# Impact of soil washing using synthetic or biological surfactants, as liquid or foam, on long-term biodegradation potential and soil microbial biodiversity in hydrocarbon-polluted soil: An experimental study

Marc Crampon, Florian Cazals, Jonathan Senechaud, Arnault Perrault, Sagyn Omirbekov, Stéfan Colombano

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## To cite this version:

Marc Crampon, Florian Cazals, Jonathan Senechaud, Arnault Perrault, Sagyn Omirbekov, et al. Impact of soil washing using synthetic or biological surfactants, as liquid or foam, on long-term biodegradation potential and soil microbial biodiversity in hydrocarbon-polluted soil: An experimental study. Journal of Hazardous Materials Advances, 2025, 19, pp.100855. 10.1016/j.hazadv.2025.100855. hal-05235739

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# Impact of soil washing using synthetic or biological surfactants, as liquid or foam, on long-term biodegradation potential and soil microbial biodiversity in hydrocarbon-polluted soil: An experimental study

Marc Crampon<sup>a,*</sup>, Florian Cazals<sup>a,b</sup>, Jonathan Senechaud<sup>c</sup>, Arnault Perrault<sup>c,†</sup>, Sagyn Omirbekov<sup>a</sup>, Stéfan Colombano<sup>a,‡</sup>

## ARTICLE INFO

Keywords:

Total petroleum hydrocarbons

Soil washing

Biostimulation

Bioaugmentation

Biodegradation

Biosurfactants

## ABSTRACT

Total petroleum hydrocarbons (TPH) sorption in contaminated soils represent a limit for their extraction and biodegradation. Their remobilization is possible with synthetic surfactants but these surfactants are generally described as more toxic for endemic bacteria at high concentrations, and they are generally described as less biodegradable compared to surfactants of biological origin. Then, the use of biosurfactants could represent a more sustainable solution. The objectives of this study are to develop a combination of treatments based on the washing by biosurfactants, directly produced from a microbial community in a bioreactor, and the subsequent biodegradation in soils impacted by pollution with hydrocarbons (TPH, PAHs, BTEX), in comparison with a chemical surfactant (sodium dodecyl sulfate) used as foam or liquids solutions. Results highlighted better remobilization of TPH from a real contaminated soil by SDS (~0.51 to 1.07 % of TPH as foam and liquids, respectively) compared to biologically produced surfactants (~ 0.02 0.13 % as foam and liquids, respectively), but also a higher toxicity towards the soils bacterial community. After a biodegradation step, it appeared that the remaining contaminants were highly more degraded in the soil treated with biosurfactants compared to SDS (~30 % biodegradation for biosurfactant conditions and 0 % for SDS), leading to a better remediation. Finally, the use of biosurfactants, even if less efficient compared to SDS for remobilization of TPH, could be interesting in the perspective of soil remediation in an on site context, provided that a second stage of natural biodegradation is considered.

### Environmental implications

Our study highlights the potential of eco-friendly (bio)surfactants for soil remediation, reducing reliance on harsh chemical treatments. While biosurfactants showed lower initial efficiency in soil washing, they enhanced long-term biodegradation, promoting natural attenuation of hydrocarbons. This approach supports microbial communities, preserving soil health and ecological functions. Interestingly, integrating only nutrients (bio-stimulation) even further accelerates pollutant breakdown. Though slower than conventional soil washing methods, this sustainable strategy minimizes environmental disruption and toxic residues. The findings emphasize the viability of nature-based solutions for large-scale site decontamination, contributing to greener remediation practices and improved long-term soil recovery.

## 1. Introduction

Soil and groundwater contaminated by Total Petroleum Hydrocarbons (TPH) has become a significant environmental issue worldwide, particularly in areas with intensive oil and gas exploration,

transportation, and storage activities. TPH are a complex mixture of organic compounds derived from crude oil, gasoline, diesel fuel, and other petroleum products. They are ubiquitous, persistent, toxic, and potentially carcinogenic, posing serious threats to human health and the environment (Kuppusamy et al., 2020; Omokpariola et al., 2011; Khan

<footnote><sup>a</sup> Bureau de Recherches Géologiques et Minières (BRGM), F-45060 Orléans, France</footnote>

<footnote><sup>b</sup> Estralab, F-78470 Bâtiment 15 – BAL 15003, Saint Rémy Les Chevreuse, France</footnote>

<footnote><sup>c</sup> Colas Environnement, F-01700 Miribel Les Echets, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: m.crampon@brgm.fr (M. Crampon).</footnote>
et al., 2018). The release of TPH into the environment can occur through accidental spills, leaks from underground storage tanks, runoff from roads and parking lots, and industrial activities such as oil refining and petrochemical manufacturing. Once released, TPH can migrate through soil and enter groundwater, potentially contaminating drinking water sources and ecosystems (Logeshwaran et al., 2018). The extent and severity of TPH contamination in soil and groundwater depend on various factors, including the type and volume of petroleum products released, the characteristics of the soil and aquifer, the presence of natural or engineered barriers, and the duration and frequency of exposure (Logeshwaran et al., 2018). To address the environmental and health risks associated with TPH contamination, numerous studies have been conducted to investigate the fate, transport, and remediation of TPH in soils and groundwater.

There are several remediation methods available to treat TPH-contaminated soils and groundwater, each with its own advantages and limitations (Kuppusamy et al., 2020; Yousaf et al., 2022; Tran et al., 2021; Zhang et al., 2021). Bioremediation is a widely used technique in soils that involves the use of microorganisms to break down TPH into less harmful compounds. This can be carried out in situ, where the contaminated soil is treated without excavation, or after excavation of the soil (on site or ex situ treatment). Bioremediation is generally cost-effective and has been shown to be effective in reducing TPH concentrations, especially in conjunction with other treatment methods, which may be mandatory prior to bioremediation in the case of heavy pollution. Phytoremediation is another technique that uses plants to remove or remediate TPH in soil. This method is often less expensive than other treatment methods and can be carried out in situ, but it may take longer time to achieve significant reductions in TPH concentrations. Chemical oxidation is a technique that involves the use of chemical agents to break down TPH. This method can be effective in treating highly contaminated soils and is often used in conjunction with other treatment methods. However, it can be expensive and may require multiple treatments. Thermal treatment, such as incineration or thermal desorption, involves heating the contaminated soil to high temperatures to volatilize or destroy the TPH. This method is effective in treating highly contaminated soils, but it can be expensive and may result in the production of air pollutants (Logeshwaran et al., 2018).

TPH biodegradation is an important mechanism for the removal of TPH from contaminated soil, where microorganisms use the hydrocarbons as a carbon and energy source (Bento et al., 2005; Wu et al., 2016). Although TPH biodegradation in soil has been studied for several decades, our understanding of the underlying processes and factors that influence biodegradation rates is still evolving (Bidja Abena et al., 2019). The biodegradation of TPH in soil is influenced by a variety of factors such as the type and composition of the petroleum product, the soil type and quality, and environmental conditions such as temperature, moisture, and nutrient availability (Admon et al., 2001; Mrozik and Piotrowska-Seget, 2010; Suja et al., 2014). It can be limited by several factors, such as the hydrocarbon concentration, the bioavailability of the TPH (i.e. weathering, organic and clay content), and the presence of toxic compounds or inhibitors. Additionally, the microbial community composition and diversity can impact the rate and extent of biodegradation (Mrozik and Piotrowska-Seget, 2010; Hassanshahian et al., 2012; Al-Hawash et al., 2018). Several approaches have been used to assess TPH biodegradation in soil, including laboratory microcosm studies, field studies, and molecular techniques such as DNA sequencing and qPCR. Laboratory studies have shown that TPH biodegradation rates can vary widely depending on the specific hydrocarbon compound and the soil microbial community (Admon et al., 2001; Suja et al., 2014). Field studies have demonstrated the effectiveness of bioremediation techniques, such as landfarming and bioaugmentation, in reducing TPH concentrations in contaminated soil (Suja et al., 2014; Chang et al., 2010). Molecular techniques have provided insight into the microbial community composition and functional genes involved in TPH biodegradation (Zhou et al., 2023). Despite the significant progress made in

understanding TPH biodegradation in soil, there are still many gaps in our knowledge. For example, the specific microbial species and functional genes involved in TPH biodegradation in different soils and environments are not fully understood (Chicca et al., 2022). In addition, the factors that limit biodegradation rates and the interactions between microbial communities and TPH are still being investigated. Further research is needed to develop more efficient and effective bioremediation strategies that can be applied to different TPH-contaminated soils and environments.

Soil washing is a technique that can be used to remediate contaminated soils and reduce TPH concentrations (Iturbe et al., 2003; Moon et al., 2016; Gautam et al., 2020). Soil washing is often jointly used with bioremediation to enhance TPH removal (Bhandari et al., 1994; Frutos et al., 2012). In a constructed biopile, a combination of biotreatment and soil washing is used to remediate TPH-contaminated soils. The process involves washing contaminated soil with a washing solution that contains surfactants and/or chelating agents. This solution helps to solubilize the TPH and desorb them from the soil (Uhmann and Aspray, 2012). The contaminated solution can then be treated with microorganisms, which break down the TPH into less harmful compounds. In a second step, the washed soil can be monitored for natural attenuation of remaining contaminants. In this case, the washing step has to ensure that the soil microbial community will not be highly impacted by the injection of surfactants into the soil, as some surfactants may be highly toxic to microbial communities (Lima et al., 2011; Reeve and Fallowfield, 2018; Rebelo et al., 2014).

