for targeted control of pollution sources, reduction of ecological risks, guarantee of agricultural product safety and sustainable development of agriculture.
II. Research Context
- Basic characteristics
The environmental behavior of PFAS is closely related to its unique chemical characteristics, which profoundly affect its occurrence in agricultural ecosystems. Exploring its migration mechanism and its occurrence dynamics in agricultural systems is the foundation for understanding the environmental effects of PFAS.
The chemical structure of PFAS is characterized by carbon fluorine bonds, and the difference in carbon chain length has a significant impact on its environmental behavior. Generally speaking, long-chain PFAS have a larger proportion of hydrophobic parts in their molecular structure, stronger affinity for soil particles, and higher soil adsorption coefficient (Kd), resulting in weaker migration ability in soil; Short chain PFAS, on the other hand, have relatively low Kd values, high solubility, are more prone to migration with water, and have stronger fluidity in soil water systems. The difference in carbon chain length directly determines the distribution range and migration rate of PFAS in environmental media, and is a key basis for analyzing its spatial diffusion law (Loganathan & Wilson, 2022).
As one of the important receptors for PFAS, the occurrence dynamics of agricultural systems are influenced by multiple input pathways (Costello et al., 2024). Irrigation water infiltration is an important pathway for PFAS to enter the soil in agricultural areas, especially when using contaminated surface water or groundwater for irrigation. PFAS can enter the cultivated soil layer with water flow and gradually accumulate; Atmospheric deposition, as a broad input method, transports PFAS from the atmosphere to the surface of farmland through dry and wet deposition, contributing to soil pollution; In addition, the application of organic fertilizers may also introduce PFAS, and some PFAS contained in animal manure or sludge compost may enter the soil system with the application of fertilizers. These pathways are intertwined and together determine the level of occurrence of PFAS in agricultural soils.
- Spatial distribution
On the basis of clarifying the basic characteristics of PFAS, the study of its spatial distribution helps to reveal regional differences in pollution, while the comparison of different regional cases and the evolution of research methods provide support for a more accurate grasp of distribution patterns.
The spatial distribution of PFAS in agricultural land shows significant regional differences worldwide. For example, PFAS hotspots in North American farmland are closely related to industrial emission sources, exhibiting a distribution pattern centered around point sources and spreading to the surrounding areas (Schroeder et al., 2021); The distribution of PFAS in East Asian irrigation areas is more influenced by irrigation water quality and agricultural production activities, and polluted areas often coincide with the distribution of irrigation water systems (Zhang et al., 2021).
The methodology of PFAS spatial distribution research has undergone a development process from simplicity to complexity, and from qualitative to quantitative. Early studies often used simple point sampling methods to obtain local PFAS concentration data by setting a small number of sampling points within the study area. This method was difficult to reflect the distribution characteristics of a larger range (Evans&Love, 1957); With the introduction of geographic information systems and geostatistics, research can draw spatial distribution maps of PFAS concentration through spatial interpolation and analysis of a large amount of sampled data, more accurately revealing its spatial heterogeneity and distribution patterns. The continuous advancement of methodology has improved the scientific nature of spatial distribution research and provided powerful tools for in-depth exploration of pollution causes.
- Source analysis
After clarifying the spatial distribution characteristics of PFAS, analyzing its sources is a key step in tracing the source of pollution and formulating prevention and control measures. Mainstream analysis models and identification markers for specific sources are the theoretical basis for conducting source analysis work.
Principal component analysis and positive definite matrix factor analysis are widely used models in PFAS source analysis. Principal component analysis can identify the main types of pollution sources that affect the distribution of PFAS by converting multiple related variables into a few unrelated principal components. However, it has certain limitations in quantitatively analyzing the contribution ratios of each pollution source; Positive definite matrix factor analysis can quantitatively calculate the contributions of various pollution sources without the need for prior spectral information of pollution source components, with higher analytical accuracy, but requires higher data quality (Giglioli et al., 2023).
In terms of source characteristics, PFAS input from agricultural sources have certain fingerprint features that can serve as their identification markers. For example, short chain PFAS are often associated with certain pesticides used in agricultural production. In farmland where specific pesticides are applied in large quantities, the concentration of short chain PFAS often shows relatively high levels; In addition, the composition ratio of PFAS in organic fertilizers may also have uniqueness, which can serve as a basis for distinguishing agricultural sources from other pollution sources. Identifying these agricultural source identification markers can improve the ability to identify agricultural sources in source analysis.
