# Pedobiological properties of a lignite spoil heap in the Provence coal mine basin (south-east of France)

Stéven Criquet, Mélanie Clouard, Fabio Ziarelli, D Borschneck, Catherine

Keller

► To cite this version:

Stéven Criquet, Mélanie Clouard, Fabio Ziarelli, D Borschneck, Catherine Keller. Pedobiological properties of a lignite spoil heap in the Provence coal mine basin (south-east of France). Geoderma Régional, 2023, 35, pp.e00711. 10.1016/j.geodrs.2023.e00711. hal-04447677

HAL Id: hal-04447677

https://amu.hal.science/hal-04447677v1

Submitted on 28 Nov 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 **Pedobiological properties of a lignite spoil heap in the Provence coal mine basin (south-east of France)**

3

4 **Stéven Criquet<sup>a*</sup>, Mélanie Clouard<sup>a,b</sup>, Daniel Borschneck<sup>b</sup>, Fabio Ziarelli<sup>c</sup>, Catherine Keller<sup>b</sup>**

5

6 <sup>(a)</sup> Aix-Marseille Univ., Faculté des Sciences de Saint-Jérôme, IMBE – Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale UMR CNRS 7263 - IRD 237 – Systèmes microbiens, Service 452, Avenue Escadrille Normandie-Niemen 13397 Marseille cedex 20, France

8

9 <sup>(b)</sup>Aix-Marseille Univ., CNRS, IRD, INRAE, CEREGE – Technopole de l'Environnement Arbois-Méditerranée, BP 80, 13545 Aix-en-Provence, Cedex 4, France

10

11 <sup>(c)</sup>Aix-Marseille Univ., Faculté des Sciences et Techniques de Saint-Jérôme, Spectropole, P.O. Box 512, Avenue Escadrille Normandie Niémen, 13397 Marseille cedex 20, France

12

13 *Corresponding author ; E-mail address: steven.criquet@imbe.fr; Postal address: Site de l'Etoile, FST de St-Jérôme, IMBE, Case 441, Avenue Escadrille Normandie-Niémen, F-13397, Marseille Cedex 20, France

14

15

16

17 E-mail addresses of co-authors: steven.criquet@imbe.fr (Stéven Criquet), melanie.clouard@dreets.gouv.fr (Mélanie Clouard), borschneck@cerege.fr (Daniel Borschneck), fabio.ziarelli@univ-amu.fr (Fabio Ziarelli), keller@cerege.fr (Catherine Keller)

18

19

20

21 Declaration of interest: none

22
23 **Abstract**

The aim of this study was to investigate the bio-physico-chemical properties of soils developed on dumps originating from lignite extraction and to decipher the respective roles of the geomorphology and the presence of lignite on the biogeochemical characteristics of these soils. Soils from the Armand spoil heap, located in Peypin (Bouches-du-Rhône, France), and natural neighboring Mediterranean soils without lignite were sampled and compared. Lignite presence was assessed by ¹³C CPMAS NMR spectroscopy allowing for the quantification of C pools such as aromatic C groups, which are characteristic signature of lignite. Physico-chemical (texture, pH, organic C, carbonate, total N, total S, CEC), mineralogical and biological (enzyme activities: β-glucosidase, arylsulfatase, acid phosphatase, arylamidase, lipase and fluorescein diacetate hydrolase; Oxitop basal respiration and Biolog® catabolic profile) parameters were analyzed in the two types of soil. Microbial activities were higher in the natural soils than in the spoil heap soils, while spoil heap samples exhibited enriched aromatic-C pools. Some variables exhibited a gradient along the slope: carbonate and sand contents were larger at the bottom of the heap while clay content, CEC, organic C and C/N ratio were larger at the top of the heap. On the contrary, microbiological activities did not follow any structured pattern according to the slope and appeared distributed as patches. Variance partitioning combined with multiple linear regressions demonstrated that the ¹³C NMR quality of soil organic matter was the main structuring factor of microbial properties, which were in most cases reduced by the presence of lignite.

41

42 **Keywords**

Lignite, ¹³C CPMAS NMR, Spoil heap, Enzymatic activities, Respirometry, CLCPs, Leptosols, Technosols

45
## 1. Introduction

Lignite, also named brown coal, is a poor-quality coal. Compared to mature coal such as anthracite, it is classified as the lowest grade coal (Van Krevelen classification) due to its high moisture content and its least carbon concentration. It has also a low heating value which explains its main use as a fossil fuel for steam-electric power generation. This fossil material can be found all around the world, with about 20 % of the reserves being located in Europe (Thielemann et al., 2007). In France, lignite was extracted in the Provence coal mine basin (south-east France) until 2003 to generate electricity in a coal-fired plant located near the pits (Meyreuil-Gardanne, France).

The mine activities were located in the sedimentary basin of the Arc, made up of old fluvio-lacustrine formations from the Upper Cretaceous with a Triassic to Jurassic basement. The Arc basin forms a 70-km-long syncline elongated from East to West (between Saint-Maximin-la-Sainte-Baume and Berre l'Etang), and 12-km-wide from North to South (between Aix-en-Provence and Marignane). The originality of the Provence mining basin is that it dates from the late Cretaceous, whereas the other French coal mining basins were formed during the Carboniferous (-359 to -299 Ma). More precisely, lignite deposits of the Provence mining basin dates from the Fuvelian, a local name corresponding to the lower Campanian (approx. 80 Ma). (Autran et al., 2014; Dheilly and Brigati, 2015; Geoderis, 2016)

The Fuvelian formation is composed of limestone (sometimes mixed with clays), clayey and/or gray lacustrine micrites, lignite layers, several levels of cement rock (i.e. a limestone containing 20-25% clay) and lateral intercalation of detrital levels with clayey or sandstone tendency. The lignite layers themselves can contain interbeds of these non-lignite substrates.

Lignite deposits were formed in a continental and lacustrine environment (i.e. a deep lake), alternately with periods of limestone sedimentation. These sedimentary characteristics were quite different from those of the carboniferous coal period (no vegetation and pedological fossilized characteristics) (Autran et al., 2014).
During its extraction, large amounts of mine overburden materials enriched in lignite were deposited
73
near the pits, forming spoil heaps. Such dumps can cause ecosystem disturbances such as
74
modifications in plant, animal and microbial community structures (Hüttl and Weber, 2001). However,
75
uncapped spoil heaps of the Provence basin have been colonized by natural vegetation and
76
pedogenetic processes have started over the past few decades, although not documented in detail
77
(Geoderis, 2020).
78

79

80

Given the unique properties of lignite, many studies have investigated its influence on soil physico-chemical properties such as SOM quality, mineralogy, pH and cation exchange capacity (Rumpel et al., 2002; Shrestha and Lal, 2011; Ogala et al., 2012). Despite its central role in soil formation and nutrient recycling, far less studies have investigated the effect of lignite on the functions of soil microbial communities (Frouz and Nováková, 2005; Helingerova et al., 2010; Lehmann et al., 2011).

Extracellular enzymes and Community-Level-Catabolic-Profiles (CLCPs) of soil microorganisms act as the major drivers of mineralizing and recycling functions. These parameters are known to vary concomitantly with the quality of C substrates (Alarcón-Gutiérrez et al., 2009) and may serve as indicators of SOM lability/recalcitrance and soil biological pedogenetic processes. Moreover, lignite effects on both physico-chemical and biological variables have been poorly documented under Mediterranean pedoclimatic conditions (Panagopoulos, 2013; Panagopoulos and Hatzistathis, 1995; Dixon and Schulze, 2002; Clouard et al., 2014). In fact, most of the studies dealing with lignite effects on soil properties were generally performed in low-pH environments and in typical North-European pedoclimatic conditions (e.g. the Lusatian District in Germany) (Fettweis et al., 2005). This was also emphasized in a review of Amoah-Antwi et al. (2020) which mentioned that alkaline soils remain largely untested about the effects of lignite on their pedobiological properties. Thus, there is unambiguously a gap of knowledge about the impact of lignite on soils with alkaline/calcareous properties. This lack of data is even more true if we consider the atypical nature of the lignite we studied (formed during the cretaceous period) compared to the other data available in the literature and which mostly concern lignite from carboniferous period with different physico-chemical properties.

100
Another gap of knowledge in literature data concerns the heterogeneity of lignite distribution in soils and its associated effects on soil properties. Indeed, to our knowledge, fine topographic assessment of the distribution of these variables on a whole spoil heap is almost never investigated, an original aspect that we aimed to investigate during our study.

Given the different gap of knowledge mentioned above, our general objective was to compare the bio-
physico-chemical properties of soils developed under natural conditions (without lignite) and those of
soils developed from overburden spoil heap material from a lignite mine, in Mediterranean
pedoclimatic conditions characterized by a calcareous parent material, alkaline pH, sclerophyllous
vegetation cover and summer drought conditions. Specific objectives were supported by two
hypotheses: (i) lignite enrichment can be evidenced by the non-destructive NMR method and its
presence can modulate the expression of microbial functions in soil samples; (ii) soil bio-physico-
chemical properties and their interactions are influenced both by the topography of the heap and by the
patchy distribution of lignite. Consequently, 4 different contour lines were sampled along the slope of
the spoil heap. Near the spoil heap, 4 plots, named controls, were also sampled on the natural slope of
a hill devoid of overburden material. Soil organic matter (SOM) quality was characterized by Solid-
State ¹³C Cross-Polarization Magic-Angle Spinning Nuclear Magnetic Resonance (SS ¹³C CPMAS
NMR), and pedological properties of soil samples (e.g. texture, pH, CEC, C, N, etc.) were measured.
Microbial functions were assessed by CLCPs (Biolog), soil basal respiration (Oxitop) and a set of
various enzymatic activities involved in the main biogeochemical CNPS cycles.

120

121 **2. Materials and methods**

2.1. Site description and sampling

123 The studied site (43°23′11.20″N/ 5°33′28.28″E) was located in Peypin, in south-east France, in the
124 mine basin of Provence near Marseille. Armand's coal heap is one of the numerous slag heaps present
125 in the mine basin of Provence. The Armand mine shaft sinks through the different layers of limestone
126 and lignite to a depth of 350 m. Lignite deposits form interstratified layers within the Fuvelian karstic
127 limestone of the late Cretaceous, with an E-W average angle of dip of 8°. Only 7 layers, named
128 "Mine" in French were exploited, 4 of them being present and exploited in all the different pitheads
(including the “Puits Armand”) of the coal basin. From the base of the lignite Fuvelian deposit these 4 layers are called “Grande Mine” (300 m deep, 2.5 – 4 m thickness), “Mauvaise Mine” (285 m deep, 0.7 – 1.7 m thickness), “Mine des 4 Pans” (238 m, 1.2 – 2 m thickness) and “Gros Rocher” (226 m deep1 – 1.2 m thickness) (Dheilly and Brigati, 2015). Detailed stratigraphic data of the arc Basin and the Armand well can be found in Espurt et al. (2012) and Monteau (2010).

