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# In situ occurrence and mobility of per and polyfluoroalkyl substances in soils amended with organic waste products

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Aurélia Marcelline Michaud<sup>a</sup>, Toyin Dunsin Saliu<sup>b</sup>, Gabriel Munoz<sup>b,c</sup>, Frédéric Feder<sup>d,e</sup>, Valérie Sappin-Didier<sup>f</sup>, Françoise Watteau<sup>g</sup>, Sabine Houot<sup>h</sup>, Sébastien Sauvé<sup>b,*</sup>

## HIGHLIGHTS

* Soil PFAS concentration and stock increased differently according to the type of organic waste products inputs.

* Short-chain PFCAs were likely produced from precursor transformation.

* PFAS leached in waters, with majority of short-chain anionic PFCAs.

* Soil/water distribution coefficients decreased with increasing water pH.

## GRAPHICAL ABSTRACT

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## ARTICLE INFO

Editor: Sergi Diez Salvador

### Keywords:

* Compost

* Municipal sludge

* Manure

* Long-term field experiment

* Soil/water distribution coefficient

* PFAS precursors

## ABSTRACT

We evaluated the in situ occurrence and soil-water distribution of 75 anionic, zwitterionic, and cationic per- and polyfluoroalkyl substances (PFAS) in soils from five field experimental sites distributed in different climatic regions in France. Four sites received agronomical doses of organic waste products (OWPs, ~5–10 t/ha per application) with repeated inputs between 1974 and 1996 (2 historical sites) and 1998–2018 (2 on-going sites), while one site received about two-fold larger amounts. Control soils without OWP application had detectable yet low PFAS levels, the Σ75PFAS remaining in most cases below 1 µg/kg. Soils amended with municipal sludge or urban composts exhibited the largest Σ75PFAS increase relative to controls (~2–20 µg/kg), with soils receiving biowaste composts displaying the lowest Σ75PFAS (~2 µg/kg). In most cases, Σ75PFAS increased significantly with time. While perfluorooctane sulfonate (PFOS) typically dominated the PFAS profiles in municipal sludge-

<footnote><sup>a</sup> INRAE, UMR SAS, Sol Agro et hydrosystème Spatialisation, 35000 Rennes, France</footnote>

<footnote><sup>b</sup> Département de Chimie, Université de Montréal, Montréal, QC H2V 0B3, Canada</footnote>

<footnote><sup>c</sup> Centre d'expertise en analyse environnementale du Québec, Ministère de l'Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs, Québec, QC G1P 3W8, Canada</footnote>

<footnote><sup>d</sup> CIRAD, UPR Recyclage et Risque, F-34398 Montpellier, France</footnote>

<footnote><sup>e</sup> Recyclage et Risque, Université Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>f</sup> ISPA, Bordeaux Sciences Agro, INRAE, F-33140 Villenave d'Ornon, France</footnote>

<footnote><sup>g</sup> INRAE, Laboratoire Sols et Environnement, Université de Lorraine, 54000 Nancy, France</footnote>

<footnote><sup>h</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 91120 Palaiseau, France</footnote>

<footnote>* Corresponding author.
E-mail address: sebastien.sauve@umontreal.ca (S. Sauvé).</footnote>
amended soils, the other prevalent PFAS classes varied with sites and years: soils from older sites also had anionic and cationic electrochemical fluorination-derived precursors (e.g., EtFOSAA and PFOSAmS), while on-going sites had increased prevalence of short-chain perfluoroalkyl carboxylates (PFCAs) (3 sites) and perfluoroalkyl phosphinates (1 site). Interestingly, 6:2 fluorotelomer sulfonamidopropyl betaine (6:2 FTAB), a major zwitterionic precursor found in French municipal sludge, was only detected at low levels in soils, indicating its transformation to degradation products. Leaching waters at a depth of 45 cm in the experimental plots had high levels of short-chain PFCAs (Mean C3-C5: 120–160 ng/L; Max C3-C5: 900–1600 ng/L), suggesting that land applied OWP containing PFCA precursors are important contributors to long-term groundwater contamination.

# 1. Introduction

A critical challenge faced by agriculture is to produce enough food to supply a growing population while ensuring its safety, in the evolving context of climate change (Richard et al., 2019). Recycling agricultural organic wastes is an age-old practice for fertilising crops. Organic waste products (OWP) from industry and urban areas have been transformed, mixed, composted and then recycled in agriculture. Such agricultural practice contributes to the circular economy through the recycling of nutrients and organic matter contained in those organic materials, all the while improving soil health and crop productivity; it also represents a valuable alternative to their landfilling and incineration (Richard et al., 2019; Bolan et al., 2021a; Moinard et al., 2021). Separate collection and biological treatment of biowaste is strongly recommended and becomes mandatory in France (France, 2020).

Depending on the origin of raw materials entering into their initial composition, land-applied OWP can, however, also introduce pathogens (e.g., *Escherichia coli*), inorganic contaminants (e.g., cadmium, lead), and organic contaminants such as residues of personal care products (e.g., pharmaceuticals and steroid hormones), polycyclic aromatic hydrocarbons, plasticizers (e.g., bisphenols) and per- and polyfluoroalkyl substances (PFAS) (Houot et al., 2014; Bourdat-Deschamps et al., 2017; Michaud et al., 2020; Munoz et al., 2021; Bolan et al., 2021a; Pepper et al., 2021; Adu et al., 2023; Saliu and Sauvé, 2024). PFAS are emerging contaminants of high current concern due to their widespread industrial uses and the risks they pose to human and environmental health (Buck et al., 2011; Podder et al., 2021; Evich et al., 2022). Due to their chemical stability, the concentrations of PFAS are expected to increase in OWP-amended soils with repeated inputs, akin to the accumulation of heavy metals (Cambier et al., 2019; Michaud et al., 2020; Bolan et al., 2021a).

When PFAS are released in terrestrial environments, they will usually leach from soils to natural waters (surface, groundwater), highlighting the need to understand PFAS behaviour in the subsurface environment (Sharifan et al., 2021). Their fate will depend on the properties of the particular PFAS (e.g., hydrophobicity, chain length, functional groups) as well as soil physicochemical properties (e.g., textural class, particle size distribution, pH, type and concentration of organic matter, mineralogical composition, among other factors) (Nguyen et al., 2020). It will also depend on several biogeochemical processes occurring in soils, including degradation of precursors and related perfluoroalkyl acids (PFAA) production over time, volatilization, adsorption and potential accumulation in the surface soil, partitioning (e.g., between soil/water), and leaching in the subsurface soil/waters (Sepulvado et al., 2011; Sharifan et al., 2021). Adsorption of PFAS to soil particles is an important factor influencing their retention and transport in the subsurface, and their concentration in soil solution (Brusseau, 2019; Mei et al., 2021). Furthermore, plants can take up PFAS from soils and waters possibly by both passive and active processes depending on PFAS concentration as well as carbon chain length, with a high percent of active uptake at lower PFAS concentrations and shorter carbon chain length (Sima and Jaffé, 2021; Qi et al., 2022). Information on the levels and distribution of PFAS between soil and soil solution is essential to understand the risks to the environment and uptake by plants. In that respect, the soil/water partitioning coefficient ($K_d$), which is sometimes

corrected for organic carbon content ($K_{OC}$), can provide an indication of the potential for leaching and related availability for organic and inorganic contaminants (Sepulvado et al., 2011; Sauvé et al., 2000, 2003).

The major PFAS exposure pathway for animals and humans is through the diet and drinking water; in the latter respect, soil contamination is an important intermediate compartment especially for drinking water produced from groundwater sources (Sörenkörd et al., 2022a). There are many reports worldwide on the occurrence and fate of PFAS in surface and groundwater especially around known major sources (Loos et al., 2010; Munoz et al., 2015, 2017; Sardina et al., 2019; Schmidt et al., 2019), such as the use of aqueous film-forming foams (AFFF) (Sharifan et al., 2021). A few studies have attempted to study PFAS concentrations in background soils (Rankin et al., 2016; Brusseau et al., 2020; Sörenkörd et al., 2022a), as well as concentrations in agricultural soils contaminated through indirect sources such as land application of contaminated or municipal biosolids/sludge (Sepulvado et al., 2011; Rankin et al., 2016; Pepper et al., 2021; Röhler et al., 2023). Furthermore, there are limited studies investigating PFAS leaching in drainage systems, piezometers and columns of biosolid-amended soils (Gottschall et al., 2010; Sepulvado et al., 2011; Gottschall et al., 2017; Gobelius et al., 2018; Pepper et al., 2021; Röhler et al., 2021; Schaefer et al., 2022; Röhler et al., 2023). Batch adsorption experiments/isotherms were conducted to evaluate the soil/water partitioning coefficients of PFAS in mineral soils and pure phases of soil constituents (Sepulvado et al., 2011; Milinovic et al., 2015; Li et al., 2019; Nguyen et al., 2020; Sima and Jaffé, 2021). Nevertheless, soil-water partitioning coefficients derived from adsorption experiments do not always predict desorption behaviour and mobility under field conditions (Sepulvado et al., 2011; Nguyen et al., 2020). Recently, Levine et al. (2023) studied the leaching of 7 PFAS in soil column materials amended with composts of biosolids and manure. These studies documented data for at least 3 PFAS (Milinovic et al., 2015) up to a maximum of 34 PFAS (Nguyen et al., 2020). Virtually no study has documented the occurrence, potential accumulation and leaching of PFAS in soils amended with various OWP recycled under field conditions.

