# Microbial diversity and geochemistry of groundwater impacted by steel slag leachates

Margot Bärenstrauch, Audrey S Vanhove, Séverine Allegra, Steve Peuble, Frédéric Gallice, Frédéric Paran, Veronique Lavastre, Françoise Girardot

► To cite this version:

Margot Bärenstrauch, Audrey S Vanhove, Séverine Allegra, Steve Peuble, Frédéric Gallice, et al. Microbial diversity and geochemistry of groundwater impacted by steel slag leachates. Science of the Total Environment, 2022, 843, pp.156987. 10.1016/j.scitotenv.2022.156987. emse-03930561

HAL Id: emse-03930561

https://hal-emse.ccsd.cnrs.fr/emse-03930561v1

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1 **TITLE**: Microbial diversity and geochemistry of groundwater impacted by steel slag leachates

2 **Authors**: Margot Bärenstrauch¹, Audrey S. Vanhove¹, Séverine Allegra¹, Steve Peuble², Frédéric

3 Gallice², Frédéric Paran², Véronique Lavastre³, Françoise Girardot¹

4 1 : Université de Lyon, Université Jean Monnet Saint-Etienne, CNRS, EVS-ISTHME UMR 5600, F-42023

5 Saint-Etienne, France

6 2 : Mines Saint-Étienne, Centre "Sciences des Processus Industriels et Naturels" (SPIN), Département

7 "Procédés pour l'Environnement et les Géo-ressources" (PEG), UMR 5600 EVS, UMR 5307 LGF, F-42023

8 Saint-Etienne, France

9 3 : Université de Lyon, Université Jean Monnet Saint-Étienne, Laboratoire de Géologie de Lyon – Terre

10 Planètes Environnement LGL-TPE, CNRS -UMR 5276, F-42023 Saint-Etienne, France

11 **Corresponding author**:

12 Françoise Girardot

13 UMR 5600 EVS-ISTHME

14 Université J. Monnet, IUT

15 Campus Métare, Dpt GBGE Bât G

16 28 av L. Jouhaux

17 42023 SAINT-ETIENNE CEDEX 2, France

18 françoise.girardot@univ-st-etienne.fr

19
1 **TITLE**: Microbial diversity and geochemistry of groundwater impacted by steel slag leachates

2 **Authors**: Margot Bärenstrauch¹, Audrey S. Vanhove¹, Séverine Allegra¹, Steve Peuble², Frédéric

3 Gallice², Frédéric Paran², Véronique Lavastre³, Françoise Girardot¹

4 1 : Université de Lyon, Université Jean Monnet Saint-Etienne, CNRS, EVS-ISTHME UMR 5600, F-42023

5 Saint-Etienne, France

6 2 : Mines Saint-Étienne, Centre "Sciences des Processus Industriels et Naturels" (SPIN), Département

7 "Procédés pour l'Environnement et les Géo-ressources" (PEG), UMR 5600 EVS, UMR 5307 LGF, F-

8 42023 Saint-Etienne, France

9 3 : Université de Lyon, Université Jean Monnet Saint-Étienne, Laboratoire de Géologie de Lyon – Terre Planètes Environnement LGL-TPE, CNRS -UMR 5276, F-42023 Saint-Etienne, France

11 **Corresponding author**:

12 Françoise Girardot

13 UMR 5600 EVS-ISTHME

14 Université J. Monnet, IUT

15 Campus Métare, Dpt GBGE Bât G

16 28 av L. Jouhaux

17 42023 SAINT-ETIENNE CEDEX 2, France

18 françoise.girardot@univ-st-etienne.fr

19
20 Abstract:

21 To understand long-term impacts of steel slag material on aquifer geochemistry and microbial
22 communities, we conducted four sampling campaigns in the Gier-alluvial groundwater (Loire,
23 France). In its northern part, the aquifer flows under a 200 000 m³ steel slag exhibiting high levels of
24 chromium and molybdenum. Geochemical analyses of the water table revealed the existence of
25 water masses with different chemical signatures. They allowed us to identify an area particularly
26 contaminated by leachates from the slag heap, whatever the sampling period. Water samples from
27 this area were compared to non-contaminated samples, with geochemical characteristics similar to
28 the river samples. To follow changes in microbial communities, the V3-V4 region of 16s rRNA gene
29 was sequenced. Overall, we observed lower diversity indices in contaminated areas, with higher
30 relative abundances of Verrucomicrobiota and Myxococcota phyla, while several Proteobacteria
31 orders exhibited lower relative abundances. In particular, one single genus among the
32 Verrucomicrobiota, Candidatus Omnitrophus, represented up to 36 % of total taxon abundance in
33 areas affected by steel slag leachates. A large proportion of taxa identified in groundwater were also
34 detected in the upstream river, indicating strong river-groundwater interactions. Our findings pave
35 the way for future research work on C. Omnitrophus remediation capacities.

36 **Keywords:** Steel industry, Geochemistry, Metabarcoding, Bacterial Diversity

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# 1. Introduction

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Slag is a mineral material formed during the successive stages of steelmaking. Between 2010 and 2020, Europe produced an average of 162 million tons of steel, generating around 46 million tons of slag each year (EUROFER, 2021). Around 40 % of steel production uses scrap metal as a starting material, resulting in the formation of so called “electric arc furnace slag” (EAF). Raw EAF slag often appears as grey or black colored lumps, resembling volcanic rocks. Changes in the steelmaking process over the last few years have allowed a better valorization of EAF slags, which can nowadays be recycled for metal recovery, road construction, cement production, or even serve as amending to raise pH in acidic soils (Branca et al., 2020). However, whether steel slags are stored in landfills, or recycled, concerns have been raised about their environmental impact (Piatak et al., 2015; Teo et al., 2020).