134

The Armand mine shaft was bored between 1887 and 1890. The mine was in activity until 1954 with a break between 1932 and 1941 (HBCM, 2002). Tailings were stored close to the well under the form of a slag heap leaning against the hill. To this end, the tailings were dumped on the top of the slag heap using tip wagons and a mechanical hauling along a chain. The structure of the slag heap was constituted by a top platform and a slope. Tailings were constituted of limestone bed rock mixed with lignite residues. At the end of the mining activity, the slag heap was left as it was. No other management intervention/rehabilitation strategy has taken place since it was abandoned (e.g. no capping layer or external inputs such as compost, soil or embankments) except for the establishment of a commercial zone on the south part of the platform (between 1996 and 2007). It was not the case for the north part of the platform and the slope (Fig. S1). Thus, the zone where our study was done has not experienced any reworking of human origin since the cessation of the mining activity. A last point that can be emphasized here concerns self-combustion. All the slag heaps of the mine basin of Provence have experienced episodes of self-combustion and this was also true for the Armand slag heap (now extinguished since several decades). This process results from exothermic oxydo-reduction reactions between lignite and pyrite. The increase in temperature can transform the limestone into quicklime then, under the action of runoff water, in slaked lime with a powdery texture, present in abundance under the soil horizons (Geoderis, 2020).

The age of the heap was determined from the end of overburden inputs making the heap 55-y-old. The heap is 45-m-high, and the mean length of its slope is about 70 m. Four topographic levels (contour lines) spaced 15 m apart were studied from the foot/bottom (level 1) to the top (level 4). Within each level, a systematic sampling procedure was used to collect a set of individual samples (Fig. S1). Morphological descriptions of the soils classified them as Spolic Technosols (Calcic) (WRB, 2015) or
artificial Anthroposols (RP, 2008) composed of four horizons, namely O, AZ, AZ/Ztc (transition) and Ztc horizons (Fig. S2) with a depth of up to 1 m until a pulverulent mine waste material that has undergone self-combustion processes was reached. The thickness of the heap soil profiles was about 0.7 m on average. The samples were collected from the AZ horizon (0.10-0.20 m thickness, average 0.14 m) every 10 m along each topographic level. Thereafter, composite samples were performed by grouping sequentially 4 to 6 contiguous individual samples along the different levels. After grouping, the different composite samples were homogenized using a manual elliptical mixer. Finally, 4 composite samples (n = 4) were generated at each level, except for the 1st level, whose extension enabled only 3 samples to be obtained (n = 3). Additionally, 4 control samples corresponding to “natural” or in-place Mediterranean soils developed without any mine tailing inputs were sampled on 4 plots located near the spoil heap (an illustration of the sampling strategy is given in Figure S1). The control soils from this natural site were Rendzic Leptosols (WRB, 2015) or Rendosols (RP, 2008) with four horizons namely O, A (average thickness of 0.13 m), A/C (transition) and C horizons (Fig. S2) with a maximum total depth of 0.25 m until the calcareous parental material was reached. After sampling, soils were aliquoted to perform both microbial and physico-chemical analyses. A subsample was air-dried at 40 °C and passed through a 2-mm sieve before physico-chemical analysis, while another subsample was sieved at 2 mm and kept fresh at 4 °C before microbial analysis within a month, according to the AFNOR standard NF ISO 18512.

## 2.2. Physico-chemical analyses

pH was measured in a 2.5 soil/water ratio after 2h of incubation at room temperature (pH meter WTW, Germany, pJ330i/SET). Cation exchange capacity (CEC) was measured with 2.5 g soil (sieved at 2 mm) suspended in 50 mL of a 50 mmol⁺·L⁻¹ hexaammine cobalt(III) chloride solution (Co(NH₃)₆Cl₃) according to the method described in AFNOR NF X 31-130. The particle size analysis was done after destruction of organic matter by H₂O₂, without decarbonation step, and using the sedimentation technique of Robinson (pipette method) described in NF X 31-107. Total carbon was analyzed after dry combustion of soil sample at 900°C and gas chromatography measurement according to NF ISO 10694 and total nitrogen according to NF ISO 13878. Carbonate content was determined using HCl
according to the gasometric method of Leo (1963). Total organic carbon (Corg) was obtained by subtracting carbonate value to total carbon value. Mineralogy was characterized by X-ray diffraction (XRD) performed on some soil samples, collected on both control and spoil heap plots. The protocol is given in Table S1.

## 2.3. SS 13C CPMAS NMR spectra acquisition

One gram of soil was pre-treated with 45 mL of a 10% (v/v) hydrofluoric acid solution according to the modified method of Schmidt et al. (1997) before NMR analyses. The HF treatment was repeated four times, followed by four rinses with distilled water. This procedure was adequate to eliminate carbonate and paramagnetic compounds that may induce broadened resonances and decreased signal intensities (Georgakopoulos, 2003). Following HF treatments, samples were further dried at 50 °C before NMR analyses. TOC content of each soil sample was determined before and after HF treatment by CHNS elemental analysis. The SS ¹³C CPMAS NMR spectra were obtained with a Bruker Advance III 400 MHz spectrometer, using a commercial two-channel, 4-mm Bruker probe head. 100 mg of air-dried and ground soil sample (particle size < 0.3 mm, Cyclotec TM1093, Foss Co., France) was placed in a 4-mm zirconium rotor and spun at magic-angle at 10 kHz. SS ¹³C CPMAS NMR was performed with a ramped ¹H pulse during a contact time of 2 msec to obtain the best signal-to-noise ratio. ¹H decoupling was performed during the acquisition to improve the resolution. Recording 8 K transients with a recycle delay of 2.5 sec represented standard conditions to obtain a good signal to-noise ratio. The ¹³C chemical shifts were referenced by tetramethylsilane (TMS) and calibrated with the glycine carbonyl signal, set at 176.05 ppm. All measurements were monitored at room temperature. The spectra chemical shift range of soil was characterized by the following dominant peaks: alkyl C (0–45 ppm), O-alkyl C (45–110 ppm), methoxyl C (50–60 ppm), aromatic C (110–140 ppm), phenolic C (140–160 ppm) and carboxyl C (160–190 ppm). Deconvolution of the NMR spectra was performed using the software DmFit (Massiot et al., 2002), and the intensity of each C region was expressed as a percentage of the total C. According to Mathers et al. (2003) and Clouard et al. (2014), to obtain C-quantified data (mg g⁻¹) instead of only C proportions, the percentages of each NMR spectrum region were multiplied by both TOC value of residual fraction (post-HF treatment) and enrichment factor (i.e.
sample residue / sample raw mass). The degree of humification was calculated according to Baldock et al. (1997) using the alkyl-C to O-alkyl-C ratio with the respective peaks of the spectra. The ratio A was calculated according to Chabbi et al. (2007) as it follows (alkyl C + aromatic C) / (O-alkyl C + carboxyl C). The aromaticity index (AI) was calculated as $AI = (A(110-160 ppm) / A(0-160 ppm))$ (Lorenz et al., 2006).

## 2.4. Enzyme assays

The following enzyme activities, arylamidase, arylsulfatase, β-glucosidase, acid and alkaline phosphomonoesterases were measured according to the respective protocols of Tabatabai and Bremmer (1969, 1970), Eivazi and Tabatabai (1988) and Acosta-Martinez and Tabatabai (2000). The substrates used for the different enzyme assays were : 8 mM L-leucine β-naphtylamide hydrochloride (arylamidase), 5 mM *p*-nitrophenyl sulphate (arylsulfatase), 5 mM *p*-nitrophenyl-β-D-glucopyranoside (β-glucosidase), 5 mM *p*-nitrophenyl phosphate (acid and alkaline phosphomonoesterases). The buffers used for the different enzyme assays were : 0.1 M tris(hydroxymethyl)aminomethane (THAM) pH 8.0 (arylamidase), 0.5 M acetate buffer pH 5.8 (arylsulfatase), modified universal buffer (MUB) pH 6.0, MUB pH 6.5 (acid phosphomonoesterase), MUB pH 11 (alkaline phosphomonoesterase).

Detailed protocols can be found in Floch et al. (2009). The method of Green et al. (2006) was modified to monitor fluorescein diacetate hydrolase activity (FDA); the pH of phosphate buffer (50 mM) used was 7.0 instead of the 7.6 as recommended by Alarcón-Gutiérrez et al. (2008b), to avoid potential non-enzymatic interferences and the substrate used was fluorescein diacetate (5 mM). Lipase, *i.e.* fluorescein dilaurate hydrolase, was measured according to the same procedure as FDA except that 5 mM fluorescein dilaurate (FDL) was used for the incubation.

For all enzyme assays, no toluene was added to the mixture because of the short incubation times.

Enzyme assays were performed in 3 replicates for each soil sample (1g) at 37°C during 1 h. Control samples, i.e. without substrate added during soil incubation step, were also realized for the different enzyme assays. A unit (U) of enzyme activity was defined as µmole of substrate hydrolyzed min⁻¹, and per g of soil dry weight (U.g⁻¹ DW).
240

241

## 2.5. Basal respiration

242

Basal respiration (BR) was carried out in triplicate using the Oxitop® (WTW, Germany) system. Jars fitted with CO₂ trap (NaOH pellets) were filled with soil sample (40 g) and were thereafter hermetically sealed. The depression in O₂ was measured during 21 days at 20 °C in jars with the automated manometric heads (Platen and Giessen-Friedberg, 2000). Basal respiration was expressed in mg O₂·g⁻¹ DW (Alarcón-Gutiérrez et al., 2008a).

243

244

245

246

247

248

## 2.6. Community-Level-Catabolic-Profiles (CLCPs)

249

The Biolog® Ecoplate technique was used to characterize the CLCPs of the soil bacterial communities. Briefly, 10 g of soil was added to 100 mL of a sterile sodium pyrophosphate solution (0.1 %) in a 250 ml flask and was shaken at 200 rpm for 20 min. The resulting mixture was 100-fold diluted with a sterile NaCl solution (0.85 %). On the basis of respirometry results and according to the protocol defined by Calbrix et al. (2005), dilutions were adjusted to obtain pre-defined and similar concentrations of microbial cells between the different soil samples. Each dilution was used to inoculate wells (31 C substrate wells + 1 control well) of the Biolog® Ecoplates (125 µL per well).