Classical biological treatment of soils in unsaturated zones and excavated soils is often carried out by bioventing and biopile. These technologies require the addition of oxygen (compressed air) and nutrients (N/P/K diluted in water). A minimum humidity is necessary for biodegradation to be effective (Khan et al., 2004). The injection of liquid in an anisotropic porous medium often follows preferential pathways (in higher permeability layers), which reduces the remediation yields of bioremediation. The injection of non-Newtonian shear-thinning liquids (foams and gels) makes it possible to scan the entire medium, using the concept of "front flattening", which is the relatively homogeneous progression of the injected fluid in the form of a flat front, attenuating the permeability contrasts of the media crossed. Fluid viscosity will decrease in low permeability layers (higher flow resistance) and will be higher in less permeable media (lower flow resistance) (Khatib et al., 1988; Lake, 1989; Tanzil et al., 2002; Mulligan and Eftekhari, 2003; Couto et al., 2009; Davarzani et al., 2022; Alamooti et al., 2023). The use of foam makes it possible to homogeneously vectorize the additives necessary for good degradation (e.g., nutrients, water, and air) and inject surfactants to make the pollutants more bioavailable.

Surfactants that may be used for soil washing can be chemical surfactants or biosurfactants, i.e. produced by microorganisms (bacteria, fungi, yeasts, algae). Depending on their nature, these (bio)surfactants will have different toxicity towards the soil microbial communities (Rebelo et al., 2014). It is generally admitted that biosurfactants are less harmful compared to synthetic surfactants. Among the most common synthetic surfactants used for soil washing, the sodium dodecyl sulfate (SDS) is the most commonly encountered and studied (Lima et al., 2011; Badmus et al., 2021). They are effective in solubilizing TPH and enhancing their removal from soil by desorption (Torres et al., 2005; Lee et al., 2013; Caetano et al., 2023). However, chemical surfactants can have negative environmental impacts, such as toxicity to aquatic organisms and persistence in the environment, and toxicity to soil microorganisms involved in TPH biodegradation (Badmus et al., 2021). Biosurfactants, on the other hand, are produced by microorganisms and have lower toxicity and environmental impact than chemical surfactants. They can be produced by a variety of bacteria and have different properties depending on the producing organism and growth conditions (Johnson et al., 2021). Biosurfactants have been shown to enhance the biodegradation of TPH by increasing their bioavailability to microorganisms (Mulligan, 2021).
Several studies have compared the effectiveness of chemical and biosurfactants in soil washing. For example, one study found that a biosurfactant produced by a *Pseudomonas aeruginosa* strain was more effective than a chemical surfactant in removing TPH from soil (Kuppusamy et al., 2020; Gutiérrez et al., 2020). The biosurfactant was able to solubilize a higher proportion of TPH and was less toxic to microorganisms than the chemical surfactant. However, this is dependant of the type of biosurfactant, and environmental conditions may affect this results. Consequently, other studies on relevant environmental conditions, on different soils and with different biosurfactants are necessary. Indeed, biosurfactants are classified into four major groups: glycolipids, lipopeptides, phospholipids, and polymeric biosurfactants, and all of them are more or less effective on hydrocarbons remobilization.

In a previous study (Cazals et al., 2020), we determined that the bacterial community used in this study to produce the biosurfactant was mainly composed by members of the *Microbacterium* genus, *Rhodanobacteraceae* family, *Pseudomonas* genus and *Enterobacteriaceae* family. Most of these bacteria present in the biosurfactant-producing community could be affiliated (at least at the family taxonomic level) to bacteria able to produce biosurfactants and to biodegrade hydrocarbons (Abouseoud et al., 2008; Jain et al., 2013; Mansouri et al., 2017; Lopez-Gutierrez et al., 2004). A clear identification of the nature of the biosurfactant was not possible, but it was determined as biosurfactant and not other surface active compounds. The community cumulated the ability of different strains: (i) produce biosurfactants and/or (ii) biodegrade hydrocarbons, that could lead to an efficient biodegradation.

The main objective of the present study is to confront this biosurfactant-producing culture to a real contaminated soil. Assays were performed to estimate (i) the efficiency to remove TPH from soil and compare this efficiency to a classically used surfactant (Sodium Dodecyl Sulfate (SDS)), both as foam or liquid mixtures and (ii) to evaluate the impact of these treatments on the soil microorganisms, and subsequent biodegradation of remaining TPH after the soil washing step.

# 2. Material and methods

## 2.1. Context, soil sampling and analyses

The sampling of the studied soil was performed on September 29th, 2020, firstly to set the biopile on site. Soil was excavated up to the unsaturated zone (around 3 m depth), and homogenized mechanically on site before setting up the biopile. Then, 4 soil samples were taken from this homogenized soil and analysed for several physico chemical parameters. The sampling of the 4 samples was performed within the biopile using a hand auger up to around 1 m depth within the biopile. A composite sample of these 4 samples was used for the column and microcosms experiments. These samples were used for the lab-scale experiments presented in this study, and the biopile was monitored in parallel on site (data not shown), with injection and recovery pipes as shown on the picture on supplementary material SM1.

## 2.2. Soil physico-chemical characterization

Initial characterization of the 4 soil samples from the constructed biopile was performed after sampling, by EUROFINS lab. The analysed parameters were soil humidity, pH H₂O, Nitrate leachable nitrate (NO₃) and nitrite, Kjeldahl nitrogen (NTK), Global nitrogen (sum of nitrogen indices), Total Organic Carbon (TOC), Phosphorus (P), Potassium (K) and Sodium (Na). Concerning contaminants, the following parameters were analysed: Volatile Hydrocarbon (C5-C10), Total Petroleum Hydrocarbons (TPH – C10-C40 separated in 8 families), Polycyclic Aromatic Hydrocarbons (PAHs - 16 PAHs listed by US EPA) and BTEX (Benzene, Toluene, Ethylbenzene and Xylenes) concentrations. BTEX (Benzene, Toluene, Ethylbenzene and Xylenes) were also analysed but were at very low concentrations (0.47 ± 0.40 mg.kg⁻¹ for the sum of 4

BTEX). Tables summarizing the results of this initial characterization are given on supplementary materials SM2 and 3. Analyses of contaminants concentrations from the samples taken during the experiments were performed by Agrolab laboratories (AL-West B.V., Deventer, Netherlands).

## 2.3. Experimental setup

### 2.3.1. Chemicals and reagents

All chemical reagents used in this study were purchased from Sigma Aldrich (Merck - Sigma-Aldrich, Steinheim, Germany). These reagents were Sodium Dodecyl Sulfates (SDS, 98.5 % purity) for the washing step, and all reagents used for the bacterial community growth in the bioreactor (Cazals et al., 2020). The biosurfactant-producing culture was grown in 5 L bioreactors, with a culture medium composed of 20 g.L⁻¹ of glucose, 2 g.L⁻¹ of NH₄NO₃, and 14.2 g.L⁻¹ of Na₂HPO₄, at 20 °C, pH 7, in tap water and with continuous injection of air to achieve a dissolved oxygen concentration of 9 mg.L⁻¹, close to saturation in water at 20 °C. Tap water was used to make the process conceivable at a real scale on site. The tap water was composed of 54.8 mg.L⁻¹ Ca²⁺, 4.7 mg.L⁻¹ Mg²⁺, 0.2 mg.L⁻¹ Fe²⁺, 3.7 mg.L⁻¹ K⁺, 13 mg.L⁻¹ Na⁺, 180.3 mg.L⁻¹ HCO₃, 4.9 mg.L⁻¹ NO₃, 0.11 mg.L⁻¹ F⁻ and 21.1 mg.L⁻¹ Cl⁻, as described previously in (Cazals et al., 2020).

### 2.3.2. Experimental setup

Experiments were performed in two steps: (i) soil washing in columns experiments with different (bio)surfactants (Fig. 1) and (ii) monitored attenuation by biodegradation of remaining hydrocarbons in batch experiments using the washed soils from step 1 (Fig. 2).

For the first step (soil washing), six columns have been set up. Both (bio)surfactants have been used as liquid solutions and foam solution by adjunction of air (ratio 4:1 for air:liquid) before injection in the columns. Columns C1 and C2 corresponded to the biosurfactant solution (injection of the bioreactor content, i.e. bacteria + culture media + biosurfactant), C1 for liquid injection and C2 for foam (liquid and air 4:1). In a previous study (Cazals et al., 2020), it was shown that the concentration of biosurfactant in the bioreactor, after similar growth period, was around 1.6 times its critical micellar concentration (CMC). All details about the setup of the bioreactor are given in this previous publication (Cazals et al., 2020). After evaluation by surface tension in the bioreactor at the beginning of the experiment, CMC of the biosurfactant in the bioreactor was around 1 CMC (surface tension SFT of 28.57 mN.m⁻¹). SFT was monitored in the columns effluents during the experiment. Columns C3 and C4 corresponded to the SDS assays, C3 for liquid and C4 for foam. SDS solution was prepared at 5 times its CMC (~10 g.L⁻¹), to ensure the formation of micelles. It must be noted that SDS and biosurfactant were not used at the same CMC as the biosurfactant solution consisted in a bacterial culture, without possibility to control the CMC, as it's dependent of biosurfactant production within the bioreactor. Then, it was decided to work on a classical CMC for SDS to ensure representativity with classical on site treatment, and with the CMC of the culture for biosurfactant, as in the case of a real on site treatment, it would have been easier to set up. Another column, C5, was set up fed with a solution containing only nutrients (the same as in bioreactor), but without initial growth of the biosurfactant producing bacterial community. This column corresponds to a biostimulation of the endemic microbial community of the initial soil by addition of nutrients, but without the impact of surfactants injection. And finally, the C6 column was set up as a control, with the injection of deionized water. For all six columns, the injection steps were as follow: (i) injection of two pore volumes (PV) of deionized water, (ii) injection of five PV the respective solutions and (iii) rinsing with two PV of deionized water. For C6 column, only deionized water was injected (9 PV). The pore volumes of the different columns were evaluated by weighting the columns with dry soil and filling with water. The difference between weights of columns without and with water (with subtraction of dead volumes) was
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Experimental setup for step 1: soil washing in soil columns using (bio)surfactants as liquid or foam solutions, C1 and C2 are for crude biosurfactant (culture) columns, C3 and C4 are for SDS surfactant, C5 corresponds to the injection of nutrients only (the same as bioreactor) and C6 was used as control with injection of deionized water only.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Experimental setup for step 2: monitored natural attenuation of remaining hydrocarbons in microcosms.</figcaption></figure>