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EAF slag material contains large amount of CaO that can result in the formation of a highly alkaline (pH >10) solution when exposed to water. As a result, steel slag leachates can disturb natural rates of mineral precipitation or dissolution in water, possibly impacting natural groundwater flow patterns (Bayless and Schulz, 2003), as well as local fish populations and benthic fauna (Koryak et al., 2002; Hull et al., 2014). Impacts of calcite precipitation and pH on surrounding aquatic environments can last for decades (Riley and Mayes, 2015). In addition, alkaline conditions can increase leaching of trace metals like Al, Cr and W, as shown during speciation experiments with slag material (Scattolin et al., 2021). Several toxic metals have been found in steel slag leachates such as aluminium, barium, chromium, molybdenum and vanadium (Bayless et al., 1998; Fällman, 2000; Chaurand et al., 2007; Matern et al., 2013; Neuhold et al., 2019), raising issues for surrounding aquatic environments especially groundwater, when located directly beneath steel slag material.

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In addition to being an important source of drinking water, groundwater also regulates many biogeochemical processes such as carbon, iron, sulfur and nitrogen cycling, for which microorganisms
are key players (Griebler and Lueders, 2009; Griebler and Avramov, 2015). In groundwater, microorganisms also participate in the transfer of organic matter along the trophic chain by providing a food source for stygofauna (Griebler and Avramov, 2015; Saccò et al., 2019). Groundwater microorganisms live in oligotrophic habitats with no light, low available organic carbon and stable temperatures, except in areas with strong interaction with rivers. Thus, they are believed to be particularly vulnerable to any environmental change, including contamination by trace metals (Griebler and Lueders, 2009). Describing bacterial communities in contaminated groundwater environments would give us a more comprehensive view of the biogeochemical processes impacted, and it would help us identifying resilient taxa. At the cell level, metals can damage membranes, inhibit essential enzyme activities or cause DNA damage (Bruins et al., 2000). At the community level, this can be reflected by loss of bacterial diversity and metabolic diversity as reported for several environments such as freshwater biofilms studied in microcosms (Ancion et al., 2010), estuary sediments (Sun et al., 2012), soils (Singh et al., 2014) and groundwater contaminated with several metals (Hemme et al., 2010, 2015; He et al., 2018). Metals can also shape bacterial communities by favoring resistant species as described in groundwater contaminated with arsenic (Li et al., 2017; Sonthiphand et al., 2021). Finally, presence of metals can enhance the transfer of multi-metal resistance genes through plasmid exchange (Hemme et al., 2016; Kothari et al., 2019). However, studies focusing on groundwater contaminated with trace metals remain scarce. It is worth noticing that the few studies which have approached steel slag contamination through the lens of microbial communities, have usually focused on highly contaminated environment with extreme alkaline conditions (Ohlsson et al., 2019; Roadcap et al., 2006).

In this study, we focused on a steelmaking site, still in activity, belonging to the Industeel - ArcelorMittal group and located at Châteauneuf (Loire, France). Several thousand tons of EAF were accumulated since 1950 and exposed to weathering. The waste produced before 2006 contains significant amounts of Cr, Mo and fluorides, exceeding the limit values to be recovered in road technique (SETRA, 2012) or admitted in landfill (Directive 1999/31/EC, n.d.). The site lies above the
alluvial groundwater of the Gier, whose permeability makes it particularly vulnerable to contamination by trace metal(oid)s. River water feeds the aquifer at the entrance of the industrial site then flows under the slag heap where leachates may enter the aquifer and finally joins the river at the site exit. Here, our main objective was to gain more insight into the impact of steel slag leachates on bacterial communities, by addressing the following questions: (1) Which trace metal(oid)s from the slag heap are leached into the groundwater and which areas of the water table (piezometers) are affected by this phenomenon? (2) To which extent are groundwater communities similar to that of the surrounding surface waters? (3) Do microbial communities affected by the steel slag differ from non-affected ones?

## 2. Material and methods

### 2.1 Study area description

The study area (Fig. 1) consists of an Electric Arc Furnace (EAF) plant of 30 ha belonging to Industeel France from the ArcelorMittal group, located at Châteauneuf (Loire, France). It is composed of a scrap metal yard, and a 200 000 m³ steel slag, with a thickness between 2 and 9 meters (Herbelin et al., 2020). The visible part of the slag is located in the eastern part, but aerial photographs taken since 1929 (https://remonterletemps.ign.fr/) show that the slag heap was historically located in the middle of the site and then progressively moved towards the north, until reaching its current position as indicated in Fig.1. The industrial site is bordered by the Gier river in its northern part and extends above the Gier alluvial aquifer, listed as body water 621A6 (Water Rhone Mediterranean Corsica agency). The aquifer consists in alluvial deposits of sands, gravels and pebbles lying on top of the substratum, composed of metamorphic rocks and coal formations. The groundwater flows from South-West to North-East, and the alluvium hydraulic conductivity is between 0.5 and 8.10⁻³ m/s. There is a hydraulic continuity between the river and the aquifer which is mainly fed by the Gier River at the upstream (near piezometer 3 area). On the eastern side of the site, the water table is also fed by groundwater from the slopes of the Massif du Pilat.-Downstream, groundwater flows back into
the Gier River. Groundwater flow direction remains the same during high and low water periods, and water table level could drop-by about 1 m during summer. Height piezometers (artificial boreholes providing access to groundwater) are located throughout the site and their characteristics are specified in Table S1. Due to their localization on recent or former steel slag materials, piezometers (PZ) 8, 9, 12, 13, 15 and 16 could be considered as potentially impacted by slag leachates, whereas, PZ1 and PZ3, located upstream the steel slag could represent control sites.