In the present work, we aimed at studying in situ and under agro-nomical conditions, the long-term effect of repeated land-application of various OWP on soil PFAS occurrence and mobility. We tested the following hypotheses: (i) OWP inputs influence the PFAS concentrations observed in soils, as a function of the origin of raw materials and history of inputs; (ii) there is transformation of precursors of PFAS in soils; and (iii) short chain PFAS are more mobile with lower soil/water partitioning coefficients. For this purpose, we studied the occurrence of 75 PFAS including zwitterionic, cationic and anionic precursors, in historic soils and soil waters sampled between 1978 and 2018 across five long-term field experimental sites. Those five field experiments received the OWP previously characterized for PFAS in Munoz et al. (2021), including dairy cattle manure (composted or not), compost and digestate of biowastes, municipal sludge (composted or not), and compost of municipal solid wastes.
## 2. Materials and methods

### 2.1. Sites description

This study was conducted within the French observatory INRAE-SOERE PRO (SOERE PRO, 2015). It is a network of long-term field experiments (i.e., randomized block-devices), monitoring at the plot-scale the evolution of agro-systems receiving OWP. The five field experiments were set up in 1974 and 1996 for the historical ones, i.e. Couhins – C (Bordeaux, Nouvelle-Aquitaine) and La Bouzule – B (Nancy, Grand-Est), and from 1998 to 2013 for the on-going ones, i.e. QualiAgro – QA (Feucherolles, Ile-de-France), PROspective – PSP (Colmar, Grand-Est) and La Réunion – RUN (French Overseas, southwestern Indian Ocean). C and B are historical sites since the application of OWP stopped, respectively, in 1993 and 1996, whereas the other sites are still active.

Each site investigates the long-term effects of repeated applications of various OWP on soil fertility and potential contamination with inorganic and organic contaminants. Table 1 summarizes the site description and the abbreviations used in illustrations (beginning year, actual status, surface, treatments, OWP average inputs, rotation, climate and main initial soil properties).

Overall, six different types of OWP were studied in the five experiments, in comparison with controls receiving no organic amendment (CN; all sites) or digestate of biowastes (CN_D; PSP), as follows (see also Supporting Information S1 for OWP composition): municipal sewage sludge (SLU); compost of green wastes and sewage sludge (C-GWS); compost of municipal biowastes (C-BIOW); compost of residual municipal solid waste (C-MSW); farmyard manure of dairy cattle (FYM-DC); and compost of farmyard manure of dairy cattle (C-FYM-DC). In all field experiments, all treatments were replicated 4 times. OWP application was made (i) every two years for composts/manure and every year for sewage sludge, and (ii) from agronomical usual doses of 5–10 tons per hectare of dry matter (DM) at 4 sites, to about two-fold larger amount with an average dose of 14.3 tons DM per hectare at 1 site (QA) in order to increase OWP effect on soil organic carbon increase. For the same type of OWP (e.g. municipal sewage sludge), their origin differed in each field experiments. Additional mineral nitrogen fertilization was applied at doses between 0 and 200 kg N per hectare to maintain per field-experiment equivalent nitrogen level between treatments. Additional mineral fertilization occurred at some sites (potassium at sites C, B and RUN; and phosphorus at sites B, PSP and RUN). All treatments and plots received similar pesticides treatments and cropping practices at each site.

### 2.2. Soil sample collections at INRAE-SOERE PRO network

Soil samples were collected at the five sites of the SOERE PRO network in the same conditions as those of OWP studied in Munoz et al. (2021). They were sampled before OWP field application (just before spreading to the agricultural fields) for analysis of classical parameters and long-term storage (i.e., archived samples). Each plot was sampled in the topsoil layer between 0 and 25 cm depth in C, B, QA and PSP sites and between 0 and 10 cm depth in RUN site (with 5–10 spadesful further pooled).

Soil samples were analysed at the sampling period by central soil testing laboratories according to current standardized procedures (see S1 for details). For PFAS analysis, archived samples were aliquoted in 50 mL polypropylene falcon tubes (~40 mL per sample) prior to shipping to the UdeM laboratory. Once received at the laboratory facilities, soil samples were crushed with mortar and pestle and sieved (2 mm mesh) prior further preparation.

Overall, 117 individual samples of soil were targeted for this study, as detailed in Table 2. Among urban OWP treatments, municipal sewage sludge (SLU; n = 26) included both soil samples collected at historical sites and those from more recent years collected at on-going sites. The other soil samples of urban OWP treatments were collected from historical and on-going sites, including compost of green wastes and municipal sewage sludge (C-GWS, n = 20), compost of municipal biowastes (C-BIOW, n = 12), and compost of residual municipal solid waste

<caption>Table 2</caption>

Soil samples information, with the number of replicates, studied treatments and sampling years.

<table><thead><tr><th></th><th>Couhins</th><th>Bouzule</th><th>QualiAgro</th><th>PROspective</th><th>Reunion</th></tr></thead><tbody><tr><td>Replicates</td><td>3</td><td>3</td><td>3</td><td>3</td><td>2</td></tr><tr><td>Treatments</td><td>SLU, CN</td><td>SLU, C-GWS, C-MSW, CN</td><td>C-GWS, C-BIOW, C-MSW, FYM-DC, CN</td><td>SLU, C-GWS, C-BIOW, FYM-DC, C-FYM-DC, CN-D</td><td>SLU, C-GWS, CN</td></tr><tr><td>Years</td><td>1978–1998<sup>a</sup></td><td>1997 and 1998<sup>b</sup></td><td>2013 and 2014<sup>c</sup></td><td>2010 and 2018<sup>d</sup></td><td>2018<sup>e</sup></td></tr></tbody></table>

<caption>Table 1</caption>

Site description and initial soil properties, with the following data presented: beginning year, actual status, surface in hectare; studied treatments; average OWP inputs in t/ha (dry matter) per spreading; main crops; climate; soil properties in the upper layer with pH (water), cationic exchange capacity (CEC) in cmol+/kg, content in organic matter (OM), clay, silt and sand expressed in %.

<table><thead><tr><th></th><th>Couhins (C)</th><th>Bouzule (B)</th><th>QualiAgro (QA)</th><th>PROspective (PSP)</th><th>Reunion (RUN)</th></tr></thead><tbody><tr><td>Beginning</td><td>1974</td><td>1996</td><td>1998</td><td>2000</td><td>2013</td></tr><tr><td>Status</td><td>Fallow</td><td>Fallow</td><td>On-going</td><td>On-going</td><td>On-going</td></tr><tr><td>Surface</td><td>0.05</td><td>0.34</td><td>6</td><td>2</td><td>1.2</td></tr><tr><td>Treatments*</td><td>SLU, CN</td><td>SLU, C-GWS, C-MSW, CN</td><td>C-GWS, C-BIOW, C-MSW, FYM-DC, CN</td><td>SLU, C-GWS, C-BIOW, FYM-DC, C-FYM-DC, CN-D</td><td>SLU, C-GWS, CN</td></tr><tr><td>OWP inputs</td><td>10</td><td>10</td><td>14.3</td><td>5.0</td><td>6.1</td></tr><tr><td>Main crops</td><td>Maize</td><td>Maize, wheat, rapeseed</td><td>Maize, wheat</td><td>Maize, wheat, barley, sugar beet</td><td>Sugar cane</td></tr><tr><td>Climate</td><td>Oceanic</td><td>Semi-continental</td><td>Oceanic</td><td>Semi-continental</td><td>Tropical</td></tr><tr><td>Soil type</td><td>Luvisol</td><td>Calcisol</td><td>Luvisol</td><td>Calcisol</td><td>Nitisol</td></tr><tr><td>pH</td><td>5.6</td><td>7.5</td><td>7.1</td><td>8.3</td><td>6.1</td></tr><tr><td>CEC</td><td>3.5</td><td>15.0</td><td>9.7</td><td>17.1</td><td>10.6</td></tr><tr><td>OM</td><td>2.8</td><td>3.0</td><td>1.8</td><td>2.5</td><td>3.6</td></tr><tr><td>Clay</td><td>4</td><td>31</td><td>15</td><td>24</td><td>55</td></tr><tr><td>Silt</td><td>7</td><td>57</td><td>78</td><td>69</td><td>35</td></tr><tr><td>Sand</td><td>89</td><td>12</td><td>7</td><td>7</td><td>10</td></tr></tbody></table>

<footnote><sup>a</sup> After 4, 12, 19 and 20 OWP inputs.</footnote>

<footnote><sup>b</sup> After 1 and 3 OWP inputs.</footnote>

<footnote><sup>c</sup> After 8 and 9 OWP inputs.</footnote>

<footnote><sup>d</sup> After 5 and 9 OWP inputs except for FYM-DC, C-FYM-DC and CN_D treatments at PSP site sampled one year (2018).</footnote>

<footnote><sup>e</sup> After 4 inputs of SLU and 2 inputs of C-GWS.</footnote>

<footnote>*Treatments: SLU (Sewage sludge), C-GWS (Compost of green wastes and sewage sludge), C-BIOW (Compost of municipal biowastes), C-MSW (Compost of residual municipal solid waste), FYM-DC (Farmyard manure of dairy cattle), C-FYM-DC (Compost of farmyard manure of dairy cattle), CN (Control with no organic amendment), CN-D (Control with digestate).</footnote>
(C-MSW, n = 12). Twelve soil samples receiving livestock manure were sampled at active sites, including farmyard manure of dairy cattle (FYM-DC, n = 9) and compost of farmyard manure of dairy cattle (C-FYM-DC, n = 3).