considered as columns pore volumes. The PV was estimated to 18.30 ± 1.25 mL (values are given in supplementary materials SM4). All columns were filled with the same initial contaminated soil, and gently compacted. The flow rate was set to 4 mL.h⁻¹ of liquid, and 16 mL.h⁻¹ for air in C2 and C4 columns (to ensure a water: air ratio of 20:80 as performed on site). For each of the nine pore volumes, water samples of effluents were taken and analyzed for pollutants concentrations. Additionally, the surface tension at the columns outlets was monitored for columns containing (bio)surfactants (C1 to C4). It was also monitored in the bioreactor at T₀ and final. Theoretical CMC values were estimated from these surface tension values.

After the washing step of the soils in the columns, washed soils were recovered and placed in microcosms to monitor the natural attenuation of hydrocarbons that could not be extracted by the different washing methods (Fig. 2). Additionally, two control microcosms were implemented: a biotic control corresponding to the initial soil without any treatment and an abiotic control corresponding to the initial soil with addition of sodium azide to sterilize the soil as much as possible. Microcosms were incubated in the dark at 20 °C. Attenuation of

hydrocarbon concentrations (TPH, PAHs, BTEX) were monitored during 91 days in the soils (6 sampling dates). Mass balances were estimated, and the impact of step 1 and the evolution of the bacterial and archaeal communities in the soil were monitored by molecular approaches (targeted on the 16S rRNA gene).

## 2.4. Microbial community analyses

### 2.4.1. Extraction and quantification of soil DNA

DNA extractions were performed for each soils excluding the sterile condition, at the beginning of step 2 and after 91 days of incubation, using the FastPrep DNA Spin Kit for soils (MP Biomedicals) following the manufacturer's instructions. The DNA in the bioreactor was also extracted at the end of step 1. Total extracted DNA (environmental DNA) was quantified using a Quantus Fluorometer (Promega) and stored at -20 °C prior to further analysis.

### 2.4.2. qPCR of 16S rRNA

Quantification of the number of 16S rRNA genes copies was
performed on all DNA samples by qPCR using a CFX Connect™ Real-Time PCR Detection System (Bio-Rad, France). The primers used were 341F (3'-CCTACGGGAGGCAGCAG-5') and 515R (3'-ATTACGCGGCTGCTGGCA-5'). qPCR reactions were performed in a total volume of 20 µL, with a mix containing 7.6 µL of sterile, nuclease- and nucleic acids-free water (MP Biomedicals, CA, USA), 10 µL of SSO Advanced Universal SYBR Green Supermix (Bio-Rad), 400 nM (16S rRNA genes) of each primer, 100 ng of T4 GP32 (16S rRNA genes), and 2 µL of template DNA (0.1 ng·µL⁻¹ to 5 ng·µL⁻¹). Sterile, nuclease- and nucleic acids-free water was used instead of DNA in negative controls. PCR reactions were performed as follows: 3 min at 95 °C, followed by 35 cycles: 30 s at 95 °C / 30 s at 60 °C / 30 s at 72 °C / 30 s at 80 °C. At the end of the qPCR, an additional step rising from 60 to 95 °C at 0.5 °C/s was applied for melt curves generation.

A calibration curve was obtained from serial dilutions of a known quantity of linearized plasmids containing known copy numbers of 16S rRNA gene. All samples, controls and standards were analyzed in duplicates. Results were reported as number of gene copies per g dry soil or per mL for soils and cultures, respectively. Generation of a specific PCR product was confirmed by melting curve analyses and agarose gel electrophoresis.

### 2.4.3. Next generation sequencing (16S rRNA gene)

Bacterial and archaeal diversity were evaluated by Illumina MiSeq sequencing of the 16S rRNA gene. Sequencing was performed on 16 samples: the initial soil, the bioreactor and the six conditions (C1 to C6) at initial and final times of the step 2 (soil batch biodegradation), with two controls (biotic and abiotic). Amplicon libraries were constructed by INRAE Transfert (Toulouse, France). Briefly, a portion of the 16S rRNA gene (V4-V5 region) was amplified using the barcoded, universal primer set 515WF - GTGYCAGCMGCCGCGGTA /928WR - CCCCGY-CAATTCMTTTRAGT (Wang and Qian, 2009). PCR reactions were performed using the AccuStart II PCR ToughMix kit, followed by cleaning (HighPrep PCR beads, Mokascience), and pooled triplicates were submitted for sequencing on Illumina MiSeq instrument at GeT-PlaGe (Auzeville, France). Fastq files were then uploaded to the Galaxy web platform (Sygenae), and the public server at usegalaxy.org was used to analyze the data, with the FROGS analysis platform (Escudié et al., 2018). Affiliations were obtained using the Silva 138_16S database. The affiliation table and sequences counts can be found in supplementary materials SM5 and rarefaction curves in SM6. The sizes of reads for R1 and R2 (forward and reverse) were 250 bp. For trimming, the FROGS Pre-process merging, denoising and dereplication tool was used (Galaxy Version r3.0–3.0). Mismatch rate was set to 0.1 (maximum rate of mismatch in the overlap region of R1 and R2 in consensus sequences), and merge calculations were performed using Flash software for an expected amplicon size of 413 bp (min 340 bp, max 450 bp). Then, clustering was performed using FROGS Clustering swarm amplicon sequence clustering tool (Galaxy Version r3.0–1.4) with denoising. The aggregation distance clustering was set to a maximum of 3 bp for inclusion into clusters. To remove the PCR chimera in each sample, the FROGS Remove chimera tool was used (Galaxy Version r3.0–7.0). This tool is based on the VSEARCH tool for chimera detection and removal (Rognes et al., 2016). Alpha diversity indices were calculated from the NGS data. The first index corresponds to the observed number of Operating Taxonomic Units (OTUs) in data from 16S rRNA gene sequencing. The Chao1 index estimates the unobserved OTUs by correcting values with OTUs that are observed only one or two times in data (correction for the rarest OTUs). The Shannon and Inverse Simpson indices give an indication of the specific richness in the samples, by linking the number of species with the specific abundance of these species in the different samples. All sequences were submitted to NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1220693.

## 2.5. Genotoxicity and cytotoxicity assays

At the end of the microcosms experiment, SOS-Chromotest® and Toxi-Chromotests® were performed on the different soil samples to explore soil genotoxicity and cytotoxicity, respectively. The experiments were performed according to the manufacturer's instructions, with adaptation concerning the dilution of samples (no dilutions here, samples were prepared on the same chromoPAD).

## 2.6. Statistical analyses

Statistical analyses were performed on XLStat software v2013.3.2 (Addinsoft). Alpha and beta diversity calculations were performed using FROGS platform (Galaxy sygenae), as well as bioinformatics treatments of raw sequence data (FastQ).

# 3. Results

## 3.1. Initial soil physico-chemical and biological parameters and contaminants concentrations

The 4 soil samples from the biopile were characterized after sampling and before further experiments. The native physico chemical properties are listed in Table 1. Briefly, humidity was found to be around 10 ± 0.4 % and pH around 8.5 ± 0.3. N values (nitrate, nitrite, Kjeldahl and sum) were low. Concerning NO₃ and NO₂, values were below the LOQ (20 mg.kg⁻¹ dw.). Kjeldahl nitrogen was measured at 0.9 ± 0.3 g. kg⁻¹ dw. as well as total nitrogen. Total organic carbon values was measured at 20,525 ± 2152 mg.kg⁻¹ dw. It corresponds to a C/N ratio of 22.8, which is high for a soil, probably due to the contribution of TPH contaminants. P, K and Na values were measured at 226 ± 27.4 mg.kg⁻¹ dw., 741 ± 65.1 mg.kg⁻¹ dw. and 186 ± 15 mg.kg⁻¹ dw., respectively. These values are high in comparison with classical soils, especially regarding sodium.

Concerning TPH concentrations, it was measured at 82.3 ± 47.8 mg. kg⁻¹ dw for the sum of C5-C10 TPH (the more volatile fraction). It must be noted here that the standard deviation is high due to only one of the 4 samples that presented lower values. Concerning the C10-C40 fraction, it was measured at 12,925 ± 776 mg.kg⁻¹ dw., with higher contribution of C24-C32 fraction (>50 % of the C10-C40 index). The sum of the 16 PAHs listed as priority by the US EPA were measured at 5.30 ± 0.81 mg. kg⁻¹. This is a low quantity compared to TPH contaminants, but it corresponds however to an anthropic source linked to the activity in the polluted site.