- Ecological risk assessment framework
After clarifying the sources and distribution of PFAS, assessing its ecological risks is an important step in determining the degree of pollution hazards and ensuring ecosystem security. It is crucial to construct a scientific assessment framework and consider the adaptability of agricultural scenarios.
The classic indicator system for PFAS ecological risk assessment covers multiple aspects, and the risk quotient compares the measured concentration of PFAS with the predicted ineffective concentration to determine its potential risk to specific ecological receptors; The potential ecological risk index comprehensively considers the toxicity and exposure levels of multiple PFAS to assess the overall ecological risk (Thomaidi et al., 2020); The species sensitivity distribution model determines the safe concentration threshold for protecting a certain proportion of species by constructing sensitivity distribution curves of different species to PFAS. These classic indicators reflect the ecological risks of PFAS from different perspectives, providing standardized methodological basis for risk assessment.
The particularity of agricultural scenarios requires corresponding adjustments to the ecological risk assessment framework. In agricultural soils, soil microbial communities are more sensitive to PFAS response, and changes in their community structure can serve as important risk assessment indicators; Meanwhile, the enrichment factor of PFAS in crops is directly related to the safety of agricultural products, and should be given a higher weight in risk assessment. Considering these characteristics of agricultural scenarios, optimizing the classic ecological risk assessment framework can make the assessment results more in line with the actual situation of agricultural ecosystems, providing more targeted basis for pollution control of agricultural land.
III. Materials and Methods
- Regional selection
This article focuses on the core agricultural sector of the Yangtze River Delta – the entire area of Taicang City, Jiangsu Province, with geographical coordinates of 31°15′-31°45′ N and 120°58′-121°20′ E. As a national commodity grain base and industrial transformation demonstration zone, this region has the characteristics of multiple and repeated pollution sources. The parent material of the soil is formed by the interaction and stacking of new alluvial deposits from the Yangtze River and lacustrine sediments. The main soil types are paddy soil and gray tide soil, with a significant gradient in surface organic matter content and pH values fluctuating between 5.8-7.6. The typical rice wheat rotation mode has an average annual irrigation water volume of 1200mm. Pollution pressure sources include fluorine chemical enterprises, sewage treatment plants, and livestock and poultry farms.
The sampling framework adopts a geographically stratified random strategy, combined with the ArcGIS 10.8 platform to construct a spatial constraint system. As shown in Figure 4, the research area is first divided into a 1km × 1km basic grid according to the “Technical Regulations for Detailed Investigation of Soil Pollution Status in China”, covering a total area of 620 km ². After excluding built-up areas and water bodies, 512 effective grids are retained. Subsequently, triple stratification will be implemented: ① Soil type layer, based on a 1:50000 soil map, rice soil subtypes and tidal soil subtypes will be delineated; ② The pressure layer of pollution sources is divided into three levels according to the Euclidean distance from the dominant pollution source, with high-intensity areas<3km, moderate intensity areas 3-10km, and low-intensity areas>10km; ③ land use layer, distinguishing three types of agricultural formats: paddy fields, dry fields, and orchards. Finally, a 5×3×3 factor combination was formed, and proportional sampling was used to ensure that the sample size of each layer was ≥ 5%. A total of 108 effective points were set up.
Figure 4 Schematic diagram of sampling strategy (considering appearance, not all effective points are shown)
- Experimental analysis
This study used an improved ISO/TS13530-2022 standard process to process soil samples.
The first step is the ultrasound assisted extraction process, which involves pre-treatment of the soil sample. 5.0g of freeze-dried soil, which has been frozen in liquid nitrogen and freeze-dried at -80 ℃ for 48 hours, is weighed and placed in a polytetrafluoroethylene centrifuge tube. Add 15mL of 0.1% ammoniated methanol solution and 1g of EDTA-2Na chelating agent to eliminate the interference of calcium and magnesium ions in the soil on subsequent analysis. Subsequently, extraction was carried out under ice bath conditions using an ultrasonic cell disruptor (Branson S-450D, 40kHz) for 30 minutes, with a pulse interval of 5 seconds and a power of 300W. The ice bath environment can effectively avoid changes in the properties of PFAS caused by thermal conductivity during the ultrasonic process, ensuring the stability of the extraction process.