## 2.3. Water sample collections at INRAE-SOERE PRO network

Water samples were collected at the three on-going sites of the SOERE PRO network with in situ lysimeters with fibreglass wicks in QA and PSP collecting leaching waters from the upper layer of the soil receiving OWP (Cambier et al., 2014), and with water/soil extractions in RUN. Water samples were analysed at the sampling/extracted period by central soil testing laboratories according to current standardized procedures used in INRA/INRAE and CIRAD.

Overall, 27 individual samples of water were targeted for this study, as detailed in Supporting Information S1 and summarised below.

In QA, in situ soil solutions were sampled at a depth of 45 cm under the topsoil layer and 100 cm during the drainage seasons. For PFAS analyses, frozen archived samples available were screened, with: 3 treatments at 45 cm depth, 3 months after OWP inputs in December 2011; 5 treatments at 45 and 100 cm depth in 2013/2014 3–8 months after OWP inputs.

In PSP, in situ soil solutions were sampled at a depth of 45 cm under the topsoil layer during the drainage seasons. For PFAS analyses, frozen available archived samples were screened, with: 4 treatments sampled at 45 cm depth in January 2018, 13 months after OWP inputs.

In RUN, estimated soil solutions were obtained from water/soil extractions with: 3 treatments corresponding to 10 cm depth, 11 months after OWP inputs.

## 2.4. PFAS chemical analyses

Soil sample preparation was adapted from optimized procedures aiming at the enhanced recovery of zwitterionic, cationic and anionic PFAS from soil (Munoz et al., 2018; Liu et al., 2021a, 2021b). The previously sieved and ground soil samples were homogenized again with a stainless-steel spatula prior to the sample intake for PFAS extraction. A 2-g dry weight aliquot of soil was introduced in a 15-mL polypropylene (PP) tube and spiked with surrogate isotope-labelled internal standards (ILIS). After a wait time of 1 h, two extraction cycles were performed, each consisting in adding 5 mL of extracting solution (50 mM ammonium acetate in methanol [MeOH]) and subjecting the samples to high-speed vortex mixing (3200 rpm) for 0.5 min and ultrasonication for 20 min. After centrifugation (6000 rpm) for 10 min, the supernatant was transferred to a fresh 15-mL PP tube. A final rinse step was performed by adding 2.5 mL of extracting solution to the remnant solids and combining the supernatant (following centrifugation) with the two previous extract fractions. Samples were then concentrated to ~2 mL (N₂, mild heating) and filtered through ENVI-Carb cartridges (250 mg) for clean-up.

Water samples were prepared using a procedure adapted from Kaboré et al. (2018). The entire content of the sample was used for analysis (available volume: ~40–100 mL, depending on the sample). Samples were spiked with surrogate ILIS and were subsequently concentrated using solid-phase extraction (Strata X-AW, 200 mg).

Following further concentration and fortification with injection ILIS, extracts of soil and water samples were aliquoted onto PP LCMS vials and ultrapure water was added to reach a final composition of 80:20 (v/v) MeOH/H₂O (Martin et al., 2019). Instrumental analysis was conducted at the Université de Montréal, using ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS Q-Exactive Orbitrap, Thermo Scientific, Waltham, MA, USA). A 15-µl aliquot of the sample was injected into a C18 column for analyte separation (Thermo Hypersil Gold C18, 100 mm × 2.1 mm, 1.9 µm particle size). Data were acquired in both negative and positive electrospray ionization modes using Full Scan MS, and Orbitrap parameters

were set as follows: AGC target 3 × 10⁶; maximum IT 50 ms; resolution 70,000 FWHM at m/z 200; mass scan range m/z 150–1000.

## 2.5. Quality assurance/quality control

Blanks were executed for each sample batch using 2 g of Ottawa sand (for soil samples) and 100 mL of commercial bottled water produced from reverse-osmosis (for water samples) following the respective extraction procedures; when PFAS were detected in blanks, the corresponding levels were subtracted from those of field samples in the corresponding batch.

The solid-phase extraction cartridge used for the pre-concentration of water samples was selected based on previous studies, with absolute extraction efficiency (without surrogate ILIS correction) in the range of 40–120 % for most PFAS and whole-method accuracy (with surrogate ILIS correction) within 70–120 % in tap water and surface water (Teymoorian et al., 2025). For soils, an optimized solvent was used to enhance the recovery of positive ion mode PFAS. For most PFAS, this resulted in absolute extraction efficiency and whole-method accuracy in the range of 70–120 % in diverse soils and sediments (Munoz et al., 2018; Munoz et al., 2025).

Eight-point based calibration curves were used for PFAS quantification, with determination coefficients required to be better than R² ≥ 0.9900. Continued calibration verification (CCV) samples were prepared in neat solvent at an intermediate in-vial level of 2.5 ng/mL, and their accuracy was required to fall within a performance objective of 70–130 %. Concentrations in the field samples were derived from the analyte to surrogate ILIS ratio in the field sample, the calibration curve equation, and the exact sample intake.

## 2.6. Calculations

Organic waste products have been incorporated into the topsoil layer. Per spreading, the estimated OWP inputs of PFAS, i.e. Q (OWP input) expressed in gram per hectare, are calculated from the PFAS concentration analysed in OWP (Munoz et al., 2021) and the measured input amounts of each OWP expressed in ton per hectare of dry matter for the corresponding spreading.

The estimated soil stocks of PFAS, i.e. PFAS amounts expressed in gram per hectare (and over a depth of 20 cm), are calculated from the following equation:

$$Q (\text{topsoil, i}) = [\text{PFAS}]_{\text{topsoil, i}} \times h \times d \times 10 \quad (1)$$

where [PFAS] is the PFAS concentration (µg/kg or mg/t), topsoil refers to the topsoil layer with the fixed value height (h) of 0.2 m, i to the treatment, d to the soil density with a fixed value of 1.3 (Cambier et al., 2019; Michaud et al., 2020; Chen et al., 2022).

Per field-experiment and sampling year, the actual amount of PFAS in topsoil resulting from one OWP spreading is estimated by calculating the part of the PFAS stock in amended-topsoil corresponding to OWP inputs expressed in gram per hectare, with the following equation:

$$Q (\text{Actual OWP topsoil}) = \frac{Q (\text{OWP}) - Q (\text{CN})}{n} \quad (2)$$

where Q (OWP) refers to the stock of ∑ 75 PFAS estimated in OWP amended-topsoil, Q (CN) refers to the stock of ∑ 75 PFAS estimated in control topsoil, and n refers to the number of applications before the soil sampling date.

The solid-solution distribution coefficient (Kₐ) is evaluated using the ratio between the total concentration of an element in the solids (in µg/kg) over the concentration of solution dissolved element (µg/L). The resulting Kₐ coefficient (Eq. (3)) is therefore reported in (L/kg) and can be expressed in log values (Sauvé et al., 2003).
$$Kd = \frac{[PFAS]_{\text{topsoil}, i}}{[PFAS]_{\text{water}, i}} \qquad (3)$$

where [PFAS] is the PFAS concentration expressed in µg/kg and µg/L, for the topsoil layer (topsoil) and the water (water), respectively, and i refers to the treatment.