The total quantity of DNA (environmental DNA) extracted from the initial soil (pool of the 4 different samples) was measured at 20.22 µg.g⁻¹ dw., corresponding to a relatively low concentration compared to the means in soils from France (RMQS, INRAE). The results of DNA quantities monitoring in the experiments (impact of soil treatment) will be presented in the next sections.

## 3.2. Experimental monitoring: soil washing step in columns

### 3.2.1. Monitoring of surface tension (SFT)

Surface tension is directly related to the (bio)surfactant concentration in aqueous solution, and so to the capacity of surfactants to produce micelles and remove contaminants. Initial surface tension of the bioreactor, before injection in the columns, was measured at 28.57 ± 0.33mN.m⁻¹, corresponding to 1 × CMC (Cazals et al., 2020). This result validates the production of biosurfactant in the culture and allowed the washing step in columns.

Initial injected solutions had a SFT of 39.47 mN.m⁻¹ for the SDS solution, 65.94 mN.m⁻¹ for the C5 column (only nutrients) and 28.57 mN.m⁻¹ for the bioreactor, corresponding theoretically to 10 × CMC for SDS, 1 × CMC for bioreactor and 0 × CMC for C5 solutions, respectively. At the C5 column outlet (only nutrients), the surface tension was measured
<caption>**Table 1**</caption>

Physicochemical properties and TPH / PAHs concentrations of the studied soil (n = 4) from the biopile and mean values / standard deviations.

<table><thead><tr><th></th><th>#1</th><th>#2</th><th>#3</th><th>#4</th><th>Mean (n = 4)</th><th>Standard deviation</th></tr></thead><tbody><tr><td>Humidity</td><td>10,3</td><td>10,3</td><td>9,6</td><td>9,7</td><td>10,0</td><td>0,4</td></tr><tr><td>pH H<sub>2</sub>O</td><td>8,5</td><td>8,5</td><td>8,7</td><td>8,1</td><td>8,5</td><td>0,3</td></tr><tr><td>Nitrate leachable (NO<sub>3</sub>) (mg/kg d.w.)</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>-</td></tr><tr><td>Nitrite leachable (NO<sub>2</sub>) (mg/kg d.w.)</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>-</td></tr><tr><td>Kjeldahl nitrogen (NTK) (g/kg d.w.)</td><td>0,6</td><td>0,9</td><td>0,6</td><td>1,3</td><td>0,9</td><td>0,3</td></tr><tr><td>Global nitrogen (NO<sub>2</sub>+NO<sub>3</sub>+NTK)(g/kg d.w.)</td><td>0,6</td><td>0,9</td><td>0,6</td><td>1,3</td><td>0,9</td><td>0,3</td></tr><tr><td>Total Organic Carbon (TOC) (mg/kg d.w.)</td><td>21,700</td><td>21,500</td><td>17,300</td><td>21,600</td><td>20,525</td><td>2152</td></tr><tr><td>Phosphorus (P) (mg/kg d.w.)</td><td>224</td><td>246</td><td>188</td><td>246</td><td>226,0</td><td>27,4</td></tr><tr><td>Potassium (K) (mg/kg d.w.)</td><td>748</td><td>790</td><td>647</td><td>779</td><td>741,0</td><td>65,1</td></tr><tr><td>Sodium (Na) (mg/kg d.w.)</td><td>189</td><td>197</td><td>164</td><td>194</td><td>186,0</td><td>15,0</td></tr><tr><td>Volatile Hydrocarbon Index (C5-C10) (mg/kg d.w.)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C5 - C6</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>-</td></tr><tr><td>C6 - C8</td><td>7,8</td><td>5,9</td><td>10</td><td>&lt;LOQ</td><td>7,9</td><td>2,1</td></tr><tr><td>C8 - C10</td><td>101</td><td>88,5</td><td>104</td><td>11,6</td><td>76,3</td><td>43,6</td></tr><tr><td>ΣC5 - C10</td><td>109</td><td>94,4</td><td>114</td><td>11,6</td><td>82,3</td><td>47,8</td></tr><tr><td>Total Petroleum Hydrocarbons (TPH) (mg/kg d.w.)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C10 ≤ C12</td><td>21</td><td>21</td><td>34</td><td>21</td><td>24</td><td>6</td></tr><tr><td>C12 ≤ C16</td><td>231</td><td>224</td><td>362</td><td>212</td><td>257</td><td>70</td></tr><tr><td>C16 ≤ C20</td><td>683</td><td>652</td><td>1036</td><td>591</td><td>741</td><td>201</td></tr><tr><td>C20 ≤ C24</td><td>2274</td><td>2012</td><td>3019</td><td>1895</td><td>2300</td><td>505</td></tr><tr><td>C24 ≤ C28</td><td>3793</td><td>3389</td><td>3768</td><td>3308</td><td>3565</td><td>252</td></tr><tr><td>C28 ≤ C32</td><td>3805</td><td>3558</td><td>2825</td><td>3416</td><td>3401</td><td>416</td></tr><tr><td>C32 ≤ C36</td><td>2154</td><td>2139</td><td>1560</td><td>1805</td><td>1915</td><td>286</td></tr><tr><td>C36 < C40</td><td>721</td><td>778</td><td>665</td><td>635</td><td>700</td><td>63</td></tr><tr><td>ΣC10-C40</td><td>13,700</td><td>12,800</td><td>13,300</td><td>11,900</td><td>12,925</td><td>776</td></tr><tr><td>Polycyclic Aromatic Hydrocarbons (PAHs) (mg/kg d.w.)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Naphthalene</td><td>0,41</td><td>0,43</td><td>0,38</td><td>0,46</td><td>0,42</td><td>0,03</td></tr><tr><td>Fluorene</td><td>0,52</td><td>0,57</td><td>0,39</td><td>0,42</td><td>0,48</td><td>0,08</td></tr><tr><td>Phenanthrene</td><td>0,26</td><td>0,52</td><td>0,42</td><td>0,44</td><td>0,41</td><td>0,11</td></tr><tr><td>Pyrene</td><td>0,63</td><td>0,77</td><td>0,67</td><td>0,57</td><td>0,66</td><td>0,08</td></tr><tr><td>Benzo[a]anthracene</td><td>0,16</td><td>0,27</td><td>0,22</td><td>0,15</td><td>0,20</td><td>0,06</td></tr><tr><td>Chrysene</td><td>0,19</td><td>0,3</td><td>0,33</td><td>0,16</td><td>0,25</td><td>0,08</td></tr><tr><td>Indeno[1,2,3-cd]Pyrene</td><td>0,15</td><td>0,26</td><td>0,18</td><td>0,18</td><td>0,19</td><td>0,05</td></tr><tr><td>Dibenzo[a,h]anthracene</td><td>0,05</td><td>0059</td><td>&lt;LOQ</td><td>0058</td><td>0,06</td><td>0,00</td></tr><tr><td>Acenaphthylene</td><td>&lt;LOQ</td><td>0094</td><td>0083</td><td>0086</td><td>0,09</td><td>0,01</td></tr><tr><td>Acenaphthene</td><td>1,2</td><td>1,1</td><td>0,87</td><td>0,88</td><td>1,01</td><td>0,16</td></tr><tr><td>Anthracene</td><td>0076</td><td>0,18</td><td>0,12</td><td>0,12</td><td>0,12</td><td>0,04</td></tr><tr><td>Fluoranthene</td><td>0,23</td><td>0,42</td><td>0,34</td><td>0,29</td><td>0,32</td><td>0,08</td></tr><tr><td>Benzo[b]fluoranthene</td><td>0,27</td><td>0,45</td><td>0,34</td><td>0,29</td><td>0,34</td><td>0,08</td></tr><tr><td>Benzo[k]fluoranthene</td><td>0093</td><td>0,15</td><td>0,1</td><td>0065</td><td>0,10</td><td>0,04</td></tr><tr><td>Benzo[a]pyrene</td><td>0,21</td><td>0,35</td><td>0,23</td><td>0,23</td><td>0,26</td><td>0,06</td></tr><tr><td>Benzo[ghi]Perylene</td><td>0,4</td><td>0,6</td><td>0,41</td><td>0,45</td><td>0,47</td><td>0,09</td></tr><tr><td>Σ16PAHs (US EPA)</td><td>4,8</td><td>6,5</td><td>5,1</td><td>4,8</td><td>5,30</td><td>0,81</td></tr></tbody></table>

at 65.94 ± 0.1 mN.m⁻¹ for the first PV and 61.04 ± 1.26 mN.m⁻¹ for the second PV (Fig. 3). It was not monitored for the next steps as it was not expected to observe significant changes in surface tension for this column, and as the SFT measured were close from the inlet solutions.

For the first PV, the surface tension of all columns was near the SFT of water (i.e. ~72 mN.m⁻¹), except for C1 column, maybe due to preferential pathways in this column leading to increased arrival of injected solution at the column outlet. For the other columns, this SFT corresponded to the mQ water flushed from the columns (coming from the 2 PV injected at the beginning of the experiment). After the first PV, SFT tended to decrease up the 3 PV for all conditions, but a higher decrease was observed for SDS conditions compared to biosurfactants. After 3 PV, SFT values tended to stagnate for C1 and C2 columns (biosurfactant), where it continues to decrease for C3 and C4 (SDS), up to ~30 mN.m⁻¹ at 5 PV. No significant difference could be observed between C3 (liquid) and C4 (foam), whereas for biosurfactant, it seems that the liquid

condition (C1) allows to obtain lower SFT compared to foam (C2). These results suggest that the TPH removal should be better for SDS compared to the biosurfactant, and that for the biosurfactant, the liquid solution should offer higher efficiency compared to foam.