250

251

252

253

254

255

256

Plates were incubated at 25 °C and absorbance at 595 nm was measured daily for 7 days. Metabolism of the different substrates in the wells resulted in the reduction of tetrazolium, which changed from colorless to purple formazan. The absorbance value of the Biolog® Ecoplates control well (containing no substrate) was subtracted from the absorbance of every other well, to eliminate background color from the soil suspension. The average well-color development (AWCD) value was calculated from the C substrate wells after 48 h of incubation (Garland and Mills, 1991). Three replicates for each soil sample were performed.

257

258

259

260

261

262

263

264

## 2.7. Statistical analysis

265

Means and standard error of means were determined for each set of composite samples. The means were compared using the Kruskal-Wallis *post hoc* test, with a significance level of *P* < 0.05.

266

267

Correlations between the different variables were calculated using Pearson coefficient and their
significance was tested at a level of 5%. The coefficient of variation (CV) of each variable was calculated by dividing the standard deviation by the mean (s(n)/μ). To perform ordination methods, we distinguished 3 subsets of variables in our study: the 1st subset contained the response variables (microbiological) and the 2nd and the 3rd were environmental explanatory variables, i.e. physico-chemical (carbonates, Corg, Ntot, C/N, clay, silt, sand, CEC) and ¹³C NMR quality subsets respectively. Prior to multivariate analyses, a detrended correspondence analysis (DCA) was carried out using detrending by segments according to ter Braak and Smilauer (2002). Since the gradient lengths extracted were always < 1 (4 being the threshold value for a unimodal response), linear responses were expected, and thus linear ordination methods were chosen. Principal component analyses (PCA) were performed to analyze the different datasets and to show relationships between physico-chemical, ¹³C CPMAS NMR and microbiological variables as well as to discriminate the different soil samples. Redundancy Discriminant Analyses (RDA) were performed according to the design given by Floch et al. (2009) in order to decompose the variance and to determine the relative contribution of both pedological and ¹³C NMR quality variables on the total variance of enzyme activities. Before running RDA, collinearities were detected and variables with high variance inflation factors (VIFs > 20) were deleted. Thereafter, the remaining variables in both environmental subsets were selected using a mixed forward and backward step by step procedure with Monte Carlo permutation tests and with a k penalty parameter of 1.2. Variance partition was finally performed by running full RDA and partial RDA (pRDA) and calculated using adjusted R² according to Peres-Neto et al. (2006) and Borcard et al. (2011). The total variation of response model was partitioned as % of net physico-chemical and % of soil ¹³C quality of explained variance. The statistical significance of the different fractions was tested with Monte Carlo permutation tests (α = 0.001). Multiple linear regression (MLR) was also performed on each individual microbial variable and its selected subsets of environmental explicative variables. Adjusted-R² and β-coefficients of each MLR model were calculated and tested at *P* < 0.05. Statistical analyses were performed using CANOCO version 4.5 (Biometris, The Netherlands) and XLSTAT (Addinsoft, 2011 version, UK) software and both ADE4 and Vegan packages of the R project for statistical computing.
296

297

## 3. Results

298

299

300

301

302

303

304

305

We distinguished 3 subsets of variables: physico-chemical (carbonates, Corg, Ntot, C/N, clay, silt, sand, CEC, pH), ¹³C NMR markers of C quality, and microbiological variables. It appeared that for most of them their coefficients of variation (CV), i.e. their dispersion, were high, which made some differences non-significant between the spoil heap levels and/or the controls from the neighboring natural site (Fig. S3). Total organic carbon (CV=0.38), aromatic C contents (CV=0.46), and lipase activity (FDL; CV=0.66)) showed the highest coefficient of variation for physico-chemical, NMR and microbiological variables respectively. To reduce the dimensionality of the data, several ordination methods were carried out on the different soil samples (PCA, DCA and RDA).

306

307

### 3.1. Soil physico-chemical and ¹³C NMR characteristics

308

309

Results of the soil physico-chemical analyses are given in Table 1. Several physico-chemical characteristics differed significantly between control soils of the natural site and spoil heap soils.

310

311

312

313

314

Control soils showed significant higher clay content (342 g kg⁻¹ on average) than soils from the slope of the slag heap (ranging on average between 135 and 175 g kg⁻¹). On the contrary, sand content in control soils (222 g kg⁻¹ on average) was significantly lower than those of the slag heap (ranging on average between 387 and 520 g kg⁻¹). According to the USDA soil textural classification, soils from the coal heap were thus mainly loam soils, while control soils were mainly clay loam soils (Fig. S4).

315

316

317

318

319

320

321

322

Carbonate content showed the lowest average value in control soils (400 g kg⁻¹ on average), which was significantly lower than carbonate contents of soils from the top (Level 4, 461 g kg⁻¹ on average) and the foot (Level 1, 656 g kg⁻¹ on average) of the slag heap. CEC of control soils (25 cmol⁺ kg⁻¹ on average) showed significant difference, but only with soils from the foot of the slag heap (39.1 cmol⁺ kg⁻¹ on average). Mineralogy differed between control soils and coal heap soils. Detailed results are given in Table S1. Briefly, calcite, illite, muscovite and quartz were present in all samples. Hematite and anorthite were only present in coal heap soils. On the contrary dolomite and clinochlore were only present in the control soils.

323
Considering only the soil samples of the slope of the spoil heap, some physico-chemical variables were structured according to an altitudinal gradient. The carbonate (656 > 524 / 562 > 461 g kg⁻¹) and sand contents (520 > 442 > 436 > 387 g kg⁻¹) decreased with increasing altitude level (from level 1 to level 4), while the inverse pattern was observed for Corg (5.1 < 8 < 9.7 < 11.3 %), C/N (10.1 < 14 < 14.1 < 19.7), clay (135 < 169 / 166 < 175 g kg⁻¹) and silt contents (346 < 389 < 395 < 439 g kg⁻¹) and CEC (25 < 34.2 < 37.1 < 40.4 cmol⁺ kg⁻¹). These trends were more evident following principal component analysis (PCA) (Fig. 1), which highlighted, except for samples from the level 2, a gradual distribution of heap samples (levels 1, 3 and 4) along the 1st axis defined by these variables.

In complement, the quality of organic carbon was assessed using SS¹³C CPMAS NMR (Tab. 2) and a PCA was also performed to discriminate samples according to their C quality (Fig. 2). Phenolic C and methoxyl C were the sole fractions to show no significant differences between samples, whether they came from the slag heap or from the control plot. Aromatic C, ratio A and aromatic index (AI) were significantly lower in control samples than in the spoil heap samples (respectively up to 3.3, 3.1 and 3 times lower), except for those of level 1, making it possible to discriminate them from heap samples on the PCA factorial map (Fig. 2). The opposite pattern was observed for O-Alkyl-C which was a fraction significantly more abundant in control soils than in all the slag heap soils (up to 2.5 times higher). The distribution of the other C signals between control and spoil heap samples was clearly heterogeneous. When considering only the samples of the heap slope, level 1, which corresponded to the foot of the spoil heap, had systematically the lowest contents in the different C fractions, while a gradual decrease was observed for some of the C signals from level 4 to level 1. This was perceptible on the 1st PCA map (Fig. 2) for recalcitrant C, such as aromatic C content, and ratios A and AI.

## 3.2. Soil microbiological variables

Results of the microbiological variables are given in the Table 3. β-glucosidase activity was significantly higher in controls (6.95 mU g⁻¹ MS on average) than in most of the spoil heap samples (ranging on average between 4.7 and 5 mU g⁻¹ MS). However, activity of the control soils was not significantly different from those of the heap foot (Level 1, 5.7 mU g⁻¹ MS on average). The acid
phosphatase activities were significantly 2.5 to 2.8 times higher in the control soils (170.7 mU g⁻¹ MS) than in the soil samples from the different levels of the slag heap (ranging on average between 61.2 and 67.4 mU g⁻¹ MS). No significant differences in acid phosphatase activities were observed between the soils of the different spoil heap levels. The arylsulfatase activities were significantly 4.2 to 5 times higher in the control soils (368.7 mU g⁻¹ MS on average) than in the soil samples from the different levels of the slag heap (ranging on average between 74.3 and 88 mU g⁻¹ MS). As also observed for the acid phosphatase and the β-glucosidase activities, no significant differences in the arylsulfatase activities were observed between the soils of the different spoil heap levels. No significant differences in respiration activities were observed between the control and the spoil heap samples. However, a gradient of respiratory activity was observed between soil samples along the slope of the slag heap with significant differences between the levels 1-2 (1.79 and 1.75 mg O₂ g⁻¹ d⁻¹ on average) and the level 4 (3 mg O₂ g⁻¹ d⁻¹ on average). No significant differences between the different samples (i.e. spoil heap levels and controls) were observed for alkaline phosphatases, arylamidases, FDA and lipases (FDL) activities. Finally, AWCD of the different samples was not significantly different between the spoil heap and the control soils, thus indicating similar CLCPs in both the spoil heap levels and the controls.

On a multivariate basis, PCA revealed that controls were clustered at the positive end of the 1st axis and thus were well discriminated from spoil heap samples (Fig.3), indicating that control soils were biologically the most active. In the case of the spoil heap samples it was not possible to discern any multivariate pattern structured according to elevation because of the patchy distribution of the different activities along the slope.

### 3.3. Correlations and variance partition

The detailed examination of univariate Pearson correlation coefficient between the different variables indicated some significant correlations (Tab. 4), the highest negative correlation detected being the one between FDA and ratio A (-0.71).

Partitioning of the variance in the response data (microbiological) into the contribution of the two subsets of environmental variables (i.e. physico-chemical and ¹³C NMR quality) was thereafter
performed on the whole (heap and controls) and the reduced (heap only) datasets (Fig. 4) as well as on each distinct response variable (heap only).

Prior to partitioning, the most significant variables in the two subsets of environmental variables were selected using a step by step procedure and RDA. This selection is given in supplementary data (Table S2). Thereafter, the selected variables were also used to perform multiple linear regressions (Tab.5).