## 2.7. Dataset availability and statistical analyses

The research dataset is available in Michaud et al. (2024). Parametric and non-parametric tests were applied after testing normality of residues

and equality of variance; in case of non-normality, further log transformation was performed in parametric regressions. Calculations proceeded when values were superior to the detection limit, as those ones under the detection limits had a non-detected status. Tests were performed as follows using XLSTAT® (V 2023.2, Addinsoft) and the R statistical software (R Core Team 2023, Vienna, Austria). Considering all experiments, the following tests were performed: Kruskal-Wallis non-parametric test to identify OWP, treatment and site differences; Principal component analyses (PCA) to study PFAS profiles, with values under the detection limit replaced by iterative regularised PCA; Linear Regression (estimated actual amount of PFAS in topsoil resulting from

<figure><img src="image_4.png" /><figcaption>Fig. 1. Soil concentration of 75 PFAS per super classes, expressed in µg per kg dry matter (a) and Ascending Hierarchical Classification (AHC) of soil 75 PFAS concentration (b). * stands for significant difference between years per each treatment at the statistical significance of 5%; ⋆ stands for significant difference between years per each treatment at the statistical significance of 10%. Treatments signification: Couhins – C, Bouzule – B, QualiAgro – QA, PROspective – PSP, Reunion – RUN; SLU (Sewage sludge), C-GWS (Compost of green wastes and sewage sludge), C-BIOW (Compost of municipal biowastes), C-MSW (Compost of residual municipal solid waste), FYM-DC (Farmyard manure of dairy cattle), C-FYM-DC (Compost of farmyard manure of dairy cattle), CN (Control with no organic amendment), CN-D (Control with digestate).</figcaption></figure>
OWP spreading; water PFAS concentrations), with data expressed in log. Per experiment the following tests were performed: Bilateral Dunn non-parametric tests with Bonferroni correction to identify significant differences between treatments per year; Friedman's non-parametric test to evaluate significant effect of years with Bilateral Nemenyi non-parametric method used for multiple comparisons for paired samples. Stepwise multiple log-log linear regressions were performed between log soil/water distribution coefficient (log *K*<sub>d</sub>) and soil/water properties (pH, SOC, DOC) for the *K*<sub>d</sub> calculated from QA and PSP (i.e. PFAS concentration in the topsoil and in situ leaching waters collected at 45 cm depth). Correlations with Pearson tests were performed between log *K*<sub>d</sub> and soil/water properties (pH, SOC, DOC) for the *K*<sub>d</sub> calculated from QA and PSP. Statistical significance was set at 5 % in all tests; additional statistical significance was set at 10 % for Friedman analyses.

# 3. Results & discussion

## 3.1. PFAS occurrence in soils

### 3.1.1. Soil PFAS concentrations in field experiments

Fig. 1 summarizes the PFAS concentrations found in the topsoil layer for the five field experiments over the 1978–2018 period (see also Supporting Information S3). Overall, the summed PFAS (Σ 75 PFAS) ranged from 0.27 to 21 µg/kg. Perfluoroalkyl acids (PFAA), i.e. perfluoroalkyl carboxylic acids (PFCA) and perfluoroalkyl sulfonic acids (PFSA), were the main classes identified in most soils, except those from La Réunion and a few samples from La Bouzule.

In controls that did not receive any OWP, soil PFAS concentrations evolved with time as discussed below. In Couhins, a significant increase (*p* < 0.05) occurred in the control treatment with PFAS levels ranging from 0.37 µg/kg in 1978 to 2.8 µg/kg in 1998 (Fig. 1a); in La Bouzule, PFAS levels in controls did not change significantly over time (mean 0.3 µg/kg DM); in QA, a slightly significant increase in summed 75 PFAS was observed in the control treatment with levels ranging from 0.49 µg/kg in 2013 to 0.66 µg/kg in 2014 (*p* < 0.10); in PSP, a slight significant PFAS increase was observed with levels ranging from 0.72 µg/kg in 2010 to 0.95 µg/kg in 2018 (*p* < 10%, S3.4). Increase in PFAS levels in the long-term control plots could reflect diffuse PFAS sources such as atmospheric deposition (Gaspéri et al., 2018; Cousins et al., 2022) in addition to use of inputs containing PFAS (e.g. fluorinated pesticides) (Ogawa et al., 2020). Such observation could also result from cross-contamination with maintenance activities such as ploughing and planting because of the close proximity of the control plots to the plots receiving OWP (e.g. contamination of controls from municipal sludge plots in Couhins), as observed previously for trace metals work in Sepulvado et al. (2011).

At Couhins (C), where urban sludge (SLU) was applied at an average dose of 10 t/ha of dry matter (Table 1), significant summed PFAS increases (*p* < 0.05) occurred in sludge-amended soils with levels ranging from 2.5 µg/kg in 1978 to 21 µg/kg in 1998 (+ 730 % in 16 applications). Soils amended with SLU exhibited significantly higher concentrations than the controls. PFOA, PFOS and EtFOSAA, belonging to the PFCA, PFSA and sulfonamides super-classes respectively, were detected in 100 % of the samples (S2 and S3.1). PFOS exhibited a significant increase with time with the highest levels measured in sludge-amended soils reaching 15 µg/kg in 1998, accounting for 73 % of the summed PFAS.

At La Bouzule (B), urban sludge composed or not (SLU, C-GWS) and compost of municipal solid waste (C-MSW) were applied at an average dose of 10 t/ha of dry matter. Significant increases occurred in the summed PFAS in soils amended with the 3 urban OWP (SLU, C-GWS, C-MSW; *p*<0.10), especially in sludge amended soils with levels ranging from 2.8 µg/kg DM in 1997 to 17 µg/kg DM in 1998 (+ 573 % in 2 applications). In 1998, soils amended with SLU exhibited significantly higher PFAS concentrations than the controls. PFOA, PFOS, PFPeA, PFHxA, PFHpA, PFNA and MeFOSAA, belonging to the PFCA, PFSA and

sulfonamides super-classes, were detected in all samples, with the highest levels observed for PFOS in sludge-amended soils reaching 5.1 µg/kg in 1998, or 30 % of the summed PFAS (S3.2).

The QualiAgro (QA) site received manure (FYM-DC) and three urban composts with urban sludge (C-GWS), biowastes (C-BIOW) and municipal solid waste (C-MSW) at an average dose of 14.3 t/ha of dry matter. A significant summed PFAS increase occurred in soils amended with C-GWS and C-MSW (+ 20 % in one application). In 2014, soils amended with C-GWS also exhibited significantly higher concentrations than the controls. The most frequently detected congeners (in 100 % of the samples) were PFOA, PFOS, PFPeA, PFBS, PFHxA, PFHpA, PFHxS, PFNA, PFDA, PFUnA, belonging to the PFCA and PFSA super-classes, with the highest levels observed for PFOS in composted-sludge amended soils reaching 11 µg/kg in 2014, representing 54 % of the summed PFAS (S3.3).

The PROspective (PSP) site received urban sludge composed or not (SLU, C-GWS) and compost of biowastes (C-BIOW), manure composted or not (FYM-DC, C-FYM-DC), and biowaste digestate (CN_D) at an average dose among OWP of 5.0 t/ha of dry matter (Table 1). Soils amended with the three urban OWP (SLU, C-GWS, C-BIOW; *p* < 0,10) had only marginal increases in PFAS levels (e.g., +9 % between 2010 and 2018 for sludge-amended soils). PFOA, PFOS, PFPeA, PFHxA, PFHpA, PFHxS, PFNA, PFDA, PFUnA, PFDoA (i.e. belonging to the PFCA and PFSA super-classes) were detected in all samples, with the highest levels observed for PFOS in sludge amended soils (6.1 µg/kg in 2018, i. e., 62 % of the summed PFAS).

At the French Overseas site (La Réunion, RUN), urban sludge composed or not (SLU, C-GWS) was applied at an average dose of 6.1 t/ha of dry matter. In 2018, soils amended with C-GWS exhibited higher concentrations than the controls with summed PFAS reaching 5.9 µg/kg. Perfluoroalkyl phosphonic acids had similar concentrations among treatments and represented from 63 % of the summed 75 PFAS in OWP-amended soils up to 83 % in controls, suggesting a local source (S3.5). This group of PFAS is widely used in textile, pharmaceutical industry, food paper-based packaging and as defoaming additives in pesticide formulations (Llorca et al., 2018). The last use as pesticides additives could result in a global contamination of soil samples. PFOA, PFOS, PFHxA, PFHpA, PFNA, PFDA, MeFOSAA, PFDoA, PFTeDA, 6:6 PFPi, 6:8 PFPi and 8:8 PFPi were detected in all samples (i.e. belonging to the PFCA, PFSA, sulfonamides and perfluoroalkyl phosphonic acids super-classes), with the highest levels observed for 6:8 PFPi and 8:8 PFPi.

Considering the five field experiments, soil samples displaying a summed 75 PFAS level higher than 3 µg/kg were grouped within a common supercluster by hierarchical clustering analysis (HCA) (Fig. 1b). HCA also gathered all soils amended by sludge composed or not (SLU and C-GWS), except those sampled at the beginning of the historic sites (i.e. C-GWS and SLU in Couhins and La Bouzule; S3), all C-MSW amended-soils sampled in QualiAgro and the control from la Réunion in which high phosphonic acid levels were found as in the other treatments. All soil samples exhibiting a summed 75 PFAS lower that 3 µg/kg were gathered within a common cluster by hierarchical clustering, with all controls (except in La Réunion), soils amended by manure (composed or not) and compost of biowastes.

The summed PFAS was in the range of the soil PFAS concentrations found in the literature and compiled in Brusseau et al. (2020) (< 0.001 to 237 µg/kg). In Sörenگård et al. (2022b), the maximal PFAS sum (26 PFAS) of 560 µg/kg was found in the perimeter of a military base (i.e. primary-contaminated site), which is far larger than background levels found in Sörenگård et al. (2022a) in Sweden with 2.3 µg/kg. This indicates that levels found in the present study, especially in the cluster 2 including controls, soils amended by compost of biowastes and manure, are in the range of background levels and far below levels found at primary-contaminated sites.