The second step is the solid-phase extraction purification step, which involves pre filtering the extracted solution with ultrasound assisted extraction through a 0.7 μ m glass fiber filter membrane to remove solid particle impurities. The pre filtered extract is then passed through a WAX weak anion exchange column (Waters Oasis) ®， 150mg/6cc）。 Prior to this, the small column needs to be activated sequentially with 4mL of methanol and 4mL of pH 4.0 acetate buffer to achieve the optimal adsorption state. The target PFAS substance will be adsorbed by the small column, while impurities will flow out with the washing solution. Afterwards, 6mL of methanol solution containing 2% formic acid was used to elute the target substance adsorbed on the small column. The composition and concentration of the eluent were optimized to efficiently and selectively elute the target PFAS from the adsorbent.
The third step is the concentration and reconstruction stage, where the eluent is concentrated by nitrogen blowing through TurboVap LV at 45 ℃ to a near dry state. The nitrogen blowing process can effectively remove organic solvents from the eluent while avoiding the influence of high temperature on PFAS. Subsequently, the concentrated residue was redissolved with 200 μ L of methanol: water (1:1) to allow PFAS to dissolve again in a suitable solution system. Finally, the solution is filtered through a 0.22 μ m PTFE needle filter to ensure that the solution entering the liquid chromatography system is free of particle impurities and to avoid damage to the instrument.
The analysis platform adopts ultra-high performance liquid chromatography triple quadrupole mass spectrometer (Waters ACQUITY UPLC I-Class/Evo TQ-S). In terms of chromatographic conditions, an ACQUITY UPLC BEH C18 column was selected with a column temperature set at 40 ℃, which helps maintain the stability of the chromatographic column. The flow rate is controlled at 0.3mL/min to ensure good separation of the sample in the chromatographic column. The mobile phase consists of a 2mmol/L ammonium acetate aqueous solution (A) and methanol containing 0.01% formic acid (B). Using a gradient elution program, the proportion of phase B increases linearly from 20% to 95% within 0-15 minutes. Through this gradient change, PFAS compounds with different properties can be effectively separated.
The mass spectrometry conditions are set as electrospray ionization anion mode (ESI -), capillary voltage 2.5 kV, desolvent gas temperature 500 ℃, and cone hole gas flow rate 150 L/h. This instrument is used to monitor 16 typical PFAS, covering C4-C14 perfluorocarboxylic acids, perfluorosulfonic acids, and fluoroalcohols. Data was collected using a multi reaction monitoring mode, and the characteristic ion pairs for each PFAS are shown in Table 1. The mass to charge ratio of short chain perfluorinated carboxylic acid parent ions increases with the growth of carbon chains, and the detection limit of the method is 0.01-0.02 μ g/kg. The concentration expands with the growth of carbon chains in the high-intensity region, and some are not detected in the low-intensity region, reflecting differences in migration and diffusion abilities; The mass to charge ratio of medium chain perfluorocarboxylic acid parent ions is 363.0-463.0, with a constant difference in sub ions. The detection limit of the method is 0.005 μ g/kg, and PFOA has the highest concentration in each intensity zone, with prominent cumulative effects; The mass to charge ratio of long-chain perfluorocarboxylic acid parent ions is 513.0-563.0, and the detection limit of the method is as low as 0.002 μ g/kg. The concentration in each intensity zone is lower than that of similar substances in medium and short chains, and some of the low-intensity zones were not detected, which is related to the reduced mobility caused by strong soil adsorption; The mass to charge ratio of perfluorosulfonic acid parent ions is 299.0-499.0, and the daughter ions are both 80.0 and 99.0. The detection limit of the method is 0.005-0.03 μ g/kg, and PFOS has the highest concentration in various intensity zones, indicating its widespread existence and strong accumulation; The mass to charge ratio of fluorinated alcohol parent ions is 315.1-415.1, and the detection limit of the method is 0.03-0.05 μ g/kg. The concentration level is relatively low, and some areas in the medium to low intensity range were not detected, indicating weak accumulation. The detection parameters and concentration distribution of various PFAS are closely related to their molecular structure. An increase in carbon chain length usually leads to a decrease in the detection limit of the method and an increase in soil adsorption capacity. PFOA and PFOS, as typical pollutants, have higher concentrations in various intensity zones.