## 2.2 Experimental design and water sampling for microbial and chemical analyses

Water samples (surface and groundwater) were collected during high water periods on April 6, 2019, June 8 and 15, 2020 and April 29, 2021. Locations of the sampling points are indicated on Fig.1. Surface waters were collected in the Gier river upstream (GAM) and downstream (GAV) the industrial site. We also collected surface waters in the Bozançon river (BOZ) which is a tributary flowing into the river Gier at the slag heap. Groundwater samples were collected through the eight piezometers displayed on the industrial site, located upstream (PZ1 and PZ3), beneath (PZ13) or downstream the steel slag (all others). Groundwater levels were first measured with a piezometric level sensor. To ensure representative sampling, piezometers were then purged using a submersible pump (Super-twister, SDEC) and a 120V battery, until the physico-chemical parameters stabilize (dissolved oxygen (DO), Electronic conductivity (EC), pH and temperature). Water chemistry parameters were measured with a multiparameter probe (HI9829-10042, Hanna Instruments). For each sampling point and each campaign, 2 to 6 L from piezometers and 2L from rivers were collected in sterile polyethylene bottles (F2000BN, Labelians) for microbial analyses, and 60mL of sample were collected in dark glass bottles and immediately acidified with 2% dilute nitric acid (HNO₃) for metal analysis. For alkalinity, total carbon and nitrogen measurements, glass bottles were filled to capacity and analysed within 72 h. All samples were transported at 4°C in a cooling box. Microbial analyses (DNA extraction) were performed on the same day.
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Since local meteorological data were not representative, we collected river flow data, which
provided information on rainfall events occurring on the days preceding the field campaign. These
data were collected daily, up to four days before each field campaign from the hydrometric station
V3114010 (Fig.1), located at Rive-de-Gier in France.

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## 2.3 Geochemical analysis

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Contents of total carbon and total nitrogen were determined by injection of 500 µL of each sample on a TOC analyser (multi N/C 2100/2100S, Analytik Jena). Standard curves were obtained from dilutions of a potassium hydrogen phtalate and potassium nitrate solution dissolved in extrapure water (2.5 to 50 mg/L). Alkalinity of water was determined in the laboratory using a motorized burette (DL50, Mettler Toledo and LabX1.2 software). Acid/base titration was performed on a volume of 20 mL with a 0.1 mol/L hydrochloric acid solution. Results are given in Table S2 in the Supplementary Materials.

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For metal analysis, samples were filtered through 8 µm filters (Fisher Scientific), stored at 4 °C and analysed within a month. The concentration of dissolved metal(oid) species (Ca, Fe, K, Mg, Mn, As, Cr, Mo, Rb, Sr) was measured using an Agilent 7800 Quadrupole Inductively Coupled Plasma- Mass Spectrometry (ICP-MS) installed at Mines Saint-Etienne. The analyses were performed on samples previously diluted 10 times in MilliQ water. The accuracy and reproducibility of the measurements were assessed using the international standard SLRS-6 (Riverine Water Reference Material for Major and Trace Metals) certified by the National Research Council of Canada. The results showed a good agreement between our replicated measurements and the certified values (Yeghicheyan et al., 2019). Results are given in Table S3 in the Supplementary Materials.

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## 2.4 DNA extraction and 16S rRNA V3-V4 gene amplicon sequencing

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Water samples were filtered through 0.45 µm cellulose nitrate filters (11406-47-ACN, Sartorius Stedim Biotech). On three separate occasions (beginning, middle and end) during the filtration process of samples, 125 mL of PCR grade water were filtered and processed like the other samples. A
Water Qiagen kit (14900-50-NF, Qiagen) was used to extract DNA from cells retained on filters, according to the supplier's instructions. DNA was eluted in 100 µl and stored at -20°C. Amplification of DNAs (2.5 ng.µL⁻¹) using primers 341F (5'-CCTACGGGAGGCAGCAG-3') and 805R (5'-ATTACCGCGGCTGCTGG-3') to target the V3-V4 region of 16s rRNA gene, and sequencing were performed by Microsynth AG (Balgach, Switzerland), ISO 9001:2015 certified and ISO/IEC 17025:2017 accredited using an Illumina Miseq instrument. The primers were preceded by 5 uncalled bases (Ns) to increase diversity in the first sequencing cycles. The following conditions were used to prepare the Illumina Nextera barcoded two-step PCR libraries: 5µl DNA template, 20µL reaction volume and 20 cycles. One internal positive control and 3 negative controls (highly purified water instead of template) were incorporated in the plate and treated like other samples. PCR products were purified with NGS Clean Beads and 5µL of purified product was used as template for the second step PCR, using indexing primers from Illumina (Nextera Index v2, Set A-D) and 10 cycles. After a second round of purification with NGS Clean beads, samples were quantified with Picogreen and equimolarly pooled. The final pool was then purified and sequenced on MiSeq. Fast QC (v. 0.11.7) was used to assess reads quality (length 2 x 150). The trimming of reads was done with Illumina's bcl2fastq software (version 2.20.0.422, no further refinement or selection) and summarized by MultiQC (version 1.7). Additionally, usearch11 was used to trim the 16S v3-v4 locus specific 341F/805R primers and reads containing uncalled bases (Ns) were discarded from trimmed reads. The raw DNA sequences were submitted to the NCBI bioproject database under the number PRJNA647634 and sequence data for all samples are available with the accession numbers going from SAMN26646205 to SAMN26646221.

## 2.5 Sequencing data processing

All analyses were performed in R environment (v4.1.1) using the package DADA2 v.1.16.0 (Callahan et al., 2016). This method produces Amplicon Sequence Variants (ASV), which have been proposed as new standard units in place of Operational Taxonomic Units (OTUs) (Callahan et al., 2017). First, the
quality of reads was verified. All samples had a median quality score above Q30. Reads were filtered using standard parameters (maxN=0, max EE=2). After estimation of the error rates, dereplication and sample inference, forward and reverse reads were finally merged, in order to build the ASV table. Non-target-length (<373 or >442 base pairs) and chimeric sequences were removed. Then, taxonomic assignment was performed using the training set Silva v.138 (McLaren, 2020). Sequences assigned as "chloroplast" or "mitochondria" were removed. To construct the phylogenetic tree, sequences were first submitted to a multiple-alignment using the DECIPHER package v.3.10 (Wright, 2016). We then constructed a first neighbor-joining tree which served as a starting point to build the final maximum likelihood tree with phangorn package v.2.4.0 (Schliep, 2011). Taxonomic table, ASV table, sample metadata and phylogenetic tree were finally combined into one single phyloseq object.