For both the whole and reduced datasets (i.e. control + spoil heap samples; spoil heap samples only), the sum of the net (marginal) explained variances (i.e. physico-chemical and ¹³C NMR) was twice higher considering only the spoil heap model (44 % vs 23 %) (Fig. 4). For both datasets, explained net variance of NMR variables was twice higher than that of physico-chemical variables (26.5 % vs 13.2 %). Explained variance of each microbiological variable was performed only on soil heap samples (Fig. 4). Strong variations of total net explained variances were observed as a function of the microbial variable considered. Arylsulfatase, alkaline phosphatase and arylamidase activities showed the highest explained variances (between 89 and 69 %) whereas AWCD (Biolog) and FDA had the lowest values (5.2 and 6.7 % respectively). Partitioning the variance between physico-chemistry and ¹³C NMR effects on microbiological variables also showed large differences. For some microbial variables, variances were more explained by net effects of ¹³C NMR variables (arylamidase 98 %, β-glucosidase 96 %, lipase (FDL) 83 %, respiration 77 %, FDA 73 %), while the variances of other microbiological variables were more explained by net effects of physico-chemical variables (acid phosphatase 85 %, arylsulfatase 67 %, alkaline phosphatase 64 %). From the environmental variables previously selected by RDA (Tab. S2), multiple linear regression analyses (MLR) were also generated for each microbial variable. Significant adjusted R² and β-coefficients are given in Table 5. The highest and significant R² were obtained with the ¹³C NMR quality. Moreover, β-coefficients were calculated and were complementary to the previous statistical calculations, enabling negative or positive effects of the different explicative variables to be visualized. The most significant effects were negative NMR effects which were mainly attributed to recalcitrant C, i.e. aromatic, phenolic and alkyl C contents. Accordingly, the best models were obtained for arylamidase (adj. R² 0.86***) and β-glucosidase (adj.
R² 0.59**) activities, which were clearly and negatively influenced by the richness in aromatic C.

Three other microbial variables, FDL, FDA and basal respiration, were less strongly but significantly influenced by C quality signature (adj. R² of 0.35*, 0.44* and 0.47* respectively), especially the content in alkyl-C. Some significant models were also obtained between some microbial variables (β-glucosidase, arylsulfatase, FDA, Oxitop BR) and physico-chemical variables (carbonate, N, clays, CEC), but their levels of significance were lower than those obtained with the sub-set of NMR variables (adj. R² ranging between 0.23* and 0.41*).

414

## 4. Discussion

Lignite or coal waste tailings have considerably modified large surface areas worldwide. This is also the case in the Provence coal mine basin, in France. Currently, only a few deposits remain visible at a landscape scale because of their natural spontaneous plant colonization. Concomitantly to the establishment of the vegetation, soils have developed on the tailings. Our objectives were to characterize these soils and to compare their properties to those developed on the neighboring natural substratum, and in particular, to decipher the role of both the geomorphology and the presence of lignite in the spatial structuring of the biogeochemical characteristics of these soils. It was not possible to directly quantify the total amount of lignite remaining in the different soil profiles, as the quantification of lignite in soils or mine coal wastes remains a challenge (Roth et al., 2012).

Characterization of the organic matter quality by solid state ¹³C NMR was thus necessary to track the presence of lignite in the surface horizons. This proxy was tested and validated by Clouard et al. (2014) and Rumpel et al. (1998), who showed that aromatic C, aromatic index and ratio A parameters were typical of the presence of lignite in soil.

429

### 4.1. Spoil heap soils significantly differ from control soils

We first aimed to compare the typology of control soils from the natural site and heap soils measuring both physico-chemical and microbiological variables. Control and spoil heap soils presented the same sequence of poorly developed horizons, but their morphology and their physico-chemical characteristics were different as detailed below.

407

408

409

410

411

412

413

415

416

417

418

419

420

421

422

423

424

425

426

427

428

430

431

432

433

434
The main morphological difference between the two types of soil was the thickness of the whole soil profile: while control soils presented a maximum of 0.25 m thickness, the average thickness of the heap soils was about 0.7 m (Fig. S2). This larger thickness may be due to the loose/powdery material characteristic in which the heap soils develop allowing for an accelerated water circulation and thus pedogenesis in the chore of the material unlike for the control soils, which are developed from hard limestone. Some physico-chemical properties also differed between control and coal heap soils.

Controls were clearly richer in clay and on the contrary poorer in sand indicating a clay loam texture, while coal heap soils were exclusively classified as loam. The texture, added to soil thickness, indicated that spoil heap possessed physically favorable characteristics for vegetation establishment as observed on site where both control and spoil heap stands were visually very similar. In addition, the thickness of the A horizon was similar on both the spoil heap and in the control soils after only 55 years, indicating an active early pedogenesis or a reduced organic matter mineralisation due to lower enzymatic activities and leading to an enhanced stock of organic matter. However, the extent of the organo-mineral aggregation and its stability would need further study to conclude on the physical fertility status of these soils. A related and interesting study was performed by Panagopoulos and Hatzistathis (1995) on spoil heaps from lignite mining in Greece, under the same bioclimatic conditions (i.e. sub-humid Mediterranean bioclimate) and on a similar mineral substratum (i.e. calcareous). On their naturally revegetated stands, the texture of the A horizons was similar (145-200 g kg⁻¹ clay, 340-460 g kg⁻¹ silt and 180-230 g kg⁻¹ sand) to that of the Armand soils. In addition, in a previous study (Clouard et al., 2014), we compared an aged soil developed on a lignite outcrop of the same origin as the lignite exploited at the Armand mine, and a nearby natural soil without lignite, similar to the Armand control soils. Control soils (without lignite) of these two sites had similar textures (clay loam / silty clay loam, (Fig. S4), a similarity which can be explained by both the same geological parent material and the same Mediterranean vegetation cover (scrubland dominated by *Quercus alba* and *Pinus Halepensis*). On the contrary, when comparing soils from these two studies developed in the presence of lignite, differences in texture were observed between spoil heap and outcrop soils. Aged soil (geological) had a finer texture (silty clay) than young soils (spoil heap), which presented a loamy texture. This comparison suggests that, on the long term, the presence of
lignite, in combination with bedrock limestone, may affect soil texture and thus soil physico-chemical quality. However, we are aware that care must be taken with this hypothesis because Clouard et al. (2014) investigated natural soils whereas our present study was done on technosols with complex historical traits of life.

Considering C contents, with mean values of respectively 550 g kg⁻¹ and 85 g kg⁻¹, carbonate and Corg contents of the Armand spoil heap soils were also similar to those reported by Panagopoulos and Hatzistathis (1995) (553 g kg⁻¹ and 73.7 g kg⁻¹ respectively). Therefore, for these physico-chemical variables, it seems that the dynamics patterns observed could represent a general trend for unmanaged young spoil heap soils under similar mineral parent material and bioclimatic conditions. However, in the case of Nₜot content and C/N values, our results diverged from those of Panagopoulos (2013), since we obtained higher Nₜot values and therefore lower C/N ratios. This can be easily explained by the age of the spoil heaps, the plant colonization chronosequence and the establishment of soil N₂ fixing microbial communities as mentioned by Baldrian et al. (2008) on brown coal heap. Indeed, the Armand spoil was 55 years old, while the spoils described by Panagopoulos (2013) were only 14 years old. Moreover, this may reflect a more evolved soil organic matter status since N and C/N values were close to those of the nearby control soils. Heap soils also exhibited some differences in their mineralogical composition. Unlike in the control plots, dolomite and clinochlore were absent in the spoil heap soils, whereas hematite and anorthite were only present in spoil heap soils. Anorthite is highly weatherable and it may favor an early pedogenesis. Lignite is known to contain pyrite/marcasite (FeS₂) (Ogala et al., 2012), which oxidation can induce the formation of hematite and thus explain its presence in the spoil heap. Alternatively, hematite could be either inherited from the calcareous rock or the result of combustion processes (Masalehdani et al., 2009) that occur in spoil heaps with lignite or coal residues (Thiery et al., 2013); as already mentioned, self-combustion is indeed an event that occurred in the past on the Armand spoil heap before its spontaneous plant colonization.
488 Microbial analyses showed that control soils had globally higher microbial activities than heap soils,
489 with acid phosphatase, arylsulfatase and β-glucosidase activities being the best differentiating
490 variables. ¹³C CPMAS NMR analysis also discriminated between control and spoil heap soils. Control
491 soils had higher contents in O-Alk-C than spoil heap soils, which were richer in structures more
492 recalcitrant to C biodegradation such as aromatic C. Soil organic matter (SOM) quality between
493 control and heap soils was clearly and significantly different, and this parameter has been recognized
494 (Alarcon-Gutierrez et al., 2009) to influence microbial biomass and microbial activities (e.g. enzyme
495 activities). Thus, the richness in easily assimilable C contents (O-Alk-C including carbohydrates) was
496 probably the main driver explaining the higher enzyme activities observed in the control soils.

497

498 4.2. Variable evolution along the slope of the spoil heap

499 One objective of our study was to test the role of the slope as a discriminating factor for soil
500 characteristics. We thus spatialized soil-bio-physico-chemical properties along the slope and
501 investigated the determinism of their interactions. Some physico-chemical properties did indeed
502 present a gradient along the slope. The foot of the spoil heap was characterized by enrichment in sand
503 and carbonate, while the highest values in clay, CEC, Corg contents and C/N were observed at the
504 highest levels. The most probable explanation for this trend is that the calcareous sand (probably
505 present in the dumped material), having no cohesion and being more sensitive to erosion, tended to
506 accumulate downhill during rainy events at the early stage of dumping, while no vegetation had yet
507 been established. There is no obvious on-going erosion, which is now prevented by the actual dense
508 vegetation. No dissolution and/or precipitation figures were observed in the soil profiles that would
509 support a soluble transport. This agrees with Nyssen and Vermeersch (2010), who found that slope
510 affects the geomorphic dynamic, the coarse elements being more abundant at the foot of a spoil. The
511 resulting larger clay content at the top of the heap may have helped stabilize organic matter and
512 therefore induce an increase in CEC and Corg. This may also explain the gradients in the ¹³C NMR
513 quality observed for some C pools: aromatic C and alkyl C were far more abundant at the top of the
514 heap probably because of the larger clay and Corg contents.
515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

## 4.3. Lignite impacts SOM quality and microbial activities

530

531

532

533

534

535

536

537

538

539

540

541

542

Despite these trends, many parameters presented high coefficients of variation, thus also revealing a patchy co-structure. A similar kind of heterogeneous spatial distribution was also observed by Frouz et al. (2011), who argued that this heterogeneity is determined by many biotic and abiotic factors, as well as their interactions. This is especially true for spoil anthropogenic soil characterized by their traits of life and the heterogeneity of the deposited material itself and the way it was dumped (Baldrian et al., 2008) which, as a result, can induce a heterogeneous vegetation development and concomitant spatially heterogeneous pedogenetic processes (Boruvka et al., 2012). The age of the pines was determined along the slope and did not show any structured pattern according to the position of the trees on the slope, and thus belongs to such a paradigm (Dr F. Guibal, personal communication). For the last subset of variables, i.e. microbial activities, neither gradient, nor any structured pattern were observed. These response variables had a patchy distribution that calls for other explanations which are discussed below.