Table 1 Quantitative ion pairs and detection limits of target PFAS compounds
| Target Compounds | Precursor Ion (m/z) | Product Ion (m/z) | LOD (μg/kg) | High-Intensity Areas (μg/kg) | Medium-Intensity Areas (μg/kg) | Low-Intensity Areas (μg/kg) |
| Short-chain Perfluorocarboxylic Acids | ||||||
| PFBA | 213.0 | 169.0* | 0.02 | 0.15–8.76 | 0.03–1.24 | ND–0.45 |
| PFPeA | 263.0 | 219.0* | 0.01 | 0.32–12.54 | 0.08–3.87 | ND–0.68 |
| PFHxA | 313.0 | 269.0* | 0.01 | 0.89–25.31 | 0.21–8.92 | 0.02–1.13 |
| Medium-chain Perfluorocarboxylic Acids | ||||||
| PFHpA | 363.0 | 319.0* | 0.005 | 1.25–34.67 | 0.45–12.56 | 0.07–3.25 |
| PFOA | 413.0 | 369.0* | 0.005 | 2.87–89.42 | 0.78–24.73 | 0.11–4.56 |
| PFNA | 463.0 | 419.0* | 0.005 | 0.94–12.58 | 0.27–4.35 | 0.03–0.98 |
| Long-chain Perfluorocarboxylic Acids | ||||||
| PFDA | 513.0 | 469.0* | 0.003 | 0.56–8.94 | 0.15–3.21 | ND–0.37 |
| PFUnDA | 563.0 | 519.0* | 0.002 | 0.21–4.62 | 0.06–1.85 | ND–0.12 |
| Perfluorosulfonic Acids | ||||||
| PFBS | 299.0 | 80.0/99.0△ | 0.03 | 0.48–14.35 | 0.12–5.43 | 0.01–0.89 |
| PFHxS | 399.0 | 80.0/99.0△ | 0.01 | 1.24–42.68 | 0.53–15.78 | 0.07–2.43 |
| PFOS | 499.0 | 80.0/99.0△ | 0.005 | 3.58–128.47 | 1.24–48.32 | 0.18–7.65 |
| Fluorotelomer Alcohols | ||||||
| 6:2 FTOH | 315.1 | 265.1* | 0.05 | 0.87–18.54 | 0.31–7.21 | ND–1.05 |
| 8:2 FTOH | 415.1 | 365.1* | 0.03 | 0.42–9.87 | 0.14–3.56 | ND–0.68 |
Note: * represents quantitative ions, △ represents qualitative ions
- Spatial distribution analysis method
Spatial distribution analysis is a key step in revealing the pattern of PFAS pollution in agricultural soil. GIS technology provides an effective tool for visualizing the spatial distribution of PFAS concentration, among which Kriging Interpolation method is widely used for accurately capturing spatial autocorrelation. This method is based on semi variogram analysis, and by modeling the spatial structure of concentration data at known sampling points, it can perform optimal unbiased estimation of unsampled areas while considering the distance and directional relationship between sample points. Importing the individual and total concentration data of PFAS into the GIS platform, after Kriging interpolation processing, a continuous concentration gradient distribution map can be generated, which clearly presents the clustering pattern of high-value areas, such as the attenuation characteristics of concentration gradients around chemical industrial parks and the distribution of pollution zones along irrigation water systems, providing intuitive basis for identifying pollution hotspots.
Specifically, based on the ArcGIS Pro 3.0 platform, a pollution spatial characterization system is constructed. The concentration surface is generated using the ordinary Kriging interpolation method to reconstruct the global PFAS concentration field. The semi variogram model is selected as the exponential model to fit the empirical semi variogram. The formula is expressed as:
（1）
Among them, c0 (block value)=0.18, c (base station value)=0.87, and a (range)=4.2 km.
The spatial gradient partition utilizes the natural breakpoint method to divide five levels of concentration gradient zones, and combines with the pollution source buffer zone to construct a spatial response model. Define the critical distance threshold using PFOS as an indicator and calculate the negative exponential decay function of the distance between the concentration and the pollution source.
The heterogeneity of soil PFAS spatial distribution can be characterized by quantitative indicators, and coefficient of variation (CV) and spatial autocorrelation analysis (Moran’s I) are commonly used measurement tools. The coefficient of variation reflects the degree of data dispersion through the ratio of standard deviation to mean. When the CV value is greater than 100%, it indicates strong spatial heterogeneity and is mostly related to the impact of point source pollution; A moderate CV value (25% -75%) suggests diffusion and mixing processes at the regional scale.
Spatial autocorrelation analysis (Moran’s I) is used to determine the spatial correlation pattern of pollutant concentrations. Positive Moran’s I values indicate the presence of spatial clustering, where high concentration areas are adjacent to high concentration areas or low concentration areas are adjacent to low concentration areas. Negative values reflect spatial dispersion, while zero values indicate random distribution.