Rarefaction curves (Fig. S1) were obtained with vegan package v.2.5.7 (Oksanen et al., 2020) and samples with a very low reads number (< 80), consistent with their bad PCR amplification level as reported by Microsynth, were removed from the dataset. As such, three replicates were obtained for all river samples and one or two replicates for groundwater samples. Table S4 gives details of 16S rRNA amplicon sequence analysis results for samples that were kept for this study. To display relative abundance of the main taxa, ASV counts from each sample were converted into their frequencies (or proportions), using the phyloseq package v.1.36.0 (McMurdie and Holmes, 2013). Eventually, reads were merged at different taxonomic levels and filtered with the genefilter package v.1.74.0 (Gentleman et al., 2021) to display only taxa above a chosen relative abundance threshold.

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## 2.6 Data analysis

Environmental data were log-transformed prior to principal component analysis (PCA), except for pH, to minimize the effect of outliers. PCA was performed using the R package "FactoMineR" v.2.4 (Lê, Josse, et Husson 2008). Alpha diversity indices (richness, Shannon, Pielou) were calculated at the order level, using the vegan package v.2.5.7 (Oksanen et al., 2020) with and without prior rarefaction of data. Indeed, if rarefying data is a common practice before calculating alpha diversity indices,

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there is currently no consensus on appropriate methods to normalize data (Weiss et al., 2017 ; Cameron et al., 2020). For rarefaction, data were resampled to an even depth of 6235 reads (depth of the smallest sample) using phyloseq package and the parameters indicating in brackets (seed = 711, replace = F). This eliminated 4745 ASVs belonging to 14 orders. Shannon index (H) was defined as H = -sum Pi *log2 (Pi), where Pi was the proportional abundance of taxa i. Pielou's evenness index (E) was defined as E = H / log2 (richness). Hierarchical clustering was also performed with vegan package, to examine how samples clustered. Phylogenetic distances and relative abundances of bacterial orders were combined using weighted Unifrac distance. Bray-Curtis dissimilarity was estimated from the relative abundances matrix (order level) and the samples were ordinated with PCoA, using functions from the phyloseq package. Similarity percentage analysis (SIMPER) was performed to identify discriminant taxa between river, control samples (PZ3, 1) and contaminated samples (PZ12, 13, 15, 16), using 100 permutations. Finally, the package DESeq2 v.1.32.0 (Love et al., 2014) was used to identify bacterial orders that were differentially abundant across contaminated samples (PZ12, 13, 15, 16) and control samples (PZ1, 3). Briefly, a new phyloseq object was created to keep only microbial orders which relative abundance was above 1 % in at least one groundwater sample. This gave a new subset of original raw counts data (i.e not transformed into proportions) containing 40 orders. This new phyloseq object was then converted into a DESeq object with phyloseq package, to generate estimates of reads dispersion. Hypothesis testing was performed using the Wald test already implemented in the DESeq function, with default parameters.

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3 Results and discussion

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3.1 Geochemical parameters, major ions and trace metals

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Analysis of environmental parameters by PCA revealed four distinct chemical signatures in groundwater (Fig.2). Changes of all parameters across field campaigns are detailed on Fig. S2 and S3.

Together, the two first PCA dimensions explained 69.7 % of the variance and metal(oid)s were the variables most correlated with samples clustering.

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The first dimension clearly separated surface waters from groundwater, except for PZ3, located in an area without EAF slag material, which clustered with surface waters. PZ3 and rivers were characterized by low levels of Cr (<5.6 and <1.3 µg.L⁻¹ for PZ3 and river samples, respectively). Mo was also scarcely detected (< LD) in BOZ, GAM and PZ3, and reached a maximum value of 12.7 µg.L⁻¹ in one downstream river sample (GAV). The clustering of river samples, apart from groundwater samples could also be explained by the higher values of dissolved oxygen and pH measured in rivers, while those displaying the lowest temperatures. Oxygen is known to decrease as the residual time of water in the groundwater increases (Mader et al., 2018). Similarly here, oxygen values were the highest in rivers (>8 mg.L⁻¹) and tended to be lower (2.1-3.4 mg.L⁻¹) in piezometers most distant from both recharge area and riverbank (PZ16, 8 and 9). pH values were stable across sites and campaigns, with a slight decrease observed for groundwater (6.8-7.9) compared to surface waters (7.3-8.2). As for temperatures, they were ranging from 13.4°C to 17.8°C and found to be stable within each campaign. However, the lowest values were recorded for river samples and PZ3, probably because this piezometer is the closest to the recharge area. Finally, no relevant pattern could be identified for total carbon concentrations, which as a result were poorly represented on the PCA.

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The second dimension of PCA allowed the distinction among piezometers. PZ1, located upstream the EAF slag material, showed low metal(oid) levels (<LD for Rb and Mo, and < 1.81 mg.L⁻¹ for Cr). All PZ1 replicates clearly clustered apart and exhibited significantly higher alkalinity (3.1-3.9 mEq.L⁻¹), calcium (72.2 - 96.1 mg.L⁻¹), magnesium (18.0-20.1 mg.L⁻¹) and to a lesser extent, total

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nitrogen levels (12.4-19.6 mg.L⁻¹). In line with those observations, significantly higher electrical conductivities were measured on the site (692-769 µS.cm⁻¹) at this location. The study region is a former mining area, and those results could coincide with intrusion of water coming from deep flooded coal mines. Indeed, mine waters are characterized by high electrical conductivities, usually explained by high calcium and magnesium concentrations (Younger, 2001; Gombert et al., 2019). They can also contain high bicarbonate levels (Gombert et al., 2019), which is consistent with the high alkalinity registered in our study. However, the high sulfate, nitrogen and chloride concentrations measured in this piezometer in previous investigations (data not shown) could also be explained by domestic water resurgence at PZ1.