Spoil heap samples were clearly enriched in aromatic C and thus had high AI and ratio A values, which are typical of a lignite signature (Chabbi et al., 2007; He et al., 2019). These results confirmed our previous results obtained with a soil developed from a lignite outcrop (Clouard et al., 2014). They are also in accordance with several other studies performed on various soils enriched in lignite and mentioning high peaks in the aromatic C region (Kögel-Knabner, 1997; Rumpel et al., 1998; Kögel-Knabner, 2000; Rumpel et al., 2000 a & b; Fettweis et al., 2005). The higher values of these proxies were concomitant with a decrease in microbial activities. From diverse ordination techniques, our results clearly evidenced that microbial activities of the spoil heap were mainly structured by the quality of SOM in relation with the presence of lignite, with lignite abundance indicators having negative effects on most of the microbial variables. Up to now, an explanation for this phenomenon has remained difficult to establish. Lignite has been suggested to be an inert material and very few studies have mentioned the possibility for microorganisms to use it as a C source (Opara et al., 2012). Rumpel et al. (2001) and Rumpel and Kögel-Knabner (2004) mentioned the possibility for soil
543 microorganisms to transform and incorporate lignite in their biomass, but the C proportion was very
544 low compared to recent soil organic matter. Considering potential effects on the composition of soil
545 microbial communities, Tran et al. (2015) reported that lignite had very limited impacts on soil
546 microbial biodiversity. In our study, we suggest that the lower microbial activities observed in the
547 spoil may be due to a relative enrichment in the C pool derived from lignite (black carbon), which is
548 more recalcitrant to degradation by microorganisms and thus limits the expression of their
549 mineralizing enzymes. We observed the same activity pattern (Clouard et al., 2014) in a soil developed
550 from a lignite outcrop. In that study, the use of a Δ 14C model (Stuiver and Polach, 1977; Rumpel et
551 al., 2003) allowed us to distinguish the functional relationships between microorganisms and the two
552 C pools, i.e. fossil black C and recent plant C. It demonstrated that the recent C pool was the sole C
553 fraction able to exert a positive influence on microbial activities, while the black C pool was devoid of
554 any stimulating effect on soil microbial metabolism. Unfortunately, C ranges between our 2 studies
555 were very different, ruling out the extrapolation to the Armand spoil of the model we used for the
556 lignite outcrop to quantify both recent and lignite C pools, probably because of the large difference in
557 soil age. Another explanation may be a direct inhibitive effect of lignite, especially on enzyme
558 activity, as it has been shown that charcoal can adsorb enzymes and can thus reduce their activities
559 (Daoud et al., 2010; Wang et al., 2011).

560

561 **5. Conclusions**

562

563 This study was performed because of the gap of knowledge about lignite effects on characteristics and
564 functioning of calcareous/alkaline soils. The approach chosen was to consider the topography of the
565 slag heap and to demonstrate : i) that soil properties were heterogeneously distributed or influenced by
566 the slope of the slag heap and ii) that lignite presence could modulate the expression of microbial
567 functions.

568 Control soils from the natural site and spoil heap soils differed in their ¹³C NMR signature with
569 aromatic C, ratio A and aromatic index (AI), typical of lignite and, which were higher in spoil heap
570 soils. They also differed in their microbiological activities, which were higher in control soils. The
lower activities observed in the spoil soils may be due to a relative enrichment in the C pool derived from lignite. The presence of lignite also discriminated soils from the heap: the patchy distribution of lignite on the heap induced a patchy distribution of microbial activities.

Heap soils were also discriminated according to their location along the slope: soils at the top of the heap had higher clay and lower carbonate contents, while soils at the bottom of the spoil heap had higher CEC, carbonate and sand contents. This trend highlights the importance of physical evolution of the heap, probably during the early stage of soil development, when vegetation is scarce. Also, control and heap soils differed in texture, with more clay in control soils than in heap soils, which can be explained by the different “parent” materials and duration of the pedogenesis processes of the two pedosystems.

This study also evidenced a very active pedogenesis since thick soil profiles developed on the spoil heap in a few decades (within 55 years). As we mentioned in the site description section (see 2.1.), the formation of slaked lime with a powdery texture following past events of self-combustion of the spoil heap is also probably of major importance for explaining the quickness of soil formation. This confirms the interest of studying lignite mine tailings in a context of calcareous/alkaline soils because, under acidic conditions, soil formation is recognized in the literature to be slower with the need of remediation procedures.

In conclusion, the pedogenesis of soils on lignite spoil heaps seems to be quite rapid and the evolution trend depends on the heap morphology and the presence of lignite, which has either a negative or at least no effect on microbiological activities. As they gradually blend into the landscape, the slag heaps become less and less easily identifiable, especially the oldest heaps such as the Armand heap.

Nevertheless, after only 55 years, age of the Armand slag heap, we can consider that the soils are still in phase of evolution and that it is difficult to predict what their future evolution will be. It would therefore be desirable to monitor these heaps over the long term in order to contribute to a better understanding of this specific anthropogenic environment and to obtain data that would be useful for mine site remediation.
598 Acknowledgements

599 This work was supported by the Human-Environment Observatories of the Mine basin of Provence
600 and the French National Center for Scientific Research (CNRS-INEE). We thank Samuel Robert
601 (CNRS-ESPACE, France) for having his knowledge of the context of the mine basin of Provence,
602 which led us to study the Armand site. We wish to thank Mr. Virgile Calvert for helping on soil
603 sampling.

604

605

606

607
608 **References**

609 Acosta-Martinez, V., Tabatabai, M.A., 2000. Arylamidase activity of soils. Soil Sci. Soc. Am. J. 64, 215-221.

611
612 Alarcón-Gutiérrez, E., Couchaud, B., Augur, C., Calvert, V., Criquet, S., 2008a. Effects of nitrogen
613 availability on microbial activities, densities and functional diversities involved in the degradation of a
614 Mediterranean evergreen oak litter (*Quercus ilex* L.). Soil Biol. Biochem. 40, 1654-1661.

615
616 Alarcón-Gutiérrez, E., Floch, C., Augur, C., Le Petit, J., Ziarelli, F., Criquet, S., 2009. Spatial
617 variations of chemical composition, microbial functional diversity, and enzyme activities in a
618 Mediterranean litter (*Quercus ilex* L.) profile. Pedobiologia 52, 387-399.

619
620 Alarcón-Gutiérrez, E., Floch, C., Ruaudel, F., Criquet, S., 2008b. Non-enzymatic hydrolysis of
621 fluorescein diacetate (FDA) in a Mediterranean oak (*Quercus ilex* L.) litter. Eur. J. Soil Sci. 59, 139-
622 146.

623
624 Autran, J., Lochard, T., Monteau, R., 2014. L'exploitation dans le bassin minier de Provence :
625 quartiers, puits et galeries. Aix-en-Provence, CNRS – OHM bassin Minier de Provence, TOHM, 2, 88
626 pages.

627
628 Baldock, J.A., Oades, J.M., Nelson, P.N., Skene, T.M., Golchin, A., Clarke, P., 1997. Assessing the
629 extent of decomposition of natural organic materials using solid-state ¹³C NMR spectroscopy. Aus. J.
630 Soil Res. 35, 1061-1083.

631
632 Baldrian, P., Trogl, J., Frouz, J., Snajdr, J., Valaskova, V., Merhautova, V., Cajthaml, T., Herinkova,
633 J., 2008. Enzyme activities and microbial biomass in topsoil layer during spontaneous succession in
634 spoil heaps after brown coal mining. Soil Biol. Biochem. 40, 2107-2115.

635
636 Borcard, D., Gillet, F., Legendre, P., 2011. Numerical Ecology with R, Springer, New York, 306 p.

637

638 Boruvka, L., Kozak, J., Muehlhanselova, M., Donatova, H., Nikodem, A., Nemecek, K., Drabek, O., 2012. Effect of covering with natural topsoil as a reclamation measure on brown-coal mining dumpsites. J. Geochem. Explor. 113, 118-123.

641

642 Calbrix, R., Laval, K., Barray, S., 2005. Analysis of the potential functional diversity of the bacterial community in soil: a reproducible procedure using sole-carbon-source utilization profiles. Eur. J. Soil Biol. 41, 11-20.

645

646 Chabbi, A., Rumpel, C., Kögel-Knabner, I., 2007. Stable carbon isotope signature and chemical composition of organic matter in lignite-containing mine soils and sediments are closely linked. Org. Geochem. 38, 835-844.

649

650 Clouard, M., Criquet, S., Borschneck, D., Ziarelli, F., Marzaioli, F., Balesdent, J., Keller, C., 2014.

651 Impact of lignite on pedogenetic processes and microbial functions in Mediterranean soils. Geoderma 232, 257-269.

653

654 Daoud, F.B.O., Kaddour, S., Sadoun, T., 2010. Adsorption of cellulase Aspergillus niger on a commercial activated carbon: Kinetics and equilibrium studies. Colloid Surface B 75, 93-99.

656

657 Dheilly, A., Brigati, B., 2015. Gestion du réservoir minier de Gardanne, Bouches du Rhône : Approches environnementales et hydrauliques du pompage et de son rejet dans la Mer Méditerranée ; Exploitations minières passées et présentes : Impacts environnementaux et sociétaux, 17, pp. 71-84, Collection EDYTEM. hal-01100951.

661

662 Dixon, J.B., Schulze, D.G., 2002. Soil mineralogy with environnemental applications, SSSA Book series 7, Madison, Wisconsin, USA, 866 p.
664

665 Eivazi, F., Tabatabai, M.A., 1988. Glucosidases and galactosidases in soils. Soil Biol. Biochem. 20,

666 601-606.

667

668 Espurt, N., Hippolyte, J.-C., Saillard, M., Bellier, O., 2012. Geometry and kinematic evolution of a

669 long-living foreland structure inferred from field data and cross section balancing, the Sainte-Victoire

670 System, Provence, France. Tectonics 31, TC4021, doi:10.1029/2011TC002988.

671

672 Fettweis, U., Bens, O., Hüttl, R.F., 2005. Accumulation and properties of soil organic carbon at

673 reclaimed sites in the Lusatian lignite mining district afforested with *Pinus* sp. Geoderma 129, 81-91.

674

675 Floch, C., Capowiez, Y., Criquet, S., 2009. Enzyme activities in apple orchard agroecosystems: How

676 are they affected by management strategy and soil properties? Soil Biol. Biochem. 41, 61-68.