These results are consistent with prior field studies and surveys where PFAS were detected in all samples, with PFOA and PFOS the most frequently detected compounds (Milinovic et al., 2015; Rankin et al.,
2016; Sörenگård et al., 2022a; Röhler et al., 2023), and PFOS often displaying the highest levels (Sepulvado et al., 2011; Brusseau et al., 2020; Sharifan et al., 2021). Average PFOA and PFOS concentrations found in the present study were far below concentrations found in primary-source contaminated sites (i.e. emissions from manufactures, AFFF-testing sites) in which concentrations of PFOA and PFOS reached 83 µg/kg and 8722 µg/kg, respectively (Brusseau et al., 2020). They were also below or in the range of secondary-source contaminated sites (i.e. application of biosolids/sludge) where the PFOS concentrations ranged from around 2 to 680 µg/kg and PFOA levels ranged from around 0.5 to 38 µg/kg, respectively (Sepulvado et al., 2011; Brusseau et al., 2020; Pepper et al., 2021; Sharifan et al., 2021). They were in the range found in previous surveys analysing background soils with median maximum background levels of 2.7–3.4 µg/kg for both PFOS and PFOA (Rankin et al., 2016; Brusseau et al., 2020). The Netherlands proposed PFAS limits in soil which are of 1.4 µg/kg DM and 1.9 µg/kg DM, for PFOS and PFOA respectively (Wintersen and Otte, 2020). In the present study, PFOA levels were lower than this PFOA limit, while PFOS content exceeded such proposed limit in soils amended by sewage sludge and compost of sewage sludge in 4 sites (C, B, QA and PSP; S3).

### 3.1.2. PFAS composition profile

Of the 75 PFAS congeners analysed, 50 congeners were found in the 117 soil samples, with at least 17 detected above the detection limit in soils amended with compost of farmyard manure (C-FYM-DC) and a maximum of 46 compounds detected in soils amended with municipal sewage sludge (SLU) (S3.6 and S3.7). PFOS and PFOA were detected in 100 % of the 117 soil samples. Seven additional congeners were detected at frequencies above 80 %, namely PFPeA, MeFOSAA, PFHpA, PFHxS, PFHxA, PFDA, and PFNA.

The archived soils extended from 1978 to 2018, allowing the investigation of the composition profile of PFAS according to the OWP type and decades. For the main congeners, we tested the first hypothesis that OWP inputs could influence their concentrations in soils as a function of the OWP nature and decades, as shown in Fig. 2. The 1st dimension of the principal components analysis (PCA) discriminated the treatments depending on the PFAS level. Control soils and soils amended with manure (composed or not) and composites of biowastes were in the left part of the correlation circle, while soils amended by SLU, C-GWS and C-MSW were in the right part of the PCA (Fig. 2b), except those sampled at the beginning of the experiment Couhins/Bouzule, as also observed in the clustering analysis (Fig. 1b). The PCA confirmed that control soils receiving no organic amendment (i.e. CN) and soils amended with manure (i.e. FYM-DC and C-FYM-DC) displayed lower PFAS concentrations compared to soils amended with urban OWP. Among urban OWP, composites of biowastes (C-BIOW) exhibited the lowest concentrations. As observed in the clustering analysis, levels found in C-BIOW were not significantly different from the soils amended with manure and controls (Fig. 1b). The second dimension of the PCA divided soil treatments according to the decades and PFAS patterns, with samples from the 1970s–1990s in the top of the PCA and samples from the 2000s–2010s in the bottom of the PCA. Sulfonamides, MeFOSAA, PFHxS, PFOS and PFSA were correlated positively to the second dimension, while PFPeA, PFHpA and PFHxA were negatively correlated to the second dimension. This indicated that soil samples from historic sites differed in PFAS patterns compared to soil samples from on-going sites. At historic sites, we observed a relatively higher prevalence of PFOS and electrochemical fluorination (ECF)-based precursors such as MeFOSAA (a PFOS precursor; Liu and Avendaño, 2013). Active sites exhibited a relatively higher composition of PFCA and related congeners, including long-chain (PFOA, PFNA, PFDA) and short-chain homologs (PFPeA, PFHpA, PFHxA) that were nearly orthogonal to the legacy PFAS found at historic sites (i.e., ECF-based precursors and long-chain PFSA).

PFCAs and PFSA together known as perfluoroalkyl acids (PFAA), are among the most studied anionic fluorinated surfactants (Buck et al.,

2011; Liu and Avendaño, 2013; Xiao, 2017). Among PFAA congeners, PFOA and PFOS are two long-chain perfluoroalkyl acids that are persistent, bioaccumulative and toxic so that PFOS was added to Annex B of the Stockholm Convention list of persistent pollutants in 2009 and PFOA was phased out from industrial products. Since then, direct emissions of PFOA and PFOS are believed to have declined in most countries (Munoz et al., 2019). Several PFAS have been used as alternatives to long-chain PFAA (Buck et al., 2011; Xiao, 2017; Munoz et al., 2019). This could partly explain the different PFAS patterns between historic and active sites. Additionally, PFAA precursors (e.g., fluorotelomers alcohols (FTOH), fluorotelomers acrylates, fluorotelomers sulfonates) are subject to (bio)transformation during product use and/or after inputs in the environment, with chain-shortening processes and formation of numerous intermediates. This leads predominantly to the formation of terminal and highly persistent PFCAs (e.g. PFOA, PFHpA, PFHxA, PFPeA, TFA) (Prevedouros et al., 2006; Buck et al., 2011; Liu and Avendaño, 2013; Hamid et al., 2018; Evich et al., 2022; Wang et al., 2024). The presence of PFOA and PFOS in recent soil samples, as well as other PFAAs could also result from (a)biotic transformation in OWP before spreading and/or in amended soils, especially after urban OWP inputs (i.e. SLU, C-GWS, C-MSW). Such degradation could explain the observed PFCA in on-going sites as well as the high levels of PFOS/PFOA across all sites.

Munoz et al. (2021) previously observed that fluorotelomer sulfonamidopropyl betaines (ESI+ FTAB) exhibited the highest relative abundance in OWP of France for land application, 6:2 FTAB being detected in 81 % of the OWP and representing 55 % of the average summed PFAS (S5). Interestingly, 6:2 FTAB was only detected in 4 % of the soil samples from the present survey (S3.7). 6:2 FTAB can be transformed to a series of degradation products, including 6:2 FTOH, 6:2 FTS, PFPeA, PFHxA, and PFBA (D'Agostino and Mabury, 2017; Xiao, 2017; Shaw et al., 2019). Degradation of 6:2 FTAB is expected to proceed slowly under field conditions, based on estimated half-lives of months if not years of fluoroalkyl sulfonamidopropyl betaines (6:2 FTAB, PFOSB) in soil microcosms (Liu et al., 2021b, 2023; Fang et al., 2024). The degradation of this and other precursors could partly explain the increase in the relative abundance of PFAA in OWP-amended soil samples, agreeing with the second hypothesis that PFAS precursors can transform in soils.

### 3.1.3. PFAS soil stocks as related to OWP inputs

Topsoil stocks of PFAS contained in plough layers, i.e., topsoils where OWP were introduced, were estimated from mean PFAS concentration per treatment and an average mass of 2600 t/ha, corresponding to a typical surface-soil bulk density of 1.3 g/cm³, a sampling depth of 20 cm, and the usual hectare area (S4) (Cambier et al., 2019; Michaud et al., 2020; Chen et al., 2022). The average topsoil stocks of 75 PFAS varied from 2.2 to 2.6 g/ha in control soils and manure-amended soils, to 21.9–28.2 g/ha in sludge-amended soils (composed or not), the last ones being significantly larger than those for controls (Fig. 3). The average topsoil stock of PFOS varied from 0.86 g/ha in control soils to 9.8–14.0 g/ha in sludge-amended soils (composed or not), while the stock of PFOA varied from 0.25 g/ha in controls to 1.95–2.38 g/ha in sludge-amended soils (composed or not). The average stock of PFOS represented 37.1 % of the total stock of measured PFAS in all treatments (33.1 % in control soils, 29.2 % in FYM-C-FYM, 36 % in C-BIOW soils, 40.0 % in C-MSW and 39.6–43.3 % in SLU/C-GWS). The stock of PFOA represented 12.6 % of the total stock (12.4 % in control soils, 15.5 % in FYM-C-FYM, 18.2 % in C-BIOW soils, 15.7 % in C-MSW and 7.8–12.2 % in SLU/C-GWS). Both PFOA and PFOS represented 49.7 % of the topsoil stock of PFAS in all treatments (45.6 % in control soils, 44.8 % in FYM/C-FYM, 51.1 % in SLU, 51.8 % in GWS, 55.7 % in C-MSW).