PZ16 and 9, located on the Pilat side, had an intermediate position along the second axis of PCA, being correlated with high magnesium levels (17.1 – 21.2 mg.L⁻¹) but small amounts of Cr, Rb and As, like in PZ1 and PZ3, despite being located beneath EAF slag material (Fig. 1). Previous investigations on the site suggested the existence of a mixture with Mg-rich water probably originating from the Pilat at this location (data not shown). Therefore, one possible explanation for those lower Cr and Rb levels could be a dilution by water flowing from the Massif du Pilat.

Finally, PZ8, 12, 13 and 15 represented a fourth group of samples, which differed from surface waters and PZ1/3 in having higher levels of dissolved K, Mo, Cr and to a lesser extent Rb. In particular, significantly higher concentrations were recorded at PZ12 for Cr (10.5 – 48.7 µg.L⁻¹), Mo (34.8– 299.9 µg.L⁻¹), Rb (36.7 – 49.2 µg.L⁻¹), As (7.1 – 8.2 µg.L⁻¹) and Sr (590-839 µg.L⁻¹). Cumulative levels of Cr, Mo, Rb and Sr were similar across field campaigns, except for Mo and Rb which reached the highest levels at PZ12 after rainfall events (campaign 2 and 3), as illustrated on Fig. S4. Altogether, cumulative levels of meta(oid)s increased in the direction of groundwater flow and PZ12, the location furthest downstream from the slag, exhibited the highest levels of contamination. These observations supports the hypothesis of metal(oid)s mobilization from EAF slag material stockpiled on the site, except for As which was present in river sample at similar concentrations (2.8 – 6.5 µg.L⁻¹). Indeed, leaching of trace metals from steel slag material was previously reported for Mo and Cr
289 (Cornelis et al., 2008). Moreover, the high values recorded for Cr are consistent with the mineral phases detected previously on the steel slag (Herbelin et al., 2020) where calcium chromite (CaCr₂O₄), brownmillerite (Ca₂(Fe, Al, Cr)₂O₅) and a spinel solid solution containing chromium ((Fe,Mg)(Fe,Mn,Cr)₂O₄) were identified. Brownmillerite in particular accounted for 20 % of the slag material volume and was the most easily destabilized Cr-bearing phase. Among all metal(oid)s detected in this study, Mo exhibited the highest concentrations which were almost five times higher that Cr concentrations. This is consistent with previous studies showing that Cr leaching from EAF slag material tended to decrease with slag ageing while Mo leaching was more constant with time (Engström et al., 2014).

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299 3.2 Characterization of microbial communities

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301 3.2.1 Comparison of river and groundwater communities

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303 Metabarcoding analyses yielded a total of 30 183 amplicon sequence variants (ASVs) from all samples retained for analysis (Table S4), for which rarefaction curves indicated a sufficient sampling depth (Fig. S1). Alpha diversity indexes calculated at the order level are presented on figure 3. Although rivers contained the lowest taxa numbers, both river and groundwater samples showed similar Shannon and Pielou's indices. This implies that taxa in rivers, even if less numerous, were more equally distributed than in groundwater samples, where a few taxa might be dominating the community.

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Across all site, bacterial community was dominant over archaeal community (Fig. 4,A). The low representation of Archaea (<1 %), is possibly due to the choice of primers that favor bacteria (Baker et al., 2010; He et al., 2021). However, Archaea usually represent a small proportion of the total freshwater microbial biomass (Bomberg et al., 2008). Bacterial communities from river and groundwater environments showed different taxonomic profiles (Fig. 4, B). In rivers, the two main phyla identified were Proteobacteria (mainly Burkholderiales and Pseudomonadales) and
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Bacteroidota (mainly Flavobacteriales and Bacteroidales), which accounted for 28-59 % and 15-28 % of the total taxon abundance, respectively. These phyla are usually highly abundant in freshwater environments (Zwart et al., 2002; Savio et al., 2015).

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In groundwater samples, the phylum Verrucomicrobiota (mainly Omnitrophales), commonly described in aquifers, (Griebler and Lueders, 2009; Wegner et al., 2019), was highly dominant (35-43 %), in PZ12, 13, 15 and 16, while PZ1 and PZ3 were dominated by Proteobacteria. This phylum, mainly represented by Burkholderiales, accounted for 22 % and 32 % of total taxon abundance in PZ1 and PZ3, respectively. Patescibacteria (15-19 %) were also highly abundant in both river and groundwater environments. Members of Patescibacteria are prevalent in freshwater environments, and particularly in groundwater, where their small size could explain their ecological success, by preventing phage adsorption (Tian et al., 2020). These very small bacterial cells are usually thought to be lost during water filtration. However, due to their reduced genome and minimal metabolic pathways, they are thought to colonize other bacterial or protists hosts (Herrmann et al., 2019; Chaudhari et al., 2021). This could be an explanation for their high occurrence here, despite filtration.