The formula for calculating Moran’s I index is as follows:
（2）
Among them, the spatial weight matrix wij adopts inverse distance squared weighting
- Source analysis method
Source analysis is the core technical step in tracing the source of PFAS pollution, and the positive definite matrix factor analysis (PMF) model, as an unsupervised multivariate statistical method, has shown significant advantages in PFAS source analysis. This model takes the PFAS homologue concentration data of each sampling point as input, and decomposes the original data matrix into a source contribution matrix and a source component spectrum matrix. It can identify the main pollution sources and calculate their contribution rates without requiring prior information about the pollution sources. In the process of model construction, it is necessary to incorporate multivariate information such as the proportion characteristics and specific biomarkers of PFAS homologues. Through iterative optimization, the objective function is minimized, and the contribution rate matrix of each pollution source to different sampling points is finally output, such as the contribution ratio of industrial emission sources, agricultural input sources, atmospheric deposition sources, etc., providing clear targets for targeted prevention and control.
PMF decomposes the chemical species matrix X matrix to obtain the source contribution matrix G and the component spectrum matrix F, expressed as follows:
（3）
The constraint conditions are gik ≥ 0, fkj ≥ 0 (p is the number of factors, eij is the residual).
The reliability of source analysis results needs to be verified through uncertainty analysis, and Bootstrap method is an effective means to evaluate the robustness of PMF model analysis results. This method constructs multiple bootstrap samples by performing repeated sampling with replacement on the original dataset, runs PMF models separately, and obtains corresponding source contribution rate results. Then, by calculating the standard deviation and confidence interval of the contribution rate, the fluctuation range of the analytical results is determined. If the mean contribution rates of different bootstrap samples are stable and the 95% confidence interval is narrow, it indicates that the model’s analytical results have high robustness; On the contrary, if there is a large fluctuation, it is necessary to optimize and adjust the model parameters based on the emission characteristics of the pollution source and the regional pollution history, in order to improve the credibility of the analytical results.
The variance expression of factor contribution rate is as follows:
（4）
Among them, N=1000, gk, b is the contribution rate of the b-th resampling factor k.
The expression for the factor stability index is as follows
（5）
Among them, θ is the spectral angular distance of the factor component.
- Ecological risk assessment model
The ecological risk assessment model can quantify the potential harm of PFAS to agricultural ecosystems by integrating pollution concentration data, providing scientific basis for risk classification. Risk Quotient (RQ) and Potential Ecological Risk Index (RI) are classic indicators for measuring PFAS ecological risk, and their calculation requires the integration of soil environmental benchmark values and biotoxicological data. The risk quotient is determined by the ratio of the measured concentration of pollutants to the predicted ineffective concentration (PNEC). When RQ>1, it indicates the presence of potential ecological risks; The potential ecological risk index is obtained by weighting and summing the risk quotient of different PFAS monomers, and the weight coefficients are determined based on the toxicity response factors of each monomer. During the calculation process, it is necessary to refer to toxicological data such as the half effect concentration of PFAS on soil microorganisms, invertebrates, and crop roots in the soil, and calibrate the benchmark values based on regional soil physical and chemical characteristics to ensure the accuracy and regional applicability of risk assessment results.
The RQ calculation expression is as follows:
（6）
Among them, PEECi is the predicted environmental exposure concentration of pollutant i.
PNECj is the predicted ineffective concentration of pollutant i on receptor j, expressed as follows:
（7）
The bioavailability correction model is expressed as follows:
（8）
Among them, foc is the fraction of organic carbon content.
The RI calculation expression is as follows:
（9）
Among them, ωj is the weight of receptor j; Ti is the toxicity response factor of pollutant i.
As shown in Table 2, based on the RQ and RI calculation results, multi endpoint ecological risk classification can be carried out, and regional control priorities can be divided according to the risk level. In the process of risk classification, it is necessary to comprehensively consider the sensitivity differences of organisms at different trophic levels, as well as the bioaccumulation and amplification effects of PFAS, to ensure that the risk classification results can fully reflect the multi-level harm of pollutants to agricultural ecosystems.
Table 2 Ecological Risk Classification
| Risk Level | RQ Threshold Range | RI Composite Index Threshold | Management Priority |
| Low Risk | RQ < 0.1 | RI < 150 | Level Ⅳ (Routine Monitoring) |
| Medium Risk | 0.1 ≤ ∃RQ ≤ 1.0 | 150–300 | Level Ⅲ (Source Regulation) |
| High Risk | RQ > 1.0 or RQ > 0.5 | RI > 300 | Level Ⅰ (Emergency Remediation) |
IV. Results
- Spatial distribution
As shown in Figure 5, the statistical characteristics of PFAS total in different intensity zones exhibit significant gradient differentiation, reflecting the spatial heterogeneity of pollution distribution.