The median stocks estimated in control soils without any OWP inputs represented 79.8 % of the stock for manure-amended soils, 56.2 % of the stock for soils amended by compost of biowastes, 40.4 % of C-MSW topsoil stock, and 7.4–12.6 % of the stock for sludge-amended soils
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Principal component analysis (PCA) of PFAS soil concentrations, with the correlation circle (a), factorial plan with individual samples for treatments (b) and decades (c) (n = 117). Treatments signification: SLU (Sewage sludge), C-GWS (Compost of green wastes and sewage sludge), C-BIOW (Compost of municipal bio-wastes), C-MSW (Compost of residual municipal solid waste), FYM-DC (Farmyard manure of dairy cattle), C-FYM-DC (Compost of farmyard manure of dairy cattle), CN (Control with no organic amendment), CN-D (Control with digestate).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Box plots of the estimated topsoil stocks of 75 PFAS (a), PFOS (b) and PFOA (c), expressed in g per hectare (and over a depth of 20 cm), median represented by a line and mean by a cross. \* stands for significant difference between treatments at the statistical significance of 5 %. Treatments signification: SLU (Sewage sludge), C-GWS (Compost of green wastes and sewage sludge), C-BIOW (Compost of municipal biowastes), C-MSW (Compost of residual municipal solid waste), FYM-DC (Farmyard manure of dairy cattle), C-FYM-DC (Compost of farmyard manure of dairy cattle), CN (Control with no organic amendment), CN-D (Control with digestate).</figcaption></figure>

(composted or not). Considering those results, we expected that the PFAS topsoil stock increased as a function of PFAS inputs by OWP. To test this hypothesis, we calculated topsoil stocks corresponding to the 44 PFAS analysed in the OWP studied in Munoz et al. (2021) (S1; Eq1, S4), in order to compare them to the estimated OWP inputs of PFAS (S5). The average estimated topsoil stocks of Σ 44 PFAS varied from 2.1 to 2.5 g/ ha in control soils and manure-amended soils, to 20.6–27.3 g/ha in sludge-amended soils (composted or not) (S4). The estimated topsoil stocks for Σ 44 PFAS and Σ 75 PFAS were significantly correlated (R² = 0.945, *p* < 0.0001) and followed the same distribution pattern (*p* = 0.913 according to the bilateral test Kolmogorov-Smirnov). We further calculated the topsoil stocks in OWP-amended soils resulting from the sole OWP origin by subtracting the PFAS stocks estimated in the corresponding controls (CN, i.e. per field experiment/year) (Eq. (2)) and we compared them to the corresponding calculated PFAS inputs from OWP (after logarithm transformation; Fig. 4). The estimated PFAS stock coming from the OWP amendments increased significantly as the OWP-PFAS inputs increased (R² = 0.754, *p* < 0.0001; log Q (Actual-OWP topsoil) = 7.844E-02 + 0.676 × log Q (PFAS OWP input)). As hypothesised, the summed PFAS stock in the soil increased as a function of the increase of PFAS inputs by OWP, with the following order in treatments: FYM/C-FYM-DC < C-BIOW < SLU ≈ C-GWS ≈ C-MSW. Our results suggest that PFAS inputs occurred in control soils without any OWP inputs and in manure-amended soils in a similar order of magnitude (Fig. 3). Such agricultural background PFAS contamination could be due to atmospheric deposition resulting from the environmental ubiquity of PFAAs in atmospheric deposition and their long-range transport and/or from fluorinated pesticides (Llorca et al., 2018; Ogawa et al., 2020; Podder et al., 2021; Cousins et al., 2022; Sörenگård et al., 2022a). The spreading of compost of biowastes doubled the amount of PFAS in the topsoil. The spreading of urban sludge and municipal solid-wastes OWP represented the main source of the PFAS in agricultural topsoil stocks, since they resulted in multiplying the background stocks by a factor of 6.2 to 13.4.

The present study confirms the potential for soils to represent an
environmental compartment accumulating PFAS, especially PFOS and
PFOA even in recent samples (Rankin et al., 2016; Washington et al.,

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Estimated increase of the topsoil stocks of 75 PFAS in amended soils as a function of estimated PFAS inputs by OWP (n = 25). Treatments signification: SLU (Sewage sludge), C-GWS (Compost of green wastes and sewage sludge), C-BIOW (Compost of municipal biowastes), C-MSW (Compost of residual municipal solid waste), FYM-DC (Farmyard manure of dairy cattle), C-FYM-DC (Compost of farmyard manure of dairy cattle).</figcaption></figure>

2019). Virtually the sole comparable study was the one of Sörenگård et al. (2022a) that estimated a spatial distribution for 28 PFAS in Sweden per m² based on a study considering 27 sites and 31 samples corresponding to mature forest soils without any known local contaminant impact obtained from the Swedish Forest soil inventory. They found a median of 28,000 ng/m² for the summed 28 PFAS and a median load of PFOS of 6600 ng/m², which corresponds to 0.28 g/ha and 0.066 g/ha for 28 PFAS and PFOS, respectively. The results obtained for forest soils devoid of known local contamination sources are an order of magnitude lower than our estimations for control and manure-amended soils, i.e.
2.2–2.6 g/ha for Σ75PFAS and 0.7–0.9 g/ha for PFOS. This is consistent with a putative higher global background level in French agricultural soils compared to Swedish forest soils as related to atmospheric deposition and additional inputs such as pesticides and mineral fertilizers.

## 3.2. PFAS mobility

### 3.2.1. Concentrations in soil waters

At active sites, the archived water samples collected from lysimeters in 2011–2018 in QualiAgro and PROspective, and the waters extracted from stored soils sampled in 2018 in La Réunion, allowed the investigation of the composition profile of PFAS in soil waters in controls and OWP treatments (Fig. 5, S6.1). In leaching waters sampled at 45 cm depth (i.e. below the plough layer amended with OWP), QualiAgro

exhibited 59 congeners belonging to various classes, with the average summed PFAS of 165 ng/L, typically dominated by ΣPFCAs (61 % of the Σ75PFAS). Even at 100 cm depth, 52 congeners were still detected and quantified at the QualiAgro site, with an average summed concentration of 39 ng/L (45 % from ΣPFCAs). In PROspective at 45 cm depth, 45 congeners were found for a summed concentration of 2340 ng/L, of which 99 % was from ΣPFCAs. Forty-nine congeners were found in the extracted waters from La Réunion, with an average summed concentration of 794 ng/L, with 92 % from ΣPFCAs. The PROspective site exhibited the largest PFAS levels going from 384 ng/L in controls to 4312 ng/L in C-GWS treatment, while at QualiAgro values went from 4 ng/L to 543 ng/L in waters leached under the amended topsoil with compost of biowastes and of municipal solid wastes, respectively. Differences in PFAS water concentrations between sites could result, in

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Soil water PFAS concentration expressed in ng per litter per super classes (a) and for PFCA compounds (b). Treatments signification: QualiAgro – QA, PROspective – PSP, Reunion – RUN; SLU (Sewage sludge), C-GWS (Compost of green wastes and sewage sludge), C-BIOW (Compost of municipal biowastes), C-MSW (Compost of residual municipal solid waste), FYM-DC (Farmyard manure of dairy cattle), CN (Control with no organic amendment).</figcaption></figure>
part, from the water sampling method and also the timing of sampling. Archived waters collected in 2014 by lysimeters in situ and analysed at QualiAgro represented the all-drainage period, while archived waters collected in 2011 at QualiAgro and in 2018 at PROspective and analysed corresponded to the beginning of the drainage period, which could explain larger concentrations found in the latter case. Production and accumulation of short-chain PFAS could occur during spring and summer presumably by higher degradation rates of precursors concomitantly to weaker leaching of waters at these periods; with following mobilization during recharge in autumn and winter, and relative dilution of leachates by winter precipitation (Hamid et al., 2018; Röhler et al., 2021). In addition, for all sites, important standard deviations occurred among water samples, suggesting heterogeneity in water samples which was not related to the treatment, as it did not induce significant differences among water PFAS concentrations for both sites. A similar observation was also made by Gottschall et al. (2017), where application of dewatered municipal biosolids did not induce significant difference in PFAAs concentrations in water sampled by piezometers.

Considering the water samples altogether, 69 congeners were detected above the detection limit, and 93 % of the summed PFAS concentration corresponded to the PFCA class (Fig. 5b, S6.2). Nineteen congeners were detected at frequencies over than 80 %, among which 8 compounds were found in 100 % of the water samples (i.e., PFPrA, PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFHxS and PFDA). Average concentrations ranked as follows: PFHpS (0.12 ng/L), PFTrDA (0.30 ng/L), 6:6 PFPI (0.34 ng/L), PFUnA (0.43 ng/L), MeFOSA (0.57 ng/L), PFPrS (0.77 ng/L), PFOS (0.83 ng/L), PFPeS (0.97 ng/L), EtFOSA (1.2 ng/L), PFHxS (1.7 ng/L), PFDA (4.5 ng/L), PFNA (5.5 ng/L), PFBS (10 ng/L), PFHpA (27 ng/L), PFOA (56 ng/L), PFHxA (65 ng/L), PFPeA (118 ng/L), PFPrA (159 ng/L), and PFBA (183 ng/L). Our results agree with previous studies reporting the prevalence of C4-C8 PFSA and C4-C9 PFCA as the dominant PFAS in waters matrices such as irrigation water, agricultural runoff and tile drainage (Gottschall et al., 2017; Podder et al., 2021; Li et al., 2022). The 6:2 FTAB was detected in 4 samples only (out of 27 analysed), with an average concentration of 0.74 ng/L in those 4 samples.