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Similarities between microbial communities were then investigated at the phylum level (Fig 5, B). As expected, rivers and groundwater samples clustered separately. Among rivers, Bozançon, which is a smaller watercourse with only a few centimeters in depth, clustered apart. Gier samples (GAM, GAV) clustered by field campaign rather than by sampling sites (upstream vs downstream), suggesting that microbial communities were highly similar along the river continuum, and that the slag heap might not impact river microbial communities downstream (GAV). This would be in line with the low levels of trace metals detected in downstream river samples, indicating that groundwater does not bring trace metals into the river after crossing the industrial site. Principal Coordinate Analysis (PCoA) further highlighted a similar clustering pattern of river and groundwater samples (Fig. 5, B). Taxa belonging to Flavobacteriales and Burkholderiales were identified among the main bacterial orders discriminating both environments and associated with river samples clustering (Fig. 5, B). Indeed, Flavobacteriales, which are frequently isolated from aquatic environments
342

(O'Sullivan et al., 2004), had high relative abundances in rivers (7-24 %) while they were poorly detected in groundwater (< 1%)(Fig. S5, A). Burkholderiales too, were more abundant in rivers (13-22%) than in groundwater (2-11 %) and genera belonging to the Rhodocyclaceae families were detected only in rivers (Fig. S5, A). Members of this order are known to occupy a wide range of habitats, including freshwater environments (Newton et al., 2011).

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Despite having dissimilar core microbiota (Fig. 4, S6), river and groundwater sites shared a large number of taxa (Fig. S5). At the genus level, 369 genera among 1105 were detected in both environments, with 10 of ten being detected in all samples and for all field campaigns. These taxa accounted for 70 to 86 % of total taxon relative abundance in groundwater (Fig. S6). Considering the high alluvial aquifer permeability (0.5 – 8.10-3 m/s) and because all field campaigns were conducted during periods of seasonally high groundwater levels, these results might indicate a river origin for at least a part of these taxa. Similar cases of community coalescence, i.e. species exchanges between local communities and their surrounding environments, have been described for freshwater and their connected environments (Mansour et al., 2018 ; Luo et al., 2020 ; Fillinger et al., 2021). For instance, significant shifts in microbial communities were reported in groundwater after surface water intrusion with increase of river-derived taxa, and the influence of surface water was shown to decrease with distance to riverbank (Fiedler et al., 2018; Fillinger et al., 2021; Korbel et al., 2022). Interestingly, among taxa identified only in groundwater sites (175 genera) we identified taxa usually isolated from soil environment (Fig. S5, C), such as members of the Acidobacteria phylum (Kalam et al., 2020). Members of the Methylococcales order, methanotrophs known to occur in deep anaerobic sediment (Orata et al., 2018), were also identified only in groundwater samples. On the reverse, genera restricted to rivers (294 genera) were identified as mainly rumen-related bacteria from the Firmicutes phylum (Lachnospirales, Oscillospirales and Lactobacillales) (Fig. S5, B). Firmicutes are fecal indicators commonly found in urban watersheds (Wu et al., 2010) and are most likely leached from surrounding farming lands and urban areas during rainfall. Likewise, in our study, Firmicutes were especially abundant in Gier river during the third campaign, which followed
368 important rainy episodes (Fig. 4, A). These results might reflect the high hydraulic connectivity

369 between river and groundwater but also highlights the existence of transient taxa, probably of soil or

370 hyporheic zone origin.

371
372 3.1.2 Comparison of groundwater communities from contaminated and non-contaminated sites
373

374 The uncontaminated PZ3 samples displayed the highest values for richness, Shannon and Pielou indices (Fig. 3). By contrast, the lowest diversity scores were observed for TM samples, where a few taxa might dominate the community. Indeed, one very abundant order (Omnitrophales) representing alone 31-40 % of all reads was identified in all these samples (Fig. 4, B). Decrease in bacterial diversity, also accompanied by a loss of functional diversity, has been reported for groundwater subjected to metal exposure (Hemme et al., 2010, 2015; He et al., 2018). Other studies have suggested that metal stress in groundwater could select for resilient species, capable of rapid acquisition of resistance genes through lateral transfer (Hemme et al., 2016; Kothari et al., 2019). Hierarchical clustering analysis and PCoA separated groundwater samples in two non-overlapping groups. PZ3 and PZ1 samples, which exhibited trace metal concentration similar to recharge water (Fig. S4), clustered together (Fig. 5, A). TM contaminated samples (PZ12, 13, 15 and 16) formed a second cluster and displayed very similar bacterial communities, both in terms of presence/absence and relative abundances. In groundwater samples, very few unique genera were identified (i.e detected in only one sample) and they accounted for 0.2-1.8 % of total taxon abundance. It reflects that groundwater communities, contaminated or not, shared the same individuals, but the core microbiota (i.e the most abundant taxa) at each site might be different.

390 To explore this idea, we used Deseq2 (Love et al., 2014) to identify bacterial orders that were differentially abundant between TM contaminated samples (PZ13, PZ15, PZ16, PZ12) and reference samples (PZ3, PZ1) (Fig. 6). Verrucomicrobiota, accounted for 34.7 – 42.4 % of the total relative abundance in TM contaminated samples. Among them, Omnitrophales, represented by one very dominant genus (> 36% of all reads), *Candidatus Omnitrophus* (Fig. S7), exhibited significantly higher relative abundance in all samples affected by the steel slag presence. Likewise, this taxa contributed most to the dissimilarity observed between TM samples and control samples (PZ1, 3) (Fig. 5, B). A
397 member of this genus isolated from lake sediments, *Candidatus Omnitrophus magneticus*, was found
398 to harbor magnetosomes (Kolinko et al. 2012). These nanocrystals, made of magnetite (Fe₃O₄) allow
399 bacterial cells to align along geomagnetic fields (Kolinko et al., 2012, 2016) and are suggested to
400 facilitate magnetotactic bacteria migration to their optimal oxygen concentration, through
401 chemotaxis and aerotaxis (Frankel et al., 1997). Interestingly, Fe-bearing minerals such as magnetite
402 were identified among the steel slag materials (Herbelin et al., 2020). Such an environment could
403 very likely attract magnetotactic bacteria and promote their metabolism. In addition, a very recent
404 study on TM contaminated soils in China revealed that Verrucomicrobiota were positively correlated
405 with Cr content (Zhang et al., 2021).