The detailed results are shown in Table 3. The total PFAS in the high-intensity zone ranges from 38.7 to 225.4, with a median of 126.8, significantly higher than that in the medium low intensity zone. This indicates that the region is strongly influenced by pollution inputs and may be closely related to the sustained release of nearby pollution sources. The coefficient of variation is 62%, which is at a moderate level, indicating that although there are some fluctuations in pollution concentration in the high-intensity zone, the overall high accumulation state remains relatively consistent. This is related to the relatively uniform pollution source impact range and environmental medium characteristics in the region. The spatial autocorrelation analysis shows that Moran’s I value is 0.51. Through significance testing, it indicates that the total amount of PFAS in high-intensity areas shows strong spatial clustering. The spatial distribution of pollutants is not random, but presents a pattern of adjacent high-value areas, further confirming the diffusion effect of point source pollution.
The total amount of PFAS in the moderate intensity zone ranges from 12.3 to 84.6, with a median of 47.2, which is about 37.3% of that in the high-intensity zone. This reflects a significant decline in pollution intensity with increasing distance. The coefficient of variation in this region has increased to 78%, higher than that in the high-intensity zone, indicating a decrease in the uniformity of pollution distribution in the medium intensity zone, which may be influenced by a combination of factors such as gradient changes in pollution source diffusion, interference from terrain and landforms on migration paths, and local input differences in agricultural activities. The Moran’s I value for spatial autocorrelation is 0.33, which also passed the significance test, but the value is lower than that of the high-intensity zone, indicating that its spatial clustering has weakened, but still maintains a certain degree of spatial correlation, reflecting the characteristics of the transition from point source pollution to surface source diffusion.
The total amount of PFAS in the low-intensity zone ranges from 1.8 to 19.4, with a median of only 8.5, which is much lower than that in the high and medium intensity zones and close to the background level, indicating that the area is less directly affected by pollution sources. Its coefficient of variation is as high as 129%, showing strong spatial heterogeneity, which is related to the complexity of pollution sources in low-intensity areas, possibly including dispersed input pathways such as long-distance atmospheric transmission and sporadic use of agricultural inputs, resulting in irregular fluctuations in pollutant concentrations in local areas. The spatial autocorrelation Moran’s I value is 0.07, which did not pass the significance test, indicating that the total amount of PFAS in the low-intensity zone exhibits a random distribution in space, further confirming the dispersion and uncertainty of pollution driving factors in this region.
Table 3 Statistical characteristics of total PFAS in different intensity zones (μ g/kg)
| Zoning | Range | Median | CV | Moran’s I |
| High-Intensity Zone | 38.7–225.4 | 126.8 | 62% | 0.51** |
| Medium-Intensity Zone | 12.3–84.6 | 47.2 | 78% | 0.33** |
| Low-Intensity Zone | 1.8–19.4 | 8.5 | 129% | 0.07 |
Note: * * indicates P<0.01, Z>2.58; N=36/zone
Figure 5 Schematic diagram of total PFAS statistics in different intensity zones
- Source analysis
As shown in Table 4, the results of the PFAS pollution source contribution table clearly reveal the differences in the contribution of different sources to the accumulation of PFAS in the environment and their characteristic identification. Industrial emission sources have become the primary source of pollution with an average contribution rate of 38.7%, and the 95% confidence interval [36.2,41.3] shows that their contribution ratio has high stability. The characteristic compound combination of this source is PFOS, PFOA, and PFNA, with a total proportion of 85 ± 6%, and the ratio of PFOS to PFOA remains stable at 1.24 ± 0.18. This fingerprint feature is directly related to the use of fluoropolymers and fire retardants in industrial production, confirming the dominant role of industrial activities in PFAS pollution.
The contribution rate of municipal sludge from agricultural sources is 27.3%, and the confidence intervals [24.1, 30.5] indicate that its contribution level is second only to industrial emission sources. This source is characterized by a combination of short chain perfluorocarboxylic acids (PFBA, PFHxA) and fluoroalcohols (6:2 FTOH), accounting for a total of 73 ± 9%. The ratio of PFBA to PFOS is as high as 6.8 ± 1.2, reflecting the enrichment characteristics of short chain PFAS in municipal sludge. This is consistent with the rule that short chain compounds are more likely to remain in sludge during sewage treatment, indicating that agricultural use of sludge is an important pathway for PFAS input into agricultural land.