### 3.2.2. TPH concentrations in effluent solutions

The TPH concentrations were monitored in the columns effluents, for each conditions and each PV (including water injection at 1/2 PV and 8/9 PV). Results are presented for two groups; (i) the more volatile fraction C5-C10 (Fig. 4a and 4b) and (ii) the heavier fraction C10-C40 (Fig. 4c and 4d).

Concerning the C5-C10 fraction, concentrations are low compared to the whole TPH in the effluents (~80 times lower compared to C10-C40). This is consistent with the initial concentrations in soil (Table 1). Significant concentrations could be detected for C2 and C4 columns from the start of the sampling, but these concentrations rapidly decreased up to 3 PV, and then increased slightly again. The cumulative concentrations (Fig. 4b) highlighted a more efficient removal of C5-C10 fraction after 9 PV for C3 and C4 columns (SDS) and C2 column (biosurfactant foam). Concerning the C10-C40 fraction, it appeared that the washing efficiency was much higher for SDS compared to biosurfactant, and that the condition foam for biosurfactant (C2) was better compared to liquid (C1), for which no quantifiable concentrations in the effluent could be detected (Fig. 4c and 4d). The opposite result could be observed for SDS: liquid condition (C3) presented better results compared to foam (C4).

Interestingly, for C2 column, the TPH concentrations were higher for heavier TPH (supplementary materials SM7). For example, concerning the C36-C40 fraction, concentrations for C2 column effluents were close

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Monitoring of surface tension at the columns outlet for columns containing (bio)surfactants, for the five pore volumes of solution injected (water injection was not monitored here).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Quantification of TPH in the columns effluents for the 6 columns and 9 PV (2°PV mQ water, 5 PV (bio)surfactants, 2 PV mQ water); legend is: (a) C5-C10 concentrations, (b) C5-C10 cumulative washed TPHs (µg), (c) C10-C40 concentrations and (d) C10-C40 cumulative washed TPHs (µg).</figcaption></figure>

to those of C4 (SDS foam), whereas it was highly lower for C10 to C24 fractions.

### 3.2.3. PAHs concentrations in effluent solutions

PAHs concentrations in soils are highly lower compared to TPH, below the quality threshold in France for excavated soils (<50 mg/kg). However, the PAHs detected in our soil could correspond to bound residues, with low bioaccessibility and consequently low biodegradation. It is then interesting to evaluate the efficiency of soil washing on these residues. As was observed for TPH, the washing efficiency was greatly higher with SDS, with concentrations in columns effluents of up to 100 µg.L⁻¹ at 5 PV vs no >5 µg.L⁻¹ with the biosurfactants (Fig. 5).

Concerning biosurfactants, the concentrations in effluents were slightly higher in C2 (foam) compared to C1 (liquid). The conditions C5 and C1 presented very similar washing efficiency, suggesting here that

the presence of biosurfactants could present a low benefit compared to a nutrient solution only (C5). Overall, the SDS solution concerning PAHs presented washing efficiency ranging from 10 to 100 times higher compared to the biosurfactants solution.

### 3.2.4. Mass balance

The initial concentrations of pollutants in the studied soil were (i) 12,925 mg.kg⁻¹ dw. for the C10-C40 fraction, (ii) 31.35 mg.kg⁻¹ (measured in abiotic control) for C5-C10 fraction, (iii) 5.3 mg.kg⁻¹ for Σ16PAHs and (iv) 0.633 mg.kg⁻¹ for the sum of BTEX. Consequently, the main issue here is about C10-C40 fraction. Concerning the washing efficiency (mass balance), considering these initial concentrations, and a mean mass of soil in the columns of 268.6 ± 1.09 g soil d.w., it is possible to evaluate a percentage of washed pollutants from the columns, according to the different conditions.

<figure><img src="image_8.png" /><figcaption>Fig. 5. Quantification of PAHs in the columns effluents for the 6 columns and 9 PV (2 PV mQ water, 5 PV (bio)surfactants, 2 PV mQ water); legend is: (a) concentrations for the sum of the 16 US EPA PAHs, (b) Cumulative washed PAHs (µg).</figcaption></figure>
For C5-C10 fraction, considering initial concentrations of 82,3 mg. kg⁻¹ (Table 1), each column contained 22.13 mg of this fraction. The removed quantities in columns were, respectively for C1 to C6, of 0, 0.049, 0.043, 0.051, 0.008 and 0 mg. The washing step consequently allowed the removal of, from C1 to C6, 0 %, 0.22 %, 0.19 %, 0.23 %, 0.04 % and 0 %, respectively.

For the C10-C40 fraction, at the end of step 1, the washed quantities were about 0.77 mg for C1; 4.48 mg for C2; 37.35 mg for C3; 17.77 mg for C4; 0.97 mg for C5 and 0 mg for C6 were washed out of the columns. These quantities correspond to a washing efficiency, respectively for C1 to C6, of 0.022 %, 0.129 %, 1.075 %, 0.511 %, 0.028 % and 0 %.

For PAHs, the total quantities (mg) washed from the columns, were respectively for C1 to C6 of 0.0056, 0.0019, 0.087, 0.053, 0.0005 and 0.00007 mg. At the end of step 1, the total washed quantities of PAHs (Σ16 PAHs US EPA) were respectively, for C1 to C6, of 0.39 %, 0.13 %, 6.10 %, 3.72 %, 0.03 % and 0.005 %.

And finally, regarding BTEX, no residual concentrations could be detected after the washing step (<LOQ), suggesting a good efficiency of the washing steps, but BTEX concentrations were initially low compared to the other hydrocarbons (0.47 ± 0.40 mg.kg⁻¹ for the sum of 4 BTEX).

These results could help dimensioning a washing of these soils, that would be efficient for a total removal of pollutants. Much more PV of solution should be injected to increase the removal of contaminants from the studied soil, and finally the concentrations of pollutants to consider for biodegradation (step 2) are not highly different compared to the native soil.

### 3.3. Experimental monitoring: biodegradation step in microcosms

The second step of the experiment consisted in recovering the washed soil from the columns and place it in microcosms to monitor

subsequent natural attenuation, and resilience of the bacterial community after the washing step. Natural soil control (biotic) was set up as well as abiotic control (initial soil with addition of NaN₃ to inhibit microbial activity).

#### 3.3.1. Monitoring of TPH concentrations in soils

Concerning the more volatile fraction (C5-C10, Fig. 6a and 6b), nearly all TPH were no longer detected (<LOQ) in all conditions except in the abiotic control. This result highlights a good efficiency of the washing step, maybe due to higher solubility/volatility of this fraction. Interestingly, the same result was obtained for the biotic control corresponding to native soil. This could be explained by a rapid biodegradation at the very beginning of the experiment, as this is the same soil as abiotic control were C5-C10 fraction is still detected at a concentration however lower compared to initial biopile soil (25 to 30 mg.kg⁻¹ dw. vs 82.3 mg.kg⁻¹ (Table 1) in the initial soil). This could be explained by dissipation during the storage of the soil samples before this step.

Fig. 6c and 6d, dealing with the C10-C40 fraction, highlight that no biodegradation occurred during ~60 days in the SDS conditions (C3 and C4), nor water conditions (C6) and abiotic control (Tabio). In the biotic control, a very low decrease in concentrations could be observed (up to ~10 % degradation), overall very close from C6 condition (water). However, a significant enhancement of the biodegradation could be highlighted in C1 and C2 conditions (biosurfactant) and C5 (nutrients), with up to ~30 % degradation for C1/C2 and up to ~40 % degradation for C5. Biodegradation occurred more rapidly in C5 condition, maybe due to an adapted microbial community that was fed by nutrients during the washing step, where C1/C2 received biosurfactants. Looking at the kinetics, for C1/C2, a lag phase of ~30 days seemed necessary for the microbial community to adapt, where in C5 it appeared that the microbial community was already adapted to biodegradation. Based on

<figure><img src="image_9.png" /><figcaption>Fig. 6. Quantification of TPH in soils during the monitored natural attenuation step; legend is: C1-C40 concentrations and (a) C5-C10 concentrations, (b) C5-C10 percentage, (c) C10-C40 percentage.</figcaption></figure>
these results, it was necessary to look at the evolution of the bacterial and archaeal communities in the different conditions (Sections 3.3.3. and 3.3.4.).

### 3.3.2. Monitoring of PAHs concentrations in soils

Concerning PAHs, naphtalene, acenaphtylene, acenaphthene and fluorene were not detected (<LOQ) in soils after washing step in any condition. These PAHs were probably efficiently removed from the soils during the washing step due to their higher solubility in water compared to the other PAHs (solubility at 25 °C of 32, 3.93, 3.42 and 1.9 mg.L⁻¹, respectively). Other PAHs were detected at low concentrations. It is then difficult to conclude on the biodegradation of the sum of the 16 PAHs listed by US EPA (Fig. 7a and 7b) as concentrations are quite low (some PAHs presented concentrations <LOQ at some sampling points). It must be noted that the initial concentrations are lower than to the initial soil, probably because of dissipation during the storage time of the soil before the microcosm experiments, even in the biotic control condition corresponding to the native soil.