Average values for PFOS and PFOA were below or in the range of the values reported by Gottschall et al. (2010) (12–17 ng/L) in drainage water collected under soils receiving biosolids. They were also below the concentrations (150–250 ng/L) observed in Levine et al. (2023) in a soil column experiment with biosolid-based compost and manure-based compost.

Considering all sites, the average concentration for PFOS+PFOA was 57 ng/L. This is lower than drinking water guidelines in Germany (100 ng/L) and Italy (90 ng/L) (Gottschall et al., 2017; Gagliano et al., 2020; Podder et al., 2021). However, this is higher than the enforceable Maximum Contaminant Levels in the USA (4 ng/L individually in drinking water), and higher than the objective value proposed by Health Canada (30 ng/L for the sum of 25 selected PFAS including PFOS and PFOA in drinking water) (Health Canada, 2024). In 2020, the European Union (2020) (EU) included PFAS into their drinking water directive, with the limit value of 0.1 µg/L for the sum of 20 PFAS and total PFAS of 0.5 µg/L (applicability effective in 2026; European Union, 2020). Considering all sites, the average sum of 75 PFAS of 663 ng/L was over the EU limit of 500 ng/L (S6.2). As many ultra-short-chain PFAS, such as trifluoroacetic acid (TFA) and triflic acid (TFMS), require a different liquid chromatography setup, the true PFAS total reported here may be underestimated.

Among the 19 most frequent congeners, all average concentrations above 10 ng/L corresponded to anionic PFCA with short chains (≤C7) and long-chain PFOA (C8). This is in line with previous studies and agrees with the relatively high water solubility of short-chain PFAS, especially PFCA (Prevedouros et al., 2006; Sepulvado et al., 2011; Röhler et al., 2021; Adu et al., 2023). The prevalence of short-chain PFAS in waters, especially PFCA, could result from the degradation of precursors in soils (Liu and Avendaño, 2013; Liu et al., 2021a, 2021b;

Bolan et al., 2021b; Röhler et al., 2021). This would be confirmed by the high proportion of PFAA observed in soils (Fig. 1, S3) contrary to those observed in land-applied OWP (Munoz et al., 2021; S5), suggesting production of PFAAs from precursors brought by OWP. In addition, the perfluorocarbon chain length is a dominant structural feature influencing sorption of PFAS and their mobility (Higgins and Luthy, 2006). The water concentration of PFCA congeners decreased as a function of chain length following nonlinear relations (S6.3 and S6.4), which is consistent with known behaviour of PFAS sorption by natural geomedia (Brusseau, 2019). PROspective site exhibited the best regression fit, with 96.6 % of the variability among the distribution of the PFCA water concentrations explained by the chain length (*p*<0.0001). In QualiAgro, 70.1 % of the variation in the water concentrations of PFCAs was explained by their chain length (*p* = 0.005), while in La Réunion, 51.7 % of the variability could be related to the chain length (*p* = 0.013). The chain length and the related hydrophobic interactions to soil components would thus better explain PFCA water concentrations at PROspective than at QualiAgro and La Réunion sites. This can be due to geochemical differences between sites, as the sorption of PFAS also depends on the soil organic carbon content and sorption sites availability (Bolan et al., 2021b; Adu et al., 2023).

### 3.2.2. Soil/water partitioning

Not only the total amount of contaminants in soils, including PFAS, but also their distribution between the solid phase and the soil solution (i.e. $K_d$) is of importance in terms of environmental fate. The $K_d$ is a widely used metrics to predict the mobility of environmental contaminants (Sauvé et al., 2000, 2003; Cambier et al., 2019; Nguyen et al., 2020). In the present study, soil/water distribution partitioning coefficients ($K_d$) were estimated for three sites with available paired soil/water concentrations. $K_d$ data are presented in Table 3 (see also S7.1). Log $K_d$ varied from -0.8 (ultrashort-chain PFPrA) to 3.7 (6:6 PFPi). A similar range of values were reported by Nguyen et al. (2020), with log $K_d$ ranging from -0.7 to 2.7. Overall, there were no significant differences in $K_d$ among treatments. However, differences were observed between sites, with typically higher log $K_d$ values of short-chain PFAAs for QualiAgro than for La Réunion and PROspective (Table 3, S7.1).

At QualiAgro, average congener's log $K_d$ values varied from 0.66 for PFPrA to 3.23 for PFOS. At la Réunion, it varies from 0.04 for PFBA to 3.69 for 6:6 PFPI, and at PROspective, from -0.78 for the shortest PFCA (PFPrA) to 3.30 for PFOS (Table 3). The highest $K_d$ values were observed for PFOS, PFDaO, PFTrDA and MeFOSAA at both sites, and 6:6/6:8 PFPi at La Réunion, which is consistent with the higher sorption propensity of long-chain hydrophobic PFAS (Milinovic et al., 2015; Nguyen et al., 2020; Mei et al., 2021).

At PROspective, PFCA $K_d$ values increased significantly as a function of chain length ($R^2_{adj} = 0.972, p < 0.0001$) (S7.2), suggesting that hydrophobicity is a main driving property for sorption (Nguyen et al., 2020; Mei et al., 2021). At la Réunion, $K_d$ values increased significantly as a function of chain length for all PFAS and PFCA ($R^2_{adj} = 0.705, p < 0.0001$; $R^2_{adj} = 0.767, p = 0.001$; respectively), but not at QualiAgro. At both sites, sorption could result partly from the chain length but also from other processes such as binding of the ionic groups of PFAS molecules with nature of soil surfaces (Nguyen et al., 2020). This could also explain the sorption to soil components for PFSA, sulfonamides and phosphonic acids exhibiting no clear relation between chain length and water concentrations (see supporting information S6.3) whereas we observe a clear trend of higher sorption (and $K_d$) for longer chain PFAS for the PFCA series (supporting information S6.4 and S7.2). Our field results thus suggested that the soil sorption/desorption mechanism would result from (i) hydrophobic interactions (i.e., decreased interactions with shorter chain lengths resulting in higher mobility) especially for PFCAs at alkaline conditions at PROspective, and (ii) electrostatic interactions between PFAS functional groups and charged soil surfaces.

To identify additional factors influencing the adsorption of PFAS
<caption>**Table 3**</caption>

Soil/water distribution coefficients (Log K<sub>d</sub>) at QA, PSP and RUN sites, with the following data presented: concentration of dissolved organic carbon (DOC) and soil organic carbon (SOC) expressed in mg/L and % respectively, pH measured in water and soil samples, log of K<sub>d</sub> for individual PFAS (chain length in brackets); mean values ± standard deviation, abc letters stand for significant difference between sites with Kruskal-Wallis and Dunn bilateral tests.

<table><thead><tr><th></th><th></th><th>QA</th><th>PSP</th><th>RUN</th></tr></thead><tbody><tr><td rowspan="10">PFCA</td><td>DOC</td><td>40.6 ± 44.1 a</td><td>28.5 ± 8.8 ab</td><td>146.7 ± 7.64 b</td></tr><tr><td>pH_water</td><td>7.79 ± 0.4 ab</td><td>8.46 ± 0.21 b</td><td>6.70 ± 0.00 a</td></tr><tr><td>SOC</td><td>1.37 ± 0.26 a</td><td>1.42 ± 0.09 a</td><td>2.43 ± 0.13 b</td></tr><tr><td>pH_soil</td><td>7.26 ± 0.44 a</td><td>8.46 ± 0.04 b</td><td>6.50 ± 0.01 a</td></tr><tr><td>log K<sub>d</sub> PFPrA (C3)</td><td>0.66 ± 0.86</td><td>-0.78 ± 0.70</td><td>na ± na</td></tr><tr><td>log K<sub>d</sub> PFBA (C4)</td><td>1.69 ± 0.70</td><td>-0.73 ± 0.25</td><td>0.04 ± 0.70</td></tr><tr><td>log K<sub>d</sub> PFPeA (C5)</td><td>1.68 ± 0.96</td><td>-0.48 ± 0.48</td><td>0.15 ± 0.52</td></tr><tr><td>log K<sub>d</sub> PFHxA (C6)</td><td>1.72 ± 0.74 b</td><td>-0.05 ± 0.45 a</td><td>0.30 ± 0.41 ab</td></tr><tr><td>log K<sub>d</sub> PFHpA (C7)</td><td>2.00 ± 0.63 b</td><td>0.40 ± 0.50 a</td><td>0.34 ± 0.42 a</td></tr><tr><td>log K<sub>d</sub> PFOA (C8)</td><td>2.02 ± 0.70 b</td><td>1.02 ± 0.49 ab</td><td>0.23 ± 0.41 a</td></tr><tr><td>log K<sub>d</sub> PFNA (C9)</td><td>2.84 ± 0.89 b</td><td>1.07 ± 0.49 ab</td><td>0.55 ± 0.43 a</td></tr><tr><td>log K<sub>d</sub> PFDA (C10)</td><td>3.13 ± 1.22 b</td><td>1.56 ± 0.54 ab</td><td>1.03 ± 0.41 a</td></tr><tr><td rowspan="4">PFSA</td><td>log K<sub>d</sub> PFDoA (C12)</td><td>1.98 ± 1.01</td><td>2.77 ± 1.13</td><td>2.74 ± 0.13</td></tr><tr><td>log K<sub>d</sub> PFTrDA (C13)</td><td>2.10 ± 1.38</td><td>2.51 ± 0.42</td><td>2.25 ± na</td></tr><tr><td>log K<sub>d</sub> PFBS (C4)</td><td>0.67 ± 0.59</td><td>-0.13 ± 0.58</td><td>0.14 ± 0.50</td></tr><tr><td>log K<sub>d</sub> PFHxS (C6)</td><td>1.48 ± 0.76</td><td>1.93 ± 0.36</td><td>0.40 ± 0.51</td></tr><tr><td rowspan="3">Sulfon. Phosph. Acids</td><td>log K<sub>d</sub> PFOS (C8)</td><td>3.23 ± 0.90</td><td>3.30 ± 0.32</td><td>2.26 ± 0.62</td></tr><tr><td>log K<sub>d</sub> MeFOSAA (C11)</td><td>1.92 ± 0.51</td><td>2.25 ± 0.3</td><td>2.78 ± 0.65</td></tr><tr><td>log K<sub>d</sub> 6,6-PFPI (C6 + C6)</td><td>1.58 ± 1.50</td><td>0.89 ± 0.96</td><td>3.69 ± 0.27</td></tr><tr><td></td><td>log K<sub>d</sub> 6,8-PFPI (C6 + C8)</td><td>0.81 ± 1.05</td><td>1.33 ± na</td><td>3.12 ± 0.45</td></tr></tbody></table>