The average contribution rate of manure application sources is 19.5%, with confidence intervals [16.7,22.3] indicating moderate contribution intensity. The characteristic compounds include PFPeA, PFHpA, and 8:2 FTOH, accounting for a total of 68 ± 11%. The ratio of 6:2 FTOH to PFOA is 2.1 ± 0.5, which is its significant fingerprint. This feature is related to the metabolic transformation of fluorine-containing additives added to livestock and poultry feed, indicating that improper application of manure in agricultural production may lead to the accumulation of PFAS in the soil.
The contribution ratio of atmospheric deposition sources is the lowest, at 14.5%, and the confidence intervals [11.8, 17.2] reflect that their contribution is relatively stable. The characteristic combination of this source is PFHxS, PFNA, and PFUnDA, accounting for a total of 62 ± 14%. The ratio of PFNA to PFHxS is 0.37 ± 0.09, which has clear identification. This result is consistent with the process of dry and wet deposition of PFAS after long-distance atmospheric transmission. Long chain compounds are more likely to enter the soil through deposition due to their strong particle adsorption, reflecting the contribution characteristics of widespread pollution.
Table 4 Source Analysis Results
| Source Type | Average Contribution Rate (%) | 95% Confidence Interval (%) | Characteristic Compound Combination | Source Characteristic Fingerprint Ratio |
| Industrial Emission Source | 38.7 | [36.2, 41.3] | PFOS/PFOA/PFNA (85±6%) | PFOS/PFOA = 1.24±0.18 |
| Municipal Sludge Agricultural Source | 27.3 | [24.1, 30.5] | PFBA/PFHxA/6:2 FTOH (73±9%) | PFBA/PFOS = 6.8±1.2 |
| Manure Application Source | 19.5 | [16.7, 22.3] | PFPeA/PFHpA/8:2 FTOH (68±11%) | 6:2 FTOH/PFOA = 2.1±0.5 |
| Atmospheric Deposition Source | 14.5 | [11.8, 17.2] | PFHxS/PFNA/PFUnDA (62±14%) | PFNA/PFHxS = 0.37±0.09 |
- Ecological risk assessment
As shown in Table 5, the results of the PFAS ecological risk table clearly present the gradient differences of ecological risks in different intensity zones and the sensitivity differentiation of biological groups. The RI index range of the high-intensity zone is 312-687, with a median of 461, and 100% of the area is at a high-risk level, indicating that the region is facing significant ecological threats. From the perspective of biospecific risk, the RQ value of earthworms for PFOS is 1.56, and the RQ value of rice for PFOS is 2.7, both exceeding the ecological safety threshold. This indicates that soil invertebrates and crops have been exposed to substantial risks in high-intensity areas, which is directly related to the accumulation of high concentrations of PFAS dominated by industrial emission sources.
The RI index in the moderate intensity zone has dropped to 158-413, with a median of 279. 71.4% of the area is still classified as high-risk, while 28.6% is classified as moderate risk, indicating a transitional risk level. The risk contribution of this region has shown new changes, with the RQ value of vegetables for PFBA rising to 1.5, becoming a significant risk point, while the RQ values of earthworms and rice, although decreasing in the high-intensity zone, are still close to or slightly above the threshold. This feature is related to the short chain PFAS input of municipal sludge agricultural sources. In the medium intensity zone, agricultural activities are intensive, and sludge application leads to the enrichment of short chain compounds in the vegetable planting layer, forming a differentiated risk pattern.
The RI index of low-intensity areas further decreased to 42-305, with a median of 134. The risk level is mainly medium risk, with 25.0% of low-risk areas and only 8.3% of high-risk areas remaining. The RQ values of various organisms are all below 1.0, among which the RQ values of PFOS for microorganisms and soil animals are 0.09 and 0.17, respectively, indicating a significant alleviation of ecosystem pressure. However, it should be noted that the mean RI in this area is still higher than the median, indicating that local areas may form risk hotspots due to the accumulation of long-chain compounds from atmospheric deposition, reflecting the potential impact of widespread pollution.