Looking at pyrene (Fig. 7c and 7d), results are more significant. It appeared that its concentration after the washing step was close from the native biopile soil, highlighting low efficiency on pyrene removal, for all conditions. After 60 days monitoring, interestingly, lower pyrene concentrations were observed for C5 and C6 conditions, corresponding to the solutions with nutrients only (biostimulation) and water only, respectively. Pyrene concentrations decreased also in C1/C2 conditions (biosurfactants), in a lower extent. Trends are not observable for C3/C4 conditions (SDS), nor in the abiotic control. In the biotic control (initial soil), the trend is very close from C1/C2 conditions.

### 3.3.3. Environmental biomass and bacterial abundance (16S rRNA)

The abundance of genomic DNA (molecular biomass) and 16S rRNA

gene in soils was evaluated at T0 (right after columns experiments) and T57 (at the end of microcosms experiments, after 57 days) for all conditions except the abiotic control. Regarding molecular biomass, we observed that soil DNA remained stable in the C6 condition and in the biotic control, as well as the abundance of bacteria (Fig. 8a and 8b, respectively), even if the bacterial abundance in the biotic control was slightly higher at T57. Concerning DNA abundance at T0 right after the washing step, it was lower in C3/C4 columns and also in C5, what is more surprising. In C1 and C2 columns, the molecular biomass is intermediate compared to the other samples, even if lower in C1, suggesting that the oxygen input in C2 (to form the foam) could have stimulated the microbial community. At T57, the molecular biomass returns to values comparable to the biotic control, with 3 exceptions: (i) high molecular biomass in C5 condition (biostimulated), (ii) C3 and C4 columns, where the molecular biomass remains very low compared to the other conditions, suggesting a higher toxicity in SDS conditions.

Concerning the bacterial abundance (Fig. 8b), results are overall comparable to molecular biomass, with some specific evolution for C2 (but the bacterial abundance was higher at T0), C3 and C4 where the bacterial community seemed to be quite resilient, but still lower compared to the other conditions. These DNA samples were then analysed by metabarcoding of the 16S rRNA gene, to explore biodiversity of the different conditions.

### 3.3.4. Bacterial and archaeal diversity (NGS illumina)

Concerning alpha diversity indices, the bioreactor was not represented in Fig. 9 as biodiversity is logically very low. Bioreactor presented a total number of observed Operational Taxonomic Units (OTUs) of 16, a Chao1 index of 16, a Shannon index of 1.38 and an inverse Simpson index of 3.28. The biodiversity in the bioreactor was logically low, but presented a diversity close from the first results (Cazals et al., 2020),

<figure><img src="image_10.png" /><figcaption>Fig. 7. Quantification of PAHs in soils during the monitored natural attenuation step; legend is: (a) concentration of the sum of the 16 US EPA PAHs, (b) percentage of the sum of the 16 US EPA PAHs, (c) pyrene concentration and (d) pyrene percentage.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Evaluation of (a) molecular biomass (genomic DNA abundance) and (b) number of copies of the 16S rRNA gene in soils at T0 (just after the washing step) and T57 (end of microcosms experiment).</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Alpha diversity indices in all conditions at T0 (after soil washing) and T57 (end of microcosms experiments). (a) number of observed OTUs; (b) Chao1 index; (c) Shannon index and (d) inverse Simpson index.</figcaption></figure>

with a bacterial community producing biosurfactants.

The number of OTUs and Chao1 index give an information on the global biodiversity of the samples (Chao1 estimates missing OTUs), when Shannon and inverse Simpson indexes give an indication on the specific richness in the samples (abundance of specific OTUs in the sample). We could observe here that the number of OTUs and Chao1 index was globally stable between T0 and T57, except for C3 and C4 conditions, where the biodiversity continued to decrease during microcosm's experiments. At T0, the lower diversity was also observed for these two conditions, when values of observed OTUs and Chao1 in all other conditions were close to the native soil (Fig. 9a and 9b).

The specific richness inside the OTUs presented different trends (Fig. 9c and 9d). In the C1, C2, C5 (only Shannon), C6 conditions, it increased between T0 and T57. That means that even for conditions where the diversity remained the same (same number of OTUs), the abundance of bacteria inside the OTUs increased.

That means that no new species appeared, but the endemic bacteria remained stable and were able to replicate. In C3/C4 conditions, on the contrary, the specific richness decreased between T0 and T57. In the presence of SDS, the total number of different OTUs decreased (Fig. 9a and 9b) and the abundance of bacteria inside the remaining OTUs also

decreased (Fig. 9c and 9d). Concerning the biotic control, both biodiversity (observed OTUs and Chao1) and specific richness (Shannon and inverse Simpson) remained stable during the microcosms experiment.

The beta diversity gives information on the global evolution of the bacterial community. Fig. 10 presents a heatmap of the nature of the bacterial communities in the different samples. The heatmaps were based on samples dissimilarities, first on weighted Unifrac algorithm distances (Fig. 10a) and Bray-Curtis distances (Fig. 10b). Again, the main observable difference here is the C3 and C4 samples (higher difference with the native soil), suggesting again that the SDS treatment strongly affected the bacterial community, whether at T0 or at T57, whereas other treatments had limited effects. Except for the bioreactor, where diversity is logically completely different from the other samples, the heatmap highlights lower differences between the other samples and the native soil.

Looking at the repartition of the bacterial (and archaeal) biodiversity at the phylum taxonomic level (relative abundance of specific OTUs, Fig. 11), the proteobacteria were highly dominant in all conditions. Bacteroidota phylum was found to be highly lower in the SDS washed soils (C3/C4) compared to the native soil. Only the biotic control presented similar results compared to the native soil. The bacteroidota
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Heatmap of the beta diversity between the 16 samples analyzed, dark green corresponds to a higher difference. (a) heatmap based on weighted Unifrac algorithm distances and (b) heatmap based on Bray-Curtis distances.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Bacterial diversity at the phylum taxonomic rank for the different studied conditions.</figcaption></figure>

phylum was particularly affected by the washing using SDS, it was nearly absent in C3 and C4 conditions, whether at T0 of T57 (no resilience of this phylum), even if it slightly reappeared in C4 at T57. Acidobacteria relative abundance was also almost zero in C3 and C4 conditions. Deinococcota phylum tended to decrease in these conditions between T0 and T57 and was globally 5 times lower in C3/C4 T57 conditions compared to the native soil, whereas values were comparable in other conditions. The firmicutes phylum presented the same evolution but in a lower extent. Actinobacteria relative abundance, on the contrary, was favored in SDS conditions at T57, but the majority of the bacterial community was composed by members of the proteobacteria phylum. Archaeal biodiversity was overall low, and was not much

different in the different conditions. It's however important to look at higher taxonomic ranks due to specificity of functions related to TPH biodegradation; this will be addressed in the discussion section. Relative abundances at higher taxonomic ranks (order and genus) can be found on supplementary materials SM8 and SM9. Verrucomicrobiota and WPS-2 phyla appeared as significantly abundant at T57 in C1, C2, C5 and C6 conditions only.

### 3.3.5. Soil genotoxicity and cytotoxicity

Images of the toxicity assays may be found on supplementary materials SM10. It appeared that both treatments did not affect significantly the soil genotoxicity (Fig. 12a). Regarding the results of cytotoxicity (Fig. 12b), characterized in this test by the inhibition of the β-galactosidase enzymatic activity, it appeared that the SDS treatment resulted in a more cytotoxic soil after soil washing step and 60 days recovery. It must be noted that for genotoxicity (Fig. 12a), a higher toxicity corresponds to a higher value (grey intensity), where for cytotoxicity, a higher toxicity corresponds to a lower grey intensity (SM10).

This result is especially visible for C3 condition, suggesting that the treatment using SDS as liquid solution resulted in a higher toxicity, maybe due to the lower oxygen abundance (foam is generated using SDS solution and injection of air), resulting in more favorable conditions in C4 condition. This result is an overview of soil toxicity but it can't give real modifications of soil bacterial community, but it tends to prove that soil washing using SDS increases soil acute toxicity on the long term. Comparing with C6 (only water) and initial soil, it appeared a slight increase in genotoxicity in soils exposed to (bio)surfactants, maybe due to an increase in bioavailability of pollutants.

<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Results of (a) SOS-Chromotest and (b) Toxi-Chromotest assays on the soils from step 2 at T57. SOS-chromotest gives the genotoxicity of the soils and TOXI-Chromotest highlights cytotoxicity of soils.</figcaption></figure>
# 4. Discussion

## 4.1. Soil washing

The results highlight that SDS is more efficient for hydrocarbons removal from contaminated soils but has a much higher impact on the soil microbial communities. Additionally, when considering a second step of monitored natural attenuation after soil washing, the efficiency of the remediation is finally better by washing using a mix of biosurfactants and producing bacteria (i.e. corresponding to both a biostimulation and bioaugmentation) compared to chemical analogs. However, at a similar CMC (5 CMC) for the biosurfactant, results would probably have been different during the washing step. Also, the impact of CMC on bacterial membrane could also affect the global toxicity in the soil, and consequently affect the biodegradation treatment efficiency or affect more strongly the soil functions (Otzen, 2017). In other words, a higher concentration would produce a higher toxicity, so increasing efficiency in step 1 would decrease efficiency of step 2. This should be evaluated in further studies, especially addressing the impact of the biosurfactant nature (glycolipids, for example, may reduce the membrane toxicity) and concentration on the efficiency of the treatment and on the functions of the microbial community. But it would have been necessary to extract and purify the biosurfactant, which is not achievable at the field scale. Consequently, even if CMC is low for the bioreactor, it is preferable to keep this kind of process on site to make the treatment possible. Moreover, with higher concentrations, the biosurfactant could also present a toxicity for the microbial communities of the soil, as well as SDS, and so the biodegradation could be affected for the remaining contaminants in soil. It must be noted that the efficiency of (bio)surfactants highly depends on soil physico chemical properties, and results could have been different in another type of soil. Indeed, it has been shown that under natural conditions, microbial surfactants mediated bioremediation depends on several factors like soil pH, temperature, and magnitude of heavy metals in soil (Datta et al., 2024). This efficiency also depends on the amount of pollution in the soil (Szulc et al., 2014; Ławniczak et al., 2013). SDS, compared to biosurfactants, could be less sensitive to these points.