406

407 Two orders of **Patescibacteria**, had significantly higher relative abundances in TM samples (Fig. 6):
408 Candidatus Giovannibacteria and Candidatus Peribacteria, yet only characterized through
409 reconstructed environmental genomes. Co-occurrence of Patescibacteria with Verrucomicrobiota, as
410 in our study has already been described in groundwater (Chaudhari et al., 2021). In a recent study,
411 both Verrucomicrobiota and Patescibacteria increased after 30 days of metal exposure in soil
412 experiments and the authors assumed that they were not sensitive to zinc oxide nanoparticles (Shi et
413 al., 2021). Patescibacteria were also found to be abundant in As contaminated aquifers impacted by
414 a tin mine (Sonthiphand et al., 2021).

415 **Methylococcales**, **Ferrovibrionales** (Alphaproteaobacteria) and **PLTA13** (Gammaproteobacteria)
416 were absent or had significantly reduced relative abundance (p.value < 0.01) in TM contaminated
417 samples (Fig. 6), reflecting the lower overall abundance of Proteobacteria in these samples. Previous
418 studies showed a decrease of Gammaproteobacteria in metal contaminated soils (Berg et al., 2012;
419 Jacquiod et al., 2018). However, to our knowledge, no negative impact on Ferrovibrionales or
420 **Methylococcales** has been reported in literature.
The **Myxococcota** phylum was more abundant in TM contaminated samples (4.4 – 6.4 %) than at PZ3 (1.8 – 2%), the dominant genera belonging to the unidentified class bacteriap25 and to orders Polyangiales and Myxococcales. A recent review demonstrated that Myxococcota are systematically identified in metal contaminated environments and could contribute to the core metal resistant microbiome (Hao et al., 2021). Myxococcota members are also suggested to be sulfate-reducing bacteria (Murphy et al., 2021). Therefore they could precipitate metals into metal sulfides and decrease their bioavailability (Luptakova et al., 2007; Neculita et al., 2007; Biswas et al., 2009).

The presence of well described metal tolerant taxa like *Bacillus*, (Chaturvedi, 2011; Ontañon et al., 2018), *Acinetobacter* (Zakaria et al., 2007; Bhattacharya et al., 2014), *Geobacter* (Liu et al., 2002; Gong et al., 2018) and *Pseudomonas* (McLean and Beveridge, 2001) known to include Cr tolerant and reducing species, was investigated. Likewise, *Bacillus*, *Pseudomonas* and several *Enterobacteriaceae* taxa were identified as Mo reducers (see Yakasai et al., 2021 for a review) and Sr resistance was reported for *Micrococcus* (Icgen and Yilmaz, 2018). Apart from *Pseudomonas* which belongs to the most abundant genera identified in all samples (contaminated or not), other genera were either absent (*Micrococcus*) or scarcely identified (*Bacillus*, *Acinetobacter*, *Geobacter*, *Enterobacteriaceae*). This is not surprising since most known metal-resistant bacteria were usually isolated from sites much more contaminated than the one of this study (Igiri et al., 2018).

Finally, sites assemblage based on environmental parameters (Fig.2) revealed the existence of four distinct water signatures in groundwater, whereas microbial community analysis allowed the discrimination of only two groups of samples (Fig. 5). For instance, PZ1 had a very unique water signature, characterized by high concentrations of calcium and total nitrogen and we expected to detect different bacterial communities at this site. In the only replicate available, we did observe a high relative abundance of *Methylomirabilota* phylum (mainly represented by *Candidatus Methylomirabilis*), which members are involved in nitrogen cycling (Wu et al., 2011). However, more replicates would be needed to confirm this trend. In the same way, several environmental
parameters indicated river intrusion in the area close to PZ3 and we might expect surface water intrusion to trigger significant changes in microbial communities. To go further, we would need to examine different regime transition (recharge and discharge periods) and long-time series to see whether regular water mixing at PZ3 could disturb or not local ecological selection in this location. As for trace metal(oid) contaminated sites, they exhibited similar community assemblage, whether they were slightly (PZ13, 16) or highly (PZ15, PZ12) contaminated. The most striking result was the dominance in every of these sites of one single genus, with potential magnetotactic abilities. We could hypothesize that these microorganisms are attracted by slag material and therefore indirectly correlated to metal(oid) contamination. In addition, here we probably have an incomplete picture of microbial communities occurring in groundwater because we mainly recovered free-living microorganisms. Therefore, microorganisms forming biofilms—were probably overlooked. Interestingly, a study indicated that Geobacter, a multimetal resistant bacteria, accounted for 24% of the sediment community but represented less than 1% of sequences of the corresponding groundwater community (Flynn et al., 2013). Sampling the surrounding soil may allow us to detect more metal-resistant bacteria.
462 4. Conclusion

In this study, we provided an extensive characterization of an alluvial aquifer impacted by former
steel industrial activities, through the microbial and geochemical lens. The presence of steel slag
materials resulted in important concentrations of leached Cr, Mo, Rb and Sr in groundwater,
especially downstream the site. Contamination by nitrogen rich waters from anthropic origin was
also detected in one piezometer. All samples affected by steel slag leachates exhibited a similar
community composition, regardless of their degree of contamination. In particular, they were
dominated by *C. omnitrophus*, a bacterial candidate with possible magnetotactic and metal-
resistance properties. We found no other study reporting similar dominance of *C. Omnitrophus* in
trace metal(oid) contaminated aquifers or soils. Several potential metal reducers and metal tolerant
species among the Gammaproteobacteria and Myxococcota phyla were also identified. A large
proportion of bacterial orders detected in groundwater were shared with river samples, in line with
the high hydraulic connectivity between surface water and groundwater in this area.

475
476 Declaration of competing interest

477 The authors declare that they have no known competing financial interests or personal relationships
478 that could have appeared to influence the work reported in this paper.