Table 5 Ecological Risk Assessment
| Indicator | High-Intensity Zone (<3 km) | Medium-Intensity Zone (3–10 km) | Low-Intensity Zone (>10 km) |
| RI Index | |||
| – Range | 312–687 | 158–413 | 42–305 |
| – Median (Mean±SD) | 461 (458±121) | 279 (287±69) | 134 (196±58) |
| RQ (Microorganisms) | |||
| – PFOS | 0.92 [0.45–1.87] | 0.51 [0.18–0.93] | 0.09 [ND–0.32] |
| – PFHxS | 0.83 [0.31–1.64] | 0.62 [0.25–1.02] | 0.14 [ND–0.41] |
| RQ (Soil Fauna) | |||
| – Earthworm (PFOS) | 1.56 [0.78–3.21]* | 0.87 [0.32–1.54] | 0.17 [ND–0.43] |
| – Springtail (PFOA) | 1.03 [0.49–2.17] | 0.63 [0.24–1.12] | 0.12 [ND–0.38] |
| RQ (Crops) | |||
| – Rice (PFOS) | 2.7 [1.8–4.3]** | 1.2 [0.7–1.9] | 0.3 [0.1–0.6] |
| – Vegetables (PFBA) | 0.9 [0.4–1.6] | 1.5 [0.9–2.4]* | 0.4 [0.1–0.8] |
| Proportion of Risk Levels | |||
| – High Risk (RI>300) | 100% | 71.4% | 8.3% |
| – Medium Risk (150≤RI≤300) | 0% | 28.6% | 66.7% |
| – Low Risk (RI<150) | 0% | 0% | 25.0% |
V. Conclusion
This study focuses on agricultural land in Taicang City, Jiangsu Province. The results show that in terms of spatial distribution, the total amount of PFAS exhibits significant gradient differentiation. The range of PFAS total amount in high-intensity areas is 38.7-225.4 μ g/kg, with a median of 126.8 μ g/kg, indicating strong spatial clustering; The total amount in the moderate intensity zone decreased to 12.3-84.6 μ g/kg, with a median of 47.2 μ g/kg, indicating a decrease in spatial clustering; The total amount of low-intensity areas is only 1.8-19.4 μ g/kg, with a median of 8.5 μ g/kg, showing a random distribution and a coefficient of variation of 129%, reflecting the dispersion of pollution sources. In terms of source analysis, industrial emission sources are the primary contributing source, with an average contribution rate of 38.7%. The characteristic compounds are PFOS, PFOA, and PFNA, and the ratio of PFOS to PFOA remains stable at 1.24 ± 0.18; Next is the agricultural source of municipal sludge, characterized by short chain perfluorocarboxylic acid and 6:2 FTOH, with a PFBA to PFOS ratio of 6.8 ± 1.2; The contributions of manure application sources and atmospheric deposition sources are relatively low, identified by PFPeA, 8:2 FTOH, PFHxS, and PFNA, respectively. In terms of ecological risk, the high-intensity zone is 100% at a high-risk level, with earthworms having an RQ of 1.56 for PFOS and rice having an RQ of 2.7 for PFOS, both exceeding the safety threshold; 71.4% of the moderate intensity zone is high-risk, 28.6% is moderate risk, and the RQ of vegetables for PFBA has risen to 1.5, becoming the main risk point; The low-intensity zone is mainly medium risk, with 8.3% being high risk. The RQ of various organisms is below 1.0, but there are local risk hotspots caused by atmospheric deposition.
Based on the research findings, in order to effectively prevent and control PFAS pollution in agricultural land and reduce ecological risks, the following suggestions are proposed: firstly, implement precise zoning control. For high-intensity areas, priority should be given to controlling industrial emissions, strictly prohibiting the use of polluted wastewater for irrigation and sludge for agriculture, and conducting soil remediation technology pilot projects; In the medium intensity zone, the application of manure and sludge should be standardized, the input of short chain PFAS should be reduced, and the monitoring of irrigation water quality should be strengthened; Low intensity areas need to pay attention to long-distance pollution caused by atmospheric deposition and establish a wide area monitoring network. Secondly, we should strengthen multi-source collaborative governance. In the industrial sector, we should promote the upgrading of production processes for fluorine-containing products, strictly control the discharge of wastewater and waste gas, and develop fluorine free alternative materials; The agricultural sector needs to establish limit standards for PFAS in organic fertilizers and sludge, promote low fluoride pesticides and fertilizers, and reduce agricultural input; At the same time, we will strengthen the research and development of soil water atmosphere multi-media migration blocking technology. The third is to improve the risk assessment and warning system, optimize the risk assessment framework for agricultural scenarios, increase specific indicators such as soil microbial community changes and crop enrichment coefficients, and establish a risk warning model for the entire chain of soil, crops, and human body; For high-risk areas, regularly monitor the PFAS content in agricultural products to ensure food safety. The fourth is to improve policies and technical support, formulate differentiated prevention and control policies based on regional pollution characteristics, and include PFAS in the key control list of soil pollution; Increase investment in research and development of low-cost remediation technologies, promote the transformation and application of technological achievements, and provide guarantees for sustainable agricultural development.
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