677

678 Frouz, J., Kalčík, J., Velichová, V., 2011. Factors causing spatial heterogeneity in soil properties, plant

679 cover, and soil fauna in a non-reclaimed post-mining site. Ecol. Eng. 37, 1910-1913.

680

681 Frouz, J., Nováková, A., 2005. Development of soil microbial properties in topsoil layer during

682 spontaneous succession in heaps after brown coal mining in relation to humus microstructure

683 development. Geoderma 129, 54-64.

684

685 Garland, J.L., Mills, A.L., 1991. Classification and characterization of heteroptrophic microbial

686 communities on the basis of patterns of community level sole-carbon-source utilization. Appl.

687 Environ. Microb. 57, 2351-2359.

688

689 Geoderis, 2016. Bassin de lignite de Provence (13). Révision et mise à jour des aléas liés à l'ancienne

690 activité minière. Rapport de Synthèse. Rapport S 2016/004DE – 16PAC22070. [WWW Document].
691 URL (last accessed 23/07/06). https://www.bouches-du-

692 rhone.gouv.fr/content/download/34133/195732/file/S2016-004DE.pdf

693

694 Geoderis, 2020. Bassin lignitifère de Provence. Révision des aléas échauffement, tassement et

695 glissement sur les dépôts liés à l'exploitation minière sur les communes de Cadolive, Fuveau, Peypin

696 et Saint-Savournin. Rapport 2020/171DE – 20PAC3602. [WWW Document]. URL (last accessed

697 23/07/06). https://www.mairiedefuveau.fr/wp-content/uploads/2022/06/2020-171DE.pdf

698

699 Georgakopoulos, A., 2003. Aspects of solid state ¹³C CPMAS NMR spectrosocopy in coals from the

700 Balkan peninsula. J. Serb. Chem. Soc. 68, 599-605.

701

702 Green, V.S., Stott, D.E., Diack, M., 2006. Assay for fluorescein diacetate hydrolytic activity:

703 Optimization for soil samples. Soil Biol. Biochem. 38, 693-701.

704

705 HBCM, 2002. Ensemble des 12 concessions du bassin de Provence. Saint-Etienne (42000). Houillères

706 de Bassin du Centre et du Midi - Direction des Sites Arrêtés 1, 95.

707

708 He, Q., Yeasmin, H., Hoadley, A., Qi, Y., 2019. Physical and chemical changes in lignite during

709 mechanical and thermal dewatering process and associated changes in the organic compounds in

710 wastewater. Int. J. Coal Prep. Util. DOI: 10.1080/19392699.2019.1682563

711

712 Helingerova, M., Frouz, J., Santruckova, H., 2010. Microbial activity in reclaimed and unreclaimed

713 post-mining sites near Sokolov (Czech Republic). Ecol. Eng. 36, 768-776.

714

715 Hüttl, R.F., Weber, E., 2001. Forest ecosystem development in post-mining landscapes : a case study

716 of the Lusatian lignite district. Naturwissenschaften 88, 322-329.

717
718 Kögel-Knaber, I., 1997. ¹³C and ¹⁵N NMR spectroscopy as a tool in soil organic matter studies.

719 Geoderma 80, 243-270.

720
721 Kögel-Knabner, I., 2000. Analytical approaches for characterizing soil organic matter. Org. Geochem.
722 31, 609-625.

723
724 Lehmann, J., Rillig, M.C., Thies, J., Masiello, C.A., Hockaday, W.C., Crowley, D., 2011. Biochar
effects on soil biota – A review. Soil Biol. Biochem. 43, 1812-1836.

726
727 Leo, M.W.M., 1963. Soil lime measurement, Determination of soil carbonates by a rapid gasometric
728 method. J. Agr. Food Chem. 11, 452-455.

729
730 Lorenz, K., Preston, C.M., Kandeler, E., 2006. Soil organic matter in urban soils: Estimation of
731 elemental carbon by thermal oxidation and characterization of organic matter by solid-state ¹³C
732 nuclear magnetic resonance (NMR) spectroscopy. Geoderma 130, 312-323.

733
734 Masalehdani, N.N., Mees, F., Dubois, M., Coquinot, Y., Potdevin, J.L., Fialin, M., Blanc-Valleron,
735 M.M., 2009. Condensate minerals from a burning coal-waste heap in Avion, Northern France. Can.
736 Mineral. 47, 573-591.

737
738 Massiot, D., Fayon, F., Capron, M., King, I., Le Calve, S., Alonso, B., Durand, J.O., Bujoli, B., Gan,
739 Z.H., Hoatson, G., 2002. Modelling one- and two-dimensional solid-state NMR spectra. Magn. Reson.
740 Chem. 40, 70-76.

741
742 Mathers, N.J., Xu, Z., Blumfield T.J., Berners-Price, S.J., Saffigna, P.G., 2003. Composition and and
743 quality of harvest residues and soil organic matter under windrow residue management in young hoop
744 pine plantations as revealed by solid-state ¹³C NMR spectroscopy. Forest Ecol. Manag. 175, 467-488.

745
746 Monteau, R., 2010. Le lignite du Bassin de l'Arc (Bouches du Rhône). A.P.B.G. 2-06-2010. [WWW

747 Document]. URL (last accessed 23/07/06). https://www.pedagogie.ac-aix-marseille.fr/upload/docs/application/pdf/2011-09/le_lignite_du_bassin_de_1_arc_apbg.pdf

748

749

750 Nyssen, J., Vermeersch, D., 2010. Slope aspect affects geomorphic dynamics of coal mining spoil

751 heaps in Belgium. Geomorphology 123, 109-121.

752

753 Ogala, J., Siavalas, G., Christianis, K., 2012. Coal petrography, mineralogy and geochemistry of lignite

754 samples from the Ogwashi-Asaba Formation, Nigeria. J. Afr. Earth Sci. 66-67, 35-45.

755

756 Opara, A., Adams, D.J., Free, M.L., McLennan, J., Hamilton, J., 2012. Microbial production of

757 methane and carbon dioxide from lignite, bituminous coal, and coal waste materials. Int. J. Coal Geol.

758 96-97, 1-8.

759

760 Panagopoulos, T. (2013). Factors of Reclamation Success at the Lignite Strip-mined Land in Northern

761 Greece. Journal of Tourism, Sustainability and Well-Being, 1 (2), 96-122.

762

763 Panagopoulos, T., Hatzistathis, A., 1995. Early growth of *Pinus nigra* and *Robinia pseudoacacia*

764 stands: contribution to soil genesis and landscape improvement on lignite spoil in Ptolemaida.

765 Landscape Urban. Plan. 32, 19-29.

766

767 Peres-Neto, P.R., Legendre, P., Dray, S., Borcard, D., 2006. Variation partitionning of species data

768 matrices: estimation and comparison of fractions. Ecology 87, 2614-2625.

769

770 Platen, H., Giessen-Friedberg, F., 2000. Validation of the OxiTop measuring system for the

771 determination of respiratory activity in soils and other solids. 1st Symposium on "Biological

772 degradability" 1, 13.

773
774 Roth, P.J., Lehndorff, E., Brodowski, S., Bornemann, L., Sanchez-Garcia, L., Gustafsson, O.,
775 Amelung, W., 2012. Differentiation of charcoal, soot and diagenetic carbon in soil : Method
776 comparison and perspectives. *Org. Geochem.* 46, 66-75.

777
778 RP, 2008. Référentiel pédologique. Baize, D., Girard, M.C. (Eds.). Editions Quae, 2009, Versailles,
779 France.

780
781 Rumpel, C., Balesdent, J., Grootes, P., Weber, E., Kogel-Knabner, I., 2003. Quantification of lignite-
782 and vegetation-derived soil carbon using 14C activity measurements in a forest chronosequence.
783 Geoderma 112, 155-166.

784
785 Rumpel, C., Grootes, P.M., Kogel-Knabner, I., 2001. Characterisation of the microbial biomass in
786 lignite-containing mine soils by radiocarbon measurements. *Soil Biol. Biochem.* 33, 2019-2021.

787
788 Rumpel, C., Knicker, H., Kögel-Knaber, I., Skjemstad, J.O., Hüttl, R.F., 1998. Types and chemical
789 composition of organic matter in reforested lignite-rich mine soils. *Geoderma* 86, 123-142.

790
791 Rumpel, C., Kogel-Knabner, I., Knicker, H., Huttl, R.F., 2000a. Composition and distribution of
792 organic matter in physical fractions of a rehabilitated mine soil rich in lignite-derived carbon.
793 Geoderma 98, 177-192.

794
795 Rumpel, C., Kogel-Knabner, I., Bruhn, F., 2002. Vertical distribution, age, and chemical composition
796 of organic carbon in two forest soils of different pedogenesis. *Org. Geochem.* 33, 1131-1142.

797
798 Rumpel, C., Kogel-Knabner, I., 2004. Microbial use of lignite compared to recent plant litter as
799 substrates in reclaimed coal mine soils. *Soil Biol. Biochem.* 36, 67-75.

800
801 Rumpel, C., Skjemstad, J.O., Knicker, H., Kögel-Knabner, I., Hüttl, R.F., 2000b. Techniques for the
differentiation of carbon types present in lignite-rich mine soils. Org. Geochem. 31, 543-551.

803
804 Schmidt, M.W.I., Knicker, H., Hatcher, P.G., Kogel-Knabner, I., 1997. Improvement of ¹³C and ¹⁵N
805 CPMAS NMR spectra of bulk soils, particle size fractions and organic material by treatment with 10%
806 hydrofluoric acid. Eur. J. Soil Sci. 48, 319-328.

807
808 Shrestha, R.K., Lal, R., 2011. Changes in physical and chemical properties of soil after surface mining
809 and reclamation. Geoderma 161, 168-176.

810
811 Stuiver, M., Polach, H.A., 1977. Reporting of ¹⁴C data - Discussion. Radiocarbon 19, 355-363. DOI
812 10.1017/S0033822200003672

813
814 Tabatabai, M.A., Bremmer, J.M., 1969. Use of p-nitrophenyl phosphate for assay of soil phosphatase
815 activity. Soil Biol. Biochem. 1, 301-307.

816
817 Tabatabai, M.A., Bremmer, J.M., 1970. Arylsulfatase activity of soils. Soil Sci. Soc. Am. J. 34, 225-
818 229.

819
820 ter Braak, C.J.F., Smilauer, P., 2002. CANOCO Reference manual and CanoDraw for Windows User's
821 guide: Software for Canonical Community Ordination (version 4.5). Ithaca, NY, USA, 500 p.