Number of samples: total = 15, QA ≤ 8, PSP ≤ 4, RUN≤3; statistics when QA = 8, PSP = 4, RUN = 3.

onto soils, correlations coefficients (R<sup>2</sup>) and multiple linear regressions were tested between log K<sub>d</sub> and soil/water properties (Fig. 6, S7.3 and S7.4). As the La Réunion site differed in its mineralogical and geochemical environments (i.e., Nitisol) and in the water sampling method, those analyses were carried out at PROspective and QualiAgro sites only. When water pH increased from 7.3 to 8.7, log K<sub>d</sub> values decreased by 2 to 4 log units depending on congeners (Fig. 6). When considering the various congeners, log K<sub>d</sub> values were significantly negatively correlated to the water pH for PFPrA, PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA and PFDA, with R<sup>2</sup> values ranging from -0.660 (PFOA) to -0.857 (PFPeA) (Fig. 6). These field results agree well with batch studies by Nguyen et al. (2020), who found that solution pH affected sorption of PFAS markedly with K<sub>d</sub> values decreasing as solution pH increased. Previous studies have reported increased mobility of

PFAAs with increasing pH, probably due to decreased sorption with increasing solution pH (due to less protonation of the adsorbent surface at higher pH), leading to fewer positive sites on the sorbent (Hamid et al., 2018).

In previous studies, K<sub>d</sub> values varied by up to two orders of magnitude, as observed in the present study among treatments at QualiAgro, which suggested that several factors would influence the sorption pattern of PFAS in soils (Milinovic et al., 2015; Li et al., 2019). Soil organic carbon is considered as a key factor controlling sorption, at least for some PFAS (Mei et al., 2021; Sima and Jaffé, 2021). Contrary to previous studies (Zareitalabad et al., 2013; Milinovic et al., 2015; Li et al., 2019), the concentration of soil organic carbon might not be a key factor influencing the adsorption of PFAS as no relation was found between K<sub>d</sub> and soil organic carbon concentration (S7.3 and S7.4).

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Log K<sub>d</sub> for individual PFAS, as a function of water pH, at QualiAgro and PROspective sites. \* *p* value <0.05, \*\* *p* value <0.01, \*\*\* *p* value <0.001 (statistical significance).</figcaption></figure>
Sharifan et al. (2021) also noted that the correlation between K<sub>d</sub> and SOC is highly variable, suggesting that SOC as the only important predictor of K<sub>d</sub> values would not hold for (all) PFAS. However, log K<sub>d</sub> regressions displaying negative significant coefficient with pH showed positive significant coefficient with dissolved organic carbon (DOC) for some congeners (i.e. PFPeA, PFHpA) (S7.4). Higher dissolved organic matter seems to alter the sorption of some, but not all PFAS. A rise in water pH and in dissolved organic carbon concentration, as can occur at alkaline conditions and after OWP inputs, could thus change significantly the soil/solution distribution and thus the resulting mobility, as observed previously for trace elements (Carrillo-Gonzalez et al., 2006; Michaud et al., 2007; Cambier et al., 2014, 2019).

## 4. Conclusions

We have studied the in situ occurrence and soil-water distribution of 75 anionic, zwitterionic, and cationic PFAS in soils from five experimental field sites representative of French agricultural practices and OWP. Our results confirm that OWP represent an important source of PFAS in soils. PFAS concentrations in soils amended with municipal sludge, composted municipal sludge, and composted residual municipal solid wastes were significantly higher (up to an order of magnitude) than controls soils. In contrast, soils amended with composites of biowastes and manure exhibited typically low PFAS levels. Our study also suggests that soils are a long-term reservoir of PFAS with the degradation of precursors after OWP inputs resulting in the formation of PFAAs, including PFOS and PFOA; while anionic mobile short-chain PFCAs (i.e. PFBA, PFPeA PFHpA, PFHxA) dominate the PFAS composition in waters.

There are still sparse studies on PFAS in situ occurrence under agricultural contexts. This study suggests that depending on the geochemical environment, land-applied OWP containing PFCA precursors (i.e. municipal sludge) could represent important contributors to long-term groundwater contamination and putatively to plant uptake from soil solution. This study also suggests the following research priorities. (i) As no one targeted mass spectrometry method can analyse the actual diversity of PFAS contained in OWP and in amended soils, future analytical methods would need to be developed to identify unknown precursors of PFAS, especially in environments with OWP inputs. (ii) It would be essential to document the PFAS levels in soils of different contexts, including agricultural, urban, and forest/semi-remote soils, in order to better differentiate between the most preserved and impacted agricultural soils. (iii) Understanding the temporal dynamics of the formation of PFAAs and their mobility, using for instance regular chronicles or asynchronous approaches, would be critical to predict the fate of these substances. Sampling at the plot scale could be performed at a finer temporal resolution (e.g., every month) to discern seasonal trends in PFAS remobilization in the subsurface. (iv) Characterisation of PFAS sources other than OWPs, including atmospheric deposition, would make it possible to establish a mass balance and calculate the respective contribution of each identified source to the long-term PFAS accumulation in soils. (v) Follow-up work should focus on documenting the temporal dynamics of PFAS uptake by crops under real in situ conditions, to complement existing studies performed in hydroponic settings. (vi) Finally, our results suggest variable PFAS mobility depending on the agro-pedo-climatic context. Future studies should evaluate PFAS transfer to crops and waters under a wider array of agricultural settings and soil/climate characteristics.

### CRediT authorship contribution statement

**Aurélia Marcelline Michaud:** Writing – original draft, Visualization, Validation, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Toyin Dunsin Saliu:** Writing – review & editing, Visualization, Validation, Investigation, Formal analysis, Data curation.
**Gabriel Munoz:** Writing – review & editing, Validation, Methodology,

**Investigation, Formal analysis, Conceptualization. Frédéric Feder:** Writing – review & editing, Resources, Investigation.
**Valérie Sappin-Didier:** Writing – review & editing, Resources, Investigation.
**Françoise Watteau:** Writing – review & editing, Resources, Investigation.
**Sabine Houot:** Writing – review & editing, Resources, Investigation.
**Sébastien Sauvé:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Aurélia Michaud reports financial support was provided by the French National Research Agency. Sébastien Sauvé reports financial support was provided by the Natural Sciences and Engineering Research Council of Canada. Sébastien Sauvé reports financial support was provided by the Strategic Environmental Research and Development Program. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

The field experiments are part of the INRAE SOERE-PRO (network of long-term experiments dedicated to the study of impacts of organic waste product recycling), integrated as a service of the infrastructure "Investment for the future" AnaEE-France, overseen by the French National Research Agency (ANR 11- INBS-0001). The QA site is supported by Veolia Research and Innovation (VERI) and the RUN site is supported by Veolia Water. We thank L. Caradec for help in statistics and R, and all those who participated in the management of the SOERE-PRO sites, especially D. Montenach, C. Resseguier and S. Colin, as well as the partners, notably the Mixed Syndicate Union for Agricultural Recycling Haut-Rhin (SMRA 68). The authors also thank the Natural Sciences and Engineering Research Council of Canada (STPGP 478774), the Fonds Québécois de la Recherche sur la Nature et les Technologies (2015-PR-183278), and the Canadian Foundation for Innovation (30044) for their financial support. We acknowledge funding by the Strategic Environmental Research and Development Program (SERDP ER19-1157).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.179708.

### Data availability

Data will be made available on request.

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