The results for step 1 are much better for SDS, but the mass balance shows that not so many pollutants were extracted from the soil, with maximum values of ~1 % of the initial pollution in C3 column. These results explain why concentrations in soils before step 2 are close in all conditions. However, it must be noted that for C10-C40 fraction, concentrations were around ~5145 ± 449 mg.kg⁻¹ (n = 8) at the beginning of step 2, so more than two times lower than initial concentrations characterized on the initial soil. This is probably due to biodegradation occurring during samples storage or in the column during step 1. This is logical because the soil was reworked, and it probably led to an increase in microbial activity and potential pollutants desorption, leading to a stimulation of degradation and potential release by volatilization. Finally, considering only a soil washing, without valorization and consideration of soil recovery after treatment, SDS appears to be the best option, by adapting injected volumes and concentration of SDS to ensure best extraction results. But it is important to consider also the impact on the soil biodiversity when considering further soil valorization after treatment, solution that could be systematically preferred to decrease the environmental impact of soil remediation. In this sense, it seems preferable to select a solution that induce a lower toxicity, and the use of biosurfactants, jointly produced in a bioreactor composed by bacteria that could be involved in pollutants biodegradation, seems to be an interesting solution to explore. It must be noted that biosurfactants may also induce a cytotoxic effect, and a virulence for the producing bacteria, depending on the concentration (Xu et al., 2024). In our study, the biosurfactant was produced directly by a bacterial culture previously selected for biosurfactant production (Cazals et al., 2020), ensuring low toxicity of the injected solution. This could be different with a purified biosurfactant injected at high concentrations.

## 4.2. Monitored natural attenuation after soil washing

After soil washing, the natural attenuation of pollutants in soils was monitored. It appeared a better pollution decrease, with up to 40 % removal for C10-C40 fraction observed for C5 column, corresponding to the addition of nutrients only (biostimulation), followed by C1/C2 column, showing ~25 to 30 % removal. In the other conditions, the C10-C40 fraction did not highlight significant change during the 57 days monitoring. This result suggests that in this case, the biostimulation was the more efficient technique for soil bioremediation, but also that the impact of SDS on the soil biodegrading community was strong, as suggested by the toxicity tests (Fig. 12). The controls did not move, confirming that without any treatment, the pollution would remain in the soil.

Other studies addressed soil washing with (bio)surfactants and/or biodegradation of hydrocarbons in the recent years, employing both synthetic and biological surfactants, often in combination with microbial consortia (endemic or via bioaugmentation) or chemical agents. These approaches reveal varying efficiencies depending on the surfactant used, soil type, and integration with other treatment methods (Table 2).

Synthetic surfactants like sodium dodecyl sulfate (SDS) and Tween 80 are widely used due to their strong solubilizing capabilities. For example, SDS was shown to achieve a TPH removal efficiency of 90.7 % in only 2 h (Caetano et al., 2024), while Tween 80 reached 80.7 % in 5 h, under optimized washing conditions using an orbital shaker and a liquid-solid ratio of 15:1. These conditions are more favorable compared to our study, but difficult to envisage at the field scale. On the other hand, biosurfactants, notably rhamnolipids, surfactin, and sophorolipids, demonstrated similarly high or superior efficiencies, often with reduced environmental risks. It was reported a removal efficiency of up to 99 % using a rhamnolipid-surfactin mixture on oily sludge in a 24 hour process at 30 °C, affirming the performance of biosurfactants in handling complex hydrocarbon matrices (Zhao et al., 2024), but again, it's difficult to compare with real on-site remediation of soils. Again, the experimental conditions here are difficult to envisage for an on-site treatment. On the other hand, it was shown that when combined with microbial consortia, surfactants can further enhance bioremediation. A reduction from 153,200 mg.kg⁻¹ to 7600 mg.kg⁻¹ of TPH in soils was observed (Wu et al., 2025). In this study, bioaugmentation after soil washing was used, it did not correspond to a natural attenuation after washing but rather to a sequence (i) soil washing and (ii) bioaugmentation. This corresponds to a double remediation, and makes it more difficult to implement on site. Lee et al. (2024) also confirmed that bioaugmentation post-washing significantly improves efficiency (Lee et al., 2025). Tween 80 alone yielded 72 % TPH removal, whereas the combination with a microbial consortium reached 94 %, showing the advantage of sequential treatment approaches, especially for the most persistent hydrocarbons. Similarly, Sanchez Huerta et al. (Sanchez-Huerta et al., 2025) used biosurfactants to solubilize hydrocarbons from a contaminated groundwater. They showed a removal efficiency of around 27 to 30 % of oil from groundwater, depending on the nature of (bio)surfactants. Better results were observed when solubilization step was combined with biodegradation. These results are consistent with our study, even if in our case the washing step showed lower efficiency, independently from (bio)surfactant nature.

Overall, the data highlight that biosurfactants are emerging as effective and environmentally sustainable alternatives to synthetic surfactants. Yet, the highest efficiencies are often obtained through combined or staged treatments, especially when targeting deep or heavily contaminated soils. Approaches that integrate surfactant washing with bioremediation or chemical oxidation offer the best trade-off between effectiveness, cost, and environmental safety. Additionally, integrating soil (bio)surfactant washing and monitored natural attenuation (i.e. not biostimulation or bioaugmentation) represent a more cost effective technique, and is easier to set up at the field scale.
<table><caption>**Table 2**
Washing efficiency observed in other recent studies for TPH.</caption><thead><tr><th>(Bio)surfactants used</th><th>Type</th><th>Treatment Method</th><th>TPH Removal Efficiency (%)</th><th>Reference</th></tr></thead><tbody><tr><td>SDS, Tween 80</td><td>Synthetic</td><td>Soil washing (ex situ)</td><td>SDS: 90.7 ± 2.8%; Tween 80: 80.7 ± 3.2%</td><td>Caetano et al. (2024) (Caetano et al., 2024)</td></tr><tr><td>Rhamnolipid, Surfactin, Sophorolipid</td><td>Biosurfactants</td><td>Soil washing</td><td>Up to 99%</td><td>Zhao et al. (2024) (Zhao et al., 2024)</td></tr><tr><td>SDS + Bioaugmentation (Bacillus, Rhodococcus)</td><td>Hybrid (synthetic + bio)</td><td>Field bioremediation + washing</td><td>>90% in topsoil; ~80% in deeper layers</td><td>Wu et al. (2025) (Wu et al., 2025)</td></tr><tr><td>Tween 80 + Biosurfactant consortia</td><td>Synthetic + bio</td><td>Soil washing + bioaugmentation</td><td>94% (combined treatment); 72% (washing only)</td><td>Lee et al. (2025) (Lee et al., 2025)</td></tr><tr><td>BS and C (no identification)</td><td>(Bio)surfactant</td><td>On groundwater with NAPL</td><td>27% (synthetic) to 30% (washing + biodegradation)</td><td>Sanchez Huerta et al. (2025) (Sanchez-Huerta et al., 2025)</td></tr><tr><td>SDS + Biosurfactant producing bacterial community</td><td>Synthetic and/or biosurfactants</td><td>Soil washing + biodegradation</td><td>25 to 40% for washing+biodegradation, max ~1% for washing only</td><td>This study</td></tr></tbody></table>

### 4.3. Impact on soil bacterial community

To understand more precisely what was the impact of the different additives used for soil washing on the soil bacterial community, metabarcoding targeting the 16S rRNA gene was performed (bacteria and archaea). First, concerning the bioreactor, it must be noted that metabarcoding did not allow fine affiliation of all the sequences, where cloning and sequencing allowed a more precise taxonomic identification in a previous study (Cazals et al., 2020). Some OTUs couldn't be characterized up to the family or genus, but it seems that the community remained quite stable compared to the previous assays. Additionally, more OTUs could be characterized here, and some of them are known for hydrocarbons biodegradation, which could be interesting both for biosurfactant production but also for enhanced biodegradation. In the bioreactor, 16 OTUs could be identified in total, but 11 of them were lower than 1 % of total abundance. Concerning the 5 more abundant OTUs, 42.8 % of the sequences were attributed to an unclassified gammaproteobacteria. It could correspond to the *Rhodanobacteraceae* family previously characterized in this bacterial consortium (Cazals et al., 2020). Then, 23.6 % of the sequences corresponded to *Enterobacterales* order, 25.1 % to *Xanthomonadaceae* family and 5 % to the *Delftia* genus, and 1.56 % of the sequences corresponded to an OTU belonging to the *Microbacteriaceae* family. Among the less abundant OTUs, that could also be important in terms of impact on hydrocarbons biodegradation or biosurfactant production, even if lower represented, we could find sequences affiliated to the *Burkholderiales*, *Rhizobiales*, *Xanthomonadales*, *Enterobacterales*, *Pseudomonadales* and *Micrococcales* families. All these families include members that were described for hydrocarbons degradation (Lee et al., 2019; Abbasian et al., 2015), and the inoculation of a contaminated site with this biosurfactant producing community could also represent a bioaugmentation increasing soil bioremediation.