822
823 Thielemann, T., Schmidt, S., Peter Gerling, J., 2007. Lignite and hard coal: Energy suppliers for world
824 needs until the year 2100 — An outlook. Int. J. Coal Geol. 72, 1-14.

825
826 Thiery, V., Sokol, E.V., Masalehdani, N-N., Guy, B., 2013. La combustion des terrils. Géochronique,
827 Bureau de recherches géologiques et minières, 2013, 127, pp.23-25. hal-00880725.

828
829 Tran, C.K.T., Rose, M.T., Cavagnaro, T.R., Patti, A.F., 2015. Lignite amendment has limited impacts
830 on soil microbial communities and mineral nitrogen availability. Appl. Soil Ecol. 95, 140-150.

831
832 Wang, K., Fu, X., Qin, Y., Sesay, S., 2011. Adsorption characteristics of lignite in China. J. Earth Sci.
833 22, 371-376.

834
835 WRB, 2015. World reference base for soil resources 2014, update 2015. International soil
836 classification system for naming soils and creating legends for soil map. World Soil Resources
837 Reports No. 106. FAO, Rome.

838
839 **Figure captions**

840

841 **Fig. 1.** PCA of physico-chemical characteristics of control and spoil heap soils (squares are control soils (C) from natural site included in the analysis as additional and passive observations). Legend of soil samples: 1st number refers to spoil heap level/belt (1 to 4); 2nd number refers to the composite soil replicate; example: 2.3. refers to the third composite sample of the level 2 of the spoil heap.

845

846 **Fig. 2.** PCA of ¹³C CPMAS NMR signals of control and spoil heap soils (squares are control soils (C) from natural site included in the analysis as additional and passive observations). Legend of soil samples: 1st number refers to spoil heap level/belt (1 to 4); 2nd number refers to the composite soil replicate; example: 2.3. refers to the third composite sample of the level 2 of the spoil heap.

850

851 **Fig. 3.** PCA of biological activities of control and spoil heap soils (squares are control soils (C) from natural site included in the analysis as additional and passive observations). Legend of soil samples: 1st number refers to spoil heap level/belt (1 to 4); 2nd number refers to the composite soil replicate. example: 2.3. refers to the third composite sample of the level 2 of the spoil heap.

855

856 **Fig. 4.** Partitioning of the net variance in the response data (microbiological) into the contribution of the two subsets of environmental variables (i.e. physico-chemical and 13C NMR quality) on the whole (heap and controls from natural site) and the reduced datasets (heap only) and on each individual microbial variable (heap only).

860

861 **Supplementary files**

862

863 File "Supplementary figures" includes :

864 - **Supplementary Figure S1.** Aerial view of the Armand spoil heap (Peypin, France)

865 and location of the collected soil samples. The sampling strategy on the heap and the making

866 of the composite samples are also detailed. Satellite image from Google Earth.
867 - **Supplementary Figure S2.** Typical a) control (natural site) and b) spoil heap profiles
868 at the Armand site. Names are given after WRB (2015).

869 - **Supplementary Figure S3.** Coefficients of variation of the different variables of the
870 Armand spoil heap.

871 - **Supplementary Figure S4.** USDA Diagram of textural classification of surface (A)
872 horizons of the Armand spoil heap and control (natural site) soils and comparison with Kirbon
873 lignite outcrop soils (Kirbon hamlet, Trets, Bouches du Rhône, France) with and without
874 lignite A and A/C horizons*; * data from Clouard et al. (2014); Textural diagram from
875 www.nrcs.usda.gov/wps/portal/nrcs/detail/soils.

876

877 File "Supplementary tables" includes :

878 - **Supplementary Table S1.** XRD analysis of minerals in soil samples.1/0 means presence /
879 absence.

880 - **Supplementary Table S2.** Selected variables in physico-chemical and ¹³C NMR subsets
881 of data. X1 and X2 refer to the variables used respectively for the physico-chemical and
882 ¹³C NMR fractions of variance partitioning.

883
Tables

<table><caption>Table 1. Physico-chemical characteristics of spoil heap and control (natural site) soils. Means (± SE, n=3 for level 1 and n=4 for level 2, 3, 4 and control) with different letters within a column are significantly different based on Kruskal-Wallis *post hoc* test (P < 0.05)</caption><thead><tr><th>Level</th><th>pH</th><th>Carbonates<br>(g kg<sup>-1</sup>)</th><th>Corg<br>(%)</th><th>Ntot<br>(%)</th><th>C/N<br>ratio</th><th>Clay<br>(g kg<sup>-1</sup>)</th><th>Silt<br>(g kg<sup>-1</sup>)</th><th>Sand<br>(g kg<sup>-1</sup>)</th><th>CEC<br>(cmol<sup>+</sup> kg<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Level 1</td><td>8.53 ± 0.1 a</td><td>656 ± 120 a</td><td>5.1 ± 2.2 a</td><td>0.5 ± 0.2 a</td><td>10.1 ± 1.6 a</td><td>135 ± 15 c</td><td>346 ± 55 b</td><td>520 ± 66 a</td><td>25.0 ± 6 b</td></tr><tr><td>Level 2</td><td>8.47 ± 0.08 a</td><td>524 ± 95 abc</td><td>8.0 ± 2.7 a</td><td>0.6 ± 0.2 a</td><td>14 ± 2.2 ab</td><td>169 ± 7 b</td><td>389 ± 102 ab</td><td>442 ± 108 a</td><td>34.2 ± 8.4 ab</td></tr><tr><td>Level 3</td><td>8.46 ± 0.15 a</td><td>562 ± 106 abc</td><td>9.7 ± 3.1 a</td><td>0.7 ± 0.1 a</td><td>14.1 ± 3 b</td><td>166 ± 18 bc</td><td>395 ± 19 ab</td><td>439 ± 31 ab</td><td>37.1 ± 2.8 a</td></tr><tr><td>Level 4</td><td>8.50 ± 0.05 a</td><td>461 ± 16 b</td><td>11.3 ± 3.3 a</td><td>0.6 ± 0.2 a</td><td>19.7 ± 2.1 b</td><td>175 ± 21 b</td><td>439 ± 24 a</td><td>387 ± 33 b</td><td>40.4 ± 3 a</td></tr><tr><td>Controls</td><td>8.40 ± 0.11 a</td><td>400 ± 56 c</td><td>10.4 ± 2.4 a</td><td>0.7 ± 0.3 a</td><td>15.2 ± 3.6 ab</td><td>342 ± 60 a</td><td>436 ± 85 ab</td><td>222 ± 32 c</td><td>39.1 ± 6.7 a</td></tr></tbody></table>
<table><caption>Table 2. Absolute intensities (mg g⁻¹) derived from the ¹³C CPMAS NMR spectra of spoil heap and control (natural site) soils. Means (± SE, n=3 for level 1 and n=4 for levels 2, 3, 4 and controls) with different letters within a column are significantly different based on Kruskal-Wallis *post hoc* test (*P* < 0.05)</caption><thead><tr><th>Level</th><th>Alkyl C</th><th>Methoxyl C</th><th>O-alkyl C</th><th>Aromatic C</th><th>Phenolic C</th><th>Carboxyl C</th><th>Ratio A</th><th>Aromatic Index</th></tr></thead><tbody><tr><td>Level 1</td><td>9.4 ± 2.65 b</td><td>2.6 ± 1.08 a</td><td>13.1 ± 4.65 b</td><td>8.6 ± 4.52 b</td><td>2.5 ± 1.3 a</td><td>3.2 ± 0.99 a</td><td>1.1 ± 0.23 b</td><td>0.2 ± 0.07 a</td></tr><tr><td>Level 2</td><td>20.2 ± 2.95 a</td><td>4.8 ± 2.06 a</td><td>19.5 ± 2.71 b</td><td>23.7 ± 9.67 a</td><td>3.5 ± 0.64 a</td><td>5.4 ± 0.67 ab</td><td>1.8 ± 0.42 a</td><td>0.3 ± 0.1 a</td></tr><tr><td>Level 3</td><td>21.6 ± 5.61 a</td><td>3.7 ± 0.78 a</td><td>23.3 ± 9.41 b</td><td>23.6 ± 4.81 a</td><td>3.1 ± 1.35 a</td><td>5.4 ± 1.58 b</td><td>1.6 ± 0.37 ab</td><td>0.3 ± 0.02 a</td></tr><tr><td>Level 4</td><td>24.9 ± 7.79 a</td><td>4.3 ± 0.97 a</td><td>20.9 ± 5.04 b</td><td>32.2 ± 10.99 a</td><td>3.3 ± 1.55 a</td><td>6.1 ± 1.15 ab</td><td>2.2 ± 0.86 a</td><td>0.3 ± 0.08 a</td></tr><tr><td>Controls</td><td>18.9 ± 5.15 a</td><td>6.1 ± 2.4 a</td><td>32.9 ± 7.69 a</td><td>9.9 ± 2.46 b</td><td>3.3 ± 0.65 a</td><td>6.7 ± 1.31 a</td><td>0.7 ± 0.17 b</td><td>0.1 ± 0.01 b</td></tr></tbody></table>
<table><caption>Table 3. Enzymatic activities (basic phosphatase, acid phosphatase, β-glucosidase, arylsulfatase, arylamidase, lipase and FDA hydrolase), basal respiration measured by Oxitop® system and functional diversity measured by Biolog® system in the different levels (1, 2, 3 and 4) on the spoil heap and in the control (natural site) soils. Means (± SE, n=3 for level 1 and n=4 for levels 2, 3, 4 and controls) with different letters within a column are significantly different based on Kruskal-Wallis *post hoc* test (P < 0.05).</caption><thead><tr><th>Level</th><th>BPH<br>(mU g<sup>-1</sup>MS)</th><th>APH<br>(mU g<sup>-1</sup>MS)</th><th>β-glu<br>(mU g<sup>-1</sup>MS)</th><th>ArylS<br>(mU g<sup>-1</sup>MS)</th><th>ArylN<br>(mU g<sup>-1</sup>MS)</th><th>FDL<br>(mU g<sup>-1</sup>MS)</th><th>FDA<br>(mU g<sup>-1</sup>MS)</th><th>Oxitop BR<br>(mg O<sub>2</sub> g<sup>-1</sup> d<sup>-1</sup>)</th><th>Biolog<br>(AWCD)</th></tr></thead><tbody><tr><td>Level 1</td><td>81.26 ± 29.37 a</td><td>64.76 ± 19.68 b</td><td>5.71 ± 1.59 ab</td><td>74.30 ± 61.01 b</td><td>10.72 ± 5.51 a</td><td>0.34 ± 0.18 a</td><td>38.36 ± 8.07 a</td><td>1.79 ± 0.73 b</td><td>0.34 ± 0.14 a</td></tr><tr><td>Level 2</td><td>66.72 ± 25.5 a</td><td>61.18 ± 5.32 b</td><td>5.00 ± 0.96 b</td><td>75.65 ± 11.32 b</td><td>11.99 ± 2.93 a</td><td>0.41 ± 0.16 a</td><td>35.66 ± 7.85 a</td><td>1.75 ± 0.45 b</td><td>0.51 ± 0.18 a</td></tr><tr><td>Level 3</td><td>86.90 ± 4.15 a</td><td>67.40 ± 9.61 b</td><td>5.25 ± 0.43 b</td><td>87.98 ± 12.15 b</td><td>13.42 ± 3.98 a</td><td>0.84 ± 0.43 a</td><td>41.11 ± 7.76 a</td><td>2.24 ± 0.73 ab</td><td>0.47 ± 0.21 a</td></tr><tr><td>Level 4</td><td>92.65 ± 15.25 a</td><td>62.20 ± 7.23 b</td><td>4.70 ± 1.1 b</td><td>77.82 ± 17.8 b</td><td>12.66 ± 2.64 a</td><td>0.72 ± 0.57 a</td><td>30.21 ± 10.27 a</td><td>3.00 ± 1.69 a</td><td>0.42 ± 0.20 a</td></tr><tr><td>Controls</td><td>101.78 ± 36.29 a</td><td>170.70 ± 34.46 a</td><td>6.95 ± 0.45 a</td><td>368.70 ± 58.03 a</td><td>8.66 ± 2.72 a</td><td>0.80 ± 0.46 a</td><td>46.81 ± 9.55 a</td><td>2.93 ± 0.21 ab</td><td>0.48 ± 0.19 a</td></tr></tbody></table>