479 Acknowledgements

480 This research was supported by the University Jean-Monnet of Saint-Etienne. The authors wish to
481 acknowledge Jean-Philippe Baron for access to the industrial site and for providing information on
482 the site history, Cyrille Connord for is help with statistics and Sandrine Hughes for her precious
483 advices on 16s data analysis. Jean-Luc Bouchardon and Olivier Faure are also acknowledged for
484 helpful discussions and help during field campaigns.

485 CRediT roles:

486 **Margot Bärenstrauch**, Formal analysis, Investigation, Writing - original draft, Visualization

487 **Audrey S. Vanhove**, Investigation, writing - review & editing.

488 **Séverine Allegra**, Investigation, writing - review & editing.

489 **Steve Peuble**: Formal analysis, writing - review & editing.

490 **Frederic Gallice**, Investigation

491 **Frédéric Paran**, Investigation, writing - review & editing.

492 **Véronique Lavastre**, Investigation, writing - review & editing.

493 **Françoise Girardot**: Conceptualization, Investigation, Funding acquisition, writing - review & editing,
494 Supervision

495
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Fig.1 → 2 column wide

**Fig.1: Studied site and hydrogeological context.** The map shows the locations of 8 piezometers (PZ1 to PZ16) and 3 rivers sampling sites (GAM: upstream GIER, GAV: downstream GIER, BOZ: tributary 1 (T1). The thick black line corresponds to the Gier river. Isopiezes refers to a reference day measurement on April 20th 2010. The flow direction during high water periods (given by the black arrows) is perpendicular to the blue contour lines from the highest to the lowest elevation. River water enters the aquifer downstream the site (PZ1, PZ3) and joins the river upstream (PZ12). Heights (Z) are given in the NGF 69 reference system. The Lambert 93 site coordinates (m) are: X = 828772/ Y = 6494522.

Fig. 2 -> 2 column wide

**Fig. 2: Discrimination of water samples on the basis of environmental data for 2019-2021 field campaigns.** A) PCA variables plot. A high cos2 value indicates a good representation of the corresponding variable on the principal component. Alca: alkalinity, DO: dissolved oxygen, EC: electrical conductivity, T°: temperature, TC: total carbon, TN : total nitrogen. B) PCA individuals plot. Colours correspond to the different chemical signatures of water identified.

Fig. 3 -> 1.5 column wide

**Fig.3: Alpha diversity indexes, at order levels.** Taxa richness, Shannon entropy and Pielou's evenness between sites (BOZ: Bozançon river, GIER: Gier river sites, TM: trace metals contaminated samples (PZ12, PZ13, PZ15, PZ16). Results are shown with (rarefied) or without prior taxa rarefaction (original).
Fig. 4 -> 2 column wide

**Fig. 4: Prokaryote community composition.** A) Relative abundances of major bacterial phyla (>1 %). TM: trace metals contaminated samples, NA: non assigned phyla. B) Detail of the taxonomic composition (Orders) of major phyla identified in surface water and groundwater. Numbers in brackets refer to the different field campaigns: (1) April 6, 2020, (2) June 8, 2020 and (3) June 15, 2020. Each bar on the chart represents one sample. BOZ: Bozançon river, GAM: upstream Gier river, GAV: downstream Gier river, PZ: piezometers.

Fig. 5-> 2 column wide

**Figure 5: Discrimination of sampling sites based on bacterial community structure (order level).** A) Hierarchical clustering of sites based on weighted Unifrac distance, obtained with Ward's linkage. BOZ: Bozançon river, GAM: upstream Gier river, GAV: downstream Gier river. Number in brackets refer to field campaigns: (1) April 6, 2019, (2) June 8, 2020, (3) June 15, 2020. B) Principal Coordinate Analysis (PCoA) score plots based on Bray-Curtis distance, showing separation between surface water samples (BOZ, GAM, GAV), non-contaminated groundwater samples (PZ1 and PZ3) and contaminated samples (PZ12, 13, 15 and 16). The top 8 taxa with the most contribution to site assemblage, based on SIMPER analysis, are listed from tax1 to tax8 in the table S5.
Fig. 6 -> 2 column wide

<caption>**Fig. 6: Differential relative abundance analysis of bacterial-orders among groundwater samples.**</caption>

**Left:** Relative abundance of microbial orders. Only the top 40 orders are shown (> 1% in at least one sample). The symbols indicate the corresponding phylum. Numbers in brackets in sample names refer to field campaign: (1) April 6, 2019 (2) June 8, 2020 and (3) June, 15 2020. **Right:** Fold change values (log2) for each bacterial order between reference samples (PZ3, PZ1) and trace metals (TM) contaminated samples (PZ12, PZ13, PZ15, PZ16). Bacterial orders exhibiting significant differential relative abundances are indicated by asterisks \*\* : p-value < 0.01; \*\*\* : p-value < 0.001.
<figure><img src="image_2.png" /></figure>

## Monts du Lyonnais

## Massif du Pilat
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
Richness

<figure><img src="image_5.png" /></figure>

Shannon entropy

<figure><img src="image_6.png" /></figure>

Pielou's evenness

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>A)</figcaption></figure>

● Surface water

■ Groundwater

<figure><img src="image_11.png" /><figcaption>B)</figcaption></figure>

tax1 : Campylobacterales

tax2 : Flavobacterales

tax3 : C. Nomurabacteria

tax4 : Pseudomonadales

tax5 : Burkholderiales

tax6 : Methylococcales

tax7 : Rokubacterales

tax8 : Omnitrophales

Axis.1 [70.3%]
<figure><img src="image_12.png" /></figure>

Relative abundance (%)

<figure><img src="image_13.png" /></figure>

Orders with lower
abundance in TM

<figure><img src="image_14.png" /></figure>

Orders with higher
abundance in TM
## Metabarcoding 16S

<figure><img src="image_15.png" /></figure>

## Trace metals (ICP-MS)

<figure><img src="image_16.png" /></figure>

## Impact on microbial communities ?

<figure><img src="image_17.png" /></figure>