Looking at global biodiversity, it appeared that SDS affected alpha diversity (diversity in the samples) significantly (Fig. 9) with a decrease in the number of observed OTUs (and Chao1 index), and no recovery after 57 days. This decrease was not (or very low) observed in the other conditions (C1, C2, C5 and C6), and the alpha diversity indices tended to increase after 57 days, highlighting a good bacterial activity in soils that have not been exposed to SDS. Looking at beta diversity (biodiversity between the samples; PCoA in Fig. 13), the SDS treatment induced a strong impact on the bacterial community structure (arrow a2 on the Fig. 13). The soil washed with biosurfactant (C1/C2) or nutrients solution (C5) presented a modification of the bacterial structure (arrow a1), probably due to bioaugmentation and/or stimulation of the soil bacterial community. This modification was not sustainable on the microbial community as the structure was very close from the native soil after 57 days, highlighting a recovery of the bacteria in soils (arrow a3 on Fig. 13). This was not observed on soils exposed to SDS, for whom the bacterial community did not return to its initial structure after 57 days (arrow a4, Fig. 13). This result suggests that the impact on soil microbial communities and functions of washing using SDS could be long and irreversible, where other methods using environmentally friendly

<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Principal Coordinate analysis (PCoA) based on Bray-Curtis dissimilarities matrix of the abundance of different OTUs in the studied samples. Arrows a1 to a4 highlight major changes in the community (a1 and a2 correspond to the impact of soil washing, a3 and a4 to the resilience of the bacterial community after microcosms step).</figcaption></figure>

surfactants could be less impactful for the soil.

Looking *At otus* diversity at the phylum level, the phylum WPS-2 could be found at T57 in C1, C2, C5 and C6 columns where it's abundance was not significant in other conditions. WPS-2 (Wittenberg polluted soil-2) is a group of phototrophic bacteria that can thrive under "extreme" environmental conditions (Sheremet et al., 2020; Ji et al., 2021), including polluted soils. SDS could have affected this phylum particularly, which could partially explain lower degradation in these columns. The bacteroidota phylum was also strongly affected by SDS. Bacteroidota are ecologically important for the proper functioning of soil (Kruczynska et al., 2023). The phylum Bacteroidota represents a versatile group of bacteria with many unique molecular mechanisms, all of which allow them to occupy distinct ecological niches (Lidbury et al., 2021). These bacteria are important carbohydrate degraders in the soil and the human gut. Bacteroidota proliferation is favored by their ability to secrete diverse sets of carbohydrate-active enzymes (CAZymes) that target highly diverse glycans in soil (Larsbrink and McKee, 2020). A very well-studied genus belonging to Bacteroidota is *Flavobacterium*, which breaks down complex organic compounds (Kraut-Cohen et al., 2023). In the case of a soil bioremediation and potential use of soils after washing and monitored natural attenuation, the loss of this phylum would clearly be a disadvantage for the normal functioning of the soil after treatment. *Verrucomicrobiota* and *acidobacteria* phyla were at higher abundance at T57 in C1, C2 and C5 conditions (also in C6 for *acidobacteria*). This
similarity of evolution between these two taxa was already described (Martínez-Cuesta et al., 2023) in ecopiles of soils contaminated by TPH, especially at the end of the monitoring after TPH biodegradation. This could highlight a synergistic activity of these two taxa for TPH biodegradation. The same trend was observed for WPS-2 phylum. This result was not observed in other conditions (SDS), highlighting again the negative impact of SDS regarding soil biodegradation capacity.

Finally, looking at higher taxonomic ranks, it is more difficult to analyze due to the high number of determined OTUs, but some interesting results appeared. Concerning the C3/C4 columns, OTU30, corresponding to *Xanthobacter autotrophicus*, was only found in C3/C4 conditions. *Xanthobacter autotrophicus* has been described as capable of degradation of some hydrocarbons (1,2-dichloroethane, propane) and methanol (Vasileva et al., 2009; Peter et al., 1997). The capacity of the soil has consequently not disappeared but was however not high enough to observe clear biodegradation. The genus KCM-B-112 nearly disappeared in C3/C4 conditions, where it represented between 11.7 to 27.7 % of the OTUs in the other conditions (and 22.7 % in native soil). This genus has been described as being predominant in petroleum contaminated soils (Mafiana et al., 2021). This could be a result of complete soil washing, but as shown after the washing step, TPH are still abundant even in C3/C4 conditions. The loss of this strain could represent a high loss in the soil's TPH biodegradation capacity and could be an explanation of the low biodegradation in step 2 observed in our experiment. The genus *Parvibaculum* was also strongly affected by the SDS soil washing, as it was nearly absent in C3/C4 conditions, where it represented around 3.5 to 9.0 % of total abundance in the other conditions (8.72 % of sequences in the native soil). *Parvibaculum* has been described as able to biodegrade hydrocarbons efficiently in soils (Li et al., 2023). The genus *Flavobacterium* represented 6.38 % of identified sequences in native soil. It was not affected in biotic control, but it seems that this OTU was affected in all other conditions. Some species of this genus have been described as able to biodegrade a high range of hydrocarbons (Chaudhary et al., 2019; Chaudhary and Kim, 2018; Jung et al., 2021). This decrease in abundance could consequently affect the soil biodegradation capacity, but biodegradation results highlight that the biodegradation occurred despite of the decrease in abundance of this strain.

## 4.4. Key conclusions

Our study demonstrates that while SDS is highly more effective in the initial removal of hydrocarbons during soil washing compared to our biosurfactant culture, it significantly disrupts soil microbial communities, with effects that persist beyond 57 days. In contrast, biosurfactants, especially when combined with producing bacteria (biostimulation or bioaugmentation), offer a more balanced solution by achieving effective remediation with lower ecological impact. Although biosurfactant-based washing was less efficient here (but lower CMC compared to SDS as dependant of production in the bioreactor), it allowed better natural attenuation in the second "natural attenuation" phase, highlighting the importance of considering both remediation steps together. Moreover, SDS-treated soils showed reduced microbial diversity and resilience, affecting long-term soil functions, including biodegradation potential. Soil recovery and microbial function were notably better preserved in biosurfactant or nutrient-treated conditions. Overall, integrated approaches that combine biosurfactant washing with monitored natural attenuation present a more sustainable and field-applicable strategy for hydrocarbon-contaminated soils. The findings underscore the importance of assessing not only pollutant removal but also the preservation of soil microbial health for effective and environmentally responsible remediation.

## 5. Conclusion and perspectives

This study aimed at testing the impact of (bio)surfactants on soil

washing, on a soil co contaminated by hydrocarbons (TPHs, PAHs and BTEX). The experimental setup proposed to study this impact on a two-step experiment: (i) washing step by injecting (bio)surfactant solutions as foam or liquid solutions and (ii) natural attenuation of hydrocarbons step in soil after the washing step. Looking at the washing efficiency, it appeared that SDS had the best extraction efficiency compared to the biosurfactant culture, and that the liquid or foam conditions did not affect significantly the hydrocarbons removal. It appeared also that the global removal of pollutants was not so high regarding the global pollutants in soil, and corresponded to 0 to 1.075 % for C10-C40 fraction, for example. However, the second step aiming at monitoring natural biodegradation after washing step highlighted better pollution reduction for the soils that were not exposed to SDS. Regarding the impact on bacterial community, it appeared that the different solutions used for soil washing impacted soil microbial community, and potentially soil functioning. The higher impact was observed for SDS, independently from conditions (as liquid or foam solutions). The impact of foam compared to liquid solutions did not present strong differences, suggesting that the impact on the soil bacteria is rather related to the chemicals themselves rather than to the conditions. The impact was characterized on various bacterial taxa, and some taxa are known to biodegrade hydrocarbons. The SDS treatment could then induce a reduction in soil biodegradation capacity, beyond global functions given by soils. However, it would have been interesting to test a higher range of concentrations to validate the efficiency and toxicity of the different additives. Concerning the biosurfactant, it would be interesting to purify the compound to be able to adjust the CMC and compare the efficiency to SDS at the same CMC, but this technique would not be reliable for a real scale treatment. Finally, the authors suggest to strongly consider the impact on soil microbial communities of soil washing and selection of surfactants, as they may modify strongly the soil global functioning and biodegradation capacity.

## CRediT authorship contribution statement

**Marc Crampon:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Florian Cazals:** Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Jonathan Senechaud:** Resources, Project administration, Funding acquisition, Conceptualization. **Arnault Perrault:** Resources, Investigation, Funding acquisition, Conceptualization. **Sagyn Omirbekov:** Formal analysis, Data curation. **Stéfan Colombano:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization.

## Declaration of competing interest

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

Colombano Stefan reports financial support was provided by Centre-Val de Loire Region. Marc Crampon reports financial support was provided by European Union. 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.

## Acknowledgements

This research was carried out as part of the CONCERTO project funded by Colas Environnement and BRGM. The authors acknowledge Colas Environnement and BRGM for their financial support. We gratefully acknowledge the financial support provided to the PIVOTS project by the Région Centre – Val de Loire (ARD 2020 program and CPER 2015 –2020) and the French Ministry of Higher Education and Research
(CPER 2015 –2020 and public service subsidy to Brgm). This operation is co-funded by European Union. Europe is committed to the Center-Val de Loire region with the European Regional Development Fund.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.hazadv.2025.100855.

## Data availability

Data will be made available on request.

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