917

918

919

920

921
<table><caption>Table 4. Pearson correlation values obtained between microbial activities and environmental variables in soil samples of the Armand spoil heap. Values in bold are significant at *P* < 0.05.</caption><thead><tr><th rowspan="2">Environmental variables</th><th colspan="9">Microbial activity</th></tr><tr><th>BPH</th><th>APH</th><th>β-glu</th><th>ArylS</th><th>ArylN</th><th>FDL (lipase)</th><th>FDA</th><th>Oxitop</th><th>AWCD</th></tr></thead><tbody><tr><td>Carbonates</td><td>-0,27</td><td>0.25</td><td>0.34</td><td>0.36</td><td>0.02</td><td>0.03</td><td>0.17</td><td>-0.25</td><td>-0.10</td></tr><tr><td>Corg</td><td>0.12</td><td>0.14</td><td>0.08</td><td>0.15</td><td>0.23</td><td>0.16</td><td>0.13</td><td>0.15</td><td>0.23</td></tr><tr><td>N</td><td>0.08</td><td>0.48</td><td>0.47</td><td>0.43</td><td>0.07</td><td>0.28</td><td>0.42</td><td>0.04</td><td>0.35</td></tr><tr><td>C/N</td><td>0.09</td><td>-0.24</td><td>-0.33</td><td>-0.14</td><td>0.32</td><td>-0.05</td><td>-0.27</td><td>0.32</td><td>0.02</td></tr><tr><td>Clay</td><td>-0.02</td><td>-0.02</td><td>-0.16</td><td>0.09</td><td>0.15</td><td>0.12</td><td>-0.47</td><td><strong>0.53</strong></td><td>0.29</td></tr><tr><td>Silt</td><td>-0.14</td><td>0.05</td><td>-0.18</td><td>0.01</td><td>-0.24</td><td>0.29</td><td>-0.25</td><td>0.29</td><td>0.11</td></tr><tr><td>Sand</td><td>0.12</td><td>-0.04</td><td>0.19</td><td>-0.04</td><td>0.16</td><td>-0.28</td><td>0.33</td><td>-0.39</td><td>-0.17</td></tr><tr><td>CEC</td><td>0.07</td><td>0.20</td><td>-0.07</td><td>0.15</td><td>0.10</td><td>0.34</td><td>0.03</td><td>0.27</td><td>0.28</td></tr><tr><td>Alkyl-C</td><td>0.09</td><td>0.09</td><td>-0.14</td><td>0.11</td><td>-0.02</td><td>0.36</td><td>-0.35</td><td>0.68</td><td>0.28</td></tr><tr><td>Methoxyl-C</td><td>-0.23</td><td>-0.11</td><td>0.43</td><td>-0.04</td><td>0.22</td><td>-0.30</td><td>-0.11</td><td>0.18</td><td>0.03</td></tr><tr><td>O-alkyl-C</td><td>0.28</td><td>0.38</td><td>0.17</td><td>0.27</td><td>0.37</td><td>0.19</td><td>0.31</td><td>0.22</td><td>0.17</td></tr><tr><td>Aromatic-C</td><td>-0.02</td><td>-0.14</td><td>-0.45</td><td>-0.18</td><td>-0.07</td><td>0.15</td><td>-0.42</td><td>0.50</td><td>-0.16</td></tr><tr><td>Phenolic-C</td><td>-0.12</td><td>0.06</td><td>-0.05</td><td>-0.21</td><td><strong>-0.54</strong></td><td>0.41</td><td>-0.23</td><td>0.23</td><td>0.27</td></tr><tr><td>Carboxyl-C</td><td>0.24</td><td>0.21</td><td>-0.04</td><td>0.09</td><td>0.17</td><td>0.25</td><td>-0.02</td><td>0.28</td><td>0.17</td></tr><tr><td>Ratio A</td><td>-0.16</td><td>-0.33</td><td><strong>-0.53</strong></td><td>-0.27</td><td>-0.28</td><td>0.15</td><td><strong>-0.71</strong></td><td>0.62</td><td>-0.14</td></tr><tr><td>AI</td><td>-0.14</td><td>-0.33</td><td><strong>-0.58</strong></td><td>-0.36</td><td>-0.18</td><td>0.13</td><td>-0.46</td><td>0.22</td><td>-0.24</td></tr></tbody></table>
<table><caption>Table 5. Multiple linear regressions between microbial responses and environmental variables (¹³C NMR OM quality and physico-chemical variables).</caption><thead><tr><th rowspan="2">Model</th><th colspan="7">β-coefficient</th></tr><tr><th>Alkyl C</th><th>Methoxyl C</th><th>O-Alkyl C</th><th>Phenolic C</th><th>Carboxyl C</th><th>Aromatic index</th><th>Adj. R²</th></tr></thead><tbody><tr><td>BPH</td><td>0.41ns</td><td>-0.58ns</td><td>0.54ns</td><td>-0.56ns</td><td></td><td></td><td>0.15ns</td></tr><tr><td>APH</td><td></td><td>-0.29ns</td><td>0.51ns</td><td></td><td></td><td>-0.33ns</td><td>0.15ns</td></tr><tr><td>Glu</td><td></td><td>-0.54*</td><td></td><td></td><td>0.52*</td><td>-0.81**</td><td>0.59**</td></tr><tr><td>ArylS</td><td></td><td></td><td></td><td>-0.51ns</td><td>0.69.</td><td>-0.57.</td><td>0.20ns</td></tr><tr><td>Aryln</td><td></td><td></td><td></td><td></td><td>-1.14***</td><td>1.12**</td><td>-0.44**</td><td>0.86***</td></tr><tr><td>FDL</td><td>0.70*</td><td>-0.66*</td><td></td><td></td><td></td><td></td><td>0.35*</td></tr><tr><td>FDA</td><td>-0.66*</td><td></td><td>0.69*</td><td></td><td></td><td>-0.25ns</td><td>0.44*</td></tr><tr><td>BR</td><td>1.07**</td><td>-0.26ns</td><td></td><td></td><td>-0.37ns</td><td></td><td>0.47*</td></tr><tr><td>AWCD</td><td>0.41ns</td><td></td><td></td><td></td><td></td><td>-0.38ns</td><td>0.08ns</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Model</th><th colspan="7">β-coefficient</th></tr><tr><th>Carbonates</th><th>N</th><th>C/N</th><th>Clay</th><th>Sand</th><th>CEC</th><th>Adj. R²</th></tr></thead><tbody><tr><td>BPH</td><td>1.31**</td><td>-0.28ns</td><td>0.57ns</td><td></td><td>0.91.</td><td>1.60*</td><td>0.40.</td></tr><tr><td>APH</td><td>0.63ns</td><td>0.36ns</td><td>-0.13ns</td><td></td><td></td><td>0.56ns</td><td>0.26ns</td></tr><tr><td>Glu</td><td>0.45.</td><td>0.56*</td><td></td><td></td><td></td><td></td><td>0.32*</td></tr><tr><td>ArylS</td><td>1.10*</td><td>0.31ns</td><td>0.29ns</td><td></td><td></td><td>0.64.</td><td>0.41*</td></tr><tr><td>Aryln</td><td>0.59ns</td><td></td><td>0.76.</td><td></td><td></td><td></td><td>0.13ns</td></tr><tr><td>FDL</td><td>0.59ns</td><td></td><td></td><td></td><td></td><td>0.77.</td><td>0.16ns</td></tr><tr><td>FDA</td><td></td><td>0.46.</td><td></td><td>0.50*</td><td></td><td></td><td>0.34*</td></tr><tr><td>BR</td><td></td><td></td><td></td><td>0.03*</td><td></td><td></td><td>0.23*</td></tr><tr><td>AWCD</td><td></td><td>0.35ns</td><td></td><td></td><td></td><td></td><td>0.05ns</td></tr></tbody></table>

<footnote>*** P < 0.001; ** P < 0.01; * P < 0.05; · P < 0.1; ns: not significant.</footnote>
<figure><img src="image_2.png" /></figure>

F1 (59.60 %)
<figure><img src="image_3.png" /></figure>

Level 1

Level 2

Level 3

Level 4

Control (Natural site)
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>
Net variance explained (%)

100

90

80

70

60

50

40

30

20

10

0

Spoil heap
& controls

Multivar.

Multivar. ArylN

ArylS

BPH

Glu

APH

FDA

BR

FDL

AWCD

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

Physico-chemical variables

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

$^{13}$C NMR variables

Spoil
heap
<figure><img src="image_8.png" /></figure>

## Main results

<figure><img src="image_9.png" /></figure>

Slope discriminate : particle size, CEC, carbonate

<figure><img src="image_10.png" /></figure>

Patchy distribution of : Lignite, microbial activities

<figure><img src="image_11.png" /></figure>

Negative effects of lignite on microbial parameters